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streams, etc. - -Usage - sources: - $ python detect.py --weights yolov5s.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - screen # screenshot - path/ # directory - list.txt # list of images - list.streams # list of streams - 'path/*.jpg' # glob - 'https://youtu.be/LNwODJXcvt4' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream - -Usage - formats: - $ python detect.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s_openvino_model # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (macOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU - yolov5s_paddle_model # PaddlePaddle -""" - -import argparse -import csv -import os -import platform -import sys -from pathlib import Path -from PIL import Image as PILImg -import shutil -import torch - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from ultralytics.utils.plotting import Annotator, colors, save_one_box - -from models.common import DetectMultiBackend -from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) -from utils.torch_utils import select_device, smart_inference_mode - - -@smart_inference_mode() -def run( - weights=ROOT / 'yolov5s.pt', # model path or triton URL - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(640, 640), # inference size (height, width) - conf_thres=0.25, # confidence threshold - iou_thres=0.45, # NMS IOU threshold - max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - save_csv=False, # save results in CSV format - save_conf=False, # save confidences in --save-txt labels - save_crop=False, # save cropped prediction boxes - nosave=False, # do not save images/videos - classes=None, # filter by class: --class 0, or --class 0 2 3 - agnostic_nms=False, # class-agnostic NMS - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'inference', # save results to project/name - name='_dhyolo', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - line_thickness=3, # bounding box thickness (pixels) - hide_labels=False, # hide labels - hide_conf=False, # hide confidences - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - vid_stride=1, # video frame-rate stride -): - source = str(source) - save_img = False #not nosave and not source.endswith('.txt') # save inference images - is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') - if is_url and is_file: - source = check_file(source) # download - - # Directories - save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Load model - device = select_device(device) - model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) - stride, names, pt = model.stride, model.names, model.pt - imgsz = check_img_size(imgsz, s=stride) # check image size - - # Dataloader - bs = 1 # batch_size - if webcam: - view_img = check_imshow(warn=True) - dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - bs = len(dataset) - elif screenshot: - dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - vid_path, vid_writer = [None] * bs, [None] * bs - - # Run inference - model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) - for path, im, im0s, vid_cap, s in dataset: - with dt[0]: - im = torch.from_numpy(im).to(model.device) - im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - if len(im.shape) == 3: - im = im[None] # expand for batch dim - - # Inference - with dt[1]: - visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred = model(im, augment=augment, visualize=visualize) - - # NMS - with dt[2]: - pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) - - # Second-stage classifier (optional) - # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) - - # Define the path for the CSV file - csv_path = save_dir / 'predictions.csv' - - # Create or append to the CSV file - def write_to_csv(image_name, prediction, confidence): - data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence} - with open(csv_path, mode='a', newline='') as f: - writer = csv.DictWriter(f, fieldnames=data.keys()) - if not csv_path.is_file(): - writer.writeheader() - writer.writerow(data) - - # Process predictions - for i, det in enumerate(pred): # per image - seen += 1 - if webcam: # batch_size >= 1 - p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' - else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - imc = im0.copy() if save_crop else im0 # for save_crop - annotator = Annotator(im0, line_width=line_thickness, example=str(names)) - if len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() - - # Print results - for c in det[:, 5].unique(): - n = (det[:, 5] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - # Write results - for *xyxy, conf, cls in reversed(det): - c = int(cls) # integer class - label = names[c] if hide_conf else f'{names[c]}' - confidence = float(conf) - confidence_str = f'{confidence:.2f}' - - if save_csv: - write_to_csv(p.name, label, confidence_str) - - if save_txt: # Write to file - xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh - line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format - with open(f'{txt_path}.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - if save_img or save_crop or view_img: # Add bbox to image - c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') - annotator.box_label(xyxy, label, color=colors(c, True)) - if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) - - # Stream results - im0 = annotator.result() - if view_img: - if platform.system() == 'Linux' and p not in windows: - windows.append(p) - cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) - cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) - cv2.imshow(str(p), im0) - cv2.waitKey(1) # 1 millisecond - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - else: # 'video' or 'stream' - if vid_path[i] != save_path: # new video - vid_path[i] = save_path - if isinstance(vid_writer[i], cv2.VideoWriter): - vid_writer[i].release() # release previous video writer - if vid_cap: # video - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer[i].write(im0) - - # Print time (inference-only) - LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") - - # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") - if update: - strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) - - # need to remove as we are not saving anything - shutil.rmtree(project, ignore_errors=True) - - return pred - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-csv', action='store_true', help='save results in CSV format') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(vars(opt)) - return opt - - -def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) - run(**vars(opt)) - - -if __name__ == '__main__': - opt = parse_opt() - main(opt) \ No newline at end of file diff --git a/iteach_toolkit/DHYOLO/export.py b/iteach_toolkit/DHYOLO/export.py deleted file mode 100644 index dfb1c06fb5e236f696b44c37f63abca63d865e61..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/export.py +++ /dev/null @@ -1,1537 +0,0 @@ -# Ultralytics YOLOv5 🚀, AGPL-3.0 license -""" -Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit - -Format | `export.py --include` | Model ---- | --- | --- -PyTorch | - | yolov5s.pt -TorchScript | `torchscript` | yolov5s.torchscript -ONNX | `onnx` | yolov5s.onnx -OpenVINO | `openvino` | yolov5s_openvino_model/ -TensorRT | `engine` | yolov5s.engine -CoreML | `coreml` | yolov5s.mlmodel -TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ -TensorFlow GraphDef | `pb` | yolov5s.pb -TensorFlow Lite | `tflite` | yolov5s.tflite -TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite -TensorFlow.js | `tfjs` | yolov5s_web_model/ -PaddlePaddle | `paddle` | yolov5s_paddle_model/ - -Requirements: - $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU - $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU - -Usage: - $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... - -Inference: - $ python detect.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s_openvino_model # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (macOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU - yolov5s_paddle_model # PaddlePaddle - -TensorFlow.js: - $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example - $ npm install - $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model - $ npm start -""" - -import argparse -import contextlib -import json -import os -import platform -import re -import subprocess -import sys -import time -import warnings -from pathlib import Path - -import pandas as pd -import torch -from torch.utils.mobile_optimizer import optimize_for_mobile - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -if platform.system() != "Windows": - ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.experimental import attempt_load -from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel -from utils.dataloaders import LoadImages -from utils.general import ( - LOGGER, - Profile, - check_dataset, - check_img_size, - check_requirements, - check_version, - check_yaml, - colorstr, - file_size, - get_default_args, - print_args, - url2file, - yaml_save, -) -from utils.torch_utils import select_device, smart_inference_mode - -MACOS = platform.system() == "Darwin" # macOS environment - - -class iOSModel(torch.nn.Module): - def __init__(self, model, im): - """ - Initializes an iOS compatible model with normalization based on image dimensions. - - Args: - model (torch.nn.Module): The PyTorch model to be adapted for iOS compatibility. - im (torch.Tensor): An input tensor representing a batch of images with shape (B, C, H, W). - - Returns: - None: This method does not return any value. - - Notes: - This initializer configures normalization based on the input image dimensions, which is critical for - ensuring the model's compatibility and proper functionality on iOS devices. The normalization step - involves dividing by the image width if the image is square; otherwise, additional conditions might apply. - """ - super().__init__() - b, c, h, w = im.shape # batch, channel, height, width - self.model = model - self.nc = model.nc # number of classes - if w == h: - self.normalize = 1.0 / w - else: - self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller) - # np = model(im)[0].shape[1] # number of points - # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger) - - def forward(self, x): - """ - Run a forward pass on the input tensor, returning class confidences and normalized coordinates. - - Args: - x (torch.Tensor): Input tensor containing the image data with shape (batch, channels, height, width). - - Returns: - torch.Tensor: Concatenated tensor with normalized coordinates (xywh), confidence scores (conf), - and class probabilities (cls), having shape (N, 4 + 1 + C), where N is the number of predictions, - and C is the number of classes. - - Examples: - ```python - model = iOSModel(pretrained_model, input_image) - output = model.forward(torch_input_tensor) - ``` - """ - xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1) - return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4) - - -def export_formats(): - """ - Returns a DataFrame of supported YOLOv5 model export formats and their properties. - - Returns: - pandas.DataFrame: A DataFrame containing supported export formats and their properties. The DataFrame - includes columns for format name, CLI argument suffix, file extension or directory name, and boolean flags - indicating if the export format supports training and detection. - - Examples: - ```python - formats = export_formats() - print(f"Supported export formats:\n{formats}") - ``` - - Notes: - The DataFrame contains the following columns: - - Format: The name of the model format (e.g., PyTorch, TorchScript, ONNX, etc.). - - Include Argument: The argument to use with the export script to include this format. - - File Suffix: File extension or directory name associated with the format. - - Supports Training: Whether the format supports training. - - Supports Detection: Whether the format supports detection. - """ - x = [ - ["PyTorch", "-", ".pt", True, True], - ["TorchScript", "torchscript", ".torchscript", True, True], - ["ONNX", "onnx", ".onnx", True, True], - ["OpenVINO", "openvino", "_openvino_model", True, False], - ["TensorRT", "engine", ".engine", False, True], - ["CoreML", "coreml", ".mlpackage", True, False], - ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], - ["TensorFlow GraphDef", "pb", ".pb", True, True], - ["TensorFlow Lite", "tflite", ".tflite", True, False], - ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], - ["TensorFlow.js", "tfjs", "_web_model", False, False], - ["PaddlePaddle", "paddle", "_paddle_model", True, True], - ] - return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]) - - -def try_export(inner_func): - """ - Log success or failure, execution time, and file size for YOLOv5 model export functions wrapped with @try_export. - - Args: - inner_func (Callable): The model export function to be wrapped by the decorator. - - Returns: - Callable: The wrapped function that logs execution details. When executed, this wrapper function returns either: - - Tuple (str | torch.nn.Module): On success — the file path of the exported model and the model instance. - - Tuple (None, None): On failure — None values indicating export failure. - - Examples: - ```python - @try_export - def export_onnx(model, filepath): - # implementation here - pass - - exported_file, exported_model = export_onnx(yolo_model, 'path/to/save/model.onnx') - ``` - - Notes: - For additional requirements and model export formats, refer to the - [Ultralytics YOLOv5 GitHub repository](https://github.com/ultralytics/ultralytics). - """ - inner_args = get_default_args(inner_func) - - def outer_func(*args, **kwargs): - """Logs success/failure and execution details of model export functions wrapped with @try_export decorator.""" - prefix = inner_args["prefix"] - try: - with Profile() as dt: - f, model = inner_func(*args, **kwargs) - LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)") - return f, model - except Exception as e: - LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") - return None, None - - return outer_func - - -@try_export -def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")): - """ - Export a YOLOv5 model to the TorchScript format. - - Args: - model (torch.nn.Module): The YOLOv5 model to be exported. - im (torch.Tensor): Example input tensor to be used for tracing the TorchScript model. - file (Path): File path where the exported TorchScript model will be saved. - optimize (bool): If True, applies optimizations for mobile deployment. - prefix (str): Optional prefix for log messages. Default is 'TorchScript:'. - - Returns: - (str | None, torch.jit.ScriptModule | None): A tuple containing the file path of the exported model - (as a string) and the TorchScript model (as a torch.jit.ScriptModule). If the export fails, both elements - of the tuple will be None. - - Notes: - - This function uses tracing to create the TorchScript model. - - Metadata, including the input image shape, model stride, and class names, is saved in an extra file (`config.txt`) - within the TorchScript model package. - - For mobile optimization, refer to the PyTorch tutorial: https://pytorch.org/tutorials/recipes/mobile_interpreter.html - - Example: - ```python - from pathlib import Path - import torch - from models.experimental import attempt_load - from utils.torch_utils import select_device - - # Load model - weights = 'yolov5s.pt' - device = select_device('') - model = attempt_load(weights, device=device) - - # Example input tensor - im = torch.zeros(1, 3, 640, 640).to(device) - - # Export model - file = Path('yolov5s.torchscript') - export_torchscript(model, im, file, optimize=False) - ``` - """ - LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...") - f = file.with_suffix(".torchscript") - - ts = torch.jit.trace(model, im, strict=False) - d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} - extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap() - if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html - optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) - else: - ts.save(str(f), _extra_files=extra_files) - return f, None - - -@try_export -def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")): - """ - Export a YOLOv5 model to ONNX format with dynamic axes support and optional model simplification. - - Args: - model (torch.nn.Module): The YOLOv5 model to be exported. - im (torch.Tensor): A sample input tensor for model tracing, usually the shape is (1, 3, height, width). - file (pathlib.Path | str): The output file path where the ONNX model will be saved. - opset (int): The ONNX opset version to use for export. - dynamic (bool): If True, enables dynamic axes for batch, height, and width dimensions. - simplify (bool): If True, applies ONNX model simplification for optimization. - prefix (str): A prefix string for logging messages, defaults to 'ONNX:'. - - Returns: - tuple[pathlib.Path | str, None]: The path to the saved ONNX model file and None (consistent with decorator). - - Raises: - ImportError: If required libraries for export (e.g., 'onnx', 'onnx-simplifier') are not installed. - AssertionError: If the simplification check fails. - - Notes: - The required packages for this function can be installed via: - ``` - pip install onnx onnx-simplifier onnxruntime onnxruntime-gpu - ``` - - Example: - ```python - from pathlib import Path - import torch - from models.experimental import attempt_load - from utils.torch_utils import select_device - - # Load model - weights = 'yolov5s.pt' - device = select_device('') - model = attempt_load(weights, map_location=device) - - # Example input tensor - im = torch.zeros(1, 3, 640, 640).to(device) - - # Export model - file_path = Path('yolov5s.onnx') - export_onnx(model, im, file_path, opset=12, dynamic=True, simplify=True) - ``` - """ - check_requirements("onnx>=1.12.0") - import onnx - - LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...") - f = str(file.with_suffix(".onnx")) - - output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"] - if dynamic: - dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) - if isinstance(model, SegmentationModel): - dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) - dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) - elif isinstance(model, DetectionModel): - dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) - - torch.onnx.export( - model.cpu() if dynamic else model, # --dynamic only compatible with cpu - im.cpu() if dynamic else im, - f, - verbose=False, - opset_version=opset, - do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False - input_names=["images"], - output_names=output_names, - dynamic_axes=dynamic or None, - ) - - # Checks - model_onnx = onnx.load(f) # load onnx model - onnx.checker.check_model(model_onnx) # check onnx model - - # Metadata - d = {"stride": int(max(model.stride)), "names": model.names} - for k, v in d.items(): - meta = model_onnx.metadata_props.add() - meta.key, meta.value = k, str(v) - onnx.save(model_onnx, f) - - # Simplify - if simplify: - try: - cuda = torch.cuda.is_available() - check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnxslim")) - import onnxslim - - LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...") - model_onnx = onnxslim.slim(model_onnx) - onnx.save(model_onnx, f) - except Exception as e: - LOGGER.info(f"{prefix} simplifier failure: {e}") - return f, model_onnx - - -@try_export -def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")): - """ - Export a YOLOv5 model to OpenVINO format with optional FP16 and INT8 quantization. - - Args: - file (Path): Path to the output file where the OpenVINO model will be saved. - metadata (dict): Dictionary including model metadata such as names and strides. - half (bool): If True, export the model with FP16 precision. - int8 (bool): If True, export the model with INT8 quantization. - data (str): Path to the dataset YAML file required for INT8 quantization. - prefix (str): Prefix string for logging purposes (default is "OpenVINO:"). - - Returns: - (str, openvino.runtime.Model | None): The OpenVINO model file path and openvino.runtime.Model object if export is - successful; otherwise, None. - - Notes: - - Requires `openvino-dev` package version 2023.0 or higher. Install with: - `$ pip install openvino-dev>=2023.0` - - For INT8 quantization, also requires `nncf` library version 2.5.0 or higher. Install with: - `$ pip install nncf>=2.5.0` - - Examples: - ```python - from pathlib import Path - from ultralytics import YOLOv5 - - model = YOLOv5('yolov5s.pt') - export_openvino(Path('yolov5s.onnx'), metadata={'names': model.names, 'stride': model.stride}, half=True, - int8=False, data='data.yaml') - ``` - - This will export the YOLOv5 model to OpenVINO with FP16 precision but without INT8 quantization, saving it to - the specified file path. - """ - check_requirements("openvino-dev>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ - import openvino.runtime as ov # noqa - from openvino.tools import mo # noqa - - LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") - f = str(file).replace(file.suffix, f"_{'int8_' if int8 else ''}openvino_model{os.sep}") - f_onnx = file.with_suffix(".onnx") - f_ov = str(Path(f) / file.with_suffix(".xml").name) - - ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half) # export - - if int8: - check_requirements("nncf>=2.5.0") # requires at least version 2.5.0 to use the post-training quantization - import nncf - import numpy as np - - from utils.dataloaders import create_dataloader - - def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4): - """Generates a DataLoader for model training or validation based on the given YAML dataset configuration.""" - data_yaml = check_yaml(yaml_path) - data = check_dataset(data_yaml) - dataloader = create_dataloader( - data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers - )[0] - return dataloader - - # noqa: F811 - - def transform_fn(data_item): - """ - Quantization transform function. - - Extracts and preprocess input data from dataloader item for quantization. - Parameters: - data_item: Tuple with data item produced by DataLoader during iteration - Returns: - input_tensor: Input data for quantization - """ - assert data_item[0].dtype == torch.uint8, "input image must be uint8 for the quantization preprocessing" - - img = data_item[0].numpy().astype(np.float32) # uint8 to fp16/32 - img /= 255.0 # 0 - 255 to 0.0 - 1.0 - return np.expand_dims(img, 0) if img.ndim == 3 else img - - ds = gen_dataloader(data) - quantization_dataset = nncf.Dataset(ds, transform_fn) - ov_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED) - - ov.serialize(ov_model, f_ov) # save - yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml - return f, None - - -@try_export -def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")): - """ - Export a YOLOv5 PyTorch model to PaddlePaddle format using X2Paddle, saving the converted model and metadata. - - Args: - model (torch.nn.Module): The YOLOv5 model to be exported. - im (torch.Tensor): Input tensor used for model tracing during export. - file (pathlib.Path): Path to the source file to be converted. - metadata (dict): Additional metadata to be saved alongside the model. - prefix (str): Prefix for logging information. - - Returns: - tuple (str, None): A tuple where the first element is the path to the saved PaddlePaddle model, and the - second element is None. - - Examples: - ```python - from pathlib import Path - import torch - - # Assume 'model' is a pre-trained YOLOv5 model and 'im' is an example input tensor - model = ... # Load your model here - im = torch.randn((1, 3, 640, 640)) # Dummy input tensor for tracing - file = Path("yolov5s.pt") - metadata = {"stride": 32, "names": ["person", "bicycle", "car", "motorbike"]} - - export_paddle(model=model, im=im, file=file, metadata=metadata) - ``` - - Notes: - Ensure that `paddlepaddle` and `x2paddle` are installed, as these are required for the export function. You can - install them via pip: - ``` - $ pip install paddlepaddle x2paddle - ``` - """ - check_requirements(("paddlepaddle", "x2paddle")) - import x2paddle - from x2paddle.convert import pytorch2paddle - - LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") - f = str(file).replace(".pt", f"_paddle_model{os.sep}") - - pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im]) # export - yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml - return f, None - - -@try_export -def export_coreml(model, im, file, int8, half, nms, mlmodel, prefix=colorstr("CoreML:")): - """ - Export a YOLOv5 model to CoreML format with optional NMS, INT8, and FP16 support. - - Args: - model (torch.nn.Module): The YOLOv5 model to be exported. - im (torch.Tensor): Example input tensor to trace the model. - file (pathlib.Path): Path object where the CoreML model will be saved. - int8 (bool): Flag indicating whether to use INT8 quantization (default is False). - half (bool): Flag indicating whether to use FP16 quantization (default is False). - nms (bool): Flag indicating whether to include Non-Maximum Suppression (default is False). - mlmodel (bool): Flag indicating whether to export as older *.mlmodel format (default is False). - prefix (str): Prefix string for logging purposes (default is 'CoreML:'). - - Returns: - tuple[pathlib.Path | None, None]: The path to the saved CoreML model file, or (None, None) if there is an error. - - Notes: - The exported CoreML model will be saved with a .mlmodel extension. - Quantization is supported only on macOS. - - Example: - ```python - from pathlib import Path - import torch - from models.yolo import Model - model = Model(cfg, ch=3, nc=80) - im = torch.randn(1, 3, 640, 640) - file = Path("yolov5s_coreml") - export_coreml(model, im, file, int8=False, half=False, nms=True, mlmodel=False) - ``` - """ - check_requirements("coremltools") - import coremltools as ct - - LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") - if mlmodel: - f = file.with_suffix(".mlmodel") - convert_to = "neuralnetwork" - precision = None - else: - f = file.with_suffix(".mlpackage") - convert_to = "mlprogram" - if half: - precision = ct.precision.FLOAT16 - else: - precision = ct.precision.FLOAT32 - - if nms: - model = iOSModel(model, im) - ts = torch.jit.trace(model, im, strict=False) # TorchScript model - ct_model = ct.convert( - ts, - inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])], - convert_to=convert_to, - compute_precision=precision, - ) - bits, mode = (8, "kmeans") if int8 else (16, "linear") if half else (32, None) - if bits < 32: - if mlmodel: - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", category=DeprecationWarning - ) # suppress numpy==1.20 float warning, fixed in coremltools==7.0 - ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) - elif bits == 8: - op_config = ct.optimize.coreml.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512) - config = ct.optimize.coreml.OptimizationConfig(global_config=op_config) - ct_model = ct.optimize.coreml.palettize_weights(ct_model, config) - ct_model.save(f) - return f, ct_model - - -@try_export -def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")): - """ - Export a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0. - - Args: - model (torch.nn.Module): YOLOv5 model to be exported. - im (torch.Tensor): Input tensor of shape (B, C, H, W). - file (pathlib.Path): Path to save the exported model. - half (bool): Set to True to export with FP16 precision. - dynamic (bool): Set to True to enable dynamic input shapes. - simplify (bool): Set to True to simplify the model during export. - workspace (int): Workspace size in GB (default is 4). - verbose (bool): Set to True for verbose logging output. - prefix (str): Log message prefix. - - Returns: - (pathlib.Path, None): Tuple containing the path to the exported model and None. - - Raises: - AssertionError: If executed on CPU instead of GPU. - RuntimeError: If there is a failure in parsing the ONNX file. - - Example: - ```python - from ultralytics import YOLOv5 - import torch - from pathlib import Path - - model = YOLOv5('yolov5s.pt') # Load a pre-trained YOLOv5 model - input_tensor = torch.randn(1, 3, 640, 640).cuda() # example input tensor on GPU - export_path = Path('yolov5s.engine') # export destination - - export_engine(model.model, input_tensor, export_path, half=True, dynamic=True, simplify=True, workspace=8, verbose=True) - ``` - """ - assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`" - try: - import tensorrt as trt - except Exception: - if platform.system() == "Linux": - check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com") - import tensorrt as trt - - if trt.__version__[0] == "7": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 - grid = model.model[-1].anchor_grid - model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] - export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 - model.model[-1].anchor_grid = grid - else: # TensorRT >= 8 - check_version(trt.__version__, "8.0.0", hard=True) # require tensorrt>=8.0.0 - export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 - onnx = file.with_suffix(".onnx") - - LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") - is_trt10 = int(trt.__version__.split(".")[0]) >= 10 # is TensorRT >= 10 - assert onnx.exists(), f"failed to export ONNX file: {onnx}" - f = file.with_suffix(".engine") # TensorRT engine file - logger = trt.Logger(trt.Logger.INFO) - if verbose: - logger.min_severity = trt.Logger.Severity.VERBOSE - - builder = trt.Builder(logger) - config = builder.create_builder_config() - if is_trt10: - config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) - else: # TensorRT versions 7, 8 - config.max_workspace_size = workspace * 1 << 30 - flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) - network = builder.create_network(flag) - parser = trt.OnnxParser(network, logger) - if not parser.parse_from_file(str(onnx)): - raise RuntimeError(f"failed to load ONNX file: {onnx}") - - inputs = [network.get_input(i) for i in range(network.num_inputs)] - outputs = [network.get_output(i) for i in range(network.num_outputs)] - for inp in inputs: - LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') - for out in outputs: - LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') - - if dynamic: - if im.shape[0] <= 1: - LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument") - profile = builder.create_optimization_profile() - for inp in inputs: - profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) - config.add_optimization_profile(profile) - - LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}") - if builder.platform_has_fast_fp16 and half: - config.set_flag(trt.BuilderFlag.FP16) - - build = builder.build_serialized_network if is_trt10 else builder.build_engine - with build(network, config) as engine, open(f, "wb") as t: - t.write(engine if is_trt10 else engine.serialize()) - return f, None - - -@try_export -def export_saved_model( - model, - im, - file, - dynamic, - tf_nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25, - keras=False, - prefix=colorstr("TensorFlow SavedModel:"), -): - """ - Export a YOLOv5 model to the TensorFlow SavedModel format, supporting dynamic axes and non-maximum suppression - (NMS). - - Args: - model (torch.nn.Module): The PyTorch model to convert. - im (torch.Tensor): Sample input tensor with shape (B, C, H, W) for tracing. - file (pathlib.Path): File path to save the exported model. - dynamic (bool): Flag to indicate whether dynamic axes should be used. - tf_nms (bool, optional): Enable TensorFlow non-maximum suppression (NMS). Default is False. - agnostic_nms (bool, optional): Enable class-agnostic NMS. Default is False. - topk_per_class (int, optional): Top K detections per class to keep before applying NMS. Default is 100. - topk_all (int, optional): Top K detections across all classes to keep before applying NMS. Default is 100. - iou_thres (float, optional): IoU threshold for NMS. Default is 0.45. - conf_thres (float, optional): Confidence threshold for detections. Default is 0.25. - keras (bool, optional): Save the model in Keras format if True. Default is False. - prefix (str, optional): Prefix for logging messages. Default is "TensorFlow SavedModel:". - - Returns: - tuple[str, tf.keras.Model | None]: A tuple containing the path to the saved model folder and the Keras model instance, - or None if TensorFlow export fails. - - Notes: - - The method supports TensorFlow versions up to 2.15.1. - - TensorFlow NMS may not be supported in older TensorFlow versions. - - If the TensorFlow version exceeds 2.13.1, it might cause issues when exporting to TFLite. - Refer to: https://github.com/ultralytics/yolov5/issues/12489 - - Example: - ```python - model, im = ... # Initialize your PyTorch model and input tensor - export_saved_model(model, im, Path("yolov5_saved_model"), dynamic=True) - ``` - """ - # YOLOv5 TensorFlow SavedModel export - try: - import tensorflow as tf - except Exception: - check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}<=2.15.1") - - import tensorflow as tf - from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 - - from models.tf import TFModel - - LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") - if tf.__version__ > "2.13.1": - helper_url = "https://github.com/ultralytics/yolov5/issues/12489" - LOGGER.info( - f"WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}" - ) # handling issue https://github.com/ultralytics/yolov5/issues/12489 - f = str(file).replace(".pt", "_saved_model") - batch_size, ch, *imgsz = list(im.shape) # BCHW - - tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) - im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow - _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) - inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size) - outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) - keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) - keras_model.trainable = False - keras_model.summary() - if keras: - keras_model.save(f, save_format="tf") - else: - spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) - m = tf.function(lambda x: keras_model(x)) # full model - m = m.get_concrete_function(spec) - frozen_func = convert_variables_to_constants_v2(m) - tfm = tf.Module() - tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) - tfm.__call__(im) - tf.saved_model.save( - tfm, - f, - options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) - if check_version(tf.__version__, "2.6") - else tf.saved_model.SaveOptions(), - ) - return f, keras_model - - -@try_export -def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")): - """ - Export YOLOv5 model to TensorFlow GraphDef (*.pb) format. - - Args: - keras_model (tf.keras.Model): The Keras model to be converted. - file (Path): The output file path where the GraphDef will be saved. - prefix (str): Optional prefix string; defaults to a colored string indicating TensorFlow GraphDef export status. - - Returns: - Tuple[Path, None]: The file path where the GraphDef model was saved and a None placeholder. - - Notes: - For more details, refer to the guide on frozen graphs: https://github.com/leimao/Frozen_Graph_TensorFlow - - Example: - ```python - from pathlib import Path - keras_model = ... # assume an existing Keras model - file = Path("model.pb") - export_pb(keras_model, file) - ``` - """ - import tensorflow as tf - from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 - - LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") - f = file.with_suffix(".pb") - - m = tf.function(lambda x: keras_model(x)) # full model - m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) - frozen_func = convert_variables_to_constants_v2(m) - frozen_func.graph.as_graph_def() - tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) - return f, None - - -@try_export -def export_tflite( - keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:") -): - # YOLOv5 TensorFlow Lite export - """ - Export a YOLOv5 model to TensorFlow Lite format with optional INT8 quantization and NMS support. - - Args: - keras_model (tf.keras.Model): The Keras model to be exported. - im (torch.Tensor): An input image tensor for normalization and model tracing. - file (Path): The file path to save the TensorFlow Lite model. - int8 (bool): Enables INT8 quantization if True. - per_tensor (bool): If True, disables per-channel quantization. - data (str): Path to the dataset for representative dataset generation in INT8 quantization. - nms (bool): Enables Non-Maximum Suppression (NMS) if True. - agnostic_nms (bool): Enables class-agnostic NMS if True. - prefix (str): Prefix for log messages. - - Returns: - (str | None, tflite.Model | None): The file path of the exported TFLite model and the TFLite model instance, or None - if the export failed. - - Example: - ```python - from pathlib import Path - import torch - import tensorflow as tf - - # Load a Keras model wrapping a YOLOv5 model - keras_model = tf.keras.models.load_model('path/to/keras_model.h5') - - # Example input tensor - im = torch.zeros(1, 3, 640, 640) - - # Export the model - export_tflite(keras_model, im, Path('model.tflite'), int8=True, per_tensor=False, data='data/coco.yaml', - nms=True, agnostic_nms=False) - ``` - - Notes: - - Ensure TensorFlow and TensorFlow Lite dependencies are installed. - - INT8 quantization requires a representative dataset to achieve optimal accuracy. - - TensorFlow Lite models are suitable for efficient inference on mobile and edge devices. - """ - import tensorflow as tf - - LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") - batch_size, ch, *imgsz = list(im.shape) # BCHW - f = str(file).replace(".pt", "-fp16.tflite") - - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - converter.target_spec.supported_types = [tf.float16] - converter.optimizations = [tf.lite.Optimize.DEFAULT] - if int8: - from models.tf import representative_dataset_gen - - dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False) - converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] - converter.target_spec.supported_types = [] - converter.inference_input_type = tf.uint8 # or tf.int8 - converter.inference_output_type = tf.uint8 # or tf.int8 - converter.experimental_new_quantizer = True - if per_tensor: - converter._experimental_disable_per_channel = True - f = str(file).replace(".pt", "-int8.tflite") - if nms or agnostic_nms: - converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) - - tflite_model = converter.convert() - open(f, "wb").write(tflite_model) - return f, None - - -@try_export -def export_edgetpu(file, prefix=colorstr("Edge TPU:")): - """ - Exports a YOLOv5 model to Edge TPU compatible TFLite format; requires Linux and Edge TPU compiler. - - Args: - file (Path): Path to the YOLOv5 model file to be exported (.pt format). - prefix (str, optional): Prefix for logging messages. Defaults to colorstr("Edge TPU:"). - - Returns: - tuple[Path, None]: Path to the exported Edge TPU compatible TFLite model, None. - - Raises: - AssertionError: If the system is not Linux. - subprocess.CalledProcessError: If any subprocess call to install or run the Edge TPU compiler fails. - - Notes: - To use this function, ensure you have the Edge TPU compiler installed on your Linux system. You can find - installation instructions here: https://coral.ai/docs/edgetpu/compiler/. - - Example: - ```python - from pathlib import Path - file = Path('yolov5s.pt') - export_edgetpu(file) - ``` - """ - cmd = "edgetpu_compiler --version" - help_url = "https://coral.ai/docs/edgetpu/compiler/" - assert platform.system() == "Linux", f"export only supported on Linux. See {help_url}" - if subprocess.run(f"{cmd} > /dev/null 2>&1", shell=True).returncode != 0: - LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") - sudo = subprocess.run("sudo --version >/dev/null", shell=True).returncode == 0 # sudo installed on system - for c in ( - "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", - 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', - "sudo apt-get update", - "sudo apt-get install edgetpu-compiler", - ): - subprocess.run(c if sudo else c.replace("sudo ", ""), shell=True, check=True) - ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] - - LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") - f = str(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model - f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model - - subprocess.run( - [ - "edgetpu_compiler", - "-s", - "-d", - "-k", - "10", - "--out_dir", - str(file.parent), - f_tfl, - ], - check=True, - ) - return f, None - - -@try_export -def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")): - """ - Convert a YOLOv5 model to TensorFlow.js format with optional uint8 quantization. - - Args: - file (Path): Path to the YOLOv5 model file to be converted, typically having a ".pt" or ".onnx" extension. - int8 (bool): If True, applies uint8 quantization during the conversion process. - prefix (str): Optional prefix for logging messages, default is 'TensorFlow.js:' with color formatting. - - Returns: - (str, None): Tuple containing the output directory path as a string and None. - - Notes: - - This function requires the `tensorflowjs` package. Install it using: - ```shell - pip install tensorflowjs - ``` - - The converted TensorFlow.js model will be saved in a directory with the "_web_model" suffix appended to the original file name. - - The conversion involves running shell commands that invoke the TensorFlow.js converter tool. - - Example: - ```python - from pathlib import Path - file = Path('yolov5.onnx') - export_tfjs(file, int8=False) - ``` - """ - check_requirements("tensorflowjs") - import tensorflowjs as tfjs - - LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") - f = str(file).replace(".pt", "_web_model") # js dir - f_pb = file.with_suffix(".pb") # *.pb path - f_json = f"{f}/model.json" # *.json path - - args = [ - "tensorflowjs_converter", - "--input_format=tf_frozen_model", - "--quantize_uint8" if int8 else "", - "--output_node_names=Identity,Identity_1,Identity_2,Identity_3", - str(f_pb), - f, - ] - subprocess.run([arg for arg in args if arg], check=True) - - json = Path(f_json).read_text() - with open(f_json, "w") as j: # sort JSON Identity_* in ascending order - subst = re.sub( - r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}}}', - r'{"outputs": {"Identity": {"name": "Identity"}, ' - r'"Identity_1": {"name": "Identity_1"}, ' - r'"Identity_2": {"name": "Identity_2"}, ' - r'"Identity_3": {"name": "Identity_3"}}}', - json, - ) - j.write(subst) - return f, None - - -def add_tflite_metadata(file, metadata, num_outputs): - """ - Adds metadata to a TensorFlow Lite (TFLite) model file, supporting multiple outputs according to TensorFlow - guidelines. - - Args: - file (str): Path to the TFLite model file to which metadata will be added. - metadata (dict): Metadata information to be added to the model, structured as required by the TFLite metadata schema. - Common keys include "name", "description", "version", "author", and "license". - num_outputs (int): Number of output tensors the model has, used to configure the metadata properly. - - Returns: - None - - Example: - ```python - metadata = { - "name": "yolov5", - "description": "YOLOv5 object detection model", - "version": "1.0", - "author": "Ultralytics", - "license": "Apache License 2.0" - } - add_tflite_metadata("model.tflite", metadata, num_outputs=4) - ``` - - Note: - TFLite metadata can include information such as model name, version, author, and other relevant details. - For more details on the structure of the metadata, refer to TensorFlow Lite - [metadata guidelines](https://www.tensorflow.org/lite/models/convert/metadata). - """ - with contextlib.suppress(ImportError): - # check_requirements('tflite_support') - from tflite_support import flatbuffers - from tflite_support import metadata as _metadata - from tflite_support import metadata_schema_py_generated as _metadata_fb - - tmp_file = Path("/tmp/meta.txt") - with open(tmp_file, "w") as meta_f: - meta_f.write(str(metadata)) - - model_meta = _metadata_fb.ModelMetadataT() - label_file = _metadata_fb.AssociatedFileT() - label_file.name = tmp_file.name - model_meta.associatedFiles = [label_file] - - subgraph = _metadata_fb.SubGraphMetadataT() - subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] - subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs - model_meta.subgraphMetadata = [subgraph] - - b = flatbuffers.Builder(0) - b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) - metadata_buf = b.Output() - - populator = _metadata.MetadataPopulator.with_model_file(file) - populator.load_metadata_buffer(metadata_buf) - populator.load_associated_files([str(tmp_file)]) - populator.populate() - tmp_file.unlink() - - -def pipeline_coreml(model, im, file, names, y, mlmodel, prefix=colorstr("CoreML Pipeline:")): - """ - Convert a PyTorch YOLOv5 model to CoreML format with Non-Maximum Suppression (NMS), handling different input/output - shapes, and saving the model. - - Args: - model (torch.nn.Module): The YOLOv5 PyTorch model to be converted. - im (torch.Tensor): Example input tensor with shape (N, C, H, W), where N is the batch size, C is the number of channels, - H is the height, and W is the width. - file (Path): Path to save the converted CoreML model. - names (dict[int, str]): Dictionary mapping class indices to class names. - y (torch.Tensor): Output tensor from the PyTorch model's forward pass. - mlmodel (bool): Flag indicating whether to export as older *.mlmodel format (default is False). - prefix (str): Custom prefix for logging messages. - - Returns: - (Path): Path to the saved CoreML model (.mlmodel). - - Raises: - AssertionError: If the number of class names does not match the number of classes in the model. - - Notes: - - This function requires `coremltools` to be installed. - - Running this function on a non-macOS environment might not support some features. - - Flexible input shapes and additional NMS options can be customized within the function. - - Examples: - ```python - from pathlib import Path - import torch - - model = torch.load('yolov5s.pt') # Load YOLOv5 model - im = torch.zeros((1, 3, 640, 640)) # Example input tensor - - names = {0: "person", 1: "bicycle", 2: "car", ...} # Define class names - - y = model(im) # Perform forward pass to get model output - - output_file = Path('yolov5s.mlmodel') # Convert to CoreML - pipeline_coreml(model, im, output_file, names, y) - ``` - """ - import coremltools as ct - from PIL import Image - - if mlmodel: - f = file.with_suffix(".mlmodel") # filename - else: - f = file.with_suffix(".mlpackage") # filename - - print(f"{prefix} starting pipeline with coremltools {ct.