diff --git a/README.md b/README.md index 7be5fc7f47d5db027d120b8024982df93db95b74..323251cc9e67702425ff17083109199efcc8f20a 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,75 @@ ---- -license: mit ---- +# EBC-ZIP + +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ebc-zip-improving-blockwise-crowd-counting/crowd-counting-on-shanghaitech-a)](https://paperswithcode.com/sota/crowd-counting-on-shanghaitech-a?p=ebc-zip-improving-blockwise-crowd-counting) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ebc-zip-improving-blockwise-crowd-counting/crowd-counting-on-shanghaitech-b)](https://paperswithcode.com/sota/crowd-counting-on-shanghaitech-b?p=ebc-zip-improving-blockwise-crowd-counting) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ebc-zip-improving-blockwise-crowd-counting/crowd-counting-on-ucf-qnrf)](https://paperswithcode.com/sota/crowd-counting-on-ucf-qnrf?p=ebc-zip-improving-blockwise-crowd-counting) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ebc-zip-improving-blockwise-crowd-counting/crowd-counting-on-nwpu-crowd-val)](https://paperswithcode.com/sota/crowd-counting-on-nwpu-crowd-val?p=ebc-zip-improving-blockwise-crowd-counting) + +The official implementation of the paper [*ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling*](https://arxiv.org/pdf/2506.19955). + +## Reults + +| **Variants** | **Size (M)** | **GFLOPS (on HD)** | **SHA (MAE)** | **SHA (RMSE)** | **SHA (NAE, %)** | **SHB (MAE)** | **SHB (RMSE)** | **SHB (NAE, %)** | **QNRF (MAE)** | **QNRF (RMSE)** | **QNRF (NAE, %)** | +|--------------|--------------|--------------------|---------------|----------------|------------------|---------------|----------------|------------------|----------------|-----------------|-------------------| +| -P (Pico) | 0.81 | 6.46 | 71.18 | 109.60 | 16.69 | 8.23 | 12.62 | 6.98 | 96.29 | 161.82 | 14.40 | +| -N (Nano) | 3.36 | 24.73 | 58.86 | 94.63 | 14.15 | 7.74 | 12.14 | 6.33 | 86.46 | 147.64 | 12.60 | +| -T (Tiny) | 10.53 | 61.39 | 56.36 | 86.09 | 13.26 | 6.67 | 9.90 | 5.52 | 76.02 | 129.40 | 11.10 | +| -S (Small) | 33.60 | 242.43 | 55.17 | 88.99 | 11.97 | 5.83 | 9.21 | 4.58 | 73.32 | 125.09 | 10.40 | +| -B (Base) | 105.60 | 800.99 | 47.81 | 75.04 | 11.06 | 5.51 | 8.63 | 4.48 | 69.46 | 121.88 | 10.18 | + +## Step 1: Install Dependencies + +```bash +pip install -r requirements.txt +``` + +## Step 2: Download Processed Datasets + +- **ShanghaiTech A**: [sha.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/sha.zip) +- **ShanghaiTech B**: [shb.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/shb.zip) +- **UCF-QNRF**: [qnrf.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/qnrf.zip), [qnrf.z01](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/qnrf.z01) +- **NWPU-Crowd**: [nwpu.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.zip), [nwpu.z01](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z01), [nwpu.z02](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z02), [nwpu.z03](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z03), [nwpu.z04](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z04), [nwpu.z05](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z05), [nwpu.z06](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z06), [nwpu.z07](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z07), [nwpu.z08](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z08) + +To unzip splitted `.zip` files, 7-Zip is recommended. You can use the following command to install 7-Zip and unzip the dataset: + +```bash +sudo apt update +sudo apt install p7zip-full + +7z x dataset.zip +``` + +## Step 3: Run Training + +Add the training code to `run.sh` and execute it: + +```bash +sh run.sh +``` + +If you want to use the zero-inflated loss, set either `--reg_loss` or `--aux_loss` to `zipnll`. For example, you can set `--reg_loss zipnll` to use the zero-inflated loss for regression. + +You can use an auxillary loss to improve the performance. For example, you might want to use the pre-defined multi-scale MAE loss by setting `--aux_loss msmae` and `--scales 1 2 4`. + +The DMCount loss can also be used together with the zero-inflated loss. For example, you can set `--reg_loss zipnll --aux_loss dmcount` to use both losses. + + +## Step 4: Test the Model + +Use `test.py` or `test.sh` to test the model. You can specify the dataset, weight path, input size, and other parameters. + +To generate the predicted counts on NWPU-Crowd Test, you need to use `test_nwpu.py` instead. + +To visualize the results, use the `notebooks/model.ipynb` notebook. + +Trained weights are also provided: +- [**ShanghaiTech A**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_sha) +- [**ShanghaiTech B**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_shb) +- [**UCF-QNRF**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_qnrf) +- [**NWPU-Crowd**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_nwpu) + +Make sure to use the processed datasets and the exact commands pre-defined in `test.sh` to reproduce the same results. + +## Step 5: Visualize the Results + +Use the `notebooks/model.ipynb` notebook to visualize the results. diff --git a/configs/bin_config.json b/configs/bin_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f897d6bf89b9808dc792eabdc2facb5ee96c9181 --- /dev/null +++ b/configs/bin_config.json @@ -0,0 +1,50 @@ +{ + "shb": { + "8": [[0, 0], [1, 1], [2, 2], [3, 3], [4, "inf"]], + "16": [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, "inf"]], + "32": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 11], [12, 12], [13, 13], [14, 14], + [15, 16], [17, 18], [19, 20], + [21, 23], [24, "inf"] + ] + }, + "sha": { + "8": [[0, 0], [1, 1], [2, 2], [3, 3], [4, "inf"]], + "16": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 12], [13, 14], [15, "inf"] + ], + "32": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 11], [12, 12], [13, 13], [14, 14], [15, 15], [16, 16], [17, 17], [18, 18], [19, 19], + [20, 21], [22, 23], [24, 25], [26, 27], [28, 29], + [30, 32], [33, 35], [36, 38], [39, 41], + [42, 45], [46, "inf"] + ] + }, + "qnrf": { + "8": [[0, 0], [1, 1], [2, 2], [3, 3], [4, "inf"]], + "16": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 12], [13, "inf"] + ], + "32": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 12], [13, 14], [15, 16], [17, 18], [19, 20], + [21, 23], [24, 26], [27, 29], [30, 33], [34, "inf"] + ] + }, + "nwpu": { + "8": [[0, 0], [1, 1], [2, 2], [3, 3], [4, "inf"]], + "16": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, "inf"] + ], + "32": [ + [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 11], [12, 13], [14, 15], [16, 17], [18, 19], + [20, 22], [23, 25], [26, 28], [29, "inf"] + ] + } +} \ No newline at end of file diff --git a/configs/nwpu.yaml b/configs/nwpu.yaml new file mode 100644 index 0000000000000000000000000000000000000000..59c122f770d115c27384f833c28fe6e549929dc3 --- /dev/null +++ b/configs/nwpu.yaml @@ -0,0 +1,34 @@ +metadata: + name: NWPU + description: Training configuration on the NWPU dataset. + +input_size: 672 +block_size: 16 +batch_size: 8 +num_crops: 1 + +aug_min_scale: 0.75 +aug_max_scale: 2.0 +aug_brightness: 0.2 +aug_contrast: 0.2 +aug_saturation: 0.15 +aug_hue: 0.0 +aug_kernel_size: 5 +aug_blur_prob: 0.2 +aug_saltiness: 0.001 +aug_spiciness: 0.001 + +lr: 0.0001 +vpt_lr: 0.0001 +adapter_lr: 0.0001 +lora_lr: 0.0001 +backbone_lr: 0.0001 + +weight_decay: 0.0001 +vpt_weight_decay: 0.0001 +adapter_weight_decay: 0.0001 +lora_weight_decay: 0.0001 +backbone_weight_decay: 0.0001 + +eval_freq: 1.0 +eval_start: 100 \ No newline at end of file diff --git a/configs/qnrf.yaml b/configs/qnrf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6c6909535dfe9aab573d0bfdf9092ecf55ad173 --- /dev/null +++ b/configs/qnrf.yaml @@ -0,0 +1,33 @@ +metadata: + name: qnrf + description: Training configuration on the UCF-QNRF dataset. + +input_size: 672 +block_size: 32 +batch_size: 8 +num_crops: 1 + +aug_min_scale: 0.75 +aug_max_scale: 2.0 +aug_brightness: 0.15 +aug_contrast: 0.15 +aug_saturation: 0.1 +aug_hue: 0.0 +aug_blur_prob: 0.0 +aug_saltiness: 0.001 +aug_spiciness: 0.001 + +lr: 0.0001 +vpt_lr: 0.0001 +adapter_lr: 0.0001 +lora_lr: 0.0001 +backbone_lr: 0.0001 + +weight_decay: 0.0001 +vpt_weight_decay: 0.0001 +adapter_weight_decay: 0.0001 +lora_weight_decay: 0.0001 +backbone_weight_decay: 0.0001 + +eval_freq: 0.5 +eval_start: 150 \ No newline at end of file diff --git a/configs/sha.yaml b/configs/sha.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4196a718923d7a9b13ec4d82584a88e0420c9356 --- /dev/null +++ b/configs/sha.yaml @@ -0,0 +1,33 @@ +metadata: + name: sha + description: Training configuration on the ShanghaiTech A dataset. + +input_size: 448 +block_size: 16 +batch_size: 8 +num_crops: 1 + +aug_min_scale: 0.75 +aug_max_scale: 2.0 +aug_brightness: 0.15 +aug_contrast: 0.15 +aug_saturation: 0.1 +aug_hue: 0.0 +aug_blur_prob: 0.0 +aug_saltiness: 0.001 +aug_spiciness: 0.001 + +lr: 0.0001 +vpt_lr: 0.0001 +adapter_lr: 0.0001 +lora_lr: 0.0001 +backbone_lr: 0.0001 + +weight_decay: 0.0001 +vpt_weight_decay: 0.0001 +adapter_weight_decay: 0.0001 +lora_weight_decay: 0.0001 +backbone_weight_decay: 0.0001 + +eval_freq: 0.25 +eval_start: 100 \ No newline at end of file diff --git a/configs/shb.yaml b/configs/shb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..660d9df51d870a6b79cd9604db3216e899f5dde3 --- /dev/null +++ b/configs/shb.yaml @@ -0,0 +1,33 @@ +metadata: + name: shb + description: Training configuration on the ShanghaiTech B dataset. + +input_size: 448 +block_size: 16 +batch_size: 8 +num_crops: 1 + +aug_min_scale: 0.75 +aug_max_scale: 2.5 +aug_brightness: 0.15 +aug_contrast: 0.15 +aug_saturation: 0.1 +aug_hue: 0.0 +aug_blur_prob: 0.0 +aug_saltiness: 0.001 +aug_spiciness: 0.001 + +lr: 0.0001 +vpt_lr: 0.0001 +adapter_lr: 0.0001 +lora_lr: 0.0001 +backbone_lr: 0.0001 + +weight_decay: 0.0001 +vpt_weight_decay: 0.0001 +adapter_weight_decay: 0.0001 +lora_weight_decay: 0.0001 +backbone_weight_decay: 0.0001 + +eval_freq: 0.25 +eval_start: 150 \ No newline at end of file diff --git a/count.py b/count.py new file mode 100644 index 0000000000000000000000000000000000000000..18d9648d0241f7c08fc6c95dbbd96eaf8aade4c6 --- /dev/null +++ b/count.py @@ -0,0 +1,253 @@ +import torch +from torch import nn +import numpy as np +import os, json +from tqdm import tqdm +from argparse import ArgumentParser +from typing import Dict + +import datasets + + +class SumPool2d(nn.Module): + def __init__(self, kernel_size: int, stride: int): + super(SumPool2d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.sum_pool = nn.AvgPool2d(kernel_size, stride, divisor_override=1) + + def forward(self, x): + return self.sum_pool(x) + + +def _update_dict(d: Dict, keys: np.ndarray, values: np.ndarray) -> Dict: + keys = keys.tolist() if isinstance(keys, np.ndarray) else keys + values = values.tolist() if isinstance(values, np.ndarray) else values + for k, v in zip(keys, values): + d[k] = d.get(k, 0) + v + + return d + + +def _get_counts( + dataset_name: str, + device: torch.device, +) -> None: + filter_4 = SumPool2d(4, 1).to(device) + filter_7 = SumPool2d(7, 1).to(device) + filter_8 = SumPool2d(8, 1).to(device) + filter_14 = SumPool2d(14, 1).to(device) + filter_16 = SumPool2d(16, 1).to(device) + filter_28 = SumPool2d(28, 1).to(device) + filter_32 = SumPool2d(32, 1).to(device) + filter_56 = SumPool2d(56, 1).to(device) + filter_64 = SumPool2d(64, 1).to(device) + counts_1, counts_4, counts_7, counts_8 = {}, {}, {}, {} + counts_14, counts_16 = {}, {} + counts_28, counts_32 = {}, {} + counts_56, counts_64 = {}, {} + + max_counts_4 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_7 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_8 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_14 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_16 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_28 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_32 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_56 = {"max": 0., "name": None, "x": None, "y": None} + max_counts_64 = {"max": 0., "name": None, "x": None, "y": None} + + counts_dir = os.path.join(os.getcwd(), "counts") + os.makedirs(counts_dir, exist_ok=True) + + dataset = datasets.Crowd(dataset=dataset_name, split="train", transforms=None, return_filename=True) + print(f"Counting {dataset_name} dataset") + + for i in tqdm(range(len(dataset))): + _, _, density, img_name = dataset[i] + density_np = density.cpu().numpy().astype(int) + uniques_, counts_ = np.unique(density_np, return_counts=True) + counts_1 = _update_dict(counts_1, uniques_, counts_) + + density = density.to(device) # Add batch dimension + window_4, window_7, window_8 = filter_4(density), filter_7(density), filter_8(density) + window_14, window_16 = filter_14(density), filter_16(density) + window_28, window_32 = filter_28(density), filter_32(density) + window_56, window_64 = filter_56(density), filter_64(density) + + window_4, window_7, window_8 = torch.round(window_4).int(), torch.round(window_7).int(), torch.round(window_8).int() + window_14, window_16 = torch.round(window_14).int(), torch.round(window_16).int() + window_28, window_32 = torch.round(window_28).int(), torch.round(window_32).int() + window_56, window_64 = torch.round(window_56).int(), torch.round(window_64).int() + + window_4, window_7, window_8 = torch.squeeze(window_4), torch.squeeze(window_7), torch.squeeze(window_8) + window_14, window_16 = torch.squeeze(window_14), torch.squeeze(window_16) + window_28, window_32 = torch.squeeze(window_28), torch.squeeze(window_32) + window_56, window_64 = torch.squeeze(window_56), torch.squeeze(window_64) + + if window_4.max().item() > max_counts_4["max"]: + max_counts_4["max"] = window_4.max().item() + max_counts_4["name"] = img_name + x, y = torch.where(window_4 == window_4.max()) + x, y = x[0].item(), y[0].item() + max_counts_4["x"] = x + max_counts_4["y"] = y + + if window_7.max().item() > max_counts_7["max"]: + max_counts_7["max"] = window_7.max().item() + max_counts_7["name"] = img_name + x, y = torch.where(window_7 == window_7.max()) + x, y = x[0].item(), y[0].item() + max_counts_7["x"] = x + max_counts_7["y"] = y + + if window_8.max().item() > max_counts_8["max"]: + max_counts_8["max"] = window_8.max().item() + max_counts_8["name"] = img_name + x, y = torch.where(window_8 == window_8.max()) + x, y = x[0].item(), y[0].item() + max_counts_8["x"] = x + max_counts_8["y"] = y + + if window_14.max().item() > max_counts_14["max"]: + max_counts_14["max"] = window_14.max().item() + max_counts_14["name"] = img_name + x, y = torch.where(window_14 == window_14.max()) + x, y = x[0].item(), y[0].item() + max_counts_14["x"] = x + max_counts_14["y"] = y + + if window_16.max().item() > max_counts_16["max"]: + max_counts_16["max"] = window_16.max().item() + max_counts_16["name"] = img_name + x, y = torch.where(window_16 == window_16.max()) + x, y = x[0].item(), y[0].item() + max_counts_16["x"] = x + max_counts_16["y"] = y + + if window_28.max().item() > max_counts_28["max"]: + max_counts_28["max"] = window_28.max().item() + max_counts_28["name"] = img_name + x, y = torch.where(window_28 == window_28.max()) + x, y = x[0].item(), y[0].item() + max_counts_28["x"] = x + max_counts_28["y"] = y + + if window_32.max().item() > max_counts_32["max"]: + max_counts_32["max"] = window_32.max().item() + max_counts_32["name"] = img_name + x, y = torch.where(window_32 == window_32.max()) + x, y = x[0].item(), y[0].item() + max_counts_32["x"] = x + max_counts_32["y"] = y + + if window_56.max().item() > max_counts_56["max"]: + max_counts_56["max"] = window_56.max().item() + max_counts_56["name"] = img_name + x, y = torch.where(window_56 == window_56.max()) + x, y = x[0].item(), y[0].item() + max_counts_56["x"] = x + max_counts_56["y"] = y + + if window_64.max().item() > max_counts_64["max"]: + max_counts_64["max"] = window_64.max().item() + max_counts_64["name"] = img_name + x, y = torch.where(window_64 == window_64.max()) + x, y = x[0].item(), y[0].item() + max_counts_64["x"] = x + max_counts_64["y"] = y + + window_4 = window_4.view(-1).cpu().numpy().astype(int) + window_7 = window_7.view(-1).cpu().numpy().astype(int) + window_8 = window_8.view(-1).cpu().numpy().astype(int) + window_14 = window_14.view(-1).cpu().numpy().astype(int) + window_16 = window_16.view(-1).cpu().numpy().astype(int) + window_28 = window_28.view(-1).cpu().numpy().astype(int) + window_32 = window_32.view(-1).cpu().numpy().astype(int) + window_56 = window_56.view(-1).cpu().numpy().astype(int) + window_64 = window_64.view(-1).cpu().numpy().astype(int) + #.view(-1).cpu().numpy().astype(int) + + uniques_, counts_ = np.unique(window_4, return_counts=True) + counts_4 = _update_dict(counts_4, uniques_, counts_) + + uniques_, counts_ = np.unique(window_7, return_counts=True) + counts_7 = _update_dict(counts_7, uniques_, counts_) + + uniques_, counts_ = np.unique(window_8, return_counts=True) + counts_8 = _update_dict(counts_8, uniques_, counts_) + + uniques_, counts_ = np.unique(window_14, return_counts=True) + counts_14 = _update_dict(counts_14, uniques_, counts_) + + uniques_, counts_ = np.unique(window_16, return_counts=True) + counts_16 = _update_dict(counts_16, uniques_, counts_) + + uniques_, counts_ = np.unique(window_28, return_counts=True) + counts_28 = _update_dict(counts_28, uniques_, counts_) + + uniques_, counts_ = np.unique(window_32, return_counts=True) + counts_32 = _update_dict(counts_32, uniques_, counts_) + + uniques_, counts_ = np.unique(window_56, return_counts=True) + counts_56 = _update_dict(counts_56, uniques_, counts_) + + uniques_, counts_ = np.unique(window_64, return_counts=True) + counts_64 = _update_dict(counts_64, uniques_, counts_) + + counts = { + 1: counts_1, + 4: counts_4, + 7: counts_7, + 8: counts_8, + 14: counts_14, + 16: counts_16, + 28: counts_28, + 32: counts_32, + 56: counts_56, + 64: counts_64 + } + + max_counts = { + 4: max_counts_4, + 7: max_counts_7, + 8: max_counts_8, + 14: max_counts_14, + 16: max_counts_16, + 28: max_counts_28, + 32: max_counts_32, + 56: max_counts_56, + 64: max_counts_64 + } + + with open(os.path.join(counts_dir, f"{dataset_name}.json"), "w") as f: + json.dump(counts, f) + + with open(os.path.join(counts_dir, f"{dataset_name}_max.json"), "w") as f: + json.dump(max_counts, f) + + +def parse_args(): + parser = ArgumentParser(description="Get local counts of the dataset") + parser.add_argument( + "--dataset", + type=str, + choices=["nwpu", "ucf_qnrf", "shanghaitech_a", "shanghaitech_b"], + required=True, + help="The dataset to use." + ) + parser.add_argument( + "--device", + type=str, + default="cuda", + help="The device to use." + ) + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = parse_args() + args.dataset = datasets.standardize_dataset_name(args.dataset) + args.device = torch.device(args.device) + _get_counts(args.dataset, args.device) diff --git a/count.sh b/count.sh new file mode 100644 index 0000000000000000000000000000000000000000..3a5e22efb98b11830bbfdb3d7b5c6fe1586665d6 --- /dev/null +++ b/count.sh @@ -0,0 +1,5 @@ +#!/bin/sh +python count.py --dataset shanghaitech_a --device cuda:0 +python count.py --dataset shanghaitech_b --device cuda:0 +python count.py --dataset nwpu --device cuda:0 +python count.py --dataset ucf_qnrf --device cuda:0 diff --git a/counts/jhu.json b/counts/jhu.json new file mode 100644 index 0000000000000000000000000000000000000000..d264c8cbf157cbe15b8a5855122050ed00926288 --- /dev/null +++ b/counts/jhu.json @@ -0,0 +1,425 @@ +{ + "1": { + "0": 5442129077, + "1": 844619 + }, + "4": { + "0": 5411259934, + "1": 13337323, + "2": 75154, + "3": 1725, + "4": 40 + }, + "7": { + "0": 5366145063, + "1": 39388535, + "2": 807008, + "3": 68635, + "4": 5975, + "5": 318, + "6": 17, + "7": 1 + }, + "8": { + "0": 5348298656, + "1": 50463806, + "2": 1400221, + "3": 154835, + "4": 19051, + "5": 1731, + "6": 121, + "7": 11 + }, + "14": { + "0": 5220148724, + "1": 129080801, + "2": 11196548, + "3": 2346703, + "4": 762426, + "5": 281109, + "6": 104707, + "7": 35659, + "8": 10533, + "9": 2989, + "10": 724, + "11": 196, + "12": 16, + "13": 1 + }, + "16": { + "0": 5172190839, + "1": 156244565, + "2": 17047061, + "3": 3987628, + "4": 1373739, + "5": 580316, + "6": 265393, + "7": 117895, + "8": 48278, + "9": 18825, + "10": 6835, + "11": 2535, + "12": 909, + "13": 209, + "14": 27, + "15": 2 + }, + "28": { + "0": 4868806093, + "1": 296210451, + "2": 64607415, + "3": 23796771, + "4": 11220229, + "5": 5869184, + "6": 3249319, + "7": 1854162, + "8": 1153843, + "9": 778472, + "10": 561910, + "11": 425259, + "12": 332715, + "13": 255032, + "14": 191332, + "15": 137704, + "16": 95475, + "17": 64842, + "18": 43528, + "19": 29738, + "20": 20028, + "21": 13687, + "22": 9609, + "23": 7228, + "24": 4847, + "25": 3457, + "26": 2563, + "27": 1831, + "28": 1349, + "29": 917, + "30": 589, + "31": 360, + "32": 213, + "33": 94, + "34": 22, + "35": 4 + }, + "32": { + "0": 4768229484, + "1": 332242168, + "2": 81810540, + "3": 32189657, + "4": 16022983, + "5": 8984314, + "6": 5419164, + "7": 3339453, + "8": 2097270, + "9": 1359271, + "10": 927341, + "11": 673849, + "12": 519302, + "13": 413081, + "14": 339682, + "15": 282493, + "16": 235154, + "17": 189365, + "18": 147778, + "19": 111779, + "20": 83938, + "21": 61440, + "22": 44843, + "23": 32312, + "24": 23514, + "25": 17003, + "26": 12718, + "27": 9671, + "28": 7115, + "29": 5853, + "30": 4515, + "31": 3342, + "32": 2525, + "33": 1880, + "34": 1522, + "35": 1199, + "36": 1034, + "37": 733, + "38": 561, + "39": 400, + "40": 287, + "41": 134, + "42": 62, + "43": 19, + "44": 4 + }, + "56": { + "0": 4222181888, + "1": 453337627, + "2": 170668322, + "3": 85503361, + "4": 50077828, + "5": 32125898, + "6": 22063372, + "7": 15687182, + "8": 11585957, + "9": 8807535, + "10": 6902417, + "11": 5494688, + "12": 4464497, + "13": 3672794, + "14": 3059884, + "15": 2569337, + "16": 2181015, + "17": 1848256, + "18": 1568914, + "19": 1327646, + "20": 1110617, + "21": 923381, + "22": 763225, + "23": 634769, + "24": 533036, + "25": 446198, + "26": 375536, + "27": 319752, + "28": 277970, + "29": 246034, + "30": 221081, + "31": 200820, + "32": 185527, + "33": 172457, + "34": 163190, + "35": 155461, + "36": 149548, + "37": 144236, + "38": 139882, + "39": 134703, + "40": 129346, + "41": 123503, + "42": 117688, + "43": 109973, + "44": 101970, + "45": 94300, + "46": 87095, + "47": 80710, + "48": 73843, + "49": 66773, + "50": 61099, + "51": 55590, + "52": 48984, + "53": 43741, + "54": 38838, + "55": 34038, + "56": 30826, + "57": 28088, + "58": 25668, + "59": 23430, + "60": 21750, + "61": 18902, + "62": 16508, + "63": 14272, + "64": 12549, + "65": 10596, + "66": 9228, + "67": 8081, + "68": 7185, + "69": 6284, + "70": 5698, + "71": 5124, + "72": 4488, + "73": 3761, + "74": 3171, + "75": 2908, + "76": 2554, + "77": 2211, + "78": 1956, + "79": 1784, + "80": 1529, + "81": 1317, + "82": 1189, + "83": 1136, + "84": 1086, + "85": 1012, + "86": 890, + "87": 914, + "88": 895, + "89": 832, + "90": 698, + "91": 607, + "92": 546, + "93": 526, + "94": 411, + "95": 386, + "96": 372, + "97": 415, + "98": 428, + "99": 487, + "100": 506, + "101": 549, + "102": 453, + "103": 475, + "104": 432, + "105": 391, + "106": 349, + "107": 307, + "108": 236, + "109": 183, + "110": 162, + "111": 128, + "112": 97, + "113": 48, + "114": 34, + "115": 14, + "116": 10, + "117": 7, + "118": 3, + "119": 1, + "120": 1 + }, + "64": { + "0": 4064136120, + "1": 469518405, + "2": 190549696, + "3": 101410734, + "4": 61441010, + "5": 40341860, + "6": 28363124, + "7": 20699526, + "8": 15647286, + "9": 12025617, + "10": 9421729, + "11": 7602900, + "12": 6244037, + "13": 5183786, + "14": 4355369, + "15": 3680829, + "16": 3145664, + "17": 2707446, + "18": 2348723, + "19": 2053730, + "20": 1802355, + "21": 1584446, + "22": 1402996, + "23": 1243258, + "24": 1087095, + "25": 947714, + "26": 818905, + "27": 707951, + "28": 615285, + "29": 531101, + "30": 459448, + "31": 397639, + "32": 343028, + "33": 295704, + "34": 259036, + "35": 229935, + "36": 207856, + "37": 189177, + "38": 173617, + "39": 158969, + "40": 147768, + "41": 139725, + "42": 132730, + "43": 127226, + "44": 122630, + "45": 118232, + "46": 115769, + "47": 114576, + "48": 111942, + "49": 107720, + "50": 105347, + "51": 101643, + "52": 98838, + "53": 96240, + "54": 91117, + "55": 87247, + "56": 82358, + "57": 77480, + "58": 72990, + "59": 68837, + "60": 65050, + "61": 61515, + "62": 57758, + "63": 53659, + "64": 50371, + "65": 45903, + "66": 42190, + "67": 39241, + "68": 35555, + "69": 32655, + "70": 29239, + "71": 26825, + "72": 24122, + "73": 22333, + "74": 21327, + "75": 19766, + "76": 18539, + "77": 16797, + "78": 15217, + "79": 13961, + "80": 12377, + "81": 11299, + "82": 9960, + "83": 8982, + "84": 7921, + "85": 7244, + "86": 6267, + "87": 5707, + "88": 5185, + "89": 4541, + "90": 4292, + "91": 3572, + "92": 3041, + "93": 2757, + "94": 2416, + "95": 2182, + "96": 1973, + "97": 1646, + "98": 1472, + "99": 1468, + "100": 1411, + "101": 1402, + "102": 1289, + "103": 1163, + "104": 983, + "105": 838, + "106": 777, + "107": 744, + "108": 689, + "109": 651, + "110": 651, + "111": 586, + "112": 523, + "113": 508, + "114": 464, + "115": 446, + "116": 428, + "117": 423, + "118": 390, + "119": 417, + "120": 363, + "121": 317, + "122": 316, + "123": 339, + "124": 340, + "125": 372, + "126": 372, + "127": 339, + "128": 403, + "129": 405, + "130": 428, + "131": 406, + "132": 409, + "133": 419, + "134": 396, + "135": 311, + "136": 288, + "137": 243, + "138": 195, + "139": 150, + "140": 158, + "141": 114, + "142": 105, + "143": 67, + "144": 33, + "145": 11, + "146": 7, + "147": 1 + } +} \ No newline at end of file diff --git a/counts/jhu_max.json b/counts/jhu_max.json new file mode 100644 index 0000000000000000000000000000000000000000..c838a67ae9f73dd941ff05e6996819b249726839 --- /dev/null +++ b/counts/jhu_max.json @@ -0,0 +1,74 @@ +{ + "4": { + "max": 4, + "name": [ + "0050.jpg" + ], + "x": 672, + "y": 1315 + }, + "7": { + "max": 7, + "name": [ + "0154.jpg" + ], + "x": 338, + "y": 1337 + }, + "8": { + "max": 7, + "name": [ + "0144.jpg" + ], + "x": 639, + "y": 943 + }, + "14": { + "max": 13, + "name": [ + "1162.jpg" + ], + "x": 604, + "y": 702 + }, + "16": { + "max": 15, + "name": [ + "0193.jpg" + ], + "x": 593, + "y": 286 + }, + "28": { + "max": 35, + "name": [ + "1162.jpg" + ], + "x": 578, + "y": 706 + }, + "32": { + "max": 44, + "name": [ + "0193.jpg" + ], + "x": 596, + "y": 263 + }, + "56": { + "max": 120, + "name": [ + "1162.jpg" + ], + "x": 562, + "y": 671 + }, + "64": { + "max": 147, + "name": [ + "1162.jpg" + ], + "x": 562, + "y": 663 + } +} \ No newline at end of file diff --git a/counts/nwpu.json b/counts/nwpu.json new file mode 100644 index 0000000000000000000000000000000000000000..9e007b41b2ba78b2ecfba70f1eb39bc22a8ef1ae --- /dev/null +++ b/counts/nwpu.json @@ -0,0 +1,761 @@ +{ + "1": { + "0": 14667500579, + "1": 1291229 + }, + "4": { + "0": 14607991573, + "1": 20424516, + "2": 101146, + "3": 3581, + "4": 173, + "5": 15, + "6": 1 + }, + "7": { + "0": 14527228244, + "1": 59342625, + "2": 1508064, + "3": 181138, + "4": 35423, + "5": 8206, + "6": 1925, + "7": 424, + "8": 92, + "9": 19, + "10": 4 + }, + "8": { + "0": 14496291716, + "1": 75535689, + "2": 2593492, + "3": 373522, + "4": 85180, + "5": 23129, + "6": 7605, + "7": 2404, + "8": 694, + "9": 170, + "10": 45, + "11": 7 + }, + "14": { + "0": 14280365725, + "1": 189868793, + "2": 17508005, + "3": 4140432, + "4": 1496968, + "5": 646243, + "6": 308292, + "7": 154512, + "8": 80925, + "9": 45696, + "10": 26811, + "11": 16841, + "12": 10489, + "13": 6798, + "14": 4437, + "15": 3038, + "16": 2097, + "17": 1426, + "18": 850, + "19": 434, + "20": 198, + "21": 105, + "22": 36, + "23": 14 + }, + "16": { + "0": 14200293041, + "1": 230337258, + "2": 25716807, + "3": 6591144, + "4": 2496616, + "5": 1151263, + "6": 597759, + "7": 328222, + "8": 186538, + "9": 107834, + "10": 64201, + "11": 40386, + "12": 26336, + "13": 17791, + "14": 12514, + "15": 8477, + "16": 6021, + "17": 4371, + "18": 3322, + "19": 2369, + "20": 1800, + "21": 1260, + "22": 892, + "23": 581, + "24": 317, + "25": 166, + "26": 88, + "27": 32, + "28": 5, + "29": 2 + }, + "28": { + "0": 13684329722, + "1": 456956241, + "2": 91566961, + "3": 34512257, + "4": 16402331, + "5": 8518065, + "6": 4898436, + "7": 3032957, + "8": 2020921, + "9": 1422203, + "10": 1041284, + "11": 785822, + "12": 600472, + "13": 463060, + "14": 356398, + "15": 278057, + "16": 220282, + "17": 175747, + "18": 141679, + "19": 115020, + "20": 92598, + "21": 75190, + "22": 61616, + "23": 50395, + "24": 40763, + "25": 33009, + "26": 26142, + "27": 21024, + "28": 16921, + "29": 14076, + "30": 11489, + "31": 10146, + "32": 8692, + "33": 7935, + "34": 7289, + "35": 6638, + "36": 5728, + "37": 5150, + "38": 4441, + "39": 3978, + "40": 3510, + "41": 3071, + "42": 2914, + "43": 2538, + "44": 2234, + "45": 1886, + "46": 1685, + "47": 1411, + "48": 1205, + "49": 1020, + "50": 817, + "51": 754, + "52": 696, + "53": 585, + "54": 540, + "55": 512, + "56": 444, + "57": 426, + "58": 364, + "59": 257, + "60": 212, + "61": 197, + "62": 157, + "63": 133, + "64": 108, + "65": 83, + "66": 95, + "67": 69, + "68": 64, + "69": 35, + "70": 21, + "71": 12, + "72": 9, + "73": 8, + "74": 3, + "75": 3 + }, + "32": { + "0": 13507181488, + "1": 523684788, + "2": 115677502, + "3": 46067053, + "4": 23384978, + "5": 13033305, + "6": 7798986, + "7": 4827879, + "8": 3222733, + "9": 2262098, + "10": 1651589, + "11": 1247118, + "12": 967386, + "13": 771426, + "14": 621546, + "15": 504368, + "16": 409418, + "17": 332421, + "18": 271277, + "19": 222138, + "20": 183772, + "21": 152433, + "22": 128423, + "23": 108428, + "24": 93487, + "25": 79093, + "26": 67728, + "27": 56196, + "28": 47634, + "29": 40579, + "30": 34355, + "31": 28984, + "32": 24565, + "33": 20972, + "34": 17931, + "35": 14995, + "36": 12377, + "37": 10307, + "38": 8797, + "39": 7610, + "40": 6846, + "41": 6271, + "42": 5855, + "43": 5378, + "44": 5294, + "45": 4945, + "46": 4528, + "47": 4172, + "48": 3883, + "49": 3522, + "50": 3246, + "51": 2948, + "52": 2646, + "53": 2401, + "54": 2102, + "55": 1889, + "56": 1689, + "57": 1444, + "58": 1354, + "59": 1166, + "60": 966, + "61": 796, + "62": 695, + "63": 629, + "64": 585, + "65": 531, + "66": 518, + "67": 482, + "68": 442, + "69": 385, + "70": 358, + "71": 335, + "72": 307, + "73": 267, + "74": 271, + "75": 220, + "76": 210, + "77": 180, + "78": 147, + "79": 124, + "80": 116, + "81": 112, + "82": 93, + "83": 69, + "84": 56, + "85": 52, + "86": 23, + "87": 17, + "88": 14, + "89": 11, + "90": 14, + "91": 6, + "92": 6, + "93": 3, + "94": 6, + "95": 1 + }, + "56": { + "0": 12465097246, + "1": 835084317, + "2": 254687121, + "3": 121720894, + "4": 71341732, + "5": 45465642, + "6": 31016406, + "7": 22117585, + "8": 16576017, + "9": 12843282, + "10": 10188871, + "11": 8166753, + "12": 6639505, + "13": 5403165, + "14": 4423601, + "15": 3641816, + "16": 2982294, + "17": 2495500, + "18": 2107822, + "19": 1777118, + "20": 1527177, + "21": 1320511, + "22": 1154409, + "23": 1016008, + "24": 902921, + "25": 805297, + "26": 717731, + "27": 639994, + "28": 578216, + "29": 522654, + "30": 471731, + "31": 430710, + "32": 391310, + "33": 360727, + "34": 333244, + "35": 306947, + "36": 285386, + "37": 266777, + "38": 248721, + "39": 231377, + "40": 213535, + "41": 197555, + "42": 182232, + "43": 168988, + "44": 156079, + "45": 144746, + "46": 135302, + "47": 124226, + "48": 114096, + "49": 104673, + "50": 95005, + "51": 87224, + "52": 81168, + "53": 76076, + "54": 71286, + "55": 67529, + "56": 64050, + "57": 62041, + "58": 58650, + "59": 55931, + "60": 51249, + "61": 47542, + "62": 44191, + "63": 41598, + "64": 38416, + "65": 36328, + "66": 33839, + "67": 32088, + "68": 30559, + "69": 27881, + "70": 26103, + "71": 24152, + "72": 22520, + "73": 20886, + "74": 19169, + "75": 17738, + "76": 16636, + "77": 15532, + "78": 14619, + "79": 14389, + "80": 13560, + "81": 13208, + "82": 12245, + "83": 11275, + "84": 10523, + "85": 10108, + "86": 9176, + "87": 8790, + "88": 8448, + "89": 8110, + "90": 7575, + "91": 7354, + "92": 6483, + "93": 6061, + "94": 5352, + "95": 5181, + "96": 4845, + "97": 4594, + "98": 4342, + "99": 4193, + "100": 3899, + "101": 3674, + "102": 3565, + "103": 3285, + "104": 3059, + "105": 2778, + "106": 2658, + "107": 2485, + "108": 2345, + "109": 2303, + "110": 2210, + "111": 2095, + "112": 1975, + "113": 1975, + "114": 2058, + "115": 1969, + "116": 1914, + "117": 1934, + "118": 1928, + "119": 1914, + "120": 1954, + "121": 1943, + "122": 1997, + "123": 2085, + "124": 1841, + "125": 1728, + "126": 1603, + "127": 1530, + "128": 1426, + "129": 1355, + "130": 1309, + "131": 1340, + "132": 1256, + "133": 1260, + "134": 1219, + "135": 1086, + "136": 1079, + "137": 1004, + "138": 987, + "139": 996, + "140": 886, + "141": 841, + "142": 786, + "143": 799, + "144": 882, + "145": 782, + "146": 718, + "147": 672, + "148": 629, + "149": 578, + "150": 592, + "151": 602, + "152": 564, + "153": 573, + "154": 551, + "155": 484, + "156": 474, + "157": 435, + "158": 410, + "159": 376, + "160": 348, + "161": 366, + "162": 299, + "163": 304, + "164": 280, + "165": 301, + "166": 298, + "167": 266, + "168": 259, + "169": 288, + "170": 259, + "171": 232, + "172": 249, + "173": 229, + "174": 197, + "175": 254, + "176": 204, + "177": 211, + "178": 208, + "179": 199, + "180": 183, + "181": 169, + "182": 169, + "183": 169, + "184": 120, + "185": 119, + "186": 151, + "187": 131, + "188": 126, + "189": 122, + "190": 107, + "191": 105, + "192": 103, + "193": 87, + "194": 71, + "195": 62, + "196": 59, + "197": 51, + "198": 40, + "199": 49, + "200": 44, + "201": 45, + "202": 43, + "203": 42, + "204": 36, + "205": 45, + "206": 36, + "207": 37, + "208": 38, + "209": 32, + "210": 27, + "211": 25, + "212": 21, + "213": 19, + "214": 30, + "215": 16, + "216": 20, + "217": 15, + "218": 14, + "219": 6, + "220": 8, + "221": 5, + "222": 3, + "223": 2 + }, + "64": { + "0": 12134170560, + "1": 910355445, + "2": 297133671, + "3": 145184087, + "4": 87626341, + "5": 57746135, + "6": 40495922, + "7": 29156512, + "8": 21919906, + "9": 16973043, + "10": 13535308, + "11": 11038546, + "12": 9149626, + "13": 7600687, + "14": 6410824, + "15": 5491781, + "16": 4677502, + "17": 3997198, + "18": 3443407, + "19": 2925959, + "20": 2507301, + "21": 2160448, + "22": 1878716, + "23": 1648075, + "24": 1450872, + "25": 1275043, + "26": 1133498, + "27": 1015835, + "28": 914243, + "29": 833304, + "30": 760872, + "31": 691863, + "32": 630584, + "33": 577966, + "34": 528643, + "35": 485362, + "36": 444354, + "37": 407675, + "38": 377100, + "39": 351641, + "40": 326893, + "41": 305689, + "42": 285689, + "43": 266757, + "44": 249514, + "45": 235532, + "46": 223892, + "47": 211932, + "48": 200323, + "49": 189578, + "50": 178068, + "51": 167402, + "52": 158785, + "53": 149971, + "54": 140597, + "55": 131198, + "56": 124442, + "57": 118109, + "58": 111071, + "59": 104882, + "60": 97607, + "61": 91490, + "62": 85286, + "63": 79531, + "64": 74921, + "65": 69722, + "66": 67061, + "67": 62855, + "68": 59431, + "69": 56425, + "70": 53389, + "71": 52205, + "72": 49130, + "73": 47540, + "74": 46130, + "75": 44031, + "76": 41069, + "77": 38590, + "78": 36372, + "79": 34739, + "80": 32483, + "81": 30821, + "82": 29084, + "83": 27658, + "84": 26356, + "85": 25296, + "86": 24161, + "87": 22766, + "88": 21596, + "89": 20576, + "90": 19734, + "91": 18715, + "92": 17676, + "93": 16389, + "94": 15235, + "95": 14115, + "96": 13051, + "97": 12336, + "98": 11769, + "99": 10974, + "100": 10731, + "101": 9897, + "102": 9661, + "103": 9456, + "104": 9255, + "105": 9143, + "106": 8863, + "107": 8535, + "108": 8059, + "109": 7377, + "110": 7024, + "111": 6470, + "112": 6426, + "113": 6009, + "114": 5748, + "115": 5535, + "116": 5244, + "117": 4876, + "118": 4586, + "119": 4234, + "120": 4118, + "121": 3789, + "122": 3695, + "123": 3622, + "124": 3493, + "125": 3318, + "126": 3359, + "127": 3420, + "128": 3353, + "129": 3224, + "130": 3222, + "131": 3038, + "132": 2831, + "133": 2743, + "134": 2751, + "135": 2703, + "136": 2517, + "137": 2404, + "138": 2360, + "139": 2069, + "140": 2037, + "141": 1829, + "142": 1693, + "143": 1599, + "144": 1588, + "145": 1482, + "146": 1408, + "147": 1386, + "148": 1339, + "149": 1401, + "150": 1313, + "151": 1276, + "152": 1276, + "153": 1179, + "154": 1242, + "155": 1267, + "156": 1184, + "157": 1245, + "158": 1187, + "159": 1113, + "160": 1095, + "161": 1059, + "162": 938, + "163": 958, + "164": 906, + "165": 920, + "166": 941, + "167": 905, + "168": 885, + "169": 873, + "170": 794, + "171": 741, + "172": 773, + "173": 713, + "174": 694, + "175": 689, + "176": 741, + "177": 770, + "178": 735, + "179": 747, + "180": 704, + "181": 670, + "182": 652, + "183": 635, + "184": 633, + "185": 682, + "186": 598, + "187": 590, + "188": 541, + "189": 526, + "190": 495, + "191": 508, + "192": 492, + "193": 501, + "194": 443, + "195": 444, + "196": 399, + "197": 363, + "198": 357, + "199": 338, + "200": 292, + "201": 273, + "202": 288, + "203": 292, + "204": 280, + "205": 260, + "206": 278, + "207": 243, + "208": 212, + "209": 241, + "210": 217, + "211": 189, + "212": 195, + "213": 181, + "214": 179, + "215": 238, + "216": 196, + "217": 195, + "218": 181, + "219": 191, + "220": 158, + "221": 154, + "222": 178, + "223": 150, + "224": 149, + "225": 155, + "226": 184, + "227": 125, + "228": 154, + "229": 135, + "230": 153, + "231": 151, + "232": 153, + "233": 124, + "234": 110, + "235": 87, + "236": 95, + "237": 76, + "238": 75, + "239": 69, + "240": 67, + "241": 60, + "242": 36, + "243": 42, + "244": 55, + "245": 41, + "246": 58, + "247": 46, + "248": 37, + "249": 33, + "250": 29, + "251": 23, + "252": 13, + "253": 3, + "254": 11, + "255": 9, + "256": 2 + } +} \ No newline at end of file diff --git a/counts/nwpu_max.json b/counts/nwpu_max.json new file mode 100644 index 0000000000000000000000000000000000000000..27e5fc511761aaa0ba28fbf28137c082d45c4837 --- /dev/null +++ b/counts/nwpu_max.json @@ -0,0 +1,74 @@ +{ + "4": { + "max": 6, + "name": [ + "0701.jpg" + ], + "x": 976, + "y": 1527 + }, + "7": { + "max": 10, + "name": [ + "0181.jpg" + ], + "x": 639, + "y": 1531 + }, + "8": { + "max": 11, + "name": [ + "1838.jpg" + ], + "x": 815, + "y": 1001 + }, + "14": { + "max": 23, + "name": [ + "1838.jpg" + ], + "x": 995, + "y": 1544 + }, + "16": { + "max": 29, + "name": [ + "1838.jpg" + ], + "x": 991, + "y": 1544 + }, + "28": { + "max": 75, + "name": [ + "1838.jpg" + ], + "x": 1003, + "y": 1706 + }, + "32": { + "max": 95, + "name": [ + "1838.jpg" + ], + "x": 1003, + "y": 1704 + }, + "56": { + "max": 223, + "name": [ + "1838.jpg" + ], + "x": 993, + "y": 1702 + }, + "64": { + "max": 256, + "name": [ + "1838.jpg" + ], + "x": 990, + "y": 1697 + } +} \ No newline at end of file diff --git a/counts/qnrf.json b/counts/qnrf.json new file mode 100644 index 0000000000000000000000000000000000000000..133756d1dae694974b15243fb10d15ff02a90dc5 --- /dev/null +++ b/counts/qnrf.json @@ -0,0 +1,569 @@ +{ + "1": { + "0": 2703096261, + "1": 1007163 + }, + "4": { + "0": 2677404968, + "1": 15969215, + "2": 59807, + "3": 1384, + "4": 97, + "5": 10 + }, + "7": { + "0": 2635421382, + "1": 45742892, + "2": 1492537, + "3": 114192, + "4": 14549, + "5": 2676, + "6": 675, + "7": 199, + "8": 47, + "9": 7 + }, + "8": { + "0": 2618651922, + "1": 57473873, + "2": 2778844, + "3": 286508, + "4": 41982, + "5": 8626, + "6": 2306, + "7": 782, + "8": 241, + "9": 77, + "10": 21, + "11": 3 + }, + "14": { + "0": 2502680139, + "1": 128220308, + "2": 19112473, + "3": 5245278, + "4": 1729894, + "5": 624274, + "6": 238250, + "7": 97230, + "8": 41347, + "9": 19325, + "10": 9833, + "11": 5696, + "12": 3361, + "13": 1972, + "14": 1035, + "15": 547, + "16": 340, + "17": 212, + "18": 112, + "19": 71, + "20": 42, + "21": 30, + "22": 14, + "23": 12, + "24": 3, + "25": 3 + }, + "16": { + "0": 2461366525, + "1": 149295686, + "2": 26297062, + "3": 8379921, + "4": 3199756, + "5": 1324146, + "6": 583234, + "7": 267593, + "8": 128122, + "9": 62843, + "10": 32265, + "11": 16540, + "12": 9297, + "13": 5835, + "14": 4037, + "15": 2616, + "16": 1660, + "17": 1066, + "18": 639, + "19": 349, + "20": 203, + "21": 183, + "22": 121, + "23": 80, + "24": 44, + "25": 30, + "26": 11, + "27": 9, + "28": 10, + "29": 6 + }, + "28": { + "0": 2217981619, + "1": 242958596, + "2": 68708089, + "3": 31034654, + "4": 17007626, + "5": 10317353, + "6": 6556090, + "7": 4298832, + "8": 2899688, + "9": 2014576, + "10": 1411981, + "11": 1007963, + "12": 718139, + "13": 516552, + "14": 375188, + "15": 273595, + "16": 199599, + "17": 144002, + "18": 106107, + "19": 79309, + "20": 60015, + "21": 45839, + "22": 35538, + "23": 27006, + "24": 21141, + "25": 16063, + "26": 11666, + "27": 8786, + "28": 6812, + "29": 5341, + "30": 4314, + "31": 3339, + "32": 2718, + "33": 2165, + "34": 1611, + "35": 1444, + "36": 1299, + "37": 1057, + "38": 930, + "39": 804, + "40": 590, + "41": 475, + "42": 361, + "43": 297, + "44": 242, + "45": 166, + "46": 125, + "47": 108, + "48": 81, + "49": 90, + "50": 74, + "51": 46, + "52": 38, + "53": 24, + "54": 13, + "55": 7, + "56": 2 + }, + "32": { + "0": 2142175706, + "1": 263796436, + "2": 81041731, + "3": 38689542, + "4": 21861748, + "5": 13731448, + "6": 9285756, + "7": 6423842, + "8": 4541778, + "9": 3251892, + "10": 2387659, + "11": 1795213, + "12": 1359736, + "13": 1036607, + "14": 790266, + "15": 606226, + "16": 469220, + "17": 361731, + "18": 281834, + "19": 218860, + "20": 168254, + "21": 130270, + "22": 100263, + "23": 78196, + "24": 61822, + "25": 49558, + "26": 39186, + "27": 32271, + "28": 26464, + "29": 21939, + "30": 17726, + "31": 14747, + "32": 11705, + "33": 9539, + "34": 7368, + "35": 5935, + "36": 4774, + "37": 3727, + "38": 3275, + "39": 2605, + "40": 2408, + "41": 1893, + "42": 1440, + "43": 1278, + "44": 1070, + "45": 915, + "46": 740, + "47": 619, + "48": 507, + "49": 484, + "50": 330, + "51": 374, + "52": 287, + "53": 244, + "54": 223, + "55": 186, + "56": 136, + "57": 120, + "58": 103, + "59": 100, + "60": 88, + "61": 35, + "62": 16, + "63": 23, + "64": 3, + "65": 4 + }, + "56": { + "0": 1753079415, + "1": 326506415, + "2": 132989783, + "3": 73569912, + "4": 47875219, + "5": 33905462, + "6": 25324896, + "7": 19351405, + "8": 14991521, + "9": 11859011, + "10": 9643110, + "11": 7988193, + "12": 6724565, + "13": 5737528, + "14": 4905936, + "15": 4234117, + "16": 3678710, + "17": 3233842, + "18": 2856180, + "19": 2528729, + "20": 2238483, + "21": 1974680, + "22": 1738379, + "23": 1522952, + "24": 1334635, + "25": 1171843, + "26": 1038446, + "27": 924884, + "28": 828510, + "29": 749323, + "30": 680155, + "31": 619173, + "32": 558209, + "33": 507896, + "34": 463642, + "35": 419398, + "36": 380125, + "37": 347601, + "38": 318828, + "39": 293043, + "40": 272483, + "41": 250724, + "42": 228696, + "43": 206140, + "44": 184636, + "45": 165534, + "46": 149696, + "47": 135099, + "48": 121824, + "49": 110240, + "50": 98425, + "51": 88515, + "52": 79279, + "53": 70978, + "54": 64994, + "55": 59099, + "56": 53268, + "57": 48134, + "58": 43611, + "59": 38300, + "60": 34909, + "61": 31681, + "62": 28393, + "63": 24688, + "64": 21934, + "65": 19803, + "66": 17598, + "67": 15593, + "68": 14189, + "69": 13168, + "70": 12483, + "71": 11762, + "72": 11066, + "73": 10447, + "74": 9606, + "75": 8747, + "76": 7574, + "77": 6921, + "78": 6340, + "79": 6088, + "80": 5448, + "81": 5380, + "82": 5144, + "83": 5114, + "84": 4775, + "85": 4632, + "86": 4332, + "87": 4082, + "88": 3949, + "89": 3821, + "90": 3476, + "91": 3406, + "92": 2973, + "93": 2766, + "94": 2489, + "95": 2253, + "96": 2087, + "97": 1763, + "98": 1560, + "99": 1322, + "100": 1243, + "101": 1150, + "102": 994, + "103": 794, + "104": 589, + "105": 538, + "106": 416, + "107": 359, + "108": 335, + "109": 309, + "110": 310, + "111": 280, + "112": 269, + "113": 279, + "114": 233, + "115": 198, + "116": 208, + "117": 211, + "118": 166, + "119": 128, + "120": 127, + "121": 119, + "122": 145, + "123": 159, + "124": 130, + "125": 115, + "126": 124, + "127": 132, + "128": 130, + "129": 114, + "130": 136, + "131": 113, + "132": 119, + "133": 92, + "134": 109, + "135": 94, + "136": 112, + "137": 108, + "138": 107, + "139": 114, + "140": 102, + "141": 63, + "142": 43, + "143": 46, + "144": 34, + "145": 17, + "146": 17, + "147": 4, + "148": 4 + }, + "64": { + "0": 1645580394, + "1": 332121950, + "2": 143857376, + "3": 82342244, + "4": 54254902, + "5": 38847202, + "6": 29417465, + "7": 23205846, + "8": 18694855, + "9": 15141642, + "10": 12371576, + "11": 10229329, + "12": 8647553, + "13": 7325344, + "14": 6295327, + "15": 5516930, + "16": 4865082, + "17": 4309391, + "18": 3842162, + "19": 3406684, + "20": 3033028, + "21": 2735522, + "22": 2473336, + "23": 2242708, + "24": 2042061, + "25": 1862630, + "26": 1687997, + "27": 1529651, + "28": 1377678, + "29": 1246699, + "30": 1127615, + "31": 1021519, + "32": 919786, + "33": 835229, + "34": 758589, + "35": 694245, + "36": 637642, + "37": 589662, + "38": 547952, + "39": 507110, + "40": 467377, + "41": 431426, + "42": 399251, + "43": 369645, + "44": 345626, + "45": 320928, + "46": 300584, + "47": 279405, + "48": 261128, + "49": 245246, + "50": 230330, + "51": 216329, + "52": 202315, + "53": 188342, + "54": 175479, + "55": 164216, + "56": 151015, + "57": 138762, + "58": 128074, + "59": 118213, + "60": 109407, + "61": 100053, + "62": 91903, + "63": 83292, + "64": 75832, + "65": 68006, + "66": 61400, + "67": 55742, + "68": 51271, + "69": 47305, + "70": 43974, + "71": 39955, + "72": 36911, + "73": 34035, + "74": 30928, + "75": 28558, + "76": 26104, + "77": 24211, + "78": 22590, + "79": 20897, + "80": 19153, + "81": 17657, + "82": 16849, + "83": 15301, + "84": 14235, + "85": 13049, + "86": 11929, + "87": 10779, + "88": 9912, + "89": 9146, + "90": 8247, + "91": 7534, + "92": 7104, + "93": 6609, + "94": 6159, + "95": 5758, + "96": 5510, + "97": 5528, + "98": 5293, + "99": 4973, + "100": 4606, + "101": 4275, + "102": 4271, + "103": 4037, + "104": 3971, + "105": 3787, + "106": 3970, + "107": 3630, + "108": 3605, + "109": 3351, + "110": 3229, + "111": 2970, + "112": 2963, + "113": 3005, + "114": 2790, + "115": 2728, + "116": 2547, + "117": 2315, + "118": 2133, + "119": 1910, + "120": 1701, + "121": 1579, + "122": 1382, + "123": 1253, + "124": 1198, + "125": 1048, + "126": 901, + "127": 847, + "128": 761, + "129": 656, + "130": 559, + "131": 543, + "132": 509, + "133": 497, + "134": 357, + "135": 353, + "136": 321, + "137": 252, + "138": 262, + "139": 215, + "140": 175, + "141": 188, + "142": 141, + "143": 138, + "144": 124, + "145": 141, + "146": 146, + "147": 147, + "148": 149, + "149": 156, + "150": 150, + "151": 122, + "152": 122, + "153": 118, + "154": 115, + "155": 142, + "156": 127, + "157": 105, + "158": 108, + "159": 96, + "160": 111, + "161": 106, + "162": 106, + "163": 99, + "164": 112, + "165": 108, + "166": 97, + "167": 106, + "168": 102, + "169": 109, + "170": 81, + "171": 118, + "172": 79, + "173": 67, + "174": 86, + "175": 34, + "176": 44, + "177": 22, + "178": 20, + "179": 20, + "180": 22, + "181": 6, + "182": 18, + "183": 12, + "184": 4, + "185": 6, + "186": 2, + "187": 2, + "188": 2 + } +} \ No newline at end of file diff --git a/counts/qnrf_max.json b/counts/qnrf_max.json new file mode 100644 index 0000000000000000000000000000000000000000..734f1c6ea609c59803470354f3d5fe050e06740a --- /dev/null +++ b/counts/qnrf_max.json @@ -0,0 +1,74 @@ +{ + "4": { + "max": 5, + "name": [ + "0862.jpg" + ], + "x": 525, + "y": 892 + }, + "7": { + "max": 9, + "name": [ + "0215.jpg" + ], + "x": 339, + "y": 701 + }, + "8": { + "max": 11, + "name": [ + "0215.jpg" + ], + "x": 339, + "y": 701 + }, + "14": { + "max": 25, + "name": [ + "0215.jpg" + ], + "x": 332, + "y": 697 + }, + "16": { + "max": 29, + "name": [ + "0215.jpg" + ], + "x": 331, + "y": 697 + }, + "28": { + "max": 56, + "name": [ + "0330.jpg" + ], + "x": 336, + "y": 1063 + }, + "32": { + "max": 65, + "name": [ + "0931.jpg" + ], + "x": 730, + "y": 1077 + }, + "56": { + "max": 148, + "name": [ + "0931.jpg" + ], + "x": 725, + "y": 1084 + }, + "64": { + "max": 188, + "name": [ + "0931.jpg" + ], + "x": 702, + "y": 1078 + } +} \ No newline at end of file diff --git a/counts/sha.json b/counts/sha.json new file mode 100644 index 0000000000000000000000000000000000000000..d51c0d82907ac1622c5df26375f1157a3b654595 --- /dev/null +++ b/counts/sha.json @@ -0,0 +1,578 @@ +{ + "1": { + "0": 221398495, + "1": 162337 + }, + "4": { + "0": 217404440, + "1": 2560651, + "2": 15919, + "3": 525, + "4": 13 + }, + "7": { + "0": 210823747, + "1": 7296555, + "2": 246197, + "3": 33928, + "4": 5933, + "5": 1100, + "6": 185, + "7": 17, + "8": 2 + }, + "8": { + "0": 208176274, + "1": 9201723, + "2": 414603, + "3": 70943, + "4": 15652, + "5": 3870, + "6": 919, + "7": 204, + "8": 33, + "9": 13, + "10": 2 + }, + "14": { + "0": 189475328, + "1": 21483041, + "2": 2585228, + "3": 687491, + "4": 268045, + "5": 123917, + "6": 62564, + "7": 32805, + "8": 17268, + "9": 9346, + "10": 5150, + "11": 2866, + "12": 1541, + "13": 822, + "14": 463, + "15": 198, + "16": 99, + "17": 48, + "18": 30, + "19": 16, + "20": 2 + }, + "16": { + "0": 182715184, + "1": 25283086, + "2": 3723263, + "3": 1075878, + "4": 428688, + "5": 212379, + "6": 116019, + "7": 66336, + "8": 38808, + "9": 22851, + "10": 13845, + "11": 8410, + "12": 5248, + "13": 3355, + "14": 1997, + "15": 1309, + "16": 818, + "17": 451, + "18": 238, + "19": 113, + "20": 65, + "21": 31, + "22": 16, + "23": 8, + "24": 8, + "25": 4, + "26": 2, + "27": 1, + "28": 1 + }, + "28": { + "0": 143735006, + "1": 40200526, + "2": 11837381, + "3": 4979488, + "4": 2499799, + "5": 1387540, + "6": 843561, + "7": 542333, + "8": 361946, + "9": 254697, + "10": 190403, + "11": 145704, + "12": 112559, + "13": 88972, + "14": 69603, + "15": 55162, + "16": 44351, + "17": 36430, + "18": 29187, + "19": 23408, + "20": 18831, + "21": 14678, + "22": 11890, + "23": 9916, + "24": 8375, + "25": 6759, + "26": 5676, + "27": 4713, + "28": 3932, + "29": 3328, + "30": 2705, + "31": 2351, + "32": 1976, + "33": 1691, + "34": 1450, + "35": 1133, + "36": 949, + "37": 790, + "38": 657, + "39": 489, + "40": 324, + "41": 244, + "42": 170, + "43": 144, + "44": 122, + "45": 85, + "46": 63, + "47": 48, + "48": 44, + "49": 38, + "50": 16, + "51": 13, + "52": 14, + "53": 4, + "54": 1, + "55": 1 + }, + "32": { + "0": 132333123, + "1": 42343779, + "2": 14212292, + "3": 6506905, + "4": 3472682, + "5": 2008349, + "6": 1259746, + "7": 835061, + "8": 580977, + "9": 411224, + "10": 298598, + "11": 226760, + "12": 177966, + "13": 142932, + "14": 116283, + "15": 95533, + "16": 78053, + "17": 64149, + "18": 52398, + "19": 43187, + "20": 36642, + "21": 31022, + "22": 26409, + "23": 22474, + "24": 19080, + "25": 15785, + "26": 12983, + "27": 10905, + "28": 9540, + "29": 8242, + "30": 7113, + "31": 5838, + "32": 4817, + "33": 4147, + "34": 3635, + "35": 3160, + "36": 2800, + "37": 2258, + "38": 2086, + "39": 1884, + "40": 1789, + "41": 1749, + "42": 1451, + "43": 1284, + "44": 1097, + "45": 849, + "46": 631, + "47": 498, + "48": 324, + "49": 294, + "50": 208, + "51": 157, + "52": 136, + "53": 119, + "54": 80, + "55": 73, + "56": 68, + "57": 58, + "58": 61, + "59": 69, + "60": 49, + "61": 40, + "62": 24, + "63": 15, + "64": 17, + "65": 5, + "66": 2 + }, + "56": { + "0": 82311154, + "1": 39190874, + "2": 22002574, + "3": 12962751, + "4": 8404996, + "5": 5832072, + "6": 4289203, + "7": 3257294, + "8": 2542514, + "9": 1997552, + "10": 1600093, + "11": 1286154, + "12": 1053852, + "13": 868200, + "14": 718305, + "15": 598864, + "16": 498449, + "17": 418687, + "18": 358697, + "19": 312381, + "20": 276011, + "21": 241729, + "22": 215353, + "23": 195921, + "24": 175559, + "25": 159251, + "26": 141084, + "27": 128022, + "28": 114886, + "29": 104495, + "30": 95802, + "31": 87751, + "32": 79668, + "33": 72856, + "34": 67187, + "35": 60598, + "36": 56041, + "37": 49833, + "38": 45739, + "39": 42100, + "40": 38922, + "41": 35683, + "42": 33222, + "43": 31037, + "44": 27306, + "45": 24412, + "46": 21939, + "47": 20087, + "48": 18312, + "49": 17285, + "50": 16026, + "51": 14905, + "52": 14599, + "53": 13990, + "54": 13420, + "55": 12785, + "56": 11938, + "57": 11445, + "58": 11094, + "59": 10387, + "60": 9826, + "61": 9605, + "62": 9270, + "63": 8533, + "64": 8157, + "65": 7849, + "66": 7121, + "67": 6586, + "68": 6083, + "69": 5424, + "70": 4978, + "71": 4867, + "72": 4364, + "73": 3995, + "74": 3771, + "75": 3567, + "76": 3107, + "77": 2871, + "78": 2630, + "79": 2162, + "80": 2096, + "81": 1907, + "82": 1872, + "83": 1792, + "84": 1838, + "85": 1703, + "86": 1629, + "87": 1545, + "88": 1388, + "89": 1298, + "90": 1310, + "91": 1258, + "92": 1175, + "93": 1174, + "94": 1013, + "95": 976, + "96": 856, + "97": 784, + "98": 711, + "99": 692, + "100": 697, + "101": 622, + "102": 639, + "103": 544, + "104": 531, + "105": 476, + "106": 481, + "107": 450, + "108": 443, + "109": 439, + "110": 443, + "111": 358, + "112": 337, + "113": 293, + "114": 264, + "115": 223, + "116": 177, + "117": 140, + "118": 143, + "119": 124, + "120": 118, + "121": 104, + "122": 100, + "123": 96, + "124": 94, + "125": 92, + "126": 69, + "127": 89, + "128": 91, + "129": 85, + "130": 70, + "131": 66, + "132": 51, + "133": 54, + "134": 77, + "135": 60, + "136": 69, + "137": 62, + "138": 75, + "139": 83, + "140": 84, + "141": 77, + "142": 63, + "143": 51, + "144": 51, + "145": 68, + "146": 44, + "147": 45, + "148": 35, + "149": 38, + "150": 39, + "151": 39, + "152": 22, + "153": 12, + "154": 19, + "155": 24, + "156": 15, + "157": 4, + "158": 3, + "159": 1 + }, + "64": { + "0": 71204848, + "1": 35431716, + "2": 22345768, + "3": 14110543, + "4": 9458039, + "5": 6781297, + "6": 5068480, + "7": 3922313, + "8": 3115679, + "9": 2546969, + "10": 2092914, + "11": 1728554, + "12": 1445669, + "13": 1226006, + "14": 1027888, + "15": 880413, + "16": 758676, + "17": 651263, + "18": 560175, + "19": 481484, + "20": 415366, + "21": 360995, + "22": 319926, + "23": 281587, + "24": 249589, + "25": 222763, + "26": 201505, + "27": 186993, + "28": 172894, + "29": 160066, + "30": 148490, + "31": 135929, + "32": 125730, + "33": 116554, + "34": 109632, + "35": 101625, + "36": 93920, + "37": 86856, + "38": 80031, + "39": 73701, + "40": 68720, + "41": 62813, + "42": 58001, + "43": 53537, + "44": 49124, + "45": 45340, + "46": 42598, + "47": 39746, + "48": 37319, + "49": 35173, + "50": 32861, + "51": 29710, + "52": 27037, + "53": 24220, + "54": 22338, + "55": 20642, + "56": 19097, + "57": 17737, + "58": 16334, + "59": 16276, + "60": 15705, + "61": 14837, + "62": 13992, + "63": 13180, + "64": 12950, + "65": 12540, + "66": 12527, + "67": 12219, + "68": 11564, + "69": 10978, + "70": 10465, + "71": 9857, + "72": 9330, + "73": 9088, + "74": 8851, + "75": 8715, + "76": 8399, + "77": 7778, + "78": 7275, + "79": 6728, + "80": 6557, + "81": 6062, + "82": 5907, + "83": 5520, + "84": 5272, + "85": 4972, + "86": 4439, + "87": 3988, + "88": 3607, + "89": 3342, + "90": 3260, + "91": 3148, + "92": 2978, + "93": 3015, + "94": 2783, + "95": 2642, + "96": 2436, + "97": 2283, + "98": 2134, + "99": 2055, + "100": 1914, + "101": 1877, + "102": 1641, + "103": 1643, + "104": 1537, + "105": 1521, + "106": 1459, + "107": 1329, + "108": 1227, + "109": 1124, + "110": 1085, + "111": 1003, + "112": 967, + "113": 837, + "114": 748, + "115": 695, + "116": 680, + "117": 662, + "118": 590, + "119": 584, + "120": 596, + "121": 630, + "122": 608, + "123": 567, + "124": 549, + "125": 535, + "126": 485, + "127": 432, + "128": 387, + "129": 379, + "130": 390, + "131": 364, + "132": 288, + "133": 321, + "134": 302, + "135": 280, + "136": 268, + "137": 287, + "138": 270, + "139": 262, + "140": 222, + "141": 196, + "142": 170, + "143": 136, + "144": 155, + "145": 122, + "146": 115, + "147": 114, + "148": 96, + "149": 98, + "150": 83, + "151": 94, + "152": 94, + "153": 84, + "154": 77, + "155": 88, + "156": 70, + "157": 66, + "158": 60, + "159": 78, + "160": 59, + "161": 57, + "162": 63, + "163": 74, + "164": 63, + "165": 52, + "166": 65, + "167": 50, + "168": 76, + "169": 63, + "170": 63, + "171": 67, + "172": 62, + "173": 47, + "174": 51, + "175": 38, + "176": 42, + "177": 44, + "178": 44, + "179": 39, + "180": 45, + "181": 42, + "182": 31, + "183": 27, + "184": 39, + "185": 21, + "186": 28, + "187": 23, + "188": 36, + "189": 24, + "190": 11, + "191": 11, + "192": 11, + "193": 6, + "194": 5, + "195": 1 + } +} \ No newline at end of file diff --git a/counts/sha_max.json b/counts/sha_max.json new file mode 100644 index 0000000000000000000000000000000000000000..4c14393089aa6821534dfcf544005871116f03de --- /dev/null +++ b/counts/sha_max.json @@ -0,0 +1,74 @@ +{ + "4": { + "max": 4, + "name": [ + "007.jpg" + ], + "x": 324, + "y": 176 + }, + "7": { + "max": 8, + "name": [ + "034.jpg" + ], + "x": 271, + "y": 341 + }, + "8": { + "max": 10, + "name": [ + "034.jpg" + ], + "x": 271, + "y": 340 + }, + "14": { + "max": 20, + "name": [ + "120.jpg" + ], + "x": 295, + "y": 762 + }, + "16": { + "max": 28, + "name": [ + "120.jpg" + ], + "x": 296, + "y": 760 + }, + "28": { + "max": 55, + "name": [ + "120.jpg" + ], + "x": 303, + "y": 652 + }, + "32": { + "max": 66, + "name": [ + "120.jpg" + ], + "x": 313, + "y": 651 + }, + "56": { + "max": 159, + "name": [ + "120.jpg" + ], + "x": 301, + "y": 655 + }, + "64": { + "max": 195, + "name": [ + "120.jpg" + ], + "x": 301, + "y": 657 + } +} \ No newline at end of file diff --git a/counts/shb.json b/counts/shb.json new file mode 100644 index 0000000000000000000000000000000000000000..9c4c3377474c967f53740908d655f000dd97f283 --- /dev/null +++ b/counts/shb.json @@ -0,0 +1,313 @@ +{ + "1": { + "0": 314523695, + "1": 49105 + }, + "4": { + "0": 311650011, + "1": 772635, + "2": 3256, + "3": 95, + "4": 3 + }, + "7": { + "0": 308004073, + "1": 2221020, + "2": 55546, + "3": 4833, + "4": 681, + "5": 181, + "6": 53, + "7": 10, + "8": 3 + }, + "8": { + "0": 306646552, + "1": 2818806, + "2": 96449, + "3": 10596, + "4": 1733, + "5": 447, + "6": 138, + "7": 57, + "8": 22 + }, + "14": { + "0": 297434203, + "1": 7041868, + "2": 636791, + "3": 139674, + "4": 42083, + "5": 15292, + "6": 6324, + "7": 3122, + "8": 1298, + "9": 595, + "10": 304, + "11": 225, + "12": 169, + "13": 45, + "14": 7 + }, + "16": { + "0": 294072360, + "1": 8559657, + "2": 922301, + "3": 225017, + "4": 75708, + "5": 29428, + "6": 12533, + "7": 6347, + "8": 3429, + "9": 1869, + "10": 913, + "11": 494, + "12": 338, + "13": 202, + "14": 192, + "15": 11, + "16": 1 + }, + "28": { + "0": 272510235, + "1": 17410504, + "2": 3291284, + "3": 1142143, + "4": 507297, + "5": 259215, + "6": 143543, + "7": 86057, + "8": 52776, + "9": 33818, + "10": 22305, + "11": 14778, + "12": 9902, + "13": 6909, + "14": 4829, + "15": 3511, + "16": 2765, + "17": 2161, + "18": 1627, + "19": 1396, + "20": 1075, + "21": 796, + "22": 639, + "23": 520, + "24": 375, + "25": 205, + "26": 92, + "27": 27, + "28": 10, + "29": 4, + "30": 2 + }, + "32": { + "0": 265135522, + "1": 20054326, + "2": 4219708, + "3": 1561515, + "4": 730071, + "5": 382477, + "6": 224559, + "7": 137037, + "8": 88156, + "9": 58687, + "10": 40153, + "11": 27989, + "12": 19367, + "13": 13555, + "14": 10126, + "15": 7417, + "16": 5593, + "17": 4242, + "18": 3235, + "19": 2714, + "20": 2136, + "21": 1687, + "22": 1343, + "23": 1093, + "24": 990, + "25": 881, + "26": 651, + "27": 428, + "28": 278, + "29": 173, + "30": 116, + "31": 83, + "32": 43, + "33": 36, + "34": 8, + "35": 3, + "36": 2 + }, + "56": { + "0": 222314024, + "1": 32191189, + "2": 9727123, + "3": 4342794, + "4": 2404979, + "5": 1505427, + "6": 1000917, + "7": 701563, + "8": 499165, + "9": 362489, + "10": 267104, + "11": 199980, + "12": 153876, + "13": 123592, + "14": 98575, + "15": 80346, + "16": 63904, + "17": 48447, + "18": 40380, + "19": 33358, + "20": 28391, + "21": 24691, + "22": 21645, + "23": 17519, + "24": 14226, + "25": 11839, + "26": 10556, + "27": 8884, + "28": 7573, + "29": 6473, + "30": 5818, + "31": 4784, + "32": 4100, + "33": 4039, + "34": 3497, + "35": 2721, + "36": 2238, + "37": 2208, + "38": 2072, + "39": 2096, + "40": 1750, + "41": 1466, + "42": 1404, + "43": 1196, + "44": 1138, + "45": 918, + "46": 786, + "47": 672, + "48": 698, + "49": 688, + "50": 610, + "51": 537, + "52": 469, + "53": 448, + "54": 346, + "55": 264, + "56": 198, + "57": 168, + "58": 131, + "59": 54, + "60": 28, + "61": 34, + "62": 22, + "63": 10, + "64": 18, + "65": 17, + "66": 16, + "67": 25, + "68": 21, + "69": 25, + "70": 11, + "71": 13, + "72": 7, + "73": 2, + "74": 4, + "76": 4 + }, + "64": { + "0": 209048823, + "1": 34905056, + "2": 11413735, + "3": 5278103, + "4": 2980067, + "5": 1886714, + "6": 1308620, + "7": 945805, + "8": 684080, + "9": 516549, + "10": 387772, + "11": 301510, + "12": 234031, + "13": 186750, + "14": 149049, + "15": 124290, + "16": 101853, + "17": 81550, + "18": 68680, + "19": 55441, + "20": 45411, + "21": 39050, + "22": 33804, + "23": 30803, + "24": 24284, + "25": 20547, + "26": 17358, + "27": 14546, + "28": 12847, + "29": 11443, + "30": 9852, + "31": 8715, + "32": 7569, + "33": 6927, + "34": 6284, + "35": 5688, + "36": 4647, + "37": 4476, + "38": 3947, + "39": 3756, + "40": 3232, + "41": 2883, + "42": 2580, + "43": 2338, + "44": 2092, + "45": 1930, + "46": 1670, + "47": 1514, + "48": 1470, + "49": 1361, + "50": 1267, + "51": 1218, + "52": 939, + "53": 852, + "54": 738, + "55": 662, + "56": 628, + "57": 690, + "58": 495, + "59": 508, + "60": 441, + "61": 401, + "62": 333, + "63": 314, + "64": 194, + "65": 130, + "66": 108, + "67": 108, + "68": 91, + "69": 72, + "70": 32, + "71": 29, + "72": 32, + "73": 20, + "74": 17, + "75": 11, + "76": 21, + "77": 15, + "78": 17, + "79": 21, + "80": 20, + "81": 13, + "82": 17, + "83": 9, + "84": 8, + "85": 5, + "86": 10, + "87": 3, + "88": 5, + "89": 4 + } +} \ No newline at end of file diff --git a/counts/shb_max.json b/counts/shb_max.json new file mode 100644 index 0000000000000000000000000000000000000000..422afc75fd60a58e444860fc0f4fee9a5db3e8c6 --- /dev/null +++ b/counts/shb_max.json @@ -0,0 +1,74 @@ +{ + "4": { + "max": 4, + "name": [ + "148.jpg" + ], + "x": 40, + "y": 549 + }, + "7": { + "max": 8, + "name": [ + "200.jpg" + ], + "x": 275, + "y": 37 + }, + "8": { + "max": 8, + "name": [ + "148.jpg" + ], + "x": 39, + "y": 550 + }, + "14": { + "max": 14, + "name": [ + "200.jpg" + ], + "x": 269, + "y": 37 + }, + "16": { + "max": 16, + "name": [ + "191.jpg" + ], + "x": 1, + "y": 257 + }, + "28": { + "max": 30, + "name": [ + "191.jpg" + ], + "x": 0, + "y": 257 + }, + "32": { + "max": 36, + "name": [ + "191.jpg" + ], + "x": 0, + "y": 256 + }, + "56": { + "max": 76, + "name": [ + "191.jpg" + ], + "x": 0, + "y": 256 + }, + "64": { + "max": 89, + "name": [ + "191.jpg" + ], + "x": 1, + "y": 254 + } +} \ No newline at end of file diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2b11095a160e95ed05aeb3fd975edcf9632fa770 --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1,12 @@ +from .crowd import Crowd, InMemoryCrowd, available_datasets, standardize_dataset_name, NWPUTest, ShanghaiTech +from .transforms import RandomCrop, Resize, RandomResizedCrop, RandomHorizontalFlip, Resize2Multiple, ZeroPad2Multiple +from .transforms import ColorJitter, RandomGrayscale, GaussianBlur, RandomApply, PepperSaltNoise +from .utils import collate_fn + + +__all__ = [ + "Crowd", "InMemoryCrowd", "available_datasets", "standardize_dataset_name", "NWPUTest", "ShanghaiTech", + "RandomCrop", "Resize", "RandomResizedCrop", "RandomHorizontalFlip", "Resize2Multiple", "ZeroPad2Multiple", + "ColorJitter", "RandomGrayscale", "GaussianBlur", "RandomApply", "PepperSaltNoise", + "collate_fn", +] diff --git a/datasets/crowd.py b/datasets/crowd.py new file mode 100644 index 0000000000000000000000000000000000000000..8c3aba2809b5d3b12cf7d0f9f91c86d1ea1c810f --- /dev/null +++ b/datasets/crowd.py @@ -0,0 +1,309 @@ +import torch +from torch import Tensor +from torch.utils.data import Dataset +from torchvision.transforms import ToTensor, Normalize, Compose +import os +from glob import glob +from tqdm import tqdm +# from PIL import Image +from turbojpeg import TurboJPEG, TJPF_RGB +jpeg_decoder = TurboJPEG() + +import numpy as np +from typing import Optional, Callable, Union, Tuple + +from .utils import get_id, generate_density_map + +curr_dir = os.path.dirname(os.path.abspath(__file__)) + +available_datasets = [ + "shanghaitech_a", "sha", + "shanghaitech_b", "shb", + "shanghaitech", "sh", + "ucf_qnrf", "qnrf", "ucf-qnrf", + "nwpu", "nwpu_crowd", "nwpu-crowd", +] + +mean = (0.48145466, 0.4578275, 0.40821073) +std = (0.26862954, 0.26130258, 0.27577711) + + +def standardize_dataset_name(dataset: str) -> str: + assert dataset.lower() in available_datasets, f"Dataset {dataset} is not available." + if dataset.lower() in ["shanghaitech_a", "sha"]: + return "sha" + elif dataset.lower() in ["shanghaitech_b", "shb"]: + return "shb" + elif dataset.lower() in ["shanghaitech", "sh"]: + return "sh" + elif dataset.lower() in ["ucf_qnrf", "qnrf", "ucf-qnrf"]: + return "qnrf" + else: + assert dataset.lower() in ["nwpu", "nwpu_crowd", "nwpu-crowd"], f"Dataset {dataset} is not available." + return "nwpu" + + +class Crowd(Dataset): + def __init__( + self, + dataset: str, + split: str, + transforms: Optional[Callable] = None, + sigma: Optional[float] = None, + return_filename: bool = False, + num_crops: int = 1, + ) -> None: + """ + Dataset for crowd counting. + """ + assert dataset.lower() in available_datasets, f"Dataset {dataset} is not available." + assert dataset.lower() not in ["shanghaitech", "sh"], "For the combined ShanghaiTech dataset, use ShanghaiTech class." + assert split in ["train", "val", "test"], f"Split {split} is not available." + assert num_crops > 0, f"num_crops should be positive, got {num_crops}." + + self.dataset = standardize_dataset_name(dataset) + self.split = split + + self.__find_root__() + self.__make_dataset__() + self.__check_sanity__() + + self.to_tensor = ToTensor() + self.normalize = Normalize(mean=mean, std=std) + self.transforms = transforms + + self.sigma = sigma + self.return_filename = return_filename + self.num_crops = num_crops + + def __find_root__(self) -> None: + self.root = os.path.join(curr_dir, "..", "data", self.dataset) + + def __make_dataset__(self) -> None: + image_names = glob(os.path.join(self.root, self.split, "images", "*.jpg")) + + label_names = glob(os.path.join(self.root, self.split, "labels", "*.npy")) + image_names = [os.path.basename(image_name) for image_name in image_names] + label_names = [os.path.basename(label_name) for label_name in label_names] + image_names.sort(key=get_id) + label_names.sort(key=get_id) + image_ids = tuple([get_id(image_name) for image_name in image_names]) + label_ids = tuple([get_id(label_name) for label_name in label_names]) + assert image_ids == label_ids, "image_ids and label_ids do not match." + self.image_names = tuple(image_names) + self.label_names = tuple(label_names) + + def __check_sanity__(self) -> None: + if self.dataset == "sha": + if self.split == "train": + assert len(self.image_names) == len(self.label_names) == 300, f"ShanghaiTech_A train split should have 300 images, but found {len(self.image_names)}." + else: + assert self.split == "val", f"Split {self.split} is not available for dataset {self.dataset}." + assert len(self.image_names) == len(self.label_names) == 182, f"ShanghaiTech_A val split should have 182 images, but found {len(self.image_names)}." + elif self.dataset == "shb": + if self.split == "train": + assert len(self.image_names) == len(self.label_names) == 399, f"ShanghaiTech_B train split should have 399 images, but found {len(self.image_names)}." + else: + assert self.split == "val", f"Split {self.split} is not available for dataset {self.dataset}." + assert len(self.image_names) == len(self.label_names) == 316, f"ShanghaiTech_B val split should have 316 images, but found {len(self.image_names)}." + elif self.dataset == "nwpu": + if self.split == "train": + assert len(self.image_names) == len(self.label_names) == 3109, f"NWPU train split should have 3109 images, but found {len(self.image_names)}." + else: + assert self.split == "val", f"Split {self.split} is not available for dataset {self.dataset}." + assert len(self.image_names) == len(self.label_names) == 500, f"NWPU val split should have 500 images, but found {len(self.image_names)}." + elif self.dataset == "qnrf": + if self.split == "train": + assert len(self.image_names) == len(self.label_names) == 1201, f"UCF_QNRF train split should have 1201 images, but found {len(self.image_names)}." + else: + assert self.split == "val", f"Split {self.split} is not available for dataset {self.dataset}." + assert len(self.image_names) == len(self.label_names) == 334, f"UCF_QNRF val split should have 334 images, but found {len(self.image_names)}." + + def __len__(self) -> int: + return len(self.image_names) + + def __getitem__(self, idx: int) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, Tensor, Tensor, str]]: + image_name = self.image_names[idx] + label_name = self.label_names[idx] + + image_path = os.path.join(self.root, self.split, "images", image_name) + label_path = os.path.join(self.root, self.split, "labels", label_name) + + with open(image_path, "rb") as f: + # image = Image.open(f).convert("RGB") + image = jpeg_decoder.decode(f.read(), pixel_format=TJPF_RGB) + image = self.to_tensor(image) + + with open(label_path, "rb") as f: + label = np.load(f) + label = torch.from_numpy(label).float() + + if self.transforms is not None: + images_labels = [self.transforms(image.clone(), label.clone()) for _ in range(self.num_crops)] + images, labels = zip(*images_labels) + else: + images = [image.clone() for _ in range(self.num_crops)] + labels = [label.clone() for _ in range(self.num_crops)] + + images = [self.normalize(img) for img in images] + density_maps = torch.stack([generate_density_map(label, image.shape[-2], image.shape[-1], sigma=self.sigma) for image, label in zip(images, labels)], 0) + image_names = [image_name] * len(images) + images = torch.stack(images, 0) + + if self.return_filename: + return images, labels, density_maps, image_names + else: + return images, labels, density_maps + + +class InMemoryCrowd(Dataset): + def __init__( + self, + dataset: str, + split: str, + transforms: Optional[Callable] = None, + sigma: Optional[float] = None, + return_filename: bool = False, + num_crops: int = 1, + ) -> None: + """ + Dataset for crowd counting, with images and labels loaded into memory. + """ + crowd = Crowd( + dataset=dataset, + split=split, + transforms=None, + sigma=sigma, + return_filename=True, + num_crops=1, + ) + print(f"Loading {len(crowd)} samples from {dataset} {split} split into memory...") + self.images, self.labels, self.image_names = [], [], [] + self.unnormalize = Compose([ + Normalize(mean=(0., 0., 0.), std=(1./std[0], 1./std[1], 1./std[2]), inplace=True), + Normalize(mean=(-mean[0], -mean[1], -mean[2]), std=(1., 1., 1.), inplace=True) + ]) + + for i in tqdm(range(len(crowd)), desc="Loading images and labels into memory"): + image, label, _, image_name = crowd[i] + self.images.append(self.unnormalize(image[0])) # recover original image + self.labels.append(label[0]) + self.image_names.append(image_name[0]) + + assert len(self.images) == len(self.labels) == len(self.image_names), "Mismatch in number of images, labels, and image names." + + self.transforms = transforms + self.sigma = sigma + self.num_crops = num_crops + self.return_filename = return_filename + self.normalize = Normalize(mean=mean, std=std, inplace=False) + + def __len__(self) -> int: + return len(self.images) + + def __getitem__(self, idx: int) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, Tensor, Tensor, str]]: + image, label, image_name = self.images[idx].clone(), self.labels[idx].clone(), self.image_names[idx] + + if self.transforms is not None: + images_labels = [self.transforms(image.clone(), label.clone()) for _ in range(self.num_crops)] + images, labels = zip(*images_labels) + else: + images = [image.clone() for _ in range(self.num_crops)] + labels = [label.clone() for _ in range(self.num_crops)] + + images = [self.normalize(img) for img in images] + density_maps = torch.stack([generate_density_map(label, image.shape[-2], image.shape[-1], sigma=self.sigma) for image, label in zip(images, labels)], 0) + image_names = [image_name] * len(images) + images = torch.stack(images, 0) + + if self.return_filename: + return images, labels, density_maps, image_names + else: + return images, labels, density_maps + + +class NWPUTest(Dataset): + def __init__( + self, + transforms: Optional[Callable] = None, + return_filename: bool = False, + ) -> None: + """ + The test set of NWPU-Crowd dataset. The test set is not labeled, so only images are returned. + """ + self.root = os.path.join(curr_dir, "..", "data", "nwpu") + image_names = glob(os.path.join(self.root, "test", "images", "*.jpg")) + + image_names = [os.path.basename(image_name) for image_name in image_names] + assert len(image_names) == 1500, f"NWPU test split should have 1500 images, but found {len(image_names)}." + image_names.sort(key=get_id) + self.image_names = tuple(image_names) + + self.to_tensor = ToTensor() + self.normalize = Normalize(mean=mean, std=std) + self.transforms = transforms + self.return_filename = return_filename + + def __len__(self) -> int: + return len(self.image_names) + + def __getitem__(self, idx: int) -> Union[Tensor, Tuple[Tensor, str]]: + image_name = self.image_names[idx] + image_path = os.path.join(self.root, "test", "images", image_name) + + with open(image_path, "rb") as f: + # image = Image.open(f).convert("RGB") + image = jpeg_decoder.decode(f.read(), pixel_format=TJPF_RGB) + image = self.to_tensor(image) + + label = torch.tensor([], dtype=torch.float) # dummy label + image, _ = self.transforms(image, label) if self.transforms is not None else (image, label) + image = self.normalize(image) + + if self.return_filename: + return image, image_name + else: + return image + + +class ShanghaiTech(Dataset): + def __init__( + self, + split: str, + transforms: Optional[Callable] = None, + sigma: Optional[float] = None, + return_filename: bool = False, + num_crops: int = 1, + ) -> None: + super().