__version__}...") - batch_size, ch, h, w = list(im.shape) # BCHW - t = time.time() - - # YOLOv5 Output shapes - spec = model.get_spec() - out0, out1 = iter(spec.description.output) - if platform.system() == "Darwin": - img = Image.new("RGB", (w, h)) # img(192 width, 320 height) - # img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection - out = model.predict({"image": img}) - out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape - else: # linux and windows can not run model.predict(), get sizes from pytorch output y - s = tuple(y[0].shape) - out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4) - - # Checks - nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height - na, nc = out0_shape - # na, nc = out0.type.multiArrayType.shape # number anchors, classes - assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check - - # Define output shapes (missing) - out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) - out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4) - # spec.neuralNetwork.preprocessing[0].featureName = '0' - - # Flexible input shapes - # from coremltools.models.neural_network import flexible_shape_utils - # s = [] # shapes - # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192)) - # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width) - # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s) - # r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges - # r.add_height_range((192, 640)) - # r.add_width_range((192, 640)) - # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r) - - # Print - print(spec.description) - - # Model from spec - weights_dir = None - if mlmodel: - weights_dir = None - else: - weights_dir = str(f / "Data/com.apple.CoreML/weights") - model = ct.models.MLModel(spec, weights_dir=weights_dir) - - # 3. Create NMS protobuf - nms_spec = ct.proto.Model_pb2.Model() - nms_spec.specificationVersion = 5 - for i in range(2): - decoder_output = model._spec.description.output[i].SerializeToString() - nms_spec.description.input.add() - nms_spec.description.input[i].ParseFromString(decoder_output) - nms_spec.description.output.add() - nms_spec.description.output[i].ParseFromString(decoder_output) - - nms_spec.description.output[0].name = "confidence" - nms_spec.description.output[1].name = "coordinates" - - output_sizes = [nc, 4] - for i in range(2): - ma_type = nms_spec.description.output[i].type.multiArrayType - ma_type.shapeRange.sizeRanges.add() - ma_type.shapeRange.sizeRanges[0].lowerBound = 0 - ma_type.shapeRange.sizeRanges[0].upperBound = -1 - ma_type.shapeRange.sizeRanges.add() - ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i] - ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i] - del ma_type.shape[:] - - nms = nms_spec.nonMaximumSuppression - nms.confidenceInputFeatureName = out0.name # 1x507x80 - nms.coordinatesInputFeatureName = out1.name # 1x507x4 - nms.confidenceOutputFeatureName = "confidence" - nms.coordinatesOutputFeatureName = "coordinates" - nms.iouThresholdInputFeatureName = "iouThreshold" - nms.confidenceThresholdInputFeatureName = "confidenceThreshold" - nms.iouThreshold = 0.45 - nms.confidenceThreshold = 0.25 - nms.pickTop.perClass = True - nms.stringClassLabels.vector.extend(names.values()) - nms_model = ct.models.MLModel(nms_spec) - - # 4. Pipeline models together - pipeline = ct.models.pipeline.Pipeline( - input_features=[ - ("image", ct.models.datatypes.Array(3, ny, nx)), - ("iouThreshold", ct.models.datatypes.Double()), - ("confidenceThreshold", ct.models.datatypes.Double()), - ], - output_features=["confidence", "coordinates"], - ) - pipeline.add_model(model) - pipeline.add_model(nms_model) - - # Correct datatypes - pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString()) - pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString()) - pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString()) - - # Update metadata - pipeline.spec.specificationVersion = 5 - pipeline.spec.description.metadata.versionString = "https://github.com/ultralytics/yolov5" - pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5" - pipeline.spec.description.metadata.author = "glenn.jocher@ultralytics.com" - pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE" - pipeline.spec.description.metadata.userDefined.update( - { - "classes": ",".join(names.values()), - "iou_threshold": str(nms.iouThreshold), - "confidence_threshold": str(nms.confidenceThreshold), - } - ) - - # Save the model - model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir) - model.input_description["image"] = "Input image" - model.input_description["iouThreshold"] = f"(optional) IOU Threshold override (default: {nms.iouThreshold})" - model.input_description["confidenceThreshold"] = ( - f"(optional) Confidence Threshold override (default: {nms.confidenceThreshold})" - ) - model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' - model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" - model.save(f) # pipelined - print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)") - - -@smart_inference_mode() -def run( - data=ROOT / "data/coco128.yaml", # 'dataset.yaml path' - weights=ROOT / "yolov5s.pt", # weights path - imgsz=(640, 640), # image (height, width) - batch_size=1, # batch size - device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu - include=("torchscript", "onnx"), # include formats - half=False, # FP16 half-precision export - inplace=False, # set YOLOv5 Detect() inplace=True - keras=False, # use Keras - optimize=False, # TorchScript: optimize for mobile - int8=False, # CoreML/TF INT8 quantization - per_tensor=False, # TF per tensor quantization - dynamic=False, # ONNX/TF/TensorRT: dynamic axes - simplify=False, # ONNX: simplify model - mlmodel=False, # CoreML: Export in *.mlmodel format - opset=12, # ONNX: opset version - verbose=False, # TensorRT: verbose log - workspace=4, # TensorRT: workspace size (GB) - nms=False, # TF: add NMS to model - agnostic_nms=False, # TF: add agnostic NMS to model - topk_per_class=100, # TF.js NMS: topk per class to keep - topk_all=100, # TF.js NMS: topk for all classes to keep - iou_thres=0.45, # TF.js NMS: IoU threshold - conf_thres=0.25, # TF.js NMS: confidence threshold -): - """ - Exports a YOLOv5 model to specified formats including ONNX, TensorRT, CoreML, and TensorFlow. - - Args: - data (str | Path): Path to the dataset YAML configuration file. Default is 'data/coco128.yaml'. - weights (str | Path): Path to the pretrained model weights file. Default is 'yolov5s.pt'. - imgsz (tuple): Image size as (height, width). Default is (640, 640). - batch_size (int): Batch size for exporting the model. Default is 1. - device (str): Device to run the export on, e.g., '0' for GPU, 'cpu' for CPU. Default is 'cpu'. - include (tuple): Formats to include in the export. Default is ('torchscript', 'onnx'). - half (bool): Flag to export model with FP16 half-precision. Default is False. - inplace (bool): Set the YOLOv5 Detect() module inplace=True. Default is False. - keras (bool): Flag to use Keras for TensorFlow SavedModel export. Default is False. - optimize (bool): Optimize TorchScript model for mobile deployment. Default is False. - int8 (bool): Apply INT8 quantization for CoreML or TensorFlow models. Default is False. - per_tensor (bool): Apply per tensor quantization for TensorFlow models. Default is False. - dynamic (bool): Enable dynamic axes for ONNX, TensorFlow, or TensorRT exports. Default is False. - simplify (bool): Simplify the ONNX model during export. Default is False. - opset (int): ONNX opset version. Default is 12. - verbose (bool): Enable verbose logging for TensorRT export. Default is False. - workspace (int): TensorRT workspace size in GB. Default is 4. - nms (bool): Add non-maximum suppression (NMS) to the TensorFlow model. Default is False. - agnostic_nms (bool): Add class-agnostic NMS to the TensorFlow model. Default is False. - topk_per_class (int): Top-K boxes per class to keep for TensorFlow.js NMS. Default is 100. - topk_all (int): Top-K boxes for all classes to keep for TensorFlow.js NMS. Default is 100. - iou_thres (float): IoU threshold for NMS. Default is 0.45. - conf_thres (float): Confidence threshold for NMS. Default is 0.25. - mlmodel (bool): Flag to use *.mlmodel for CoreML export. Default is False. - - Returns: - None - - Notes: - - Model export is based on the specified formats in the 'include' argument. - - Be cautious of combinations where certain flags are mutually exclusive, such as `--half` and `--dynamic`. - - Example: - ```python - run( - data="data/coco128.yaml", - weights="yolov5s.pt", - imgsz=(640, 640), - batch_size=1, - device="cpu", - include=("torchscript", "onnx"), - half=False, - inplace=False, - keras=False, - optimize=False, - int8=False, - per_tensor=False, - dynamic=False, - simplify=False, - opset=12, - verbose=False, - mlmodel=False, - workspace=4, - nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25, - ) - ``` - """ - t = time.time() - include = [x.lower() for x in include] # to lowercase - fmts = tuple(export_formats()["Argument"][1:]) # --include arguments - flags = [x in include for x in fmts] - assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}" - jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans - file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights) # PyTorch weights - - # Load PyTorch model - device = select_device(device) - if half: - assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0" - assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both" - model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model - - # Checks - imgsz *= 2 if len(imgsz) == 1 else 1 # expand - if optimize: - assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu" - - # Input - gs = int(max(model.stride)) # grid size (max stride) - imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples - im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection - - # Update model - model.eval() - for k, m in model.named_modules(): - if isinstance(m, Detect): - m.inplace = inplace - m.dynamic = dynamic - m.export = True - - for _ in range(2): - y = model(im) # dry runs - if half and not coreml: - im, model = im.half(), model.half() # to FP16 - shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape - metadata = {"stride": int(max(model.stride)), "names": model.names} # model metadata - LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") - - # Exports - f = [""] * len(fmts) # exported filenames - warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning) # suppress TracerWarning - if jit: # TorchScript - f[0], _ = export_torchscript(model, im, file, optimize) - if engine: # TensorRT required before ONNX - f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose) - if onnx or xml: # OpenVINO requires ONNX - f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) - if xml: # OpenVINO - f[3], _ = export_openvino(file, metadata, half, int8, data) - if coreml: # CoreML - f[4], ct_model = export_coreml(model, im, file, int8, half, nms, mlmodel) - if nms: - pipeline_coreml(ct_model, im, file, model.names, y, mlmodel) - if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats - assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type." - assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported." - f[5], s_model = export_saved_model( - model.cpu(), - im, - file, - dynamic, - tf_nms=nms or agnostic_nms or tfjs, - agnostic_nms=agnostic_nms or tfjs, - topk_per_class=topk_per_class, - topk_all=topk_all, - iou_thres=iou_thres, - conf_thres=conf_thres, - keras=keras, - ) - if pb or tfjs: # pb prerequisite to tfjs - f[6], _ = export_pb(s_model, file) - if tflite or edgetpu: - f[7], _ = export_tflite( - s_model, im, file, int8 or edgetpu, per_tensor, data=data, nms=nms, agnostic_nms=agnostic_nms - ) - if edgetpu: - f[8], _ = export_edgetpu(file) - add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) - if tfjs: - f[9], _ = export_tfjs(file, int8) - if paddle: # PaddlePaddle - f[10], _ = export_paddle(model, im, file, metadata) - - # Finish - f = [str(x) for x in f if x] # filter out '' and None - if any(f): - cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type - det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) - dir = Path("segment" if seg else "classify" if cls else "") - h = "--half" if half else "" # --half FP16 inference arg - s = ( - "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" - if cls - else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" - if seg - else "" - ) - LOGGER.info( - f'\nExport complete ({time.time() - t:.1f}s)' - f"\nResults saved to {colorstr('bold', file.parent.resolve())}" - f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" - f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" - f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" - f'\nVisualize: https://netron.app' - ) - return f # return list of exported files/dirs - - -def parse_opt(known=False): - """ - Parse command-line options for YOLOv5 model export configurations. - - Args: - known (bool): If True, uses `argparse.ArgumentParser.parse_known_args`; otherwise, uses `argparse.ArgumentParser.parse_args`. - Default is False. - - Returns: - argparse.Namespace: Object containing parsed command-line arguments. - - Example: - ```python - opts = parse_opt() - print(opts.data) - print(opts.weights) - ``` - """ - parser = argparse.ArgumentParser() - parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") - parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model.pt path(s)") - parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)") - parser.add_argument("--batch-size", type=int, default=1, help="batch size") - parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") - parser.add_argument("--half", action="store_true", help="FP16 half-precision export") - parser.add_argument("--inplace", action="store_true", help="set YOLOv5 Detect() inplace=True") - parser.add_argument("--keras", action="store_true", help="TF: use Keras") - parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile") - parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization") - parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization") - parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes") - parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model") - parser.add_argument("--mlmodel", action="store_true", help="CoreML: Export in *.mlmodel format") - parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version") - parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log") - parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)") - parser.add_argument("--nms", action="store_true", help="TF: add NMS to model") - parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model") - parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep") - parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep") - parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold") - parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold") - parser.add_argument( - "--include", - nargs="+", - default=["torchscript"], - help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle", - ) - opt = parser.parse_known_args()[0] if known else parser.parse_args() - print_args(vars(opt)) - return opt - - -def main(opt): - """Run(**vars(opt)) # Execute the run function with parsed options.""" - for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]: - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/iteach_toolkit/DHYOLO/hubconf.py b/iteach_toolkit/DHYOLO/hubconf.py deleted file mode 100644 index 98e3994218098e8f70c3059e644d8ba24a564394..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/hubconf.py +++ /dev/null @@ -1,510 +0,0 @@ -# Ultralytics YOLOv5 🚀, AGPL-3.0 license -""" -PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5 - -Usage: - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model - model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch - model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model - model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo -""" - -import torch - - -def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - """ - Creates or loads a YOLOv5 model, with options for pretrained weights and model customization. - - Args: - name (str): Model name (e.g., 'yolov5s') or path to the model checkpoint (e.g., 'path/to/best.pt'). - pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True. - channels (int, optional): Number of input channels the model expects. Defaults to 3. - classes (int, optional): Number of classes the model is expected to detect. Defaults to 80. - autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for various input formats. Defaults to True. - verbose (bool, optional): If True, prints detailed information during the model creation/loading process. Defaults to True. - device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). If None, selects - the best available device. Defaults to None. - - Returns: - (DetectMultiBackend | AutoShape): The loaded YOLOv5 model, potentially wrapped with AutoShape if specified. - - Examples: - ```python - import torch - from ultralytics import _create - - # Load an official YOLOv5s model with pretrained weights - model = _create('yolov5s') - - # Load a custom model from a local checkpoint - model = _create('path/to/custom_model.pt', pretrained=False) - - # Load a model with specific input channels and classes - model = _create('yolov5s', channels=1, classes=10) - ``` - - Notes: - For more information on model loading and customization, visit the - [YOLOv5 PyTorch Hub Documentation](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading). - """ - from pathlib import Path - - from models.common import AutoShape, DetectMultiBackend - from models.experimental import attempt_load - from models.yolo import ClassificationModel, DetectionModel, SegmentationModel - from utils.downloads import attempt_download - from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging - from utils.torch_utils import select_device - - if not verbose: - LOGGER.setLevel(logging.WARNING) - check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop")) - name = Path(name) - path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path - try: - device = select_device(device) - if pretrained and channels == 3 and classes == 80: - try: - model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model - if autoshape: - if model.pt and isinstance(model.model, ClassificationModel): - LOGGER.warning( - "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. " - "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)." - ) - elif model.pt and isinstance(model.model, SegmentationModel): - LOGGER.warning( - "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. " - "You will not be able to run inference with this model." - ) - else: - model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS - except Exception: - model = attempt_load(path, device=device, fuse=False) # arbitrary model - else: - cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path - model = DetectionModel(cfg, channels, classes) # create model - if pretrained: - ckpt = torch.load(attempt_download(path), map_location=device) # load - csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 - csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect - model.load_state_dict(csd, strict=False) # load - if len(ckpt["model"].names) == classes: - model.names = ckpt["model"].names # set class names attribute - if not verbose: - LOGGER.setLevel(logging.INFO) # reset to default - return model.to(device) - - except Exception as e: - help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" - s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help." - raise Exception(s) from e - - -def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None): - """ - Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification. - - Args: - path (str): Path to the custom model file (e.g., 'path/to/model.pt'). - autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model if True, enabling compatibility with various input - types (default is True). - _verbose (bool): If True, prints all informational messages to the screen; otherwise, operates silently - (default is True). - device (str | torch.device | None): Device to load the model on, e.g., 'cpu', 'cuda', torch.device('cuda:0'), etc. - (default is None, which automatically selects the best available device). - - Returns: - torch.nn.Module: A YOLOv5 model loaded with the specified parameters. - - Notes: - For more details on loading models from PyTorch Hub: - https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading - - Examples: - ```python - # Load model from a given path with autoshape enabled on the best available device - model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') - - # Load model from a local path without autoshape on the CPU device - model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local', autoshape=False, device='cpu') - ``` - """ - return _create(path, autoshape=autoshape, verbose=_verbose, device=device) - - -def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping, - verbosity, and device. - - Args: - pretrained (bool): If True, loads pretrained weights into the model. Defaults to True. - channels (int): Number of input channels for the model. Defaults to 3. - classes (int): Number of classes for object detection. Defaults to 80. - autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper to the model for various formats (file/URI/PIL/ - cv2/np) and non-maximum suppression (NMS) during inference. Defaults to True. - _verbose (bool): If True, prints detailed information to the screen. Defaults to True. - device (str | torch.device | None): Specifies the device to use for model computation. If None, uses the best device - available (i.e., GPU if available, otherwise CPU). Defaults to None. - - Returns: - DetectionModel | ClassificationModel | SegmentationModel: The instantiated YOLOv5-nano model, potentially with - pretrained weights and autoshaping applied. - - Notes: - For further details on loading models from PyTorch Hub, refer to [PyTorch Hub models](https://pytorch.org/hub/ - ultralytics_yolov5). - - Examples: - ```python - import torch - from ultralytics import yolov5n - - # Load the YOLOv5-nano model with defaults - model = yolov5n() - - # Load the YOLOv5-nano model with a specific device - model = yolov5n(device='cuda') - ``` - """ - return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping, - verbosity, and device configuration. - - Args: - pretrained (bool, optional): Flag to load pretrained weights into the model. Defaults to True. - channels (int, optional): Number of input channels. Defaults to 3. - classes (int, optional): Number of model classes. Defaults to 80. - autoshape (bool, optional): Whether to wrap the model with YOLOv5's .autoshape() for handling various input formats. - Defaults to True. - _verbose (bool, optional): Flag to print detailed information regarding model loading. Defaults to True. - device (str | torch.device | None, optional): Device to use for model computation, can be 'cpu', 'cuda', or - torch.device instances. If None, automatically selects the best available device. Defaults to None. - - Returns: - torch.nn.Module: The YOLOv5-small model configured and loaded according to the specified parameters. - - Example: - ```python - import torch - - # Load the official YOLOv5-small model with pretrained weights - model = torch.hub.load('ultralytics/yolov5', 'yolov5s') - - # Load the YOLOv5-small model from a specific branch - model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') - - # Load a custom YOLOv5-small model from a local checkpoint - model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') - - # Load a local YOLOv5-small model specifying source as local repository - model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') - ``` - - Notes: - For more details on model loading and customization, visit - the [YOLOv5 PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5). - """ - return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping, - verbosity, and device. - - Args: - pretrained (bool, optional): Whether to load pretrained weights into the model. Default is True. - channels (int, optional): Number of input channels. Default is 3. - classes (int, optional): Number of model classes. Default is 80. - autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model for handling various input formats. - Default is True. - _verbose (bool, optional): Whether to print detailed information to the screen. Default is True. - device (str | torch.device | None, optional): Device specification to use for model parameters (e.g., 'cpu', 'cuda'). - Default is None. - - Returns: - torch.nn.Module: The instantiated YOLOv5-medium model. - - Usage Example: - ```python - import torch - - model = torch.hub.load('ultralytics/yolov5', 'yolov5m') # Load YOLOv5-medium from Ultralytics repository - model = torch.hub.load('ultralytics/yolov5:master', 'yolov5m') # Load from the master branch - model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m.pt') # Load a custom/local YOLOv5-medium model - model = torch.hub.load('.', 'custom', 'yolov5m.pt', source='local') # Load from a local repository - ``` - - For more information, visit https://pytorch.org/hub/ultralytics_yolov5. - """ - return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device - selection. - - Args: - pretrained (bool): Load pretrained weights into the model. Default is True. - channels (int): Number of input channels. Default is 3. - classes (int): Number of model classes. Default is 80. - autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model. Default is True. - _verbose (bool): Print all information to screen. Default is True. - device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', or a torch.device instance. - Default is None. - - Returns: - YOLOv5 model (torch.nn.Module): The YOLOv5-large model instantiated with specified configurations and possibly - pretrained weights. - - Examples: - ```python - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5l') - ``` - - Notes: - For additional details, refer to the PyTorch Hub models documentation: - https://pytorch.org/hub/ultralytics_yolov5 - """ - return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Perform object detection using the YOLOv5-xlarge model with options for pretraining, input channels, class count, - autoshaping, verbosity, and device specification. - - Args: - pretrained (bool): If True, loads pretrained weights into the model. Defaults to True. - channels (int): Number of input channels for the model. Defaults to 3. - classes (int): Number of model classes for object detection. Defaults to 80. - autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper for handling different input formats. Defaults to - True. - _verbose (bool): If True, prints detailed information during model loading. Defaults to True. - device (str | torch.device | None): Device specification for computing the model, e.g., 'cpu', 'cuda:0', torch.device('cuda'). - Defaults to None. - - Returns: - torch.nn.Module: The YOLOv5-xlarge model loaded with the specified parameters, optionally with pretrained weights and - autoshaping applied. - - Example: - ```python - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5x') - ``` - - For additional details, refer to the official YOLOv5 PyTorch Hub models documentation: - https://pytorch.org/hub/ultralytics_yolov5 - """ - return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device. - - Args: - pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True. - channels (int, optional): Number of input channels. Default is 3. - classes (int, optional): Number of model classes. Default is 80. - autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model. Default is True. - _verbose (bool, optional): If True, prints all information to screen. Default is True. - device (str | torch.device | None, optional): Device to use for model parameters. Can be 'cpu', 'cuda', or None. - Default is None. - - Returns: - torch.nn.Module: YOLOv5-nano-P6 model loaded with the specified configurations. - - Example: - ```python - import torch - model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda') - ``` - - Notes: - For more information on PyTorch Hub models, visit: https://pytorch.org/hub/ultralytics_yolov5 - """ - return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping, - verbosity, and device selection. - - Args: - pretrained (bool): If True, loads pretrained weights. Default is True. - channels (int): Number of input channels. Default is 3. - classes (int): Number of object detection classes. Default is 80. - autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model, allowing for varied input formats. - Default is True. - _verbose (bool): If True, prints detailed information during model loading. Default is True. - device (str | torch.device | None): Device specification for model parameters (e.g., 'cpu', 'cuda', or torch.device). - Default is None, which selects an available device automatically. - - Returns: - torch.nn.Module: The YOLOv5-small-P6 model instance. - - Usage: - ```python - import torch - - model = torch.hub.load('ultralytics/yolov5', 'yolov5s6') - model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s6') # load from a specific branch - model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5s6.pt') # custom/local model - model = torch.hub.load('.', 'custom', 'path/to/yolov5s6.pt', source='local') # local repo model - ``` - - Notes: - - For more information, refer to the PyTorch Hub models documentation at https://pytorch.org/hub/ultralytics_yolov5 - - Raises: - Exception: If there is an error during model creation or loading, with a suggestion to visit the YOLOv5 - tutorials for help. - """ - return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, and - device. - - Args: - pretrained (bool): If True, loads pretrained weights. Default is True. - channels (int): Number of input channels. Default is 3. - classes (int): Number of model classes. Default is 80. - autoshape (bool): Apply YOLOv5 .autoshape() wrapper to the model for file/URI/PIL/cv2/np inputs and NMS. - Default is True. - _verbose (bool): If True, prints detailed information to the screen. Default is True. - device (str | torch.device | None): Device to use for model parameters. Default is None, which uses the - best available device. - - Returns: - torch.nn.Module: The YOLOv5-medium-P6 model. - - Refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 for additional details. - - Example: - ```python - import torch - - # Load YOLOv5-medium-P6 model - model = torch.hub.load('ultralytics/yolov5', 'yolov5m6') - ``` - - Notes: - - The model can be loaded with pre-trained weights for better performance on specific tasks. - - The autoshape feature simplifies input handling by allowing various popular data formats. - """ - return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping, - verbosity, and device selection. - - Args: - pretrained (bool, optional): If True, load pretrained weights into the model. Default is True. - channels (int, optional): Number of input channels. Default is 3. - classes (int, optional): Number of model classes. Default is 80. - autoshape (bool, optional): If True, apply YOLOv5 .autoshape() wrapper to the model for input flexibility. Default is True. - _verbose (bool, optional): If True, print all information to the screen. Default is True. - device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda', or torch.device. - If None, automatically selects the best available device. Default is None. - - Returns: - torch.nn.Module: The instantiated YOLOv5-large-P6 model. - - Example: - ```python - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # official model - model = torch.hub.load('ultralytics/yolov5:master', 'yolov5l6') # from specific branch - model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5l6.pt') # custom/local model - model = torch.hub.load('.', 'custom', 'path/to/yolov5l6.pt', source='local') # local repository - ``` - - Note: - Refer to [PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5) for additional usage instructions. - """ - return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device) - - -def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): - """ - Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count, autoshaping, - verbosity, and device selection. - - Args: - pretrained (bool): If True, loads pretrained weights into the model. Default is True. - channels (int): Number of input channels. Default is 3. - classes (int): Number of model classes. Default is 80. - autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model. Default is True. - _verbose (bool): If True, prints all information to the screen. Default is True. - device (str | torch.device | None): Device to use for model parameters, can be a string, torch.device object, or - None for default device selection. Default is None. - - Returns: - torch.nn.Module: The instantiated YOLOv5-xlarge-P6 model. - - Example: - ```python - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5x6') # load the YOLOv5-xlarge-P6 model - ``` - - Note: - For more information on YOLOv5 models, visit the official documentation: - https://docs.ultralytics.com/yolov5 - """ - return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device) - - -if __name__ == "__main__": - import argparse - from pathlib import Path - - import numpy as np - from PIL import Image - - from utils.general import cv2, print_args - - # Argparser - parser = argparse.ArgumentParser() - parser.add_argument("--model", type=str, default="yolov5s", help="model name") - opt = parser.parse_args() - print_args(vars(opt)) - - # Model - model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) - # model = custom(path='path/to/model.pt') # custom - - # Images - imgs = [ - "data/images/zidane.jpg", # filename - Path("data/images/zidane.jpg"), # Path - "https://ultralytics.com/images/zidane.jpg", # URI - cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV - Image.open("data/images/bus.jpg"), # PIL - np.zeros((320, 640, 3)), - ] # numpy - - # Inference - results = model(imgs, size=320) # batched inference - - # Results - results.print() - results.save() diff --git a/iteach_toolkit/DHYOLO/model.py b/iteach_toolkit/DHYOLO/model.py deleted file mode 100644 index d463023d107f3e87638e531584601f6960159f06..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/model.py +++ /dev/null @@ -1,174 +0,0 @@ -from .detect import run as run_detection -import torch -import cv2 -import logging - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -class DHYOLODetector: - """ - A class to encapsulate the YOLO model prediction for object detection. - - Attributes: - model_path (str): Path to the trained YOLO model weights. - - Methods: - predict(image_path, conf_thres, iou_thres, max_det): - Runs object detection on the given image using the YOLO model. - Logs errors in case of failure and handles exceptions. - plot_bboxes(): - Plots bounding boxes on the input image based on YOLO predictions and returns the modified image. - """ - - def __init__(self, model_path): - """ - Initializes DHYOLODetector with the path to the YOLO model weights. - - Args: - model_path (str): Path to the YOLO model weights. - """ - self.model_path = model_path - self.image = None - self.preds = None - - def predict(self, image_path, conf_thres=0.25, iou_thres=0.45, max_det=1000): - """ - Runs object detection on the provided image using the YOLO model. - - Args: - image_path (str): Path to the image file (file path or a web URL). - conf_thres (float): Confidence threshold for YOLO detections (default is 0.25). - iou_thres (float): IOU threshold for non-max suppression (default is 0.45). - max_det (int): Maximum number of detections per image (default is 1000). - - Returns: - tuple: A tuple containing: - - numpy.ndarray: The image read as a NumPy array. - - dict: A dictionary with the bounding boxes in xyxy format, confidence scores, and class labels: - - 'boxes': List of bounding boxes in xyxy format. - - 'confidences': List of confidence scores for each detection. - - 'class_labels': List of class labels corresponding to each detection. - - Raises: - FileNotFoundError: If the image file does not exist or cannot be opened. - Exception: For any general errors during prediction. - """ - try: - logger.info(f"Starting prediction for image: {image_path} with model: {self.model_path}") - - - self.image = cv2.imread(image_path) # Read the image as a NumPy array - self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB - - if self.image is None: - raise FileNotFoundError(f"Image file not found or cannot be opened: {image_path}") - - # Run YOLO detection with custom parameters - result = run_detection( - weights=self.model_path, - source=image_path, - conf_thres=conf_thres, # Confidence threshold - iou_thres=iou_thres, # IOU threshold - max_det=max_det # Maximum detections - ) - - self.preds = result[0] # We are only running for one image - - # Prepare predictions as a dictionary - detections_dict = { - 'boxes': [], # List of bounding boxes in xyxy format - 'confidences': [], # List of confidence scores - 'class_labels': [] # List of class labels - } - - for detection in self.preds: - if isinstance(detection, torch.Tensor): - detection = detection.cpu().numpy() - - # Extract details - x1, y1, x2, y2, conf, cls = detection[:6] - detections_dict['boxes'].append([float(x1), float(y1), float(x2), float(y2)]) # xyxy format - detections_dict['confidences'].append(float(conf)) - detections_dict['class_labels'].append(int(cls)) - - logger.info(f"Prediction completed successfully for image: {image_path}") - return self.image, detections_dict # Return image and detections as a tuple - - except FileNotFoundError as e: - logger.error(f"Image file not found: {image_path}. Exception: {e}") - raise - - except Exception as e: - logger.error(f"An error occurred during prediction. Exception: {e}") - raise - - def plot_bboxes(self, attach_watermark=False): - """ - Plots bounding boxes on the input image based on YOLO predictions and returns the modified image. - - Args: - attach_watermark (bool): Whether to attach a watermark text to the image (default is False). - - Returns: - tuple: The original image and the image with bounding boxes plotted. - """ - class_labels = {0: "door", 1: "handle"} - class_colors = { - 0: (255, 0, 0), # Red in RGB format for doors - 1: (255, 255, 0) # Yellow in RGB format for handles - } - - bbox_img = self.image.copy() # Create a copy of the original image - - # Check if there are predictions - if self.preds is None or len(self.preds) == 0: - logger.warning("No predictions to display.") - return bbox_img, bbox_img # Return the original image if no predictions - - # Iterate through detections and plot each bounding box - for detection in self.preds: - if isinstance(detection, torch.Tensor): - detection = detection.cpu().numpy() - - conf = detection[4] - x1, y1, x2, y2, _, cls = detection[:6].astype(float) # Ensure float for bounding box coordinates - label = class_labels[int(cls)] - - # Draw the rectangle on the bbox_img - cv2.rectangle(bbox_img, (int(x1), int(y1)), (int(x2), int(y2)), class_colors[int(cls)], 2) - - # Prepare text with confidence score - text = f'{label} ({conf:.2f})' # Include confidence score in the text - text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_DUPLEX, 0.5, 1)[0] - - # Set text position directly above the bounding box - text_x = int(x1) - text_y = int(y1) - 2 # Adjust for a slight overlap with the bounding box - - # Set text color based on class - text_color = (0, 0, 0) if cls == 1 else (255, 255, 255) # Black for handle, white for door - - # Draw a background rectangle for the text - cv2.rectangle(bbox_img, (text_x, text_y - text_size[1] - 2), (text_x + text_size[0], text_y), class_colors[int(cls)], cv2.FILLED) - - # Put the label text on the bbox_img - cv2.putText(bbox_img, text, (text_x, text_y - 2), cv2.FONT_HERSHEY_DUPLEX, 0.5, text_color, 1, cv2.LINE_AA) - - # Attach watermark if specified - if attach_watermark: - watermark_text = "Predictions by DH-YOLO" - watermark_color = (200, 200, 200) # Greyish color for watermark - watermark_scale = 0.4 # Reduced scale for the watermark text - watermark_thickness = 1 # Decreased thickness for the watermark text - - # Get the text size for positioning - text_size = cv2.getTextSize(watermark_text, cv2.FONT_HERSHEY_DUPLEX, watermark_scale, watermark_thickness)[0] - text_x = bbox_img.shape[1] - text_size[0] - 10 # 10 pixels from right - text_y = bbox_img.shape[0] - 10 # 10 pixels from bottom - - # Put the watermark text on the bbox_img - cv2.putText(bbox_img, watermark_text, (text_x, text_y), cv2.FONT_HERSHEY_DUPLEX, watermark_scale, watermark_color, watermark_thickness, cv2.LINE_AA) - - return self.image, bbox_img # Return the original image and modified image with bounding boxes diff --git 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contextlib -import json -import math -import platform -import warnings -import zipfile -from collections import OrderedDict, namedtuple -from copy import copy -from pathlib import Path -from urllib.parse import urlparse - -import cv2 -import numpy as np -import pandas as pd -import requests -import torch -import torch.nn as nn -from PIL import Image -from torch.cuda import amp - -# Import 'ultralytics' package or install if if missing -try: - import ultralytics - - assert hasattr(ultralytics, '__version__') # verify package is not directory -except (ImportError, AssertionError): - import os - - os.system('pip install -U ultralytics') - import ultralytics - -from ultralytics.utils.plotting import Annotator, colors, save_one_box - -from utils import TryExcept -from utils.dataloaders import exif_transpose, letterbox -from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, - increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, - xyxy2xywh, yaml_load) -from utils.torch_utils import copy_attr, smart_inference_mode - - -def autopad(k, p=None, d=1): # kernel, padding, dilation - # Pad to 'same' shape outputs - if d > 1: - k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -class Conv(nn.Module): - # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) - default_act = nn.SiLU() # default activation - - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): - super().__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def forward_fuse(self, x): - return self.act(self.conv(x)) - - -class DWConv(Conv): - # Depth-wise convolution - def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation - super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) - - -class DWConvTranspose2d(nn.ConvTranspose2d): - # Depth-wise transpose convolution - def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out - super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) - - -class TransformerLayer(nn.Module): - # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) - def __init__(self, c, num_heads): - super().__init__() - self.q = nn.Linear(c, c, bias=False) - self.k = nn.Linear(c, c, bias=False) - self.v = nn.Linear(c, c, bias=False) - self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) - self.fc1 = nn.Linear(c, c, bias=False) - self.fc2 = nn.Linear(c, c, bias=False) - - def forward(self, x): - x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x - x = self.fc2(self.fc1(x)) + x - return x - - -class TransformerBlock(nn.Module): - # Vision Transformer https://arxiv.org/abs/2010.11929 - def __init__(self, c1, c2, num_heads, num_layers): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - self.linear = nn.Linear(c2, c2) # learnable position embedding - self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) - self.c2 = c2 - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - b, _, w, h = x.shape - p = x.flatten(2).permute(2, 0, 1) - return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) - - -class Bottleneck(nn.Module): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2, 3, 1, g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class BottleneckCSP(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) - self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) - self.cv4 = Conv(2 * c_, c2, 1, 1) - self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) - self.act = nn.SiLU() - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) - - -class CrossConv(nn.Module): - # Cross Convolution Downsample - def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): - # ch_in, ch_out, kernel, stride, groups, expansion, shortcut - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, (1, k), (1, s)) - self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class C3(nn.Module): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) - - -class C3x(C3): - # C3 module with cross-convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) - - -class C3TR(C3): - # C3 module with TransformerBlock() - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = TransformerBlock(c_, c_, 4, n) - - -class C3SPP(C3): - # C3 module with SPP() - def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = SPP(c_, c_, k) - - -class C3Ghost(C3): - # C3 module with GhostBottleneck() - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) - - -class SPP(nn.Module): - # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 - def __init__(self, c1, c2, k=(5, 9, 13)): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - - def forward(self, x): - x = self.cv1(x) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class SPPF(nn.Module): - # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher - def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * 4, c2, 1, 1) - self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) - - def forward(self, x): - x = self.cv1(x) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) - - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) - # self.contract = Contract(gain=2) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) - # return self.conv(self.contract(x)) - - -class GhostConv(nn.Module): - # Ghost Convolution https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups - super().__init__() - c_ = c2 // 2 # hidden channels - self.cv1 = Conv(c1, c_, k, s, None, g, act=act) - self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) - - def forward(self, x): - y = self.cv1(x) - return torch.cat((y, self.cv2(y)), 1) - - -class GhostBottleneck(nn.Module): - # Ghost Bottleneck https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride - super().__init__() - c_ = c2 // 2 - self.conv = nn.Sequential( - GhostConv(c1, c_, 1, 1), # pw - DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw - GhostConv(c_, c2, 1, 1, act=False)) # pw-linear - self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, - act=False)) if s == 2 else nn.Identity() - - def forward(self, x): - return self.conv(x) + self.shortcut(x) - - -class Contract(nn.Module): - # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' - s = self.gain - x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) - return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) - - -class Expand(nn.Module): - # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' - s = self.gain - x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) - x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) - return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) - - -class Concat(nn.Module): - # Concatenate a list of tensors along dimension - def __init__(self, dimension=1): - super().__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class DetectMultiBackend(nn.Module): - # YOLOv5 MultiBackend class for python inference on various backends - def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True): - # Usage: - # PyTorch: weights = *.pt - # TorchScript: *.torchscript - # ONNX Runtime: *.onnx - # ONNX OpenCV DNN: *.onnx --dnn - # OpenVINO: *_openvino_model - # CoreML: *.mlmodel - # TensorRT: *.engine - # TensorFlow SavedModel: *_saved_model - # TensorFlow GraphDef: *.pb - # TensorFlow Lite: *.tflite - # TensorFlow Edge TPU: *_edgetpu.tflite - # PaddlePaddle: *_paddle_model - from models.experimental import attempt_download, attempt_load # scoped to avoid circular import - - super().__init__() - w = str(weights[0] if isinstance(weights, list) else weights) - pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) - fp16 &= pt or jit or onnx or engine or triton # FP16 - nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) - stride = 32 # default stride - cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA - if not (pt or triton): - w = attempt_download(w) # download if not local - - if pt: # PyTorch - model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) - stride = max(int(model.stride.max()), 32) # model stride - names = model.module.names if hasattr(model, 'module') else model.names # get class names - model.half() if fp16 else model.float() - self.model = model # explicitly assign for to(), cpu(), cuda(), half() - elif jit: # TorchScript - LOGGER.info(f'Loading {w} for TorchScript inference...') - extra_files = {'config.txt': ''} # model metadata - model = torch.jit.load(w, _extra_files=extra_files, map_location=device) - model.half() if fp16 else model.float() - if extra_files['config.txt']: # load metadata dict - d = json.loads(extra_files['config.txt'], - object_hook=lambda d: { - int(k) if k.isdigit() else k: v - for k, v in d.items()}) - stride, names = int(d['stride']), d['names'] - elif dnn: # ONNX OpenCV DNN - LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') - check_requirements('opencv-python>=4.5.4') - net = cv2.dnn.readNetFromONNX(w) - elif onnx: # ONNX Runtime - LOGGER.info(f'Loading {w} for ONNX Runtime inference...') - check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) - import onnxruntime - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] - session = onnxruntime.InferenceSession(w, providers=providers) - output_names = [x.name for x in session.get_outputs()] - meta = session.get_modelmeta().custom_metadata_map # metadata - if 'stride' in meta: - stride, names = int(meta['stride']), eval(meta['names']) - elif xml: # OpenVINO - LOGGER.info(f'Loading {w} for OpenVINO inference...') - check_requirements('openvino>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/ - from openvino.runtime import Core, Layout, get_batch - core = Core() - if not Path(w).is_file(): # if not *.xml - w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir - ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin')) - if ov_model.get_parameters()[0].get_layout().empty: - ov_model.get_parameters()[0].set_layout(Layout('NCHW')) - batch_dim = get_batch(ov_model) - if batch_dim.is_static: - batch_size = batch_dim.get_length() - ov_compiled_model = core.compile_model(ov_model, device_name='AUTO') # AUTO selects best available device - stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata - elif engine: # TensorRT - LOGGER.info(f'Loading {w} for TensorRT inference...') - import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download - check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 - if device.type == 'cpu': - device = torch.device('cuda:0') - Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) - logger = trt.Logger(trt.Logger.INFO) - with open(w, 'rb') as f, trt.Runtime(logger) as runtime: - model = runtime.deserialize_cuda_engine(f.read()) - context = model.create_execution_context() - bindings = OrderedDict() - output_names = [] - fp16 = False # default updated below - dynamic = False - for i in range(model.num_bindings): - name = model.get_binding_name(i) - dtype = trt.nptype(model.get_binding_dtype(i)) - if model.binding_is_input(i): - if -1 in tuple(model.get_binding_shape(i)): # dynamic - dynamic = True - context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) - if dtype == np.float16: - fp16 = True - else: # output - output_names.append(name) - shape = tuple(context.get_binding_shape(i)) - im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) - bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) - binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) - batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size - elif coreml: # CoreML - LOGGER.info(f'Loading {w} for CoreML inference...') - import coremltools as ct - model = ct.models.MLModel(w) - elif saved_model: # TF SavedModel - LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') - import tensorflow as tf - keras = False # assume TF1 saved_model - model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) - elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt - LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') - import tensorflow as tf - - def wrap_frozen_graph(gd, inputs, outputs): - x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped - ge = x.graph.as_graph_element - return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) - - def gd_outputs(gd): - name_list, input_list = [], [] - for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef - name_list.append(node.name) - input_list.extend(node.input) - return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) - - gd = tf.Graph().as_graph_def() # TF GraphDef - with open(w, 'rb') as f: - gd.ParseFromString(f.read()) - frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd)) - elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python - try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu - from tflite_runtime.interpreter import Interpreter, load_delegate - except ImportError: - import tensorflow as tf - Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, - if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime - LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') - delegate = { - 'Linux': 'libedgetpu.so.1', - 'Darwin': 'libedgetpu.1.dylib', - 'Windows': 'edgetpu.dll'}[platform.system()] - interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) - else: # TFLite - LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') - interpreter = Interpreter(model_path=w) # load TFLite model - interpreter.allocate_tensors() # allocate - input_details = interpreter.get_input_details() # inputs - output_details = interpreter.get_output_details() # outputs - # load metadata - with contextlib.suppress(zipfile.BadZipFile): - with zipfile.ZipFile(w, 'r') as model: - meta_file = model.namelist()[0] - meta = ast.literal_eval(model.read(meta_file).decode('utf-8')) - stride, names = int(meta['stride']), meta['names'] - elif tfjs: # TF.js - raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') - elif paddle: # PaddlePaddle - LOGGER.info(f'Loading {w} for PaddlePaddle inference...') - check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') - import paddle.inference as pdi - if not Path(w).is_file(): # if not *.pdmodel - w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir - weights = Path(w).with_suffix('.pdiparams') - config = pdi.Config(str(w), str(weights)) - if cuda: - config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) - predictor = pdi.create_predictor(config) - input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) - output_names = predictor.get_output_names() - elif triton: # NVIDIA Triton Inference Server - LOGGER.info(f'Using {w} as Triton Inference Server...') - check_requirements('tritonclient[all]') - from utils.triton import TritonRemoteModel - model = TritonRemoteModel(url=w) - nhwc = model.runtime.startswith('tensorflow') - else: - raise NotImplementedError(f'ERROR: {w} is not a supported format') - - # class names - if 'names' not in locals(): - names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} - if names[0] == 'n01440764' and len(names) == 1000: # ImageNet - names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names - - self.__dict__.update(locals()) # assign all variables to self - - def forward(self, im, augment=False, visualize=False): - # YOLOv5 MultiBackend inference - b, ch, h, w = im.shape # batch, channel, height, width - if self.fp16 and im.dtype != torch.float16: - im = im.half() # to FP16 - if self.nhwc: - im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) - - if self.pt: # PyTorch - y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) - elif self.jit: # TorchScript - y = self.model(im) - elif self.dnn: # ONNX OpenCV DNN - im = im.cpu().numpy() # torch to numpy - self.net.setInput(im) - y = self.net.forward() - elif self.onnx: # ONNX Runtime - im = im.cpu().numpy() # torch to numpy - y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) - elif self.xml: # OpenVINO - im = im.cpu().numpy() # FP32 - y = list(self.ov_compiled_model(im).values()) - elif self.engine: # TensorRT - if self.dynamic and im.shape != self.bindings['images'].shape: - i = self.model.get_binding_index('images') - self.context.set_binding_shape(i, im.shape) # reshape if dynamic - self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) - for name in self.output_names: - i = self.model.get_binding_index(name) - self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) - s = self.bindings['images'].shape - assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" - self.binding_addrs['images'] = int(im.data_ptr()) - self.context.execute_v2(list(self.binding_addrs.values())) - y = [self.bindings[x].data for x in sorted(self.output_names)] - elif self.coreml: # CoreML - im = im.cpu().numpy() - im = Image.fromarray((im[0] * 255).astype('uint8')) - # im = im.resize((192, 320), Image.BILINEAR) - y = self.model.predict({'image': im}) # coordinates are xywh normalized - if 'confidence' in y: - box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels - conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) - y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) - else: - y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) - elif self.paddle: # PaddlePaddle - im = im.cpu().numpy().astype(np.float32) - self.input_handle.copy_from_cpu(im) - self.predictor.run() - y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] - elif self.triton: # NVIDIA Triton Inference Server - y = self.model(im) - else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) - im = im.cpu().numpy() - if self.saved_model: # SavedModel - y = self.model(im, training=False) if self.keras else self.model(im) - elif self.pb: # GraphDef - y = self.frozen_func(x=self.tf.constant(im)) - else: # Lite or Edge TPU - input = self.input_details[0] - int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model - if int8: - scale, zero_point = input['quantization'] - im = (im / scale + zero_point).astype(np.uint8) # de-scale - self.interpreter.set_tensor(input['index'], im) - self.interpreter.invoke() - y = [] - for output in self.output_details: - x = self.interpreter.get_tensor(output['index']) - if int8: - scale, zero_point = output['quantization'] - x = (x.astype(np.float32) - zero_point) * scale # re-scale - y.append(x) - y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] - y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels - - if isinstance(y, (list, tuple)): - return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] - else: - return self.from_numpy(y) - - def from_numpy(self, x): - return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x - - def warmup(self, imgsz=(1, 3, 640, 640)): - # Warmup model by running inference once - warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton - if any(warmup_types) and (self.device.type != 'cpu' or self.triton): - im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input - for _ in range(2 if self.jit else 1): # - self.forward(im) # warmup - - @staticmethod - def _model_type(p='path/to/model.pt'): - # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx - # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] - from export import export_formats - from utils.downloads import is_url - sf = list(export_formats().Suffix) # export suffixes - if not is_url(p, check=False): - check_suffix(p, sf) # checks - url = urlparse(p) # if url may be Triton inference server - types = [s in Path(p).name for s in sf] - types[8] &= not types[9] # tflite &= not edgetpu - triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc]) - return types + [triton] - - @staticmethod - def _load_metadata(f=Path('path/to/meta.yaml')): - # Load metadata from meta.yaml if it exists - if f.exists(): - d = yaml_load(f) - return d['stride'], d['names'] # assign stride, names - return None, None - - -class AutoShape(nn.Module): - # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - agnostic = False # NMS class-agnostic - multi_label = False # NMS multiple labels per box - classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs - max_det = 1000 # maximum number of detections per image - amp = False # Automatic Mixed Precision (AMP) inference - - def __init__(self, model, verbose=True): - super().