__init__() + self.sha = Crowd( + dataset="sha", + split=split, + transforms=transforms, + sigma=sigma, + return_filename=return_filename, + num_crops=num_crops, + ) + self.shb = Crowd( + dataset="shb", + split=split, + transforms=transforms, + sigma=sigma, + return_filename=return_filename, + num_crops=num_crops, + ) + self.dataset = "sh" + self.split = split + self.transforms = transforms + self.sigma = sigma + self.return_filename = return_filename + self.num_crops = num_crops + + def __len__(self) -> int: + return len(self.sha) + len(self.shb) + + def __getitem__(self, idx: int) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, Tensor, Tensor, str]]: + if idx < len(self.sha): + return self.sha[idx] + else: + return self.shb[idx - len(self.sha)] diff --git a/datasets/transforms.py b/datasets/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..474aab3bc9a88a696448521364f146c25fad9f29 --- /dev/null +++ b/datasets/transforms.py @@ -0,0 +1,262 @@ +import torch +from torch import Tensor +from torchvision.transforms import ColorJitter as _ColorJitter +import torchvision.transforms.functional as TF +import numpy as np +from typing import Tuple, Union, Optional, Callable + + +def _crop( + image: Tensor, + label: Tensor, + top: int, + left: int, + height: int, + width: int, +) -> Tuple[Tensor, Tensor]: + image = TF.crop(image, top, left, height, width) + if len(label) > 0: + label[:, 0] -= left + label[:, 1] -= top + label_mask = (label[:, 0] >= 0) & (label[:, 0] < width) & (label[:, 1] >= 0) & (label[:, 1] < height) + label = label[label_mask] + + return image, label + + +def _resize( + image: Tensor, + label: Tensor, + height: int, + width: int, +) -> Tuple[Tensor, Tensor]: + image_height, image_width = image.shape[-2:] + image = TF.resize(image, (height, width), interpolation=TF.InterpolationMode.BICUBIC, antialias=True) if (image_height != height or image_width != width) else image + if len(label) > 0 and (image_height != height or image_width != width): + label[:, 0] = label[:, 0] * width / image_width + label[:, 1] = label[:, 1] * height / image_height + label[:, 0] = label[:, 0].clamp(min=0, max=width - 1) + label[:, 1] = label[:, 1].clamp(min=0, max=height - 1) + + return image, label + + +class RandomCrop(object): + def __init__(self, size: Tuple[int, int]) -> None: + self.size = size + assert len(self.size) == 2, f"size should be a tuple (h, w), got {self.size}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + crop_height, crop_width = self.size + image_height, image_width = image.shape[-2:] + assert crop_height <= image_height and crop_width <= image_width, \ + f"crop size should be no larger than image size, got crop size {self.size} and image size {image.shape}." + + top = torch.randint(0, image_height - crop_height + 1, (1,)).item() + left = torch.randint(0, image_width - crop_width + 1, (1,)).item() + return _crop(image, label, top, left, crop_height, crop_width) + + +class Resize(object): + def __init__(self, size: Tuple[int, int]) -> None: + self.size = size + assert len(self.size) == 2, f"size should be a tuple (h, w), got {self.size}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + return _resize(image, label, self.size[0], self.size[1]) + + +class Resize2Multiple(object): + """ + Resize the image so that it satisfies: + img_h = window_h + stride_h * n_h + img_w = window_w + stride_w * n_w + """ + def __init__( + self, + window_size: Tuple[int, int], + stride: Tuple[int, int], + ) -> None: + window_size = (int(window_size), int(window_size)) if isinstance(window_size, (int, float)) else window_size + window_size = tuple(window_size) + stride = (int(stride), int(stride)) if isinstance(stride, (int, float)) else stride + stride = tuple(stride) + assert len(window_size) == 2, f"window_size should be a tuple (h, w), got {window_size}." + assert len(stride) == 2, f"stride should be a tuple (h, w), got {stride}." + assert all(s > 0 for s in window_size), f"window_size should be positive, got {window_size}." + assert all(s > 0 for s in stride), f"stride should be positive, got {stride}." + assert stride[0] <= window_size[0] and stride[1] <= window_size[1], f"stride should be no larger than window_size, got {stride} and {window_size}." + self.window_size = window_size + self.stride = stride + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + image_height, image_width = image.shape[-2:] + window_height, window_width = self.window_size + stride_height, stride_width = self.stride + new_height = int(max(round((image_height - window_height) / stride_height), 0) * stride_height + window_height) + new_width = int(max(round((image_width - window_width) / stride_width), 0) * stride_width + window_width) + + if new_height == image_height and new_width == image_width: + return image, label + else: + return _resize(image, label, new_height, new_width) + + +class ZeroPad2Multiple(object): + def __init__( + self, + window_size: Tuple[int, int], + stride: Tuple[int, int], + ) -> None: + window_size = (int(window_size), int(window_size)) if isinstance(window_size, (int, float)) else window_size + window_size = tuple(window_size) + stride = (int(stride), int(stride)) if isinstance(stride, (int, float)) else stride + stride = tuple(stride) + assert len(window_size) == 2, f"window_size should be a tuple (h, w), got {window_size}." + assert len(stride) == 2, f"stride should be a tuple (h, w), got {stride}." + assert all(s > 0 for s in window_size), f"window_size should be positive, got {window_size}." + assert all(s > 0 for s in stride), f"stride should be positive, got {stride}." + assert stride[0] <= window_size[0] and stride[1] <= window_size[1], f"stride should be no larger than window_size, got {stride} and {window_size}." + self.window_size = window_size + self.stride = stride + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + image_height, image_width = image.shape[-2:] + window_height, window_width = self.window_size + stride_height, stride_width = self.stride + new_height = int(max(np.ceil((image_height - window_height) / stride_height), 0) * stride_height + window_height) + new_width = int(max(np.ceil((image_width - window_width) / stride_width), 0) * stride_width + window_width) + + if new_height == image_height and new_width == image_width: + return image, label + else: + assert new_height >= image_height and new_width >= image_width, f"new size should be no less than the original size, got {new_height} and {new_width}." + pad_height, pad_width = new_height - image_height, new_width - image_width + return TF.pad(image, (0, 0, pad_width, pad_height), fill=0), label # only pad the right and bottom sides so that the label coordinates are not affected + + +class RandomResizedCrop(object): + def __init__( + self, + size: Tuple[int, int], + scale: Tuple[float, float] = (0.75, 1.25), + ) -> None: + """ + Randomly crop an image and resize it to a given size. The aspect ratio is preserved during this process. + """ + self.size = size + self.scale = scale + assert len(self.size) == 2, f"size should be a tuple (h, w), got {self.size}." + assert 0 < self.scale[0] <= self.scale[1], f"scale should satisfy 0 < scale[0] <= scale[1], got {self.scale}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + out_height, out_width = self.size + # out_ratio = out_width / out_height + + scale = torch.empty(1).uniform_(self.scale[0], self.scale[1]).item() # if scale < 1, then the image will be zoomed in, otherwise zoomed out + in_height, in_width = image.shape[-2:] + + # if in_width / in_height < out_ratio: # Image is too tall + # crop_width = int(in_width * scale) + # crop_height = int(crop_width / out_ratio) + # else: # Image is too wide + # crop_height = int(in_height * scale) + # crop_width = int(crop_height * out_ratio) + + crop_height, crop_width = int(out_height * scale), int(out_width * scale) + + if crop_height <= in_height and crop_width <= in_width: # directly crop and resize the image + top = torch.randint(0, in_height - crop_height + 1, (1,)).item() + left = torch.randint(0, in_width - crop_width + 1, (1,)).item() + + else: # resize the image and then crop + ratio = max(crop_height / in_height, crop_width / in_width) # keep the aspect ratio + resize_height, resize_width = int(in_height * ratio) + 1, int(in_width * ratio) + 1 # add 1 to make sure the resized image is no less than the crop size + image, label = _resize(image, label, resize_height, resize_width) + + top = torch.randint(0, resize_height - crop_height + 1, (1,)).item() + left = torch.randint(0, resize_width - crop_width + 1, (1,)).item() + + image, label = _crop(image, label, top, left, crop_height, crop_width) + return _resize(image, label, out_height, out_width) + + +class RandomHorizontalFlip(object): + def __init__(self, p: float = 0.5) -> None: + self.p = p + assert 0 <= self.p <= 1, f"p should be in range [0, 1], got {self.p}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + if torch.rand(1) < self.p: + image = TF.hflip(image) + + if len(label) > 0: + label[:, 0] = image.shape[-1] - 1 - label[:, 0] # if width is 256, then 0 -> 255, 1 -> 254, 2 -> 253, etc. + label[:, 0] = label[:, 0].clamp(min=0, max=image.shape[-1] - 1) + + return image, label + + +class ColorJitter(object): + def __init__( + self, + brightness: Union[float, Tuple[float, float]] = 0.4, + contrast: Union[float, Tuple[float, float]] = 0.4, + saturation: Union[float, Tuple[float, float]] = 0.4, + hue: Union[float, Tuple[float, float]] = 0.2, + ) -> None: + self.color_jitter = _ColorJitter(brightness=brightness, contrast=contrast, saturation=saturation, hue=hue) + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + return self.color_jitter(image), label + + +class RandomGrayscale(object): + def __init__(self, p: float = 0.1) -> None: + self.p = p + assert 0 <= self.p <= 1, f"p should be in range [0, 1], got {self.p}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + if torch.rand(1) < self.p: + image = TF.rgb_to_grayscale(image, num_output_channels=3) + + return image, label + + +class GaussianBlur(object): + def __init__(self, kernel_size: int, sigma: Tuple[float, float] = (0.1, 2.0)) -> None: + self.kernel_size = kernel_size + self.sigma = sigma + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + return TF.gaussian_blur(image, self.kernel_size, self.sigma), label + + +class RandomApply(object): + def __init__(self, transforms: Tuple[Callable, ...], p: Union[float, Tuple[float, ...]] = 0.5) -> None: + self.transforms = transforms + p = [p] * len(transforms) if isinstance(p, float) else p + assert all(0 <= p_ <= 1 for p_ in p), f"p should be in range [0, 1], got {p}." + assert len(p) == len(transforms), f"p should be a float or a tuple of floats with the same length as transforms, got {p}." + self.p = p + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + for transform, p in zip(self.transforms, self.p): + if torch.rand(1) < p: + image, label = transform(image, label) + + return image, label + + +class PepperSaltNoise(object): + def __init__(self, saltiness: float = 0.001, spiciness: float = 0.001) -> None: + self.saltiness = saltiness + self.spiciness = spiciness + assert 0 <= self.saltiness <= 1, f"saltiness should be in range [0, 1], got {self.saltiness}." + assert 0 <= self.spiciness <= 1, f"spiciness should be in range [0, 1], got {self.spiciness}." + + def __call__(self, image: Tensor, label: Tensor) -> Tuple[Tensor, Tensor]: + noise = torch.rand_like(image) + image = torch.where(noise < self.saltiness, 1., image) # Salt + image = torch.where(noise > 1 - self.spiciness, 0., image) # Pepper + return image, label diff --git a/datasets/utils.py b/datasets/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8538d3558d304801c31f169df9a1bb96b8b44ade --- /dev/null +++ b/datasets/utils.py @@ -0,0 +1,63 @@ +import torch +from torch import Tensor +from scipy.ndimage import gaussian_filter +from typing import Optional, List, Tuple + + +def get_id(x: str) -> int: + return int(x.split(".")[0]) + + +def generate_density_map(label: Tensor, height: int, width: int, sigma: Optional[float] = None) -> Tensor: + """ + Generate the density map based on the dot annotations provided by the label. + """ + density_map = torch.zeros((1, height, width), dtype=torch.float32) + + if len(label) > 0: + assert len(label.shape) == 2 and label.shape[1] == 2, f"label should be a Nx2 tensor, got {label.shape}." + label_ = label.long() + label_[:, 0] = label_[:, 0].clamp(min=0, max=width - 1) + label_[:, 1] = label_[:, 1].clamp(min=0, max=height - 1) + density_map[0, label_[:, 1], label_[:, 0]] = 1.0 + + if sigma is not None: + assert sigma > 0, f"sigma should be positive if not None, got {sigma}." + density_map = torch.from_numpy(gaussian_filter(density_map, sigma=sigma)) + + return density_map + + +def collate_fn(batch: List[Tensor]) -> Tuple[Tensor, List[Tensor], Tensor]: + batch = list(zip(*batch)) + images = batch[0] + assert len(images[0].shape) == 4, f"images should be a 4D tensor, got {images[0].shape}." + if len(batch) == 4: # image, label, density_map, image_name + images = torch.cat(images, 0) + points = batch[1] # list of lists of tensors, flatten it + points = [p for points_ in points for p in points_] + densities = torch.cat(batch[2], 0) + image_names = batch[3] # list of lists of strings, flatten it + image_names = [name for names_ in image_names for name in names_] + + return images, points, densities, image_names + + elif len(batch) == 3: # image, label, density_map + images = torch.cat(images, 0) + points = batch[1] + points = [p for points_ in points for p in points_] + densities = torch.cat(batch[2], 0) + + return images, points, densities + + elif len(batch) == 2: # image, image_name. NWPU test dataset + images = torch.cat(images, 0) + image_names = batch[1] + image_names = [name for names_ in image_names for name in names_] + + return images, image_names + + else: + images = torch.cat(images, 0) + + return images diff --git a/efficiency.py b/efficiency.py new file mode 100644 index 0000000000000000000000000000000000000000..a5ea2f21433ed0e86e69f65aca7b6c51b66a0247 --- /dev/null +++ b/efficiency.py @@ -0,0 +1,163 @@ +from argparse import ArgumentParser +import time +import os +import torch +import torchvision.transforms as transforms +from contextlib import nullcontext +import json +from models import get_model + + +parser = ArgumentParser(description="Train an EBC model.") +parser.add_argument("--model_info_path", type=str, required=True, help="Path to the model information file.") + +parser.add_argument("--batch_size", type=int, default=1, help="Batch size for the model.") +parser.add_argument("--height", type=int, default=768, help="Height of the input image.") +parser.add_argument("--width", type=int, default=1024, help="Width of the input image.") + +parser.add_argument("--num_iterations", type=int, default=200, help="Number of iterations to run the model.") +parser.add_argument("--num_warmup", type=int, default=20, help="Dispose of the first N iterations.") + +parser.add_argument("--device", type=str, choices=["cpu", "cuda", "mps"], help="Device to run the model on. Options are 'cpu', 'cuda', or 'mps'.") +parser.add_argument("--amp", action="store_true", help="Enable autocast mixed precision (fp16/bf16).") +parser.add_argument("--half", action="store_true", help="Use half precision for the model.") +parser.add_argument("--channels_last", action="store_true", help="Use NHWC memory format (recommended for CUDA).") +parser.add_argument("--compile", action="store_true", help="Enable torch.compile if available.") +parser.add_argument("--threads", type=int, default=None, help="torch.set_num_threads(threads) for CPU") +parser.add_argument("--sleep_time", type=float, default=0.0, help="Seconds to sleep after *each* iteration (cool-down).") + +_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + +def _dummy_input(bs, h, w, device, half, channels_last): + x = torch.rand(bs, 3, h, w, device=device) + x = _normalize(x) + if half: + x = x.half() + if channels_last: + x = x.to(memory_format=torch.channels_last) + return x + + +def _maybe_sync(dev): + if dev.type == "cuda": + torch.cuda.synchronize() + + +@torch.inference_mode() +def benchmark( + model: torch.nn.Module, + inp: torch.Tensor, + warmup: int, + steps: int, + amp: bool, + sleep_time: float = 0.0 +): + cm = torch.autocast(device_type=inp.device.type) if amp else nullcontext() + + # --- warm-up --- + for _ in range(warmup): + with cm: + _ = model(inp) + _maybe_sync(inp.device) + + # --- timed loop --- + total_time = 0.0 + for _ in range(steps): + tic = time.perf_counter() + with cm: + _ = model(inp) + + toc = time.perf_counter() + total_time += toc - tic + + if sleep_time > 0: + time.sleep(sleep_time) + + _maybe_sync(inp.device) + + fps = steps / total_time + return fps, total_time / steps + + +def main(args): + assert os.path.isfile(args.model_info_path), \ + f"{args.model_info_path} not found" + + model = get_model(model_info_path=args.model_info_path) + model.eval() + + if args.channels_last: + model = model.to(memory_format=torch.channels_last) + if args.half: + model = model.half() + + device = torch.device(args.device) + model = model.to(device) + + if args.compile and hasattr(torch, "compile"): + model = torch.compile(model, mode="reduce-overhead") + + if args.threads: + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + + inp = _dummy_input( + args.batch_size, + args.height, + args.width, + device, + args.half, + args.channels_last + ) + + fps, t_avg = benchmark( + model, + inp, + warmup=args.num_warmup, + steps=args.num_iterations, + amp=args.amp, + sleep_time=args.sleep_time + ) + + cfg = vars(args) + cfg.pop("model_info_path") + print(json.dumps(cfg, indent=2)) + print(f"\nAverage latency: {t_avg*1000:6.2f} ms | FPS: {fps:,.2f}") + + +if __name__ == "__main__": + main(parser.parse_args()) + + +# CUDA @FP16 + channels_last + torch.compile +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device cuda --half --amp --channels_last --compile + +# CUDA @AMP + channels_last + torch.compile +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device cuda --amp --channels_last --compile + +# CUDA @FP32 + channels_last + torch.compile +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device cuda --channels_last --compile + +# AMD 5900X (12 Core) + channels_last + torch.compile +# export OMP_NUM_THREADS=12; export MKL_NUM_THREADS=12 +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device cpu --threads 12 --channels_last --compile + +# Apple M1 Pro (6 Performance Cores). Compiling makes it slower. +# export OMP_NUM_THREADS=6; export VECLIB_MAXIMUM_THREADS=6 +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device cpu --threads 6 + +# Apple M1 Pro MPS @FP32 + torch.compile +# python efficiency.py \ +# --model_info_path checkpoints/shb/ebc_p/best_mae.pth \ +# --device mps --channels_last --compile \ No newline at end of file diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..7b48e25e02702c0657555670af3c9c7fb8e85897 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,84 @@ +import torch +from torch.amp import autocast +import torch.nn.functional as F +import torch.distributed as dist +from torch import nn, Tensor +from torch.utils.data import DataLoader +from typing import Tuple, Optional +from tqdm import tqdm +import numpy as np + +from utils import sliding_window_predict, barrier, calculate_errors + + +def evaluate( + model: nn.Module, + data_loader: DataLoader, + sliding_window: bool, + max_input_size: int = 4096, + window_size: int = 224, + stride: int = 224, + max_num_windows: int = 64, + device: torch.device = torch.device("cuda"), + amp: bool = False, + local_rank: int = 0, + nprocs: int = 1, + progress_bar: bool = True, +) -> Tuple[Tensor, Tensor]: + ddp = nprocs > 1 + model = model.to(device) + model.eval() + pred_counts, gt_counts = [], [] + data_iter = tqdm(data_loader) if (local_rank == 0 and progress_bar) else data_loader + + for image, gt_points, _ in data_iter: + image = image.to(device) + image_height, image_width = image.shape[-2:] + gt_counts.extend([len(p) for p in gt_points]) + + # Resize image if it's smaller than the window size + aspect_ratio = image_width / image_height + if image_height < window_size: + new_height = window_size + new_width = int(new_height * aspect_ratio) + image = F.interpolate(image, size=(new_height, new_width), mode="bicubic", align_corners=False) + image_height, image_width = new_height, new_width + if image_width < window_size: + new_width = window_size + new_height = int(new_width / aspect_ratio) + image = F.interpolate(image, size=(new_height, new_width), mode="bicubic", align_corners=False) + image_height, image_width = new_height, new_width + + with torch.set_grad_enabled(False), autocast(device_type="cuda", enabled=amp): + if sliding_window or (image_height * image_width) > max_input_size ** 2: + pred_den_maps = sliding_window_predict(model, image, window_size, stride, max_num_windows) + else: + pred_den_maps = model(image) + + pred_counts.extend(pred_den_maps.sum(dim=(-1, -2, -3)).cpu().numpy().tolist()) + + barrier(ddp) + assert len(pred_counts) == len(gt_counts), f"Length of predictions and ground truths should be equal, but got {len(pred_counts)} and {len(gt_counts)}" + + if ddp: + pred_counts, gt_counts = torch.tensor(pred_counts, device=device), torch.tensor(gt_counts, device=device) + # Pad `pred_counts` and `gt_counts` to the same length across all processes. + local_length = torch.tensor([len(pred_counts)], device=device) + lengths = [torch.zeros_like(local_length) for _ in range(nprocs)] + dist.all_gather(lengths, local_length) + max_length = max([l.item() for l in lengths]) + padded_pred_counts, padded_gt_counts = torch.full((max_length,), float("nan"), device=device), torch.full((max_length,), float("nan"), device=device) + padded_pred_counts[:len(pred_counts)], padded_gt_counts[:len(gt_counts)] = pred_counts, gt_counts + gathered_pred_counts, gathered_gt_counts = [torch.zeros_like(padded_pred_counts) for _ in range(nprocs)], [torch.zeros_like(padded_gt_counts) for _ in range(nprocs)] + dist.all_gather(gathered_pred_counts, padded_pred_counts) + dist.all_gather(gathered_gt_counts, padded_gt_counts) + # Concatenate predictions and ground truths from all processes and remove padding (nan values). + pred_counts, gt_counts = torch.cat(gathered_pred_counts).cpu(), torch.cat(gathered_gt_counts).cpu() + pred_counts, gt_counts = pred_counts[~torch.isnan(pred_counts)], gt_counts[~torch.isnan(gt_counts)] + pred_counts, gt_counts = pred_counts.numpy(), gt_counts.numpy() + + else: + pred_counts, gt_counts = np.array(pred_counts), np.array(gt_counts) + + torch.cuda.empty_cache() + return calculate_errors(pred_counts, gt_counts) diff --git a/losses/__init__.py b/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..284f8dbcbb190c55efa88228ff43b220b08bd2f1 --- /dev/null +++ b/losses/__init__.py @@ -0,0 +1,7 @@ +from .loss import QuadLoss +from .bregman_pytorch import sinkhorn + +__all__ = [ + "QuadLoss", + "sinkhorn", +] diff --git a/losses/bregman_pytorch.py b/losses/bregman_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..b152f9b6fc104f9c532256744f3775ea39eafd33 --- /dev/null +++ b/losses/bregman_pytorch.py @@ -0,0 +1,70 @@ +# Code modified from https://github.com/cvlab-stonybrook/DM-Count/blob/master/losses/bregman_pytorch.py +import torch +from torch.amp import autocast +from torch import Tensor +from typing import Union, Tuple, Dict + +M_EPS = 1e-16 + + +@torch.no_grad() +@autocast(device_type="cuda", enabled=True, dtype=torch.float32) +def sinkhorn( + a: Tensor, + b: Tensor, + C: Tensor, + reg: float = 1e-1, + maxIter: int = 1000, + stopThr: float = 1e-9, + verbose: bool = False, + log: bool = True, + eval_freq: int = 10, + print_freq: int = 200, +) -> Union[Tensor, Tuple[Tensor, Dict[str, Tensor]]]: + device = a.device + na, nb = C.shape + assert na == a.shape[0] and nb == b.shape[0], f"Shapes of a ({a.shape}) or b ({b.shape}) do not match that of C ({C.shape})" + assert reg > 0, f"reg should be greater than 0. Found reg = {reg}" + assert a.min() >= 0. and b.min() >= 0., f"Elements in a and b should be nonnegative. Found a.min() = {a.min()}, b.min() = {b.min()}" + + if log: + log = {"err": []} + + u = torch.ones(na, dtype=a.dtype, device=device) / na + v = torch.ones(nb, dtype=b.dtype, device=device) / nb + K = torch.exp(-C / reg) + + it, err = 1, 1 + while (err > stopThr and it <= maxIter): + u_pre, v_pre = u.clone(), v.clone() + KTu = torch.matmul(K.T, u) + v = b / (KTu + M_EPS) + Kv = torch.matmul(K, v) + u = a / (Kv + M_EPS) + + if torch.any(torch.isnan(u)) or torch.any(torch.isnan(v)) or torch.any(torch.isinf(u)) or torch.any(torch.isinf(v)): + print("Warning: numerical errors at iteration", it) + u, v = u_pre, v_pre + break + + if log and it % eval_freq == 0: + b_hat = torch.matmul(u, K) * v + err = (b - b_hat).pow(2).sum().item() + log["err"].append(err) + + if verbose and it % print_freq == 0: + print(f"Iteration {it}, constraint error {err}") + + it += 1 + + if log: + log["u"] = u + log["v"] = v + log["alpha"] = reg * torch.log(u + M_EPS) + log["beta"] = reg * torch.log(v + M_EPS) + + P = u.view(-1, 1) * K * v.view(1, -1) + if log: + return P, log + else: + return P diff --git a/losses/dm_loss.py b/losses/dm_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0127827b10784fa61d75f99eef203337b8274079 --- /dev/null +++ b/losses/dm_loss.py @@ -0,0 +1,142 @@ +import torch +from torch import nn, Tensor +from torch.amp import autocast +from typing import List, Tuple, Dict + +from .bregman_pytorch import sinkhorn +from .utils import _reshape_density + +EPS = 1e-8 + + +class OTLoss(nn.Module): + def __init__( + self, + input_size: int, + block_size: int, + numItermax: int = 100, + regularization: float = 10.0 + ) -> None: + super().__init__() + assert input_size % block_size == 0 + + self.input_size = input_size + self.block_size = block_size + self.num_blocks_h = input_size // block_size + self.num_blocks_w = input_size // block_size + self.numItermax = numItermax + self.regularization = regularization + + # coordinate is same to image space, set to constant since crop size is same + self.coords_h = torch.arange(0, input_size, step=block_size, dtype=torch.float32) + block_size / 2 + self.coords_w = torch.arange(0, input_size, step=block_size, dtype=torch.float32) + block_size / 2 + self.coords_h, self.coords_w = self.coords_h.unsqueeze(0), self.coords_w.unsqueeze(0) # [1, #coordinates] + + def set_numItermax(self, numItermax: int) -> None: + self.numItermax = numItermax + + @autocast(device_type="cuda", enabled=True, dtype=torch.float32) # avoid numerical instability + def forward(self, pred_den_map: Tensor, pred_den_map_normed: Tensor, gt_points: List[Tensor]) -> Tuple[Tensor, Tensor, Tensor]: + assert pred_den_map.shape[1:] == pred_den_map_normed.shape[1:] == (1, self.num_blocks_h, self.num_blocks_w), f"Expected pred_den_map to have shape (B, 1, {self.num_blocks_h}, {self.num_blocks_w}), but got {pred_den_map.shape} and {pred_den_map_normed.shape}" + assert len(gt_points) == pred_den_map.shape[0] == pred_den_map_normed.shape[0], f"Expected gt_points to have length {pred_den_map_normed.shape[0]}, but got {len(gt_points)}" + device = pred_den_map.device + + loss = torch.zeros(1, device=device) + ot_obj_values = torch.zeros(1, device=device) + w_dist = torch.zeros(1, device=device) # Wasserstein distance + coords_h, coords_w = self.coords_h.to(device), self.coords_w.to(device) # [1, #coordinates] + for idx, points in enumerate(gt_points): + if len(points) > 0: + # compute l2 square distance, it should be source target distance. [#gt, #coordinates * #coordinates] + x, y = points[:, 0].unsqueeze(1), points[:, 1].unsqueeze(1) # [#gt, 1] + x_dist = -2 * torch.matmul(x, coords_w) + x * x + coords_w * coords_w # [#gt, #coordinates] + y_dist = -2 * torch.matmul(y, coords_h) + y * y + coords_h * coords_h # [#gt, #coordinates] + dist = x_dist.unsqueeze(1) + y_dist.unsqueeze(2) + dist = dist.view((dist.shape[0], -1)) # size of [#gt, #coordinates * #coordinates] + + source_prob = pred_den_map_normed[idx].view(-1).detach() + target_prob = (torch.ones(len(points)) / len(points)).to(device) + # use sinkhorn to solve OT, compute optimal beta. + P, log = sinkhorn( + a=target_prob, + b=source_prob, + C=dist, + reg=self.regularization, + maxIter=self.numItermax, + log=True + ) + beta = log["beta"] # size is the same as source_prob: [#coordinates * #coordinates] + w_dist += (dist * P).sum() + ot_obj_values += (pred_den_map_normed[idx] * beta.view(1, self.num_blocks_h, self.num_blocks_w)).sum() + # compute the gradient of OT loss to predicted density (pred_den_map). + # im_grad = beta / source_count - < beta, source_density> / (source_count)^2 + source_density = pred_den_map[idx].view(-1).detach() + source_count = source_density.sum() + gradient_1 = (source_count) / (source_count * source_count+ EPS) * beta # size of [#coordinates * #coordinates] + gradient_2 = (source_density * beta).sum() / (source_count * source_count + EPS) # size of 1 + gradient = gradient_1 - gradient_2 + gradient = gradient.detach().view(1, self.num_blocks_h, self.num_blocks_w) + # Define loss = . The gradient of loss w.r.t predicted density is im_grad. + loss += torch.sum(pred_den_map[idx] * gradient) + + return loss, w_dist, ot_obj_values + + +class DMLoss(nn.Module): + def __init__( + self, + input_size: int, + block_size: int, + numItermax: int = 100, + regularization: float = 10.0, + weight_ot: float = 0.1, + weight_tv: float = 0.01, + weight_cnt: float = 1.0, + ) -> None: + super().__init__() + self.input_size = input_size + self.block_size = block_size + self.weight_ot = weight_ot + self.weight_tv = weight_tv + self.weight_cnt = weight_cnt + + self.ot_loss = OTLoss( + input_size=self.input_size, + block_size=self.block_size, + numItermax=numItermax, + regularization=regularization, + ) + self.tv_loss = nn.L1Loss(reduction="none") + self.cnt_loss = nn.L1Loss(reduction="mean") + self.weight_ot = weight_ot + self.weight_tv = weight_tv + + @autocast(device_type="cuda", enabled=True, dtype=torch.float32) # avoid numerical instability + def forward(self, pred_den_map: Tensor, gt_den_map: Tensor, gt_points: List[Tensor]) -> Tuple[Tensor, Dict[str, Tensor]]: + gt_den_map = _reshape_density(gt_den_map, block_size=self.ot_loss.block_size) if gt_den_map.shape[-2:] != pred_den_map.shape[-2:] else gt_den_map + assert pred_den_map.shape == gt_den_map.shape, f"Expected pred_den_map and gt_den_map to have the same shape, got {pred_den_map.shape} and {gt_den_map.shape}" + + pred_cnt = pred_den_map.view(pred_den_map.shape[0], -1).sum(dim=1) + pred_den_map_normed = pred_den_map / (pred_cnt.view(-1, 1, 1, 1) + EPS) + gt_cnt = torch.tensor([len(p) for p in gt_points], dtype=torch.float32).to(pred_den_map.device) + gt_den_map_normed = gt_den_map / (gt_cnt.view(-1, 1, 1, 1) + EPS) + + ot_loss, w_dist, _ = self.ot_loss(pred_den_map, pred_den_map_normed, gt_points) + + tv_loss = (self.tv_loss(pred_den_map_normed, gt_den_map_normed).sum(dim=(1, 2, 3)) * gt_cnt).mean() if self.weight_tv > 0 else 0 + + cnt_loss = self.cnt_loss(pred_cnt, gt_cnt) if self.weight_cnt > 0 else 0 + + loss = ot_loss * self.weight_ot + tv_loss * self.weight_tv + cnt_loss * self.weight_cnt + + loss_info = { + "ot_loss": ot_loss.detach(), + "dm_loss": loss.detach(), + "w_dist": w_dist.detach(), + } + if self.weight_tv > 0: + loss_info["tv_loss"] = tv_loss.detach() + if self.weight_cnt > 0: + loss_info["cnt_loss"] = cnt_loss.detach() + + return loss, loss_info diff --git a/losses/dual_loss.py b/losses/dual_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..47efc2baa594bf4029e711b501b04f2d3f247b5b --- /dev/null +++ b/losses/dual_loss.py @@ -0,0 +1,175 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from typing import List, Tuple, Dict + +from .dm_loss import DMLoss +from .multiscale_mae import MultiscaleMAE +from .utils import _reshape_density + + + +class DualLoss(nn.Module): + def __init__( + self, + input_size: int, + block_size: int, + bins: List[Tuple[float, float]], + bin_centers: List[float], + cls_loss: str = "ce", + reg_loss: str = "dm", + weight_tv: float = 0.01, + weight_cls: float = 0.1, + weight_reg: float = 0.1, + numItermax: int = 100, + regularization: float = 10.0, + scales: List[int] = [1, 2, 4], + min_scale_weight: float = 0.25, + max_scale_weight: float = 0.75, + alpha: float = 0.5, + ) -> None: + super().__init__() + assert len(bins) == len(bin_centers) >= 2, f"Expected bins and bin_centers to have at least 2 elements, got {len(bins)} and {len(bin_centers)}" + assert all([len(b) == 2 for b in bins]), f"Expected all bins to be of length 2, got {bins}" + assert all(b[0] <= p <= b[1] for b, p in zip(bins, bin_centers)), f"Expected bin_centers to be within the range of the corresponding bin, got {bins} and {bin_centers}" + assert cls_loss in ["ce", "mae", "mse", "none"], f"Expected cls_loss to be one of ['ce', 'mae', 'mse', 'none'], got {cls_loss}" + assert reg_loss in ["dm", "msmae", "mae", "mse", "none"], f"Expected reg_loss to be one of ['dm', 'msmae', 'mae', 'mse', 'none'], got {reg_loss}" + assert not (cls_loss == "none" and reg_loss == "none"), "Expected at least one of cls_loss and reg_loss to be provided" + assert weight_cls is None or weight_cls >= 0, f"Expected weight_cls to be non-negative, got {weight_cls}" + assert weight_reg is None or weight_reg >= 0, f"Expected weight_reg to be non-negative, got {weight_reg}" + assert weight_tv is None or weight_tv >= 0, f"Expected weight_tv to be non-negative, got {weight_tv}" + assert min_scale_weight is None or max_scale_weight is None or max_scale_weight >= min_scale_weight > 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {min_scale_weight} and {max_scale_weight}" + assert alpha is None or 1 > alpha > 0, f"Expected alpha to be between 0 and 1, got {alpha}" + + if reg_loss == "dm": + assert numItermax is not None and numItermax > 0, f"Expected numItermax to be a positive integer, got {numItermax}" + assert regularization is not None and regularization > 0, f"Expected regularization to be a positive float, got {regularization}" + assert weight_tv is not None and weight_tv >= 0, f"Expected weight_tv to be non-negative, got {weight_tv}" + else: + weight_tv, numItermax, regularization = None, None, None + + if reg_loss == "msmae": + assert isinstance(scales, (list, tuple)) and len(scales) > 0 and all(isinstance(s, int) and s > 0 for s in scales), f"Expected scales to be a list of positive integers, got {scales}" + assert max_scale_weight >= min_scale_weight > 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {min_scale_weight} and {max_scale_weight}" + assert 1 > alpha > 0, f"Expected alpha to be between 0 and 1, got {alpha}" + else: + scales = None + min_scale_weight, max_scale_weight = None, None + alpha = None + + weight_cls = weight_cls if weight_cls is not None else 0 + weight_reg = weight_reg if weight_reg is not None else 0 + + self.input_size, self.block_size = input_size, block_size + self.num_blocks_h, self.num_blocks_w = input_size // block_size, input_size // block_size + self.bins, self.bin_centers, self.num_bins = bins, bin_centers, len(bins) + self.cls_loss, self.reg_loss = cls_loss, reg_loss + self.weight_cls, self.weight_reg = weight_cls, weight_reg + self.numItermax, self.regularization = numItermax, regularization + self.weight_tv = weight_tv + self.scales = scales + self.min_scale_weight, self.max_scale_weight = min_scale_weight, max_scale_weight + + if cls_loss == "ce": + self.cls_loss_fn = nn.CrossEntropyLoss(reduction="none") + self.weight_cls = 1.0 + elif cls_loss == "mae": + self.cls_loss_fn = nn.L1Loss(reduction="none") + self.weight_cls = weight_cls + elif cls_loss == "mse": + self.cls_loss_fn = nn.MSELoss(reduction="none") + self.weight_cls = weight_cls + else: # cls_loss == "none" + self.cls_loss_fn = None + self.weight_cls = 0 + + if reg_loss == "dm": + self.reg_loss_fn = DMLoss( + input_size=input_size, + block_size=block_size, + numItermax=numItermax, + regularization=regularization, + weight_ot=weight_reg, + weight_tv=weight_tv, + weight_cnt=0, # Calculate the count loss separately + ) + self.weight_reg = 1.0 + elif reg_loss == "msmae": + self.reg_loss_fn = MultiscaleMAE(scales=scales, weights=None, min_scale_weight=min_scale_weight, max_scale_weight=max_scale_weight, alpha=alpha) + self.weight_reg = 1.0 + elif reg_loss == "mae": + self.reg_loss_fn = nn.L1Loss(reduction="none") + self.weight_reg = weight_reg + elif reg_loss == "mse": + self.reg_loss_fn = nn.MSELoss(reduction="none") + self.weight_reg = weight_reg + else: + self.reg_loss_fn = None + self.weight_reg = 0 + + self.cnt_loss_fn = nn.L1Loss(reduction="none") + + def _bin_count(self, density_map: Tensor) -> Tensor: + class_map = torch.zeros_like(density_map, dtype=torch.long) + for idx, (low, high) in enumerate(self.bins): + mask = (density_map >= low) & (density_map <= high) + class_map[mask] = idx + return class_map.squeeze(1) # remove channel dimension + + def forward( + self, + pred_logit_map: Tensor, + pred_den_map: Tensor, + gt_den_map: Tensor, + gt_points: List[Tensor] + ) -> Tuple[Tensor, Dict[str, Tensor]]: + B = pred_logit_map.shape[0] + assert pred_logit_map.shape == (B, self.num_bins, self.num_blocks_h, self.num_blocks_w), f"Expected pred_logit_map to have shape {B, self.num_bins, self.num_blocks_h, self.num_blocks_w}, got {pred_logit_map.shape}" + if gt_den_map.shape[-2:] != (self.num_blocks_h, self.num_blocks_w): + assert gt_den_map.shape[-2:] == (self.input_size, self.input_size), f"Expected gt_den_map to have shape {B, 1, self.input_size, self.input_size}, got {gt_den_map.shape}" + gt_den_map = _reshape_density(gt_den_map, block_size=self.block_size) + assert pred_den_map.shape == gt_den_map.shape == (B, 1, self.num_blocks_h, self.num_blocks_w), f"Expected pred_den_map and gt_den_map to have shape (B, 1, H, W), got {pred_den_map.shape} and {gt_den_map.shape}" + assert len(gt_points) == B, f"Expected gt_points to have length B, got {len(gt_points)}" + + loss_info = {} + + if self.weight_cls > 0: + gt_class_map = self._bin_count(gt_den_map) + if self.cls_loss == "ce": + cls_loss = self.cls_loss_fn(pred_logit_map, gt_class_map).sum(dim=(-1, -2)).mean() + loss_info["cls_ce_loss"] = cls_loss.detach() + else: # self.cls_loss in ["mae", "mse"] + gt_prob_map = F.one_hot(gt_class_map, num_classes=self.num_bins).float() # B, H, W -> B, H, W, N + gt_prob_map = gt_prob_map.permute(0, 3, 1, 2) # B, H, W, N -> B, N, H, W + pred_prob_map = pred_logit_map.softmax(dim=1) + cls_loss = self.cls_loss_fn(pred_prob_map, gt_prob_map).sum(dim=(-1, -2)).mean() + loss_info[f"cls_{self.cls_loss}_loss"] = cls_loss.detach() + else: + cls_loss = 0 + + if self.weight_reg > 0: + if self.reg_loss == "dm": + reg_loss, reg_loss_info = self.reg_loss_fn( + pred_den_map=pred_den_map, + gt_den_map=gt_den_map, + gt_points=gt_points, + ) + loss_info.update({f"reg_{k}": v for k, v in reg_loss_info.items()}) + elif self.reg_loss == "msmae": + reg_loss, reg_loss_info = self.reg_loss_fn(pred_den_map, gt_den_map) + loss_info.update({f"reg_{k}": v for k, v in reg_loss_info.items()}) + else: # self.reg_loss in ["mae", "mse"] + reg_loss = self.reg_loss_fn(pred_den_map, gt_den_map).sum(dim=(-1, -2)).mean() + loss_info[f"reg_{self.reg_loss}_loss"] = reg_loss.detach() + else: + reg_loss = 0 + + gt_cnt = torch.tensor([len(p) for p in gt_points], dtype=torch.float32, device=pred_den_map.device) + cnt_loss = self.cnt_loss_fn(pred_den_map.sum(dim=(1, 2, 3)), gt_cnt).mean() + loss_info["cnt_loss"] = cnt_loss.detach() + + total_loss = self.weight_cls * cls_loss + self.weight_reg * reg_loss + cnt_loss + loss_info["total_loss"] = total_loss.detach() + loss_info = dict(sorted(loss_info.items())) # sort by key for nicer printing + + return total_loss, loss_info \ No newline at end of file diff --git a/losses/loss.py b/losses/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..650318bd51d8eeda54f5022c553c2bf1d6260875 --- /dev/null +++ b/losses/loss.py @@ -0,0 +1,204 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from typing import List, Dict, Optional, Tuple, Union + +from .dm_loss import DMLoss +from .multiscale_mae import MultiscaleMAE +from .poisson_nll import PoissonNLL +from .zero_inflated_poisson_nll import ZIPoissonNLL, ZICrossEntropy +from .utils import _reshape_density, _bin_count + + +EPS = 1e-8 + + +class QuadLoss(nn.Module): + def __init__( + self, + input_size: int, + block_size: int, + bins: List[Tuple[float, float]], + reg_loss: str = "zipnll", + aux_loss: str = "none", + weight_cls: float = 1.0, + weight_reg: float = 1.0, + weight_aux: Optional[float] = None, + numItermax: Optional[int] = 100, + regularization: Optional[int] = 10.0, + scales: Optional[List[int]] = [[1, 2, 4]], + min_scale_weight: Optional[float] = 0.0, + max_scale_weight: Optional[float] = 1.0, + alpha: Optional[float] = 0.5, + ) -> None: + super().__init__() + assert input_size % block_size == 0, f"Expected input_size to be divisible by block_size, got {input_size} and {block_size}" + assert len(bins) >= 2, f"Expected bins to have at least 2 elements, got {len(bins)}" + assert all([len(b) == 2 for b in bins]), f"Expected all bins to be of length 2, got {bins}" + bins = [(float(low), float(high)) for low, high in bins] + assert all([b[0] <= b[1] for b in bins]), f"Expected each bin to have bin[0] <= bin[1], got {bins}" + assert reg_loss in ["zipnll", "pnll", "dm", "msmae", "mae", "mse"], f"Expected reg_loss to be one of ['zipnll', 'pnll', 'dm', 'msmae', 'mae', 'mse'], got {reg_loss}" + assert aux_loss in ["zipnll", "pnll", "dm", "msmae", "mae", "mse", "none"], f"Expected aux_loss to be one of ['zipnll', 'pnll', 'dm', 'msmae', 'mae', 'mse', 'none'], got {aux_loss}" + + assert weight_cls >= 0, f"Expected weight_cls to be non-negative, got {weight_cls}" + assert weight_reg >= 0, f"Expected weight_reg to be non-negative, got {weight_reg}" + assert not (weight_cls == 0 and weight_reg == 0), "Expected at least one of weight_cls and weight_reg to be non-zero" + weight_aux = 0 if aux_loss == "none" or weight_aux is None else weight_aux + assert weight_aux >= 0, f"Expected weight_aux to be non-negative, got {weight_aux}" + + self.input_size = input_size + self.block_size = block_size + self.bins = bins + self.reg_loss = reg_loss + self.aux_loss = aux_loss + self.weight_cls = weight_cls + self.weight_reg = weight_reg + self.weight_aux = weight_aux + + self.num_bins = len(bins) + self.num_blocks_h = input_size // block_size + self.num_blocks_w = input_size // block_size + + if reg_loss == "zipnll": + self.cls_loss = "zice" + self.cls_loss_fn = ZICrossEntropy(bins=bins, reduction="mean") + self.reg_loss_fn = ZIPoissonNLL(reduction="mean") + else: + self.cls_loss = "ce" + self.cls_loss_fn = nn.CrossEntropyLoss(reduction="none") + if reg_loss == "pnll": + self.reg_loss_fn = PoissonNLL(reduction="mean") + elif reg_loss == "dm": + assert numItermax is not None and numItermax > 0, f"Expected numItermax to be a positive integer, got {numItermax}" + assert regularization is not None and regularization > 0, f"Expected regularization to be a positive float, got {regularization}" + self.reg_loss_fn = DMLoss( + input_size=input_size, + block_size=block_size, + numItermax=numItermax, + regularization=regularization, + weight_ot=0.1, + weight_tv=0.01, + weight_cnt=0, # count loss will be calculated separately in this module. + ) + elif reg_loss == "msmae": + assert isinstance(scales, (list, tuple)) and len(scales) > 0 and all(isinstance(s, int) and s > 0 for s in scales), f"Expected scales to be a list of positive integers, got {scales}" + assert max_scale_weight >= min_scale_weight >= 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {min_scale_weight} and {max_scale_weight}" + assert 1 > alpha > 0, f"Expected alpha to be between 0 and 1, got {alpha}" + self.reg_loss_fn = MultiscaleMAE( + scales=sorted(scales), + min_scale_weight=min_scale_weight, + max_scale_weight=max_scale_weight, + alpha=alpha, + ) + elif reg_loss == "mae": + self.reg_loss_fn = nn.L1Loss(reduction="none") + elif reg_loss == "mse": + self.reg_loss_fn = nn.MSELoss(reduction="none") + else: # reg_loss == "none" + self.reg_loss_fn = None + + if aux_loss == "zipnll": + self.aux_loss_fn = ZIPoissonNLL(reduction="mean") + elif aux_loss == "pnll": + self.aux_loss_fn = PoissonNLL(reduction="mean") + elif aux_loss == "dm": + assert numItermax is not None and numItermax > 0, f"Expected numItermax to be a positive integer, got {numItermax}" + assert regularization is not None and regularization > 0, f"Expected regularization to be a positive float, got {regularization}" + self.aux_loss_fn = DMLoss( + input_size=input_size, + block_size=block_size, + numItermax=numItermax, + regularization=regularization, + weight_ot=0.1, + weight_tv=0.01, + weight_cnt=0, # count loss will be calculated separately in this module. + ) + elif aux_loss == "msmae": + assert isinstance(scales, (list, tuple)) and len(scales) > 0 and all(isinstance(s, int) and s > 0 for s in scales), f"Expected scales to be a list of positive integers, got {scales}" + assert max_scale_weight >= min_scale_weight >= 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {min_scale_weight} and {max_scale_weight}" + assert 1 > alpha > 0, f"Expected alpha to be between 0 and 1, got {alpha}" + self.aux_loss_fn = MultiscaleMAE( + scales=sorted(scales), + min_scale_weight=min_scale_weight, + max_scale_weight=max_scale_weight, + alpha=alpha, + ) + elif aux_loss == "mae": + self.aux_loss_fn = nn.L1Loss(reduction="none") + elif aux_loss == "mse": + self.aux_loss_fn = nn.MSELoss(reduction="none") + else: # aux_loss == "none" + self.aux_loss_fn = None + + self.cnt_loss_fn = nn.L1Loss(reduction="mean") + + def forward( + self, + pred_logit_map: Tensor, + pred_den_map: Tensor, + gt_den_map: Tensor, + gt_points: List[Tensor], + pred_logit_pi_map: Optional[Tensor] = None, + pred_lambda_map: Optional[Tensor] = None, + ) -> Tuple[Tensor, Dict[str, Tensor]]: + B = pred_den_map.shape[0] + assert pred_logit_map.shape[-2:] == (self.num_blocks_h, self.num_blocks_w), f"Expected pred_logit_map to have the spatial dimension of {self.num_blocks_h}x{self.num_blocks_w}, got {pred_logit_map.shape}" + if gt_den_map.shape[-2:] != (self.num_blocks_h, self.num_blocks_w): + assert gt_den_map.shape[-2:] == (self.input_size, self.input_size), f"Expected gt_den_map to have shape {B, 1, self.input_size, self.input_size}, got {gt_den_map.shape}" + gt_den_map = _reshape_density(gt_den_map, block_size=self.block_size) + assert pred_den_map.shape == gt_den_map.shape == (B, 1, self.num_blocks_h, self.num_blocks_w), f"Expected pred_den_map and gt_den_map to have shape (B, 1, H, W), got {pred_den_map.shape} and {gt_den_map.shape}" + assert len(gt_points) == B, f"Expected gt_points to have length B, got {len(gt_points)}" + + if self.reg_loss == "zipnll" or self.aux_loss == "zipnll": + assert pred_logit_pi_map is not None and pred_logit_pi_map.shape == (B, 2, self.num_blocks_h, self.num_blocks_w), f"Expected pred_logit_pi_map to have shape {B, 2, self.num_blocks_h, self.num_blocks_w}, got {pred_logit_pi_map.shape}" + assert pred_lambda_map is not None and pred_lambda_map.shape == (B, 1, self.num_blocks_h, self.num_blocks_w), f"Expected pred_lambda_map to have shape {B, 1, self.num_blocks_h, self.num_blocks_w}, got {pred_lambda_map.shape}" + + loss_info = {} + if self.weight_cls > 0: + gt_class_map = _bin_count(gt_den_map, bins=self.bins) + if self.cls_loss == "ce": + cls_loss = self.cls_loss_fn(pred_logit_map, gt_class_map).sum(dim=(-1, -2)).mean() + loss_info["cls_ce_loss"] = cls_loss.detach() + else: # cls_loss == "zice" + cls_loss, cls_loss_info = self.cls_loss_fn(pred_logit_map, gt_den_map) + loss_info.update(cls_loss_info) + else: + cls_loss = 0 + + if self.weight_reg > 0: + if self.reg_loss == "zipnll": + reg_loss, reg_loss_info = self.reg_loss_fn(pred_logit_pi_map, pred_lambda_map, gt_den_map) + elif self.reg_loss == "dm": + reg_loss, reg_loss_info = self.reg_loss_fn(pred_den_map, gt_den_map, gt_points) + elif self.reg_loss in ["pnll", "msmae"]: + reg_loss, reg_loss_info = self.reg_loss_fn(pred_den_map, gt_den_map) + else: # reg_loss in ["mae", "mse"] + reg_loss = self.reg_loss_fn(pred_den_map, gt_den_map).sum(dim=(-1, -2)).mean() + reg_loss_info = {f"{self.reg_loss}": reg_loss.detach()} + reg_loss_info = {f"reg_{k}": v for k, v in reg_loss_info.items()} + loss_info.update(reg_loss_info) + else: + reg_loss = 0 + + if self.weight_aux > 0: + if self.aux_loss == "zipnll": + aux_loss, aux_loss_info = self.aux_loss_fn(pred_logit_pi_map, pred_lambda_map, gt_den_map) + elif self.aux_loss in ["pnll", "msmae"]: + aux_loss, aux_loss_info = self.aux_loss_fn(pred_den_map, gt_den_map) + elif self.aux_loss == "dm": + aux_loss, aux_loss_info = self.aux_loss_fn(pred_den_map, gt_den_map, gt_points) + else: + aux_loss = self.aux_loss_fn(pred_den_map, gt_den_map).sum(dim=(-1, -2)).mean() + aux_loss_info = {f"{self.aux_loss}": aux_loss.detach()} + aux_loss_info = {f"aux_{k}": v for k, v in aux_loss_info.items()} + loss_info.update(aux_loss_info) + else: + aux_loss = 0 + + gt_cnt = torch.tensor([len(p) for p in gt_points], dtype=torch.float32, device=pred_den_map.device) + cnt_loss = self.cnt_loss_fn(pred_den_map.sum(dim=(1, 2, 3)), gt_cnt) + loss_info["cnt_loss"] = cnt_loss.detach() + + total_loss = self.weight_cls * cls_loss + self.weight_reg * reg_loss + self.weight_aux * aux_loss + cnt_loss + return total_loss, loss_info + \ No newline at end of file diff --git a/losses/multiscale_mae.py b/losses/multiscale_mae.py new file mode 100644 index 0000000000000000000000000000000000000000..f58404905cf1f6a50188031e6deb440c59033691 --- /dev/null +++ b/losses/multiscale_mae.py @@ -0,0 +1,55 @@ +from torch import nn, Tensor +import math +from typing import List, Optional, Dict, Tuple + + +class MultiscaleMAE(nn.Module): + def __init__( + self, + scales: List[int] = [1, 2, 4], + min_scale_weight: float = 0.0, + max_scale_weight: float = 1.0, + alpha: float = 0.5, + weights: Optional[List[float]] = None, + ) -> None: + super().__init__() + assert isinstance(scales, (list, tuple)) and len(scales) > 0 and all(isinstance(s, int) and s > 0 for s in scales), f"Expected scales to be a list of positive integers, got {scales}" + assert max_scale_weight >= min_scale_weight >= 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {min_scale_weight} and {max_scale_weight}" + assert 1 > alpha > 0, f"Expected alpha to be between 0 and 1, got {alpha}" + self.min_scale_weight, self.max_scale_weight = min_scale_weight, max_scale_weight + + scales = sorted(scales) # sort scales in ascending order so that the last one is the largest + weights = [min_scale_weight + (max_scale_weight - min_scale_weight) * alpha ** (math.log2(scales[-1] / s)) for s in scales] if weights is None else weights # e.g., [1, 2, 4, 8] -> [0.125, 0.25, 0.5, 1] + + assert len(scales) == len(weights), f"Expected scales and weights to have the same length, got {len(scales)} and {len(weights)}" + self.scales, self.weights = scales, weights + + for idx in range(len(scales)): + setattr(self, f"pool_{scales[idx]}", nn.AvgPool2d(kernel_size=scales[idx], stride=scales[idx]) if scales[idx] > 1 else nn.Identity()) + setattr(self, f"weight_{scales[idx]}", weights[idx]) + setattr(self, f"mae_loss_fn_{scales[idx]}", nn.L1Loss(reduction="none")) + + def forward( + self, + pred_den_map: Tensor, + gt_den_map: Tensor, + ) -> Tuple[Tensor, Dict]: + assert len(pred_den_map.shape) == 4, f"Expected pred_den_map to have 4 dimensions, got {len(pred_den_map.shape)}" + assert len(gt_den_map.shape) == 4, f"Expected gt_den_map to have 4 dimensions, got {len(gt_den_map.shape)}" + assert pred_den_map.shape[1] == gt_den_map.shape[1] == 1, f"Expected pred_den_map and gt_den_map to have 1 channel, got {pred_den_map.shape[1]} and {gt_den_map.shape[1]}" + assert pred_den_map.shape == gt_den_map.shape, f"Expected pred_den_map and gt_den_map to have the same shape, got {pred_den_map.shape} and {gt_den_map.shape}" + + loss, loss_info = 0, {} + for idx in range(len(self.scales)): + pool = getattr(self, f"pool_{self.scales[idx]}") + weight = getattr(self, f"weight_{self.scales[idx]}") + loss_fn = getattr(self, f"mae_loss_fn_{self.scales[idx]}") + + pred_den_map_pool = pool(pred_den_map) + gt_den_map_pool = pool(gt_den_map) + + mae_loss_scale = loss_fn(pred_den_map_pool, gt_den_map_pool).sum(dim=(-1, -2)).mean() + loss += weight * mae_loss_scale + loss_info[f"mae_loss_{self.scales[idx]}"] = mae_loss_scale.detach() + + return loss, loss_info diff --git a/losses/poisson_nll.py b/losses/poisson_nll.py new file mode 100644 index 0000000000000000000000000000000000000000..31852ce28079b330bd5f3b22453df7472f930466 --- /dev/null +++ b/losses/poisson_nll.py @@ -0,0 +1,46 @@ +import torch +from torch import nn, Tensor +from .utils import _reshape_density + + +EPS = 1e-8 + + +class PoissonNLL(nn.Module): + def __init__( + self, + reduction: str = "mean", + ) -> None: + super().__init__() + assert reduction in ["none", "mean", "sum"], f"Expected reduction to be one of ['none', 'mean', 'sum'], got {reduction}." + self.reduction = reduction + + def forward(self, pred_den_map: Tensor, gt_den_map: Tensor) -> Tensor: + """ + Args: + pred_den_map: predicted λ map, shape (B, 1, H, W) + gt_den_map: ground truth density map, shape (B, 1, H, W) + Returns: + Poisson loss + """ + assert len(pred_den_map.shape) == 4, f"Expected pred_den_map to have 4 dimensions, got {len(pred_den_map.shape)}" + assert len(gt_den_map.shape) == 4, f"Expected gt_den_map to have 4 dimensions, got {len(gt_den_map.shape)}" + assert pred_den_map.shape[1] == gt_den_map.shape[1] == 1, f"Expected pred_den_map and gt_den_map to have 1 channel, got {pred_den_map.shape[1]} and {gt_den_map.shape[1]}" + if gt_den_map.shape != pred_den_map.shape: + gt_h, gt_w = gt_den_map.shape[-2], gt_den_map.shape[-1] + pred_h, pred_w = pred_den_map.shape[-2], pred_den_map.shape[-1] + assert gt_h % pred_h == 0 and gt_w % pred_w == 0 and gt_h // pred_h == gt_w // pred_w, f"Expected the spatial dimension of gt_den_map to be a multiple of that of pred_den_map, got {gt_den_map.shape} and {pred_den_map.shape}" + gt_den_map = _reshape_density(gt_den_map, block_size=gt_h // pred_h) + + assert gt_den_map.shape == pred_den_map.shape, f"Expected gt_den_map and pred_den_map to have the same shape, got {gt_den_map.shape} and {pred_den_map.shape}" + + gt_den_map = gt_den_map.to(pred_den_map.device) + + loss = (pred_den_map - gt_den_map * torch.log(pred_den_map + EPS)).sum(dim=(-1, -2)) # sum over H and W + + if self.reduction == "mean": + loss = loss.mean() + elif self.reduction == "sum": + loss = loss.sum() + + return loss, {"pnll": loss.detach()} diff --git a/losses/utils.py b/losses/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e52ad2b9ee3479475676fa0ba7cacbff3fb6b956 --- /dev/null +++ b/losses/utils.py @@ -0,0 +1,19 @@ +import torch +from torch import Tensor +from typing import List, Tuple + + +def _reshape_density(density: Tensor, block_size: int) -> Tensor: + assert len(density.shape) == 4, f"Expected 4D (B, 1, H, W) tensor, got {density.shape}" + assert density.shape[1] == 1, f"Expected 1 channel, got {density.shape[1]}" + assert density.shape[2] % block_size == 0, f"Expected height to be divisible by {block_size}, got {density.shape[2]}" + assert density.shape[3] % block_size == 0, f"Expected width to be divisible by {block_size}, got {density.shape[3]}" + return density.reshape(density.shape[0], 1, density.shape[2] // block_size, block_size, density.shape[3] // block_size, block_size).sum(dim=(-1, -3)) + + +def _bin_count(density_map: Tensor, bins: List[Tuple[int, int]]) -> Tensor: + class_map = torch.zeros_like(density_map, dtype=torch.long) + for idx, (low, high) in enumerate(bins): + mask = (density_map >= low) & (density_map <= high) + class_map[mask] = idx + return class_map.squeeze(1) # remove channel dimension diff --git a/losses/zero_inflated_poisson_nll.py b/losses/zero_inflated_poisson_nll.py new file mode 100644 index 0000000000000000000000000000000000000000..5432b8c008a17973fcecdedcb3b03b1ad6a35975 --- /dev/null +++ b/losses/zero_inflated_poisson_nll.py @@ -0,0 +1,96 @@ +import torch +from torch import nn, Tensor +from einops import rearrange +from typing import List, Tuple +from .utils import _reshape_density, _bin_count + +EPS = 1e-8 + + +class ZIPoissonNLL(nn.Module): + def __init__( + self, + reduction: str = "mean", + ) -> None: + super().__init__() + assert reduction in ["none", "mean", "sum"], f"Expected reduction to be one of ['none', 'mean', 'sum'], got {reduction}." + self.reduction = reduction + + def forward( + self, + logit_pi_maps: Tensor, + lambda_maps: Tensor, + gt_den_maps: Tensor, + ) -> Tensor: + assert len(logit_pi_maps.shape) == len(lambda_maps.shape) == len(gt_den_maps.shape) == 4, f"Expected 4D (B, C, H, W) tensor, got {logit_pi_maps.shape}, {lambda_maps.shape}, and {gt_den_maps.shape}" + B, _, H, W = lambda_maps.shape + assert logit_pi_maps.shape == (B, 2, H, W), f"Expected logit_pi_maps to have shape (B, 2, H, W), got {logit_pi_maps.shape}" + assert lambda_maps.shape == (B, 1, H, W), f"Expected lambda_maps to have shape (B, 1, H, W), got {lambda_maps.shape}" + if gt_den_maps.shape[2:] != (H, W): + gt_h, gt_w = gt_den_maps.shape[-2], gt_den_maps.shape[-1] + assert gt_h % H == 0 and gt_w % W == 0 and gt_h // H == gt_w // W, f"Expected the spatial dimension of gt_den_maps to be a multiple of that of lambda_maps, got {gt_den_maps.shape} and {lambda_maps.shape}" + gt_den_maps = _reshape_density(gt_den_maps, block_size=gt_h // H) + assert gt_den_maps.shape == (B, 1, H, W), f"Expected gt_den_maps to have shape (B, 1, H, W), got {gt_den_maps.shape}" + + pi_maps = logit_pi_maps.softmax(dim=1) + zero_indices = (gt_den_maps == 0).float() + zero_loss = -torch.log(pi_maps[:, 0:1] + pi_maps[:, 1:] * torch.exp(-lambda_maps) + EPS) * zero_indices + + poisson_log_p = gt_den_maps * torch.log(lambda_maps + EPS) - lambda_maps + nonzero_loss = (-torch.log(pi_maps[:, 1:] + EPS) - poisson_log_p) * (1.0 - zero_indices) + + loss = (zero_loss + nonzero_loss).sum(dim=(-1, -2)) + if self.reduction == "mean": + loss = loss.mean() + elif self.reduction == "sum": + loss = loss.sum() + + return loss, {"zipnll": loss.detach()} + + +class ZICrossEntropy(nn.Module): + def __init__( + self, + bins: List[Tuple[int, int]], + reduction: str = "mean", + ) -> None: + super().__init__() + assert all([low <= high for low, high in bins]), f"Expected bins to be a list of tuples (low, high) where low <= high, got {bins}" + assert reduction in ["mean", "sum"], f"Expected reduction to be one of ['none', 'mean', 'sum'], got {reduction}." + + self.bins = bins + self.reduction = reduction + self.ce_loss_fn = nn.CrossEntropyLoss(reduction="none") + + def forward( + self, + logit_maps: Tensor, + gt_den_maps: Tensor, + ) -> Tensor: + assert len(logit_maps.shape) == len(gt_den_maps.shape) == 4, f"Expected 4D (B, C, H, W) tensor, got {logit_maps.shape} and {gt_den_maps.shape}" + B, _, H, W = logit_maps.shape + assert logit_maps.shape[0] == B and logit_maps.shape[2:] == (H, W), f"Expected logit_maps to have shape (B, C, H, W), got {logit_maps.shape}" + if gt_den_maps.shape[2:] != (H, W): + gt_h, gt_w = gt_den_maps.shape[-2], gt_den_maps.shape[-1] + assert gt_h % H == 0 and gt_w % W == 0 and gt_h // H == gt_w // W, f"Expected the spatial dimension of gt_den_maps to be a multiple of that of logit_maps, got {gt_den_maps.shape} and {logit_maps.shape}" + gt_den_maps = _reshape_density(gt_den_maps, block_size=gt_h // H) + assert gt_den_maps.shape == (B, 1, H, W), f"Expected gt_den_maps to have shape (B, 1, H, W), got {gt_den_maps.shape}" + + gt_class_maps = _bin_count(gt_den_maps, bins=self.bins) + gt_class_maps = rearrange(gt_class_maps, "B H W -> B (H W)") # flatten spatial dimensions + logit_maps = rearrange(logit_maps, "B C H W -> B (H W) C") # flatten spatial dimensions + + loss = 0.0 + for idx in range(gt_class_maps.shape[0]): + gt_class_map, logit_map = gt_class_maps[idx], logit_maps[idx] + mask = gt_class_map > 0 + # Find gt_class_map values and logit_maps values where gt_class_map > 0 + gt_class_map = gt_class_map[mask] - 1 + logit_map = logit_map[mask] + loss += self.ce_loss_fn(logit_map, gt_class_map).sum() + + if self.reduction == "mean": + loss /= gt_class_maps.shape[0] + + return loss, {"cls_zice": loss.detach()} + \ No newline at end of file diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..facb4bef0ae5ff2d46d30defea58112262656dba --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,155 @@ +import os, torch +from typing import List, Tuple, Optional, Union, Dict + +from .ebc import _ebc, EBC +from .clip_ebc import _clip_ebc, CLIP_EBC + + +def get_model( + model_info_path: str, + model_name: Optional[str] = None, + block_size: Optional[int] = None, + bins: Optional[List[Tuple[float, float]]] = None, + bin_centers: Optional[List[float]] = None, + zero_inflated: Optional[bool] = True, + # parameters for CLIP_EBC + clip_weight_name: Optional[str] = None, + num_vpt: Optional[int] = None, + vpt_drop: Optional[float] = None, + input_size: Optional[int] = None, + adapter: bool = False, + adapter_reduction: Optional[int] = None, + lora: bool = False, + lora_rank: Optional[int] = None, + lora_alpha: Optional[int] = None, + lora_dropout: Optional[float] = None, + norm: str = "none", + act: str = "none", + text_prompts: Optional[List[str]] = None +) -> Union[EBC, CLIP_EBC]: + if os.path.exists(model_info_path): + model_info = torch.load(model_info_path, map_location="cpu", weights_only=False) + + model_name = model_info["config"]["model_name"] + block_size = model_info["config"]["block_size"] + bins = model_info["config"]["bins"] + bin_centers = model_info["config"]["bin_centers"] + zero_inflated = model_info["config"]["zero_inflated"] + + clip_weight_name = model_info["config"].get("clip_weight_name", None) + + num_vpt = model_info["config"].get("num_vpt", None) + vpt_drop = model_info["config"].get("vpt_drop", None) + + + adapter = model_info["config"].get("adapter", False) + adapter_reduction = model_info["config"].get("adapter_reduction", None) + + lora = model_info["config"].get("lora", False) + lora_rank = model_info["config"].get("lora_rank", None) + lora_alpha = model_info["config"].get("lora_alpha", None) + lora_dropout = model_info["config"].get("lora_dropout", None) + + input_size = model_info["config"].get("input_size", None) + text_prompts = model_info["config"].get("text_prompts", None) + + norm = model_info["config"].get("norm", "none") + act = model_info["config"].get("act", "none") + + weights = model_info["weights"] + + else: + assert model_name is not None, "model_name should be provided if model_info_path is not provided" + assert block_size is not None, "block_size should be provided" + assert bins is not None, "bins should be provided" + assert bin_centers is not None, "bin_centers should be provided" + weights = None + + if "ViT" in model_name: + assert num_vpt is not None, f"num_vpt should be provided for ViT models, got {num_vpt}" + assert vpt_drop is not None, f"vpt_drop should be provided for ViT models, got {vpt_drop}" + + if model_name.startswith("CLIP_") or model_name.startswith("CLIP-"): + assert clip_weight_name is not None, f"clip_weight_name should be provided for CLIP models, got {clip_weight_name}" + model = _clip_ebc( + model_name=model_name[5:], + weight_name=clip_weight_name, + block_size=block_size, + bins=bins, + bin_centers=bin_centers, + zero_inflated=zero_inflated, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + input_size=input_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + text_prompts=text_prompts, + norm=norm, + act=act + ) + model_config = { + "model_name": model_name, + "block_size": block_size, + "bins": bins, + "bin_centers": bin_centers, + "zero_inflated": zero_inflated, + "clip_weight_name": clip_weight_name, + "num_vpt": num_vpt, + "vpt_drop": vpt_drop, + "input_size": input_size, + "adapter": adapter, + "adapter_reduction": adapter_reduction, + "lora": lora, + "lora_rank": lora_rank, + "lora_alpha": lora_alpha, + "lora_dropout": lora_dropout, + "text_prompts": model.text_prompts, + "norm": norm, + "act": act + } + + else: + assert not adapter, "adapter for non-CLIP models is not implemented yet" + assert not lora, "lora for non-CLIP models is not implemented yet" + model = _ebc( + model_name=model_name, + block_size=block_size, + bins=bins, + bin_centers=bin_centers, + zero_inflated=zero_inflated, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + input_size=input_size, + norm=norm, + act=act + ) + model_config = { + "model_name": model_name, + "block_size": block_size, + "bins": bins, + "bin_centers": bin_centers, + "zero_inflated": zero_inflated, + "num_vpt": num_vpt, + "vpt_drop": vpt_drop, + "input_size": input_size, + "norm": norm, + "act": act + } + + model.config = model_config + model_info = {"config": model_config, "weights": weights} + + if weights is not None: + model.load_state_dict(weights) + + if not os.path.exists(model_info_path): + torch.save(model_info, model_info_path) + + return model + + +__all__ = ["get_model"] diff --git a/models/clip_ebc/__init__.py b/models/clip_ebc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2448575b7fa674c224f48027693bd7208fa7a9d0 --- /dev/null +++ b/models/clip_ebc/__init__.py @@ -0,0 +1,7 @@ +from .model import CLIP_EBC, _clip_ebc + + +__all__ = [ + "CLIP_EBC", + "_clip_ebc", +] diff --git a/models/clip_ebc/__pycache__/__init__.cpython-312.pyc b/models/clip_ebc/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5040aca42087d39938f50c09d2085ea3292e6ef0 Binary files /dev/null and b/models/clip_ebc/__pycache__/__init__.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/convnext.cpython-312.pyc b/models/clip_ebc/__pycache__/convnext.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5cb190e4d27ce03408fa56a09e08456472d4fdc5 Binary files /dev/null and b/models/clip_ebc/__pycache__/convnext.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/mobileclip.cpython-312.pyc b/models/clip_ebc/__pycache__/mobileclip.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98107cb1c38feceb8736a16ad0a2d9c9878f52f5 Binary files /dev/null and b/models/clip_ebc/__pycache__/mobileclip.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/model.cpython-312.pyc b/models/clip_ebc/__pycache__/model.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c17540e4a2f85933df076b9e394d06d3958991a3 Binary files /dev/null and b/models/clip_ebc/__pycache__/model.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/resnet.cpython-312.pyc b/models/clip_ebc/__pycache__/resnet.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8ae3584f79c905a633d40e7962c8ecaf13c3403d Binary files /dev/null and b/models/clip_ebc/__pycache__/resnet.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/utils.cpython-312.pyc b/models/clip_ebc/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b374a2de0bcbbbd77f2711e7150db88967ed718 Binary files /dev/null and b/models/clip_ebc/__pycache__/utils.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/vit.cpython-312.pyc b/models/clip_ebc/__pycache__/vit.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5280ae98efbd319757d1fb6c6cfe67fd9bc467a5 Binary files /dev/null and b/models/clip_ebc/__pycache__/vit.cpython-312.pyc differ diff --git a/models/clip_ebc/__pycache__/vit_siglip.cpython-312.pyc b/models/clip_ebc/__pycache__/vit_siglip.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b711144eed9984a41e9721c7a844dd1417d464fa Binary files /dev/null and b/models/clip_ebc/__pycache__/vit_siglip.cpython-312.pyc differ diff --git a/models/clip_ebc/convnext.py b/models/clip_ebc/convnext.py new file mode 100644 index 0000000000000000000000000000000000000000..52a3c167879739933703e9892f42d64ccaf47b4e --- /dev/null +++ b/models/clip_ebc/convnext.py @@ -0,0 +1,199 @@ +from torch import nn, Tensor +import open_clip +from peft import get_peft_model, LoraConfig + +from ..utils import ConvRefine, ConvAdapter +from ..utils import ConvUpsample, _get_norm_layer, _get_activation + + +convnext_names_and_weights = { + "convnext_base": ["laion400m_s13b_b51k"], # 107.49M + "convnext_base_w": ["laion2b_s13b_b82k", "laion2b_s13b_b82k_augreg", "laion_aesthetic_s13b_b82k"], # 107.75M + "convnext_base_w_320": ["laion_aesthetic_s13b_b82k", "laion_aesthetic_s13b_b82k_augreg"], # 107.75M + "convnext_large_d": ["laion2b_s26b_b102k_augreg"], # 217.46M + "convnext_large_d_320": ["laion2b_s29b_b131k_ft", "laion2b_s29b_b131k_ft_soup"], # 217.46M + "convnext_xxlarge": ["laion2b_s34b_b82k_augreg", "laion2b_s34b_b82k_augreg_rewind", "laion2b_s34b_b82k_augreg_soup"] # 896.88M +} + +refiner_channels = { + "convnext_base": 1024, + "convnext_base_w": 1024, + "convnext_base_w_320": 1024, + "convnext_large_d": 1536, + "convnext_large_d_320": 1536, + "convnext_xxlarge": 3072, +} + +refiner_groups = { + "convnext_base": 1, + "convnext_base_w": 1, + "convnext_base_w_320": 1, + "convnext_large_d": refiner_channels["convnext_large_d"] // 512, # 3 + "convnext_large_d_320": refiner_channels["convnext_large_d_320"] // 512, # 3 + "convnext_xxlarge": refiner_channels["convnext_xxlarge"] // 512, # 6 +} + + + +class ConvNeXt(nn.Module): + def __init__( + self, + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + norm: str = "none", + act: str = "none" + ) -> None: + super(ConvNeXt, self).__init__() + assert model_name in convnext_names_and_weights, f"Model name should be one of {list(convnext_names_and_weights.keys())}, but got {model_name}." + assert weight_name in convnext_names_and_weights[model_name], f"Pretrained should be one of {convnext_names_and_weights[model_name]}, but got {weight_name}." + assert block_size in [32, 16, 8], f"block_size should be one of [32, 16, 8], got {block_size}" + self.model_name, self.weight_name = model_name, weight_name + self.block_size = block_size + + model = open_clip.create_model_from_pretrained(model_name, weight_name, return_transform=False).visual + + self.adapter = adapter + if adapter: + self.adapter_reduction = adapter_reduction + for param in model.parameters(): + param.requires_grad = False + + self.stem = model.trunk.stem + self.depth = len(model.trunk.stages) + for idx, stage in enumerate(model.trunk.stages): + setattr(self, f"stage{idx}", stage) + if adapter: + setattr(self, f"adapter{idx}", ConvAdapter( + in_channels=stage.blocks[-1].mlp.fc2.out_features, + bottleneck_channels=stage.blocks[-1].mlp.fc2.out_features // adapter_reduction, + ) if idx < self.depth - 1 else nn.Identity()) # No adapter for the last stage + + if self.model_name in ["convnext_base", "convnext_base_w", "convnext_base_w_320", "convnext_xxlarge"]: + self.in_features, self.out_features = model.head.proj.in_features, model.head.proj.out_features + else: # "convnext_large_d", "convnext_large_d_320": + self.in_features, self.out_features = model.head.mlp.fc1.in_features, model.head.mlp.fc2.out_features + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + if block_size == 32: + self.refiner = ConvRefine( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + elif block_size == 16: + self.refiner = ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + else: # block_size == 8 + self.refiner = nn.Sequential( + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ) + + def train(self, mode: bool = True): + if self.adapter and mode: + # training: + self.stem.eval() + + for idx in range(self.depth): + getattr(self, f"stage{idx}").eval() + getattr(self, f"adapter{idx}").train() + + self.refiner.train() + + else: + # evaluation: + for module in self.children(): + module.train(mode) + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + + for idx in range(self.depth): + x = getattr(self, f"stage{idx}")(x) + if self.adapter: + x = getattr(self, f"adapter{idx}")(x) + + x = self.refiner(x) + return x + + +def _convnext( + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + lora: bool = False, + lora_rank: int = 16, + lora_alpha: float = 32.0, + lora_dropout: float = 0.1, + norm: str = "none", + act: str = "none" +) -> ConvNeXt: + assert not (lora and adapter), "Lora and adapter cannot be used together." + model = ConvNeXt( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + norm=norm, + act=act + ) + + if lora: + target_modules = [] + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)) and "refiner" not in name: + target_modules.append(name) + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + bias="none", + target_modules=target_modules, + ) + model = get_peft_model(model, lora_config) + + # Unfreeze refiner + for name, module in model.named_modules(): + if "refiner" in name: + module.requires_grad_(True) + + return model \ No newline at end of file diff --git a/models/clip_ebc/mobileclip.py b/models/clip_ebc/mobileclip.py new file mode 100644 index 0000000000000000000000000000000000000000..00ed77b28677c9dc6e09e7c9da3fe4a34397406b --- /dev/null +++ b/models/clip_ebc/mobileclip.py @@ -0,0 +1,197 @@ +from torch import nn, Tensor +import open_clip +from peft import get_peft_model, LoraConfig + +from ..utils import ConvRefine, ConvUpsample, ConvAdapter +from ..utils import _get_norm_layer, _get_activation + + +mobileclip_names_and_weights = { + "MobileCLIP-S1": ["datacompdr"], + "MobileCLIP-S2": ["datacompdr"], +} + + +refiner_channels = { + "MobileCLIP-S1": 1024, + "MobileCLIP-S2": 1280, +} + +refiner_groups = { + "MobileCLIP-S1": 2, + "MobileCLIP-S2": 2, +} + + +class MobileCLIP(nn.Module): + def __init__( + self, + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in mobileclip_names_and_weights, f"Model name should be one of {list(mobileclip_names_and_weights.keys())}, but got {model_name}." + assert weight_name in mobileclip_names_and_weights[model_name], f"Pretrained should be one of {mobileclip_names_and_weights[model_name]}, but got {weight_name}." + assert block_size in [32, 16, 8], f"block_size should be one of [32, 16, 8], got {block_size}" + self.model_name, self.weight_name = model_name, weight_name + self.block_size = block_size + + model = open_clip.create_model_from_pretrained(model_name, weight_name, return_transform=False).visual + + self.adapter = adapter + if adapter: + for param in model.parameters(): + param.requires_grad = False + + self.stem = model.trunk.stem + self.stages = model.trunk.stages + + self.depth = len(model.trunk.stages) + for idx, stage in enumerate(model.trunk.stages): + if adapter: + setattr(self, f"adapter{idx}", ConvAdapter( + in_channels=stage.blocks[-1].mlp.fc2.out_channels, + bottleneck_channels=stage.blocks[-1].mlp.fc2.out_channels // adapter_reduction, + )) + + self.final_conv = model.trunk.final_conv + + self.in_features, self.out_features = model.trunk.head.fc.in_features, model.trunk.head.fc.out_features + + # refine_block = LightConvRefine if model_name == "MobileCLIP-S1" else ConvRefine + # upsample_block = LightConvUpsample if model_name == "MobileCLIP-S1" else ConvUpsample + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + if block_size == 32: + self.refiner = ConvRefine( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[model_name], + ) + elif block_size == 16: + self.refiner = ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + else: # block_size == 8 + self.refiner = nn.Sequential( + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ) + + def train(self, mode: bool = True): + if self.adapter and mode: + # training: + self.stem.eval() + + for idx in range(self.depth): + getattr(self, f"stage{idx}").eval() + getattr(self, f"adapter{idx}").train() + + self.final_conv.eval() + self.refiner.train() + + else: + # evaluation: + for module in self.children(): + module.train(mode) + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + + for idx in range(self.depth): + x = self.stages[idx](x) + if self.adapter: + x = getattr(self, f"adapter{idx}")(x) + + x = self.final_conv(x) + + x = self.refiner(x) + return x + + +def _mobileclip( + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + lora: bool = False, + lora_rank: int = 16, + lora_alpha: float = 32.0, + lora_dropout: float = 0.1, + norm: str = "none", + act: str = "none" +) -> MobileCLIP: + assert not (lora and adapter), "Lora and adapter cannot be used together." + model = MobileCLIP( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + norm=norm, + act=act + ) + + if lora: + target_modules = [] + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + target_modules.append(name) + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + bias="none", + target_modules=target_modules, + ) + model = get_peft_model(model, lora_config) + + # Unfreeze the BN layers + for name, module in model.named_modules() and "refiner" not in name: + if isinstance(module, nn.BatchNorm2d): + module.requires_grad_(True) + + # Unfreeze refiner + for name, module in model.named_modules(): + if "refiner" in name: + module.requires_grad_(True) + + return model \ No newline at end of file diff --git a/models/clip_ebc/model.py b/models/clip_ebc/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c912ebc64fbdf46e341298e78e85356373ed4d3c --- /dev/null +++ b/models/clip_ebc/model.py @@ -0,0 +1,272 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +import numpy as np +from typing import List, Optional, Dict, Tuple +from copy import deepcopy + +from .vit import vit_names_and_weights, _vit +from .convnext import convnext_names_and_weights, _convnext +from .resnet import resnet_names_and_weights, _resnet +from .mobileclip import mobileclip_names_and_weights, _mobileclip + +from .utils import encode_text, optimize_text_prompts +from ..utils import conv1x1 + +supported_models_and_weights = deepcopy(vit_names_and_weights) +supported_models_and_weights.update(convnext_names_and_weights) +supported_models_and_weights.update(resnet_names_and_weights) +supported_models_and_weights.update(mobileclip_names_and_weights) + + +class CLIP_EBC(nn.Module): + def __init__( + self, + model_name: str, + weight_name: str, + block_size: Optional[int] = None, + bins: Optional[List[Tuple[float, float]]] = None, + bin_centers: Optional[List[float]] = None, + zero_inflated: Optional[bool] = True, + num_vpt: Optional[int] = None, + vpt_drop: Optional[float] = None, + input_size: Optional[int] = None, + adapter: Optional[bool] = False, + adapter_reduction: Optional[int] = None, + lora: Optional[bool] = False, + lora_rank: Optional[int] = None, + lora_alpha: Optional[float] = None, + lora_dropout: Optional[float] = None, + text_prompts: Optional[Dict[str, List[str]]] = None, + norm: Optional[str] = "none", + act: Optional[str] = "none", + ) -> None: + super().