__init__() - if verbose: - LOGGER.info('Adding AutoShape... ') - copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes - self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance - self.pt = not self.dmb or model.pt # PyTorch model - self.model = model.eval() - if self.pt: - m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() - m.inplace = False # Detect.inplace=False for safe multithread inference - m.export = True # do not output loss values - - def _apply(self, fn): - # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers - self = super()._apply(fn) - if self.pt: - m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() - m.stride = fn(m.stride) - m.grid = list(map(fn, m.grid)) - if isinstance(m.anchor_grid, list): - m.anchor_grid = list(map(fn, m.anchor_grid)) - return self - - @smart_inference_mode() - def forward(self, ims, size=640, augment=False, profile=False): - # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: - # file: ims = 'data/images/zidane.jpg' # str or PosixPath - # URI: = 'https://ultralytics.com/images/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) - # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) - # numpy: = np.zeros((640,1280,3)) # HWC - # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - dt = (Profile(), Profile(), Profile()) - with dt[0]: - if isinstance(size, int): # expand - size = (size, size) - p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param - autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference - if isinstance(ims, torch.Tensor): # torch - with amp.autocast(autocast): - return self.model(ims.to(p.device).type_as(p), augment=augment) # inference - - # Pre-process - n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images - shape0, shape1, files = [], [], [] # image and inference shapes, filenames - for i, im in enumerate(ims): - f = f'image{i}' # filename - if isinstance(im, (str, Path)): # filename or uri - im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im - im = np.asarray(exif_transpose(im)) - elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = max(size) / max(s) # gain - shape1.append([int(y * g) for y in s]) - ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update - shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape - x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad - x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 - - with amp.autocast(autocast): - # Inference - with dt[1]: - y = self.model(x, augment=augment) # forward - - # Post-process - with dt[2]: - y = non_max_suppression(y if self.dmb else y[0], - self.conf, - self.iou, - self.classes, - self.agnostic, - self.multi_label, - max_det=self.max_det) # NMS - for i in range(n): - scale_boxes(shape1, y[i][:, :4], shape0[i]) - - return Detections(ims, y, files, dt, self.names, x.shape) - - -class Detections: - # YOLOv5 detections class for inference results - def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): - super().__init__() - d = pred[0].device # device - gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations - self.ims = ims # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.files = files # image filenames - self.times = times # profiling times - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) # number of images (batch size) - self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) - self.s = tuple(shape) # inference BCHW shape - - def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): - s, crops = '', [] - for i, (im, pred) in enumerate(zip(self.ims, self.pred)): - s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string - if pred.shape[0]: - for c in pred[:, -1].unique(): - n = (pred[:, -1] == c).sum() # detections per class - s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - s = s.rstrip(', ') - if show or save or render or crop: - annotator = Annotator(im, example=str(self.names)) - for *box, conf, cls in reversed(pred): # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' - if crop: - file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None - crops.append({ - 'box': box, - 'conf': conf, - 'cls': cls, - 'label': label, - 'im': save_one_box(box, im, file=file, save=save)}) - else: # all others - annotator.box_label(box, label if labels else '', color=colors(cls)) - im = annotator.im - else: - s += '(no detections)' - - im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np - if show: - if is_jupyter(): - from IPython.display import display - display(im) - else: - im.show(self.files[i]) - if save: - f = self.files[i] - im.save(save_dir / f) # save - if i == self.n - 1: - LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") - if render: - self.ims[i] = np.asarray(im) - if pprint: - s = s.lstrip('\n') - return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t - if crop: - if save: - LOGGER.info(f'Saved results to {save_dir}\n') - return crops - - @TryExcept('Showing images is not supported in this environment') - def show(self, labels=True): - self._run(show=True, labels=labels) # show results - - def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): - save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir - self._run(save=True, labels=labels, save_dir=save_dir) # save results - - def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): - save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None - return self._run(crop=True, save=save, save_dir=save_dir) # crop results - - def render(self, labels=True): - self._run(render=True, labels=labels) # render results - return self.ims - - def pandas(self): - # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) - new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): - a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update - setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) - return new - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - r = range(self.n) # iterable - x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] - # for d in x: - # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: - # setattr(d, k, getattr(d, k)[0]) # pop out of list - return x - - def print(self): - LOGGER.info(self.__str__()) - - def __len__(self): # override len(results) - return self.n - - def __str__(self): # override print(results) - return self._run(pprint=True) # print results - - def __repr__(self): - return f'YOLOv5 {self.__class__} instance\n' + self.__str__() - - -class Proto(nn.Module): - # YOLOv5 mask Proto module for segmentation models - def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks - super().__init__() - self.cv1 = Conv(c1, c_, k=3) - self.upsample = nn.Upsample(scale_factor=2, mode='nearest') - self.cv2 = Conv(c_, c_, k=3) - self.cv3 = Conv(c_, c2) - - def forward(self, x): - return self.cv3(self.cv2(self.upsample(self.cv1(x)))) - - -class Classify(nn.Module): - # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2) - def __init__(self, - c1, - c2, - k=1, - s=1, - p=None, - g=1, - dropout_p=0.0): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability - super().__init__() - c_ = 1280 # efficientnet_b0 size - self.conv = Conv(c1, c_, k, s, autopad(k, p), g) - self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) - self.drop = nn.Dropout(p=dropout_p, inplace=True) - self.linear = nn.Linear(c_, c2) # to x(b,c2) - - def forward(self, x): - if isinstance(x, list): - x = torch.cat(x, 1) - return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) diff --git a/iteach_toolkit/DHYOLO/models/doordetectweights.yaml b/iteach_toolkit/DHYOLO/models/doordetectweights.yaml deleted file mode 100644 index a76900c5a2e2602d59ece8645bbd67e0dc454311..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/doordetectweights.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/experimental.py b/iteach_toolkit/DHYOLO/models/experimental.py deleted file mode 100644 index 11f75e2254b3054fc8c3b5be6e3fa0a994912a97..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/experimental.py +++ /dev/null @@ -1,111 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Experimental modules -""" -import math - -import numpy as np -import torch -import torch.nn as nn - -from utils.downloads import attempt_download - - -class Sum(nn.Module): - # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 - def __init__(self, n, weight=False): # n: number of inputs - super().__init__() - self.weight = weight # apply weights boolean - self.iter = range(n - 1) # iter object - if weight: - self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights - - def forward(self, x): - y = x[0] # no weight - if self.weight: - w = torch.sigmoid(self.w) * 2 - for i in self.iter: - y = y + x[i + 1] * w[i] - else: - for i in self.iter: - y = y + x[i + 1] - return y - - -class MixConv2d(nn.Module): - # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 - def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy - super().__init__() - n = len(k) # number of convolutions - if equal_ch: # equal c_ per group - i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices - c_ = [(i == g).sum() for g in range(n)] # intermediate channels - else: # equal weight.numel() per group - b = [c2] + [0] * n - a = np.eye(n + 1, n, k=-1) - a -= np.roll(a, 1, axis=1) - a *= np.array(k) ** 2 - a[0] = 1 - c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b - - self.m = nn.ModuleList([ - nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() - - def forward(self, x): - return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) - - -class Ensemble(nn.ModuleList): - # Ensemble of models - def __init__(self): - super().__init__() - - def forward(self, x, augment=False, profile=False, visualize=False): - y = [module(x, augment, profile, visualize)[0] for module in self] - # y = torch.stack(y).max(0)[0] # max ensemble - # y = torch.stack(y).mean(0) # mean ensemble - y = torch.cat(y, 1) # nms ensemble - return y, None # inference, train output - - -def attempt_load(weights, device=None, inplace=True, fuse=True): - # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a - from models.yolo import Detect, Model - - model = Ensemble() - for w in weights if isinstance(weights, list) else [weights]: - ckpt = torch.load(attempt_download(w), map_location='cpu') # load - ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model - - # Model compatibility updates - if not hasattr(ckpt, 'stride'): - ckpt.stride = torch.tensor([32.]) - if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)): - ckpt.names = dict(enumerate(ckpt.names)) # convert to dict - - model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode - - # Module updates - for m in model.modules(): - t = type(m) - if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): - m.inplace = inplace - if t is Detect and not isinstance(m.anchor_grid, list): - delattr(m, 'anchor_grid') - setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) - elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): - m.recompute_scale_factor = None # torch 1.11.0 compatibility - - # Return model - if len(model) == 1: - return model[-1] - - # Return detection ensemble - print(f'Ensemble created with {weights}\n') - for k in 'names', 'nc', 'yaml': - setattr(model, k, getattr(model[0], k)) - model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride - assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' - return model diff --git a/iteach_toolkit/DHYOLO/models/hub/anchors.yaml b/iteach_toolkit/DHYOLO/models/hub/anchors.yaml deleted file mode 100644 index df2f668b022ca363b47e62ef95bdf20d418fe0e3..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/anchors.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -# Default anchors for COCO data - - -# P5 ------------------------------------------------------------------------------------------------------------------- -# P5-640: -anchors_p5_640: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - - -# P6 ------------------------------------------------------------------------------------------------------------------- -# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 -anchors_p6_640: - - [9,11, 21,19, 17,41] # P3/8 - - [43,32, 39,70, 86,64] # P4/16 - - [65,131, 134,130, 120,265] # P5/32 - - [282,180, 247,354, 512,387] # P6/64 - -# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 -anchors_p6_1280: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 -anchors_p6_1920: - - [28,41, 67,59, 57,141] # P3/8 - - [144,103, 129,227, 270,205] # P4/16 - - [209,452, 455,396, 358,812] # P5/32 - - [653,922, 1109,570, 1387,1187] # P6/64 - - -# P7 ------------------------------------------------------------------------------------------------------------------- -# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 -anchors_p7_640: - - [11,11, 13,30, 29,20] # P3/8 - - [30,46, 61,38, 39,92] # P4/16 - - [78,80, 146,66, 79,163] # P5/32 - - [149,150, 321,143, 157,303] # P6/64 - - [257,402, 359,290, 524,372] # P7/128 - -# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 -anchors_p7_1280: - - [19,22, 54,36, 32,77] # P3/8 - - [70,83, 138,71, 75,173] # P4/16 - - [165,159, 148,334, 375,151] # P5/32 - - [334,317, 251,626, 499,474] # P6/64 - - [750,326, 534,814, 1079,818] # P7/128 - -# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 -anchors_p7_1920: - - [29,34, 81,55, 47,115] # P3/8 - - [105,124, 207,107, 113,259] # P4/16 - - [247,238, 222,500, 563,227] # P5/32 - - [501,476, 376,939, 749,711] # P6/64 - - [1126,489, 801,1222, 1618,1227] # P7/128 diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov3-spp.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov3-spp.yaml deleted file mode 100644 index 4a71ed405277ce10a3c3f386834764ff0a82d53c..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov3-spp.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# darknet53 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [32, 3, 1]], # 0 - [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 - [-1, 1, Bottleneck, [64]], - [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 - [-1, 2, Bottleneck, [128]], - [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 - [-1, 8, Bottleneck, [256]], - [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 - [-1, 8, Bottleneck, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 - [-1, 4, Bottleneck, [1024]], # 10 - ] - -# YOLOv3-SPP head -head: - [[-1, 1, Bottleneck, [1024, False]], - [-1, 1, SPP, [512, [5, 9, 13]]], - [-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Bottleneck, [256, False]], - [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - - [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov3-tiny.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov3-tiny.yaml deleted file mode 100644 index 50b47e282df482b6f4f8dfb485c914ab1cbf6274..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov3-tiny.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,14, 23,27, 37,58] # P4/16 - - [81,82, 135,169, 344,319] # P5/32 - -# YOLOv3-tiny backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [16, 3, 1]], # 0 - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 - [-1, 1, Conv, [32, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 - [-1, 1, Conv, [64, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 - [-1, 1, Conv, [128, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 - [-1, 1, Conv, [256, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 - [-1, 1, Conv, [512, 3, 1]], - [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 - [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 - ] - -# YOLOv3-tiny head -head: - [[-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) - - [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov3.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov3.yaml deleted file mode 100644 index c5e21098f89379487dde6f236b78667fad0ad57f..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov3.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# darknet53 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [32, 3, 1]], # 0 - [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 - [-1, 1, Bottleneck, [64]], - [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 - [-1, 2, Bottleneck, [128]], - [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 - [-1, 8, Bottleneck, [256]], - [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 - [-1, 8, Bottleneck, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 - [-1, 4, Bottleneck, [1024]], # 10 - ] - -# YOLOv3 head -head: - [[-1, 1, Bottleneck, [1024, False]], - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Bottleneck, [256, False]], - [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - - [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-bifpn.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-bifpn.yaml deleted file mode 100644 index 9dbdd4ee0580c4a5613548607b6970aefda8c03e..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-bifpn.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 BiFPN head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-fpn.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-fpn.yaml deleted file mode 100644 index 2292eb1185a0d4c14985c3039d01fcffa26b32fd..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-fpn.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 FPN head -head: - [[-1, 3, C3, [1024, False]], # 10 (P5/32-large) - - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Conv, [512, 1, 1]], - [-1, 3, C3, [512, False]], # 14 (P4/16-medium) - - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Conv, [256, 1, 1]], - [-1, 3, C3, [256, False]], # 18 (P3/8-small) - - [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-p2.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-p2.yaml deleted file mode 100644 index 2c0ae44841cc967bceee310c3b5606c7d191c6f7..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-p2.yaml +++ /dev/null @@ -1,54 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 2], 1, Concat, [1]], # cat backbone P2 - [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) - - [-1, 1, Conv, [128, 3, 2]], - [[-1, 18], 1, Concat, [1]], # cat head P3 - [-1, 3, C3, [256, False]], # 24 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 27 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 30 (P5/32-large) - - [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-p34.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-p34.yaml deleted file mode 100644 index 60ae3b4b6f30d0b1a5ba901d021de77d14953161..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-p34.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 6, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 1024 ] ], - [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 - ] - -# YOLOv5 v6.0 head with (P3, P4) outputs -head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 13 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) - - [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-p6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-p6.yaml deleted file mode 100644 index a9e1b5f90c72dbe0c7257001761da7190c7b235b..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-p6.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-p7.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-p7.yaml deleted file mode 100644 index a502412f08877bd233580bf048558758f8d0c1c4..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-p7.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 - [-1, 3, C3, [1280]], - [-1, 1, SPPF, [1280, 5]], # 13 - ] - -# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs -head: - [[-1, 1, Conv, [1024, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 10], 1, Concat, [1]], # cat backbone P6 - [-1, 3, C3, [1024, False]], # 17 - - [-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 21 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 25 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 29 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 26], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 32 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 22], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 35 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 18], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) - - [-1, 1, Conv, [1024, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P7 - [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) - - [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5-panet.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5-panet.yaml deleted file mode 100644 index 5595e25738235718d5b4eb8672fc50301f0c043d..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5-panet.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 PANet head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5l6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5l6.yaml deleted file mode 100644 index 651dbb0251ae89817e6292e215e57ab7ddc9a92a..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5l6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5m6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5m6.yaml deleted file mode 100644 index 059b12b46929cc481a014298a5ab5ae2b2bdaf68..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5m6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5n6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5n6.yaml deleted file mode 100644 index 5052e7cbfc8b972a577af8c1668e0d475728268c..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5n6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.25 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5s-LeakyReLU.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5s-LeakyReLU.yaml deleted file mode 100644 index 0368a78dcbb42c690a2ee81789c248d41009d665..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5s-LeakyReLU.yaml +++ /dev/null @@ -1,49 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5s-ghost.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5s-ghost.yaml deleted file mode 100644 index ce5238fa5dfcd629b01d5a4a29388d6a7646d6ec..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5s-ghost.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3Ghost, [128]], - [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3Ghost, [256]], - [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3Ghost, [512]], - [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3Ghost, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, GhostConv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3Ghost, [512, False]], # 13 - - [-1, 1, GhostConv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) - - [-1, 1, GhostConv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) - - [-1, 1, GhostConv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5s-transformer.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5s-transformer.yaml deleted file mode 100644 index f5267163453c059a6b948567ec1fe5a9af18f7e5..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5s-transformer.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5s6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5s6.yaml deleted file mode 100644 index 2f39b0379e74dbfdd584896561337572d47ee580..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5s6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/hub/yolov5x6.yaml b/iteach_toolkit/DHYOLO/models/hub/yolov5x6.yaml deleted file mode 100644 index e1edbcb8634c7c8abc68fa99bf53a2106700129c..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/hub/yolov5x6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/iteach_toolkit/DHYOLO/models/segment/yolov5l-seg.yaml b/iteach_toolkit/DHYOLO/models/segment/yolov5l-seg.yaml deleted file mode 100644 index 71f80cc0805490c21adbb27bc093a04c4bc7b882..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/segment/yolov5l-seg.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/segment/yolov5m-seg.yaml b/iteach_toolkit/DHYOLO/models/segment/yolov5m-seg.yaml deleted file mode 100644 index 2b8e1db2818acbbcd6d2b340c2d950fc3108d4d4..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/segment/yolov5m-seg.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/segment/yolov5n-seg.yaml b/iteach_toolkit/DHYOLO/models/segment/yolov5n-seg.yaml deleted file mode 100644 index 1f67f8e3dfb05af091943100d7578e5f30770455..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/segment/yolov5n-seg.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.25 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/segment/yolov5s-seg.yaml b/iteach_toolkit/DHYOLO/models/segment/yolov5s-seg.yaml deleted file mode 100644 index 2ff2524ca9b559fa416854dba8af9d3e16eb8323..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/segment/yolov5s-seg.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.5 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/segment/yolov5x-seg.yaml b/iteach_toolkit/DHYOLO/models/segment/yolov5x-seg.yaml deleted file mode 100644 index 589f65c76f954c4db6bc88e4fb0a6d26be70556e..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/segment/yolov5x-seg.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/tf.py b/iteach_toolkit/DHYOLO/models/tf.py deleted file mode 100644 index 62ba3ebf07820baa799b0b316666f79fc56ce498..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/tf.py +++ /dev/null @@ -1,608 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -TensorFlow, Keras and TFLite versions of YOLOv5 -Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 - -Usage: - $ python models/tf.py --weights yolov5s.pt - -Export: - $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs -""" - -import argparse -import sys -from copy import deepcopy -from pathlib import Path - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -# ROOT = ROOT.relative_to(Path.cwd()) # relative - -import numpy as np -import tensorflow as tf -import torch -import torch.nn as nn -from tensorflow import keras - -from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv, - DWConvTranspose2d, Focus, autopad) -from models.experimental import MixConv2d, attempt_load -from models.yolo import Detect, Segment -from utils.activations import SiLU -from utils.general import LOGGER, make_divisible, print_args - - -class TFBN(keras.layers.Layer): - # TensorFlow BatchNormalization wrapper - def __init__(self, w=None): - super().__init__() - self.bn = keras.layers.BatchNormalization( - beta_initializer=keras.initializers.Constant(w.bias.numpy()), - gamma_initializer=keras.initializers.Constant(w.weight.numpy()), - moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), - moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), - epsilon=w.eps) - - def call(self, inputs): - return self.bn(inputs) - - -class TFPad(keras.layers.Layer): - # Pad inputs in spatial dimensions 1 and 2 - def __init__(self, pad): - super().__init__() - if isinstance(pad, int): - self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) - else: # tuple/list - self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) - - def call(self, inputs): - return tf.pad(inputs, self.pad, mode='constant', constant_values=0) - - -class TFConv(keras.layers.Layer): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): - # ch_in, ch_out, weights, kernel, stride, padding, groups - super().__init__() - assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" - # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) - # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch - conv = keras.layers.Conv2D( - filters=c2, - kernel_size=k, - strides=s, - padding='SAME' if s == 1 else 'VALID', - use_bias=not hasattr(w, 'bn'), - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) - self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) - self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity - self.act = activations(w.act) if act else tf.identity - - def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) - - -class TFDWConv(keras.layers.Layer): - # Depthwise convolution - def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): - # ch_in, ch_out, weights, kernel, stride, padding, groups - super().__init__() - assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels' - conv = keras.layers.DepthwiseConv2D( - kernel_size=k, - depth_multiplier=c2 // c1, - strides=s, - padding='SAME' if s == 1 else 'VALID', - use_bias=not hasattr(w, 'bn'), - depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) - self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) - self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity - self.act = activations(w.act) if act else tf.identity - - def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) - - -class TFDWConvTranspose2d(keras.layers.Layer): - # Depthwise ConvTranspose2d - def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): - # ch_in, ch_out, weights, kernel, stride, padding, groups - super().__init__() - assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels' - assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1' - weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() - self.c1 = c1 - self.conv = [ - keras.layers.Conv2DTranspose(filters=1, - kernel_size=k, - strides=s, - padding='VALID', - output_padding=p2, - use_bias=True, - kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]), - bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)] - - def call(self, inputs): - return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] - - -class TFFocus(keras.layers.Layer): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): - # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) - - def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) - # inputs = inputs / 255 # normalize 0-255 to 0-1 - inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] - return self.conv(tf.concat(inputs, 3)) - - -class TFBottleneck(keras.layers.Layer): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) - self.add = shortcut and c1 == c2 - - def call(self, inputs): - return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) - - -class TFCrossConv(keras.layers.Layer): - # Cross Convolution - def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) - self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) - self.add = shortcut and c1 == c2 - - def call(self, inputs): - return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) - - -class TFConv2d(keras.layers.Layer): - # Substitution for PyTorch nn.Conv2D - def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): - super().__init__() - assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" - self.conv = keras.layers.Conv2D(filters=c2, - kernel_size=k, - strides=s, - padding='VALID', - use_bias=bias, - kernel_initializer=keras.initializers.Constant( - w.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None) - - def call(self, inputs): - return self.conv(inputs) - - -class TFBottleneckCSP(keras.layers.Layer): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): - # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) - self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) - self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) - self.bn = TFBN(w.bn) - self.act = lambda x: keras.activations.swish(x) - self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) - - def call(self, inputs): - y1 = self.cv3(self.m(self.cv1(inputs))) - y2 = self.cv2(inputs) - return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) - - -class TFC3(keras.layers.Layer): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): - # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) - self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) - self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) - - def call(self, inputs): - return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) - - -class TFC3x(keras.layers.Layer): - # 3 module with cross-convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): - # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) - self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) - self.m = keras.Sequential([ - TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]) - - def call(self, inputs): - return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) - - -class TFSPP(keras.layers.Layer): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13), w=None): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) - self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] - - def call(self, inputs): - x = self.cv1(inputs) - return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) - - -class TFSPPF(keras.layers.Layer): - # Spatial pyramid pooling-Fast layer - def __init__(self, c1, c2, k=5, w=None): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) - self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME') - - def call(self, inputs): - x = self.cv1(inputs) - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) - - -class TFDetect(keras.layers.Layer): - # TF YOLOv5 Detect layer - def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer - super().__init__() - self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) - self.nc = nc # number of classes - self.no = nc + 5 # number of outputs per anchor - self.nl = len(anchors) # number of detection layers - self.na = len(anchors[0]) // 2 # number of anchors - self.grid = [tf.zeros(1)] * self.nl # init grid - self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) - self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) - self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] - self.training = False # set to False after building model - self.imgsz = imgsz - for i in range(self.nl): - ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] - self.grid[i] = self._make_grid(nx, ny) - - def call(self, inputs): - z = [] # inference output - x = [] - for i in range(self.nl): - x.append(self.m[i](inputs[i])) - # x(bs,20,20,255) to x(bs,3,20,20,85) - ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] - x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) - - if not self.training: # inference - y = x[i] - grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 - anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 - xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy - wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid - # Normalize xywh to 0-1 to reduce calibration error - xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) - wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) - y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1) - z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) - - return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), ) - - @staticmethod - def _make_grid(nx=20, ny=20): - # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) - # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() - xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) - return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) - - -class TFSegment(TFDetect): - # YOLOv5 Segment head for segmentation models - def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): - super().__init__(nc, anchors, ch, imgsz, w) - self.nm = nm # number of masks - self.npr = npr # number of protos - self.no = 5 + nc + self.nm # number of outputs per anchor - self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv - self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos - self.detect = TFDetect.call - - def call(self, x): - p = self.proto(x[0]) - # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos - p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) - x = self.detect(self, x) - return (x, p) if self.training else (x[0], p) - - -class TFProto(keras.layers.Layer): - - def __init__(self, c1, c_=256, c2=32, w=None): - super().__init__() - self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) - self.upsample = TFUpsample(None, scale_factor=2, mode='nearest') - self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) - self.cv3 = TFConv(c_, c2, w=w.cv3) - - def call(self, inputs): - return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) - - -class TFUpsample(keras.layers.Layer): - # TF version of torch.nn.Upsample() - def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' - super().__init__() - assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2' - self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) - # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) - # with default arguments: align_corners=False, half_pixel_centers=False - # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, - # size=(x.shape[1] * 2, x.shape[2] * 2)) - - def call(self, inputs): - return self.upsample(inputs) - - -class TFConcat(keras.layers.Layer): - # TF version of torch.concat() - def __init__(self, dimension=1, w=None): - super().__init__() - assert dimension == 1, 'convert only NCHW to NHWC concat' - self.d = 3 - - def call(self, inputs): - return tf.concat(inputs, self.d) - - -def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) - LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] - na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors - no = na * (nc + 5) # number of outputs = anchors * (classes + 5) - - layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args - m_str = m - m = eval(m) if isinstance(m, str) else m # eval strings - for j, a in enumerate(args): - try: - args[j] = eval(a) if isinstance(a, str) else a # eval strings - except NameError: - pass - - n = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [ - nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, - BottleneckCSP, C3, C3x]: - c1, c2 = ch[f], args[0] - c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 - - args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3, C3x]: - args.insert(2, n) - n = 1 - elif m is nn.BatchNorm2d: - args = [ch[f]] - elif m is Concat: - c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) - elif m in [Detect, Segment]: - args.append([ch[x + 1] for x in f]) - if isinstance(args[1], int): # number of anchors - args[1] = [list(range(args[1] * 2))] * len(f) - if m is Segment: - args[3] = make_divisible(args[3] * gw, 8) - args.append(imgsz) - else: - c2 = ch[f] - - tf_m = eval('TF' + m_str.replace('nn.', '')) - m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ - else tf_m(*args, w=model.model[i]) # module - - torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module - t = str(m)[8:-2].replace('__main__.', '') # module type - np = sum(x.numel() for x in torch_m_.parameters()) # number params - m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print - save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist - layers.append(m_) - ch.append(c2) - return keras.Sequential(layers), sorted(save) - - -class TFModel: - # TF YOLOv5 model - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes - super().__init__() - if isinstance(cfg, dict): - self.yaml = cfg # model dict - else: # is *.yaml - import yaml # for torch hub - self.yaml_file = Path(cfg).name - with open(cfg) as f: - self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict - - # Define model - if nc and nc != self.yaml['nc']: - LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value - self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) - - def predict(self, - inputs, - tf_nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25): - y = [] # outputs - x = inputs - for m in self.model.layers: - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - - x = m(x) # run - y.append(x if m.i in self.savelist else None) # save output - - # Add TensorFlow NMS - if tf_nms: - boxes = self._xywh2xyxy(x[0][..., :4]) - probs = x[0][:, :, 4:5] - classes = x[0][:, :, 5:] - scores = probs * classes - if agnostic_nms: - nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) - else: - boxes = tf.expand_dims(boxes, 2) - nms = tf.image.combined_non_max_suppression(boxes, - scores, - topk_per_class, - topk_all, - iou_thres, - conf_thres, - clip_boxes=False) - return (nms, ) - return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] - # x = x[0] # [x(1,6300,85), ...] to x(6300,85) - # xywh = x[..., :4] # x(6300,4) boxes - # conf = x[..., 4:5] # x(6300,1) confidences - # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes - # return tf.concat([conf, cls, xywh], 1) - - @staticmethod - def _xywh2xyxy(xywh): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) - return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) - - -class AgnosticNMS(keras.layers.Layer): - # TF Agnostic NMS - def call(self, input, topk_all, iou_thres, conf_thres): - # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 - return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), - input, - fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), - name='agnostic_nms') - - @staticmethod - def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS - boxes, classes, scores = x - class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) - scores_inp = tf.reduce_max(scores, -1) - selected_inds = tf.image.non_max_suppression(boxes, - scores_inp, - max_output_size=topk_all, - iou_threshold=iou_thres, - score_threshold=conf_thres) - selected_boxes = tf.gather(boxes, selected_inds) - padded_boxes = tf.pad(selected_boxes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], - mode='CONSTANT', - constant_values=0.0) - selected_scores = tf.gather(scores_inp, selected_inds) - padded_scores = tf.pad(selected_scores, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode='CONSTANT', - constant_values=-1.0) - selected_classes = tf.gather(class_inds, selected_inds) - padded_classes = tf.pad(selected_classes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode='CONSTANT', - constant_values=-1.0) - valid_detections = tf.shape(selected_inds)[0] - return padded_boxes, padded_scores, padded_classes, valid_detections - - -def activations(act=nn.SiLU): - # Returns TF activation from input PyTorch activation - if isinstance(act, nn.LeakyReLU): - return lambda x: keras.activations.relu(x, alpha=0.1) - elif isinstance(act, nn.Hardswish): - return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 - elif isinstance(act, (nn.SiLU, SiLU)): - return lambda x: keras.activations.swish(x) - else: - raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}') - - -def representative_dataset_gen(dataset, ncalib=100): - # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays - for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): - im = np.transpose(img, [1, 2, 0]) - im = np.expand_dims(im, axis=0).astype(np.float32) - im /= 255 - yield [im] - if n >= ncalib: - break - - -def run( - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=(640, 640), # inference size h,w - batch_size=1, # batch size - dynamic=False, # dynamic batch size -): - # PyTorch model - im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image - model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False) - _ = model(im) # inference - model.info() - - # TensorFlow model - im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image - tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) - _ = tf_model.predict(im) # inference - - # Keras model - im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) - keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) - keras_model.summary() - - LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.') - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--dynamic', action='store_true', help='dynamic batch size') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(vars(opt)) - return opt - - -def main(opt): - run(**vars(opt)) - - -if __name__ == '__main__': - opt = parse_opt() - main(opt) diff --git a/iteach_toolkit/DHYOLO/models/yolo.py b/iteach_toolkit/DHYOLO/models/yolo.py deleted file mode 100644 index 891e1e87f5670b9ede8e8f6ad7920a9ff9b8f09d..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolo.py +++ /dev/null @@ -1,394 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -YOLO-specific modules - -Usage: - $ python models/yolo.py --cfg yolov5s.yaml -""" - -import argparse -import contextlib -import os -import platform -import sys -from copy import deepcopy -from pathlib import Path - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -if platform.system() != 'Windows': - ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import * # noqa -from models.experimental import * # noqa -from utils.autoanchor import check_anchor_order -from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args -from utils.plots import feature_visualization -from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, - time_sync) - -try: - import thop # for FLOPs computation -except ImportError: - thop = None - - -class Detect(nn.Module): - # YOLOv5 Detect head for detection models - stride = None # strides computed during build - dynamic = False # force grid reconstruction - export = False # export mode - - def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer - super().__init__() - self.nc = nc # number of classes - self.no = nc + 5 # number of outputs per anchor - self.nl = len(anchors) # number of detection layers - self.na = len(anchors[0]) // 2 # number of anchors - self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid - self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid - self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) - self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv - self.inplace = inplace # use inplace ops (e.g. slice assignment) - - def forward(self, x): - z = [] # inference output - for i in range(self.nl): - x[i] = self.m[i](x[i]) # conv - bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) - x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() - - if not self.training: # inference - if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: - self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) - - if isinstance(self, Segment): # (boxes + masks) - xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) - xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy - wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh - y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) - else: # Detect (boxes only) - xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) - xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy - wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh - y = torch.cat((xy, wh, conf), 4) - z.append(y.view(bs, self.na * nx * ny, self.no)) - - return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x) - - def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')): - d = self.anchors[i].device - t = self.anchors[i].dtype - shape = 1, self.na, ny, nx, 2 # grid shape - y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) - yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility - grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 - anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) - return grid, anchor_grid - - -class Segment(Detect): - # YOLOv5 Segment head for segmentation models - def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): - super().__init__(nc, anchors, ch, inplace) - self.nm = nm # number of masks - self.npr = npr # number of protos - self.no = 5 + nc + self.nm # number of outputs per anchor - self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv - self.proto = Proto(ch[0], self.npr, self.nm) # protos - self.detect = Detect.forward - - def forward(self, x): - p = self.proto(x[0]) - x = self.detect(self, x) - return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) - - -class BaseModel(nn.Module): - # YOLOv5 base model - def forward(self, x, profile=False, visualize=False): - return self._forward_once(x, profile, visualize) # single-scale inference, train - - def _forward_once(self, x, profile=False, visualize=False): - y, dt = [], [] # outputs - for m in self.model: - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - if profile: - self._profile_one_layer(m, x, dt) - x = m(x) # run - y.append(x if m.i in self.save else None) # save output - if visualize: - feature_visualization(x, m.type, m.i, save_dir=visualize) - return x - - def _profile_one_layer(self, m, x, dt): - c = m == self.model[-1] # is final layer, copy input as inplace fix - o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs - t = time_sync() - for _ in range(10): - m(x.copy() if c else x) - dt.append((time_sync() - t) * 100) - if m == self.model[0]: - LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") - LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') - if c: - LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") - - def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers - LOGGER.info('Fusing layers... ') - for m in self.model.modules(): - if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): - m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv - delattr(m, 'bn') # remove batchnorm - m.forward = m.forward_fuse # update forward - self.info() - return self - - def info(self, verbose=False, img_size=640): # print model information - model_info(self, verbose, img_size) - - def _apply(self, fn): - # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers - self = super()._apply(fn) - m = self.model[-1] # Detect() - if isinstance(m, (Detect, Segment)): - m.stride = fn(m.stride) - m.grid = list(map(fn, m.grid)) - if isinstance(m.anchor_grid, list): - m.anchor_grid = list(map(fn, m.anchor_grid)) - return self - - -class DetectionModel(BaseModel): - # YOLOv5 detection model - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes - super().__init__() - if isinstance(cfg, dict): - self.yaml = cfg # model dict - else: # is *.yaml - import yaml # for torch hub - self.yaml_file = Path(cfg).name - with open(cfg, encoding='ascii', errors='ignore') as f: - self.yaml = yaml.safe_load(f) # model dict - - # Define model - ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels - if nc and nc != self.yaml['nc']: - LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value - if anchors: - LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') - self.yaml['anchors'] = round(anchors) # override yaml value - - self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist - self.names = [str(i) for i in range(self.yaml['nc'])] # default names - self.inplace = self.yaml.get('inplace', True) - - # Build strides, anchors - m = self.model[-1] # Detect() - if isinstance(m, (Detect, Segment)): - s = 256 # 2x min stride - m.inplace = self.inplace - forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) - m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward - check_anchor_order(m) - m.anchors /= m.stride.view(-1, 1, 1) - self.stride = m.stride - self._initialize_biases() # only run once - - # Init weights, biases - initialize_weights(self) - self.info() - LOGGER.info('') - - def forward(self, x, augment=False, profile=False, visualize=False): - if augment: - return self._forward_augment(x) # augmented inference, None - return self._forward_once(x, profile, visualize) # single-scale inference, train - - def _forward_augment(self, x): - img_size = x.shape[-2:] # height, width - s = [1, 0.83, 0.67] # scales - f = [None, 3, None] # flips (2-ud, 3-lr) - y = [] # outputs - for si, fi in zip(s, f): - xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) - yi = self._forward_once(xi)[0] # forward - # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save - yi = self._descale_pred(yi, fi, si, img_size) - y.append(yi) - y = self._clip_augmented(y) # clip augmented tails - return torch.cat(y, 1), None # augmented inference, train - - def _descale_pred(self, p, flips, scale, img_size): - # de-scale predictions following augmented inference (inverse operation) - if self.inplace: - p[..., :4] /= scale # de-scale - if flips == 2: - p[..., 1] = img_size[0] - p[..., 1] # de-flip ud - elif flips == 3: - p[..., 0] = img_size[1] - p[..., 0] # de-flip lr - else: - x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale - if flips == 2: - y = img_size[0] - y # de-flip ud - elif flips == 3: - x = img_size[1] - x # de-flip lr - p = torch.cat((x, y, wh, p[..., 4:]), -1) - return p - - def _clip_augmented(self, y): - # Clip YOLOv5 augmented inference tails - nl = self.model[-1].nl # number of detection layers (P3-P5) - g = sum(4 ** x for x in range(nl)) # grid points - e = 1 # exclude layer count - i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices - y[0] = y[0][:, :-i] # large - i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices - y[-1] = y[-1][:, i:] # small - return y - - def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency - # https://arxiv.org/abs/1708.02002 section 3.3 - # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. - m = self.model[-1] # Detect() module - for mi, s in zip(m.m, m.stride): # from - b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) - b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) - b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls - mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) - - -Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility - - -class SegmentationModel(DetectionModel): - # YOLOv5 segmentation model - def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): - super().__init__(cfg, ch, nc, anchors) - - -class ClassificationModel(BaseModel): - # YOLOv5 classification model - def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index - super().__init__() - self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) - - def _from_detection_model(self, model, nc=1000, cutoff=10): - # Create a YOLOv5 classification model from a YOLOv5 detection model - if isinstance(model, DetectMultiBackend): - model = model.model # unwrap DetectMultiBackend - model.model = model.model[:cutoff] # backbone - m = model.model[-1] # last layer - ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module - c = Classify(ch, nc) # Classify() - c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type - model.model[-1] = c # replace - self.model = model.model - self.stride = model.stride - self.save = [] - self.nc = nc - - def _from_yaml(self, cfg): - # Create a YOLOv5 classification model from a *.yaml file - self.model = None - - -def parse_model(d, ch): # model_dict, input_channels(3) - # Parse a YOLOv5 model.yaml dictionary - LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - print(f"FFFFFFFF: {d}") - - anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') - if act: - Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() - LOGGER.info(f"{colorstr('activation:')} {act}") # print - na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors - no = na * (nc + 5) # number of outputs = anchors * (classes + 5) - - layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args - m = eval(m) if isinstance(m, str) else m # eval strings - for j, a in enumerate(args): - with contextlib.suppress(NameError): - args[j] = eval(a) if isinstance(a, str) else a # eval strings - - n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in { - Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, - BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: - c1, c2 = ch[f], args[0] - if c2 != no: # if not output - c2 = make_divisible(c2 * gw, 8) - - args = [c1, c2, *args[1:]] - if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: - args.insert(2, n) # number of repeats - n = 1 - elif m is nn.BatchNorm2d: - args = [ch[f]] - elif m is Concat: - c2 = sum(ch[x] for x in f) - # TODO: channel, gw, gd - elif m in {Detect, Segment}: - args.append([ch[x] for x in f]) - if isinstance(args[1], int): # number of anchors - args[1] = [list(range(args[1] * 2))] * len(f) - if m is Segment: - args[3] = make_divisible(args[3] * gw, 8) - elif m is Contract: - c2 = ch[f] * args[0] ** 2 - elif m is Expand: - c2 = ch[f] // args[0] ** 2 - else: - c2 = ch[f] - - m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module - t = str(m)[8:-2].replace('__main__.', '') # module type - np = sum(x.numel() for x in m_.parameters()) # number params - m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print - save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist - layers.append(m_) - if i == 0: - ch = [] - ch.append(c2) - return nn.Sequential(*layers), sorted(save) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') - parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--profile', action='store_true', help='profile model speed') - parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer') - parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') - opt = parser.parse_args() - opt.cfg = check_yaml(opt.cfg) # check YAML - print_args(vars(opt)) - device = select_device(opt.device) - - # Create model - im = torch.rand(opt.batch_size, 3, 640, 640).to(device) - model = Model(opt.cfg).to(device) - - # Options - if opt.line_profile: # profile layer by layer - model(im, profile=True) - - elif opt.profile: # profile forward-backward - results = profile(input=im, ops=[model], n=3) - - elif opt.test: # test all models - for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): - try: - _ = Model(cfg) - except Exception as e: - print(f'Error in {cfg}: {e}') - - else: # report fused model summary - model.fuse() diff --git a/iteach_toolkit/DHYOLO/models/yolov5l.yaml b/iteach_toolkit/DHYOLO/models/yolov5l.yaml deleted file mode 100644 index 31362f8769327bad3afdb65d39a3b940397ecfae..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolov5l.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/yolov5m.yaml b/iteach_toolkit/DHYOLO/models/yolov5m.yaml deleted file mode 100644 index a76900c5a2e2602d59ece8645bbd67e0dc454311..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolov5m.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/yolov5n.yaml b/iteach_toolkit/DHYOLO/models/yolov5n.yaml deleted file mode 100644 index aba96cfc54f48c2cb2fe16aae2b0b94e38826c9d..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolov5n.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.25 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/yolov5s.yaml b/iteach_toolkit/DHYOLO/models/yolov5s.yaml deleted file mode 100644 index 5d05364c49363adb2b863ecf163e45516679dc03..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolov5s.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/models/yolov5x.yaml b/iteach_toolkit/DHYOLO/models/yolov5x.yaml deleted file mode 100644 index 4bdd93915da550d62c4ada3684c5660ff5034404..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/models/yolov5x.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/iteach_toolkit/DHYOLO/utils/__init__.py b/iteach_toolkit/DHYOLO/utils/__init__.py deleted file mode 100644 index 7497a757bc88916a656fb784c6694ede2dfdb7b4..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/__init__.py +++ /dev/null @@ -1,107 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -utils/initialization -""" - -import contextlib -import platform -import threading -import signal - - -def emojis(str=''): - # Return platform-dependent emoji-safe version of string - return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str - - -class TryExcept(contextlib.ContextDecorator): - # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager - def __init__(self, msg=''): - self.msg = msg - - def __enter__(self): - pass - - def __exit__(self, exc_type, value, traceback): - if value: - print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) - return True - - -def threaded(func): - # Multi-threads a target function and returns thread. Usage: @threaded decorator - def wrapper(*args, **kwargs): - thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) - thread.start() - return thread - - return wrapper - - -def join_threads(verbose=False): - # Join all daemon threads, i.e. atexit.register(lambda: join_threads()) - main_thread = threading.current_thread() - for t in threading.enumerate(): - if t is not main_thread: - if verbose: - print(f'Joining thread {t.name}') - t.join() - - -def notebook_init(verbose=True): - # Check system software and hardware - print('Checking setup...') - - import os - import shutil - - from ultralytics.utils.checks import check_requirements - - from utils.general import check_font, is_colab - from utils.torch_utils import select_device # imports - - check_font() - - import psutil - - if check_requirements('wandb', install=False): - os.system('pip uninstall -y wandb') # eliminate unexpected account creation prompt with infinite hang - if is_colab(): - shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory - - # System info - display = None - if verbose: - gb = 1 << 30 # bytes to GiB (1024 ** 3) - ram = psutil.virtual_memory().total - total, used, free = shutil.disk_usage('/') - with contextlib.suppress(Exception): # clear display if ipython is installed - from IPython import display - display.clear_output() - s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' - else: - s = '' - - select_device(newline=False) - print(emojis(f'Setup complete ✅ {s}')) - return display - - -class Timeout(contextlib.ContextDecorator): - # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager - def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): - self.seconds = int(seconds) - self.timeout_message = timeout_msg - self.suppress = bool(suppress_timeout_errors) - - def _timeout_handler(self, signum, frame): - 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index 243eabaf3669c1b7e5b18ef0818ce1036614db06..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/__pycache__/torch_utils.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/activations.py b/iteach_toolkit/DHYOLO/utils/activations.py deleted file mode 100644 index e4d4bbde5ec8610a5ff13fe2ef2281721c14ca1a..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/activations.py +++ /dev/null @@ -1,103 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Activation functions -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class SiLU(nn.Module): - # SiLU activation https://arxiv.org/pdf/1606.08415.pdf - @staticmethod - def forward(x): - return x * torch.sigmoid(x) - - -class Hardswish(nn.Module): - # Hard-SiLU activation - @staticmethod - def forward(x): - # return x * F.hardsigmoid(x) # for TorchScript and CoreML - return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX - - -class Mish(nn.Module): - # Mish activation https://github.com/digantamisra98/Mish - @staticmethod - def forward(x): - return x * F.softplus(x).tanh() - - -class MemoryEfficientMish(nn.Module): - # Mish activation memory-efficient - class F(torch.autograd.Function): - - @staticmethod - def forward(ctx, x): - ctx.save_for_backward(x) - return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) - - @staticmethod - def backward(ctx, grad_output): - x = ctx.saved_tensors[0] - sx = torch.sigmoid(x) - fx = F.softplus(x).tanh() - return grad_output * (fx + x * sx * (1 - fx * fx)) - - def forward(self, x): - return self.F.apply(x) - - -class FReLU(nn.Module): - # FReLU activation https://arxiv.org/abs/2007.11824 - def __init__(self, c1, k=3): # ch_in, kernel - super().__init__() - self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) - self.bn = nn.BatchNorm2d(c1) - - def forward(self, x): - return torch.max(x, self.bn(self.conv(x))) - - -class AconC(nn.Module): - r""" ACON activation (activate or not) - AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1): - super().__init__() - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) - - def forward(self, x): - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x - - -class MetaAconC(nn.Module): - r""" ACON activation (activate or not) - MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r - super().__init__() - c2 = max(r, c1 // r) - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) - self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) - # self.bn1 = nn.BatchNorm2d(c2) - # self.bn2 = nn.BatchNorm2d(c1) - - def forward(self, x): - y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) - # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 - # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable - beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/iteach_toolkit/DHYOLO/utils/augmentations.py b/iteach_toolkit/DHYOLO/utils/augmentations.py deleted file mode 100644 index 1e609303e2092235e7e73ae7045185e6d22020ce..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/augmentations.py +++ /dev/null @@ -1,397 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Image augmentation functions -""" - -import math -import random - -import cv2 -import numpy as np -import torch -import torchvision.transforms as T -import torchvision.transforms.functional as TF - -from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy -from utils.metrics import bbox_ioa - -IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean -IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation - - -class Albumentations: - # YOLOv5 Albumentations class (optional, only used if package is installed) - def __init__(self, size=640): - self.transform = None - prefix = colorstr('albumentations: ') - try: - import albumentations as A - check_version(A.__version__, '1.0.3', hard=True) # version requirement - - T = [ - A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), - A.Blur(p=0.01), - A.MedianBlur(p=0.01), - A.ToGray(p=0.01), - A.CLAHE(p=0.01), - A.RandomBrightnessContrast(p=0.0), - A.RandomGamma(p=0.0), - A.ImageCompression(quality_lower=75, p=0.0)] # transforms - self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) - - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) - except ImportError: # package not installed, skip - pass - except Exception as e: - LOGGER.info(f'{prefix}{e}') - - def __call__(self, im, labels, p=1.0): - if self.transform and random.random() < p: - new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed - im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) - return im, labels - - -def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): - # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std - return TF.normalize(x, mean, std, inplace=inplace) - - -def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): - # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean - for i in range(3): - x[:, i] = x[:, i] * std[i] + mean[i] - return x - - -def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): - # HSV color-space augmentation - if hgain or sgain or vgain: - r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains - hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) - dtype = im.dtype # uint8 - - x = np.arange(0, 256, dtype=r.dtype) - lut_hue = ((x * r[0]) % 180).astype(dtype) - lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) - lut_val = np.clip(x * r[2], 0, 255).astype(dtype) - - im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) - cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed - - -def hist_equalize(im, clahe=True, bgr=False): - # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 - yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) - if clahe: - c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) - yuv[:, :, 0] = c.apply(yuv[:, :, 0]) - else: - yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram - return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB - - -def replicate(im, labels): - # Replicate labels - h, w = im.shape[:2] - boxes = labels[:, 1:].astype(int) - x1, y1, x2, y2 = boxes.T - s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) - for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices - x1b, y1b, x2b, y2b = boxes[i] - bh, bw = y2b - y1b, x2b - x1b - yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y - x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] - im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] - labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) - - return im, labels - - -def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): - # Resize and pad image while meeting stride-multiple constraints - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better val mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border - return im, ratio, (dw, dh) - - -def random_perspective(im, - targets=(), - segments=(), - degrees=10, - translate=.1, - scale=.1, - shear=10, - perspective=0.0, - border=(0, 0)): - # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) - # targets = [cls, xyxy] - - height = im.shape[0] + border[0] * 2 # shape(h,w,c) - width = im.shape[1] + border[1] * 2 - - # Center - C = np.eye(3) - C[0, 2] = -im.shape[1] / 2 # x translation (pixels) - C[1, 2] = -im.shape[0] / 2 # y translation (pixels) - - # Perspective - P = np.eye(3) - P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) - P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) - - # Rotation and Scale - R = np.eye(3) - a = random.uniform(-degrees, degrees) - # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations - s = random.uniform(1 - scale, 1 + scale) - # s = 2 ** random.uniform(-scale, scale) - R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) - - # Shear - S = np.eye(3) - S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) - S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) - - # Translation - T = np.eye(3) - T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) - T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) - - # Combined rotation matrix - M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT - if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed - if perspective: - im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) - else: # affine - im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) - - # Visualize - # import matplotlib.pyplot as plt - # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() - # ax[0].imshow(im[:, :, ::-1]) # base - # ax[1].imshow(im2[:, :, ::-1]) # warped - - # Transform label coordinates - n = len(targets) - if n: - use_segments = any(x.any() for x in segments) and len(segments) == n - new = np.zeros((n, 4)) - if use_segments: # warp segments - segments = resample_segments(segments) # upsample - for i, segment in enumerate(segments): - xy = np.ones((len(segment), 3)) - xy[:, :2] = segment - xy = xy @ M.T # transform - xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine - - # clip - new[i] = segment2box(xy, width, height) - - else: # warp boxes - xy = np.ones((n * 4, 3)) - xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 - xy = xy @ M.T # transform - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # clip - new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) - new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) - - # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) - targets = targets[i] - targets[:, 1:5] = new[i] - - return im, targets - - -def copy_paste(im, labels, segments, p=0.5): - # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) - n = len(segments) - if p and n: - h, w, c = im.shape # height, width, channels - im_new = np.zeros(im.shape, np.uint8) - for j in random.sample(range(n), k=round(p * n)): - l, s = labels[j], segments[j] - box = w - l[3], l[2], w - l[1], l[4] - ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area - if (ioa < 0.30).all(): # allow 30% obscuration of existing labels - labels = np.concatenate((labels, [[l[0], *box]]), 0) - segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) - cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) - - result = cv2.flip(im, 1) # augment segments (flip left-right) - i = cv2.flip(im_new, 1).astype(bool) - im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug - - return im, labels, segments - - -def cutout(im, labels, p=0.5): - # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 - if random.random() < p: - h, w = im.shape[:2] - scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction - for s in scales: - mask_h = random.randint(1, int(h * s)) # create random masks - mask_w = random.randint(1, int(w * s)) - - # box - xmin = max(0, random.randint(0, w) - mask_w // 2) - ymin = max(0, random.randint(0, h) - mask_h // 2) - xmax = min(w, xmin + mask_w) - ymax = min(h, ymin + mask_h) - - # apply random color mask - im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] - - # return unobscured labels - if len(labels) and s > 0.03: - box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) - ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area - labels = labels[ioa < 0.60] # remove >60% obscured labels - - return labels - - -def mixup(im, labels, im2, labels2): - # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf - r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 - im = (im * r + im2 * (1 - r)).astype(np.uint8) - labels = np.concatenate((labels, labels2), 0) - return im, labels - - -def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) - # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio - w1, h1 = box1[2] - box1[0], box1[3] - box1[1] - w2, h2 = box2[2] - box2[0], box2[3] - box2[1] - ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio - return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates - - -def classify_albumentations( - augment=True, - size=224, - scale=(0.08, 1.0), - ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 - hflip=0.5, - vflip=0.0, - jitter=0.4, - mean=IMAGENET_MEAN, - std=IMAGENET_STD, - auto_aug=False): - # YOLOv5 classification Albumentations (optional, only used if package is installed) - prefix = colorstr('albumentations: ') - try: - import albumentations as A - from albumentations.pytorch import ToTensorV2 - check_version(A.__version__, '1.0.3', hard=True) # version requirement - if augment: # Resize and crop - T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] - if auto_aug: - # TODO: implement AugMix, AutoAug & RandAug in albumentation - LOGGER.info(f'{prefix}auto augmentations are currently not supported') - else: - if hflip > 0: - T += [A.HorizontalFlip(p=hflip)] - if vflip > 0: - T += [A.VerticalFlip(p=vflip)] - if jitter > 0: - color_jitter = (float(jitter), ) * 3 # repeat value for brightness, contrast, satuaration, 0 hue - T += [A.ColorJitter(*color_jitter, 0)] - else: # Use fixed crop for eval set (reproducibility) - T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] - T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) - return A.Compose(T) - - except ImportError: # package not installed, skip - LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') - except Exception as e: - LOGGER.info(f'{prefix}{e}') - - -def classify_transforms(size=224): - # Transforms to apply if albumentations not installed - assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' - # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) - return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) - - -class LetterBox: - # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) - def __init__(self, size=(640, 640), auto=False, stride=32): - super().__init__() - self.h, self.w = (size, size) if isinstance(size, int) else size - self.auto = auto # pass max size integer, automatically solve for short side using stride - self.stride = stride # used with auto - - def __call__(self, im): # im = np.array HWC - imh, imw = im.shape[:2] - r = min(self.h / imh, self.w / imw) # ratio of new/old - h, w = round(imh * r), round(imw * r) # resized image - hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w - top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) - im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) - im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) - return im_out - - -class CenterCrop: - # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) - def __init__(self, size=640): - super().__init__() - self.h, self.w = (size, size) if isinstance(size, int) else size - - def __call__(self, im): # im = np.array HWC - imh, imw = im.shape[:2] - m = min(imh, imw) # min dimension - top, left = (imh - m) // 2, (imw - m) // 2 - return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) - - -class ToTensor: - # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) - def __init__(self, half=False): - super().__init__() - self.half = half - - def __call__(self, im): # im = np.array HWC in BGR order - im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous - im = torch.from_numpy(im) # to torch - im = im.half() if self.half else im.float() # uint8 to fp16/32 - im /= 255.0 # 0-255 to 0.0-1.0 - return im diff --git a/iteach_toolkit/DHYOLO/utils/autoanchor.py b/iteach_toolkit/DHYOLO/utils/autoanchor.py deleted file mode 100644 index 4c11ab3decec6f30f46fcd6121a3cfd5bc7957c2..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/autoanchor.py +++ /dev/null @@ -1,169 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -AutoAnchor utils -""" - -import random - -import numpy as np -import torch -import yaml -from tqdm import tqdm - -from utils import TryExcept -from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr - -PREFIX = colorstr('AutoAnchor: ') - - -def check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - if da and (da.sign() != ds.sign()): # same order - LOGGER.info(f'{PREFIX}Reversing anchor order') - m.anchors[:] = m.anchors.flip(0) - - -@TryExcept(f'{PREFIX}ERROR') -def check_anchors(dataset, model, thr=4.0, imgsz=640): - # Check anchor fit to data, recompute if necessary - m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() - shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) - scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale - wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh - - def metric(k): # compute metric - r = wh[:, None] / k[None] - x = torch.min(r, 1 / r).min(2)[0] # ratio metric - best = x.max(1)[0] # best_x - aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold - bpr = (best > 1 / thr).float().mean() # best possible recall - return bpr, aat - - stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides - anchors = m.anchors.clone() * stride # current anchors - bpr, aat = metric(anchors.cpu().view(-1, 2)) - s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' - if bpr > 0.98: # threshold to recompute - LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅') - else: - LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') - na = m.anchors.numel() // 2 # number of anchors - anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) - new_bpr = metric(anchors)[0] - if new_bpr > bpr: # replace anchors - anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) - m.anchors[:] = anchors.clone().view_as(m.anchors) - check_anchor_order(m) # must be in pixel-space (not grid-space) - m.anchors /= stride - s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' - else: - s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' - LOGGER.info(s) - - -def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): - """ Creates kmeans-evolved anchors from training dataset - - Arguments: - dataset: path to data.yaml, or a loaded dataset - n: number of anchors - img_size: image size used for training - thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 - gen: generations to evolve anchors using genetic algorithm - verbose: print all results - - Return: - k: kmeans evolved anchors - - Usage: - from utils.autoanchor import *; _ = kmean_anchors() - """ - from scipy.cluster.vq import kmeans - - npr = np.random - thr = 1 / thr - - def metric(k, wh): # compute metrics - r = wh[:, None] / k[None] - x = torch.min(r, 1 / r).min(2)[0] # ratio metric - # x = wh_iou(wh, torch.tensor(k)) # iou metric - return x, x.max(1)[0] # x, best_x - - def anchor_fitness(k): # mutation fitness - _, best = metric(torch.tensor(k, dtype=torch.float32), wh) - return (best * (best > thr).float()).mean() # fitness - - def print_results(k, verbose=True): - k = k[np.argsort(k.prod(1))] # sort small to large - x, best = metric(k, wh0) - bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr - s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ - f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \ - f'past_thr={x[x > thr].mean():.3f}-mean: ' - for x in k: - s += '%i,%i, ' % (round(x[0]), round(x[1])) - if verbose: - LOGGER.info(s[:-2]) - return k - - if isinstance(dataset, str): # *.yaml file - with open(dataset, errors='ignore') as f: - data_dict = yaml.safe_load(f) # model dict - from utils.dataloaders import LoadImagesAndLabels - dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) - - # Get label wh - shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) - wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh - - # Filter - i = (wh0 < 3.0).any(1).sum() - if i: - LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size') - wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels - # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 - - # Kmeans init - try: - LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') - assert n <= len(wh) # apply overdetermined constraint - s = wh.std(0) # sigmas for whitening - k = kmeans(wh / s, n, iter=30)[0] * s # points - assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar - except Exception: - LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init') - k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init - wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) - k = print_results(k, verbose=False) - - # Plot - # k, d = [None] * 20, [None] * 20 - # for i in tqdm(range(1, 21)): - # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance - # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) - # ax = ax.ravel() - # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') - # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh - # ax[0].hist(wh[wh[:, 0]<100, 0],400) - # ax[1].hist(wh[wh[:, 1]<100, 1],400) - # fig.savefig('wh.png', dpi=200) - - # Evolve - f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma - pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar - for _ in pbar: - v = np.ones(sh) - while (v == 1).all(): # mutate until a change occurs (prevent duplicates) - v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) - kg = (k.copy() * v).clip(min=2.0) - fg = anchor_fitness(kg) - if fg > f: - f, k = fg, kg.copy() - pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' - if verbose: - print_results(k, verbose) - - return print_results(k).astype(np.float32) diff --git a/iteach_toolkit/DHYOLO/utils/autobatch.py b/iteach_toolkit/DHYOLO/utils/autobatch.py deleted file mode 100644 index aa763b888462a3dabf7ae161c24d9599fcfd8d9a..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/autobatch.py +++ /dev/null @@ -1,72 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Auto-batch utils -""" - -from copy import deepcopy - -import numpy as np -import torch - -from utils.general import LOGGER, colorstr -from utils.torch_utils import profile - - -def check_train_batch_size(model, imgsz=640, amp=True): - # Check YOLOv5 training batch size - with torch.cuda.amp.autocast(amp): - return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size - - -def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): - # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory - # Usage: - # import torch - # from utils.autobatch import autobatch - # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) - # print(autobatch(model)) - - # Check device - prefix = colorstr('AutoBatch: ') - LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') - device = next(model.parameters()).device # get model device - if device.type == 'cpu': - LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') - return batch_size - if torch.backends.cudnn.benchmark: - LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') - return batch_size - - # Inspect CUDA memory - gb = 1 << 30 # bytes to GiB (1024 ** 3) - d = str(device).upper() # 'CUDA:0' - properties = torch.cuda.get_device_properties(device) # device properties - t = properties.total_memory / gb # GiB total - r = torch.cuda.memory_reserved(device) / gb # GiB reserved - a = torch.cuda.memory_allocated(device) / gb # GiB allocated - f = t - (r + a) # GiB free - LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') - - # Profile batch sizes - batch_sizes = [1, 2, 4, 8, 16] - try: - img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] - results = profile(img, model, n=3, device=device) - except Exception as e: - LOGGER.warning(f'{prefix}{e}') - - # Fit a solution - y = [x[2] for x in results if x] # memory [2] - p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit - b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) - if None in results: # some sizes failed - i = results.index(None) # first fail index - if b >= batch_sizes[i]: # y intercept above failure point - b = batch_sizes[max(i - 1, 0)] # select prior safe point - if b < 1 or b > 1024: # b outside of safe range - b = batch_size - LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.') - - fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted - LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') - return b diff --git a/iteach_toolkit/DHYOLO/utils/aws/__init__.py b/iteach_toolkit/DHYOLO/utils/aws/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/iteach_toolkit/DHYOLO/utils/aws/mime.sh b/iteach_toolkit/DHYOLO/utils/aws/mime.sh deleted file mode 100644 index c319a83cfbdf09bea634c3bd9fca737c0b1dd505..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/aws/mime.sh +++ /dev/null @@ -1,26 +0,0 @@ -# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ -# This script will run on every instance restart, not only on first start -# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- - -Content-Type: multipart/mixed; boundary="//" -MIME-Version: 1.0 - ---// -Content-Type: text/cloud-config; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="cloud-config.txt" - -#cloud-config -cloud_final_modules: -- [scripts-user, always] - ---// -Content-Type: text/x-shellscript; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="userdata.txt" - -#!/bin/bash -# --- paste contents of userdata.sh here --- ---// diff --git a/iteach_toolkit/DHYOLO/utils/aws/resume.py b/iteach_toolkit/DHYOLO/utils/aws/resume.py deleted file mode 100644 index b21731c979a121ab8227280351b70d6062efd983..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/aws/resume.py +++ /dev/null @@ -1,40 +0,0 @@ -# Resume all interrupted trainings in yolov5/ dir including DDP trainings -# Usage: $ python utils/aws/resume.py - -import os -import sys -from pathlib import Path - -import torch -import yaml - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[2] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): - ckpt = torch.load(last) - if ckpt['optimizer'] is None: - continue - - # Load opt.yaml - with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: - opt = yaml.safe_load(f) - - # Get device count - d = opt['device'].split(',') # devices - nd = len(d) # number of devices - ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel - - if ddp: # multi-GPU - port += 1 - cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' - else: # single-GPU - cmd = f'python train.py --resume {last}' - - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread - print(cmd) - os.system(cmd) diff --git a/iteach_toolkit/DHYOLO/utils/aws/userdata.sh b/iteach_toolkit/DHYOLO/utils/aws/userdata.sh deleted file mode 100644 index 5fc1332ac1b0d1794cf8f8c5f6918059ae5dc381..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/aws/userdata.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html -# This script will run only once on first instance start (for a re-start script see mime.sh) -# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir -# Use >300 GB SSD - -cd home/ubuntu -if [ ! -d yolov5 ]; then - echo "Running first-time script." # install dependencies, download COCO, pull Docker - git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 - cd yolov5 - bash data/scripts/get_coco.sh && echo "COCO done." & - sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & - python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & - wait && echo "All tasks done." # finish background tasks -else - echo "Running re-start script." # resume interrupted runs - i=0 - list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' - while IFS= read -r id; do - ((i++)) - echo "restarting container $i: $id" - sudo docker start $id - # sudo docker exec -it $id python train.py --resume # single-GPU - sudo docker exec -d $id python utils/aws/resume.py # multi-scenario - done <<<"$list" -fi diff --git a/iteach_toolkit/DHYOLO/utils/callbacks.py b/iteach_toolkit/DHYOLO/utils/callbacks.py deleted file mode 100644 index c90fa824cdb4c99e9e2ab6863b160ece626a9a28..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/callbacks.py +++ /dev/null @@ -1,76 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Callback utils -""" - -import threading - - -class Callbacks: - """" - Handles all registered callbacks for YOLOv5 Hooks - """ - - def __init__(self): - # Define the available callbacks - self._callbacks = { - 'on_pretrain_routine_start': [], - 'on_pretrain_routine_end': [], - 'on_train_start': [], - 'on_train_epoch_start': [], - 'on_train_batch_start': [], - 'optimizer_step': [], - 'on_before_zero_grad': [], - 'on_train_batch_end': [], - 'on_train_epoch_end': [], - 'on_val_start': [], - 'on_val_batch_start': [], - 'on_val_image_end': [], - 'on_val_batch_end': [], - 'on_val_end': [], - 'on_fit_epoch_end': [], # fit = train + val - 'on_model_save': [], - 'on_train_end': [], - 'on_params_update': [], - 'teardown': [], } - self.stop_training = False # set True to interrupt training - - def register_action(self, hook, name='', callback=None): - """ - Register a new action to a callback hook - - Args: - hook: The callback hook name to register the action to - name: The name of the action for later reference - callback: The callback to fire - """ - assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" - assert callable(callback), f"callback '{callback}' is not callable" - self._callbacks[hook].append({'name': name, 'callback': callback}) - - def get_registered_actions(self, hook=None): - """" - Returns all the registered actions by callback hook - - Args: - hook: The name of the hook to check, defaults to all - """ - return self._callbacks[hook] if hook else self._callbacks - - def run(self, hook, *args, thread=False, **kwargs): - """ - Loop through the registered actions and fire all callbacks on main thread - - Args: - hook: The name of the hook to check, defaults to all - args: Arguments to receive from YOLOv5 - thread: (boolean) Run callbacks in daemon thread - kwargs: Keyword Arguments to receive from YOLOv5 - """ - - assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" - for logger in self._callbacks[hook]: - if thread: - threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start() - else: - logger['callback'](*args, **kwargs) diff --git a/iteach_toolkit/DHYOLO/utils/dataloaders.py b/iteach_toolkit/DHYOLO/utils/dataloaders.py deleted file mode 100644 index 26201c3c78fcf6ee030be9ffdc2f9b7128f2b2b5..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/dataloaders.py +++ /dev/null @@ -1,1222 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Dataloaders and dataset utils -""" - -import contextlib -import glob -import hashlib -import json -import math -import os -import random -import shutil -import time -from itertools import repeat -from multiprocessing.pool import Pool, ThreadPool -from pathlib import Path -from threading import Thread -from urllib.parse import urlparse - -import numpy as np -import psutil -import torch -import torch.nn.functional as F -import torchvision -import yaml -from PIL import ExifTags, Image, ImageOps -from torch.utils.data import DataLoader, Dataset, dataloader, distributed -from tqdm import tqdm - -from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, - letterbox, mixup, random_perspective) -from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements, - check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, - xywh2xyxy, xywhn2xyxy, xyxy2xywhn) -from utils.torch_utils import torch_distributed_zero_first - -# Parameters -HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data' -IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes -VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders - -# Get orientation exif tag -for orientation in ExifTags.TAGS.keys(): - if ExifTags.TAGS[orientation] == 'Orientation': - break - - -def get_hash(paths): - # Returns a single hash value of a list of paths (files or dirs) - size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes - h = hashlib.sha256(str(size).encode()) # hash sizes - h.update(''.join(paths).encode()) # hash paths - return h.hexdigest() # return hash - - -def exif_size(img): - # Returns exif-corrected PIL size - s = img.size # (width, height) - with contextlib.suppress(Exception): - rotation = dict(img._getexif().items())[orientation] - if rotation in [6, 8]: # rotation 270 or 90 - s = (s[1], s[0]) - return s - - -def exif_transpose(image): - """ - Transpose a PIL image accordingly if it has an EXIF Orientation tag. - Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() - - :param image: The image to transpose. - :return: An image. - """ - exif = image.getexif() - orientation = exif.get(0x0112, 1) # default 1 - if orientation > 1: - method = { - 2: Image.FLIP_LEFT_RIGHT, - 3: Image.ROTATE_180, - 4: Image.FLIP_TOP_BOTTOM, - 5: Image.TRANSPOSE, - 6: Image.ROTATE_270, - 7: Image.TRANSVERSE, - 8: Image.ROTATE_90}.get(orientation) - if method is not None: - image = image.transpose(method) - del exif[0x0112] - image.info['exif'] = exif.tobytes() - return image - - -def seed_worker(worker_id): - # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader - worker_seed = torch.initial_seed() % 2 ** 32 - np.random.seed(worker_seed) - random.seed(worker_seed) - - -def create_dataloader(path, - imgsz, - batch_size, - stride, - single_cls=False, - hyp=None, - augment=False, - cache=False, - pad=0.0, - rect=False, - rank=-1, - workers=8, - image_weights=False, - quad=False, - prefix='', - shuffle=False, - seed=0): - if rect and shuffle: - LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') - shuffle = False - with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP - dataset = LoadImagesAndLabels( - path, - imgsz, - batch_size, - augment=augment, # augmentation - hyp=hyp, # hyperparameters - rect=rect, # rectangular batches - cache_images=cache, - single_cls=single_cls, - stride=int(stride), - pad=pad, - image_weights=image_weights, - prefix=prefix) - - batch_size = min(batch_size, len(dataset)) - nd = torch.cuda.device_count() # number of CUDA devices - nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers - sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) - loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates - generator = torch.Generator() - generator.manual_seed(6148914691236517205 + seed + RANK) - return loader(dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=PIN_MEMORY, - collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, - worker_init_fn=seed_worker, - generator=generator), dataset - - -class InfiniteDataLoader(dataloader.DataLoader): - """ Dataloader that reuses workers - - Uses same syntax as vanilla DataLoader - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) - self.iterator = super().__iter__() - - def __len__(self): - return len(self.batch_sampler.sampler) - - def __iter__(self): - for _ in range(len(self)): - yield next(self.iterator) - - -class _RepeatSampler: - """ Sampler that repeats forever - - Args: - sampler (Sampler) - """ - - def __init__(self, sampler): - self.sampler = sampler - - def __iter__(self): - while True: - yield from iter(self.sampler) - - -class LoadScreenshots: - # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` - def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): - # source = [screen_number left top width height] (pixels) - check_requirements('mss') - import mss - - source, *params = source.split() - self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 - if len(params) == 1: - self.screen = int(params[0]) - elif len(params) == 4: - left, top, width, height = (int(x) for x in params) - elif len(params) == 5: - self.screen, left, top, width, height = (int(x) for x in params) - self.img_size = img_size - self.stride = stride - self.transforms = transforms - self.auto = auto - self.mode = 'stream' - self.frame = 0 - self.sct = mss.mss() - - # Parse monitor shape - monitor = self.sct.monitors[self.screen] - self.top = monitor['top'] if top is None else (monitor['top'] + top) - self.left = monitor['left'] if left is None else (monitor['left'] + left) - self.width = width or monitor['width'] - self.height = height or monitor['height'] - self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height} - - def __iter__(self): - return self - - def __next__(self): - # mss screen capture: get raw pixels from the screen as np array - im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR - s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: ' - - if self.transforms: - im = self.transforms(im0) # transforms - else: - im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize - im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - im = np.ascontiguousarray(im) # contiguous - self.frame += 1 - return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s - - -class LoadImages: - # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` - def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): - if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line - path = Path(path).read_text().rsplit() - files = [] - for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: - p = str(Path(p).resolve()) - if '*' in p: - files.extend(sorted(glob.glob(p, recursive=True))) # glob - elif os.path.isdir(p): - files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir - elif os.path.isfile(p): - files.append(p) # files - else: - raise FileNotFoundError(f'{p} does not exist') - - images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] - videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] - ni, nv = len(images), len(videos) - - self.img_size = img_size - self.stride = stride - self.files = images + videos - self.nf = ni + nv # number of files - self.video_flag = [False] * ni + [True] * nv - self.mode = 'image' - self.auto = auto - self.transforms = transforms # optional - self.vid_stride = vid_stride # video frame-rate stride - if any(videos): - self._new_video(videos[0]) # new video - else: - self.cap = None - assert self.nf > 0, f'No images or videos found in {p}. ' \ - f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' - - def __iter__(self): - self.count = 0 - return self - - def __next__(self): - if self.count == self.nf: - raise StopIteration - path = self.files[self.count] - - if self.video_flag[self.count]: - # Read video - self.mode = 'video' - for _ in range(self.vid_stride): - self.cap.grab() - ret_val, im0 = self.cap.retrieve() - while not ret_val: - self.count += 1 - self.cap.release() - if self.count == self.nf: # last video - raise StopIteration - path = self.files[self.count] - self._new_video(path) - ret_val, im0 = self.cap.read() - - self.frame += 1 - # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False - s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' - - else: - # Read image - self.count += 1 - im0 = cv2.imread(path) # BGR - assert im0 is not None, f'Image Not Found {path}' - s = f'image {self.count}/{self.nf} {path}: ' - - if self.transforms: - im = self.transforms(im0) # transforms - else: - im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize - im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - im = np.ascontiguousarray(im) # contiguous - - return path, im, im0, self.cap, s - - def _new_video(self, path): - # Create a new video capture object - self.frame = 0 - self.cap = cv2.VideoCapture(path) - self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) - self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees - # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 - - def _cv2_rotate(self, im): - # Rotate a cv2 video manually - if self.orientation == 0: - return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) - elif self.orientation == 180: - return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) - elif self.orientation == 90: - return cv2.rotate(im, cv2.ROTATE_180) - return im - - def __len__(self): - return self.nf # number of files - - -class LoadStreams: - # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` - def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): - torch.backends.cudnn.benchmark = True # faster for fixed-size inference - self.mode = 'stream' - self.img_size = img_size - self.stride = stride - self.vid_stride = vid_stride # video frame-rate stride - sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] - n = len(sources) - self.sources = [clean_str(x) for x in sources] # clean source names for later - self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n - for i, s in enumerate(sources): # index, source - # Start thread to read frames from video stream - st = f'{i + 1}/{n}: {s}... ' - if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video - # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' - check_requirements(('pafy', 'youtube_dl==2020.12.2')) - import pafy - s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL - s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam - if s == 0: - assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' - assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' - cap = cv2.VideoCapture(s) - assert cap.isOpened(), f'{st}Failed to open {s}' - w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan - self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback - self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback - - _, self.imgs[i] = cap.read() # guarantee first frame - self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) - LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)') - self.threads[i].start() - LOGGER.info('') # newline - - # check for common shapes - s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) - self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal - self.auto = auto and self.rect - self.transforms = transforms # optional - if not self.rect: - LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') - - def update(self, i, cap, stream): - # Read stream `i` frames in daemon thread - n, f = 0, self.frames[i] # frame number, frame array - while cap.isOpened() and n < f: - n += 1 - cap.grab() # .read() = .grab() followed by .retrieve() - if n % self.vid_stride == 0: - success, im = cap.retrieve() - if success: - self.imgs[i] = im - else: - LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') - self.imgs[i] = np.zeros_like(self.imgs[i]) - cap.open(stream) # re-open stream if signal was lost - time.sleep(0.0) # wait time - - def __iter__(self): - self.count = -1 - return self - - def __next__(self): - self.count += 1 - if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit - cv2.destroyAllWindows() - raise StopIteration - - im0 = self.imgs.copy() - if self.transforms: - im = np.stack([self.transforms(x) for x in im0]) # transforms - else: - im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize - im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW - im = np.ascontiguousarray(im) # contiguous - - return self.sources, im, im0, None, '' - - def __len__(self): - return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years - - -def img2label_paths(img_paths): - # Define label paths as a function of image paths - sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings - return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] - - -class LoadImagesAndLabels(Dataset): - # YOLOv5 train_loader/val_loader, loads images and labels for training and validation - cache_version = 0.6 # dataset labels *.cache version - rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] - - def __init__(self, - path, - img_size=640, - batch_size=16, - augment=False, - hyp=None, - rect=False, - image_weights=False, - cache_images=False, - single_cls=False, - stride=32, - pad=0.0, - min_items=0, - prefix=''): - self.img_size = img_size - self.augment = augment - self.hyp = hyp - self.image_weights = image_weights - self.rect = False if image_weights else rect - self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) - self.mosaic_border = [-img_size // 2, -img_size // 2] - self.stride = stride - self.path = path - self.albumentations = Albumentations(size=img_size) if augment else None - - try: - f = [] # image files - for p in path if isinstance(path, list) else [path]: - p = Path(p) # os-agnostic - if p.is_dir(): # dir - f += glob.glob(str(p / '**' / '*.*'), recursive=True) - # f = list(p.rglob('*.