__init__() + if "mobileclip" in model_name.lower() or "vit" in model_name.lower(): + model_name = model_name.replace("_", "-") + assert model_name in supported_models_and_weights, f"Model name should be one of {list(supported_models_and_weights.keys())}, but got {model_name}." + assert weight_name in supported_models_and_weights[model_name], f"Pretrained should be one of {supported_models_and_weights[model_name]}, but got {weight_name}." + assert len(bins) == len(bin_centers), f"Expected bins and bin_centers to have the same length, got {len(bins)} and {len(bin_centers)}" + assert len(bins) >= 2, f"Expected at least 2 bins, got {len(bins)}" + assert all(len(b) == 2 for b in bins), f"Expected bins to be a list of tuples of length 2, got {bins}" + bins = [(float(b[0]), float(b[1])) for b in bins] + assert all(bin[0] <= p <= bin[1] for bin, p in zip(bins, bin_centers)), f"Expected bin_centers to be within the range of the corresponding bin, got {bins} and {bin_centers}" + + self.model_name = model_name + self.weight_name = weight_name + self.block_size = block_size + self.bins = bins + self.register_buffer("bin_centers", torch.tensor(bin_centers, dtype=torch.float32, requires_grad=False).view(1, -1, 1, 1)) + self.zero_inflated = zero_inflated + self.text_prompts = text_prompts + + # Image encoder + if model_name in vit_names_and_weights: + assert num_vpt is not None and num_vpt >= 0, f"Number of VPT tokens should be greater than 0, but got {num_vpt}." + vpt_drop = 0. if vpt_drop is None else vpt_drop + self.backbone = _vit( + model_name=model_name, + weight_name=weight_name, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + input_size=(input_size, input_size), + norm=norm, + act=act + ) + elif model_name in convnext_names_and_weights: + self.backbone = _convnext( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + norm=norm, + act=act + ) + elif model_name in resnet_names_and_weights: + self.backbone = _resnet( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + norm=norm, + act=act + ) + elif model_name in mobileclip_names_and_weights: + self.backbone = _mobileclip( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + norm=norm, + act=act + ) + + self._build_text_feats() + self._build_head() + + def _build_text_feats(self) -> None: + model_name, weight_name = self.model_name, self.weight_name + text_prompts = self.text_prompts + + if text_prompts is None: + bins = [b[0] if b[0] == b[1] else b for b in self.bins] # if the bin is a single value (e.g., [0, 0]), use that value + if self.zero_inflated: # separate 0 from the rest + assert bins[0] == 0, f"Expected the first bin to be 0, got {bins[0]}." + bins_pi = [0, (1, float("inf"))] + bins_lambda = bins[1:] + pi_text_prompts = optimize_text_prompts(model_name, weight_name, bins_pi) + lambda_text_prompts = optimize_text_prompts(model_name, weight_name, bins_lambda) + self.text_prompts = {"pi": pi_text_prompts, "lambda": lambda_text_prompts} + pi_text_feats = encode_text(model_name, weight_name, pi_text_prompts) + lambda_text_feats = encode_text(model_name, weight_name, lambda_text_prompts) + pi_text_feats.requires_grad = False + lambda_text_feats.requires_grad = False + self.register_buffer("pi_text_feats", pi_text_feats) + self.register_buffer("lambda_text_feats", lambda_text_feats) + + else: + text_prompts = optimize_text_prompts(model_name, weight_name, bins) + self.text_prompts = text_prompts + text_feats = encode_text(model_name, weight_name, text_prompts) + text_feats.requires_grad = False + self.register_buffer("text_feats", text_feats) + + else: + if self.zero_inflated: + assert "pi" in text_prompts and "lambda" in text_prompts, f"Expected text_prompts to have keys 'pi' and 'lambda', got {text_prompts.keys()}." + pi_text_prompts = text_prompts["pi"] + lambda_text_prompts = text_prompts["lambda"] + pi_text_feats = encode_text(model_name, weight_name, pi_text_prompts) + lambda_text_feats = encode_text(model_name, weight_name, lambda_text_prompts) + pi_text_feats.requires_grad = False + lambda_text_feats.requires_grad = False + self.register_buffer("pi_text_feats", pi_text_feats) + self.register_buffer("lambda_text_feats", lambda_text_feats) + + else: + text_feats = encode_text(model_name, weight_name, text_prompts) + text_feats.requires_grad = False + self.register_buffer("text_feats", text_feats) + + def _build_head(self) -> None: + in_channels = self.backbone.in_features + out_channels = self.backbone.out_features + if self.zero_inflated: + self.pi_logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07), requires_grad=True) + self.lambda_logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07), requires_grad=True) + + self.pi_head = conv1x1(in_channels, out_channels, bias=False) + self.lambda_head = conv1x1(in_channels, out_channels, bias=False) + + else: + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07), requires_grad=True) + self.head = conv1x1(in_channels, out_channels, bias=False) + + def forward(self, image: Tensor): + image_feats = self.backbone(image) + # image_feats = F.normalize(image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C) + + if self.zero_inflated: + pi_image_feats, lambda_image_feats = self.pi_head(image_feats), self.lambda_head(image_feats) + pi_image_feats = F.normalize(pi_image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C) + lambda_image_feats = F.normalize(lambda_image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C) + + pi_text_feats, lambda_text_feats = self.pi_text_feats, self.lambda_text_feats + pi_logit_scale, lambda_logit_scale = self.pi_logit_scale.exp(), self.lambda_logit_scale.exp() + + pi_logit_map = pi_logit_scale * pi_image_feats @ pi_text_feats.t() # (B, H, W, 2), logits per image + lambda_logit_map = lambda_logit_scale * lambda_image_feats @ lambda_text_feats.t() # (B, H, W, N - 1), logits per image + + pi_logit_map = pi_logit_map.permute(0, 3, 1, 2) # (B, 2, H, W) + lambda_logit_map = lambda_logit_map.permute(0, 3, 1, 2) # (B, N - 1, H, W) + + lambda_map = (lambda_logit_map.softmax(dim=1) * self.bin_centers[:, 1:]).sum(dim=1, keepdim=True) # (B, 1, H, W) + + # pi_logit_map.softmax(dim=1)[:, 0] is the probability of zeros + den_map = pi_logit_map.softmax(dim=1)[:, 1:] * lambda_map # (B, 1, H, W) + + if self.training: + return pi_logit_map, lambda_logit_map, lambda_map, den_map + else: + return den_map + + else: + image_feats = self.head(image_feats) + image_feats = F.normalize(image_feats.permute(0, 2, 3, 1), p=2, dim=-1) + + text_feats = self.text_feats + logit_scale = self.logit_scale.exp() + + logit_map = logit_scale * image_feats @ text_feats.t() # (B, H, W, N), logits per image + logit_map = logit_map.permute(0, 3, 1, 2) # (B, N, H, W) + + den_map = (logit_map.softmax(dim=1) * self.bin_centers).sum(dim=1, keepdim=True) # (B, 1, H, W) + + if self.training: + return logit_map, den_map + else: + return den_map + + +def _clip_ebc( + model_name: str, + weight_name: str, + block_size: Optional[int] = None, + bins: Optional[List[Tuple[float, float]]] = None, + bin_centers: Optional[List[float]] = None, + zero_inflated: Optional[bool] = True, + num_vpt: Optional[int] = None, + vpt_drop: Optional[float] = None, + input_size: Optional[int] = None, + adapter: Optional[bool] = False, + adapter_reduction: Optional[int] = None, + lora: Optional[bool] = False, + lora_rank: Optional[int] = None, + lora_alpha: Optional[float] = None, + lora_dropout: Optional[float] = None, + text_prompts: Optional[List[str]] = None, + norm: Optional[str] = "none", + act: Optional[str] = "none", +) -> CLIP_EBC: + return CLIP_EBC( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + bins=bins, + bin_centers=bin_centers, + zero_inflated=zero_inflated, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + input_size=input_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + lora=lora, + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + text_prompts=text_prompts, + norm=norm, + act=act, + ) \ No newline at end of file diff --git a/models/clip_ebc/resnet.py b/models/clip_ebc/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b351c01b0003fff3012fba581542bcd6dc1b8abe --- /dev/null +++ b/models/clip_ebc/resnet.py @@ -0,0 +1,236 @@ +from torch import nn, Tensor +import open_clip +from peft import get_peft_model, LoraConfig + +from ..utils import ConvRefine, ConvUpsample, ConvAdapter +from ..utils import _get_norm_layer, _get_activation + + +resnet_names_and_weights = { + "RN50": ["openai", "yfcc15m", "cc12m"], + "RN101": ["openai", "yfcc15m", "cc12m"], + "RN50x4": ["openai", "yfcc15m", "cc12m"], + "RN50x16": ["openai", "yfcc15m", "cc12m"], + "RN50x64": ["openai", "yfcc15m", "cc12m"], +} + +refiner_channels = { + "RN50": 2048, + "RN101": 2048, + "RN50x4": 2560, + "RN50x16": 3072, + "RN50x64": 4096, +} + +refiner_groups = { + "RN50": refiner_channels["RN50"] // 512, # 4 + "RN101": refiner_channels["RN101"] // 512, # 4 + "RN50x4": refiner_channels["RN50x4"] // 512, # 5 + "RN50x16": refiner_channels["RN50x16"] // 512, # 6 + "RN50x64": refiner_channels["RN50x64"] // 512, # 8 +} + + +class ResNet(nn.Module): + def __init__( + self, + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + norm: str = "none", + act: str = "none" + ) -> None: + super(ResNet, self).__init__() + assert model_name in resnet_names_and_weights, f"Model name should be one of {list(resnet_names_and_weights.keys())}, but got {model_name}." + assert weight_name in resnet_names_and_weights[model_name], f"Pretrained should be one of {resnet_names_and_weights[model_name]}, but got {weight_name}." + assert block_size in [32, 16, 8], f"block_size should be one of [32, 16, 8], got {block_size}" + self.model_name, self.weight_name = model_name, weight_name + self.block_size = block_size + + model = open_clip.create_model_from_pretrained(model_name, weight_name, return_transform=False).visual + + self.adapter = adapter + if adapter: + for param in model.parameters(): + param.requires_grad = False + + # Stem + self.conv1 = model.conv1 + self.bn1 = model.bn1 + self.act1 = model.act1 + self.conv2 = model.conv2 + self.bn2 = model.bn2 + self.act2 = model.act2 + self.conv3 = model.conv3 + self.bn3 = model.bn3 + self.act3 = model.act3 + self.avgpool = model.avgpool + # Stem: reduction = 4 + + # Layers + for idx in range(1, 5): + setattr(self, f"layer{idx}", getattr(model, f"layer{idx}")) + if adapter: + setattr(self, f"adapter{idx}", ConvAdapter( + in_channels=getattr(model, f"layer{idx}")[-1].conv3.out_channels, + bottleneck_channels=getattr(model, f"layer{idx}")[-1].conv3.out_channels // adapter_reduction, + ) if idx < 4 else nn.Identity()) # No adapter for the last layer + + self.in_features = model.attnpool.c_proj.weight.shape[1] + self.out_features = model.attnpool.c_proj.weight.shape[0] + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + if block_size == 32: + self.refiner = ConvRefine( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + elif block_size == 16: + self.refiner = ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + else: # block_size == 8 + self.refiner = nn.Sequential( + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ) + + def train(self, mode: bool = True): + if self.adapter and mode: + # training: + self.conv1.eval() + self.bn1.eval() + self.act1.eval() + self.conv2.eval() + self.bn2.eval() + self.act2.eval() + self.conv3.eval() + self.bn3.eval() + self.act3.eval() + self.avgpool.eval() + + for idx in range(1, 5): + getattr(self, f"layer{idx}").eval() + getattr(self, f"adapter{idx}").train() + + self.refiner.train() + + else: + # evaluation: + for module in self.children(): + module.train(mode) + + def stem(self, x: Tensor) -> Tensor: + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + + x = self.layer1(x) + if self.adapter: + x = self.adapter1(x) + + x = self.layer2(x) + if self.adapter: + x = self.adapter2(x) + + x = self.layer3(x) + if self.adapter: + x = self.adapter3(x) + + x = self.layer4(x) + if self.adapter: + x = self.adapter4(x) + + x = self.refiner(x) + return x + + +def _resnet( + model_name: str, + weight_name: str, + block_size: int = 16, + adapter: bool = False, + adapter_reduction: int = 4, + lora: bool = False, + lora_rank: int = 16, + lora_alpha: float = 32.0, + lora_dropout: float = 0.1, + norm: str = "none", + act: str = "none" +) -> ResNet: + assert not (lora and adapter), "Lora and adapter cannot be used together." + model = ResNet( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + adapter=adapter, + adapter_reduction=adapter_reduction, + norm=norm, + act=act + ) + + if lora: + target_modules = [] + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + target_modules.append(name) + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + bias="none", + target_modules=target_modules, + ) + model = get_peft_model(model, lora_config) + + # Unfreeze BN layers + for name, module in model.named_modules(): + if isinstance(module, nn.BatchNorm2d) and "refiner" not in name: + module.requires_grad_(True) + + # Unfreeze refiner + for name, module in model.named_modules(): + if "refiner" in name: + module.requires_grad_(True) + + return model diff --git a/models/clip_ebc/utils.py b/models/clip_ebc/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c3da4376806acb3b13f5fdafa08ede5de75c34fe --- /dev/null +++ b/models/clip_ebc/utils.py @@ -0,0 +1,137 @@ +import torch +from torch import Tensor, nn +import torch.nn.functional as F +import open_clip +from tqdm import tqdm +import numpy as np +from typing import Union, Tuple, List + + +num_to_word = { + "0": "zero", "1": "one", "2": "two", "3": "three", "4": "four", "5": "five", "6": "six", "7": "seven", "8": "eight", "9": "nine", + "10": "ten", "11": "eleven", "12": "twelve", "13": "thirteen", "14": "fourteen", "15": "fifteen", "16": "sixteen", "17": "seventeen", "18": "eighteen", "19": "nineteen", + "20": "twenty", "21": "twenty-one", "22": "twenty-two", "23": "twenty-three", "24": "twenty-four", "25": "twenty-five", "26": "twenty-six", "27": "twenty-seven", "28": "twenty-eight", "29": "twenty-nine", + "30": "thirty", "31": "thirty-one", "32": "thirty-two", "33": "thirty-three", "34": "thirty-four", "35": "thirty-five", "36": "thirty-six", "37": "thirty-seven", "38": "thirty-eight", "39": "thirty-nine", + "40": "forty", "41": "forty-one", "42": "forty-two", "43": "forty-three", "44": "forty-four", "45": "forty-five", "46": "forty-six", "47": "forty-seven", "48": "forty-eight", "49": "forty-nine", + "50": "fifty", "51": "fifty-one", "52": "fifty-two", "53": "fifty-three", "54": "fifty-four", "55": "fifty-five", "56": "fifty-six", "57": "fifty-seven", "58": "fifty-eight", "59": "fifty-nine", + "60": "sixty", "61": "sixty-one", "62": "sixty-two", "63": "sixty-three", "64": "sixty-four", "65": "sixty-five", "66": "sixty-six", "67": "sixty-seven", "68": "sixty-eight", "69": "sixty-nine", + "70": "seventy", "71": "seventy-one", "72": "seventy-two", "73": "seventy-three", "74": "seventy-four", "75": "seventy-five", "76": "seventy-six", "77": "seventy-seven", "78": "seventy-eight", "79": "seventy-nine", + "80": "eighty", "81": "eighty-one", "82": "eighty-two", "83": "eighty-three", "84": "eighty-four", "85": "eighty-five", "86": "eighty-six", "87": "eighty-seven", "88": "eighty-eight", "89": "eighty-nine", + "90": "ninety", "91": "ninety-one", "92": "ninety-two", "93": "ninety-three", "94": "ninety-four", "95": "ninety-five", "96": "ninety-six", "97": "ninety-seven", "98": "ninety-eight", "99": "ninety-nine", + "100": "one hundred" +} + +prefixes = [ + "", + "A photo of", "A block of", "An image of", "A picture of", + "There are", + "The image contains", "The photo contains", "The picture contains", + "The image shows", "The photo shows", "The picture shows", + ] +arabic_numeral = [True, False] +compares = [ + "more than", "greater than", "higher than", "larger than", "bigger than", "greater than or equal to", + "at least", "no less than", "not less than", "not fewer than", "not lower than", "not smaller than", "not less than or equal to", + "over", "above", "beyond", "exceeding", "surpassing", +] +suffixes = [ + "people", "persons", "individuals", "humans", "faces", "heads", "figures", "", +] + + +def num2word(num: Union[int, str]) -> str: + """ + Convert the input number to the corresponding English word. For example, 1 -> "one", 2 -> "two", etc. + """ + num = str(int(num)) + return num_to_word.get(num, num) + + +def format_count( + bins: List[Union[float, Tuple[float, float]]], +) -> List[List[str]]: + text_prompts = [] + for prefix in prefixes: + for numeral in arabic_numeral: + for compare in compares: + for suffix in suffixes: + prompts = [] + for bin in bins: + if isinstance(bin, (int, float)): # count is a single number + count = int(bin) + if count == 0 or count == 1: + count = num2word(count) if not numeral else count + prefix_ = "There is" if prefix == "There are" else prefix + suffix_ = "person" if suffix == "people" else suffix[:-1] + prompt = f"{prefix_} {count} {suffix_}" + else: # count > 1 + count = num2word(count) if not numeral else count + prompt = f"{prefix} {count} {suffix}" + + elif bin[1] == float("inf"): # count is (lower_bound, inf) + count = int(bin[0]) + count = num2word(count) if not numeral else count + prompt = f"{prefix} {compare} {count} {suffix}" + + else: # bin is (lower_bound, upper_bound) + left, right = int(bin[0]), int(bin[1]) + left, right = num2word(left) if not numeral else left, num2word(right) if not numeral else right + prompt = f"{prefix} between {left} and {right} {suffix}" + + # Remove starting and trailing whitespaces + prompt = prompt.strip() + "." + + prompts.append(prompt) + + text_prompts.append(prompts) + + return text_prompts + + +def encode_text( + model_name: str, + weight_name: str, + text: List[str] +) -> Tensor: + if torch.cuda.is_available(): + device = torch.device("cuda") + elif torch.mps.is_available(): + device = torch.device("mps") + else: + device = torch.device("cpu") + text = open_clip.get_tokenizer(model_name)(text).to(device) + model = open_clip.create_model_from_pretrained(model_name, weight_name, return_transform=False).to(device) + model.eval() + with torch.no_grad(): + text_feats = model.encode_text(text) + text_feats = F.normalize(text_feats, p=2, dim=-1).detach().cpu() + return text_feats + + +def optimize_text_prompts( + model_name: str, + weight_name: str, + flat_bins: List[Union[float, Tuple[float, float]]], + batch_size: int = 1024, +) -> List[str]: + text_prompts = format_count(flat_bins) + + # Find the template that has the smallest average similarity of bin prompts. + print("Finding the best setup for text prompts...") + text_prompts_ = [prompt for prompts in text_prompts for prompt in prompts] # flatten the list + text_feats = [] + for i in tqdm(range(0, len(text_prompts_), batch_size)): + text_feats.append(encode_text(model_name, weight_name, text_prompts_[i: min(i + batch_size, len(text_prompts_))])) + text_feats = torch.cat(text_feats, dim=0) + + sims = [] + for idx, prompts in enumerate(text_prompts): + text_feats_ = text_feats[idx * len(prompts): (idx + 1) * len(prompts)] + sim = torch.mm(text_feats_, text_feats_.T) + sim = sim[~torch.eye(sim.shape[0], dtype=bool)].mean().item() + sims.append(sim) + + optimal_prompts = text_prompts[np.argmin(sims)] + sim = sims[np.argmin(sims)] + print(f"Found the best text prompts: {optimal_prompts} (similarity: {sim:.2f})") + return optimal_prompts diff --git a/models/clip_ebc/vit.py b/models/clip_ebc/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..43e916aacef76dc5bbd5ad8e4333389abe921b52 --- /dev/null +++ b/models/clip_ebc/vit.py @@ -0,0 +1,372 @@ +import torch +from torch import nn, Tensor +import math +from einops import rearrange +import open_clip +from peft import get_peft_model, LoraConfig +from typing import Optional, Tuple + +from ..utils import interpolate_pos_embed, ViTAdapter +# from ..utils import TransformerRefine, TransformerDownsample, TransformerUpsample +from ..utils import ConvRefine, ConvDownsample, ConvUpsample +from ..utils import _get_norm_layer, _get_activation + + +vit_names_and_weights = { + "ViT-B-32": [ + "openai", + "laion400m_e31", "laion400m_e32", "laion2b_e16", "laion2b_s34b_b79k", + "datacomp_xl_s13b_b90k", "datacomp_m_s128m_b4k", "datacomp_s_s13m_b4k", + "commonpool_m_clip_s128m_b4k", "commonpool_m_laion_s128m_b4k", "commonpool_m_image_s128m_b4k", "commonpool_m_text_s128m_b4k", "commonpool_m_basic_s128m_b4k", "commonpool_m_s128m_b4k", + "commonpool_s_clip_s13m_b4k", "commonpool_s_laion_s13m_b4k", "commonpool_s_image_s13m_b4k", "commonpool_s_text_s13m_b4k", "commonpool_s_basic_s13m_b4k", "commonpool_s_s13m_b4k", + ], + "ViT-B_32-256": ["datacomp_s34b_b86k"], + "ViT-B-16": [ + "openai", + "laion400m_e31", "laion400m_e32", "laion2b_s34b_b88k", + "datacomp_xl_s13b_b90k", "datacomp_l_s1b_b8k", + "commonpool_l_clip_s1b_b8k", "commonpool_l_laion_s1b_b8k", "commonpool_l_image_s1b_b8k", "commonpool_l_text_s1b_b8k", "commonpool_l_basic_s1b_b8k", "commonpool_l_s1b_b8k", + "dfn2b" + ], + "ViT-L-14": [ + "openai", + "laion400m_e31", "laion400m_e32", "laion2b_s32b_b82k", + "datacomp_xl_s13b_b90k", + "commonpool_xl_clip_s13b_b90k", "commonpool_xl_laion_s13b_b90k", "commonpool_xl_s13b_b90k" + ], + "ViT-L-14-336": ["openai"], + "ViT-H-14": ["laion2b_s32b_b79k"], + "ViT-g-14": ["laion2b_s12b_b42k", "laion2b_s34b_b88k"], + "ViT-bigG-14": ["laion2b_s39b_b160k"], +} + + +refiner_channels = { + "ViT-B-32": 768, + "ViT-B-32-256": 768, + "ViT-B-16": 768, + "ViT-L-14": 1024, + "ViT-L-14-336": 1024, + "ViT-H-14": 1280, + "ViT-g-14": 1408, + "ViT-bigG-14": 1664, +} + +refiner_groups = { + "ViT-B-32": 1, + "ViT-B-32-256": 1, + "ViT-B-16": 1, + "ViT-L-14": 1, + "ViT-L-14-336": 1, + "ViT-H-14": 1, + "ViT-g-14": refiner_channels["ViT-g-14"] // 704, # 2 + "ViT-bigG-14": refiner_channels["ViT-bigG-14"] // 416, # 4 +} + + + +class ViT(nn.Module): + def __init__( + self, + model_name: str, + weight_name: str, + block_size: int = 16, + num_vpt: int = 32, + vpt_drop: float = 0.0, + adapter: bool = False, + adapter_reduction: int = 4, + input_size: Optional[Tuple[int, int]] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super(ViT, self).__init__() + assert model_name in vit_names_and_weights, f"Model name should be one of {list(vit_names_and_weights.keys())}, but got {model_name}." + assert weight_name in vit_names_and_weights[model_name], f"Pretrained should be one of {vit_names_and_weights[model_name]}, but got {weight_name}." + if adapter: + assert num_vpt is None or num_vpt == 0, "num_vpt should be None or 0 when using adapter." + assert vpt_drop is None or vpt_drop == 0.0, "vpt_drop should be None or 0.0 when using adapter." + else: + assert num_vpt > 0, f"Number of VPT tokens should be greater than 0, but got {num_vpt}." + assert 0.0 <= vpt_drop < 1.0, f"VPT dropout should be in [0.0, 1.0), but got {vpt_drop}." + + self.model_name, self.weight_name = model_name, weight_name + self.block_size = block_size + self.num_vpt = num_vpt + self.vpt_drop = vpt_drop + self.adapter = adapter + + model = open_clip.create_model_from_pretrained(model_name, weight_name, return_transform=False).visual + + # Always freeze the parameters of the model + for param in model.parameters(): + param.requires_grad = False + + # Setup the model + self.input_size = input_size if input_size is not None else model.image_size + self.pretrain_size = model.image_size + self.patch_size = model.patch_size + self.class_embedding = model.class_embedding + self.positional_embedding = model.positional_embedding + self.embed_dim = model.class_embedding.shape[-1] + + self.conv1 = model.conv1 + self.ln_pre = model.ln_pre + self.resblocks = model.transformer.resblocks + self.num_layers = len(self.resblocks) + self.ln_post = model.ln_post + + # Setup VPT tokens + val = math.sqrt(6. / float(3 * self.patch_size[0] + self.embed_dim)) + for idx in range(self.num_layers): + if self.adapter: + setattr(self, f"adapter{idx}", ViTAdapter( + in_channels=self.embed_dim, + bottleneck_channels=self.embed_dim // adapter_reduction, + )) + else: + setattr(self, f"vpt_{idx}", nn.Parameter(torch.empty(self.num_vpt, self.embed_dim))) + nn.init.uniform_(getattr(self, f"vpt_{idx}"), -val, val) + setattr(self, f"vpt_drop_{idx}", nn.Dropout(self.vpt_drop)) + + # Adjust the positional embedding to match the new input size + self._adjust_pos_embed() + + in_features, out_features = model.proj.shape + self.in_features = in_features + self.out_features = out_features + + patch_size = self.patch_size[0] + if patch_size in [16, 32]: + assert block_size in [8, 16, 32], f"Patch size is 32, but got block size {block_size}." + else: # patch_size == 14 + assert block_size in [7, 14, 28], f"Patch size is 14, but got block size {block_size}." + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + if block_size == patch_size: + self.refiner = ConvRefine( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + + elif block_size < patch_size: # upsample + if block_size == 8 and patch_size == 32: + self.refiner = nn.Sequential( + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ), + ) + else: + self.refiner = ConvUpsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + + else: # downsample + assert block_size // patch_size == 2, f"Block size {block_size} should be 2 times the patch size {patch_size}." + self.refiner = ConvDownsample( + in_channels=self.in_features, + out_channels=self.in_features, + norm_layer=norm_layer, + activation=activation, + groups=refiner_groups[self.model_name], + ) + + def _adjust_pos_embed(self) -> Tensor: + """ + Adjust the positional embedding to match the spatial resolution of the feature map. + + Args: + orig_h, orig_w: The original spatial resolution of the image. + new_h, new_w: The new spatial resolution of the image. + """ + self.positional_embedding = nn.Parameter(self._interpolate_pos_embed(self.pretrain_size[0], self.pretrain_size[1], self.input_size[0], self.input_size[1]), requires_grad=False) + + def _interpolate_pos_embed(self, orig_h: int, orig_w: int, new_h: int, new_w: int) -> Tensor: + """ + Interpolate the positional embedding to match the spatial resolution of the feature map. + + Args: + orig_h, orig_w: The original spatial resolution of the image. + new_h, new_w: The new spatial resolution of the image. + """ + if (orig_h, orig_w) == (new_h, new_w): + return self.positional_embedding + + orig_h_patches, orig_w_patches = orig_h // self.patch_size[0], orig_w // self.patch_size[1] + new_h_patches, new_w_patches = new_h // self.patch_size[0], new_w // self.patch_size[1] + class_pos_embed, patch_pos_embed = self.positional_embedding[:1, :], self.positional_embedding[1:, :] + patch_pos_embed = rearrange(patch_pos_embed, "(h w) d -> d h w", h=orig_h_patches, w=orig_w_patches) + patch_pos_embed = interpolate_pos_embed(patch_pos_embed, size=(new_h_patches, new_w_patches)) + patch_pos_embed = rearrange(patch_pos_embed, "d h w -> (h w) d") + pos_embed = torch.cat((class_pos_embed, patch_pos_embed), dim=0) + return pos_embed + + def train(self, mode: bool = True): + if mode: + # training: + self.conv1.eval() + self.ln_pre.eval() + self.resblocks.eval() + self.ln_post.eval() + + for idx in range(self.num_layers): + getattr(self, f"vpt_drop_{idx}").train() + + self.refiner.train() + + else: + # evaluation: + for module in self.children(): + module.train(mode) + + def _prepare_vpt(self, layer: int, batch_size: int, device: torch.device) -> Tensor: + vpt = getattr(self, f"vpt_{layer}").unsqueeze(0).expand(batch_size, -1, -1).to(device) # (batch_size, num_vpt, embed_dim) + vpt = getattr(self, f"vpt_drop_{layer}")(vpt) + + return vpt + + def _forward_patch_embed(self, x: Tensor) -> Tensor: + # This step performs 1) embed x into patches; 2) append the class token; 3) add positional embeddings. + assert len(x.shape) == 4, f"Expected input to have shape (batch_size, 3, height, width), but got {x.shape}" + batch_size, _, height, width = x.shape + + # Step 1: Embed x into patches + x = self.conv1(x) + + # Step 2: Append the class token + class_embedding = self.class_embedding.expand(batch_size, 1, -1) + x = rearrange(x, "b d h w -> b (h w) d") + x = torch.cat([class_embedding, x], dim=1) + + # Step 3: Add positional embeddings + pos_embed = self._interpolate_pos_embed(orig_h=self.input_size[0], orig_w=self.input_size[1], new_h=height, new_w=width).expand(batch_size, -1, -1) + x = x + pos_embed + + x = self.ln_pre(x) + return x + + def _forward_vpt(self, x: Tensor, idx: int) -> Tensor: + batch_size = x.shape[0] + device = x.device + + # Assemble + vpt = self._prepare_vpt(idx, batch_size, device) + x = torch.cat([ + x[:, :1, :], # class token + vpt, + x[:, 1:, :] # patches + ], dim=1) + + # Forward + x = self.resblocks[idx](x) + + # Disassemble + x = torch.cat([ + x[:, :1, :], # class token + x[:, 1 + self.num_vpt:, :] # patches + ], dim=1) + + return x + + def _forward_adapter(self, x: Tensor, idx: int) -> Tensor: + return getattr(self, f"adapter{idx}")(x) + + def forward_encoder(self, x: Tensor) -> Tensor: + x = self._forward_patch_embed(x) + for idx in range(self.num_layers): + x = self._forward_adapter(x, idx) if self.adapter else self._forward_vpt(x, idx) + x = self.ln_post(x) + return x + + def forward(self, x: Tensor) -> Tensor: + orig_h, orig_w = x.shape[-2:] + num_patches_h, num_patches_w = orig_h // self.patch_size[0], orig_w // self.patch_size[1] + x = self.forward_encoder(x) + x = x[:, 1:, :] # remove the class token + x = rearrange(x, "b (h w) d -> b d h w", h=num_patches_h, w=num_patches_w) + + x = self.refiner(x) + return x + + +def _vit( + model_name: str, + weight_name: str, + block_size: int = 16, + num_vpt: int = 32, + vpt_drop: float = 0.1, + adapter: bool = False, + adapter_reduction: int = 4, + lora: bool = False, + lora_rank: int = 16, + lora_alpha: float = 32.0, + lora_dropout: float = 0.1, + input_size: Optional[Tuple[int, int]] = None, + norm: str = "none", + act: str = "none" +) -> ViT: + assert not (lora and adapter), "LoRA and adapter cannot be used together." + model = ViT( + model_name=model_name, + weight_name=weight_name, + block_size=block_size, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + adapter=adapter, + adapter_reduction=adapter_reduction, + input_size=input_size, + norm=norm, + act=act + ) + + if lora: + target_modules = [] + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d, nn.MultiheadAttention)) and "refiner" not in name: + target_modules.append(name) + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + bias="none", + target_modules=target_modules, + ) + model = get_peft_model(model, lora_config) + + # Unfreeze refiner + for name, module in model.named_modules(): + if "refiner" in name: + module.requires_grad_(True) + + return model diff --git a/models/ebc/__init__.py b/models/ebc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..72bc84f9940eb4885387aa08fac76186e8bda8ba --- /dev/null +++ b/models/ebc/__init__.py @@ -0,0 +1,3 @@ +from .model import EBC, _ebc + +__all__ = ["EBC", "_ebc"] diff --git a/models/ebc/__pycache__/__init__.cpython-312.pyc b/models/ebc/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f94824371ebabd62934a36e56cdf37c4f486dd8c Binary files /dev/null and b/models/ebc/__pycache__/__init__.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/cannet.cpython-312.pyc b/models/ebc/__pycache__/cannet.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cf3f564a3f8b39131e2420777a319759ea9bac9c Binary files /dev/null and b/models/ebc/__pycache__/cannet.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/csrnet.cpython-312.pyc b/models/ebc/__pycache__/csrnet.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..860bb06f60553763a17a8f24cebf09b55ac9c710 Binary files /dev/null and b/models/ebc/__pycache__/csrnet.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/hrnet.cpython-312.pyc b/models/ebc/__pycache__/hrnet.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0dc97b2f8dc79f74cf8127f62ee56284a9c82aa5 Binary files /dev/null and b/models/ebc/__pycache__/hrnet.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/model.cpython-312.pyc b/models/ebc/__pycache__/model.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..73d3f6ba769875369511ce01acf12c2018b08702 Binary files /dev/null and b/models/ebc/__pycache__/model.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/timm_models.cpython-312.pyc b/models/ebc/__pycache__/timm_models.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..948ae1b28e2e0d54819108cff2594252c5529175 Binary files /dev/null and b/models/ebc/__pycache__/timm_models.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/utils.cpython-312.pyc b/models/ebc/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..412b3007fb85a8f9151b91a2497e7ad72b63f973 Binary files /dev/null and b/models/ebc/__pycache__/utils.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/vgg.cpython-312.pyc b/models/ebc/__pycache__/vgg.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..62b272e17a3f18ef302265e8808b2514c601a0a6 Binary files /dev/null and b/models/ebc/__pycache__/vgg.cpython-312.pyc differ diff --git a/models/ebc/__pycache__/vit.cpython-312.pyc b/models/ebc/__pycache__/vit.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..05fc195a437c15267a6d20ba16366880c2a2f79d Binary files /dev/null and b/models/ebc/__pycache__/vit.cpython-312.pyc differ diff --git a/models/ebc/cannet.py b/models/ebc/cannet.py new file mode 100644 index 0000000000000000000000000000000000000000..db4af4af7e26ea419c3a3081d04632bcb4f285d1 --- /dev/null +++ b/models/ebc/cannet.py @@ -0,0 +1,105 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from typing import List, Optional + +from .csrnet import _csrnet, _csrnet_bn +from ..utils import _init_weights + +EPS = 1e-6 + + +class ContextualModule(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int = 512, + scales: List[int] = [1, 2, 3, 6], + ) -> None: + super().__init__() + self.scales = scales + self.multiscale_modules = nn.ModuleList([self.__make_scale__(in_channels, size) for size in scales]) + self.bottleneck = nn.Conv2d(in_channels * 2, out_channels, kernel_size=1) + self.relu = nn.ReLU(inplace=True) + self.weight_net = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.apply(_init_weights) + + def __make_weight__(self, feature: Tensor, scale_feature: Tensor) -> Tensor: + weight_feature = feature - scale_feature + weight_feature = self.weight_net(weight_feature) + return F.sigmoid(weight_feature) + + def __make_scale__(self, channels: int, size: int) -> nn.Module: + return nn.Sequential( + nn.AdaptiveAvgPool2d(output_size=(size, size)), + nn.Conv2d(channels, channels, kernel_size=1, bias=False), + ) + + def forward(self, feature: Tensor) -> Tensor: + h, w = feature.shape[-2:] + multiscale_feats = [F.interpolate(input=scale(feature), size=(h, w), mode="bilinear") for scale in self.multiscale_modules] + weights = [self.__make_weight__(feature, scale_feature) for scale_feature in multiscale_feats] + multiscale_feats = sum([multiscale_feats[i] * weights[i] for i in range(len(weights))]) / (sum(weights) + EPS) + overall_features = torch.cat([multiscale_feats, feature], dim=1) + overall_features = self.bottleneck(overall_features) + overall_features = self.relu(overall_features) + return overall_features + + +class CANNet(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none", + scales: List[int] = [1, 2, 3, 6], + ) -> None: + super().