*')) # pathlib - elif p.is_file(): # file - with open(p) as t: - t = t.read().strip().splitlines() - parent = str(p.parent) + os.sep - f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t] # to global path - # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) - else: - raise FileNotFoundError(f'{prefix}{p} does not exist') - self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) - # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib - assert self.im_files, f'{prefix}No images found' - except Exception as e: - raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e - - # Check cache - self.label_files = img2label_paths(self.im_files) # labels - cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') - try: - cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict - assert cache['version'] == self.cache_version # matches current version - assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash - except Exception: - cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops - - # Display cache - nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total - if exists and LOCAL_RANK in {-1, 0}: - d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' - tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results - if cache['msgs']: - LOGGER.info('\n'.join(cache['msgs'])) # display warnings - assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}' - - # Read cache - [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items - labels, shapes, self.segments = zip(*cache.values()) - nl = len(np.concatenate(labels, 0)) # number of labels - assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' - self.labels = list(labels) - self.shapes = np.array(shapes) - self.im_files = list(cache.keys()) # update - self.label_files = img2label_paths(cache.keys()) # update - - # Filter images - if min_items: - include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) - LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset') - self.im_files = [self.im_files[i] for i in include] - self.label_files = [self.label_files[i] for i in include] - self.labels = [self.labels[i] for i in include] - self.segments = [self.segments[i] for i in include] - self.shapes = self.shapes[include] # wh - - # Create indices - n = len(self.shapes) # number of images - bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index - nb = bi[-1] + 1 # number of batches - self.batch = bi # batch index of image - self.n = n - self.indices = range(n) - - # Update labels - include_class = [] # filter labels to include only these classes (optional) - self.segments = list(self.segments) - include_class_array = np.array(include_class).reshape(1, -1) - for i, (label, segment) in enumerate(zip(self.labels, self.segments)): - if include_class: - j = (label[:, 0:1] == include_class_array).any(1) - self.labels[i] = label[j] - if segment: - self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem] - if single_cls: # single-class training, merge all classes into 0 - self.labels[i][:, 0] = 0 - - # Rectangular Training - if self.rect: - # Sort by aspect ratio - s = self.shapes # wh - ar = s[:, 1] / s[:, 0] # aspect ratio - irect = ar.argsort() - self.im_files = [self.im_files[i] for i in irect] - self.label_files = [self.label_files[i] for i in irect] - self.labels = [self.labels[i] for i in irect] - self.segments = [self.segments[i] for i in irect] - self.shapes = s[irect] # wh - ar = ar[irect] - - # Set training image shapes - shapes = [[1, 1]] * nb - for i in range(nb): - ari = ar[bi == i] - mini, maxi = ari.min(), ari.max() - if maxi < 1: - shapes[i] = [maxi, 1] - elif mini > 1: - shapes[i] = [1, 1 / mini] - - self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride - - # Cache images into RAM/disk for faster training - if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix): - cache_images = False - self.ims = [None] * n - self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] - if cache_images: - b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes - self.im_hw0, self.im_hw = [None] * n, [None] * n - fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image - results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) - pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) - for i, x in pbar: - if cache_images == 'disk': - b += self.npy_files[i].stat().st_size - else: # 'ram' - self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) - b += self.ims[i].nbytes - pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' - pbar.close() - - def check_cache_ram(self, safety_margin=0.1, prefix=''): - # Check image caching requirements vs available memory - b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes - n = min(self.n, 30) # extrapolate from 30 random images - for _ in range(n): - im = cv2.imread(random.choice(self.im_files)) # sample image - ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio - b += im.nbytes * ratio ** 2 - mem_required = b * self.n / n # GB required to cache dataset into RAM - mem = psutil.virtual_memory() - cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question - if not cache: - LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, ' - f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' - f"{'caching images ✅' if cache else 'not caching images ⚠️'}") - return cache - - def cache_labels(self, path=Path('./labels.cache'), prefix=''): - # Cache dataset labels, check images and read shapes - x = {} # dict - nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages - desc = f'{prefix}Scanning {path.parent / path.stem}...' - with Pool(NUM_THREADS) as pool: - pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), - desc=desc, - total=len(self.im_files), - bar_format=TQDM_BAR_FORMAT) - for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: - nm += nm_f - nf += nf_f - ne += ne_f - nc += nc_f - if im_file: - x[im_file] = [lb, shape, segments] - if msg: - msgs.append(msg) - pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' - - pbar.close() - if msgs: - LOGGER.info('\n'.join(msgs)) - if nf == 0: - LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') - x['hash'] = get_hash(self.label_files + self.im_files) - x['results'] = nf, nm, ne, nc, len(self.im_files) - x['msgs'] = msgs # warnings - x['version'] = self.cache_version # cache version - try: - np.save(path, x) # save cache for next time - path.with_suffix('.cache.npy').rename(path) # remove .npy suffix - LOGGER.info(f'{prefix}New cache created: {path}') - except Exception as e: - LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable - return x - - def __len__(self): - return len(self.im_files) - - # def __iter__(self): - # self.count = -1 - # print('ran dataset iter') - # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) - # return self - - def __getitem__(self, index): - index = self.indices[index] # linear, shuffled, or image_weights - - hyp = self.hyp - mosaic = self.mosaic and random.random() < hyp['mosaic'] - if mosaic: - # Load mosaic - img, labels = self.load_mosaic(index) - shapes = None - - # MixUp augmentation - if random.random() < hyp['mixup']: - img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) - - else: - # Load image - img, (h0, w0), (h, w) = self.load_image(index) - - # Letterbox - shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape - img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) - shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling - - labels = self.labels[index].copy() - if labels.size: # normalized xywh to pixel xyxy format - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) - - if self.augment: - img, labels = random_perspective(img, - labels, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) - - nl = len(labels) # number of labels - if nl: - labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) - - if self.augment: - # Albumentations - img, labels = self.albumentations(img, labels) - nl = len(labels) # update after albumentations - - # HSV color-space - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) - - # Flip up-down - if random.random() < hyp['flipud']: - img = np.flipud(img) - if nl: - labels[:, 2] = 1 - labels[:, 2] - - # Flip left-right - if random.random() < hyp['fliplr']: - img = np.fliplr(img) - if nl: - labels[:, 1] = 1 - labels[:, 1] - - # Cutouts - # labels = cutout(img, labels, p=0.5) - # nl = len(labels) # update after cutout - - labels_out = torch.zeros((nl, 6)) - if nl: - labels_out[:, 1:] = torch.from_numpy(labels) - - # Convert - img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - img = np.ascontiguousarray(img) - - return torch.from_numpy(img), labels_out, self.im_files[index], shapes - - def load_image(self, i): - # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw) - im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i], - if im is None: # not cached in RAM - if fn.exists(): # load npy - im = np.load(fn) - else: # read image - im = cv2.imread(f) # BGR - assert im is not None, f'Image Not Found {f}' - h0, w0 = im.shape[:2] # orig hw - r = self.img_size / max(h0, w0) # ratio - if r != 1: # if sizes are not equal - interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA - im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) - return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized - return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized - - def cache_images_to_disk(self, i): - # Saves an image as an *.npy file for faster loading - f = self.npy_files[i] - if not f.exists(): - np.save(f.as_posix(), cv2.imread(self.im_files[i])) - - def load_mosaic(self, index): - # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic - labels4, segments4 = [], [] - s = self.img_size - yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y - indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices - random.shuffle(indices) - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = self.load_image(index) - - # place img in img4 - if i == 0: # top left - img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) - x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) - elif i == 1: # top right - x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc - x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h - elif i == 2: # bottom left - x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) - x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) - elif i == 3: # bottom right - x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) - x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) - - img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] - padw = x1a - x1b - padh = y1a - y1b - - # Labels - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padw, padh) for x in segments] - labels4.append(labels) - segments4.extend(segments) - - # Concat/clip labels - labels4 = np.concatenate(labels4, 0) - for x in (labels4[:, 1:], *segments4): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img4, labels4 = replicate(img4, labels4) # replicate - - # Augment - img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) - img4, labels4 = random_perspective(img4, - labels4, - segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - - return img4, labels4 - - def load_mosaic9(self, index): - # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic - labels9, segments9 = [], [] - s = self.img_size - indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices - random.shuffle(indices) - hp, wp = -1, -1 # height, width previous - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = self.load_image(index) - - # place img in img9 - if i == 0: # center - img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - h0, w0 = h, w - c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates - elif i == 1: # top - c = s, s - h, s + w, s - elif i == 2: # top right - c = s + wp, s - h, s + wp + w, s - elif i == 3: # right - c = s + w0, s, s + w0 + w, s + h - elif i == 4: # bottom right - c = s + w0, s + hp, s + w0 + w, s + hp + h - elif i == 5: # bottom - c = s + w0 - w, s + h0, s + w0, s + h0 + h - elif i == 6: # bottom left - c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h - elif i == 7: # left - c = s - w, s + h0 - h, s, s + h0 - elif i == 8: # top left - c = s - w, s + h0 - hp - h, s, s + h0 - hp - - padx, pady = c[:2] - x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords - - # Labels - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padx, pady) for x in segments] - labels9.append(labels) - segments9.extend(segments) - - # Image - img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] - hp, wp = h, w # height, width previous - - # Offset - yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y - img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] - - # Concat/clip labels - labels9 = np.concatenate(labels9, 0) - labels9[:, [1, 3]] -= xc - labels9[:, [2, 4]] -= yc - c = np.array([xc, yc]) # centers - segments9 = [x - c for x in segments9] - - for x in (labels9[:, 1:], *segments9): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img9, labels9 = replicate(img9, labels9) # replicate - - # Augment - img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste']) - img9, labels9 = random_perspective(img9, - labels9, - segments9, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - - return img9, labels9 - - @staticmethod - def collate_fn(batch): - im, label, path, shapes = zip(*batch) # transposed - for i, lb in enumerate(label): - lb[:, 0] = i # add target image index for build_targets() - return torch.stack(im, 0), torch.cat(label, 0), path, shapes - - @staticmethod - def collate_fn4(batch): - im, label, path, shapes = zip(*batch) # transposed - n = len(shapes) // 4 - im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] - - ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) - wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) - s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale - for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW - i *= 4 - if random.random() < 0.5: - im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', - align_corners=False)[0].type(im[i].type()) - lb = label[i] - else: - im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) - lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s - im4.append(im1) - label4.append(lb) - - for i, lb in enumerate(label4): - lb[:, 0] = i # add target image index for build_targets() - - return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 - - -# Ancillary functions -------------------------------------------------------------------------------------------------- -def flatten_recursive(path=DATASETS_DIR / 'coco128'): - # Flatten a recursive directory by bringing all files to top level - new_path = Path(f'{str(path)}_flat') - if os.path.exists(new_path): - shutil.rmtree(new_path) # delete output folder - os.makedirs(new_path) # make new output folder - for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)): - shutil.copyfile(file, new_path / Path(file).name) - - -def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes() - # Convert detection dataset into classification dataset, with one directory per class - path = Path(path) # images dir - shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing - files = list(path.rglob('*.*')) - n = len(files) # number of files - for im_file in tqdm(files, total=n): - if im_file.suffix[1:] in IMG_FORMATS: - # image - im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB - h, w = im.shape[:2] - - # labels - lb_file = Path(img2label_paths([str(im_file)])[0]) - if Path(lb_file).exists(): - with open(lb_file) as f: - lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels - - for j, x in enumerate(lb): - c = int(x[0]) # class - f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename - if not f.parent.is_dir(): - f.parent.mkdir(parents=True) - - b = x[1:] * [w, h, w, h] # box - # b[2:] = b[2:].max() # rectangle to square - b[2:] = b[2:] * 1.2 + 3 # pad - b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) - - b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image - b[[1, 3]] = np.clip(b[[1, 3]], 0, h) - assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' - - -def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): - """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files - Usage: from utils.dataloaders import *; autosplit() - Arguments - path: Path to images directory - weights: Train, val, test weights (list, tuple) - annotated_only: Only use images with an annotated txt file - """ - path = Path(path) # images dir - files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only - n = len(files) # number of files - random.seed(0) # for reproducibility - indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split - - txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files - for x in txt: - if (path.parent / x).exists(): - (path.parent / x).unlink() # remove existing - - print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) - for i, img in tqdm(zip(indices, files), total=n): - if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label - with open(path.parent / txt[i], 'a') as f: - f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file - - -def verify_image_label(args): - # Verify one image-label pair - im_file, lb_file, prefix = args - nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments - try: - # verify images - im = Image.open(im_file) - im.verify() # PIL verify - shape = exif_size(im) # image size - assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' - assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' - if im.format.lower() in ('jpg', 'jpeg'): - with open(im_file, 'rb') as f: - f.seek(-2, 2) - if f.read() != b'\xff\xd9': # corrupt JPEG - ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) - msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved' - - # verify labels - if os.path.isfile(lb_file): - nf = 1 # label found - with open(lb_file) as f: - lb = [x.split() for x in f.read().strip().splitlines() if len(x)] - if any(len(x) > 6 for x in lb): # is segment - classes = np.array([x[0] for x in lb], dtype=np.float32) - segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) - lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) - lb = np.array(lb, dtype=np.float32) - nl = len(lb) - if nl: - assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' - assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' - assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' - _, i = np.unique(lb, axis=0, return_index=True) - if len(i) < nl: # duplicate row check - lb = lb[i] # remove duplicates - if segments: - segments = [segments[x] for x in i] - msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed' - else: - ne = 1 # label empty - lb = np.zeros((0, 5), dtype=np.float32) - else: - nm = 1 # label missing - lb = np.zeros((0, 5), dtype=np.float32) - return im_file, lb, shape, segments, nm, nf, ne, nc, msg - except Exception as e: - nc = 1 - msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}' - return [None, None, None, None, nm, nf, ne, nc, msg] - - -class HUBDatasetStats(): - """ Class for generating HUB dataset JSON and `-hub` dataset directory - - Arguments - path: Path to data.yaml or data.zip (with data.yaml inside data.zip) - autodownload: Attempt to download dataset if not found locally - - Usage - from utils.dataloaders import HUBDatasetStats - stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 - stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 - stats.get_json(save=False) - stats.process_images() - """ - - def __init__(self, path='coco128.yaml', autodownload=False): - # Initialize class - zipped, data_dir, yaml_path = self._unzip(Path(path)) - try: - with open(check_yaml(yaml_path), errors='ignore') as f: - data = yaml.safe_load(f) # data dict - if zipped: - data['path'] = data_dir - except Exception as e: - raise Exception('error/HUB/dataset_stats/yaml_load') from e - - check_dataset(data, autodownload) # download dataset if missing - self.hub_dir = Path(data['path'] + '-hub') - self.im_dir = self.hub_dir / 'images' - self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images - self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary - self.data = data - - @staticmethod - def _find_yaml(dir): - # Return data.yaml file - files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive - assert files, f'No *.yaml file found in {dir}' - if len(files) > 1: - files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name - assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' - assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' - return files[0] - - def _unzip(self, path): - # Unzip data.zip - if not str(path).endswith('.zip'): # path is data.yaml - return False, None, path - assert Path(path).is_file(), f'Error unzipping {path}, file not found' - unzip_file(path, path=path.parent) - dir = path.with_suffix('') # dataset directory == zip name - assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/' - return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path - - def _hub_ops(self, f, max_dim=1920): - # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing - f_new = self.im_dir / Path(f).name # dataset-hub image filename - try: # use PIL - im = Image.open(f) - r = max_dim / max(im.height, im.width) # ratio - if r < 1.0: # image too large - im = im.resize((int(im.width * r), int(im.height * r))) - im.save(f_new, 'JPEG', quality=50, optimize=True) # save - except Exception as e: # use OpenCV - LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') - im = cv2.imread(f) - im_height, im_width = im.shape[:2] - r = max_dim / max(im_height, im_width) # ratio - if r < 1.0: # image too large - im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) - cv2.imwrite(str(f_new), im) - - def get_json(self, save=False, verbose=False): - # Return dataset JSON for Ultralytics HUB - def _round(labels): - # Update labels to integer class and 6 decimal place floats - return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] - - for split in 'train', 'val', 'test': - if self.data.get(split) is None: - self.stats[split] = None # i.e. no test set - continue - dataset = LoadImagesAndLabels(self.data[split]) # load dataset - x = np.array([ - np.bincount(label[:, 0].astype(int), minlength=self.data['nc']) - for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80) - self.stats[split] = { - 'instance_stats': { - 'total': int(x.sum()), - 'per_class': x.sum(0).tolist()}, - 'image_stats': { - 'total': dataset.n, - 'unlabelled': int(np.all(x == 0, 1).sum()), - 'per_class': (x > 0).sum(0).tolist()}, - 'labels': [{ - str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]} - - # Save, print and return - if save: - stats_path = self.hub_dir / 'stats.json' - print(f'Saving {stats_path.resolve()}...') - with open(stats_path, 'w') as f: - json.dump(self.stats, f) # save stats.json - if verbose: - print(json.dumps(self.stats, indent=2, sort_keys=False)) - return self.stats - - def process_images(self): - # Compress images for Ultralytics HUB - for split in 'train', 'val', 'test': - if self.data.get(split) is None: - continue - dataset = LoadImagesAndLabels(self.data[split]) # load dataset - desc = f'{split} images' - for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): - pass - print(f'Done. All images saved to {self.im_dir}') - return self.im_dir - - -# Classification dataloaders ------------------------------------------------------------------------------------------- -class ClassificationDataset(torchvision.datasets.ImageFolder): - """ - YOLOv5 Classification Dataset. - Arguments - root: Dataset path - transform: torchvision transforms, used by default - album_transform: Albumentations transforms, used if installed - """ - - def __init__(self, root, augment, imgsz, cache=False): - super().__init__(root=root) - self.torch_transforms = classify_transforms(imgsz) - self.album_transforms = classify_albumentations(augment, imgsz) if augment else None - self.cache_ram = cache is True or cache == 'ram' - self.cache_disk = cache == 'disk' - self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im - - def __getitem__(self, i): - f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image - if self.cache_ram and im is None: - im = self.samples[i][3] = cv2.imread(f) - elif self.cache_disk: - if not fn.exists(): # load npy - np.save(fn.as_posix(), cv2.imread(f)) - im = np.load(fn) - else: # read image - im = cv2.imread(f) # BGR - if self.album_transforms: - sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] - else: - sample = self.torch_transforms(im) - return sample, j - - -def create_classification_dataloader(path, - imgsz=224, - batch_size=16, - augment=True, - cache=False, - rank=-1, - workers=8, - shuffle=True): - # Returns Dataloader object to be used with YOLOv5 Classifier - with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP - dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) - batch_size = min(batch_size, len(dataset)) - nd = torch.cuda.device_count() - nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) - sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) - generator = torch.Generator() - generator.manual_seed(6148914691236517205 + RANK) - return InfiniteDataLoader(dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=PIN_MEMORY, - worker_init_fn=seed_worker, - generator=generator) # or DataLoader(persistent_workers=True) diff --git a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile b/iteach_toolkit/DHYOLO/utils/docker/Dockerfile deleted file mode 100644 index 4346fc823c1611cfdd1a1f88a17d6100a886bee9..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile +++ /dev/null @@ -1,74 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 -# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference - -# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch -FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime - -# Downloads to user config dir -ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ - -# Install linux packages -ENV DEBIAN_FRONTEND noninteractive -RUN apt update -RUN TZ=Etc/UTC apt install -y tzdata -RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg -# RUN alias python=python3 - -# Security updates -# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 -RUN apt upgrade --no-install-recommends -y openssl - -# Create working directory -RUN rm -rf /usr/src/app && mkdir -p /usr/src/app -WORKDIR /usr/src/app - -# Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app - -# Install pip packages -COPY requirements.txt . -RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ - coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' - # tensorflow tensorflowjs \ - -# Set environment variables -ENV OMP_NUM_THREADS=1 - -# Cleanup -ENV DEBIAN_FRONTEND teletype - - -# Usage Examples ------------------------------------------------------------------------------------------------------- - -# Build and Push -# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t - -# Pull and Run -# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t - -# Pull and Run with local directory access -# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t - -# Kill all -# sudo docker kill $(sudo docker ps -q) - -# Kill all image-based -# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) - -# DockerHub tag update -# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew - -# Clean up -# sudo docker system prune -a --volumes - -# Update Ubuntu drivers -# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ - -# DDP test -# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 - -# GCP VM from Image -# docker.io/ultralytics/yolov5:latest diff --git a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-arm64 b/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-arm64 deleted file mode 100644 index 7b5c610e5071b2aa712b7e521c39145c11016773..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-arm64 +++ /dev/null @@ -1,41 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 -# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi - -# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM arm64v8/ubuntu:22.10 - -# Downloads to user config dir -ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ - -# Install linux packages -ENV DEBIAN_FRONTEND noninteractive -RUN apt update -RUN TZ=Etc/UTC apt install -y tzdata -RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev -# RUN alias python=python3 - -# Install pip packages -COPY requirements.txt . -RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ - coremltools onnx onnxruntime - # tensorflow-aarch64 tensorflowjs \ - -# Create working directory -RUN mkdir -p /usr/src/app -WORKDIR /usr/src/app - -# Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app -ENV DEBIAN_FRONTEND teletype - - -# Usage Examples ------------------------------------------------------------------------------------------------------- - -# Build and Push -# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t - -# Pull and Run -# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-cpu b/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-cpu deleted file mode 100644 index 17316986c9d56df1e42250aaf300fbc73eb1c2d3..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/docker/Dockerfile-cpu +++ /dev/null @@ -1,43 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 -# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments - -# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM ubuntu:lunar-20230615 - -# Downloads to user config dir -ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ - -# Install linux packages -# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package -RUN apt update \ - && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 -# RUN alias python=python3 - -# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error -RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED - -# Install pip packages -COPY requirements.txt . -RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ - coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \ - # tensorflow tensorflowjs \ - --extra-index-url https://download.pytorch.org/whl/cpu - -# Create working directory -RUN mkdir -p /usr/src/app -WORKDIR /usr/src/app - -# Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app - - -# Usage Examples ------------------------------------------------------------------------------------------------------- - -# Build and Push -# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t - -# Pull and Run -# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/iteach_toolkit/DHYOLO/utils/downloads.py b/iteach_toolkit/DHYOLO/utils/downloads.py deleted file mode 100644 index 9298259d4ab183516d7e144f71084de3e219b987..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/downloads.py +++ /dev/null @@ -1,127 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Download utils -""" - -import logging -import subprocess -import urllib -from pathlib import Path - -import requests -import torch - - -def is_url(url, check=True): - # Check if string is URL and check if URL exists - try: - url = str(url) - result = urllib.parse.urlparse(url) - assert all([result.scheme, result.netloc]) # check if is url - return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online - except (AssertionError, urllib.request.HTTPError): - return False - - -def gsutil_getsize(url=''): - # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8') - if output: - return int(output.split()[0]) - return 0 - - -def url_getsize(url='https://ultralytics.com/images/bus.jpg'): - # Return downloadable file size in bytes - response = requests.head(url, allow_redirects=True) - return int(response.headers.get('content-length', -1)) - - -def curl_download(url, filename, *, silent: bool = False) -> bool: - """ - Download a file from a url to a filename using curl. - """ - silent_option = 'sS' if silent else '' # silent - proc = subprocess.run([ - 'curl', - '-#', - f'-{silent_option}L', - url, - '--output', - filename, - '--retry', - '9', - '-C', - '-', ]) - return proc.returncode == 0 - - -def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): - # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes - from utils.general import LOGGER - - file = Path(file) - assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" - try: # url1 - LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) - assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check - except Exception as e: # url2 - if file.exists(): - file.unlink() # remove partial downloads - LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...') - # curl download, retry and resume on fail - curl_download(url2 or url, file) - finally: - if not file.exists() or file.stat().st_size < min_bytes: # check - if file.exists(): - file.unlink() # remove partial downloads - LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}') - LOGGER.info('') - - -def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'): - # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc. - from utils.general import LOGGER - - def github_assets(repository, version='latest'): - # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...]) - if version != 'latest': - version = f'tags/{version}' # i.e. tags/v7.0 - response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api - return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets - - file = Path(str(file).strip().replace("'", '')) - if not file.exists(): - # URL specified - name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. - if str(file).startswith(('http:/', 'https:/')): # download - url = str(file).replace(':/', '://') # Pathlib turns :// -> :/ - file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth... - if Path(file).is_file(): - LOGGER.info(f'Found {url} locally at {file}') # file already exists - else: - safe_download(file=file, url=url, min_bytes=1E5) - return file - - # GitHub assets - assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default - try: - tag, assets = github_assets(repo, release) - except Exception: - try: - tag, assets = github_assets(repo) # latest release - except Exception: - try: - tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] - except Exception: - tag = release - - if name in assets: - file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) - safe_download(file, - url=f'https://github.com/{repo}/releases/download/{tag}/{name}', - min_bytes=1E5, - error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}') - - return str(file) diff --git a/iteach_toolkit/DHYOLO/utils/flask_rest_api/README.md b/iteach_toolkit/DHYOLO/utils/flask_rest_api/README.md deleted file mode 100644 index a726acbd92043458311dd949cc09c0195cd35400..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/flask_rest_api/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# Flask REST API - -[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are -commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API -created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). - -## Requirements - -[Flask](https://palletsprojects.com/p/flask/) is required. Install with: - -```shell -$ pip install Flask -``` - -## Run - -After Flask installation run: - -```shell -$ python3 restapi.py --port 5000 -``` - -Then use [curl](https://curl.se/) to perform a request: - -```shell -$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' -``` - -The model inference results are returned as a JSON response: - -```json -[ - { - "class": 0, - "confidence": 0.8900438547, - "height": 0.9318675399, - "name": "person", - "width": 0.3264600933, - "xcenter": 0.7438579798, - "ycenter": 0.5207948685 - }, - { - "class": 0, - "confidence": 0.8440024257, - "height": 0.7155083418, - "name": "person", - "width": 0.6546785235, - "xcenter": 0.427829951, - "ycenter": 0.6334488392 - }, - { - "class": 27, - "confidence": 0.3771208823, - "height": 0.3902671337, - "name": "tie", - "width": 0.0696444362, - "xcenter": 0.3675483763, - "ycenter": 0.7991207838 - }, - { - "class": 27, - "confidence": 0.3527112305, - "height": 0.1540903747, - "name": "tie", - "width": 0.0336618312, - "xcenter": 0.7814827561, - "ycenter": 0.5065554976 - } -] -``` - -An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given -in `example_request.py` diff --git a/iteach_toolkit/DHYOLO/utils/flask_rest_api/example_request.py b/iteach_toolkit/DHYOLO/utils/flask_rest_api/example_request.py deleted file mode 100644 index 256ad1319c82abf941a50f2d690a4ec1244616bd..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/flask_rest_api/example_request.py +++ /dev/null @@ -1,19 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Perform test request -""" - -import pprint - -import requests - -DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s' -IMAGE = 'zidane.jpg' - -# Read image -with open(IMAGE, 'rb') as f: - image_data = f.read() - -response = requests.post(DETECTION_URL, files={'image': image_data}).json() - -pprint.pprint(response) diff --git a/iteach_toolkit/DHYOLO/utils/flask_rest_api/restapi.py b/iteach_toolkit/DHYOLO/utils/flask_rest_api/restapi.py deleted file mode 100644 index ae4756b276e4b5d4215d29ee1761e520adc05f54..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/flask_rest_api/restapi.py +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Run a Flask REST API exposing one or more YOLOv5s models -""" - -import argparse -import io - -import torch -from flask import Flask, request -from PIL import Image - -app = Flask(__name__) -models = {} - -DETECTION_URL = '/v1/object-detection/' - - -@app.route(DETECTION_URL, methods=['POST']) -def predict(model): - if request.method != 'POST': - return - - if request.files.get('image'): - # Method 1 - # with request.files["image"] as f: - # im = Image.open(io.BytesIO(f.read())) - - # Method 2 - im_file = request.files['image'] - im_bytes = im_file.read() - im = Image.open(io.BytesIO(im_bytes)) - - if model in models: - results = models[model](im, size=640) # reduce size=320 for faster inference - return results.pandas().xyxy[0].to_json(orient='records') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') - parser.add_argument('--port', default=5000, type=int, help='port number') - parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') - opt = parser.parse_args() - - for m in opt.model: - models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) - - app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat diff --git a/iteach_toolkit/DHYOLO/utils/general.py b/iteach_toolkit/DHYOLO/utils/general.py deleted file mode 100644 index 135141e2143602f3a7482b4ac0853aad54e044e3..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/general.py +++ /dev/null @@ -1,1118 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -General utils -""" - -import contextlib -import glob -import inspect -import logging -import logging.config -import math -import os -import platform -import random -import re -import signal -import subprocess -import sys -import time -import urllib -from copy import deepcopy -from datetime import datetime -from itertools import repeat -from multiprocessing.pool import ThreadPool -from pathlib import Path -from subprocess import check_output -from tarfile import is_tarfile -from typing import Optional -from zipfile import ZipFile, is_zipfile - -import cv2 -import numpy as np -import pandas as pd -import pkg_resources as pkg -import torch -import torchvision -import yaml - -# Import 'ultralytics' package or install if if missing -try: - import ultralytics - - assert hasattr(ultralytics, '__version__') # verify package is not directory -except (ImportError, AssertionError): - os.system('pip install -U ultralytics') - import ultralytics - -from ultralytics.utils.checks import check_requirements - -from utils import TryExcept, emojis -from utils.downloads import curl_download, gsutil_getsize -from utils.metrics import box_iou, fitness - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -RANK = int(os.getenv('RANK', -1)) - -# Settings -NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads -DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory -AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode -VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode -TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format -FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf - -torch.set_printoptions(linewidth=320, precision=5, profile='long') -np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 -pd.options.display.max_columns = 10 -cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) -os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads -os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab - - -def is_ascii(s=''): - # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) - s = str(s) # convert list, tuple, None, etc. to str - return len(s.encode().decode('ascii', 'ignore')) == len(s) - - -def is_chinese(s='人工智能'): - # Is string composed of any Chinese characters? - return bool(re.search('[\u4e00-\u9fff]', str(s))) - - -def is_colab(): - # Is environment a Google Colab instance? - return 'google.colab' in sys.modules - - -def is_jupyter(): - """ - Check if the current script is running inside a Jupyter Notebook. - Verified on Colab, Jupyterlab, Kaggle, Paperspace. - - Returns: - bool: True if running inside a Jupyter Notebook, False otherwise. - """ - with contextlib.suppress(Exception): - from IPython import get_ipython - return get_ipython() is not None - return False - - -def is_kaggle(): - # Is environment a Kaggle Notebook? - return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com' - - -def is_docker() -> bool: - """Check if the process runs inside a docker container.""" - if Path('/.dockerenv').exists(): - return True - try: # check if docker is in control groups - with open('/proc/self/cgroup') as file: - return any('docker' in line for line in file) - except OSError: - return False - - -def is_writeable(dir, test=False): - # Return True if directory has write permissions, test opening a file with write permissions if test=True - if not test: - return os.access(dir, os.W_OK) # possible issues on Windows - file = Path(dir) / 'tmp.txt' - try: - with open(file, 'w'): # open file with write permissions - pass - file.unlink() # remove file - return True - except OSError: - return False - - -LOGGING_NAME = 'yolov5' - - -def set_logging(name=LOGGING_NAME, verbose=True): - # sets up logging for the given name - rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings - level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR - logging.config.dictConfig({ - 'version': 1, - 'disable_existing_loggers': False, - 'formatters': { - name: { - 'format': '%(message)s'}}, - 'handlers': { - name: { - 'class': 'logging.StreamHandler', - 'formatter': name, - 'level': level, }}, - 'loggers': { - name: { - 'level': level, - 'handlers': [name], - 'propagate': False, }}}) - - -set_logging(LOGGING_NAME) # run before defining LOGGER -LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) -if platform.system() == 'Windows': - for fn in LOGGER.info, LOGGER.warning: - setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging - - -def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): - # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. - env = os.getenv(env_var) - if env: - path = Path(env) # use environment variable - else: - cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs - path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir - path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable - path.mkdir(exist_ok=True) # make if required - return path - - -CONFIG_DIR = user_config_dir() # Ultralytics settings dir - - -class Profile(contextlib.ContextDecorator): - # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager - def __init__(self, t=0.0): - self.t = t - self.cuda = torch.cuda.is_available() - - def __enter__(self): - self.start = self.time() - return self - - def __exit__(self, type, value, traceback): - self.dt = self.time() - self.start # delta-time - self.t += self.dt # accumulate dt - - def time(self): - if self.cuda: - torch.cuda.synchronize() - return time.time() - - -class Timeout(contextlib.ContextDecorator): - # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager - def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): - self.seconds = int(seconds) - self.timeout_message = timeout_msg - self.suppress = bool(suppress_timeout_errors) - - def _timeout_handler(self, signum, frame): - raise TimeoutError(self.timeout_message) - - def __enter__(self): - if platform.system() != 'Windows': # not supported on Windows - signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM - signal.alarm(self.seconds) # start countdown for SIGALRM to be raised - - def __exit__(self, exc_type, exc_val, exc_tb): - if platform.system() != 'Windows': - signal.alarm(0) # Cancel SIGALRM if it's scheduled - if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError - return True - - -class WorkingDirectory(contextlib.ContextDecorator): - # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager - def __init__(self, new_dir): - self.dir = new_dir # new dir - self.cwd = Path.cwd().resolve() # current dir - - def __enter__(self): - os.chdir(self.dir) - - def __exit__(self, exc_type, exc_val, exc_tb): - os.chdir(self.cwd) - - -def methods(instance): - # Get class/instance methods - return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')] - - -def print_args(args: Optional[dict] = None, show_file=True, show_func=False): - # Print function arguments (optional args dict) - x = inspect.currentframe().f_back # previous frame - file, _, func, _, _ = inspect.getframeinfo(x) - if args is None: # get args automatically - args, _, _, frm = inspect.getargvalues(x) - args = {k: v for k, v in frm.items() if k in args} - try: - file = Path(file).resolve().relative_to(ROOT).with_suffix('') - except ValueError: - file = Path(file).stem - s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '') - LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items())) - - -def init_seeds(seed=0, deterministic=False): - # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe - # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 - if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 - torch.use_deterministic_algorithms(True) - torch.backends.cudnn.deterministic = True - os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' - os.environ['PYTHONHASHSEED'] = str(seed) - - -def intersect_dicts(da, db, exclude=()): - # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values - return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} - - -def get_default_args(func): - # Get func() default arguments - signature = inspect.signature(func) - return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} - - -def get_latest_run(search_dir='.'): - # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) - last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) - return max(last_list, key=os.path.getctime) if last_list else '' - - -def file_age(path=__file__): - # Return days since last file update - dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta - return dt.days # + dt.seconds / 86400 # fractional days - - -def file_date(path=__file__): - # Return human-readable file modification date, i.e. '2021-3-26' - t = datetime.fromtimestamp(Path(path).stat().st_mtime) - return f'{t.year}-{t.month}-{t.day}' - - -def file_size(path): - # Return file/dir size (MB) - mb = 1 << 20 # bytes to MiB (1024 ** 2) - path = Path(path) - if path.is_file(): - return path.stat().st_size / mb - elif path.is_dir(): - return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb - else: - return 0.0 - - -def check_online(): - # Check internet connectivity - import socket - - def run_once(): - # Check once - try: - socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility - return True - except OSError: - return False - - return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues - - -def git_describe(path=ROOT): # path must be a directory - # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe - try: - assert (Path(path) / '.git').is_dir() - return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1] - except Exception: - return '' - - -@TryExcept() -@WorkingDirectory(ROOT) -def check_git_status(repo='ultralytics/yolov5', branch='master'): - # YOLOv5 status check, recommend 'git pull' if code is out of date - url = f'https://github.com/{repo}' - msg = f', for updates see {url}' - s = colorstr('github: ') # string - assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg - assert check_online(), s + 'skipping check (offline)' + msg - - splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode()) - matches = [repo in s for s in splits] - if any(matches): - remote = splits[matches.index(True) - 1] - else: - remote = 'ultralytics' - check_output(f'git remote add {remote} {url}', shell=True) - check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch - local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out - n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind - if n > 0: - pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}' - s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." - else: - s += f'up to date with {url} ✅' - LOGGER.info(s) - - -@WorkingDirectory(ROOT) -def check_git_info(path='.'): - # YOLOv5 git info check, return {remote, branch, commit} - check_requirements('gitpython') - import git - try: - repo = git.Repo(path) - remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5' - commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' - try: - branch = repo.active_branch.name # i.e. 'main' - except TypeError: # not on any branch - branch = None # i.e. 'detached HEAD' state - return {'remote': remote, 'branch': branch, 'commit': commit} - except git.exc.InvalidGitRepositoryError: # path is not a git dir - return {'remote': None, 'branch': None, 'commit': None} - - -def check_python(minimum='3.8.0'): - # Check current python version vs. required python version - check_version(platform.python_version(), minimum, name='Python ', hard=True) - - -def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): - # Check version vs. required version - current, minimum = (pkg.parse_version(x) for x in (current, minimum)) - result = (current == minimum) if pinned else (current >= minimum) # bool - s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string - if hard: - assert result, emojis(s) # assert min requirements met - if verbose and not result: - LOGGER.warning(s) - return result - - -def check_img_size(imgsz, s=32, floor=0): - # Verify image size is a multiple of stride s in each dimension - if isinstance(imgsz, int): # integer i.e. img_size=640 - new_size = max(make_divisible(imgsz, int(s)), floor) - else: # list i.e. img_size=[640, 480] - imgsz = list(imgsz) # convert to list if tuple - new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] - if new_size != imgsz: - LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') - return new_size - - -def check_imshow(warn=False): - # Check if environment supports image displays - try: - assert not is_jupyter() - assert not is_docker() - cv2.imshow('test', np.zeros((1, 1, 3))) - cv2.waitKey(1) - cv2.destroyAllWindows() - cv2.waitKey(1) - return True - except Exception as e: - if warn: - LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}') - return False - - -def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''): - # Check file(s) for acceptable suffix - if file and suffix: - if isinstance(suffix, str): - suffix = [suffix] - for f in file if isinstance(file, (list, tuple)) else [file]: - s = Path(f).suffix.lower() # file suffix - if len(s): - assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}' - - -def check_yaml(file, suffix=('.yaml', '.yml')): - # Search/download YAML file (if necessary) and return path, checking suffix - return check_file(file, suffix) - - -def check_file(file, suffix=''): - # Search/download file (if necessary) and return path - check_suffix(file, suffix) # optional - file = str(file) # convert to str() - if os.path.isfile(file) or not file: # exists - return file - elif file.startswith(('http:/', 'https:/')): # download - url = file # warning: Pathlib turns :// -> :/ - file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth - if os.path.isfile(file): - LOGGER.info(f'Found {url} locally at {file}') # file already exists - else: - LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check - return file - elif file.startswith('clearml://'): # ClearML Dataset ID - assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." - return file - else: # search - files = [] - for d in 'data', 'models', 'utils': # search directories - files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file - assert len(files), f'File not found: {file}' # assert file was found - assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique - return files[0] # return file - - -def check_font(font=FONT, progress=False): - # Download font to CONFIG_DIR if necessary - font = Path(font) - file = CONFIG_DIR / font.name - if not font.exists() and not file.exists(): - url = f'https://ultralytics.com/assets/{font.name}' - LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, str(file), progress=progress) - - -def check_dataset(data, autodownload=True): - # Download, check and/or unzip dataset if not found locally - - # Download (optional) - extract_dir = '' - if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): - download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1) - data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) - extract_dir, autodownload = data.parent, False - - # Read yaml (optional) - if isinstance(data, (str, Path)): - data = yaml_load(data) # dictionary - - # Checks - for k in 'train', 'val', 'names': - assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") - if isinstance(data['names'], (list, tuple)): # old array format - data['names'] = dict(enumerate(data['names'])) # convert to dict - assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car' - data['nc'] = len(data['names']) - - # Resolve paths - path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.' - if not path.is_absolute(): - path = (ROOT / path).resolve() - data['path'] = path # download scripts - for k in 'train', 'val', 'test': - if data.get(k): # prepend path - if isinstance(data[k], str): - x = (path / data[k]).resolve() - if not x.exists() and data[k].startswith('../'): - x = (path / data[k][3:]).resolve() - data[k] = str(x) - else: - data[k] = [str((path / x).resolve()) for x in data[k]] - - # Parse yaml - train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) - if val: - val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path - if not all(x.exists() for x in val): - LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]) - if not s or not autodownload: - raise Exception('Dataset not found ❌') - t = time.time() - if s.startswith('http') and s.endswith('.zip'): # URL - f = Path(s).name # filename - LOGGER.info(f'Downloading {s} to {f}...') - torch.hub.download_url_to_file(s, f) - Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root - unzip_file(f, path=DATASETS_DIR) # unzip - Path(f).unlink() # remove zip - r = None # success - elif s.startswith('bash '): # bash script - LOGGER.info(f'Running {s} ...') - r = subprocess.run(s, shell=True) - else: # python script - r = exec(s, {'yaml': data}) # return None - dt = f'({round(time.time() - t, 1)}s)' - s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌' - LOGGER.info(f'Dataset download {s}') - check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts - return data # dictionary - - -def check_amp(model): - # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation - from models.common import AutoShape, DetectMultiBackend - - def amp_allclose(model, im): - # All close FP32 vs AMP results - m = AutoShape(model, verbose=False) # model - a = m(im).xywhn[0] # FP32 inference - m.amp = True - b = m(im).xywhn[0] # AMP inference - return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance - - prefix = colorstr('AMP: ') - device = next(model.parameters()).device # get model device - if device.type in ('cpu', 'mps'): - return False # AMP only used on CUDA devices - f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check - im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3)) - try: - assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im) - LOGGER.info(f'{prefix}checks passed ✅') - return True - except Exception: - help_url = 'https://github.com/ultralytics/yolov5/issues/7908' - LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}') - return False - - -def yaml_load(file='data.yaml'): - # Single-line safe yaml loading - with open(file, errors='ignore') as f: - return yaml.safe_load(f) - - -def yaml_save(file='data.yaml', data={}): - # Single-line safe yaml saving - with open(file, 'w') as f: - yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) - - -def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): - # Unzip a *.zip file to path/, excluding files containing strings in exclude list - if path is None: - path = Path(file).parent # default path - with ZipFile(file) as zipObj: - for f in zipObj.namelist(): # list all archived filenames in the zip - if all(x not in f for x in exclude): - zipObj.extract(f, path=path) - - -def url2file(url): - # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt - url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ - return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth - - -def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3): - # Multithreaded file download and unzip function, used in data.yaml for autodownload - def download_one(url, dir): - # Download 1 file - success = True - if os.path.isfile(url): - f = Path(url) # filename - else: # does not exist - f = dir / Path(url).name - LOGGER.info(f'Downloading {url} to {f}...') - for i in range(retry + 1): - if curl: - success = curl_download(url, f, silent=(threads > 1)) - else: - torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download - success = f.is_file() - if success: - break - elif i < retry: - LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...') - else: - LOGGER.warning(f'❌ Failed to download {url}...') - - if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)): - LOGGER.info(f'Unzipping {f}...') - if is_zipfile(f): - unzip_file(f, dir) # unzip - elif is_tarfile(f): - subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip - elif f.suffix == '.gz': - subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip - if delete: - f.unlink() # remove zip - - dir = Path(dir) - dir.mkdir(parents=True, exist_ok=True) # make directory - if threads > 1: - pool = ThreadPool(threads) - pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded - pool.close() - pool.join() - else: - for u in [url] if isinstance(url, (str, Path)) else url: - download_one(u, dir) - - -def make_divisible(x, divisor): - # Returns nearest x divisible by divisor - if isinstance(divisor, torch.Tensor): - divisor = int(divisor.max()) # to int - return math.ceil(x / divisor) * divisor - - -def clean_str(s): - # Cleans a string by replacing special characters with underscore _ - return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s) - - -def one_cycle(y1=0.0, y2=1.0, steps=100): - # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf - return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 - - -def colorstr(*input): - # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') - *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string - colors = { - 'black': '\033[30m', # basic colors - 'red': '\033[31m', - 'green': '\033[32m', - 'yellow': '\033[33m', - 'blue': '\033[34m', - 'magenta': '\033[35m', - 'cyan': '\033[36m', - 'white': '\033[37m', - 'bright_black': '\033[90m', # bright colors - 'bright_red': '\033[91m', - 'bright_green': '\033[92m', - 'bright_yellow': '\033[93m', - 'bright_blue': '\033[94m', - 'bright_magenta': '\033[95m', - 'bright_cyan': '\033[96m', - 'bright_white': '\033[97m', - 'end': '\033[0m', # misc - 'bold': '\033[1m', - 'underline': '\033[4m'} - return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] - - -def labels_to_class_weights(labels, nc=80): - # Get class weights (inverse frequency) from training labels - if labels[0] is None: # no labels loaded - return torch.Tensor() - - labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO - classes = labels[:, 0].astype(int) # labels = [class xywh] - weights = np.bincount(classes, minlength=nc) # occurrences per class - - # Prepend gridpoint count (for uCE training) - # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image - # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start - - weights[weights == 0] = 1 # replace empty bins with 1 - weights = 1 / weights # number of targets per class - weights /= weights.sum() # normalize - return torch.from_numpy(weights).float() - - -def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): - # Produces image weights based on class_weights and image contents - # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample - class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) - return (class_weights.reshape(1, nc) * class_counts).sum(1) - - -def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) - # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ - # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') - # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') - # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco - # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet - return [ - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] - - -def xyxy2xywh(x): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center - y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center - y[..., 2] = x[..., 2] - x[..., 0] # width - y[..., 3] = x[..., 3] - x[..., 1] # height - return y - - -def xywh2xyxy(x): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x - y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y - y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x - y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y - return y - - -def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): - # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x - y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y - y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x - y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y - return y - - -def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right - if clip: - clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center - y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center - y[..., 2] = (x[..., 2] - x[..., 0]) / w # width - y[..., 3] = (x[..., 3] - x[..., 1]) / h # height - return y - - -def xyn2xy(x, w=640, h=640, padw=0, padh=0): - # Convert normalized segments into pixel segments, shape (n,2) - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = w * x[..., 0] + padw # top left x - y[..., 1] = h * x[..., 1] + padh # top left y - return y - - -def segment2box(segment, width=640, height=640): - # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) - x, y = segment.T # segment xy - inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) - x, y, = x[inside], y[inside] - return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy - - -def segments2boxes(segments): - # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) - boxes = [] - for s in segments: - x, y = s.T # segment xy - boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy - return xyxy2xywh(np.array(boxes)) # cls, xywh - - -def resample_segments(segments, n=1000): - # Up-sample an (n,2) segment - for i, s in enumerate(segments): - s = np.concatenate((s, s[0:1, :]), axis=0) - x = np.linspace(0, len(s) - 1, n) - xp = np.arange(len(s)) - segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy - return segments - - -def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): - # Rescale boxes (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - boxes[..., [0, 2]] -= pad[0] # x padding - boxes[..., [1, 3]] -= pad[1] # y padding - boxes[..., :4] /= gain - clip_boxes(boxes, img0_shape) - return boxes - - -def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - segments[:, 0] -= pad[0] # x padding - segments[:, 1] -= pad[1] # y padding - segments /= gain - clip_segments(segments, img0_shape) - if normalize: - segments[:, 0] /= img0_shape[1] # width - segments[:, 1] /= img0_shape[0] # height - return segments - - -def clip_boxes(boxes, shape): - # Clip boxes (xyxy) to image shape (height, width) - if isinstance(boxes, torch.Tensor): # faster individually - boxes[..., 0].clamp_(0, shape[1]) # x1 - boxes[..., 1].clamp_(0, shape[0]) # y1 - boxes[..., 2].clamp_(0, shape[1]) # x2 - boxes[..., 3].clamp_(0, shape[0]) # y2 - else: # np.array (faster grouped) - boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 - - -def clip_segments(segments, shape): - # Clip segments (xy1,xy2,...) to image shape (height, width) - if isinstance(segments, torch.Tensor): # faster individually - segments[:, 0].clamp_(0, shape[1]) # x - segments[:, 1].clamp_(0, shape[0]) # y - else: # np.array (faster grouped) - segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x - segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y - - -def non_max_suppression( - prediction, - conf_thres=0.25, - iou_thres=0.45, - classes=None, - agnostic=False, - multi_label=False, - labels=(), - max_det=300, - nm=0, # number of masks -): - """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - # Checks - assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' - assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' - if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) - prediction = prediction[0] # select only inference output - - device = prediction.device - mps = 'mps' in device.type # Apple MPS - if mps: # MPS not fully supported yet, convert tensors to CPU before NMS - prediction = prediction.cpu() - bs = prediction.shape[0] # batch size - nc = prediction.shape[2] - nm - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # min_wh = 2 # (pixels) minimum box width and height - max_wh = 7680 # (pixels) maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 0.5 + 0.05 * bs # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - mi = 5 + nc # mask start index - output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - lb = labels[xi] - v = torch.zeros((len(lb), nc + nm + 5), device=x.device) - v[:, :4] = lb[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box/Mask - box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) - mask = x[:, mi:] # zero columns if no masks - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) - else: # best class only - conf, j = x[:, 5:mi].max(1, keepdim=True) - x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - i = i[:max_det] # limit detections - if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if mps: - output[xi] = output[xi].to(device) - if (time.time() - t) > time_limit: - LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') - break # time limit exceeded - - return output - - -def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() - # Strip optimizer from 'f' to finalize training, optionally save as 's' - x = torch.load(f, map_location=torch.device('cpu')) - if x.get('ema'): - x['model'] = x['ema'] # replace model with ema - for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys - x[k] = None - x['epoch'] = -1 - x['model'].half() # to FP16 - for p in x['model'].parameters(): - p.requires_grad = False - torch.save(x, s or f) - mb = os.path.getsize(s or f) / 1E6 # filesize - LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") - - -def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')): - evolve_csv = save_dir / 'evolve.csv' - evolve_yaml = save_dir / 'hyp_evolve.yaml' - keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] - keys = tuple(x.strip() for x in keys) - vals = results + tuple(hyp.values()) - n = len(keys) - - # Download (optional) - if bucket: - url = f'gs://{bucket}/evolve.csv' - if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): - subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local - - # Log to evolve.csv - s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header - with open(evolve_csv, 'a') as f: - f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') - - # Save yaml - with open(evolve_yaml, 'w') as f: - data = pd.read_csv(evolve_csv, skipinitialspace=True) - data = data.rename(columns=lambda x: x.strip()) # strip keys - i = np.argmax(fitness(data.values[:, :4])) # - generations = len(data) - f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' + - f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + - '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') - yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) - - # Print to screen - LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix + - ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}' - for x in vals) + '\n\n') - - if bucket: - subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload - - -def apply_classifier(x, model, img, im0): - # Apply a second stage classifier to YOLO outputs - # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() - im0 = [im0] if isinstance(im0, np.ndarray) else im0 - for i, d in enumerate(x): # per image - if d is not None and len(d): - d = d.clone() - - # Reshape and pad cutouts - b = xyxy2xywh(d[:, :4]) # boxes - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square - b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad - d[:, :4] = xywh2xyxy(b).long() - - # Rescale boxes from img_size to im0 size - scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) - - # Classes - pred_cls1 = d[:, 5].long() - ims = [] - for a in d: - cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] - im = cv2.resize(cutout, (224, 224)) # BGR - - im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - ims.append(im) - - pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction - x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections - - return x - - -def increment_path(path, exist_ok=False, sep='', mkdir=False): - # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. - path = Path(path) # os-agnostic - if path.exists() and not exist_ok: - path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') - - # Method 1 - for n in range(2, 9999): - p = f'{path}{sep}{n}{suffix}' # increment path - if not os.path.exists(p): # - break - path = Path(p) - - # Method 2 (deprecated) - # dirs = glob.glob(f"{path}{sep}*") # similar paths - # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] - # i = [int(m.groups()[0]) for m in matches if m] # indices - # n = max(i) + 1 if i else 2 # increment number - # path = Path(f"{path}{sep}{n}{suffix}") # increment path - - if mkdir: - path.mkdir(parents=True, exist_ok=True) # make directory - - return path - - -# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ -imshow_ = cv2.imshow # copy to avoid recursion errors - - -def imread(filename, flags=cv2.IMREAD_COLOR): - return cv2.imdecode(np.fromfile(filename, np.uint8), flags) - - -def imwrite(filename, img): - try: - cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) - return True - except Exception: - return False - - -def imshow(path, im): - imshow_(path.encode('unicode_escape').decode(), im) - - -if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: - cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine - -# Variables ------------------------------------------------------------------------------------------------------------ diff --git a/iteach_toolkit/DHYOLO/utils/google_app_engine/Dockerfile b/iteach_toolkit/DHYOLO/utils/google_app_engine/Dockerfile deleted file mode 100644 index 0155618f475104e9858b81470339558156c94e13..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/google_app_engine/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM gcr.io/google-appengine/python - -# Create a virtualenv for dependencies. This isolates these packages from -# system-level packages. -# Use -p python3 or -p python3.7 to select python version. Default is version 2. -RUN virtualenv /env -p python3 - -# Setting these environment variables are the same as running -# source /env/bin/activate. -ENV VIRTUAL_ENV /env -ENV PATH /env/bin:$PATH - -RUN apt-get update && apt-get install -y python-opencv - -# Copy the application's requirements.txt and run pip to install all -# dependencies into the virtualenv. -ADD requirements.txt /app/requirements.txt -RUN pip install -r /app/requirements.txt - -# Add the application source code. -ADD . /app - -# Run a WSGI server to serve the application. gunicorn must be declared as -# a dependency in requirements.txt. -CMD gunicorn -b :$PORT main:app diff --git a/iteach_toolkit/DHYOLO/utils/google_app_engine/additional_requirements.txt b/iteach_toolkit/DHYOLO/utils/google_app_engine/additional_requirements.txt deleted file mode 100644 index fce1511588e3f09711e4b2d8f0490a5effc7dc0f..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/google_app_engine/additional_requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -# add these requirements in your app on top of the existing ones -pip==21.1 -Flask==2.3.2 -gunicorn==19.10.0 -werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability diff --git a/iteach_toolkit/DHYOLO/utils/google_app_engine/app.yaml b/iteach_toolkit/DHYOLO/utils/google_app_engine/app.yaml deleted file mode 100644 index 5056b7c1186d6ad278957bbd6e976c3a0f169a30..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/google_app_engine/app.yaml +++ /dev/null @@ -1,14 +0,0 @@ -runtime: custom -env: flex - -service: yolov5app - -liveness_check: - initial_delay_sec: 600 - -manual_scaling: - instances: 1 -resources: - cpu: 1 - memory_gb: 4 - disk_size_gb: 20 diff --git a/iteach_toolkit/DHYOLO/utils/loggers/__init__.py b/iteach_toolkit/DHYOLO/utils/loggers/__init__.py deleted file mode 100644 index ba7d2790e613e61f06db0c5173d7acf943b1dc4d..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/__init__.py +++ /dev/null @@ -1,401 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Logging utils -""" - -import os -import warnings -from pathlib import Path - -import pkg_resources as pkg -import torch - -from utils.general import LOGGER, colorstr, cv2 -from utils.loggers.clearml.clearml_utils import ClearmlLogger -from utils.loggers.wandb.wandb_utils import WandbLogger -from utils.plots import plot_images, plot_labels, plot_results -from utils.torch_utils import de_parallel - -LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML -RANK = int(os.getenv('RANK', -1)) - -try: - from torch.utils.tensorboard import SummaryWriter -except ImportError: - SummaryWriter = lambda *args: None # None = SummaryWriter(str) - -try: - import wandb - - assert hasattr(wandb, '__version__') # verify package import not local dir - if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}: - try: - wandb_login_success = wandb.login(timeout=30) - except wandb.errors.UsageError: # known non-TTY terminal issue - wandb_login_success = False - if not wandb_login_success: - wandb = None -except (ImportError, AssertionError): - wandb = None - -try: - import clearml - - assert hasattr(clearml, '__version__') # verify package import not local dir -except (ImportError, AssertionError): - clearml = None - -try: - if RANK in {0, -1}: - import comet_ml - - assert hasattr(comet_ml, '__version__') # verify package import not local dir - from utils.loggers.comet import CometLogger - - else: - comet_ml = None -except (ImportError, AssertionError): - comet_ml = None - - -class Loggers(): - # YOLOv5 Loggers class - def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): - self.save_dir = save_dir - self.weights = weights - self.opt = opt - self.hyp = hyp - self.plots = not opt.noplots # plot results - self.logger = logger # for printing results to console - self.include = include - self.keys = [ - 'train/box_loss', - 'train/obj_loss', - 'train/cls_loss', # train loss - 'metrics/precision', - 'metrics/recall', - 'metrics/mAP_0.5', - 'metrics/mAP_0.5:0.95', # metrics - 'val/box_loss', - 'val/obj_loss', - 'val/cls_loss', # val loss - 'x/lr0', - 'x/lr1', - 'x/lr2'] # params - self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95'] - for k in LOGGERS: - setattr(self, k, None) # init empty logger dictionary - self.csv = True # always log to csv - - # Messages - if not comet_ml: - prefix = colorstr('Comet: ') - s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" - self.logger.info(s) - # TensorBoard - s = self.save_dir - if 'tb' in self.include and not self.opt.evolve: - prefix = colorstr('TensorBoard: ') - self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") - self.tb = SummaryWriter(str(s)) - - # W&B - if wandb and 'wandb' in self.include: - self.opt.hyp = self.hyp # add hyperparameters - self.wandb = WandbLogger(self.opt) - else: - self.wandb = None - - # ClearML - if clearml and 'clearml' in self.include: - try: - self.clearml = ClearmlLogger(self.opt, self.hyp) - except Exception: - self.clearml = None - prefix = colorstr('ClearML: ') - LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' - f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme') - - else: - self.clearml = None - - # Comet - if comet_ml and 'comet' in self.include: - if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'): - run_id = self.opt.resume.split('/')[-1] - self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) - - else: - self.comet_logger = CometLogger(self.opt, self.hyp) - - else: - self.comet_logger = None - - @property - def remote_dataset(self): - # Get data_dict if custom dataset artifact link is provided - data_dict = None - if self.clearml: - data_dict = self.clearml.data_dict - if self.wandb: - data_dict = self.wandb.data_dict - if self.comet_logger: - data_dict = self.comet_logger.data_dict - - return data_dict - - def on_train_start(self): - if self.comet_logger: - self.comet_logger.on_train_start() - - def on_pretrain_routine_start(self): - if self.comet_logger: - self.comet_logger.on_pretrain_routine_start() - - def on_pretrain_routine_end(self, labels, names): - # Callback runs on pre-train routine end - if self.plots: - plot_labels(labels, names, self.save_dir) - paths = self.save_dir.glob('*labels*.jpg') # training labels - if self.wandb: - self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]}) - # if self.clearml: - # pass # ClearML saves these images automatically using hooks - if self.comet_logger: - self.comet_logger.on_pretrain_routine_end(paths) - - def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): - log_dict = dict(zip(self.keys[:3], vals)) - # Callback runs on train batch end - # ni: number integrated batches (since train start) - if self.plots: - if ni < 3: - f = self.save_dir / f'train_batch{ni}.jpg' # filename - plot_images(imgs, targets, paths, f) - if ni == 0 and self.tb and not self.opt.sync_bn: - log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) - if ni == 10 and (self.wandb or self.clearml): - files = sorted(self.save_dir.glob('train*.jpg')) - if self.wandb: - self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) - if self.clearml: - self.clearml.log_debug_samples(files, title='Mosaics') - - if self.comet_logger: - self.comet_logger.on_train_batch_end(log_dict, step=ni) - - def on_train_epoch_end(self, epoch): - # Callback runs on train epoch end - if self.wandb: - self.wandb.current_epoch = epoch + 1 - - if self.comet_logger: - self.comet_logger.on_train_epoch_end(epoch) - - def on_val_start(self): - if self.comet_logger: - self.comet_logger.on_val_start() - - def on_val_image_end(self, pred, predn, path, names, im): - # Callback runs on val image end - if self.wandb: - self.wandb.val_one_image(pred, predn, path, names, im) - if self.clearml: - self.clearml.log_image_with_boxes(path, pred, names, im) - - def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): - if self.comet_logger: - self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) - - def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): - # Callback runs on val end - if self.wandb or self.clearml: - files = sorted(self.save_dir.glob('val*.jpg')) - if self.wandb: - self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]}) - if self.clearml: - self.clearml.log_debug_samples(files, title='Validation') - - if self.comet_logger: - self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) - - def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): - # Callback runs at the end of each fit (train+val) epoch - x = dict(zip(self.keys, vals)) - if self.csv: - file = self.save_dir / 'results.csv' - n = len(x) + 1 # number of cols - s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header - with open(file, 'a') as f: - f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') - - if self.tb: - for k, v in x.items(): - self.tb.add_scalar(k, v, epoch) - elif self.clearml: # log to ClearML if TensorBoard not used - for k, v in x.items(): - title, series = k.split('/') - self.clearml.task.get_logger().report_scalar(title, series, v, epoch) - - if self.wandb: - if best_fitness == fi: - best_results = [epoch] + vals[3:7] - for i, name in enumerate(self.best_keys): - self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary - self.wandb.log(x) - self.wandb.end_epoch() - - if self.clearml: - self.clearml.current_epoch_logged_images = set() # reset epoch image limit - self.clearml.current_epoch += 1 - - if self.comet_logger: - self.comet_logger.on_fit_epoch_end(x, epoch=epoch) - - def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): - # Callback runs on model save event - if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: - if self.wandb: - self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) - if self.clearml: - self.clearml.task.update_output_model(model_path=str(last), - model_name='Latest Model', - auto_delete_file=False) - - if self.comet_logger: - self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) - - def on_train_end(self, last, best, epoch, results): - # Callback runs on training end, i.e. saving best model - if self.plots: - plot_results(file=self.save_dir / 'results.csv') # save results.png - files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] - files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter - self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") - - if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles - for f in files: - self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') - - if self.wandb: - self.wandb.log(dict(zip(self.keys[3:10], results))) - self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]}) - # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model - if not self.opt.evolve: - wandb.log_artifact(str(best if best.exists() else last), - type='model', - name=f'run_{self.wandb.wandb_run.id}_model', - aliases=['latest', 'best', 'stripped']) - self.wandb.finish_run() - - if self.clearml and not self.opt.evolve: - self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), - name='Best Model', - auto_delete_file=False) - - if self.comet_logger: - final_results = dict(zip(self.keys[3:10], results)) - self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) - - def on_params_update(self, params: dict): - # Update hyperparams or configs of the experiment - if self.wandb: - self.wandb.wandb_run.config.update(params, allow_val_change=True) - if self.comet_logger: - self.comet_logger.on_params_update(params) - - -class GenericLogger: - """ - YOLOv5 General purpose logger for non-task specific logging - Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...) - Arguments - opt: Run arguments - console_logger: Console logger - include: loggers to include - """ - - def __init__(self, opt, console_logger, include=('tb', 'wandb')): - # init default loggers - self.save_dir = Path(opt.save_dir) - self.include = include - self.console_logger = console_logger - self.csv = self.save_dir / 'results.csv' # CSV logger - if 'tb' in self.include: - prefix = colorstr('TensorBoard: ') - self.console_logger.info( - f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/") - self.tb = SummaryWriter(str(self.save_dir)) - - if wandb and 'wandb' in self.include: - self.wandb = wandb.init(project=web_project_name(str(opt.project)), - name=None if opt.name == 'exp' else opt.name, - config=opt) - else: - self.wandb = None - - def log_metrics(self, metrics, epoch): - # Log metrics dictionary to all loggers - if self.csv: - keys, vals = list(metrics.keys()), list(metrics.values()) - n = len(metrics) + 1 # number of cols - s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header - with open(self.csv, 'a') as f: - f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') - - if self.tb: - for k, v in metrics.items(): - self.tb.add_scalar(k, v, epoch) - - if self.wandb: - self.wandb.log(metrics, step=epoch) - - def log_images(self, files, name='Images', epoch=0): - # Log images to all loggers - files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path - files = [f for f in files if f.exists()] # filter by exists - - if self.tb: - for f in files: - self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') - - if self.wandb: - self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) - - def log_graph(self, model, imgsz=(640, 640)): - # Log model graph to all loggers - if self.tb: - log_tensorboard_graph(self.tb, model, imgsz) - - def log_model(self, model_path, epoch=0, metadata={}): - # Log model to all loggers - if self.wandb: - art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) - art.add_file(str(model_path)) - wandb.log_artifact(art) - - def update_params(self, params): - # Update the parameters logged - if self.wandb: - wandb.run.config.update(params, allow_val_change=True) - - -def log_tensorboard_graph(tb, model, imgsz=(640, 640)): - # Log model graph to TensorBoard - try: - p = next(model.parameters()) # for device, type - imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand - im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress jit trace warning - tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) - except Exception as e: - LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}') - - -def web_project_name(project): - # Convert local project name to web project name - if not project.startswith('runs/train'): - return project - suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else '' - return f'YOLOv5{suffix}' diff --git a/iteach_toolkit/DHYOLO/utils/loggers/__pycache__/__init__.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index 00634409c959257df5b63ce29a93a4521cd15bfc..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/README.md b/iteach_toolkit/DHYOLO/utils/loggers/clearml/README.md deleted file mode 100644 index ca41c040193c1d8817a870404af09871b511f7ed..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/clearml/README.md +++ /dev/null @@ -1,237 +0,0 @@ -# ClearML Integration - -Clear|MLClear|ML - -## About ClearML - -[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. - -🔨 Track every YOLOv5 training run in the experiment manager - -🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool - -🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent - -🔬 Get the very best mAP using ClearML Hyperparameter Optimization - -🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving - -
-And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! -
-
- -![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) - -
-
- -## 🦾 Setting Things Up - -To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: - -Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! - -1. Install the `clearml` python package: - - ```bash - pip install clearml - ``` - -1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: - - ```bash - clearml-init - ``` - -That's it! You're done 😎 - -
- -## 🚀 Training YOLOv5 With ClearML - -To enable ClearML experiment tracking, simply install the ClearML pip package. - -```bash -pip install clearml>=1.2.0 -``` - -This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. - -If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. -PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name! - -```bash -python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache -``` - -or with custom project and task name: - -```bash -python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache -``` - -This will capture: - -- Source code + uncommitted changes -- Installed packages -- (Hyper)parameters -- Model files (use `--save-period n` to save a checkpoint every n epochs) -- Console output -- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...) -- General info such as machine details, runtime, creation date etc. -- All produced plots such as label correlogram and confusion matrix -- Images with bounding boxes per epoch -- Mosaic per epoch -- Validation images per epoch -- ... - -That's a lot right? 🤯 -Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them! - -There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! - -
- -## 🔗 Dataset Version Management - -Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! - -![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif) - -### Prepare Your Dataset - -The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure: - -``` -.. -|_ yolov5 -|_ datasets - |_ coco128 - |_ images - |_ labels - |_ LICENSE - |_ README.txt -``` - -But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure. - -Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls. - -Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`. - -``` -.. -|_ yolov5 -|_ datasets - |_ coco128 - |_ images - |_ labels - |_ coco128.yaml # <---- HERE! - |_ LICENSE - |_ README.txt -``` - -### Upload Your Dataset - -To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: - -```bash -cd coco128 -clearml-data sync --project YOLOv5 --name coco128 --folder . -``` - -The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other: - -```bash -# Optionally add --parent if you want to base -# this version on another dataset version, so no duplicate files are uploaded! -clearml-data create --name coco128 --project YOLOv5 -clearml-data add --files . -clearml-data close -``` - -### Run Training Using A ClearML Dataset - -Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! - -```bash -python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache -``` - -
- -## 👀 Hyperparameter Optimization - -Now that we have our experiments and data versioned, it's time to take a look at what we can build on top! - -Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does! - -To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters. - -You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead. - -```bash -# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch -pip install optuna -python utils/loggers/clearml/hpo.py -``` - -![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png) - -## 🤯 Remote Execution (advanced) - -Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. -This is where the ClearML Agent comes into play. Check out what the agent can do here: - -- [YouTube video](https://youtu.be/MX3BrXnaULs) -- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) - -In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. - -You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: - -```bash -clearml-agent daemon --queue [--docker] -``` - -### Cloning, Editing And Enqueuing - -With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too! - -🪄 Clone the experiment by right-clicking it - -🎯 Edit the hyperparameters to what you wish them to be - -⏳ Enqueue the task to any of the queues by right-clicking it - -![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif) - -### Executing A Task Remotely - -Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on! - -To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated: - -```python -# ... -# Loggers -data_dict = None -if RANK in {-1, 0}: - loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance - if loggers.clearml: - loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE - # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML - data_dict = loggers.clearml.data_dict -# ... -``` - -When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead! - -### Autoscaling workers - -ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying! - -Check out the autoscalers getting started video below. - -[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/__init__.py b/iteach_toolkit/DHYOLO/utils/loggers/clearml/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/__init__.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index 6fa3503174ec14aacb49beff02f7cffc94d3331e..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/clearml_utils.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/clearml_utils.cpython-39.pyc deleted file mode 100644 index 86e03c8347a31ab3bd0bb8bd50ead696995dfb27..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/clearml/__pycache__/clearml_utils.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/clearml_utils.py b/iteach_toolkit/DHYOLO/utils/loggers/clearml/clearml_utils.py deleted file mode 100644 index 4e999bfee5dbf03fbb0a826ee458315912af4d7e..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/clearml/clearml_utils.py +++ /dev/null @@ -1,163 +0,0 @@ -"""Main Logger class for ClearML experiment tracking.""" -import glob -import re -from pathlib import Path - -import numpy as np -import yaml -from ultralytics.utils.plotting import Annotator, colors - -try: - import clearml - from clearml import Dataset, Task - - assert hasattr(clearml, '__version__') # verify package import not local dir -except (ImportError, AssertionError): - clearml = None - - -def construct_dataset(clearml_info_string): - """Load in a clearml dataset and fill the internal data_dict with its contents. - """ - dataset_id = clearml_info_string.replace('clearml://', '') - dataset = Dataset.get(dataset_id=dataset_id) - dataset_root_path = Path(dataset.get_local_copy()) - - # We'll search for the yaml file definition in the dataset - yaml_filenames = list(glob.glob(str(dataset_root_path / '*.yaml')) + glob.glob(str(dataset_root_path / '*.yml'))) - if len(yaml_filenames) > 1: - raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains ' - 'the dataset definition this way.') - elif len(yaml_filenames) == 0: - raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file ' - 'inside the dataset root path.') - with open(yaml_filenames[0]) as f: - dataset_definition = yaml.safe_load(f) - - assert set(dataset_definition.keys()).issuperset( - {'train', 'test', 'val', 'nc', 'names'} - ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" - - data_dict = dict() - data_dict['train'] = str( - (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None - data_dict['test'] = str( - (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None - data_dict['val'] = str( - (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None - data_dict['nc'] = dataset_definition['nc'] - data_dict['names'] = dataset_definition['names'] - - return data_dict - - -class ClearmlLogger: - """Log training runs, datasets, models, and predictions to ClearML. - - This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, - this information includes hyperparameters, system configuration and metrics, model metrics, code information and - basic data metrics and analyses. - - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. - """ - - def __init__(self, opt, hyp): - """ - - Initialize ClearML Task, this object will capture the experiment - - Upload dataset version to ClearML Data if opt.upload_dataset is True - - arguments: - opt (namespace) -- Commandline arguments for this run - hyp (dict) -- Hyperparameters for this run - - """ - self.current_epoch = 0 - # Keep tracked of amount of logged images to enforce a limit - self.current_epoch_logged_images = set() - # Maximum number of images to log to clearML per epoch - self.max_imgs_to_log_per_epoch = 16 - # Get the interval of epochs when bounding box images should be logged - self.bbox_interval = opt.bbox_interval - self.clearml = clearml - self.task = None - self.data_dict = None - if self.clearml: - self.task = Task.init( - project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5', - task_name=opt.name if opt.name != 'exp' else 'Training', - tags=['YOLOv5'], - output_uri=True, - reuse_last_task_id=opt.exist_ok, - auto_connect_frameworks={'pytorch': False} - # We disconnect pytorch auto-detection, because we added manual model save points in the code - ) - # ClearML's hooks will already grab all general parameters - # Only the hyperparameters coming from the yaml config file - # will have to be added manually! - self.task.connect(hyp, name='Hyperparameters') - self.task.connect(opt, name='Args') - - # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent - self.task.set_base_docker('ultralytics/yolov5:latest', - docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', - docker_setup_bash_script='pip install clearml') - - # Get ClearML Dataset Version if requested - if opt.data.startswith('clearml://'): - # data_dict should have the following keys: - # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) - self.data_dict = construct_dataset(opt.data) - # Set data to data_dict because wandb will crash without this information and opt is the best way - # to give it to them - opt.data = self.data_dict - - def log_debug_samples(self, files, title='Debug Samples'): - """ - Log files (images) as debug samples in the ClearML task. - - arguments: - files (List(PosixPath)) a list of file paths in PosixPath format - title (str) A title that groups together images with the same values - """ - for f in files: - if f.exists(): - it = re.search(r'_batch(\d+)', f.name) - iteration = int(it.groups()[0]) if it else 0 - self.task.get_logger().report_image(title=title, - series=f.name.replace(it.group(), ''), - local_path=str(f), - iteration=iteration) - - def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): - """ - Draw the bounding boxes on a single image and report the result as a ClearML debug sample. - - arguments: - image_path (PosixPath) the path the original image file - boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] - class_names (dict): dict containing mapping of class int to class name - image (Tensor): A torch tensor containing the actual image data - """ - if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0: - # Log every bbox_interval times and deduplicate for any intermittend extra eval runs - if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images: - im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) - annotator = Annotator(im=im, pil=True) - for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): - color = colors(i) - - class_name = class_names[int(class_nr)] - confidence_percentage = round(float(conf) * 100, 2) - label = f'{class_name}: {confidence_percentage}%' - - if conf > conf_threshold: - annotator.rectangle(box.cpu().numpy(), outline=color) - annotator.box_label(box.cpu().numpy(), label=label, color=color) - - annotated_image = annotator.result() - self.task.get_logger().report_image(title='Bounding Boxes', - series=image_path.name, - iteration=self.current_epoch, - image=annotated_image) - self.current_epoch_logged_images.add(image_path) diff --git a/iteach_toolkit/DHYOLO/utils/loggers/clearml/hpo.py b/iteach_toolkit/DHYOLO/utils/loggers/clearml/hpo.py deleted file mode 100644 index ee518b0fbfc89ee811b51bbf85341eee4f685be1..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/clearml/hpo.py +++ /dev/null @@ -1,84 +0,0 @@ -from clearml import Task -# Connecting ClearML with the current process, -# from here on everything is logged automatically -from clearml.automation import HyperParameterOptimizer, UniformParameterRange -from clearml.automation.optuna import OptimizerOptuna - -task = Task.init(project_name='Hyper-Parameter Optimization', - task_name='YOLOv5', - task_type=Task.TaskTypes.optimizer, - reuse_last_task_id=False) - -# Example use case: -optimizer = HyperParameterOptimizer( - # This is the experiment we want to optimize - base_task_id='', - # here we define the hyper-parameters to optimize - # Notice: The parameter name should exactly match what you see in the UI: / - # For Example, here we see in the base experiment a section Named: "General" - # under it a parameter named "batch_size", this becomes "General/batch_size" - # If you have `argparse` for example, then arguments will appear under the "Args" section, - # and you should instead pass "Args/batch_size" - hyper_parameters=[ - UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1), - UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0), - UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98), - UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001), - UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0), - UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95), - UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2), - UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2), - UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0), - UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0), - UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0), - UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0), - UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7), - UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0), - UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0), - UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1), - UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0), - UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0), - UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001), - UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)], - # this is the objective metric we want to maximize/minimize - objective_metric_title='metrics', - objective_metric_series='mAP_0.5', - # now we decide if we want to maximize it or minimize it (accuracy we maximize) - objective_metric_sign='max', - # let us limit the number of concurrent experiments, - # this in turn will make sure we do dont bombard the scheduler with experiments. - # if we have an auto-scaler connected, this, by proxy, will limit the number of machine - max_number_of_concurrent_tasks=1, - # this is the optimizer class (actually doing the optimization) - # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) - optimizer_class=OptimizerOptuna, - # If specified only the top K performing Tasks will be kept, the others will be automatically archived - save_top_k_tasks_only=5, # 5, - compute_time_limit=None, - total_max_jobs=20, - min_iteration_per_job=None, - max_iteration_per_job=None, -) - -# report every 10 seconds, this is way too often, but we are testing here -optimizer.set_report_period(10 / 60) -# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent -# an_optimizer.start_locally(job_complete_callback=job_complete_callback) -# set the time limit for the optimization process (2 hours) -optimizer.set_time_limit(in_minutes=120.0) -# Start the optimization process in the local environment -optimizer.start_locally() -# wait until process is done (notice we are controlling the optimization process in the background) -optimizer.wait() -# make sure background optimization stopped -optimizer.stop() - -print('We are done, good bye') diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/README.md b/iteach_toolkit/DHYOLO/utils/loggers/comet/README.md deleted file mode 100644 index 3ad52b01b4e9374e1ff7e93cc6d2f2dea061cb94..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/comet/README.md +++ /dev/null @@ -1,258 +0,0 @@ - - -# YOLOv5 with Comet - -This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) - -# About Comet - -Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. - -Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! -Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! - -# Getting Started - -## Install Comet - -```shell -pip install comet_ml -``` - -## Configure Comet Credentials - -There are two ways to configure Comet with YOLOv5. - -You can either set your credentials through environment variables - -**Environment Variables** - -```shell -export COMET_API_KEY= -export COMET_PROJECT_NAME= # This will default to 'yolov5' -``` - -Or create a `.comet.config` file in your working directory and set your credentials there. - -**Comet Configuration File** - -``` -[comet] -api_key= -project_name= # This will default to 'yolov5' -``` - -## Run the Training Script - -```shell -# Train YOLOv5s on COCO128 for 5 epochs -python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt -``` - -That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI - -yolo-ui - -# Try out an Example! - -Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) - -Or better yet, try it out yourself in this Colab Notebook - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb) - -# Log automatically - -By default, Comet will log the following items - -## Metrics - -- Box Loss, Object Loss, Classification Loss for the training and validation data -- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. -- Precision and Recall for the validation data - -## Parameters - -- Model Hyperparameters -- All parameters passed through the command line options - -## Visualizations - -- Confusion Matrix of the model predictions on the validation data -- Plots for the PR and F1 curves across all classes -- Correlogram of the Class Labels - -# Configure Comet Logging - -Comet can be configured to log additional data either through command line flags passed to the training script -or through environment variables. - -```shell -export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online -export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 -export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true -export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. -export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false -export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' -export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. -export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions -``` - -## Logging Checkpoints with Comet - -Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the -logged checkpoints to Comet based on the interval value provided by `save-period` - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---save-period 1 -``` - -## Logging Model Predictions - -By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. - -You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. - -**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. - -Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 2 -``` - -### Controlling the number of Prediction Images logged to Comet - -When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. - -```shell -env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 1 -``` - -### Logging Class Level Metrics - -Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. - -```shell -env COMET_LOG_PER_CLASS_METRICS=true python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt -``` - -## Uploading a Dataset to Comet Artifacts - -If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. - -The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---upload_dataset -``` - -You can find the uploaded dataset in the Artifacts tab in your Comet Workspace -artifact-1 - -You can preview the data directly in the Comet UI. -artifact-2 - -Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file -artifact-3 - -### Using a saved Artifact - -If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. - -``` -# contents of artifact.yaml file -path: "comet:///:" -``` - -Then pass this file to your training script in the following way - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data artifact.yaml \ ---weights yolov5s.pt -``` - -Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. -artifact-4 - -## Resuming a Training Run - -If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. - -The Run Path has the following format `comet:////`. - -This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI - -```shell -python train.py \ ---resume "comet://" -``` - -## Hyperparameter Search with the Comet Optimizer - -YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI. - -### Configuring an Optimizer Sweep - -To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` - -```shell -python utils/loggers/comet/hpo.py \ - --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" -``` - -The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after -the script. - -```shell -python utils/loggers/comet/hpo.py \ - --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ - --save-period 1 \ - --bbox_interval 1 -``` - -### Running a Sweep in Parallel - -```shell -comet optimizer -j utils/loggers/comet/hpo.py \ - utils/loggers/comet/optimizer_config.json" -``` - -### Visualizing Results - -Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) - -hyperparameter-yolo diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/__init__.py b/iteach_toolkit/DHYOLO/utils/loggers/comet/__init__.py deleted file mode 100644 index c14a5f885696650084b7d59913a55acdb091df51..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/comet/__init__.py +++ /dev/null @@ -1,518 +0,0 @@ -import glob -import json -import logging -import os -import sys -from pathlib import Path - -logger = logging.getLogger(__name__) - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[3] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -try: - import comet_ml - - # Project Configuration - config = comet_ml.config.get_config() - COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') -except ImportError: - comet_ml = None - COMET_PROJECT_NAME = None - -import PIL -import torch -import torchvision.transforms as T -import yaml - -from utils.dataloaders import img2label_paths -from utils.general import check_dataset, scale_boxes, xywh2xyxy -from utils.metrics import box_iou - -COMET_PREFIX = 'comet://' - -COMET_MODE = os.getenv('COMET_MODE', 'online') - -# Model Saving Settings -COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') - -# Dataset Artifact Settings -COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true' - -# Evaluation Settings -COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true') -COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true' -COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100)) - -# Confusion Matrix Settings -CONF_THRES = float(os.getenv('CONF_THRES', 0.001)) -IOU_THRES = float(os.getenv('IOU_THRES', 0.6)) - -# Batch Logging Settings -COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true') -COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1) -COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1) -COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true') - -RANK = int(os.getenv('RANK', -1)) - -to_pil = T.ToPILImage() - - -class CometLogger: - """Log metrics, parameters, source code, models and much more - with Comet - """ - - def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None: - self.job_type = job_type - self.opt = opt - self.hyp = hyp - - # Comet Flags - self.comet_mode = COMET_MODE - - self.save_model = opt.save_period > -1 - self.model_name = COMET_MODEL_NAME - - # Batch Logging Settings - self.log_batch_metrics = COMET_LOG_BATCH_METRICS - self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL - - # Dataset Artifact Settings - self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET - self.resume = self.opt.resume - - # Default parameters to pass to Experiment objects - self.default_experiment_kwargs = { - 'log_code': False, - 'log_env_gpu': True, - 'log_env_cpu': True, - 'project_name': COMET_PROJECT_NAME, } - self.default_experiment_kwargs.update(experiment_kwargs) - self.experiment = self._get_experiment(self.comet_mode, run_id) - self.experiment.set_name(self.opt.name) - - self.data_dict = self.check_dataset(self.opt.data) - self.class_names = self.data_dict['names'] - self.num_classes = self.data_dict['nc'] - - self.logged_images_count = 0 - self.max_images = COMET_MAX_IMAGE_UPLOADS - - if run_id is None: - self.experiment.log_other('Created from', 'YOLOv5') - if not isinstance(self.experiment, comet_ml.OfflineExperiment): - workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:] - self.experiment.log_other( - 'Run Path', - f'{workspace}/{project_name}/{experiment_id}', - ) - self.log_parameters(vars(opt)) - self.log_parameters(self.opt.hyp) - self.log_asset_data( - self.opt.hyp, - name='hyperparameters.json', - metadata={'type': 'hyp-config-file'}, - ) - self.log_asset( - f'{self.opt.save_dir}/opt.yaml', - metadata={'type': 'opt-config-file'}, - ) - - self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX - - if hasattr(self.opt, 'conf_thres'): - self.conf_thres = self.opt.conf_thres - else: - self.conf_thres = CONF_THRES - if hasattr(self.opt, 'iou_thres'): - self.iou_thres = self.opt.iou_thres - else: - self.iou_thres = IOU_THRES - - self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres}) - - self.comet_log_predictions = COMET_LOG_PREDICTIONS - if self.opt.bbox_interval == -1: - self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10) - else: - self.comet_log_prediction_interval = self.opt.bbox_interval - - if self.comet_log_predictions: - self.metadata_dict = {} - self.logged_image_names = [] - - self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS - - self.experiment.log_others({ - 'comet_mode': COMET_MODE, - 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS, - 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS, - 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS, - 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX, - 'comet_model_name': COMET_MODEL_NAME, }) - - # Check if running the Experiment with the Comet Optimizer - if hasattr(self.opt, 'comet_optimizer_id'): - self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id) - self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective) - self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric) - self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp)) - - def _get_experiment(self, mode, experiment_id=None): - if mode == 'offline': - if experiment_id is not None: - return comet_ml.ExistingOfflineExperiment( - previous_experiment=experiment_id, - **self.default_experiment_kwargs, - ) - - return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, ) - - else: - try: - if experiment_id is not None: - return comet_ml.ExistingExperiment( - previous_experiment=experiment_id, - **self.default_experiment_kwargs, - ) - - return comet_ml.Experiment(**self.default_experiment_kwargs) - - except ValueError: - logger.warning('COMET WARNING: ' - 'Comet credentials have not been set. ' - 'Comet will default to offline logging. ' - 'Please set your credentials to enable online logging.') - return self._get_experiment('offline', experiment_id) - - return - - def log_metrics(self, log_dict, **kwargs): - self.experiment.log_metrics(log_dict, **kwargs) - - def log_parameters(self, log_dict, **kwargs): - self.experiment.log_parameters(log_dict, **kwargs) - - def log_asset(self, asset_path, **kwargs): - self.experiment.log_asset(asset_path, **kwargs) - - def log_asset_data(self, asset, **kwargs): - self.experiment.log_asset_data(asset, **kwargs) - - def log_image(self, img, **kwargs): - self.experiment.log_image(img, **kwargs) - - def log_model(self, path, opt, epoch, fitness_score, best_model=False): - if not self.save_model: - return - - model_metadata = { - 'fitness_score': fitness_score[-1], - 'epochs_trained': epoch + 1, - 'save_period': opt.save_period, - 'total_epochs': opt.epochs, } - - model_files = glob.glob(f'{path}/*.pt') - for model_path in model_files: - name = Path(model_path).name - - self.experiment.log_model( - self.model_name, - file_or_folder=model_path, - file_name=name, - metadata=model_metadata, - overwrite=True, - ) - - def check_dataset(self, data_file): - with open(data_file) as f: - data_config = yaml.safe_load(f) - - path = data_config.get('path') - if path and path.startswith(COMET_PREFIX): - path = data_config['path'].replace(COMET_PREFIX, '') - data_dict = self.download_dataset_artifact(path) - - return data_dict - - self.log_asset(self.opt.data, metadata={'type': 'data-config-file'}) - - return check_dataset(data_file) - - def log_predictions(self, image, labelsn, path, shape, predn): - if self.logged_images_count >= self.max_images: - return - detections = predn[predn[:, 4] > self.conf_thres] - iou = box_iou(labelsn[:, 1:], detections[:, :4]) - mask, _ = torch.where(iou > self.iou_thres) - if len(mask) == 0: - return - - filtered_detections = detections[mask] - filtered_labels = labelsn[mask] - - image_id = path.split('/')[-1].split('.')[0] - image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}' - if image_name not in self.logged_image_names: - native_scale_image = PIL.Image.open(path) - self.log_image(native_scale_image, name=image_name) - self.logged_image_names.append(image_name) - - metadata = [] - for cls, *xyxy in filtered_labels.tolist(): - metadata.append({ - 'label': f'{self.class_names[int(cls)]}-gt', - 'score': 100, - 'box': { - 'x': xyxy[0], - 'y': xyxy[1], - 'x2': xyxy[2], - 'y2': xyxy[3]}, }) - for *xyxy, conf, cls in filtered_detections.tolist(): - metadata.append({ - 'label': f'{self.class_names[int(cls)]}', - 'score': conf * 100, - 'box': { - 'x': xyxy[0], - 'y': xyxy[1], - 'x2': xyxy[2], - 'y2': xyxy[3]}, }) - - self.metadata_dict[image_name] = metadata - self.logged_images_count += 1 - - return - - def preprocess_prediction(self, image, labels, shape, pred): - nl, _ = labels.shape[0], pred.shape[0] - - # Predictions - if self.opt.single_cls: - pred[:, 5] = 0 - - predn = pred.clone() - scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) - - labelsn = None - if nl: - tbox = xywh2xyxy(labels[:, 1:5]) # target boxes - scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels - labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels - scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred - - return predn, labelsn - - def add_assets_to_artifact(self, artifact, path, asset_path, split): - img_paths = sorted(glob.glob(f'{asset_path}/*')) - label_paths = img2label_paths(img_paths) - - for image_file, label_file in zip(img_paths, label_paths): - image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) - - try: - artifact.add( - image_file, - logical_path=image_logical_path, - metadata={'split': split}, - ) - artifact.add( - label_file, - logical_path=label_logical_path, - metadata={'split': split}, - ) - except ValueError as e: - logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.') - logger.error(f'COMET ERROR: {e}') - continue - - return artifact - - def upload_dataset_artifact(self): - dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset') - path = str((ROOT / Path(self.data_dict['path'])).resolve()) - - metadata = self.data_dict.copy() - for key in ['train', 'val', 'test']: - split_path = metadata.get(key) - if split_path is not None: - metadata[key] = split_path.replace(path, '') - - artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata) - for key in metadata.keys(): - if key in ['train', 'val', 'test']: - if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): - continue - - asset_path = self.data_dict.get(key) - if asset_path is not None: - artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) - - self.experiment.log_artifact(artifact) - - return - - def download_dataset_artifact(self, artifact_path): - logged_artifact = self.experiment.get_artifact(artifact_path) - artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) - logged_artifact.download(artifact_save_dir) - - metadata = logged_artifact.metadata - data_dict = metadata.copy() - data_dict['path'] = artifact_save_dir - - metadata_names = metadata.get('names') - if isinstance(metadata_names, dict): - data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()} - elif isinstance(metadata_names, list): - data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} - else: - raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" - - return self.update_data_paths(data_dict) - - def update_data_paths(self, data_dict): - path = data_dict.get('path', '') - - for split in ['train', 'val', 'test']: - if data_dict.get(split): - split_path = data_dict.get(split) - data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [ - f'{path}/{x}' for x in split_path]) - - return data_dict - - def on_pretrain_routine_end(self, paths): - if self.opt.resume: - return - - for path in paths: - self.log_asset(str(path)) - - if self.upload_dataset: - if not self.resume: - self.upload_dataset_artifact() - - return - - def on_train_start(self): - self.log_parameters(self.hyp) - - def on_train_epoch_start(self): - return - - def on_train_epoch_end(self, epoch): - self.experiment.curr_epoch = epoch - - return - - def on_train_batch_start(self): - return - - def on_train_batch_end(self, log_dict, step): - self.experiment.curr_step = step - if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): - self.log_metrics(log_dict, step=step) - - return - - def on_train_end(self, files, save_dir, last, best, epoch, results): - if self.comet_log_predictions: - curr_epoch = self.experiment.curr_epoch - self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch) - - for f in files: - self.log_asset(f, metadata={'epoch': epoch}) - self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch}) - - if not self.opt.evolve: - model_path = str(best if best.exists() else last) - name = Path(model_path).name - if self.save_model: - self.experiment.log_model( - self.model_name, - file_or_folder=model_path, - file_name=name, - overwrite=True, - ) - - # Check if running Experiment with Comet Optimizer - if hasattr(self.opt, 'comet_optimizer_id'): - metric = results.get(self.opt.comet_optimizer_metric) - self.experiment.log_other('optimizer_metric_value', metric) - - self.finish_run() - - def on_val_start(self): - return - - def on_val_batch_start(self): - return - - def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): - if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): - return - - for si, pred in enumerate(outputs): - if len(pred) == 0: - continue - - image = images[si] - labels = targets[targets[:, 0] == si, 1:] - shape = shapes[si] - path = paths[si] - predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) - if labelsn is not None: - self.log_predictions(image, labelsn, path, shape, predn) - - return - - def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): - if self.comet_log_per_class_metrics: - if self.num_classes > 1: - for i, c in enumerate(ap_class): - class_name = self.class_names[c] - self.experiment.log_metrics( - { - 'mAP@.5': ap50[i], - 'mAP@.5:.95': ap[i], - 'precision': p[i], - 'recall': r[i], - 'f1': f1[i], - 'true_positives': tp[i], - 'false_positives': fp[i], - 'support': nt[c], }, - prefix=class_name, - ) - - if self.comet_log_confusion_matrix: - epoch = self.experiment.curr_epoch - class_names = list(self.class_names.values()) - class_names.append('background') - num_classes = len(class_names) - - self.experiment.log_confusion_matrix( - matrix=confusion_matrix.matrix, - max_categories=num_classes, - labels=class_names, - epoch=epoch, - column_label='Actual Category', - row_label='Predicted Category', - file_name=f'confusion-matrix-epoch-{epoch}.json', - ) - - def on_fit_epoch_end(self, result, epoch): - self.log_metrics(result, epoch=epoch) - - def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): - if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: - self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) - - def on_params_update(self, params): - self.log_parameters(params) - - def finish_run(self): - self.experiment.end() diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/__init__.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index 1b9370f67c3219e0d4d2dde7aba5c81adccd582c..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/comet_utils.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/comet_utils.cpython-39.pyc deleted file mode 100644 index 8f9075d81c9c252c4c45d9a5937e94b37af64d2b..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/comet/__pycache__/comet_utils.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/comet_utils.py b/iteach_toolkit/DHYOLO/utils/loggers/comet/comet_utils.py deleted file mode 100644 index 27600761ad2843a6ab66aa22ad06782bb4b7eea7..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/comet/comet_utils.py +++ /dev/null @@ -1,150 +0,0 @@ -import logging -import os -from urllib.parse import urlparse - -try: - import comet_ml -except (ModuleNotFoundError, ImportError): - comet_ml = None - -import yaml - -logger = logging.getLogger(__name__) - -COMET_PREFIX = 'comet://' -COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') -COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt') - - -def download_model_checkpoint(opt, experiment): - model_dir = f'{opt.project}/{experiment.name}' - os.makedirs(model_dir, exist_ok=True) - - model_name = COMET_MODEL_NAME - model_asset_list = experiment.get_model_asset_list(model_name) - - if len(model_asset_list) == 0: - logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}') - return - - model_asset_list = sorted( - model_asset_list, - key=lambda x: x['step'], - reverse=True, - ) - logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list} - - resource_url = urlparse(opt.weights) - checkpoint_filename = resource_url.query - - if checkpoint_filename: - asset_id = logged_checkpoint_map.get(checkpoint_filename) - else: - asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) - checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME - - if asset_id is None: - logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment') - return - - try: - logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}') - asset_filename = checkpoint_filename - - model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) - model_download_path = f'{model_dir}/{asset_filename}' - with open(model_download_path, 'wb') as f: - f.write(model_binary) - - opt.weights = model_download_path - - except Exception as e: - logger.warning('COMET WARNING: Unable to download checkpoint from Comet') - logger.exception(e) - - -def set_opt_parameters(opt, experiment): - """Update the opts Namespace with parameters - from Comet's ExistingExperiment when resuming a run - - Args: - opt (argparse.Namespace): Namespace of command line options - experiment (comet_ml.APIExperiment): Comet API Experiment object - """ - asset_list = experiment.get_asset_list() - resume_string = opt.resume - - for asset in asset_list: - if asset['fileName'] == 'opt.yaml': - asset_id = asset['assetId'] - asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) - opt_dict = yaml.safe_load(asset_binary) - for key, value in opt_dict.items(): - setattr(opt, key, value) - opt.resume = resume_string - - # Save hyperparameters to YAML file - # Necessary to pass checks in training script - save_dir = f'{opt.project}/{experiment.name}' - os.makedirs(save_dir, exist_ok=True) - - hyp_yaml_path = f'{save_dir}/hyp.yaml' - with open(hyp_yaml_path, 'w') as f: - yaml.dump(opt.hyp, f) - opt.hyp = hyp_yaml_path - - -def check_comet_weights(opt): - """Downloads model weights from Comet and updates the - weights path to point to saved weights location - - Args: - opt (argparse.Namespace): Command Line arguments passed - to YOLOv5 training script - - Returns: - None/bool: Return True if weights are successfully downloaded - else return None - """ - if comet_ml is None: - return - - if isinstance(opt.weights, str): - if opt.weights.startswith(COMET_PREFIX): - api = comet_ml.API() - resource = urlparse(opt.weights) - experiment_path = f'{resource.netloc}{resource.path}' - experiment = api.get(experiment_path) - download_model_checkpoint(opt, experiment) - return True - - return None - - -def check_comet_resume(opt): - """Restores run parameters to its original state based on the model checkpoint - and logged Experiment parameters. - - Args: - opt (argparse.Namespace): Command Line arguments passed - to YOLOv5 training script - - Returns: - None/bool: Return True if the run is restored successfully - else return None - """ - if comet_ml is None: - return - - if isinstance(opt.resume, str): - if opt.resume.startswith(COMET_PREFIX): - api = comet_ml.API() - resource = urlparse(opt.resume) - experiment_path = f'{resource.netloc}{resource.path}' - experiment = api.get(experiment_path) - set_opt_parameters(opt, experiment) - download_model_checkpoint(opt, experiment) - - return True - - return None diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/hpo.py b/iteach_toolkit/DHYOLO/utils/loggers/comet/hpo.py deleted file mode 100644 index fc49115c13581554bebe1ddddaf3d5e10caaae07..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/comet/hpo.py +++ /dev/null @@ -1,118 +0,0 @@ -import argparse -import json -import logging -import os -import sys -from pathlib import Path - -import comet_ml - -logger = logging.getLogger(__name__) - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[3] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -from train import train -from utils.callbacks import Callbacks -from utils.general import increment_path -from utils.torch_utils import select_device - -# Project Configuration -config = comet_ml.config.get_config() -COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') - - -def get_args(known=False): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=300, help='total training epochs') - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--noval', action='store_true', help='only validate final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') - parser.add_argument('--noplots', action='store_true', help='save no plot files') - parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--seed', type=int, default=0, help='Global training seed') - parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') - - # Weights & Biases arguments - parser.add_argument('--entity', default=None, help='W&B: Entity') - parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') - parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') - parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') - - # Comet Arguments - parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.') - parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.') - parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.") - parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.') - parser.add_argument('--comet_optimizer_workers', - type=int, - default=1, - help='Comet: Number of Parallel Workers to use with the Comet Optimizer.') - - return parser.parse_known_args()[0] if known else parser.parse_args() - - -def run(parameters, opt): - hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']} - - opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) - opt.batch_size = parameters.get('batch_size') - opt.epochs = parameters.get('epochs') - - device = select_device(opt.device, batch_size=opt.batch_size) - train(hyp_dict, opt, device, callbacks=Callbacks()) - - -if __name__ == '__main__': - opt = get_args(known=True) - - opt.weights = str(opt.weights) - opt.cfg = str(opt.cfg) - opt.data = str(opt.data) - opt.project = str(opt.project) - - optimizer_id = os.getenv('COMET_OPTIMIZER_ID') - if optimizer_id is None: - with open(opt.comet_optimizer_config) as f: - optimizer_config = json.load(f) - optimizer = comet_ml.Optimizer(optimizer_config) - else: - optimizer = comet_ml.Optimizer(optimizer_id) - - opt.comet_optimizer_id = optimizer.id - status = optimizer.status() - - opt.comet_optimizer_objective = status['spec']['objective'] - opt.comet_optimizer_metric = status['spec']['metric'] - - logger.info('COMET INFO: Starting Hyperparameter Sweep') - for parameter in optimizer.get_parameters(): - run(parameter['parameters'], opt) diff --git a/iteach_toolkit/DHYOLO/utils/loggers/comet/optimizer_config.json b/iteach_toolkit/DHYOLO/utils/loggers/comet/optimizer_config.json deleted file mode 100644 index 83ddddab6f2084b4bdf84dca1e61696de200d1b8..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/comet/optimizer_config.json +++ /dev/null @@ -1,209 +0,0 @@ -{ - "algorithm": "random", - "parameters": { - "anchor_t": { - "type": "discrete", - "values": [ - 2, - 8 - ] - }, - "batch_size": { - "type": "discrete", - "values": [ - 16, - 32, - 64 - ] - }, - "box": { - "type": "discrete", - "values": [ - 0.02, - 0.2 - ] - }, - "cls": { - "type": "discrete", - "values": [ - 0.2 - ] - }, - "cls_pw": { - "type": "discrete", - "values": [ - 0.5 - ] - }, - "copy_paste": { - "type": "discrete", - "values": [ - 1 - ] - }, - "degrees": { - "type": "discrete", - "values": [ - 0, - 45 - ] - }, - "epochs": { - "type": "discrete", - "values": [ - 5 - ] - }, - "fl_gamma": { - "type": "discrete", - "values": [ - 0 - ] - }, - "fliplr": { - "type": "discrete", - "values": [ - 0 - ] - }, - "flipud": { - "type": "discrete", - "values": [ - 0 - ] - }, - "hsv_h": { - "type": "discrete", - "values": [ - 0 - ] - }, - "hsv_s": { - "type": "discrete", - "values": [ - 0 - ] - }, - "hsv_v": { - "type": "discrete", - "values": [ - 0 - ] - }, - "iou_t": { - "type": "discrete", - "values": [ - 0.7 - ] - }, - "lr0": { - "type": "discrete", - "values": [ - 1e-05, - 0.1 - ] - }, - "lrf": { - "type": "discrete", - "values": [ - 0.01, - 1 - ] - }, - "mixup": { - "type": "discrete", - "values": [ - 1 - ] - }, - "momentum": { - "type": "discrete", - "values": [ - 0.6 - ] - }, - "mosaic": { - "type": "discrete", - "values": [ - 0 - ] - }, - "obj": { - "type": "discrete", - "values": [ - 0.2 - ] - }, - "obj_pw": { - "type": "discrete", - "values": [ - 0.5 - ] - }, - "optimizer": { - "type": "categorical", - "values": [ - "SGD", - "Adam", - "AdamW" - ] - }, - "perspective": { - "type": "discrete", - "values": [ - 0 - ] - }, - "scale": { - "type": "discrete", - "values": [ - 0 - ] - }, - "shear": { - "type": "discrete", - "values": [ - 0 - ] - }, - "translate": { - "type": "discrete", - "values": [ - 0 - ] - }, - "warmup_bias_lr": { - "type": "discrete", - "values": [ - 0, - 0.2 - ] - }, - "warmup_epochs": { - "type": "discrete", - "values": [ - 5 - ] - }, - "warmup_momentum": { - "type": "discrete", - "values": [ - 0, - 0.95 - ] - }, - "weight_decay": { - "type": "discrete", - "values": [ - 0, - 0.001 - ] - } - }, - "spec": { - "maxCombo": 0, - "metric": "metrics/mAP_0.5", - "objective": "maximize" - }, - "trials": 1 -} diff --git a/iteach_toolkit/DHYOLO/utils/loggers/wandb/__init__.py b/iteach_toolkit/DHYOLO/utils/loggers/wandb/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/__init__.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index f13731de80ab2dfea01ae6f1743f84eeb64aec6a..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/wandb_utils.cpython-39.pyc b/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/wandb_utils.cpython-39.pyc deleted file mode 100644 index c282aee55da064f675b3b1a792dc602ca62177d6..0000000000000000000000000000000000000000 Binary files a/iteach_toolkit/DHYOLO/utils/loggers/wandb/__pycache__/wandb_utils.cpython-39.pyc and /dev/null differ diff --git a/iteach_toolkit/DHYOLO/utils/loggers/wandb/wandb_utils.py b/iteach_toolkit/DHYOLO/utils/loggers/wandb/wandb_utils.py deleted file mode 100644 index 4ea32b1d4c6ec62920a9e90af085346d0f7a5f2c..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loggers/wandb/wandb_utils.py +++ /dev/null @@ -1,193 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license - -# WARNING ⚠️ wandb is deprecated and will be removed in future release. -# See supported integrations at https://github.com/ultralytics/yolov5#integrations - -import logging -import os -import sys -from contextlib import contextmanager -from pathlib import Path - -from utils.general import LOGGER, colorstr - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[3] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -RANK = int(os.getenv('RANK', -1)) -DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \ - f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' - -try: - import wandb - - assert hasattr(wandb, '__version__') # verify package import not local dir - LOGGER.warning(DEPRECATION_WARNING) -except (ImportError, AssertionError): - wandb = None - - -class WandbLogger(): - """Log training runs, datasets, models, and predictions to Weights & Biases. - - This logger sends information to W&B at wandb.ai. By default, this information - includes hyperparameters, system configuration and metrics, model metrics, - and basic data metrics and analyses. - - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. - - For more on how this logger is used, see the Weights & Biases documentation: - https://docs.wandb.com/guides/integrations/yolov5 - """ - - def __init__(self, opt, run_id=None, job_type='Training'): - """ - - Initialize WandbLogger instance - - Upload dataset if opt.upload_dataset is True - - Setup training processes if job_type is 'Training' - - arguments: - opt (namespace) -- Commandline arguments for this run - run_id (str) -- Run ID of W&B run to be resumed - job_type (str) -- To set the job_type for this run - - """ - # Pre-training routine -- - self.job_type = job_type - self.wandb, self.wandb_run = wandb, wandb.run if wandb else None - self.val_artifact, self.train_artifact = None, None - self.train_artifact_path, self.val_artifact_path = None, None - self.result_artifact = None - self.val_table, self.result_table = None, None - self.max_imgs_to_log = 16 - self.data_dict = None - if self.wandb: - self.wandb_run = wandb.init(config=opt, - resume='allow', - project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, - entity=opt.entity, - name=opt.name if opt.name != 'exp' else None, - job_type=job_type, - id=run_id, - allow_val_change=True) if not wandb.run else wandb.run - - if self.wandb_run: - if self.job_type == 'Training': - if isinstance(opt.data, dict): - # This means another dataset manager has already processed the dataset info (e.g. ClearML) - # and they will have stored the already processed dict in opt.data - self.data_dict = opt.data - self.setup_training(opt) - - def setup_training(self, opt): - """ - Setup the necessary processes for training YOLO models: - - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX - - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded - - Setup log_dict, initialize bbox_interval - - arguments: - opt (namespace) -- commandline arguments for this run - - """ - self.log_dict, self.current_epoch = {}, 0 - self.bbox_interval = opt.bbox_interval - if isinstance(opt.resume, str): - model_dir, _ = self.download_model_artifact(opt) - if model_dir: - self.weights = Path(model_dir) / 'last.pt' - config = self.wandb_run.config - opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str( - self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ - config.hyp, config.imgsz - - if opt.bbox_interval == -1: - self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 - if opt.evolve or opt.noplots: - self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval - - def log_model(self, path, opt, epoch, fitness_score, best_model=False): - """ - Log the model checkpoint as W&B artifact - - arguments: - path (Path) -- Path of directory containing the checkpoints - opt (namespace) -- Command line arguments for this run - epoch (int) -- Current epoch number - fitness_score (float) -- fitness score for current epoch - best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. - """ - model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', - type='model', - metadata={ - 'original_url': str(path), - 'epochs_trained': epoch + 1, - 'save period': opt.save_period, - 'project': opt.project, - 'total_epochs': opt.epochs, - 'fitness_score': fitness_score}) - model_artifact.add_file(str(path / 'last.pt'), name='last.pt') - wandb.log_artifact(model_artifact, - aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) - LOGGER.info(f'Saving model artifact on epoch {epoch + 1}') - - def val_one_image(self, pred, predn, path, names, im): - pass - - def log(self, log_dict): - """ - save the metrics to the logging dictionary - - arguments: - log_dict (Dict) -- metrics/media to be logged in current step - """ - if self.wandb_run: - for key, value in log_dict.items(): - self.log_dict[key] = value - - def end_epoch(self): - """ - commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. - - arguments: - best_result (boolean): Boolean representing if the result of this evaluation is best or not - """ - if self.wandb_run: - with all_logging_disabled(): - try: - wandb.log(self.log_dict) - except BaseException as e: - LOGGER.info( - f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}' - ) - self.wandb_run.finish() - self.wandb_run = None - self.log_dict = {} - - def finish_run(self): - """ - Log metrics if any and finish the current W&B run - """ - if self.wandb_run: - if self.log_dict: - with all_logging_disabled(): - wandb.log(self.log_dict) - wandb.run.finish() - LOGGER.warning(DEPRECATION_WARNING) - - -@contextmanager -def all_logging_disabled(highest_level=logging.CRITICAL): - """ source - https://gist.github.com/simon-weber/7853144 - A context manager that will prevent any logging messages triggered during the body from being processed. - :param highest_level: the maximum logging level in use. - This would only need to be changed if a custom level greater than CRITICAL is defined. - """ - previous_level = logging.root.manager.disable - logging.disable(highest_level) - try: - yield - finally: - logging.disable(previous_level) diff --git a/iteach_toolkit/DHYOLO/utils/loss.py b/iteach_toolkit/DHYOLO/utils/loss.py deleted file mode 100644 index 26cca8797315a425b26d1c8c083bd321d7b52fff..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/loss.py +++ /dev/null @@ -1,234 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Loss functions -""" - -import torch -import torch.nn as nn - -from utils.metrics import bbox_iou -from utils.torch_utils import de_parallel - - -def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets - return 1.0 - 0.5 * eps, 0.5 * eps - - -class BCEBlurWithLogitsLoss(nn.Module): - # BCEwithLogitLoss() with reduced missing label effects. - def __init__(self, alpha=0.05): - super().__init__() - self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() - self.alpha = alpha - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - pred = torch.sigmoid(pred) # prob from logits - dx = pred - true # reduce only missing label effects - # dx = (pred - true).abs() # reduce missing label and false label effects - alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) - loss *= alpha_factor - return loss.mean() - - -class FocalLoss(nn.Module): - # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super().__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - # p_t = torch.exp(-loss) - # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability - - # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py - pred_prob = torch.sigmoid(pred) # prob from logits - p_t = true * pred_prob + (1 - true) * (1 - pred_prob) - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = (1.0 - p_t) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class QFocalLoss(nn.Module): - # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super().__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - - pred_prob = torch.sigmoid(pred) # prob from logits - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = torch.abs(true - pred_prob) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class ComputeLoss: - sort_obj_iou = False - - # Compute losses - def __init__(self, model, autobalance=False): - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - m = de_parallel(model).model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 - self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance - self.na = m.na # number of anchors - self.nc = m.nc # number of classes - self.nl = m.nl # number of layers - self.anchors = m.anchors - self.device = device - - def __call__(self, p, targets): # predictions, targets - lcls = torch.zeros(1, device=self.device) # class loss - lbox = torch.zeros(1, device=self.device) # box loss - lobj = torch.zeros(1, device=self.device) # object loss - tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj - - n = b.shape[0] # number of targets - if n: - # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 - pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions - - # Regression - pxy = pxy.sigmoid() * 2 - 0.5 - pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - iou = iou.detach().clamp(0).type(tobj.dtype) - if self.sort_obj_iou: - j = iou.argsort() - b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] - if self.gr < 1: - iou = (1.0 - self.gr) + self.gr * iou - tobj[b, a, gj, gi] = iou # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(pcls, self.cn, device=self.device) # targets - t[range(n), tcls[i]] = self.cp - lcls += self.BCEcls(pcls, t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(7, device=self.device) # normalized to gridspace gain - ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor( - [ - [0, 0], - [1, 0], - [0, 1], - [-1, 0], - [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], - device=self.device).float() * g # offsets - - for i in range(self.nl): - anchors, shape = self.anchors[i], p[i].shape - gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain # shape(3,n,7) - if nt: - # Matches - r = t[..., 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1 < g) & (gxy > 1)).T - l, m = ((gxi % 1 < g) & (gxi > 1)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors - a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class - gij = (gxy - offsets).long() - gi, gj = gij.T # grid indices - - # Append - indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - - return tcls, tbox, indices, anch diff --git a/iteach_toolkit/DHYOLO/utils/metrics.py b/iteach_toolkit/DHYOLO/utils/metrics.py deleted file mode 100644 index 5646f40e9860f90648e1dc8d074277de9b827b97..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/metrics.py +++ /dev/null @@ -1,360 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Model validation metrics -""" - -import math -import warnings -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch - -from utils import TryExcept, threaded - - -def fitness(x): - # Model fitness as a weighted combination of metrics - w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] - return (x[:, :4] * w).sum(1) - - -def smooth(y, f=0.05): - # Box filter of fraction f - nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) - p = np.ones(nf // 2) # ones padding - yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded - return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed - - -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''): - """ Compute the average precision, given the recall and precision curves. - Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. - # Arguments - tp: True positives (nparray, nx1 or nx10). - conf: Objectness value from 0-1 (nparray). - pred_cls: Predicted object classes (nparray). - target_cls: True object classes (nparray). - plot: Plot precision-recall curve at mAP@0.5 - save_dir: Plot save directory - # Returns - The average precision as computed in py-faster-rcnn. - """ - - # Sort by objectness - i = np.argsort(-conf) - tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] - - # Find unique classes - unique_classes, nt = np.unique(target_cls, return_counts=True) - nc = unique_classes.shape[0] # number of classes, number of detections - - # Create Precision-Recall curve and compute AP for each class - px, py = np.linspace(0, 1, 1000), [] # for plotting - ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) - for ci, c in enumerate(unique_classes): - i = pred_cls == c - n_l = nt[ci] # number of labels - n_p = i.sum() # number of predictions - if n_p == 0 or n_l == 0: - continue - - # Accumulate FPs and TPs - fpc = (1 - tp[i]).cumsum(0) - tpc = tp[i].cumsum(0) - - # Recall - recall = tpc / (n_l + eps) # recall curve - r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases - - # Precision - precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score - - # AP from recall-precision curve - for j in range(tp.shape[1]): - ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and j == 0: - py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - - # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + eps) - names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data - names = dict(enumerate(names)) # to dict - if plot: - plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names) - plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1') - plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision') - plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall') - - i = smooth(f1.mean(0), 0.1).argmax() # max F1 index - p, r, f1 = p[:, i], r[:, i], f1[:, i] - tp = (r * nt).round() # true positives - fp = (tp / (p + eps) - tp).round() # false positives - return tp, fp, p, r, f1, ap, unique_classes.astype(int) - - -def compute_ap(recall, precision): - """ Compute the average precision, given the recall and precision curves - # Arguments - recall: The recall curve (list) - precision: The precision curve (list) - # Returns - Average precision, precision curve, recall curve - """ - - # Append sentinel values to beginning and end - mrec = np.concatenate(([0.0], recall, [1.0])) - mpre = np.concatenate(([1.0], precision, [0.0])) - - # Compute the precision envelope - mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) - - # Integrate area under curve - method = 'interp' # methods: 'continuous', 'interp' - if method == 'interp': - x = np.linspace(0, 1, 101) # 101-point interp (COCO) - ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate - else: # 'continuous' - i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve - - return ap, mpre, mrec - - -class ConfusionMatrix: - # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix - def __init__(self, nc, conf=0.25, iou_thres=0.45): - self.matrix = np.zeros((nc + 1, nc + 1)) - self.nc = nc # number of classes - self.conf = conf - self.iou_thres = iou_thres - - def process_batch(self, detections, labels): - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - detections (Array[N, 6]), x1, y1, x2, y2, conf, class - labels (Array[M, 5]), class, x1, y1, x2, y2 - Returns: - None, updates confusion matrix accordingly - """ - if detections is None: - gt_classes = labels.int() - for gc in gt_classes: - self.matrix[self.nc, gc] += 1 # background FN - return - - detections = detections[detections[:, 4] > self.conf] - gt_classes = labels[:, 0].int() - detection_classes = detections[:, 5].int() - iou = box_iou(labels[:, 1:], detections[:, :4]) - - x = torch.where(iou > self.iou_thres) - if x[0].shape[0]: - matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - else: - matches = np.zeros((0, 3)) - - n = matches.shape[0] > 0 - m0, m1, _ = matches.transpose().astype(int) - for i, gc in enumerate(gt_classes): - j = m0 == i - if n and sum(j) == 1: - self.matrix[detection_classes[m1[j]], gc] += 1 # correct - else: - self.matrix[self.nc, gc] += 1 # true background - - if n: - for i, dc in enumerate(detection_classes): - if not any(m1 == i): - self.matrix[dc, self.nc] += 1 # predicted background - - def tp_fp(self): - tp = self.matrix.diagonal() # true positives - fp = self.matrix.sum(1) - tp # false positives - # fn = self.matrix.sum(0) - tp # false negatives (missed detections) - return tp[:-1], fp[:-1] # remove background class - - @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure') - def plot(self, normalize=True, save_dir='', names=()): - import seaborn as sn - - array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns - array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) - - fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) - nc, nn = self.nc, len(names) # number of classes, names - sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size - labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels - ticklabels = (names + ['background']) if labels else 'auto' - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered - sn.heatmap(array, - ax=ax, - annot=nc < 30, - annot_kws={ - 'size': 8}, - cmap='Blues', - fmt='.2f', - square=True, - vmin=0.0, - xticklabels=ticklabels, - yticklabels=ticklabels).set_facecolor((1, 1, 1)) - ax.set_xlabel('True') - ax.set_ylabel('Predicted') - ax.set_title('Confusion Matrix') - fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) - plt.close(fig) - - def print(self): - for i in range(self.nc + 1): - print(' '.join(map(str, self.matrix[i]))) - - -def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) - - # Get the coordinates of bounding boxes - if xywh: # transform from xywh to xyxy - (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) - w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 - b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ - b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ - else: # x1, y1, x2, y2 = box1 - b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) - b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) - w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) - w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) - - # Intersection area - inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \ - (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0) - - # Union Area - union = w1 * h1 + w2 * h2 - inter + eps - - # IoU - iou = inter / union - if CIoU or DIoU or GIoU: - cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width - ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height - if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared - rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 - if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) - with torch.no_grad(): - alpha = v / (v - iou + (1 + eps)) - return iou - (rho2 / c2 + v * alpha) # CIoU - return iou - rho2 / c2 # DIoU - c_area = cw * ch + eps # convex area - return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf - return iou # IoU - - -def box_iou(box1, box2, eps=1e-7): - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) - inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) - - # IoU = inter / (area1 + area2 - inter) - return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) - - -def bbox_ioa(box1, box2, eps=1e-7): - """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 - box1: np.array of shape(4) - box2: np.array of shape(nx4) - returns: np.array of shape(n) - """ - - # Get the coordinates of bounding boxes - b1_x1, b1_y1, b1_x2, b1_y2 = box1 - b2_x1, b2_y1, b2_x2, b2_y2 = box2.T - - # Intersection area - inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ - (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) - - # box2 area - box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps - - # Intersection over box2 area - return inter_area / box2_area - - -def wh_iou(wh1, wh2, eps=1e-7): - # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 - wh1 = wh1[:, None] # [N,1,2] - wh2 = wh2[None] # [1,M,2] - inter = torch.min(wh1, wh2).prod(2) # [N,M] - return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) - - -# Plots ---------------------------------------------------------------------------------------------------------------- - - -@threaded -def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()): - # Precision-recall curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - py = np.stack(py, axis=1) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) - else: - ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) - - ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) - ax.set_xlabel('Recall') - ax.set_ylabel('Precision') - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') - ax.set_title('Precision-Recall Curve') - fig.savefig(save_dir, dpi=250) - plt.close(fig) - - -@threaded -def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'): - # Metric-confidence curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) - else: - ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) - - y = smooth(py.mean(0), 0.05) - ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') - ax.set_title(f'{ylabel}-Confidence Curve') - fig.savefig(save_dir, dpi=250) - plt.close(fig) diff --git a/iteach_toolkit/DHYOLO/utils/plots.py b/iteach_toolkit/DHYOLO/utils/plots.py deleted file mode 100644 index 7fb9bf16fb7fcbe0d3e8dfa6e05861158767884b..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/plots.py +++ /dev/null @@ -1,447 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Plotting utils -""" - -import contextlib -import math -import os -from copy import copy -from pathlib import Path - -import cv2 -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sn -import torch -from PIL import Image, ImageDraw -from scipy.ndimage.filters import gaussian_filter1d -from ultralytics.utils.plotting import Annotator - -from utils import TryExcept, threaded, Timeout -from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh -from utils.metrics import fitness - -# Settings -RANK = int(os.getenv('RANK', -1)) -matplotlib.rc('font', **{'size': 11}) -matplotlib.use('Agg') # for writing to files only - - -class Colors: - # Ultralytics color palette https://ultralytics.com/ - def __init__(self): - # hex = matplotlib.colors.TABLEAU_COLORS.values() - hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', - '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') - self.palette = [self.hex2rgb(f'#{c}') for c in hexs] - self.n = len(self.palette) - - def __call__(self, i, bgr=False): - c = self.palette[int(i) % self.n] - return (c[2], c[1], c[0]) if bgr else c - - @staticmethod - def hex2rgb(h): # rgb order (PIL) - return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) - - -colors = Colors() # create instance for 'from utils.plots import colors' - - -def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')): - """ - x: Features to be visualized - module_type: Module type - stage: Module stage within model - n: Maximum number of feature maps to plot - save_dir: Directory to save results - """ - if 'Detect' not in module_type: - batch, channels, height, width = x.shape # batch, channels, height, width - if height > 1 and width > 1: - f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename - - blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels - n = min(n, channels) # number of plots - fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols - ax = ax.ravel() - plt.subplots_adjust(wspace=0.05, hspace=0.05) - for i in range(n): - ax[i].imshow(blocks[i].squeeze()) # cmap='gray' - ax[i].axis('off') - - LOGGER.info(f'Saving {f}... ({n}/{channels})') - plt.savefig(f, dpi=300, bbox_inches='tight') - plt.close() - np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save - - -def hist2d(x, y, n=100): - # 2d histogram used in labels.png and evolve.png - xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) - hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) - xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) - yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) - return np.log(hist[xidx, yidx]) - - -def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): - from scipy.signal import butter, filtfilt - - # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy - def butter_lowpass(cutoff, fs, order): - nyq = 0.5 * fs - normal_cutoff = cutoff / nyq - return butter(order, normal_cutoff, btype='low', analog=False) - - b, a = butter_lowpass(cutoff, fs, order=order) - return filtfilt(b, a, data) # forward-backward filter - - -def output_to_target(output, max_det=300): - # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting - targets = [] - for i, o in enumerate(output): - box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) - j = torch.full((conf.shape[0], 1), i) - targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) - return torch.cat(targets, 0).numpy() - - -@threaded -def plot_images(images, targets, paths=None, fname='images.jpg', names=None): - # Plot image grid with labels - if isinstance(images, torch.Tensor): - images = images.cpu().float().numpy() - if isinstance(targets, torch.Tensor): - targets = targets.cpu().numpy() - - max_size = 1920 # max image size - max_subplots = 16 # max image subplots, i.e. 4x4 - bs, _, h, w = images.shape # batch size, _, height, width - bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) - if np.max(images[0]) <= 1: - images *= 255 # de-normalise (optional) - - # Build Image - mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init - for i, im in enumerate(images): - if i == max_subplots: # if last batch has fewer images than we expect - break - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im - - # Resize (optional) - scale = max_size / ns / max(h, w) - if scale < 1: - h = math.ceil(scale * h) - w = math.ceil(scale * w) - mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) - - # Annotate - fs = int((h + w) * ns * 0.01) # font size - annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) - for i in range(i + 1): - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders - if paths: - annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames - if len(targets) > 0: - ti = targets[targets[:, 0] == i] # image targets - boxes = xywh2xyxy(ti[:, 2:6]).T - classes = ti[:, 1].astype('int') - labels = ti.shape[1] == 6 # labels if no conf column - conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) - - if boxes.shape[1]: - if boxes.max() <= 1.01: # if normalized with tolerance 0.01 - boxes[[0, 2]] *= w # scale to pixels - boxes[[1, 3]] *= h - elif scale < 1: # absolute coords need scale if image scales - boxes *= scale - boxes[[0, 2]] += x - boxes[[1, 3]] += y - for j, box in enumerate(boxes.T.tolist()): - cls = classes[j] - color = colors(cls) - cls = names[cls] if names else cls - if labels or conf[j] > 0.25: # 0.25 conf thresh - label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' - annotator.box_label(box, label, color=color) - annotator.im.save(fname) # save - - -def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): - # Plot LR simulating training for full epochs - optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals - y = [] - for _ in range(epochs): - scheduler.step() - y.append(optimizer.param_groups[0]['lr']) - plt.plot(y, '.-', label='LR') - plt.xlabel('epoch') - plt.ylabel('LR') - plt.grid() - plt.xlim(0, epochs) - plt.ylim(0) - plt.savefig(Path(save_dir) / 'LR.png', dpi=200) - plt.close() - - -def plot_val_txt(): # from utils.plots import *; plot_val() - # Plot val.txt histograms - x = np.loadtxt('val.txt', dtype=np.float32) - box = xyxy2xywh(x[:, :4]) - cx, cy = box[:, 0], box[:, 1] - - fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) - ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) - ax.set_aspect('equal') - plt.savefig('hist2d.png', dpi=300) - - fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) - ax[0].hist(cx, bins=600) - ax[1].hist(cy, bins=600) - plt.savefig('hist1d.png', dpi=200) - - -def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() - # Plot targets.txt histograms - x = np.loadtxt('targets.txt', dtype=np.float32).T - s = ['x targets', 'y targets', 'width targets', 'height targets'] - fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) - ax = ax.ravel() - for i in range(4): - ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') - ax[i].legend() - ax[i].set_title(s[i]) - plt.savefig('targets.jpg', dpi=200) - - -def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study() - # Plot file=study.txt generated by val.py (or plot all study*.txt in dir) - save_dir = Path(file).parent if file else Path(dir) - plot2 = False # plot additional results - if plot2: - ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() - - fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: - for f in sorted(save_dir.glob('study*.txt')): - y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T - x = np.arange(y.shape[1]) if x is None else np.array(x) - if plot2: - s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)'] - for i in range(7): - ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) - ax[i].set_title(s[i]) - - j = y[3].argmax() + 1 - ax2.plot(y[5, 1:j], - y[3, 1:j] * 1E2, - '.-', - linewidth=2, - markersize=8, - label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) - - ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], - 'k.