__init__() + assert model_name in ["csrnet", "csrnet_bn"], f"Model name should be one of ['csrnet', 'csrnet_bn'], but got {model_name}." + assert block_size is None or block_size in [8, 16, 32], f"block_size should be one of [8, 16, 32], but got {block_size}." + assert isinstance(scales, (tuple, list)), f"scales should be a list or tuple, got {type(scales)}." + assert len(scales) > 0, f"Expected at least one size, got {len(scales)}." + assert all([isinstance(size, int) for size in scales]), f"Expected all size to be int, got {scales}." + self.model_name = model_name + self.scales = scales + + csrnet = _csrnet(block_size=block_size, norm=norm, act=act) if model_name == "csrnet" else _csrnet_bn(block_size=block_size, norm=norm, act=act) + self.block_size = csrnet.block_size + + self.encoder = csrnet.encoder + self.encoder_channels = csrnet.encoder_channels + self.encoder_reduction = csrnet.encoder_reduction # feature map size compared to input size + + self.refiner = nn.Sequential( + csrnet.refiner, + ContextualModule(csrnet.refine_channels, 512, scales) + ) + self.refiner_channels = 512 + self.refiner_reduction = csrnet.refiner_reduction # feature map size compared to input size + + self.decoder = csrnet.decoder + self.decoder_channels = csrnet.decoder_channels + self.decoder_reduction = csrnet.decoder_reduction + + def encode(self, x: Tensor) -> Tensor: + return self.encoder(x) + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +def _cannet(block_size: Optional[int] = None, norm: str = "none", act: str = "none", scales: List[int] = [1, 2, 3, 6]) -> CANNet: + return CANNet("csrnet", block_size=block_size, norm=norm, act=act, scales=scales) + +def _cannet_bn(block_size: Optional[int] = None, norm: str = "none", act: str = "none", scales: List[int] = [1, 2, 3, 6]) -> CANNet: + return CANNet("csrnet_bn", block_size=block_size, norm=norm, act=act, scales=scales) diff --git a/models/ebc/csrnet.py b/models/ebc/csrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ec7f08b7748e4042b654727410f52214534018 --- /dev/null +++ b/models/ebc/csrnet.py @@ -0,0 +1,104 @@ +from torch import nn, Tensor +from torch.hub import load_state_dict_from_url +from typing import Optional + +from .vgg import VGG +from .utils import make_vgg_layers, vgg_urls +from ..utils import _init_weights, ConvDownsample, _get_activation, _get_norm_layer + +EPS = 1e-6 + + +encoder_cfg = [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512] +decoder_cfg = [512, 512, 512, 256, 128] + + +class CSRNet(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in ["vgg16", "vgg16_bn"], f"Model name should be one of ['vgg16', 'vgg16_bn'], but got {model_name}." + assert block_size is None or block_size in [8, 16, 32], f"block_size should be one of [8, 16, 32], but got {block_size}." + self.model_name = model_name + + vgg = VGG(make_vgg_layers(encoder_cfg, in_channels=3, batch_norm="bn" in model_name, dilation=1)) + vgg.load_state_dict(load_state_dict_from_url(vgg_urls[model_name]), strict=False) + self.encoder = vgg.features + self.encoder_reduction = 8 + self.encoder_channels = 512 + self.block_size = block_size if block_size is not None else 8 + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(vgg) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(vgg) + + if self.block_size == self.encoder_reduction: + self.refiner = nn.Identity() + elif self.block_size > self.encoder_reduction: + if self.block_size == 32: + self.refiner = nn.Sequential( + ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ), + ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + ) + elif self.block_size == 16: + self.refiner = ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + self.refiner_channels = self.encoder_channels + self.refiner_reduction = self.block_size + + decoder = make_vgg_layers(decoder_cfg, in_channels=512, batch_norm="bn" in model_name, dilation=2) + decoder.apply(_init_weights) + self.decoder = decoder + self.decoder_channels = decoder_cfg[-1] + self.decoder_reduction = self.refiner_reduction + + def encode(self, x: Tensor) -> Tensor: + return self.encoder(x) + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +def _csrnet(block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> CSRNet: + return CSRNet("vgg16", block_size=block_size, norm=norm, act=act) + +def _csrnet_bn(block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> CSRNet: + return CSRNet("vgg16_bn", block_size=block_size, norm=norm, act=act) diff --git a/models/ebc/hrnet.py b/models/ebc/hrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..af87785868150d8056914c83492ad52e3a947a5d --- /dev/null +++ b/models/ebc/hrnet.py @@ -0,0 +1,195 @@ +import timm +import torch.nn.functional as F +from torch import nn, Tensor +from functools import partial +from typing import Optional + +from ..utils import ConvRefine, _get_norm_layer, _get_activation + + +available_hrnets = [ + "hrnet_w18", "hrnet_w18_small", "hrnet_w18_small_v2", + "hrnet_w30", "hrnet_w32", "hrnet_w40", "hrnet_w44", "hrnet_w48", "hrnet_w64", +] + + +class HRNet(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in available_hrnets, f"Model name should be one of {available_hrnets}" + assert block_size is None or block_size in [8, 16, 32], f"block_size should be one of [8, 16, 32], but got {block_size}." + self.model_name = model_name + self.block_size = block_size if block_size is not None else 32 + + model = timm.create_model(model_name, pretrained=True) + + self.conv1 = model.conv1 + self.bn1 = model.bn1 + self.act1 = model.act1 + self.conv2 = model.conv2 + self.bn2 = model.bn2 + self.act2 = model.act2 + + self.layer1 = model.layer1 + + self.transition1 = model.transition1 + self.stage2 = model.stage2 + + self.transition2 = model.transition2 + self.stage3 = model.stage3 + + self.transition3 = model.transition3 + self.stage4 = model.stage4 + + incre_modules = model.incre_modules + downsamp_modules = model.downsamp_modules + + assert len(incre_modules) == 4, f"Expected 4 incre_modules, got {len(self.incre_modules)}" + assert len(downsamp_modules) == 3, f"Expected 3 downsamp_modules, got {len(self.downsamp_modules)}" + + self.out_channels_4 = incre_modules[0][0].downsample[0].out_channels + self.out_channels_8 = incre_modules[1][0].downsample[0].out_channels + self.out_channels_16 = incre_modules[2][0].downsample[0].out_channels + self.out_channels_32 = incre_modules[3][0].downsample[0].out_channels + + if self.block_size == 8: + self.encoder_reduction = 8 + self.encoder_channels = self.out_channels_8 + self.incre_modules = incre_modules[:2] + self.downsamp_modules = downsamp_modules[:1] + + self.refiner = nn.Identity() + self.refiner_reduction = 8 + self.refiner_channels = self.out_channels_8 + + elif self.block_size == 16: + self.encoder_reduction = 16 + self.encoder_channels = self.out_channels_16 + self.incre_modules = incre_modules[:3] + self.downsamp_modules = downsamp_modules[:2] + + self.refiner = nn.Identity() + self.refiner_reduction = 16 + self.refiner_channels = self.out_channels_16 + + else: # self.block_size == 32 + self.encoder_reduction = 32 + self.encoder_channels = self.out_channels_32 + self.incre_modules = incre_modules + self.downsamp_modules = downsamp_modules + + self.refiner = nn.Identity() + self.refiner_reduction = 32 + self.refiner_channels = self.out_channels_32 + + # define the decoder + if self.refiner_channels <= 512: + groups = 1 + elif self.refiner_channels <= 1024: + groups = 2 + elif self.refiner_channels <= 2048: + groups = 4 + else: + groups = 8 + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + decoder_block = partial(ConvRefine, groups=groups, norm_layer=norm_layer, activation=activation) + if self.refiner_channels <= 256: + self.decoder = nn.Identity() + self.decoder_channels = self.refiner_channels + elif self.refiner_channels <= 512: + self.decoder = decoder_block(self.refiner_channels, self.refiner_channels // 2) + self.decoder_channels = self.refiner_channels // 2 + elif self.refiner_channels <= 1024: + self.decoder = nn.Sequential( + decoder_block(self.refiner_channels, self.refiner_channels // 2), + decoder_block(self.refiner_channels // 2, self.refiner_channels // 4), + ) + self.decoder_channels = self.refiner_channels // 4 + else: + self.decoder = nn.Sequential( + decoder_block(self.refiner_channels, self.refiner_channels // 2), + decoder_block(self.refiner_channels // 2, self.refiner_channels // 4), + decoder_block(self.refiner_channels // 4, self.refiner_channels // 8), + ) + self.decoder_channels = self.refiner_channels // 8 + + self.decoder_reduction = self.refiner_reduction + + def _interpolate(self, x: Tensor) -> Tensor: + # This method adjust the spatial dimensions of the input tensor so that it can be divided by 32. + if x.shape[-1] % 32 != 0 or x.shape[-2] % 32 != 0: + new_h = int(round(x.shape[-2] / 32) * 32) + new_w = int(round(x.shape[-1] / 32) * 32) + return F.interpolate(x, size=(new_h, new_w), mode="bicubic", align_corners=False) + + return x + + + def encode(self, x: Tensor) -> Tensor: + x = self.conv1(x) + x = self.bn1(x) + x = self.act1(x) + + x = self.conv2(x) + x = self.bn2(x) + x = self.act2(x) + + x = self.layer1(x) + + x = [t(x) for t in self.transition1] + x = self.stage2(x) + + x = [t(x[-1]) if not isinstance(t, nn.Identity) else x[i] for i, t in enumerate(self.transition2)] + x = self.stage3(x) + + x = [t(x[-1]) if not isinstance(t, nn.Identity) else x[i] for i, t in enumerate(self.transition3)] + x = self.stage4(x) + + assert len(x) == 4, f"Expected 4 outputs, got {len(x)}" + + feats = None + for i, incre in enumerate(self.incre_modules): + if feats is None: + feats = incre(x[i]) + else: + down = self.downsamp_modules[i - 1] # needed for torchscript module indexing + feats = incre(x[i]) + down.forward(feats) + + return feats + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self._interpolate(x) + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +def _hrnet(model_name: str, block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> HRNet: + return HRNet(model_name, block_size, norm, act) \ No newline at end of file diff --git a/models/ebc/model.py b/models/ebc/model.py new file mode 100644 index 0000000000000000000000000000000000000000..37a936d5fadbe9263506341e1f96f842f01b7ed8 --- /dev/null +++ b/models/ebc/model.py @@ -0,0 +1,199 @@ +import torch +from torch import nn, Tensor +from einops import rearrange + +from typing import Tuple, Union, Dict, Optional, List +from functools import partial + +from .cannet import _cannet, _cannet_bn +from .csrnet import _csrnet, _csrnet_bn +from .vgg import _vgg_encoder_decoder, _vgg_encoder +from .vit import _vit, supported_vit_backbones +from .timm_models import _timm_model +from .timm_models import regular_models as timm_regular_models, heavy_models as timm_heavy_models, light_models as timm_light_models, lighter_models as timm_lighter_models +from .hrnet import _hrnet, available_hrnets + +from ..utils import conv1x1 + + +regular_models = [ + "csrnet", "csrnet_bn", + "cannet", "cannet_bn", + "vgg11", "vgg11_bn", "vgg13", "vgg13_bn", "vgg16", "vgg16_bn", "vgg19", "vgg19_bn", + "vgg11_ae", "vgg11_bn_ae", "vgg13_ae", "vgg13_bn_ae", "vgg16_ae", "vgg16_bn_ae", "vgg19_ae", "vgg19_bn_ae", + *timm_regular_models, + *available_hrnets, +] + +heavy_models = timm_heavy_models + +light_models = timm_light_models + +lighter_models = timm_lighter_models + +transformer_models = supported_vit_backbones + +supported_models = regular_models + heavy_models + light_models + lighter_models + transformer_models + + + +class EBC(nn.Module): + def __init__( + self, + model_name: str, + block_size: int, + bins: List[Tuple[float, float]], + bin_centers: List[float], + zero_inflated: bool = True, + num_vpt: Optional[int] = None, + vpt_drop: Optional[float] = None, + input_size: Optional[int] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in supported_models, f"Model name should be one of {supported_models}, but got {model_name}." + self.model_name = model_name + + if input_size is not None: + input_size = (input_size, input_size) if isinstance(input_size, int) else input_size + assert len(input_size) == 2 and input_size[0] > 0 and input_size[1] > 0, f"Expected input_size to be a tuple of two positive integers, got {input_size}" + self.input_size = input_size + + assert len(bins) == len(bin_centers), f"Expected bins and bin_centers to have the same length, got {len(bins)} and {len(bin_centers)}" + assert len(bins) >= 2, f"Expected at least 2 bins, got {len(bins)}" + assert all(len(b) == 2 for b in bins), f"Expected bins to be a list of tuples of length 2, got {bins}" + bins = [(float(b[0]), float(b[1])) for b in bins] + assert all(bin[0] <= p <= bin[1] for bin, p in zip(bins, bin_centers)), f"Expected bin_centers to be within the range of the corresponding bin, got {bins} and {bin_centers}" + + self.block_size = block_size + self.bins = bins + self.register_buffer("bin_centers", torch.tensor(bin_centers, dtype=torch.float32, requires_grad=False).view(1, -1, 1, 1)) + + self.zero_inflated = zero_inflated + self.num_vpt = num_vpt + self.vpt_drop = vpt_drop + self.input_size = input_size + + self.norm = norm + self.act = act + + self._build_backbone() + self._build_head() + + def _build_backbone(self) -> None: + model_name = self.model_name + if model_name == "csrnet": + self.backbone = _csrnet(self.block_size, self.norm, self.act) + elif model_name == "csrnet_bn": + self.backbone = _csrnet_bn(self.block_size, self.norm, self.act) + elif model_name == "cannet": + self.backbone = _cannet(self.block_size, self.norm, self.act) + elif model_name == "cannet_bn": + self.backbone = _cannet_bn(self.block_size, self.norm, self.act) + elif model_name == "vgg11": + self.backbone = _vgg_encoder("vgg11", self.block_size, self.norm, self.act) + elif model_name == "vgg11_ae": + self.backbone = _vgg_encoder_decoder("vgg11", self.block_size, self.norm, self.act) + elif model_name == "vgg11_bn": + self.backbone = _vgg_encoder("vgg11_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg11_bn_ae": + self.backbone = _vgg_encoder_decoder("vgg11_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg13": + self.backbone = _vgg_encoder("vgg13", self.block_size, self.norm, self.act) + elif model_name == "vgg13_ae": + self.backbone = _vgg_encoder_decoder("vgg13", self.block_size, self.norm, self.act) + elif model_name == "vgg13_bn": + self.backbone = _vgg_encoder("vgg13_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg13_bn_ae": + self.backbone = _vgg_encoder_decoder("vgg13_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg16": + self.backbone = _vgg_encoder("vgg16", self.block_size, self.norm, self.act) + elif model_name == "vgg16_ae": + self.backbone = _vgg_encoder_decoder("vgg16", self.block_size, self.norm, self.act) + elif model_name == "vgg16_bn": + self.backbone = _vgg_encoder("vgg16_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg16_bn_ae": + self.backbone = _vgg_encoder_decoder("vgg16_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg19": + self.backbone = _vgg_encoder("vgg19", self.block_size, self.norm, self.act) + elif model_name == "vgg19_ae": + self.backbone = _vgg_encoder_decoder("vgg19", self.block_size, self.norm, self.act) + elif model_name == "vgg19_bn": + self.backbone = _vgg_encoder("vgg19_bn", self.block_size, self.norm, self.act) + elif model_name == "vgg19_bn_ae": + self.backbone = _vgg_encoder_decoder("vgg19_bn", self.block_size, self.norm, self.act) + elif model_name in supported_vit_backbones: + self.backbone = _vit(model_name, block_size=self.block_size, num_vpt=self.num_vpt, vpt_drop=self.vpt_drop, input_size=self.input_size, norm=self.norm, act=self.act) + elif model_name in available_hrnets: + self.backbone = _hrnet(model_name, block_size=self.block_size, norm=self.norm, act=self.act) + else: + self.backbone = _timm_model(model_name, self.block_size, self.norm, self.act) + + def _build_head(self) -> None: + channels = self.backbone.decoder_channels + if self.zero_inflated: + self.bin_head = conv1x1( + in_channels=channels, + out_channels=len(self.bins) - 1, + ) + self.pi_head = conv1x1( + in_channels=channels, + out_channels=2, + ) # this models structural 0s. + else: + self.bin_head = conv1x1( + in_channels=channels, + out_channels=len(self.bins), + ) + + def forward(self, x: Tensor) -> Union[Tensor, Tuple[Tensor, Tensor]]: + x = self.backbone(x) + + if self.zero_inflated: + logit_pi_maps = self.pi_head(x) # shape: (B, 2, H, W) + logit_maps = self.bin_head(x) # shape: (B, C, H, W) + lambda_maps = (logit_maps.softmax(dim=1) * self.bin_centers[:, 1:]).sum(dim=1, keepdim=True) # shape: (B, 1, H, W) + + # logit_pi_maps.softmax(dim=1)[:, 0] is the probability of zeros + den_maps = logit_pi_maps.softmax(dim=1)[:, 1:] * lambda_maps # expectation of the Poisson distribution + + if self.training: + return logit_pi_maps, logit_maps, lambda_maps, den_maps + else: + return den_maps + + else: + logit_maps = self.bin_head(x) + den_maps = (logit_maps.softmax(dim=1) * self.bin_centers).sum(dim=1, keepdim=True) + + if self.training: + return logit_maps, den_maps + else: + return den_maps + + +def _ebc( + model_name: str, + block_size: int, + bins: List[Tuple[float, float]], + bin_centers: List[float], + zero_inflated: bool = True, + num_vpt: Optional[int] = None, + vpt_drop: Optional[float] = None, + input_size: Optional[int] = None, + norm: str = "none", + act: str = "none" +) -> EBC: + return EBC( + model_name=model_name, + block_size=block_size, + bins=bins, + bin_centers=bin_centers, + zero_inflated=zero_inflated, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + input_size=input_size, + norm=norm, + act=act + ) diff --git a/models/ebc/timm_models.py b/models/ebc/timm_models.py new file mode 100644 index 0000000000000000000000000000000000000000..0e9b1e7bb81f43d334966e1ec06152c49d6ca642 --- /dev/null +++ b/models/ebc/timm_models.py @@ -0,0 +1,318 @@ +from timm import create_model +from torch import nn, Tensor +from typing import Optional +from functools import partial + +from ..utils import _get_activation, _get_norm_layer, ConvUpsample, ConvDownsample +from ..utils import LightConvUpsample, LightConvDownsample, LighterConvUpsample, LighterConvDownsample +from ..utils import ConvRefine, LightConvRefine, LighterConvRefine + +regular_models = [ + "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", + "convnext_nano", "convnext_tiny", "convnext_small", "convnext_base", + "mobilenetv4_conv_large", +] + +heavy_models = [ + "convnext_large", "convnext_xlarge", "convnext_xxlarge", +] + +light_models = [ + "mobilenetv1_100", "mobilenetv1_125", + "mobilenetv2_100", "mobilenetv2_140", + "mobilenetv3_large_100", + "mobilenetv4_conv_medium", + +] + +lighter_models = [ + "mobilenetv2_050", + "mobilenetv3_small_050", "mobilenetv3_small_075", "mobilenetv3_small_100", + "mobilenetv4_conv_small_050", "mobilenetv4_conv_small" +] + +supported_models = regular_models + heavy_models + light_models + lighter_models + + +refiner_in_channels = { + # ResNet + "resnet18": 512, + "resnet34": 512, + "resnet50": 2048, + "resnet101": 2048, + "resnet152": 2048, + # ConvNeXt + "convnext_nano": 640, + "convnext_tiny": 768, + "convnext_small": 768, + "convnext_base": 1024, + "convnext_large": 1536, + "convnext_xlarge": 2048, + "convnext_xxlarge": 3072, + # MobileNet V1 + "mobilenetv1_100": 1024, + "mobilenetv1_125": 1280, + # MobileNet V2 + "mobilenetv2_050": 160, + "mobilenetv2_100": 320, + "mobilenetv2_140": 448, + # MobileNet V3 + "mobilenetv3_small_050": 288, + "mobilenetv3_small_075": 432, + "mobilenetv3_small_100": 576, + "mobilenetv3_large_100": 960, + # MobileNet V4 + "mobilenetv4_conv_small_050": 480, + "mobilenetv4_conv_small": 960, + "mobilenetv4_conv_medium": 960, + "mobilenetv4_conv_large": 960, +} + + +refiner_out_channels = { + # ResNet + "resnet18": 512, + "resnet34": 512, + "resnet50": 2048, + "resnet101": 2048, + "resnet152": 2048, + # ConvNeXt + "convnext_nano": 640, + "convnext_tiny": 768, + "convnext_small": 768, + "convnext_base": 1024, + "convnext_large": 1536, + "convnext_xlarge": 2048, + "convnext_xxlarge": 3072, + # MobileNet V1 + "mobilenetv1_100": 512, + "mobilenetv1_125": 640, + # MobileNet V2 + "mobilenetv2_050": 160, + "mobilenetv2_100": 320, + "mobilenetv2_140": 448, + # MobileNet V3 + "mobilenetv3_small_050": 288, + "mobilenetv3_small_075": 432, + "mobilenetv3_small_100": 576, + "mobilenetv3_large_100": 480, + # MobileNet V4 + "mobilenetv4_conv_small_050": 480, + "mobilenetv4_conv_small": 960, + "mobilenetv4_conv_medium": 960, + "mobilenetv4_conv_large": 960, +} + + +groups = { + # ResNet + "resnet18": 1, + "resnet34": 1, + "resnet50": refiner_in_channels["resnet50"] // 512, + "resnet101": refiner_in_channels["resnet101"] // 512, + "resnet152": refiner_in_channels["resnet152"] // 512, + # ConvNeXt + "convnext_nano": 8, + "convnext_tiny": 8, + "convnext_small": 8, + "convnext_base": 8, + "convnext_large": refiner_in_channels["convnext_large"] // 512, + "convnext_xlarge": refiner_in_channels["convnext_xlarge"] // 512, + "convnext_xxlarge": refiner_in_channels["convnext_xxlarge"] // 512, + # MobileNet V1 + "mobilenetv1_100": None, + "mobilenetv1_125": None, + # MobileNet V2 + "mobilenetv2_050": None, + "mobilenetv2_100": None, + "mobilenetv2_140": None, + # MobileNet V3 + "mobilenetv3_small_050": None, + "mobilenetv3_small_075": None, + "mobilenetv3_small_100": None, + "mobilenetv3_large_100": None, + # MobileNet V4 + "mobilenetv4_conv_small_050": None, + "mobilenetv4_conv_small": None, + "mobilenetv4_conv_medium": None, + "mobilenetv4_conv_large": 1, +} + + +class TIMMModel(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in supported_models, f"Backbone {model_name} not supported. Supported models are {supported_models}" + assert block_size is None or block_size in [8, 16, 32], f"Block size should be one of [8, 16, 32], but got {block_size}." + self.model_name = model_name + self.encoder = create_model(model_name, pretrained=True, features_only=True, out_indices=[-1]) + self.encoder_channels = self.encoder.feature_info.channels()[-1] + self.encoder_reduction = self.encoder.feature_info.reduction()[-1] + self.block_size = block_size if block_size is not None else self.encoder_reduction + + if model_name in lighter_models: + upsample_block = LighterConvUpsample + downsample_block = LighterConvDownsample + decoder_block = LighterConvRefine + elif model_name in light_models: + upsample_block = LightConvUpsample + downsample_block = LightConvDownsample + decoder_block = LightConvRefine + else: + upsample_block = partial(ConvUpsample, groups=groups[model_name]) + downsample_block = partial(ConvDownsample, groups=groups[model_name]) + decoder_block = partial(ConvRefine, groups=groups[model_name]) + + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(self.encoder) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(self.encoder) + + if self.block_size > self.encoder_reduction: + if self.block_size > self.encoder_reduction * 2: + assert self.block_size == self.encoder_reduction * 4, f"Block size {self.block_size} is not supported for model {self.model_name}. Supported block sizes are {self.encoder_reduction}, {self.encoder_reduction * 2}, and {self.encoder_reduction * 4}." + self.refiner = nn.Sequential( + downsample_block( + in_channels=self.encoder_channels, + out_channels=refiner_in_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ), + downsample_block( + in_channels=refiner_in_channels[self.model_name], + out_channels=refiner_out_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ) + ) + else: + assert self.block_size == self.encoder_reduction * 2, f"Block size {self.block_size} is not supported for model {self.model_name}. Supported block sizes are {self.encoder_reduction}, {self.encoder_reduction * 2}, and {self.encoder_reduction * 4}." + self.refiner = downsample_block( + in_channels=self.encoder_channels, + out_channels=refiner_out_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ) + + self.refiner_channels = refiner_out_channels[self.model_name] + + elif self.block_size < self.encoder_reduction: + if self.block_size < self.encoder_reduction // 2: + assert self.block_size == self.encoder_reduction // 4, f"Block size {self.block_size} is not supported for model {self.model_name}. Supported block sizes are {self.encoder_reduction}, {self.encoder_reduction // 2}, and {self.encoder_reduction // 4}." + self.refiner = nn.Sequential( + upsample_block( + in_channels=self.encoder_channels, + out_channels=refiner_in_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ), + upsample_block( + in_channels=refiner_in_channels[self.model_name], + out_channels=refiner_out_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ) + ) + else: + assert self.block_size == self.encoder_reduction // 2, f"Block size {self.block_size} is not supported for model {self.model_name}. Supported block sizes are {self.encoder_reduction}, {self.encoder_reduction // 2}, and {self.encoder_reduction // 4}." + self.refiner = upsample_block( + in_channels=self.encoder_channels, + out_channels=refiner_out_channels[self.model_name], + norm_layer=norm_layer, + activation=activation, + ) + + self.refiner_channels = refiner_out_channels[self.model_name] + + else: + self.refiner = nn.Identity() + self.refiner_channels = self.encoder_channels + + self.refiner_reduction = self.block_size + + if self.refiner_channels <= 256: + self.decoder = nn.Identity() + self.decoder_channels = self.refiner_channels + elif self.refiner_channels <= 512: + self.decoder = decoder_block( + in_channels=self.refiner_channels, + out_channels=self.refiner_channels // 2, + norm_layer=norm_layer, + activation=activation, + ) + self.decoder_channels = self.refiner_channels // 2 + elif self.refiner_channels <= 1024: + self.decoder = nn.Sequential( + decoder_block( + in_channels=self.refiner_channels, + out_channels=self.refiner_channels // 2, + norm_layer=norm_layer, + activation=activation, + ), + decoder_block( + in_channels=self.refiner_channels // 2, + out_channels=self.refiner_channels // 4, + norm_layer=norm_layer, + activation=activation, + ), + ) + self.decoder_channels = self.refiner_channels // 4 + else: + self.decoder = nn.Sequential( + decoder_block( + in_channels=self.refiner_channels, + out_channels=self.refiner_channels // 2, + norm_layer=norm_layer, + activation=activation, + ), + decoder_block( + in_channels=self.refiner_channels // 2, + out_channels=self.refiner_channels // 4, + norm_layer=norm_layer, + activation=activation, + ), + decoder_block( + in_channels=self.refiner_channels // 4, + out_channels=self.refiner_channels // 8, + norm_layer=norm_layer, + activation=activation, + ), + ) + self.decoder_channels = self.refiner_channels // 8 + + self.decoder_reduction = self.refiner_reduction + + def encode(self, x: Tensor) -> Tensor: + return self.encoder(x)[0] + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +def _timm_model(model_name: str, block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> TIMMModel: + return TIMMModel(model_name, block_size=block_size, norm=norm, act=act) diff --git a/models/ebc/utils.py b/models/ebc/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..12c39a4d61c4d7a40098c3ebb94e1feae1a00bac --- /dev/null +++ b/models/ebc/utils.py @@ -0,0 +1,37 @@ +from torch import nn +from typing import Union, List, List + + +vgg_urls = { + "vgg11": "https://download.pytorch.org/models/vgg11-8a719046.pth", + "vgg11_bn": "https://download.pytorch.org/models/vgg11_bn-6002323d.pth", + "vgg13": "https://download.pytorch.org/models/vgg13-19584684.pth", + "vgg13_bn": "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth", + "vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth", + "vgg16_bn": "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth", + "vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth", + "vgg19_bn": "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth", +} + + +vgg_cfgs = { + "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512], + "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512], + "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512], + "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512] +} + + +def make_vgg_layers(cfg: List[Union[str, int]], in_channels: int = 3, batch_norm: bool = False, dilation: int = 1) -> nn.Sequential: + layers = [] + for v in cfg: + if v == "M": + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=dilation, dilation=dilation) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) diff --git a/models/ebc/vgg.py b/models/ebc/vgg.py new file mode 100644 index 0000000000000000000000000000000000000000..100478e1f269c3e27f2fd83cf957b8134c2dce9c --- /dev/null +++ b/models/ebc/vgg.py @@ -0,0 +1,255 @@ +from torch import nn, Tensor +from torch.hub import load_state_dict_from_url +from typing import Optional + +from .utils import make_vgg_layers, vgg_cfgs, vgg_urls +from ..utils import _init_weights, _get_norm_layer, _get_activation +from ..utils import ConvDownsample, ConvUpsample + + +vgg_models = [ + "vgg11", "vgg11_bn", + "vgg13", "vgg13_bn", + "vgg16", "vgg16_bn", + "vgg19", "vgg19_bn", +] + +decoder_cfg = [512, 256, 128] + + +class VGGEncoder(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none", + ) -> None: + super().__init__() + assert model_name in vgg_models, f"Model name should be one of {vgg_models}, but got {model_name}." + assert block_size is None or block_size in [8, 16, 32], f"Block size should be one of [8, 16, 32], but got {block_size}." + self.model_name = model_name + + if model_name == "vgg11": + self.encoder = vgg11() + elif model_name == "vgg11_bn": + self.encoder = vgg11_bn() + elif model_name == "vgg13": + self.encoder = vgg13() + elif model_name == "vgg13_bn": + self.encoder = vgg13_bn() + elif model_name == "vgg16": + self.encoder = vgg16() + elif model_name == "vgg16_bn": + self.encoder = vgg16_bn() + elif model_name == "vgg19": + self.encoder = vgg19() + else: # model_name == "vgg19_bn" + self.encoder = vgg19_bn() + + self.encoder_channels = 512 + self.encoder_reduction = 16 + self.block_size = block_size if block_size is not None else self.encoder_reduction + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(self.encoder) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(self.encoder) + + if self.encoder_reduction >= self.block_size: # 8, 16 + self.refiner = ConvUpsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + scale_factor=self.encoder_reduction // self.block_size, + norm_layer=norm_layer, + activation=activation, + ) + else: # 32 + self.refiner = ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + self.refiner_channels = self.encoder_channels + self.refiner_reduction = self.block_size + + self.decoder = nn.Identity() + self.decoder_channels = self.encoder_channels + self.decoder_reduction = self.refiner_reduction + + def encode(self, x: Tensor) -> Tensor: + return self.encoder(x) + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +class VGGEncoderDecoder(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + norm: str = "none", + act: str = "none", + ) -> None: + super().__init__() + assert model_name in vgg_models, f"Model name should be one of {vgg_models}, but got {model_name}." + assert block_size is None or block_size in [8, 16, 32], f"Block size should be one of [8, 16, 32], but got {block_size}." + self.model_name = model_name + + if model_name == "vgg11": + encoder = vgg11() + elif model_name == "vgg11_bn": + encoder = vgg11_bn() + elif model_name == "vgg13": + encoder = vgg13() + elif model_name == "vgg13_bn": + encoder = vgg13_bn() + elif model_name == "vgg16": + encoder = vgg16() + elif model_name == "vgg16_bn": + encoder = vgg16_bn() + elif model_name == "vgg19": + encoder = vgg19() + else: # model_name == "vgg19_bn" + encoder = vgg19_bn() + + encoder_channels = 512 + encoder_reduction = 16 + decoder = make_vgg_layers(decoder_cfg, in_channels=encoder_channels, batch_norm="bn" in model_name, dilation=1) + decoder.apply(_init_weights) + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(encoder) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(encoder) + + self.encoder = nn.Sequential(encoder, decoder) + self.encoder_channels = decoder_cfg[-1] + self.encoder_reduction = encoder_reduction + self.block_size = block_size if block_size is not None else self.encoder_reduction + + if self.encoder_reduction >= self.block_size: + self.refiner = ConvUpsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + scale_factor=self.encoder_reduction // self.block_size, + norm_layer=norm_layer, + activation=activation, + ) + else: + self.refiner = ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + self.refiner_channels = self.encoder_channels + self.refiner_reduction = self.block_size + + self.decoder = nn.Identity() + self.decoder_channels = self.refiner_channels + self.decoder_reduction = self.refiner_reduction + + def encode(self, x: Tensor) -> Tensor: + return self.encoder(x) + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +class VGG(nn.Module): + def __init__( + self, + features: nn.Module, + ) -> None: + super().__init__() + self.features = features + + def forward(self, x: Tensor) -> Tensor: + x = self.features(x) + return x + + +def vgg11() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["A"])) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg11"]), strict=False) + return model + +def vgg11_bn() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["A"], batch_norm=True)) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg11_bn"]), strict=False) + return model + +def vgg13() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["B"])) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg13"]), strict=False) + return model + +def vgg13_bn() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["B"], batch_norm=True)) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg13_bn"]), strict=False) + return model + +def vgg16() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["D"])) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg16"]), strict=False) + return model + +def vgg16_bn() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["D"], batch_norm=True)) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg16_bn"]), strict=False) + return model + +def vgg19() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["E"])) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg19"]), strict=False) + return model + +def vgg19_bn() -> VGG: + model = VGG(make_vgg_layers(vgg_cfgs["E"], batch_norm=True)) + model.load_state_dict(state_dict=load_state_dict_from_url(vgg_urls["vgg19_bn"]), strict=False) + return model + +def _vgg_encoder(model_name: str, block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> VGGEncoder: + return VGGEncoder(model_name, block_size, norm=norm, act=act) + +def _vgg_encoder_decoder(model_name: str, block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> VGGEncoderDecoder: + return VGGEncoderDecoder(model_name, block_size, norm=norm, act=act) diff --git a/models/ebc/vit.py b/models/ebc/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..1718c66aa8f4da7d4b5497147bed870c98a9adec --- /dev/null +++ b/models/ebc/vit.py @@ -0,0 +1,323 @@ +import torch +from torch import nn, Tensor +import timm +from einops import rearrange +import torch.nn.functional as F + +import math +from typing import Optional, Tuple +from ..utils import ConvUpsample, ConvDownsample, _get_activation, _get_norm_layer, ConvRefine + + +supported_vit_backbones = [ + # Tiny + "vit_tiny_patch16_224", "vit_tiny_patch16_384", + # Small + "vit_small_patch8_224", + "vit_small_patch16_224", "vit_small_patch16_384", + "vit_small_patch32_224", "vit_small_patch32_384", + # Base + "vit_base_patch8_224", + "vit_base_patch16_224", "vit_base_patch16_384", + "vit_base_patch32_224", "vit_base_patch32_384", + # Large + "vit_large_patch16_224", "vit_large_patch16_384", + "vit_large_patch32_224", "vit_large_patch32_384", + # Huge + "vit_huge_patch14_224", +] + + +refiner_channels = { + "vit_tiny_patch16_224": 192, + "vit_tiny_patch16_384": 192, + "vit_small_patch8_224": 384, + "vit_small_patch16_224": 384, + "vit_small_patch16_384": 384, + "vit_small_patch32_224": 384, + "vit_small_patch32_384": 384, + "vit_base_patch8_224": 768, + "vit_base_patch16_224": 768, + "vit_base_patch16_384": 768, + "vit_base_patch32_224": 768, + "vit_base_patch32_384": 768, + "vit_large_patch16_224": 1024, + "vit_large_patch16_384": 1024, + "vit_large_patch32_224": 1024, + "vit_large_patch32_384": 1024, +} + +refiner_groups = { + "vit_tiny_patch16_224": 1, + "vit_tiny_patch16_384": 1, + "vit_small_patch8_224": 1, + "vit_small_patch16_224": 1, + "vit_small_patch16_384": 1, + "vit_small_patch32_224": 1, + "vit_small_patch32_384": 1, + "vit_base_patch8_224": 1, + "vit_base_patch16_224": 1, + "vit_base_patch16_384": 1, + "vit_base_patch32_224": 1, + "vit_base_patch32_384": 1, + "vit_large_patch16_224": 1, + "vit_large_patch16_384": 1, + "vit_large_patch32_224": 1, + "vit_large_patch32_384": 1, +} + + +class ViT(nn.Module): + def __init__( + self, + model_name: str, + block_size: Optional[int] = None, + num_vpt: int = 32, + vpt_drop: float = 0.0, + input_size: Optional[Tuple[int, int]] = None, + norm: str = "none", + act: str = "none" + ) -> None: + super().