-', - linewidth=2, - markersize=8, - alpha=.25, - label='EfficientDet') - - ax2.grid(alpha=0.2) - ax2.set_yticks(np.arange(20, 60, 5)) - ax2.set_xlim(0, 57) - ax2.set_ylim(25, 55) - ax2.set_xlabel('GPU Speed (ms/img)') - ax2.set_ylabel('COCO AP val') - ax2.legend(loc='lower right') - f = save_dir / 'study.png' - print(f'Saving {f}...') - plt.savefig(f, dpi=300) - - -@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 -@Timeout(30) # known issue https://github.com/ultralytics/yolov5/issues/5611 -def plot_labels(labels, names=(), save_dir=Path('')): - # plot dataset labels - LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") - c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes - nc = int(c.max() + 1) # number of classes - x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) - - # seaborn correlogram - sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) - plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) - plt.close() - - # matplotlib labels - matplotlib.use('svg') # faster - ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() - y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) - with contextlib.suppress(Exception): # color histogram bars by class - [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 - ax[0].set_ylabel('instances') - if 0 < len(names) < 30: - ax[0].set_xticks(range(len(names))) - ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) - else: - ax[0].set_xlabel('classes') - sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) - sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) - - # rectangles - labels[:, 1:3] = 0.5 # center - labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 - img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) - for cls, *box in labels[:1000]: - ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot - ax[1].imshow(img) - ax[1].axis('off') - - for a in [0, 1, 2, 3]: - for s in ['top', 'right', 'left', 'bottom']: - ax[a].spines[s].set_visible(False) - - plt.savefig(save_dir / 'labels.jpg', dpi=200) - matplotlib.use('Agg') - plt.close() - - -def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')): - # Show classification image grid with labels (optional) and predictions (optional) - from utils.augmentations import denormalize - - names = names or [f'class{i}' for i in range(1000)] - blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im), - dim=0) # select batch index 0, block by channels - n = min(len(blocks), nmax) # number of plots - m = min(8, round(n ** 0.5)) # 8 x 8 default - fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols - ax = ax.ravel() if m > 1 else [ax] - # plt.subplots_adjust(wspace=0.05, hspace=0.05) - for i in range(n): - ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) - ax[i].axis('off') - if labels is not None: - s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '') - ax[i].set_title(s, fontsize=8, verticalalignment='top') - plt.savefig(f, dpi=300, bbox_inches='tight') - plt.close() - if verbose: - LOGGER.info(f'Saving {f}') - if labels is not None: - LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax])) - if pred is not None: - LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax])) - return f - - -def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve() - # Plot evolve.csv hyp evolution results - evolve_csv = Path(evolve_csv) - data = pd.read_csv(evolve_csv) - keys = [x.strip() for x in data.columns] - x = data.values - f = fitness(x) - j = np.argmax(f) # max fitness index - plt.figure(figsize=(10, 12), tight_layout=True) - matplotlib.rc('font', **{'size': 8}) - print(f'Best results from row {j} of {evolve_csv}:') - for i, k in enumerate(keys[7:]): - v = x[:, 7 + i] - mu = v[j] # best single result - plt.subplot(6, 5, i + 1) - plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') - plt.plot(mu, f.max(), 'k+', markersize=15) - plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters - if i % 5 != 0: - plt.yticks([]) - print(f'{k:>15}: {mu:.3g}') - f = evolve_csv.with_suffix('.png') # filename - plt.savefig(f, dpi=200) - plt.close() - print(f'Saved {f}') - - -def plot_results(file='path/to/results.csv', dir=''): - # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') - save_dir = Path(file).parent if file else Path(dir) - fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) - ax = ax.ravel() - files = list(save_dir.glob('results*.csv')) - assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' - for f in files: - try: - data = pd.read_csv(f) - s = [x.strip() for x in data.columns] - x = data.values[:, 0] - for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): - y = data.values[:, j].astype('float') - # y[y == 0] = np.nan # don't show zero values - ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) # actual results - ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2) # smoothing line - ax[i].set_title(s[j], fontsize=12) - # if j in [8, 9, 10]: # share train and val loss y axes - # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) - except Exception as e: - LOGGER.info(f'Warning: Plotting error for {f}: {e}') - ax[1].legend() - fig.savefig(save_dir / 'results.png', dpi=200) - plt.close() - - -def profile_idetection(start=0, stop=0, labels=(), save_dir=''): - # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() - ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() - s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] - files = list(Path(save_dir).glob('frames*.txt')) - for fi, f in enumerate(files): - try: - results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows - n = results.shape[1] # number of rows - x = np.arange(start, min(stop, n) if stop else n) - results = results[:, x] - t = (results[0] - results[0].min()) # set t0=0s - results[0] = x - for i, a in enumerate(ax): - if i < len(results): - label = labels[fi] if len(labels) else f.stem.replace('frames_', '') - a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) - a.set_title(s[i]) - a.set_xlabel('time (s)') - # if fi == len(files) - 1: - # a.set_ylim(bottom=0) - for side in ['top', 'right']: - a.spines[side].set_visible(False) - else: - a.remove() - except Exception as e: - print(f'Warning: Plotting error for {f}; {e}') - ax[1].legend() - plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) - - -def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): - # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop - xyxy = torch.tensor(xyxy).view(-1, 4) - b = xyxy2xywh(xyxy) # boxes - if square: - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square - b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad - xyxy = xywh2xyxy(b).long() - clip_boxes(xyxy, im.shape) - crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] - if save: - file.parent.mkdir(parents=True, exist_ok=True) # make directory - f = str(increment_path(file).with_suffix('.jpg')) - # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue - Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB - return crop diff --git a/iteach_toolkit/DHYOLO/utils/segment/__init__.py b/iteach_toolkit/DHYOLO/utils/segment/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/iteach_toolkit/DHYOLO/utils/segment/augmentations.py b/iteach_toolkit/DHYOLO/utils/segment/augmentations.py deleted file mode 100644 index f8154b834869acd87f80c0152c870b7631a918ba..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/augmentations.py +++ /dev/null @@ -1,104 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Image augmentation functions -""" - -import math -import random - -import cv2 -import numpy as np - -from ..augmentations import box_candidates -from ..general import resample_segments, segment2box - - -def mixup(im, labels, segments, im2, labels2, segments2): - # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf - r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 - im = (im * r + im2 * (1 - r)).astype(np.uint8) - labels = np.concatenate((labels, labels2), 0) - segments = np.concatenate((segments, segments2), 0) - return im, labels, segments - - -def random_perspective(im, - targets=(), - segments=(), - degrees=10, - translate=.1, - scale=.1, - shear=10, - perspective=0.0, - border=(0, 0)): - # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) - # targets = [cls, xyxy] - - height = im.shape[0] + border[0] * 2 # shape(h,w,c) - width = im.shape[1] + border[1] * 2 - - # Center - C = np.eye(3) - C[0, 2] = -im.shape[1] / 2 # x translation (pixels) - C[1, 2] = -im.shape[0] / 2 # y translation (pixels) - - # Perspective - P = np.eye(3) - P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) - P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) - - # Rotation and Scale - R = np.eye(3) - a = random.uniform(-degrees, degrees) - # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations - s = random.uniform(1 - scale, 1 + scale) - # s = 2 ** random.uniform(-scale, scale) - R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) - - # Shear - S = np.eye(3) - S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) - S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) - - # Translation - T = np.eye(3) - T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels) - T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels) - - # Combined rotation matrix - M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT - if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed - if perspective: - im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) - else: # affine - im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) - - # Visualize - # import matplotlib.pyplot as plt - # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() - # ax[0].imshow(im[:, :, ::-1]) # base - # ax[1].imshow(im2[:, :, ::-1]) # warped - - # Transform label coordinates - n = len(targets) - new_segments = [] - if n: - new = np.zeros((n, 4)) - segments = resample_segments(segments) # upsample - for i, segment in enumerate(segments): - xy = np.ones((len(segment), 3)) - xy[:, :2] = segment - xy = xy @ M.T # transform - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine - - # clip - new[i] = segment2box(xy, width, height) - new_segments.append(xy) - - # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) - targets = targets[i] - targets[:, 1:5] = new[i] - new_segments = np.array(new_segments)[i] - - return im, targets, new_segments diff --git a/iteach_toolkit/DHYOLO/utils/segment/dataloaders.py b/iteach_toolkit/DHYOLO/utils/segment/dataloaders.py deleted file mode 100644 index 3ee826dba69cb0cda00c48b82710784cd39c5a81..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/dataloaders.py +++ /dev/null @@ -1,332 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Dataloaders -""" - -import os -import random - -import cv2 -import numpy as np -import torch -from torch.utils.data import DataLoader, distributed - -from ..augmentations import augment_hsv, copy_paste, letterbox -from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker -from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn -from ..torch_utils import torch_distributed_zero_first -from .augmentations import mixup, random_perspective - -RANK = int(os.getenv('RANK', -1)) - - -def create_dataloader(path, - imgsz, - batch_size, - stride, - single_cls=False, - hyp=None, - augment=False, - cache=False, - pad=0.0, - rect=False, - rank=-1, - workers=8, - image_weights=False, - quad=False, - prefix='', - shuffle=False, - mask_downsample_ratio=1, - overlap_mask=False, - seed=0): - if rect and shuffle: - LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') - shuffle = False - with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP - dataset = LoadImagesAndLabelsAndMasks( - path, - imgsz, - batch_size, - augment=augment, # augmentation - hyp=hyp, # hyperparameters - rect=rect, # rectangular batches - cache_images=cache, - single_cls=single_cls, - stride=int(stride), - pad=pad, - image_weights=image_weights, - prefix=prefix, - downsample_ratio=mask_downsample_ratio, - overlap=overlap_mask) - - batch_size = min(batch_size, len(dataset)) - nd = torch.cuda.device_count() # number of CUDA devices - nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers - sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) - loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates - generator = torch.Generator() - generator.manual_seed(6148914691236517205 + seed + RANK) - return loader( - dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=True, - collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, - worker_init_fn=seed_worker, - generator=generator, - ), dataset - - -class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing - - def __init__( - self, - path, - img_size=640, - batch_size=16, - augment=False, - hyp=None, - rect=False, - image_weights=False, - cache_images=False, - single_cls=False, - stride=32, - pad=0, - min_items=0, - prefix='', - downsample_ratio=1, - overlap=False, - ): - super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, - stride, pad, min_items, prefix) - self.downsample_ratio = downsample_ratio - self.overlap = overlap - - def __getitem__(self, index): - index = self.indices[index] # linear, shuffled, or image_weights - - hyp = self.hyp - mosaic = self.mosaic and random.random() < hyp['mosaic'] - masks = [] - if mosaic: - # Load mosaic - img, labels, segments = self.load_mosaic(index) - shapes = None - - # MixUp augmentation - if random.random() < hyp['mixup']: - img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) - - else: - # Load image - img, (h0, w0), (h, w) = self.load_image(index) - - # Letterbox - shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape - img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) - shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling - - labels = self.labels[index].copy() - # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy - segments = self.segments[index].copy() - if len(segments): - for i_s in range(len(segments)): - segments[i_s] = xyn2xy( - segments[i_s], - ratio[0] * w, - ratio[1] * h, - padw=pad[0], - padh=pad[1], - ) - if labels.size: # normalized xywh to pixel xyxy format - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) - - if self.augment: - img, labels, segments = random_perspective(img, - labels, - segments=segments, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) - - nl = len(labels) # number of labels - if nl: - labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) - if self.overlap: - masks, sorted_idx = polygons2masks_overlap(img.shape[:2], - segments, - downsample_ratio=self.downsample_ratio) - masks = masks[None] # (640, 640) -> (1, 640, 640) - labels = labels[sorted_idx] - else: - masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) - - masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] // - self.downsample_ratio, img.shape[1] // - self.downsample_ratio)) - # TODO: albumentations support - if self.augment: - # Albumentations - # there are some augmentation that won't change boxes and masks, - # so just be it for now. - img, labels = self.albumentations(img, labels) - nl = len(labels) # update after albumentations - - # HSV color-space - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) - - # Flip up-down - if random.random() < hyp['flipud']: - img = np.flipud(img) - if nl: - labels[:, 2] = 1 - labels[:, 2] - masks = torch.flip(masks, dims=[1]) - - # Flip left-right - if random.random() < hyp['fliplr']: - img = np.fliplr(img) - if nl: - labels[:, 1] = 1 - labels[:, 1] - masks = torch.flip(masks, dims=[2]) - - # Cutouts # labels = cutout(img, labels, p=0.5) - - labels_out = torch.zeros((nl, 6)) - if nl: - labels_out[:, 1:] = torch.from_numpy(labels) - - # Convert - img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - img = np.ascontiguousarray(img) - - return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) - - def load_mosaic(self, index): - # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic - labels4, segments4 = [], [] - s = self.img_size - yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y - - # 3 additional image indices - indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = self.load_image(index) - - # place img in img4 - if i == 0: # top left - img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) - x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) - elif i == 1: # top right - x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc - x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h - elif i == 2: # bottom left - x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) - x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) - elif i == 3: # bottom right - x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) - x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) - - img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] - padw = x1a - x1b - padh = y1a - y1b - - labels, segments = self.labels[index].copy(), self.segments[index].copy() - - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padw, padh) for x in segments] - labels4.append(labels) - segments4.extend(segments) - - # Concat/clip labels - labels4 = np.concatenate(labels4, 0) - for x in (labels4[:, 1:], *segments4): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img4, labels4 = replicate(img4, labels4) # replicate - - # Augment - img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) - img4, labels4, segments4 = random_perspective(img4, - labels4, - segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - return img4, labels4, segments4 - - @staticmethod - def collate_fn(batch): - img, label, path, shapes, masks = zip(*batch) # transposed - batched_masks = torch.cat(masks, 0) - for i, l in enumerate(label): - l[:, 0] = i # add target image index for build_targets() - return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks - - -def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): - """ - Args: - img_size (tuple): The image size. - polygons (np.ndarray): [N, M], N is the number of polygons, - M is the number of points(Be divided by 2). - """ - mask = np.zeros(img_size, dtype=np.uint8) - polygons = np.asarray(polygons) - polygons = polygons.astype(np.int32) - shape = polygons.shape - polygons = polygons.reshape(shape[0], -1, 2) - cv2.fillPoly(mask, polygons, color=color) - nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) - # NOTE: fillPoly firstly then resize is trying the keep the same way - # of loss calculation when mask-ratio=1. - mask = cv2.resize(mask, (nw, nh)) - return mask - - -def polygons2masks(img_size, polygons, color, downsample_ratio=1): - """ - Args: - img_size (tuple): The image size. - polygons (list[np.ndarray]): each polygon is [N, M], - N is the number of polygons, - M is the number of points(Be divided by 2). - """ - masks = [] - for si in range(len(polygons)): - mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) - masks.append(mask) - return np.array(masks) - - -def polygons2masks_overlap(img_size, segments, downsample_ratio=1): - """Return a (640, 640) overlap mask.""" - masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), - dtype=np.int32 if len(segments) > 255 else np.uint8) - areas = [] - ms = [] - for si in range(len(segments)): - mask = polygon2mask( - img_size, - [segments[si].reshape(-1)], - downsample_ratio=downsample_ratio, - color=1, - ) - ms.append(mask) - areas.append(mask.sum()) - areas = np.asarray(areas) - index = np.argsort(-areas) - ms = np.array(ms)[index] - for i in range(len(segments)): - mask = ms[i] * (i + 1) - masks = masks + mask - masks = np.clip(masks, a_min=0, a_max=i + 1) - return masks, index diff --git a/iteach_toolkit/DHYOLO/utils/segment/general.py b/iteach_toolkit/DHYOLO/utils/segment/general.py deleted file mode 100644 index f1b2f1dd120ff47eec618e0c25239c28c4d88475..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/general.py +++ /dev/null @@ -1,160 +0,0 @@ -import cv2 -import numpy as np -import torch -import torch.nn.functional as F - - -def crop_mask(masks, boxes): - """ - "Crop" predicted masks by zeroing out everything not in the predicted bbox. - Vectorized by Chong (thanks Chong). - - Args: - - masks should be a size [n, h, w] tensor of masks - - boxes should be a size [n, 4] tensor of bbox coords in relative point form - """ - - n, h, w = masks.shape - x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) - r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) - c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) - - return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) - - -def process_mask_upsample(protos, masks_in, bboxes, shape): - """ - Crop after upsample. - protos: [mask_dim, mask_h, mask_w] - masks_in: [n, mask_dim], n is number of masks after nms - bboxes: [n, 4], n is number of masks after nms - shape: input_image_size, (h, w) - - return: h, w, n - """ - - c, mh, mw = protos.shape # CHW - masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW - masks = crop_mask(masks, bboxes) # CHW - return masks.gt_(0.5) - - -def process_mask(protos, masks_in, bboxes, shape, upsample=False): - """ - Crop before upsample. - proto_out: [mask_dim, mask_h, mask_w] - out_masks: [n, mask_dim], n is number of masks after nms - bboxes: [n, 4], n is number of masks after nms - shape:input_image_size, (h, w) - - return: h, w, n - """ - - c, mh, mw = protos.shape # CHW - ih, iw = shape - masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW - - downsampled_bboxes = bboxes.clone() - downsampled_bboxes[:, 0] *= mw / iw - downsampled_bboxes[:, 2] *= mw / iw - downsampled_bboxes[:, 3] *= mh / ih - downsampled_bboxes[:, 1] *= mh / ih - - masks = crop_mask(masks, downsampled_bboxes) # CHW - if upsample: - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW - return masks.gt_(0.5) - - -def process_mask_native(protos, masks_in, bboxes, shape): - """ - Crop after upsample. - protos: [mask_dim, mask_h, mask_w] - masks_in: [n, mask_dim], n is number of masks after nms - bboxes: [n, 4], n is number of masks after nms - shape: input_image_size, (h, w) - - return: h, w, n - """ - c, mh, mw = protos.shape # CHW - masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) - gain = min(mh / shape[0], mw / shape[1]) # gain = old / new - pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding - top, left = int(pad[1]), int(pad[0]) # y, x - bottom, right = int(mh - pad[1]), int(mw - pad[0]) - masks = masks[:, top:bottom, left:right] - - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW - masks = crop_mask(masks, bboxes) # CHW - return masks.gt_(0.5) - - -def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): - """ - img1_shape: model input shape, [h, w] - img0_shape: origin pic shape, [h, w, 3] - masks: [h, w, num] - """ - # Rescale coordinates (xyxy) from im1_shape to im0_shape - if ratio_pad is None: # calculate from im0_shape - gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new - pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding - else: - pad = ratio_pad[1] - top, left = int(pad[1]), int(pad[0]) # y, x - bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) - - if len(masks.shape) < 2: - raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') - masks = masks[top:bottom, left:right] - # masks = masks.permute(2, 0, 1).contiguous() - # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] - # masks = masks.permute(1, 2, 0).contiguous() - masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) - - if len(masks.shape) == 2: - masks = masks[:, :, None] - return masks - - -def mask_iou(mask1, mask2, eps=1e-7): - """ - mask1: [N, n] m1 means number of predicted objects - mask2: [M, n] m2 means number of gt objects - Note: n means image_w x image_h - - return: masks iou, [N, M] - """ - intersection = torch.matmul(mask1, mask2.t()).clamp(0) - union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection - return intersection / (union + eps) - - -def masks_iou(mask1, mask2, eps=1e-7): - """ - mask1: [N, n] m1 means number of predicted objects - mask2: [N, n] m2 means number of gt objects - Note: n means image_w x image_h - - return: masks iou, (N, ) - """ - intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) - union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection - return intersection / (union + eps) - - -def masks2segments(masks, strategy='largest'): - # Convert masks(n,160,160) into segments(n,xy) - segments = [] - for x in masks.int().cpu().numpy().astype('uint8'): - c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] - if c: - if strategy == 'concat': # concatenate all segments - c = np.concatenate([x.reshape(-1, 2) for x in c]) - elif strategy == 'largest': # select largest segment - c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) - else: - c = np.zeros((0, 2)) # no segments found - segments.append(c.astype('float32')) - return segments diff --git a/iteach_toolkit/DHYOLO/utils/segment/loss.py b/iteach_toolkit/DHYOLO/utils/segment/loss.py deleted file mode 100644 index caeff3cad586b4367990aa4626ed6c326b04baf3..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/loss.py +++ /dev/null @@ -1,185 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..general import xywh2xyxy -from ..loss import FocalLoss, smooth_BCE -from ..metrics import bbox_iou -from ..torch_utils import de_parallel -from .general import crop_mask - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False, overlap=False): - self.sort_obj_iou = False - self.overlap = overlap - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - m = de_parallel(model).model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 - self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance - self.na = m.na # number of anchors - self.nc = m.nc # number of classes - self.nl = m.nl # number of layers - self.nm = m.nm # number of masks - self.anchors = m.anchors - self.device = device - - def __call__(self, preds, targets, masks): # predictions, targets, model - p, proto = preds - bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width - lcls = torch.zeros(1, device=self.device) - lbox = torch.zeros(1, device=self.device) - lobj = torch.zeros(1, device=self.device) - lseg = torch.zeros(1, device=self.device) - tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj - - n = b.shape[0] # number of targets - if n: - pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions - - # Box regression - pxy = pxy.sigmoid() * 2 - 0.5 - pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - iou = iou.detach().clamp(0).type(tobj.dtype) - if self.sort_obj_iou: - j = iou.argsort() - b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] - if self.gr < 1: - iou = (1.0 - self.gr) + self.gr * iou - tobj[b, a, gj, gi] = iou # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(pcls, self.cn, device=self.device) # targets - t[range(n), tcls[i]] = self.cp - lcls += self.BCEcls(pcls, t) # BCE - - # Mask regression - if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample - masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] - marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized - mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) - for bi in b.unique(): - j = b == bi # matching index - if self.overlap: - mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) - else: - mask_gti = masks[tidxs[i]][j] - lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - lseg *= self.hyp['box'] / bs - - loss = lbox + lobj + lcls + lseg - return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() - - def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): - # Mask loss for one image - pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) - loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') - return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] - gain = torch.ones(8, device=self.device) # normalized to gridspace gain - ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - if self.overlap: - batch = p[0].shape[0] - ti = [] - for i in range(batch): - num = (targets[:, 0] == i).sum() # find number of targets of each image - ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) - ti = torch.cat(ti, 1) # (na, nt) - else: - ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) - targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor( - [ - [0, 0], - [1, 0], - [0, 1], - [-1, 0], - [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], - device=self.device).float() * g # offsets - - for i in range(self.nl): - anchors, shape = self.anchors[i], p[i].shape - gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain # shape(3,n,7) - if nt: - # Matches - r = t[..., 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1 < g) & (gxy > 1)).T - l, m = ((gxi % 1 < g) & (gxi > 1)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors - (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class - gij = (gxy - offsets).long() - gi, gj = gij.T # grid indices - - # Append - indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - tidxs.append(tidx) - xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized - - return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/iteach_toolkit/DHYOLO/utils/segment/metrics.py b/iteach_toolkit/DHYOLO/utils/segment/metrics.py deleted file mode 100644 index 787961bee1bf00731274ae87cf04e1bc49248e64..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/metrics.py +++ /dev/null @@ -1,210 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Model validation metrics -""" - -import numpy as np - -from ..metrics import ap_per_class - - -def fitness(x): - # Model fitness as a weighted combination of metrics - w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] - return (x[:, :8] * w).sum(1) - - -def ap_per_class_box_and_mask( - tp_m, - tp_b, - conf, - pred_cls, - target_cls, - plot=False, - save_dir='.', - names=(), -): - """ - Args: - tp_b: tp of boxes. - tp_m: tp of masks. - other arguments see `func: ap_per_class`. - """ - results_boxes = ap_per_class(tp_b, - conf, - pred_cls, - target_cls, - plot=plot, - save_dir=save_dir, - names=names, - prefix='Box')[2:] - results_masks = ap_per_class(tp_m, - conf, - pred_cls, - target_cls, - plot=plot, - save_dir=save_dir, - names=names, - prefix='Mask')[2:] - - results = { - 'boxes': { - 'p': results_boxes[0], - 'r': results_boxes[1], - 'ap': results_boxes[3], - 'f1': results_boxes[2], - 'ap_class': results_boxes[4]}, - 'masks': { - 'p': results_masks[0], - 'r': results_masks[1], - 'ap': results_masks[3], - 'f1': results_masks[2], - 'ap_class': results_masks[4]}} - return results - - -class Metric: - - def __init__(self) -> None: - self.p = [] # (nc, ) - self.r = [] # (nc, ) - self.f1 = [] # (nc, ) - self.all_ap = [] # (nc, 10) - self.ap_class_index = [] # (nc, ) - - @property - def ap50(self): - """AP@0.5 of all classes. - Return: - (nc, ) or []. - """ - return self.all_ap[:, 0] if len(self.all_ap) else [] - - @property - def ap(self): - """AP@0.5:0.95 - Return: - (nc, ) or []. - """ - return self.all_ap.mean(1) if len(self.all_ap) else [] - - @property - def mp(self): - """mean precision of all classes. - Return: - float. - """ - return self.p.mean() if len(self.p) else 0.0 - - @property - def mr(self): - """mean recall of all classes. - Return: - float. - """ - return self.r.mean() if len(self.r) else 0.0 - - @property - def map50(self): - """Mean AP@0.5 of all classes. - Return: - float. - """ - return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 - - @property - def map(self): - """Mean AP@0.5:0.95 of all classes. - Return: - float. - """ - return self.all_ap.mean() if len(self.all_ap) else 0.0 - - def mean_results(self): - """Mean of results, return mp, mr, map50, map""" - return (self.mp, self.mr, self.map50, self.map) - - def class_result(self, i): - """class-aware result, return p[i], r[i], ap50[i], ap[i]""" - return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) - - def get_maps(self, nc): - maps = np.zeros(nc) + self.map - for i, c in enumerate(self.ap_class_index): - maps[c] = self.ap[i] - return maps - - def update(self, results): - """ - Args: - results: tuple(p, r, ap, f1, ap_class) - """ - p, r, all_ap, f1, ap_class_index = results - self.p = p - self.r = r - self.all_ap = all_ap - self.f1 = f1 - self.ap_class_index = ap_class_index - - -class Metrics: - """Metric for boxes and masks.""" - - def __init__(self) -> None: - self.metric_box = Metric() - self.metric_mask = Metric() - - def update(self, results): - """ - Args: - results: Dict{'boxes': Dict{}, 'masks': Dict{}} - """ - self.metric_box.update(list(results['boxes'].values())) - self.metric_mask.update(list(results['masks'].values())) - - def mean_results(self): - return self.metric_box.mean_results() + self.metric_mask.mean_results() - - def class_result(self, i): - return self.metric_box.class_result(i) + self.metric_mask.class_result(i) - - def get_maps(self, nc): - return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) - - @property - def ap_class_index(self): - # boxes and masks have the same ap_class_index - return self.metric_box.ap_class_index - - -KEYS = [ - 'train/box_loss', - 'train/seg_loss', # train loss - 'train/obj_loss', - 'train/cls_loss', - 'metrics/precision(B)', - 'metrics/recall(B)', - 'metrics/mAP_0.5(B)', - 'metrics/mAP_0.5:0.95(B)', # metrics - 'metrics/precision(M)', - 'metrics/recall(M)', - 'metrics/mAP_0.5(M)', - 'metrics/mAP_0.5:0.95(M)', # metrics - 'val/box_loss', - 'val/seg_loss', # val loss - 'val/obj_loss', - 'val/cls_loss', - 'x/lr0', - 'x/lr1', - 'x/lr2', ] - -BEST_KEYS = [ - 'best/epoch', - 'best/precision(B)', - 'best/recall(B)', - 'best/mAP_0.5(B)', - 'best/mAP_0.5:0.95(B)', - 'best/precision(M)', - 'best/recall(M)', - 'best/mAP_0.5(M)', - 'best/mAP_0.5:0.95(M)', ] diff --git a/iteach_toolkit/DHYOLO/utils/segment/plots.py b/iteach_toolkit/DHYOLO/utils/segment/plots.py deleted file mode 100644 index f9938cd1b06a072f085a3bb5322cb032d164ba7a..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/segment/plots.py +++ /dev/null @@ -1,143 +0,0 @@ -import contextlib -import math -from pathlib import Path - -import cv2 -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import torch - -from .. import threaded -from ..general import xywh2xyxy -from ..plots import Annotator, colors - - -@threaded -def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None): - # Plot image grid with labels - if isinstance(images, torch.Tensor): - images = images.cpu().float().numpy() - if isinstance(targets, torch.Tensor): - targets = targets.cpu().numpy() - if isinstance(masks, torch.Tensor): - masks = masks.cpu().numpy().astype(int) - - max_size = 1920 # max image size - max_subplots = 16 # max image subplots, i.e. 4x4 - bs, _, h, w = images.shape # batch size, _, height, width - bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) - if np.max(images[0]) <= 1: - images *= 255 # de-normalise (optional) - - # Build Image - mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init - for i, im in enumerate(images): - if i == max_subplots: # if last batch has fewer images than we expect - break - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im - - # Resize (optional) - scale = max_size / ns / max(h, w) - if scale < 1: - h = math.ceil(scale * h) - w = math.ceil(scale * w) - mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) - - # Annotate - fs = int((h + w) * ns * 0.01) # font size - annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) - for i in range(i + 1): - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders - if paths: - annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames - if len(targets) > 0: - idx = targets[:, 0] == i - ti = targets[idx] # image targets - - boxes = xywh2xyxy(ti[:, 2:6]).T - classes = ti[:, 1].astype('int') - labels = ti.shape[1] == 6 # labels if no conf column - conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) - - if boxes.shape[1]: - if boxes.max() <= 1.01: # if normalized with tolerance 0.01 - boxes[[0, 2]] *= w # scale to pixels - boxes[[1, 3]] *= h - elif scale < 1: # absolute coords need scale if image scales - boxes *= scale - boxes[[0, 2]] += x - boxes[[1, 3]] += y - for j, box in enumerate(boxes.T.tolist()): - cls = classes[j] - color = colors(cls) - cls = names[cls] if names else cls - if labels or conf[j] > 0.25: # 0.25 conf thresh - label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' - annotator.box_label(box, label, color=color) - - # Plot masks - if len(masks): - if masks.max() > 1.0: # mean that masks are overlap - image_masks = masks[[i]] # (1, 640, 640) - nl = len(ti) - index = np.arange(nl).reshape(nl, 1, 1) + 1 - image_masks = np.repeat(image_masks, nl, axis=0) - image_masks = np.where(image_masks == index, 1.0, 0.0) - else: - image_masks = masks[idx] - - im = np.asarray(annotator.im).copy() - for j, box in enumerate(boxes.T.tolist()): - if labels or conf[j] > 0.25: # 0.25 conf thresh - color = colors(classes[j]) - mh, mw = image_masks[j].shape - if mh != h or mw != w: - mask = image_masks[j].astype(np.uint8) - mask = cv2.resize(mask, (w, h)) - mask = mask.astype(bool) - else: - mask = image_masks[j].astype(bool) - with contextlib.suppress(Exception): - im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 - annotator.fromarray(im) - annotator.im.save(fname) # save - - -def plot_results_with_masks(file='path/to/results.csv', dir='', best=True): - # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') - save_dir = Path(file).parent if file else Path(dir) - fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) - ax = ax.ravel() - files = list(save_dir.glob('results*.csv')) - assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' - for f in files: - try: - data = pd.read_csv(f) - index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + - 0.1 * data.values[:, 11]) - s = [x.strip() for x in data.columns] - x = data.values[:, 0] - for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): - y = data.values[:, j] - # y[y == 0] = np.nan # don't show zero values - ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2) - if best: - # best - ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3) - ax[i].set_title(s[j] + f'\n{round(y[index], 5)}') - else: - # last - ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3) - ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}') - # if j in [8, 9, 10]: # share train and val loss y axes - # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) - except Exception as e: - print(f'Warning: Plotting error for {f}: {e}') - ax[1].legend() - fig.savefig(save_dir / 'results.png', dpi=200) - plt.close() diff --git a/iteach_toolkit/DHYOLO/utils/torch_utils.py b/iteach_toolkit/DHYOLO/utils/torch_utils.py deleted file mode 100644 index 13a356f3238c53356907153e8ded9598c2a4a448..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/torch_utils.py +++ /dev/null @@ -1,432 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -PyTorch utils -""" - -import math -import os -import platform -import subprocess -import time -import warnings -from contextlib import contextmanager -from copy import deepcopy -from pathlib import Path - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.parallel import DistributedDataParallel as DDP - -from utils.general import LOGGER, check_version, colorstr, file_date, git_describe - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) - -try: - import thop # for FLOPs computation -except ImportError: - thop = None - -# Suppress PyTorch warnings -warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') -warnings.filterwarnings('ignore', category=UserWarning) - - -def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): - # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator - def decorate(fn): - return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) - - return decorate - - -def smartCrossEntropyLoss(label_smoothing=0.0): - # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 - if check_version(torch.__version__, '1.10.0'): - return nn.CrossEntropyLoss(label_smoothing=label_smoothing) - if label_smoothing > 0: - LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0') - return nn.CrossEntropyLoss() - - -def smart_DDP(model): - # Model DDP creation with checks - assert not check_version(torch.__version__, '1.12.0', pinned=True), \ - 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ - 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' - if check_version(torch.__version__, '1.11.0'): - return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) - else: - return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) - - -def reshape_classifier_output(model, n=1000): - # Update a TorchVision classification model to class count 'n' if required - from models.common import Classify - name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module - if isinstance(m, Classify): # YOLOv5 Classify() head - if m.linear.out_features != n: - m.linear = nn.Linear(m.linear.in_features, n) - elif isinstance(m, nn.Linear): # ResNet, EfficientNet - if m.out_features != n: - setattr(model, name, nn.Linear(m.in_features, n)) - elif isinstance(m, nn.Sequential): - types = [type(x) for x in m] - if nn.Linear in types: - i = types.index(nn.Linear) # nn.Linear index - if m[i].out_features != n: - m[i] = nn.Linear(m[i].in_features, n) - elif nn.Conv2d in types: - i = types.index(nn.Conv2d) # nn.Conv2d index - if m[i].out_channels != n: - m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) - - -@contextmanager -def torch_distributed_zero_first(local_rank: int): - # Decorator to make all processes in distributed training wait for each local_master to do something - if local_rank not in [-1, 0]: - dist.barrier(device_ids=[local_rank]) - yield - if local_rank == 0: - dist.barrier(device_ids=[0]) - - -def device_count(): - # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows - assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' - try: - cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows - return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) - except Exception: - return 0 - - -def select_device(device='', batch_size=0, newline=True): - # device = None or 'cpu' or 0 or '0' or '0,1,2,3' - s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' - device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0' - cpu = device == 'cpu' - mps = device == 'mps' # Apple Metal Performance Shaders (MPS) - if cpu or mps: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False - elif device: # non-cpu device requested - os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() - assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ - f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" - - if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available - devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 - n = len(devices) # device count - if n > 1 and batch_size > 0: # check batch_size is divisible by device_count - assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' - space = ' ' * (len(s) + 1) - for i, d in enumerate(devices): - p = torch.cuda.get_device_properties(i) - s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB - arg = 'cuda:0' - elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available - s += 'MPS\n' - arg = 'mps' - else: # revert to CPU - s += 'CPU\n' - arg = 'cpu' - - if not newline: - s = s.rstrip() - LOGGER.info(s) - return torch.device(arg) - - -def time_sync(): - # PyTorch-accurate time - if torch.cuda.is_available(): - torch.cuda.synchronize() - return time.time() - - -def profile(input, ops, n=10, device=None): - """ YOLOv5 speed/memory/FLOPs profiler - Usage: - input = torch.randn(16, 3, 640, 640) - m1 = lambda x: x * torch.sigmoid(x) - m2 = nn.SiLU() - profile(input, [m1, m2], n=100) # profile over 100 iterations - """ - results = [] - if not isinstance(device, torch.device): - device = select_device(device) - print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" - f"{'input':>24s}{'output':>24s}") - - for x in input if isinstance(input, list) else [input]: - x = x.to(device) - x.requires_grad = True - for m in ops if isinstance(ops, list) else [ops]: - m = m.to(device) if hasattr(m, 'to') else m # device - m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m - tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward - try: - flops = thop.profile(m, inputs=(x, ), verbose=False)[0] / 1E9 * 2 # GFLOPs - except Exception: - flops = 0 - - try: - for _ in range(n): - t[0] = time_sync() - y = m(x) - t[1] = time_sync() - try: - _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() - t[2] = time_sync() - except Exception: # no backward method - # print(e) # for debug - t[2] = float('nan') - tf += (t[1] - t[0]) * 1000 / n # ms per op forward - tb += (t[2] - t[1]) * 1000 / n # ms per op backward - mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) - s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes - p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') - results.append([p, flops, mem, tf, tb, s_in, s_out]) - except Exception as e: - print(e) - results.append(None) - torch.cuda.empty_cache() - return results - - -def is_parallel(model): - # Returns True if model is of type DP or DDP - return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) - - -def de_parallel(model): - # De-parallelize a model: returns single-GPU model if model is of type DP or DDP - return model.module if is_parallel(model) else model - - -def initialize_weights(model): - for m in model.modules(): - t = type(m) - if t is nn.Conv2d: - pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif t is nn.BatchNorm2d: - m.eps = 1e-3 - m.momentum = 0.03 - elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: - m.inplace = True - - -def find_modules(model, mclass=nn.Conv2d): - # Finds layer indices matching module class 'mclass' - return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] - - -def sparsity(model): - # Return global model sparsity - a, b = 0, 0 - for p in model.parameters(): - a += p.numel() - b += (p == 0).sum() - return b / a - - -def prune(model, amount=0.3): - # Prune model to requested global sparsity - import torch.nn.utils.prune as prune - for name, m in model.named_modules(): - if isinstance(m, nn.Conv2d): - prune.l1_unstructured(m, name='weight', amount=amount) # prune - prune.remove(m, 'weight') # make permanent - LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') - - -def fuse_conv_and_bn(conv, bn): - # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ - fusedconv = nn.Conv2d(conv.in_channels, - conv.out_channels, - kernel_size=conv.kernel_size, - stride=conv.stride, - padding=conv.padding, - dilation=conv.dilation, - groups=conv.groups, - bias=True).requires_grad_(False).to(conv.weight.device) - - # Prepare filters - w_conv = conv.weight.clone().view(conv.out_channels, -1) - w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) - fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) - - # Prepare spatial bias - b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias - b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) - fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) - - return fusedconv - - -def model_info(model, verbose=False, imgsz=640): - # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] - n_p = sum(x.numel() for x in model.parameters()) # number parameters - n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients - if verbose: - print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") - for i, (name, p) in enumerate(model.named_parameters()): - name = name.replace('module_list.', '') - print('%5g %40s %9s %12g %20s %10.3g %10.3g' % - (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) - - try: # FLOPs - p = next(model.parameters()) - stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride - im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format - flops = thop.profile(deepcopy(model), inputs=(im, ), verbose=False)[0] / 1E9 * 2 # stride GFLOPs - imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float - fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs - except Exception: - fs = '' - - name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' - LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}') - - -def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) - # Scales img(bs,3,y,x) by ratio constrained to gs-multiple - if ratio == 1.0: - return img - h, w = img.shape[2:] - s = (int(h * ratio), int(w * ratio)) # new size - img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize - if not same_shape: # pad/crop img - h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) - return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean - - -def copy_attr(a, b, include=(), exclude=()): - # Copy attributes from b to a, options to only include [...] and to exclude [...] - for k, v in b.__dict__.items(): - if (len(include) and k not in include) or k.startswith('_') or k in exclude: - continue - else: - setattr(a, k, v) - - -def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): - # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay - g = [], [], [] # optimizer parameter groups - bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() - for v in model.modules(): - for p_name, p in v.named_parameters(recurse=0): - if p_name == 'bias': # bias (no decay) - g[2].append(p) - elif p_name == 'weight' and isinstance(v, bn): # weight (no decay) - g[1].append(p) - else: - g[0].append(p) # weight (with decay) - - if name == 'Adam': - optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum - elif name == 'AdamW': - optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) - elif name == 'RMSProp': - optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) - elif name == 'SGD': - optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) - else: - raise NotImplementedError(f'Optimizer {name} not implemented.') - - optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay - optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights) - LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " - f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias') - return optimizer - - -def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): - # YOLOv5 torch.hub.load() wrapper with smart error/issue handling - if check_version(torch.__version__, '1.9.1'): - kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors - if check_version(torch.__version__, '1.12.0'): - kwargs['trust_repo'] = True # argument required starting in torch 0.12 - try: - return torch.hub.load(repo, model, **kwargs) - except Exception: - return torch.hub.load(repo, model, force_reload=True, **kwargs) - - -def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): - # Resume training from a partially trained checkpoint - best_fitness = 0.0 - start_epoch = ckpt['epoch'] + 1 - if ckpt['optimizer'] is not None: - optimizer.load_state_dict(ckpt['optimizer']) # optimizer - best_fitness = ckpt['best_fitness'] - if ema and ckpt.get('ema'): - ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA - ema.updates = ckpt['updates'] - if resume: - assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ - f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" - LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') - if epochs < start_epoch: - LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") - epochs += ckpt['epoch'] # finetune additional epochs - return best_fitness, start_epoch, epochs - - -class EarlyStopping: - # YOLOv5 simple early stopper - def __init__(self, patience=30): - self.best_fitness = 0.0 # i.e. mAP - self.best_epoch = 0 - self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop - self.possible_stop = False # possible stop may occur next epoch - - def __call__(self, epoch, fitness): - if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training - self.best_epoch = epoch - self.best_fitness = fitness - delta = epoch - self.best_epoch # epochs without improvement - self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch - stop = delta >= self.patience # stop training if patience exceeded - if stop: - LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' - f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' - f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' - f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') - return stop - - -class ModelEMA: - """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models - Keeps a moving average of everything in the model state_dict (parameters and buffers) - For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage - """ - - def __init__(self, model, decay=0.9999, tau=2000, updates=0): - # Create EMA - self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA - self.updates = updates # number of EMA updates - self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) - for p in self.ema.parameters(): - p.requires_grad_(False) - - def update(self, model): - # Update EMA parameters - self.updates += 1 - d = self.decay(self.updates) - - msd = de_parallel(model).state_dict() # model state_dict - for k, v in self.ema.state_dict().items(): - if v.dtype.is_floating_point: # true for FP16 and FP32 - v *= d - v += (1 - d) * msd[k].detach() - # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' - - def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): - # Update EMA attributes - copy_attr(self.ema, model, include, exclude) diff --git a/iteach_toolkit/DHYOLO/utils/triton.py b/iteach_toolkit/DHYOLO/utils/triton.py deleted file mode 100644 index b5153dad940ddeceda4d8e39ac3d90e3efa66448..0000000000000000000000000000000000000000 --- a/iteach_toolkit/DHYOLO/utils/triton.py +++ /dev/null @@ -1,85 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" Utils to interact with the Triton Inference Server -""" - -import typing -from urllib.parse import urlparse - -import torch - - -class TritonRemoteModel: - """ A wrapper over a model served by the Triton Inference Server. It can - be configured to communicate over GRPC or HTTP. It accepts Torch Tensors - as input and returns them as outputs. - """ - - def __init__(self, url: str): - """ - Keyword arguments: - url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000 - """ - - parsed_url = urlparse(url) - if parsed_url.scheme == 'grpc': - from tritonclient.grpc import InferenceServerClient, InferInput - - self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client - model_repository = self.client.get_model_repository_index() - self.model_name = model_repository.models[0].name - self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) - - def create_input_placeholders() -> typing.List[InferInput]: - return [ - InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] - - else: - from tritonclient.http import InferenceServerClient, InferInput - - self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client - model_repository = self.client.get_model_repository_index() - self.model_name = model_repository[0]['name'] - self.metadata = self.client.get_model_metadata(self.model_name) - - def create_input_placeholders() -> typing.List[InferInput]: - return [ - InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] - - self._create_input_placeholders_fn = create_input_placeholders - - @property - def runtime(self): - """Returns the model runtime""" - return self.metadata.get('backend', self.metadata.get('platform')) - - def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: - """ Invokes the model. Parameters can be provided via args or kwargs. - args, if provided, are assumed to match the order of inputs of the model. - kwargs are matched with the model input names. - """ - inputs = self._create_inputs(*args, **kwargs) - response = self.client.infer(model_name=self.model_name, inputs=inputs) - result = [] - for output in self.metadata['outputs']: - tensor = torch.as_tensor(response.as_numpy(output['name'])) - result.append(tensor) - return result[0] if len(result) == 1 else result - - def _create_inputs(self, *args, **kwargs): - args_len, kwargs_len = len(args), len(kwargs) - if not args_len and not kwargs_len: - raise RuntimeError('No inputs provided.') - if args_len and kwargs_len: - raise RuntimeError('Cannot specify args and kwargs at the same time') - - placeholders = self._create_input_placeholders_fn() - if args_len: - if args_len != len(placeholders): - raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.') - for input, value in zip(placeholders, args): - input.set_data_from_numpy(value.cpu().numpy()) - else: - for input in placeholders: - value = kwargs[input.name] - input.set_data_from_numpy(value.cpu().numpy()) - return placeholders