__init__() + assert model_name in supported_vit_backbones, f"Model {model_name} not supported" + assert num_vpt >= 0, f"Number of VPT tokens should be greater than 0, but got {num_vpt}." + self.model_name = model_name + + self.num_vpt = num_vpt + self.vpt_drop = vpt_drop + + model = timm.create_model(model_name, pretrained=True) + + self.input_size = input_size if input_size is not None else model.patch_embed.img_size + self.pretrain_size = model.patch_embed.img_size + self.patch_size = model.patch_embed.patch_size + + if self.patch_size[0] in [8, 16, 32]: + assert block_size is None or block_size in [8, 16, 32], f"Block size should be one of [8, 16, 32], but got {block_size}." + else: # patch_size == 14 + assert block_size is None or block_size in [7, 14, 28], f"Block size should be one of [7, 14, 28], but got {block_size}." + + self.num_layers = len(model.blocks) + self.embed_dim = model.cls_token.shape[-1] + + if self.num_vpt > 0: # Use visual prompt tuning so freeze the backbone + for param in model.parameters(): + param.requires_grad = False + + # Setup VPT tokens + val = math.sqrt(6. / float(3 * self.patch_size[0] + self.embed_dim)) + for idx in range(self.num_layers): + setattr(self, f"vpt_{idx}", nn.Parameter(torch.empty(self.num_vpt, self.embed_dim))) + nn.init.uniform_(getattr(self, f"vpt_{idx}"), -val, val) + setattr(self, f"vpt_drop_{idx}", nn.Dropout(self.vpt_drop)) + + self.patch_embed = model.patch_embed + self.cls_token = model.cls_token + self.pos_embed = model.pos_embed + self.pos_drop = model.pos_drop + self.patch_drop = model.patch_drop + self.norm_pre = model.norm_pre + + self.blocks = model.blocks + self.norm = model.norm + + self.encoder_channels = self.embed_dim + self.encoder_reduction = self.patch_size[0] + self.block_size = block_size if block_size is not None else self.encoder_reduction + + if norm == "bn": + norm_layer = nn.BatchNorm2d + elif norm == "ln": + norm_layer = nn.LayerNorm + else: + norm_layer = _get_norm_layer(model) + + if act == "relu": + activation = nn.ReLU(inplace=True) + elif act == "gelu": + activation = nn.GELU() + else: + activation = _get_activation(model) + + if self.block_size < self.encoder_reduction: + assert self.block_size == self.encoder_reduction // 2, f"Block size should be half of the encoder reduction, but got {self.block_size} and {self.encoder_reduction}." + self.refiner = ConvUpsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + elif self.block_size > self.encoder_reduction: + assert self.block_size == self.encoder_reduction * 2, f"Block size should be double of the encoder reduction, but got {self.block_size} and {self.encoder_reduction}." + self.refiner = ConvDownsample( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + else: + self.refiner = ConvRefine( + in_channels=self.encoder_channels, + out_channels=self.encoder_channels, + norm_layer=norm_layer, + activation=activation, + ) + + self.refiner_channels = self.encoder_channels + self.refiner_reduction = self.block_size + + self.decoder = nn.Identity() + self.decoder_channels = self.refiner_channels + self.reduction = self.refiner_reduction + + # Adjust the positional embedding to match the new input size + self._adjust_pos_embed() + + def _adjust_pos_embed(self) -> Tensor: + """ + Adjust the positional embedding to match the spatial resolution of the feature map. + + Args: + orig_h, orig_w: The original spatial resolution of the image. + new_h, new_w: The new spatial resolution of the image. + """ + self.pos_embed = nn.Parameter(self._interpolate_pos_embed(self.pretrain_size[0], self.pretrain_size[1], self.input_size[0], self.input_size[1]), requires_grad=self.num_vpt == 0) + + def _interpolate_pos_embed(self, orig_h: int, orig_w: int, new_h: int, new_w: int) -> Tensor: + """ + Interpolate the positional embedding to match the spatial resolution of the feature map. + + Args: + orig_h, orig_w: The original spatial resolution of the image. + new_h, new_w: The new spatial resolution of the image. + """ + if (orig_h, orig_w) == (new_h, new_w): + return self.pos_embed # (1, (h * w + 1), d) + + orig_h_patches, orig_w_patches = orig_h // self.patch_size[0], orig_w // self.patch_size[1] + new_h_patches, new_w_patches = new_h // self.patch_size[0], new_w // self.patch_size[1] + class_pos_embed, patch_pos_embed = self.pos_embed[:, :1, :], self.pos_embed[:, 1:, :] + patch_pos_embed = rearrange(patch_pos_embed, "1 (h w) d -> 1 d h w", h=orig_h_patches, w=orig_w_patches) + patch_pos_embed = F.interpolate(patch_pos_embed, size=(new_h_patches, new_w_patches), mode="bicubic", antialias=True) + patch_pos_embed = rearrange(patch_pos_embed, "1 d h w -> 1 (h w) d") + pos_embed = torch.cat((class_pos_embed, patch_pos_embed), dim=1) + return pos_embed + + def train(self, mode: bool = True): + if self.num_vpt > 0 and mode: + self.patch_embed.eval() + self.pos_drop.eval() + self.patch_drop.eval() + self.norm_pre.eval() + + self.blocks.eval() + self.norm.eval() + + for idx in range(self.num_layers): + getattr(self, f"vpt_drop_{idx}").train() + + self.refiner.train() + self.decoder.train() + + else: + for module in self.children(): + module.train(mode) + + def _prepare_vpt(self, layer: int, batch_size: int, device: torch.device) -> Tensor: + vpt = getattr(self, f"vpt_{layer}").unsqueeze(0).expand(batch_size, -1, -1).to(device) # (batch_size, num_vpt, embed_dim) + vpt = getattr(self, f"vpt_drop_{layer}")(vpt) + + return vpt + + def _forward_patch_embed(self, x: Tensor) -> Tensor: + # This step performs 1) embed x into patches; 2) append the class token; 3) add positional embeddings. + assert len(x.shape) == 4, f"Expected input to have shape (batch_size, 3, height, width), but got {x.shape}" + batch_size, _, height, width = x.shape + + # Step 1: Embed x into patches + x = self.patch_embed(x) # (b, h * w, d) + + # Step 2: Append the class token + cls_token = self.cls_token.expand(batch_size, 1, -1) + x = torch.cat([cls_token, x], dim=1) + + # Step 3: Add positional embeddings + pos_embed = self._interpolate_pos_embed(orig_h=self.input_size[0], orig_w=self.input_size[1], new_h=height, new_w=width).expand(batch_size, -1, -1) + x = self.pos_drop(x + pos_embed) + return x + + def _forward_vpt(self, x: Tensor, idx: int) -> Tensor: + batch_size = x.shape[0] + device = x.device + + # Assemble + vpt = self._prepare_vpt(idx, batch_size, device) + x = torch.cat([ + x[:, :1, :], # class token + vpt, + x[:, 1:, :] # patches + ], dim=1) + + # Forward + x = self.blocks[idx](x) + + # Disassemble + x = torch.cat([ + x[:, :1, :], # class token + x[:, 1 + self.num_vpt:, :] # patches + ], dim=1) + + return x + + def _forward(self, x: Tensor, idx: int) -> Tensor: + x = self.blocks[idx](x) + return x + + def encode(self, x: Tensor) -> Tensor: + orig_h, orig_w = x.shape[-2:] + num_patches_h, num_patches_w = orig_h // self.patch_size[0], orig_w // self.patch_size[1] + + x = self._forward_patch_embed(x) + x = self.patch_drop(x) + x = self.norm_pre(x) + + for idx in range(self.num_layers): + x = self._forward_vpt(x, idx) if self.num_vpt > 0 else self._forward(x, idx) + + x = self.norm(x) + x = x[:, 1:, :] + x = rearrange(x, "b (h w) d -> b d h w", h=num_patches_h, w=num_patches_w) + return x + + def refine(self, x: Tensor) -> Tensor: + return self.refiner(x) + + def decode(self, x: Tensor) -> Tensor: + return self.decoder(x) + + def forward(self, x: Tensor) -> Tensor: + x = self.encode(x) + x = self.refine(x) + x = self.decode(x) + return x + + +def _vit( + model_name: str, + block_size: Optional[int] = None, + num_vpt: int = 32, + vpt_drop: float = 0.0, + input_size: Optional[Tuple[int, int]] = None, + norm: str = "none", + act: str = "none" +) -> ViT: + model = ViT( + model_name=model_name, + block_size=block_size, + num_vpt=num_vpt, + vpt_drop=vpt_drop, + input_size=input_size, + norm=norm, + act=act + ) + return model diff --git a/models/utils/__init__.py b/models/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9c2c5f7dc320a2da56045795b1f5d2b6bd5c94bf --- /dev/null +++ b/models/utils/__init__.py @@ -0,0 +1,56 @@ +from torch import nn +from typing import Optional +from functools import partial + +from .utils import _init_weights, interpolate_pos_embed +from .blocks import DepthSeparableConv2d, conv1x1, conv3x3, Conv2dLayerNorm +from .refine import ConvRefine, LightConvRefine, LighterConvRefine +from .downsample import ConvDownsample, LightConvDownsample, LighterConvDownsample +from .upsample import ConvUpsample, LightConvUpsample, LighterConvUpsample +from .multi_scale import MultiScale +from .blocks import ConvAdapter, ViTAdapter + + +def _get_norm_layer(model: nn.Module) -> Optional[nn.Module]: + for module in model.modules(): + if isinstance(module, nn.BatchNorm2d): + return nn.BatchNorm2d + elif isinstance(module, nn.GroupNorm): + num_groups = module.num_groups + return partial(nn.GroupNorm, num_groups=num_groups) + elif isinstance(module, (nn.LayerNorm, Conv2dLayerNorm)): + return Conv2dLayerNorm + return None + + +def _get_activation(model: nn.Module) -> Optional[nn.Module]: + for module in model.modules(): + if isinstance(module, nn.BatchNorm2d): + return nn.ReLU(inplace=True) + elif isinstance(module, nn.GroupNorm): + return nn.ReLU(inplace=True) + elif isinstance(module, (nn.LayerNorm, Conv2dLayerNorm)): + return nn.GELU() + return nn.GELU() + + + +__all__ = [ + "_init_weights", "_check_norm_layer", "_check_activation", + "conv1x1", + "conv3x3", + "Conv2dLayerNorm", + "interpolate_pos_embed", + "DepthSeparableConv2d", + "ConvRefine", + "LightConvRefine", + "LighterConvRefine", + "ConvDownsample", + "LightConvDownsample", + "LighterConvDownsample", + "ConvUpsample", + "LightConvUpsample", + "LighterConvUpsample", + "MultiScale", + "ConvAdapter", "ViTAdapter", +] diff --git a/models/utils/__pycache__/__init__.cpython-312.pyc b/models/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6b8b031c4f638d1fab7a24bc9b6b5485c8a103c7 Binary files /dev/null and b/models/utils/__pycache__/__init__.cpython-312.pyc differ diff --git a/models/utils/__pycache__/blocks.cpython-312.pyc b/models/utils/__pycache__/blocks.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7c236d139a708cf83bb33aa70a69dddfa6adf413 Binary files /dev/null and b/models/utils/__pycache__/blocks.cpython-312.pyc differ diff --git a/models/utils/__pycache__/downsample.cpython-312.pyc b/models/utils/__pycache__/downsample.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5e53661820cee69d76e8c9fe270ad95d7f84fda4 Binary files /dev/null and b/models/utils/__pycache__/downsample.cpython-312.pyc differ diff --git a/models/utils/__pycache__/multi_scale.cpython-312.pyc b/models/utils/__pycache__/multi_scale.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..feec32474ebf96f018aca63488bfd7079c7f7601 Binary files /dev/null and b/models/utils/__pycache__/multi_scale.cpython-312.pyc differ diff --git a/models/utils/__pycache__/multi_scale_block.cpython-312.pyc b/models/utils/__pycache__/multi_scale_block.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bd640759d775ed4a69efc2485f4c12653e15d336 Binary files /dev/null and b/models/utils/__pycache__/multi_scale_block.cpython-312.pyc differ diff --git a/models/utils/__pycache__/refine.cpython-312.pyc b/models/utils/__pycache__/refine.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0fda80647ace458ce628a8f850b72fab24b89506 Binary files /dev/null and b/models/utils/__pycache__/refine.cpython-312.pyc differ diff --git a/models/utils/__pycache__/upsample.cpython-312.pyc b/models/utils/__pycache__/upsample.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1cdb5a41ed84e17624449476f7f03b6f19a55155 Binary files /dev/null and b/models/utils/__pycache__/upsample.cpython-312.pyc differ diff --git a/models/utils/__pycache__/utils.cpython-312.pyc b/models/utils/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..95b589767fab1daa92b3aa2d9aa295bc8da177ea Binary files /dev/null and b/models/utils/__pycache__/utils.cpython-312.pyc differ diff --git a/models/utils/blocks.py b/models/utils/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..28caf8d3ac7d5226fc20a24401f5b8336085f8b1 --- /dev/null +++ b/models/utils/blocks.py @@ -0,0 +1,617 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange + +from typing import Callable, Optional, Sequence, Tuple, Union, List, List +import warnings + +from .utils import _init_weights, _make_ntuple, _log_api_usage_once + + +def conv3x3( + in_channels: int, + out_channels: int, + stride: int = 1, + groups: int = 1, + dilation: int = 1, + bias: bool = True, +) -> nn.Conv2d: + """3x3 convolution with padding""" + conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=bias, + dilation=dilation, + ) + conv.apply(_init_weights) + return conv + + +def conv1x1( + in_channels: int, + out_channels: int, + stride: int = 1, + bias: bool = True, +) -> nn.Conv2d: + """1x1 convolution""" + conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=bias) + conv.apply(_init_weights) + return conv + + +class DepthSeparableConv2d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int = 0, + dilation: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + ) -> None: + super().__init__() + # Depthwise convolution: one filter per input channel. + self.depthwise = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias, + padding_mode=padding_mode + ) + # Pointwise convolution: combine the features across channels. + self.pointwise = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=bias, + padding_mode=padding_mode + ) + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + return self.pointwise(self.depthwise(x)) + + +class SEBlock(nn.Module): + def __init__(self, channels: int, reduction: int = 16): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + B, C, _, _ = x.shape + y = self.avg_pool(x).view(B, C) + y = self.fc(y).view(B, C, 1, 1) + return x * y + + +class BasicBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + ) -> None: + super().__init__() + assert isinstance(groups, int) and groups > 0, f"Expected groups to be a positive integer, but got {groups}" + assert in_channels % groups == 0, f"Expected in_channels to be divisible by groups, but got {in_channels} % {groups}" + assert out_channels % groups == 0, f"Expected out_channels to be divisible by groups, but got {out_channels} % {groups}" + self.grouped_conv = groups > 1 + self.conv1 = conv3x3( + in_channels=in_channels, + out_channels=out_channels, + stride=1, + bias=not norm_layer, + groups=groups, + ) + if self.grouped_conv: + self.conv1_1x1 = conv1x1(out_channels, out_channels, stride=1, bias=not norm_layer) + + self.norm1 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = conv3x3( + in_channels=out_channels, + out_channels=out_channels, + stride=1, + bias=not norm_layer, + groups=groups, + ) + if self.grouped_conv: + self.conv2_1x1 = conv1x1(out_channels, out_channels, stride=1, bias=not norm_layer) + + self.norm2 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act2 = activation + + if in_channels != out_channels: + self.downsample = nn.Sequential( + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + else: + self.downsample = nn.Identity() + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.conv1_1x1(out) if self.grouped_conv else out + out = self.norm1(out) + out = self.act1(out) + + out = self.conv2(out) + out = self.conv2_1x1(out) if self.grouped_conv else out + out = self.norm2(out) + + out += self.downsample(identity) + out = self.act2(out) + + return out + + +class LightBasicBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + self.conv1 = DepthSeparableConv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=not norm_layer, + ) + self.norm1 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = DepthSeparableConv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=not norm_layer, + ) + self.norm2 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act2 = activation + + if in_channels != out_channels: + self.downsample = nn.Sequential( + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + else: + self.downsample = nn.Identity() + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.act1(out) + + out = self.conv2(out) + out = self.norm2(out) + + out += self.downsample(identity) + out = self.act2(out) + + return out + + +class Bottleneck(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + base_width: int = 64, + expansion: float = 2.0, + ) -> None: + super().__init__() + assert isinstance(groups, int) and groups > 0, f"Expected groups to be a positive integer, but got {groups}" + assert expansion > 0, f"Expected expansion to be greater than 0, but got {expansion}" + assert base_width > 0, f"Expected base_width to be greater than 0, but got {base_width}" + bottleneck_channels = int(in_channels * (base_width / 64.0) * expansion) + assert bottleneck_channels % groups == 0, f"Expected bottleneck_channels to be divisible by groups, but got {bottleneck_channels} % {groups}" + self.grouped_conv = groups > 1 + self.expansion, self.base_width = expansion, base_width + + self.conv_in = conv1x1(in_channels, bottleneck_channels, stride=1, bias=not norm_layer) + self.norm_in = norm_layer(bottleneck_channels) + self.act_in = activation + + self.se_in = SEBlock(bottleneck_channels) if bottleneck_channels > in_channels else nn.Identity() + + self.conv_block_1 = nn.Sequential( + conv3x3( + in_channels=bottleneck_channels, + out_channels=bottleneck_channels, + stride=1, + groups=groups, + bias=not norm_layer + ), + conv1x1(bottleneck_channels, bottleneck_channels, stride=1, bias=not norm_layer) if groups > 1 else nn.Identity(), + norm_layer(bottleneck_channels) if norm_layer else nn.Identity(), + activation, + ) + + self.conv_block_2 = nn.Sequential( + conv3x3( + in_channels=bottleneck_channels, + out_channels=bottleneck_channels, + stride=1, + groups=groups, + bias=not norm_layer + ), + conv1x1(bottleneck_channels, bottleneck_channels, stride=1, bias=not norm_layer) if groups > 1 else nn.Identity(), + norm_layer(bottleneck_channels) if norm_layer else nn.Identity(), + activation, + ) + + self.conv_out = conv1x1(bottleneck_channels, out_channels, stride=1, bias=not norm_layer) + self.norm_out = norm_layer(out_channels) + self.act_out = activation + self.se_out = SEBlock(out_channels) if out_channels > bottleneck_channels else nn.Identity() + + if in_channels != out_channels: + self.downsample = nn.Sequential( + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + else: + self.downsample = nn.Identity() + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + # expand + out = self.conv_in(x) + out = self.norm_in(out) + out = self.act_in(out) + out = self.se_in(out) + + # conv + out = self.conv_block_1(out) + out = self.conv_block_2(out) + + # reduce + out = self.conv_out(out) + out = self.norm_out(out) + out = self.se_out(out) + + out += self.downsample(identity) + out = self.act_out(out) + return out + + +class ConvASPP(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dilations: List[int] = [1, 2, 4], + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + base_width: int = 64, + expansion: float = 2.0, + ) -> None: + super().__init__() + assert isinstance(groups, int) and groups > 0, f"Expected groups to be a positive integer, but got {groups}" + assert expansion > 0, f"Expected expansion to be greater than 0, but got {expansion}" + assert base_width > 0, f"Expected base_width to be greater than 0, but got {base_width}" + bottleneck_channels = int(in_channels * (base_width / 64.0) * expansion) + assert bottleneck_channels % groups == 0, f"Expected bottleneck_channels to be divisible by groups, but got {bottleneck_channels} % {groups}" + self.expansion, self.base_width = expansion, base_width + + self.conv_in = conv1x1(in_channels, bottleneck_channels, stride=1, bias=not norm_layer) + self.norm_in = norm_layer(bottleneck_channels) + self.act_in = activation + + conv_blocks = [nn.Sequential( + conv1x1(bottleneck_channels, bottleneck_channels, stride=1, bias=not norm_layer), + norm_layer(bottleneck_channels), + activation + )] + + for dilation in dilations: + conv_blocks.append(nn.Sequential( + conv3x3( + in_channels=bottleneck_channels, + out_channels=bottleneck_channels, + stride=1, + groups=groups, + dilation=dilation, + bias=not norm_layer + ), + conv1x1(bottleneck_channels, bottleneck_channels, stride=1, bias=not norm_layer) if groups > 1 else nn.Identity(), + norm_layer(bottleneck_channels) if norm_layer else nn.Identity(), + activation + )) + + self.convs = nn.ModuleList(conv_blocks) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.conv_avg = conv1x1(bottleneck_channels, bottleneck_channels, stride=1, bias=not norm_layer) + self.norm_avg = norm_layer(bottleneck_channels) + self.act_avg = activation + + self.se = SEBlock(bottleneck_channels * (len(dilations) + 2)) + + self.conv_out = conv1x1(bottleneck_channels * (len(dilations) + 2), out_channels, stride=1, bias=not norm_layer) + self.norm_out = norm_layer(out_channels) + self.act_out = activation + + if in_channels != out_channels: + self.downsample = nn.Sequential( + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + else: + self.downsample = nn.Identity() + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + height, width = x.shape[-2:] + identity = x + + # expand + out = self.conv_in(x) + out = self.norm_in(out) + out = self.act_in(out) + + outs = [] + for conv in self.convs: + outs.append(conv(out)) + + avg = self.avgpool(out) + avg = self.conv_avg(avg) + avg = self.norm_avg(avg) + avg = self.act_avg(avg) # (B, C, 1, 1) + avg = avg.repeat(1, 1, height, width) + + outs = torch.cat([*outs, avg], dim=1) # (B, C * (len(dilations) + 2), H, W) + outs = self.se(outs) + + # reduce + outs = self.conv_out(outs) + outs = self.norm_out(outs) + + outs += self.downsample(identity) + outs = self.act_out(outs) + return outs + + +class ViTBlock(nn.Module): + def __init__( + self, + embed_dim: int, + num_heads: int = 8, + dropout: float = 0.0, + mlp_ratio: float = 4.0, + ) -> None: + super().__init__() + assert embed_dim % num_heads == 0, f"Embedding dimension {embed_dim} should be divisible by number of heads {num_heads}" + self.embed_dim, self.num_heads = embed_dim, num_heads + self.dropout, self.mlp_ratio = dropout, mlp_ratio + + self.norm1 = nn.LayerNorm(embed_dim) + self.attn = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True + ) + + self.norm2 = nn.LayerNorm(embed_dim) + self.mlp = nn.Sequential( + nn.Linear(embed_dim, int(embed_dim * mlp_ratio)), + nn.GELU(), + nn.Dropout(dropout) if dropout > 0 else nn.Identity(), + nn.Linear(int(embed_dim * mlp_ratio), embed_dim), + nn.Dropout(dropout) if dropout > 0 else nn.Identity() + ) + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + assert len(x.shape) == 3, f"Expected input to have shape (B, N, C), but got {x.shape}" + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + + +class Conv2dLayerNorm(nn.Sequential): + """ + Layer normalization applied in a convolutional fashion. + """ + def __init__(self, dim: int) -> None: + super().__init__( + Rearrange("B C H W -> B H W C"), + nn.LayerNorm(dim), + Rearrange("B H W C -> B C H W") + ) + self.apply(_init_weights) + + +class CvTAttention(nn.Module): + def __init__( + self, + embed_dim: int, + num_heads: int = 8, + dropout: float = 0.0, + q_stride: int = 1, # controls downsampling rate + kv_stride: int = 1, + ) -> None: + super().__init__() + assert embed_dim % num_heads == 0, f"Embedding dimension {embed_dim} should be divisible by number of heads {num_heads}" + self.embed_dim, self.num_heads, self.dim_head = embed_dim, num_heads, embed_dim // num_heads + self.scale = self.dim_head ** -0.5 + self.q_stride, self.kv_stride = q_stride, kv_stride + + self.attend = nn.Softmax(dim=-1) + self.dropout = nn.Dropout(dropout) + + self.to_q = DepthSeparableConv2d( + in_channels=embed_dim, + out_channels=embed_dim, + kernel_size=3, + stride=q_stride, + padding=1, + bias=False + ) + self.to_k = DepthSeparableConv2d( + in_channels=embed_dim, + out_channels=embed_dim, + kernel_size=3, + stride=kv_stride, + padding=1, + bias=False + ) + self.to_v = DepthSeparableConv2d( + in_channels=embed_dim, + out_channels=embed_dim, + kernel_size=3, + stride=kv_stride, + padding=1, + bias=False + ) + + self.to_out = nn.Sequential( + conv1x1(embed_dim, embed_dim, stride=1), + nn.Dropout(dropout) if dropout > 0 else nn.Identity() + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + assert len(x.shape) == 4, f"Expected input to have shape (B, C, H, W), but got {x.shape}" + assert x.shape[1] == self.embed_dim, f"Expected input to have embedding dimension {self.embed_dim}, but got {x.shape[1]}" + + q, k, v = self.to_q(x), self.to_k(x), self.to_v(x) + B, _, H, W = q.shape + q, k, v = map(lambda t: rearrange(t, "B (num_heads head_dim) H W -> (B num_heads) (H W) head_dim", num_heads=self.num_heads), (q, k, v)) + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = self.attend(attn) + attn = self.dropout(attn) + + out = attn @ v + out = rearrange(out, "(B num_heads) (H W) head_dim -> B (num_heads head_dim) H W", B=B, H=H, W=W, num_heads=self.num_heads) + out = self.to_out(out) + + return out + + +class CvTBlock(nn.Module): + """ + Implement convolutional vision transformer block. + """ + def __init__( + self, + embed_dim: int, + num_heads: int = 8, + dropout: float = 0.0, + mlp_ratio: float = 4.0, + q_stride: int = 1, + kv_stride: int = 1, + ) -> None: + super().__init__() + assert embed_dim % num_heads == 0, f"Embedding dimension {embed_dim} should be divisible by number of heads {num_heads}." + self.embed_dim, self.num_heads = embed_dim, num_heads + + self.norm1 = Conv2dLayerNorm(embed_dim) + self.attn = CvTAttention(embed_dim, num_heads, dropout, q_stride, kv_stride) + + self.pool = nn.AvgPool2d(kernel_size=q_stride, stride=q_stride) if q_stride > 1 else nn.Identity() + + self.norm2 = Conv2dLayerNorm(embed_dim) + self.mlp = nn.Sequential( + nn.Conv2d(embed_dim, int(embed_dim * mlp_ratio), kernel_size=1), + nn.GELU(), + nn.Dropout(dropout) if dropout > 0 else nn.Identity(), + nn.Conv2d(int(embed_dim * mlp_ratio), embed_dim, kernel_size=1), + nn.Dropout(dropout) if dropout > 0 else nn.Identity() + ) + + def forward(self, x: Tensor) -> Tensor: + x = self.pool(x) + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + + +class ConvAdapter(nn.Module): + def __init__( + self, + in_channels: int, + bottleneck_channels: int = 16, + ) -> None: + super().__init__() + assert in_channels > 0, f"Expected input_channels to be greater than 0, but got {in_channels}" + assert bottleneck_channels > 0, f"Expected bottleneck_channels to be greater than 0, but got {bottleneck_channels}" + + self.adapter = nn.Sequential( + nn.Conv2d(in_channels, bottleneck_channels, kernel_size=1), + nn.GELU(), + nn.Conv2d(bottleneck_channels, in_channels, kernel_size=1), + ) + nn.init.zeros_(self.adapter[2].weight) + nn.init.zeros_(self.adapter[2].bias) + + def forward(self, x: Tensor) -> Tensor: + assert len(x.shape) == 4, f"Expected input to have shape (B, C, H, W), but got {x.shape}" + return x + self.adapter(x) + + +class ViTAdapter(nn.Module): + def __init__(self, input_dim, bottleneck_dim): + super().__init__() + self.adapter = nn.Sequential( + nn.Linear(input_dim, bottleneck_dim), + nn.GELU(), # ViT中常用GELU作为激活函数 + nn.Linear(bottleneck_dim, input_dim) + ) + nn.init.zeros_(self.adapter[2].weight) + nn.init.zeros_(self.adapter[2].bias) + + def forward(self, x: Tensor) -> Tensor: + assert len(x.shape) == 3, f"Expected input to have shape (B, N, C), but got {x.shape}" + return x + self.adapter(x) + \ No newline at end of file diff --git a/models/utils/carafe.py b/models/utils/carafe.py new file mode 100644 index 0000000000000000000000000000000000000000..3011742baaeaad1c7af82a7b8798a52de3c2615f --- /dev/null +++ b/models/utils/carafe.py @@ -0,0 +1,203 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +def carafe_forward( + features: torch.Tensor, + masks: torch.Tensor, + kernel_size: int, + group_size: int, + scale_factor: int +) -> torch.Tensor: + """ + Pure-PyTorch implementation of the CARAFE upsampling operator. + + Args: + features (Tensor): Input feature map of shape (N, C, H, W). + masks (Tensor): Reassembly kernel weights of shape + (N, kernel_size*kernel_size*group_size, H_out, W_out), + where H_out = H*scale_factor and W_out = W*scale_factor. + kernel_size (int): The spatial size of the reassembly kernel. + group_size (int): The group size to divide channels. Must divide C. + scale_factor (int): The upsampling factor. + + Returns: + Tensor: Upsampled feature map of shape (N, C, H*scale_factor, W*scale_factor). + """ + N, C, H, W = features.size() + out_H, out_W = H * scale_factor, W * scale_factor + num_channels = C // group_size # channels per group + + # Reshape features to (N, group_size, num_channels, H, W) + features = features.view(N, group_size, num_channels, H, W) + # Merge batch and group dims for unfolding + features_reshaped = features.view(N * group_size, num_channels, H, W) + # Extract local patches; use padding so that output spatial dims match input + patches = F.unfold(features_reshaped, kernel_size=kernel_size, + padding=(kernel_size - 1) // 2) + # patches shape: (N*group_size, num_channels*kernel_size*kernel_size, H*W) + # Reshape to (N, group_size, num_channels, kernel_size*kernel_size, H, W) + patches = patches.view(N, group_size, num_channels, kernel_size * kernel_size, H, W) + # Flatten spatial dimensions: now (N, group_size, num_channels, kernel_size*kernel_size, H*W) + patches = patches.view(N, group_size, num_channels, kernel_size * kernel_size, H * W) + + # For each output pixel location, determine the corresponding base input index. + # For an output coordinate (oh, ow), the corresponding input index is: + # h = oh // scale_factor, w = ow // scale_factor, linear index = h * W + w. + device = features.device + # Create coordinate indices for output + h_idx = torch.div(torch.arange(out_H, device=device), scale_factor, rounding_mode='floor') # (out_H,) + w_idx = torch.div(torch.arange(out_W, device=device), scale_factor, rounding_mode='floor') # (out_W,) + # Form a 2D grid of base indices (shape: out_H x out_W) + h_idx = h_idx.unsqueeze(1).expand(out_H, out_W) # (out_H, out_W) + w_idx = w_idx.unsqueeze(0).expand(out_H, out_W) # (out_H, out_W) + base_idx = (h_idx * W + w_idx).view(-1) # (out_H*out_W,) + + # Expand base_idx so that it can index the last dimension of patches: + # Desired shape for gathering: (N, group_size, num_channels, kernel_size*kernel_size, out_H*out_W) + base_idx = base_idx.view(1, 1, 1, 1, -1).expand(N, group_size, num_channels, kernel_size * kernel_size, -1) + # Gather patches corresponding to each output location + gathered_patches = torch.gather(patches, -1, base_idx) + # Reshape gathered patches to (N, group_size, num_channels, kernel_size*kernel_size, out_H, out_W) + gathered_patches = gathered_patches.view(N, group_size, num_channels, kernel_size * kernel_size, out_H, out_W) + + # Reshape masks to separate groups. + # Expected mask shape: (N, kernel_size*kernel_size*group_size, out_H, out_W) + # Reshape to: (N, group_size, kernel_size*kernel_size, out_H, out_W) + masks = masks.view(N, group_size, kernel_size * kernel_size, out_H, out_W) + # For multiplication, add a channel dimension so that masks shape becomes + # (N, group_size, 1, kernel_size*kernel_size, out_H, out_W) + masks = masks.unsqueeze(2) + # Expand masks to match gathered_patches: (N, group_size, num_channels, kernel_size*kernel_size, out_H, out_W) + masks = masks.expand(-1, -1, num_channels, -1, -1, -1) + + # Multiply patches with masks and sum over the kernel dimension. + # This yields the reassembled features for each output location. + out = (gathered_patches * masks).sum(dim=3) # shape: (N, group_size, num_channels, out_H, out_W) + # Reshape back to (N, C, out_H, out_W) + out = out.view(N, C, out_H, out_W) + return out + + +class CARAFE(nn.Module): + """ + CARAFE: Content-Aware ReAssembly of Features + + This PyTorch module implements the CARAFE upsampling operator in pure Python. + Given an input feature map and its corresponding reassembly masks, the module + reassembles features from local patches to produce a higher-resolution output. + + Args: + kernel_size (int): Reassembly kernel size. + group_size (int): Group size for channel grouping (must divide number of channels). + scale_factor (int): Upsample ratio. + """ + def __init__(self, kernel_size: int, group_size: int, scale_factor: int): + super(CARAFE, self).__init__() + self.kernel_size = kernel_size + self.group_size = group_size + self.scale_factor = scale_factor + + def forward(self, features: torch.Tensor, masks: torch.Tensor) -> torch.Tensor: + return carafe_forward(features, masks, self.kernel_size, self.group_size, self.scale_factor) + + +class CARAFEPack(nn.Module): + """ + A unified package of the CARAFE upsampler that contains: + 1) A channel compressor. + 2) A content encoder that predicts reassembly masks. + 3) The CARAFE operator. + + This is modeled after the official CARAFE package. + + Args: + channels (int): Number of input feature channels. + scale_factor (int): Upsample ratio. + up_kernel (int): Kernel size for the CARAFE operator. + up_group (int): Group size for the CARAFE operator. + encoder_kernel (int): Kernel size of the content encoder. + encoder_dilation (int): Dilation rate for the content encoder. + compressed_channels (int): Output channels for the channel compressor. + """ + def __init__( + self, + channels: int, + scale_factor: int, + up_kernel: int = 5, + up_group: int = 1, + encoder_kernel: int = 3, + encoder_dilation: int = 1, + compressed_channels: int = 64 + ): + super(CARAFEPack, self).__init__() + self.channels = channels + self.scale_factor = scale_factor + self.up_kernel = up_kernel + self.up_group = up_group + self.encoder_kernel = encoder_kernel + self.encoder_dilation = encoder_dilation + self.compressed_channels = compressed_channels + + # Compress input channels. + self.channel_compressor = nn.Conv2d(channels, compressed_channels, kernel_size=1) + # Predict reassembly masks. + self.content_encoder = nn.Conv2d( + compressed_channels, + up_kernel * up_kernel * up_group * scale_factor * scale_factor, + kernel_size=encoder_kernel, + padding=int((encoder_kernel - 1) * encoder_dilation / 2), + dilation=encoder_dilation + ) + # Initialize weights (using Xavier for conv layers). + nn.init.xavier_uniform_(self.channel_compressor.weight) + nn.init.xavier_uniform_(self.content_encoder.weight) + if self.channel_compressor.bias is not None: + nn.init.constant_(self.channel_compressor.bias, 0) + if self.content_encoder.bias is not None: + nn.init.constant_(self.content_encoder.bias, 0) + + def kernel_normalizer(self, mask: torch.Tensor) -> torch.Tensor: + """ + Normalize and reshape the mask. + Applies pixel shuffle to upsample the predicted kernel weights and then + applies softmax normalization across the kernel dimension. + + Args: + mask (Tensor): Predicted mask of shape (N, out_channels, H, W). + + Returns: + Tensor: Normalized mask of shape (N, up_group * up_kernel^2, H*scale, W*scale). + """ + # Pixel shuffle to rearrange and upsample the mask. + mask = F.pixel_shuffle(mask, self.scale_factor) + N, mask_c, H, W = mask.size() + # Determine the number of channels per kernel + mask_channel = mask_c // (self.up_kernel ** 2) + mask = mask.view(N, mask_channel, self.up_kernel ** 2, H, W) + mask = F.softmax(mask, dim=2) + mask = mask.view(N, mask_channel * self.up_kernel ** 2, H, W).contiguous() + return mask + + def feature_reassemble(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + return carafe_forward(x, mask, self.up_kernel, self.up_group, self.scale_factor) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + compressed_x = self.channel_compressor(x) + mask = self.content_encoder(compressed_x) + mask = self.kernel_normalizer(mask) + out = self.feature_reassemble(x, mask) + return out + + +# === Example Usage === +if __name__ == '__main__': + # Create dummy input: batch size 2, 64 channels, 32x32 spatial resolution. + x = torch.randn(2, 64, 32, 32).cuda() # assuming GPU available + # Define CARAFEPack with upsample ratio 2. + # For example, use kernel size 5, group size 1. + upsampler = CARAFEPack(channels=64, scale_factor=2, up_kernel=5, up_group=1).cuda() + # Get upsampled feature map. + out = upsampler(x) + print("Input shape: ", x.shape) + print("Output shape:", out.shape) # Expected shape: (2, 64, 64, 64) diff --git a/models/utils/downsample.py b/models/utils/downsample.py new file mode 100644 index 0000000000000000000000000000000000000000..6cdc2c41e67de3db4c57d28f22306eb62d661630 --- /dev/null +++ b/models/utils/downsample.py @@ -0,0 +1,239 @@ +from torch import nn, Tensor + +from typing import Union + +from .blocks import DepthSeparableConv2d, conv1x1, conv3x3 +from .utils import _init_weights + + +class ConvDownsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + ) -> None: + super().__init__() + assert isinstance(groups, int) and groups > 0, f"Number of groups should be an integer greater than 0, but got {groups}." + assert in_channels % groups == 0, f"Number of input channels {in_channels} should be divisible by number of groups {groups}." + assert out_channels % groups == 0, f"Number of output channels {out_channels} should be divisible by number of groups {groups}." + self.grouped_conv = groups > 1 + + # conv1 is used for downsampling + # self.conv1 = nn.Conv2d( + # in_channels=in_channels, + # out_channels=in_channels, + # kernel_size=2, + # stride=2, + # padding=0, + # bias=not norm_layer, + # groups=groups, + # ) + # if self.grouped_conv: + # self.conv1_1x1 = conv1x1(in_channels, in_channels, stride=1, bias=not norm_layer) + self.conv1 = nn.AvgPool2d(kernel_size=2, stride=2) # downsample by 2 + if self.grouped_conv: + self.conv1_1x1 = nn.Identity() + + self.norm1 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = conv3x3( + in_channels=in_channels, + out_channels=in_channels, + stride=1, + groups=groups, + bias=not norm_layer, + ) + if self.grouped_conv: + self.conv2_1x1 = conv1x1(in_channels, in_channels, stride=1, bias=not norm_layer) + + self.norm2 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act2 = activation + + self.conv3 = conv3x3( + in_channels=in_channels, + out_channels=out_channels, + stride=1, + groups=groups, + bias=not norm_layer, + ) + if self.grouped_conv: + self.conv3_1x1 = conv1x1(out_channels, out_channels, stride=1, bias=not norm_layer) + + self.norm3 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act3 = activation + + self.downsample = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2), # make sure the spatial sizes match + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + # downsample + out = self.conv1(x) + out = self.conv1_1x1(out) if self.grouped_conv else out + out = self.norm1(out) + out = self.act1(out) + + out = self.conv2(out) + out = self.conv2_1x1(out) if self.grouped_conv else out + out = self.norm2(out) + out = self.act2(out) + + out = self.conv3(out) + out = self.conv3_1x1(out) if self.grouped_conv else out + out = self.norm3(out) + + # shortcut + out += self.downsample(identity) + out = self.act3(out) + return out + + +class LightConvDownsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + self.conv1 = DepthSeparableConv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=2, + stride=2, + padding=0, + bias=not norm_layer, + ) + self.norm1 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = DepthSeparableConv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=not norm_layer, + ) + self.norm2 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act2 = activation + + self.conv3 = DepthSeparableConv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=not norm_layer, + ) + self.norm3 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act3 = activation + + self.downsample = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2), # make sure the spatial sizes match + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + # downsample + out = self.conv1(x) + out = self.norm1(out) + out = self.act1(out) + + # refine 1 + out = self.conv2(out) + out = self.norm2(out) + out = self.act2(out) + + # refine 2 + out = self.conv3(out) + out = self.norm3(out) + + # shortcut + out += self.downsample(identity) + out = self.act3(out) + return x + + +class LighterConvDownsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + self.conv1 = DepthSeparableConv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=2, + stride=2, + padding=0, + bias=not norm_layer, + ) + self.norm1 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = conv3x3( + in_channels=in_channels, + out_channels=in_channels, + stride=1, + groups=in_channels, + bias=not norm_layer, + ) + self.norm2 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act2 = activation + + self.conv3 = conv1x1( + in_channels=in_channels, + out_channels=out_channels, + stride=1, + bias=not norm_layer, + ) + self.norm3 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act3 = activation + + self.downsample = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2), # make sure the spatial sizes match + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + # downsample + out = self.conv1(x) + out = self.norm1(out) + out = self.act1(out) + + # refine, depthwise conv + out = self.conv2(out) + out = self.norm2(out) + out = self.act2(out) + + # refine, pointwise conv + out = self.conv3(out) + out = self.norm3(out) + + # shortcut + out += self.downsample(identity) + out = self.act3(out) + return out diff --git a/models/utils/multi_scale.py b/models/utils/multi_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..c0362a84ae78c163e707fa9a5267b691c9156458 --- /dev/null +++ b/models/utils/multi_scale.py @@ -0,0 +1,112 @@ +import torch +from torch import nn, Tensor +from typing import List +from einops import rearrange + +from .blocks import conv3x3, conv1x1, Conv2dLayerNorm, _init_weights + + +class MultiScale(nn.Module): + def __init__( + self, + channels: int, + scales: List[int], + heads: int = 8, + groups: int = 1, + mlp_ratio: float = 4.0, + ) -> None: + super().__init__() + assert channels > 0, "channels should be a positive integer" + assert isinstance(scales, (list, tuple)) and len(scales) > 0 and all([scale > 0 for scale in scales]), "scales should be a list or tuple of positive integers" + assert heads > 0 and channels % heads == 0, "heads should be a positive integer and channels should be divisible by heads" + assert groups > 0 and channels % groups == 0, "groups should be a positive integer and channels should be divisible by groups" + scales = sorted(scales) + self.scales = scales + self.num_scales = len(scales) + 1 # +1 for the original feature map + self.heads = heads + self.groups = groups + + # modules that generate multi-scale feature maps + self.scale_0 = nn.Sequential( + conv1x1(channels, channels, stride=1, bias=False), + Conv2dLayerNorm(channels), + nn.GELU(), + ) + for scale in scales: + setattr(self, f"conv_{scale}", nn.Sequential( + conv3x3( + in_channels=channels, + out_channels=channels, + stride=1, + groups=groups, + dilation=scale, + bias=False, + ), + conv1x1(channels, channels, stride=1, bias=False) if groups > 1 else nn.Identity(), + Conv2dLayerNorm(channels), + nn.GELU(), + )) + + # modules that fuse multi-scale feature maps + self.norm_attn = Conv2dLayerNorm(channels) + self.pos_embed = nn.Parameter(torch.randn(1, self.num_scales + 1, channels, 1, 1) / channels ** 0.5) + self.to_q = conv1x1(channels, channels, stride=1, bias=False) + self.to_k = conv1x1(channels, channels, stride=1, bias=False) + self.to_v = conv1x1(channels, channels, stride=1, bias=False) + + self.scale = (channels // heads) ** -0.5 + + self.attend = nn.Softmax(dim=-1) + + self.to_out = conv1x1(channels, channels, stride=1) + + # modules that refine multi-scale feature maps + self.norm_mlp = Conv2dLayerNorm(channels) + self.mlp = nn.Sequential( + conv1x1(channels, channels * mlp_ratio, stride=1), + nn.GELU(), + conv1x1(channels * mlp_ratio, channels, stride=1), + ) + + self.apply(_init_weights) + + def _forward_attn(self, x: Tensor) -> Tensor: + assert len(x.shape) == 4, f"Expected input to have shape (B, C, H, W), but got {x.shape}" + x = [self.scale_0(x)] + [getattr(self, f"conv_{scale}")(x) for scale in self.scales] + + x = torch.stack(x, dim=1) # (B, S, C, H, W) + x = torch.cat([x.mean(dim=1, keepdim=True), x], dim=1) # (B, S+1, C, H, W) + x = x + self.pos_embed # (B, S+1, C, H, W) + + x = rearrange(x, "B S C H W -> (B S) C H W") # (B*(S+1), C, H, W) + x = self.norm_attn(x) # (B*(S+1), C, H, W) + x = rearrange(x, "(B S) C H W -> B S C H W", S=self.num_scales + 1) # (B, S+1, C, H, W) + + q = self.to_q(x[:, 0]) # (B, C, H, W) + k = self.to_k(rearrange(x, "B S C H W -> (B S) C H W")) + v = self.to_v(rearrange(x, "B S C H W -> (B S) C H W")) + + q = rearrange(q, "B (h d) H W -> B h H W 1 d", h=self.heads) + k = rearrange(k, "(B S) (h d) H W -> B h H W S d", S=self.num_scales + 1, h=self.heads) + v = rearrange(v, "(B S) (h d) H W -> B h H W S d", S=self.num_scales + 1, h=self.heads) + + attn = q @ k.transpose(-2, -1) * self.scale # (B, h, H, W, 1, S+1) + attn = self.attend(attn) # (B, h, H, W, 1, S+1) + out = attn @ v # (B, h, H, W, 1, d) + + out = rearrange(out, "B h H W 1 d -> B (h d) H W") # (B, C, H, W) + + out = self.to_out(out) # (B, C, H, W) + return out + + def _forward_mlp(self, x: Tensor) -> Tensor: + assert len(x.shape) == 4, f"Expected input to have shape (B, C, H, W), but got {x.shape}" + x = self.norm_mlp(x) + x = self.mlp(x) + return x + + def forward(self, x: Tensor) -> Tensor: + x = x + self._forward_attn(x) + x = x + self._forward_mlp(x) + return x + \ No newline at end of file diff --git a/models/utils/refine.py b/models/utils/refine.py new file mode 100644 index 0000000000000000000000000000000000000000..a0c245356f69b3a44ed78a6efccaf658530f768c --- /dev/null +++ b/models/utils/refine.py @@ -0,0 +1,103 @@ +from torch import nn, Tensor +from typing import Union + +from .utils import _init_weights +from .blocks import BasicBlock, LightBasicBlock, conv1x1, conv3x3 + + +class ConvRefine(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + ) -> None: + super().__init__() + self.refine = BasicBlock( + in_channels=in_channels, + out_channels=out_channels, + norm_layer=norm_layer, + activation=activation, + groups=groups, + ) + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + return self.refine(x) + + +class LightConvRefine(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + self.refine = LightBasicBlock( + in_channels=in_channels, + out_channels=out_channels, + norm_layer=norm_layer, + activation=activation, + ) + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + return self.refine(x) + + +class LighterConvRefine(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + # depthwise separable convolution + self.conv1 = conv3x3( + in_channels=in_channels, + out_channels=in_channels, + stride=1, + groups=in_channels, + bias=not norm_layer, + ) + self.norm1 = norm_layer(in_channels) if norm_layer else nn.Identity() + self.act1 = activation + + self.conv2 = conv1x1( + in_channels=in_channels, + out_channels=out_channels, + stride=1, + bias=not norm_layer, + ) + self.norm2 = norm_layer(out_channels) if norm_layer else nn.Identity() + self.act2 = activation + + if in_channels != out_channels: + self.downsample = nn.Sequential( + conv1x1(in_channels, out_channels, stride=1, bias=not norm_layer), + norm_layer(out_channels) if norm_layer else nn.Identity(), + ) + else: + self.downsample = nn.Identity() + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.act1(out) + + out = self.conv2(out) + out = self.norm2(out) + + out += self.downsample(identity) + out = self.act2(out) + return out diff --git a/models/utils/upsample.py b/models/utils/upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..7b32bbfca1c6f93c71115639d1eebd2a0f728eb8 --- /dev/null +++ b/models/utils/upsample.py @@ -0,0 +1,118 @@ +from torch import nn, Tensor +from torch.nn import functional as F + +from typing import Union +from functools import partial + +from .utils import _init_weights +from .refine import ConvRefine, LightConvRefine, LighterConvRefine + + +class ConvUpsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + scale_factor: int = 2, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + groups: int = 1, + ) -> None: + super().__init__() + assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}" + self.scale_factor = scale_factor + self.upsample = partial( + F.interpolate, + scale_factor=scale_factor, + mode="bilinear", + align_corners=False, + recompute_scale_factor=False, + antialias=False, + ) if scale_factor > 1 else nn.Identity() + + self.refine = ConvRefine( + in_channels=in_channels, + out_channels=out_channels, + norm_layer=norm_layer, + activation=activation, + groups=groups, + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + x = self.upsample(x) + x = self.refine(x) + return x + + +class LightConvUpsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + scale_factor: int = 2, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}" + self.scale_factor = scale_factor + self.upsample = partial( + F.interpolate, + scale_factor=scale_factor, + mode="bilinear", + align_corners=False, + recompute_scale_factor=False, + antialias=False, + ) if scale_factor > 1 else nn.Identity() + + self.refine = LightConvRefine( + in_channels=in_channels, + out_channels=out_channels, + norm_layer=norm_layer, + activation=activation, + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + x = self.upsample(x) + x = self.refine(x) + return x + + +class LighterConvUpsample(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + scale_factor: int = 2, + norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d, + activation: nn.Module = nn.ReLU(inplace=True), + ) -> None: + super().__init__() + assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}" + self.scale_factor = scale_factor + self.upsample = partial( + F.interpolate, + scale_factor=scale_factor, + mode="bilinear", + align_corners=False, + recompute_scale_factor=False, + antialias=False, + ) if scale_factor > 1 else nn.Identity() + + self.refine = LighterConvRefine( + in_channels=in_channels, + out_channels=out_channels, + norm_layer=norm_layer, + activation=activation, + ) + + self.apply(_init_weights) + + def forward(self, x: Tensor) -> Tensor: + x = self.upsample(x) + x = self.refine(x) + return x diff --git a/models/utils/utils.py b/models/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..98e5d8ab35791a5875412f9cbc4ef7741ab9033d --- /dev/null +++ b/models/utils/utils.py @@ -0,0 +1,77 @@ +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from typing import Tuple, Any, Optional, Union +from types import FunctionType +from itertools import repeat +from collections.abc import Iterable + + +def _log_api_usage_once(obj: Any) -> None: + + """ + Logs API usage(module and name) within an organization. + In a large ecosystem, it's often useful to track the PyTorch and + TorchVision APIs usage. This API provides the similar functionality to the + logging module in the Python stdlib. It can be used for debugging purpose + to log which methods are used and by default it is inactive, unless the user + manually subscribes a logger via the `SetAPIUsageLogger method `_. + Please note it is triggered only once for the same API call within a process. + It does not collect any data from open-source users since it is no-op by default. + For more information, please refer to + * PyTorch note: https://pytorch.org/docs/stable/notes/large_scale_deployments.html#api-usage-logging; + * Logging policy: https://github.com/pytorch/vision/issues/5052; + + Args: + obj (class instance or method): an object to extract info from. + """ + module = obj.__module__ + if not module.startswith("torchvision"): + module = f"torchvision.internal.{module}" + name = obj.__class__.__name__ + if isinstance(obj, FunctionType): + name = obj.__name__ + torch._C._log_api_usage_once(f"{module}.{name}") + + +def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]: + """ + Make n-tuple from input x. If x is an iterable, then we just convert it to tuple. + Otherwise, we will make a tuple of length n, all with value of x. + reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8 + + Args: + x (Any): input value + n (int): length of the resulting tuple + """ + if isinstance(x, Iterable): + return tuple(x) + return tuple(repeat(x, n)) + + +def _init_weights(model: nn.Module) -> None: + for m in model.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0.) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, nn.LayerNorm)): + nn.init.constant_(m.weight, 1.) + if m.bias is not None: + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=0.01) + if m.bias is not None: + nn.init.constant_(m.bias, 0.) + + +def interpolate_pos_embed(pos_embed: Tensor, size: Optional[Union[int, Tuple[int, int]]] = None, scale_factor: Optional[float] = None) -> Tensor: + assert len(pos_embed.shape) == 3, f"Positional embedding should be 3D tensor (C, H, W), but got {pos_embed.shape}." + return F.interpolate( + pos_embed.unsqueeze(0), + size=size, + scale_factor=scale_factor, + mode="bicubic", + align_corners=False, + antialias=True, + ).squeeze(0) diff --git a/notebooks/dataset.ipynb b/notebooks/dataset.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b18f3a10a0ae5b6ebed6441d55331cd1743a72b5 --- /dev/null +++ b/notebooks/dataset.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current directory: /Users/yiming/Documents/EBC-ZIP/notebooks; Parent directory: /Users/yiming/Documents/EBC-ZIP\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import numpy as np\n", + "import torch\n", + "from torch import Tensor\n", + "from scipy.ndimage import gaussian_filter\n", + "from torchvision.transforms.functional import normalize, to_pil_image\n", + "from torchvision.transforms.v2 import Compose\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from typing import Tuple, Optional\n", + "\n", + "current_dir = os.getcwd()\n", + "parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))\n", + "print(f\"Current directory: {current_dir}; Parent directory: {parent_dir}\")\n", + "sys.path.insert(0, parent_dir)\n", + "\n", + "import datasets\n", + "\n", + "rng = np.random.default_rng(42)\n", + "\n", + "\n", + "def plot_img_and_ann(\n", + " img: Tensor,\n", + " ann: Tensor,\n", + " density_map: Optional[Tensor] = None,\n", + " title: Optional[str] = None,\n", + " mean: Tuple[float, float, float] = (0.485, 0.456, 0.406),\n", + " std: Tuple[float, float, float] = (0.229, 0.224, 0.225),\n", + " sigma: float = 4.0,\n", + " # dpi: int = 200,\n", + " alpha: float = 0.2,\n", + " markersize: int = 1,\n", + " markercolor: str = \"deeppink\",\n", + ") -> plt.Figure:\n", + " img = normalize(img, mean=(0., 0., 0.), std = (1. / std[0], 1. / std[1], 1. / std[2]))\n", + " img = normalize(img, mean=(-mean[0], -mean[1], -mean[2]), std=(1., 1., 1.))\n", + " img = to_pil_image(img)\n", + "\n", + " ann = ann.numpy()\n", + "\n", + " if density_map is not None:\n", + " density_map = density_map.numpy().squeeze()\n", + " density_map = gaussian_filter(density_map, sigma=sigma)\n", + "\n", + " # fig, ax = plt.subplots(dpi=dpi)\n", + " # fig.set_dpi(dpi)\n", + " fig, ax = plt.subplots()\n", + " ax.imshow(img)\n", + "\n", + " if len(ann) > 0:\n", + " ax.scatter(ann[:, 0], ann[:, 1], s=markersize, c=markercolor)\n", + " if density_map is not None:\n", + " ax.imshow(density_map, cmap=\"jet\", alpha=alpha)\n", + "\n", + " if title is not None:\n", + " ax.set_title(title)\n", + " ax.axis(\"off\")\n", + " fig.tight_layout()\n", + " return fig\n", + "\n", + "crop_size = 672\n", + "transforms = Compose([\n", + " datasets.RandomResizedCrop((crop_size, crop_size), scale=(1, 2)),\n", + " datasets.RandomHorizontalFlip(),\n", + " datasets.RandomApply([\n", + " datasets.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.),\n", + " datasets.GaussianBlur(kernel_size=5),\n", + " datasets.PepperSaltNoise(),\n", + " ], p=(1, 1, 0.5)),\n", + " # datasets.RandomGrayscale(p=0.1),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_name = \"nwpu\"\n", + "split = \"val\"\n", + "\n", + "dataset = datasets.Crowd(\n", + " dataset_name,\n", + " split=split,\n", + " transforms=transforms,\n", + " sigma=8,\n", + " return_filename=True,\n", + ")\n", + "idx = rng.integers(0, len(dataset))\n", + "img, ann, density_map, file_path = dataset[idx]\n", + "img = img.squeeze(0)\n", + "ann = ann[0]\n", + "density_map = density_map.squeeze(0)\n", + "file_path = file_path[0]\n", + "file_name = os.path.basename(file_path)\n", + "fig = plot_img_and_ann(img, ann, density_map, f\"file: {dataset_name}/{split}/{file_name}; count: {len(ann)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset = datasets.NWPUTest(\n", + " transforms=None,\n", + " return_filename=True,\n", + ")\n", + "idx = rng.integers(0, len(dataset))\n", + "img, file_path = dataset[idx]\n", + "file_name = os.path.basename(file_path)\n", + "fig = plot_img_and_ann(img, torch.tensor([], dtype=torch.float32), None, file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/distribution.ipynb b/notebooks/distribution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..47ad0665125113554a2673593226eacfe448ec17 --- /dev/null +++ b/notebooks/distribution.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8251ed25", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from collections import defaultdict\n", + "\n", + "# copied from the counts folder\n", + "datasets = {\n", + " \"ShanghaiTech A\": {\n", + " \"0\": 182715307,\n", + " \"1\": 25282990,\n", + " \"2\": 3723241,\n", + " \"3\": 1075878,\n", + " \"4\": 428683,\n", + " \"5\": 212379,\n", + " \"6\": 116019,\n", + " \"7\": 66336,\n", + " \"8\": 38808,\n", + " \"9\": 22851,\n", + " \"10\": 13845,\n", + " \"11\": 8410,\n", + " \"12\": 5248,\n", + " \"13\": 3355,\n", + " \"14\": 1997,\n", + " \"15\": 1309,\n", + " \"16\": 818,\n", + " \"17\": 451,\n", + " \"18\": 238,\n", + " \"19\": 113,\n", + " \"20\": 65,\n", + " \"21\": 31,\n", + " \"22\": 16,\n", + " \"23\": 8,\n", + " \"24\": 8,\n", + " \"25\": 4,\n", + " \"26\": 2,\n", + " \"27\": 1,\n", + " \"28\": 1\n", + " },\n", + " \"ShanghaiTech B\": {\n", + " \"0\": 294072360,\n", + " \"1\": 8559657,\n", + " \"2\": 922301,\n", + " \"3\": 225017,\n", + " \"4\": 75708,\n", + " \"5\": 29428,\n", + " \"6\": 12533,\n", + " \"7\": 6347,\n", + " \"8\": 3429,\n", + " \"9\": 1869,\n", + " \"10\": 913,\n", + " \"11\": 494,\n", + " \"12\": 338,\n", + " \"13\": 202,\n", + " \"14\": 192,\n", + " \"15\": 11,\n", + " \"16\": 1\n", + " },\n", + " \"UCF-QNRF\": {\n", + " \"0\": 8451989906,\n", + " \"1\": 189577152,\n", + " \"2\": 19349594,\n", + " \"3\": 4927861,\n", + " \"4\": 1576908,\n", + " \"5\": 556543,\n", + " \"6\": 226325,\n", + " \"7\": 104066,\n", + " \"8\": 51679,\n", + " \"9\": 24615,\n", + " \"10\": 11391,\n", + " \"11\": 4737,\n", + " \"12\": 2041,\n", + " \"13\": 921,\n", + " \"14\": 450,\n", + " \"15\": 236,\n", + " \"16\": 120,\n", + " \"17\": 54,\n", + " \"18\": 20,\n", + " \"19\": 4,\n", + " \"20\": 2\n", + " },\n", + " \"NWPU-Crowd\": {\n", + " \"0\": 28037670189,\n", + " \"1\": 262823214,\n", + " \"2\": 20627370,\n", + " \"3\": 4559000,\n", + " \"4\": 1377101,\n", + " \"5\": 488567,\n", + " \"6\": 205419,\n", + " \"7\": 94266,\n", + " \"8\": 44455,\n", + " \"9\": 22605,\n", + " \"10\": 11771,\n", + " \"11\": 6703,\n", + " \"12\": 3736,\n", + " \"13\": 2433,\n", + " \"14\": 1408,\n", + " \"15\": 680,\n", + " \"16\": 271,\n", + " \"17\": 89,\n", + " \"18\": 41,\n", + " \"19\": 10,\n", + " \"20\": 3,\n", + " \"21\": 2\n", + " }\n", + "}\n", + "data_by_value = defaultdict(dict)\n", + "\n", + "colors = {\n", + " \"ShanghaiTech A\": \"#4477AA\",\n", + " \"ShanghaiTech B\": \"#EE6677\",\n", + " \"UCF-QNRF\": \"#228833\",\n", + " \"NWPU-Crowd\": \"#AA3377\"\n", + "}\n", + "\n", + "\n", + "for name, data in datasets.items():\n", + " total = sum(data.values())\n", + " counts = {k: 0 for k in range(4)}\n", + " counts[\"≥4\"] = 0\n", + " for k, v in data.items():\n", + " if int(k) < 4:\n", + " counts[int(k)] += v\n", + " else:\n", + " counts[\"≥4\"] += v\n", + " for k in counts:\n", + " data_by_value[k][name] = counts[k] / total * 100 # 转为百分比\n", + "\n", + "# 准备绘图数据\n", + "x_labels = [0, 1, 2, 3, \"≥4\"]\n", + "datasets_order = list(datasets.keys())\n", + "bar_width = 0.2\n", + "x = list(range(len(x_labels)))\n", + "\n", + "# 创建图形\n", + "fig, ax = plt.subplots(1, 1, figsize=(15, 8), dpi=200)\n", + "\n", + "# 绘图\n", + "for i, dataset in enumerate(datasets_order):\n", + " y_vals = [data_by_value[label][dataset] for label in x_labels]\n", + " x_positions = [val + i * bar_width for val in x]\n", + " bars = ax.bar(x_positions, y_vals, width=bar_width, label=dataset, edgecolor='black', color=colors[dataset])\n", + " \n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " text = f'{height:.1f}' if height > 1.0 else f'{height:.2f}'\n", + " ax.annotate(\n", + " text,\n", + " xy=(bar.get_x() + bar.get_width() / 2, height),\n", + " xytext=(0, 2),\n", + " textcoords=\"offset points\",\n", + " ha='center',\n", + " va='bottom',\n", + " fontsize=18,\n", + " weight=\"medium\",\n", + " color=colors[dataset],\n", + " )\n", + "\n", + "# 设置坐标轴、标题等\n", + "ax.set_xlabel('Blockwise Count Value', fontsize=25)\n", + "ax.set_ylabel('Percentage of Blocks', fontsize=25)\n", + "ax.set_yscale('log')\n", + "\n", + "x = list(range(len(x_labels))) # [0, 1, 2, 3, 4]\n", + "x_positions = [x_val + i * bar_width for x_val in x]\n", + "# 设置紧凑型横轴\n", + "ax.set_xticks([r + bar_width * 1.5 for r in x]) # 保持标签居中\n", + "ax.set_xticklabels(x_labels)\n", + "\n", + "# 缩小左右空隙\n", + "left_margin = x[0] - bar_width * 0.75 # 起始位置\n", + "right_margin = x[-1] + bar_width * (len(datasets_order) - 0.25) # 结束位置\n", + "ax.set_xlim(left_margin, right_margin)\n", + "\n", + "# ax.set_xticks([r + bar_width * 1.5 for r in x])\n", + "# ax.set_xticklabels(x_labels)\n", + "\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=22.5)\n", + "ax.tick_params(axis=\"both\", which=\"minor\", labelsize=22.5)\n", + "\n", + "ax.legend(title='Dataset', title_fontsize=25, fontsize=22.5)\n", + "ax.grid(axis='y', linestyle='--', alpha=0.8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"count_distribution.png\", dpi=300, bbox_inches='tight', pad_inches=0.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d594a601", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/model.ipynb b/notebooks/model.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..71646500b96b3380e2f10da0a2ac987dbaf2fec3 --- /dev/null +++ b/notebooks/model.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CLIP_EBC(\n", + " (backbone): ConvNeXt(\n", + " (stem): Sequential(\n", + " (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n", + " (1): LayerNorm2d((128,), eps=1e-06, elementwise_affine=True)\n", + " )\n", + " (stage0): ConvNeXtStage(\n", + " (downsample): Identity()\n", + " (blocks): Sequential(\n", + " (0): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)\n", + " (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=512, out_features=128, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): Identity()\n", + " )\n", + " (1): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)\n", + " (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=512, out_features=128, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.003)\n", + " )\n", + " (2): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)\n", + " (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=512, out_features=128, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.006)\n", + " )\n", + " )\n", + " )\n", + " (stage1): ConvNeXtStage(\n", + " (downsample): Sequential(\n", + " (0): LayerNorm2d((128,), eps=1e-06, elementwise_affine=True)\n", + " (1): Conv2d(128, 256, kernel_size=(2, 2), stride=(2, 2))\n", + " )\n", + " (blocks): Sequential(\n", + " (0): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)\n", + " (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=256, out_features=1024, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=1024, out_features=256, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.009)\n", + " )\n", + " (1): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)\n", + " (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=256, out_features=1024, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=1024, out_features=256, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.011)\n", + " )\n", + " (2): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)\n", + " (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=256, out_features=1024, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=1024, out_features=256, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.014)\n", + " )\n", + " )\n", + " )\n", + " (stage2): ConvNeXtStage(\n", + " (downsample): Sequential(\n", + " (0): LayerNorm2d((256,), eps=1e-06, elementwise_affine=True)\n", + " (1): Conv2d(256, 512, kernel_size=(2, 2), stride=(2, 2))\n", + " )\n", + " (blocks): Sequential(\n", + " (0): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.017)\n", + " )\n", + " (1): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.020)\n", + " )\n", + " (2): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.023)\n", + " )\n", + " (3): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.026)\n", + " )\n", + " (4): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.029)\n", + " )\n", + " (5): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.031)\n", + " )\n", + " (6): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.034)\n", + " )\n", + " (7): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.037)\n", + " )\n", + " (8): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.040)\n", + " )\n", + " (9): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.043)\n", + " )\n", + " (10): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.046)\n", + " )\n", + " (11): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.049)\n", + " )\n", + " (12): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.051)\n", + " )\n", + " (13): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.054)\n", + " )\n", + " (14): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.057)\n", + " )\n", + " (15): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.060)\n", + " )\n", + " (16): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.063)\n", + " )\n", + " (17): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.066)\n", + " )\n", + " (18): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.069)\n", + " )\n", + " (19): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.071)\n", + " )\n", + " (20): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.074)\n", + " )\n", + " (21): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.077)\n", + " )\n", + " (22): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.080)\n", + " )\n", + " (23): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.083)\n", + " )\n", + " (24): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.086)\n", + " )\n", + " (25): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.089)\n", + " )\n", + " (26): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)\n", + " (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.091)\n", + " )\n", + " )\n", + " )\n", + " (stage3): ConvNeXtStage(\n", + " (downsample): Sequential(\n", + " (0): LayerNorm2d((512,), eps=1e-06, elementwise_affine=True)\n", + " (1): Conv2d(512, 1024, kernel_size=(2, 2), stride=(2, 2))\n", + " )\n", + " (blocks): Sequential(\n", + " (0): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)\n", + " (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.094)\n", + " )\n", + " (1): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)\n", + " (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.097)\n", + " )\n", + " (2): ConvNeXtBlock(\n", + " (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)\n", + " (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (act): GELU()\n", + " (drop1): Dropout(p=0.0, inplace=False)\n", + " (norm): Identity()\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " (drop2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (shortcut): Identity()\n", + " (drop_path): DropPath(drop_prob=0.100)\n", + " )\n", + " )\n", + " )\n", + " (refiner): Sequential(\n", + " (0): ConvUpsample(\n", + " (refine): ConvRefine(\n", + " (refine): BasicBlock(\n", + " (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (norm1): Conv2dLayerNorm(\n", + " (0): Rearrange('B C H W -> B H W C')\n", + " (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (2): Rearrange('B H W C -> B C H W')\n", + " )\n", + " (act1): GELU(approximate='none')\n", + " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (norm2): Conv2dLayerNorm(\n", + " (0): Rearrange('B C H W -> B H W C')\n", + " (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (2): Rearrange('B H W C -> B C H W')\n", + " )\n", + " (act2): GELU(approximate='none')\n", + " (downsample): Identity()\n", + " )\n", + " )\n", + " )\n", + " (1): ConvUpsample(\n", + " (refine): ConvRefine(\n", + " (refine): BasicBlock(\n", + " (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (norm1): Conv2dLayerNorm(\n", + " (0): Rearrange('B C H W -> B H W C')\n", + " (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (2): Rearrange('B H W C -> B C H W')\n", + " )\n", + " (act1): GELU(approximate='none')\n", + " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (norm2): Conv2dLayerNorm(\n", + " (0): Rearrange('B C H W -> B H W C')\n", + " (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (2): Rearrange('B H W C -> B C H W')\n", + " )\n", + " (act2): GELU(approximate='none')\n", + " (downsample): Identity()\n", + " )\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pi_head): Conv2d(1024, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (lambda_head): Conv2d(1024, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + ")" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import sys\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms.functional import normalize, to_pil_image\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))\n", + "sys.path.insert(0, parent_dir)\n", + "\n", + "import datasets\n", + "from models import get_model\n", + "from utils import resize_density_map\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "dataset_name = \"nwpu\"\n", + "split = \"val\"\n", + "\n", + "model_info_path = os.path.join(parent_dir, \"checkpoints\", \"nwpu\", \"ebc_b_best\", \"best_mae.pth\")\n", + "\n", + "model = get_model(model_info_path)\n", + "\n", + "model = model.to(device)\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = datasets.Crowd(dataset=dataset_name, split=split, sigma=8, return_filename=True)\n", + "dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0, collate_fn=datasets.collate_fn)\n", + "data_iter = iter(dataloader)\n", + "mean = (0.485, 0.456, 0.406)\n", + "std = (0.229, 0.224, 0.225)\n", + "alpha = 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GT Count: 1307\n", + "Pred Count: 1305.81\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 1.19, NAE: 0.09%\n" + ] + } + ], + "source": [ + "idx = 4\n", + "\n", + "image, points, density, image_name = dataset[idx]\n", + "image_height, image_width = image.shape[-2:]\n", + "image = image.to(device)\n", + "\n", + "with torch.no_grad():\n", + " image_feats = model.backbone(image)\n", + " # image_feats = F.normalize(image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C)\n", + " pi_image_feats, lambda_image_feats = model.pi_head(image_feats), model.lambda_head(image_feats)\n", + " pi_image_feats = F.normalize(pi_image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C)\n", + " lambda_image_feats = F.normalize(lambda_image_feats.permute(0, 2, 3, 1), p=2, dim=-1) # shape (B, H, W, C)\n", + "\n", + " pi_text_feats, lambda_text_feats = model.pi_text_feats, model.lambda_text_feats\n", + " pi_logit_scale, lambda_logit_scale = model.pi_logit_scale.exp(), model.lambda_logit_scale.exp()\n", + "\n", + " pi_logit_map = pi_logit_scale * pi_image_feats @ pi_text_feats.t() # (B, H, W, 2), logits per image\n", + " lambda_logit_map = lambda_logit_scale * lambda_image_feats @ lambda_text_feats.t() # (B, H, W, N - 1), logits per image\n", + "\n", + " pi_logit_map = pi_logit_map.permute(0, 3, 1, 2) # (B, 2, H, W)\n", + " lambda_logit_map = lambda_logit_map.permute(0, 3, 1, 2) # (B, N - 1, H, W)\n", + "\n", + " lambda_map = (lambda_logit_map.softmax(dim=1) * model.bin_centers[:, 1:]).sum(dim=1, keepdim=True) # (B, 1, H, W)\n", + " \n", + " # pi_logit_map.softmax(dim=1)[:, 0] is the probability of zeros\n", + " den_map = pi_logit_map.softmax(dim=1)[:, 1:] * lambda_map # (B, 1, H, W)\n", + " count = den_map.sum().item() # total count\n", + "\n", + " pi_map = pi_logit_map.softmax(dim=1)[:, 0:1] # (B, 1, H, W)\n", + "\n", + " den_map = F.interpolate(den_map, size=(image_height, image_width), mode=\"bilinear\", align_corners=False)\n", + " pi_map = F.interpolate(pi_map, size=(image_height, image_width), mode=\"bilinear\", align_corners=False)\n", + " lambda_map = F.interpolate(lambda_map, size=(image_height, image_width), mode=\"bilinear\", align_corners=False)\n", + "\n", + "\n", + "image = normalize(image, mean=(0., 0., 0.), std=(1. / std[0], 1. / std[1], 1. / std[2]))\n", + "image = normalize(image, mean=(-mean[0], -mean[1], -mean[2]), std=(1., 1., 1.))\n", + "image = to_pil_image(image.squeeze(0))\n", + "\n", + "density = density.squeeze().cpu().numpy()\n", + "points = points[0]\n", + "den_map = den_map.squeeze().cpu().numpy()\n", + "pi_map = pi_map.squeeze().cpu().numpy()\n", + "lambda_map = lambda_map.squeeze().cpu().numpy()\n", + "image_name = image_name[0].split(\".\")[0]\n", + "\n", + "fig, axes = plt.subplots(2, 4, dpi=200, tight_layout=True, frameon=False)\n", + "axes = axes.flatten()\n", + "\n", + "axes[0].imshow(image)\n", + "# Optional: plot the ground truth points\n", + "# if len(points) > 0:\n", + "# axes[0].scatter(points[:, 0], points[:, 1], s=1, c=\"white\")\n", + "axes[0].axis(\"off\")\n", + "axes[0].set_title(f\"{image_name}\")\n", + "print(f\"GT Count: {len(points)}\")\n", + "\n", + "axes[1].imshow(image)\n", + "axes[1].imshow(density, cmap=\"jet\", alpha=alpha)\n", + "axes[1].axis(\"off\")\n", + "axes[1].set_title(f\"GT Density\")\n", + "\n", + "axes[2].imshow(image)\n", + "axes[2].imshow(den_map, cmap=\"jet\", alpha=alpha)\n", + "axes[2].axis(\"off\")\n", + "axes[2].set_title(f\"Pred Density\")\n", + "print(f\"Pred Count: {count:.2f}\")\n", + "\n", + "axes[3].imshow(image)\n", + "axes[3].imshow(lambda_map, cmap=\"jet\", alpha=alpha)\n", + "axes[3].axis(\"off\")\n", + "axes[3].set_title(\"Lambda\")\n", + "\n", + "axes[4].imshow(image)\n", + "axes[4].imshow(pi_map, cmap=\"jet\", alpha=alpha)\n", + "axes[4].axis(\"off\")\n", + "axes[4].set_title(\"Structural Zero\")\n", + "\n", + "axes[5].imshow(image)\n", + "axes[5].imshow((1 - pi_map) * np.exp(-lambda_map), cmap=\"jet\", alpha=alpha)\n", + "axes[5].axis(\"off\")\n", + "axes[5].set_title(\"Sampling Zero\")\n", + "\n", + "axes[6].imshow(image)\n", + "axes[6].imshow(pi_map + (1 - pi_map) * np.exp(-lambda_map), cmap=\"jet\", alpha=alpha)\n", + "axes[6].axis(\"off\")\n", + "axes[6].set_title(\"Complete Zero\")\n", + "\n", + "axes[7].set_visible(False)\n", + "plt.show()\n", + "\n", + "mae = abs(count - len(points))\n", + "nae = mae / len(points) if len(points) > 0 else 0.0\n", + "print(f\"MAE: {mae:.2f}, NAE: {nae:.2%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/preprocess.py b/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..89a864a4a26408c769df5b61c29ba5e661987584 --- /dev/null +++ b/preprocess.py @@ -0,0 +1,386 @@ +import os +from glob import glob +from scipy.io import loadmat +import cv2 +from argparse import ArgumentParser +from tqdm import tqdm +import numpy as np +from typing import Tuple, Optional +from warnings import warn + +from datasets import standardize_dataset_name + + +def _calc_size( + img_w: int, + img_h: int, + min_size: int, + max_size: int, + base: int = 32 +) -> Tuple[int, int]: + """ + This function generates a new size for an image while keeping the aspect ratio. The new size should be within the given range (min_size, max_size). + + Args: + img_w (int): The width of the image. + img_h (int): The height of the image. + min_size (int): The minimum size of the edges of the image. + max_size (int): The maximum size of the edges of the image. + # base (int): The base number to which the new size should be a multiple of. + """ + assert min_size % base == 0, f"min_size ({min_size}) must be a multiple of {base}" + if max_size != float("inf"): + assert max_size % base == 0, f"max_size ({max_size}) must be a multiple of {base} if provided" + + assert min_size <= max_size, f"min_size ({min_size}) must be less than or equal to max_size ({max_size})" + + aspect_ratios = (img_w / img_h, img_h / img_w) + if min_size / max_size <= min(aspect_ratios) <= max(aspect_ratios) <= max_size / min_size: # possible to resize and preserve the aspect ratio + if min_size <= min(img_w, img_h) <= max(img_w, img_h) <= max_size: # already within the range, no need to resize + ratio = 1. + elif min(img_w, img_h) < min_size: # smaller than the minimum size, resize to the minimum size + ratio = min_size / min(img_w, img_h) + else: # larger than the maximum size, resize to the maximum size + ratio = max_size / max(img_w, img_h) + + new_w, new_h = int(round(img_w * ratio / base) * base), int(round(img_h * ratio / base) * base) + new_w = max(min_size, min(max_size, new_w)) + new_h = max(min_size, min(max_size, new_h)) + return new_w, new_h + + else: # impossible to resize and preserve the aspect ratio + msg = f"Impossible to resize {img_w}x{img_h} image while preserving the aspect ratio to a size within the range ({min_size}, {max_size}). Will not limit the maximum size." + warn(msg) + return _calc_size(img_w, img_h, min_size, float("inf"), base) + + +def _resize(image: np.ndarray, label: np.ndarray, min_size: int, max_size: int) -> Tuple[np.ndarray, np.ndarray, bool]: + image_h, image_w, _ = image.shape + new_size = _calc_size(image_w, image_h, min_size, max_size) + if new_size is None: + return image, label, False + else: + new_w, new_h = new_size + image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4) if (new_w, new_h) != (image_w, image_h) else image + label = label * np.array([[new_w / image_w, new_h / image_h]]) if len(label) > 0 and (new_w, new_h) != (image_w, image_h) else label + return image, label, True + + +def _preprocess( + dataset: str, + data_src_dir: str, + data_dst_dir: str, + min_size: int, + max_size: int, +) -> None: + """ + This function organizes the data into the following structure: + data_dst_dir + ├── train + │ ├── images + │ │ ├── 0001.jpg + │ │ ├── 0002.jpg + │ │ ├── ... + │ │ + │ ├── labels + │ ├── 0001.npy + │ ├── 0002.npy + │ ├── ... + │ + ├── val + ├── images + │ ├── 0001.jpg + │ ├── 0002.jpg + │ ├── ... + │ + ├── labels + │ ├── 0001.npy + │ ├── 0002.npy + │ ├── ... + """ + dataset = standardize_dataset_name(dataset) + assert os.path.isdir(data_src_dir), f"{data_src_dir} does not exist" + os.makedirs(data_dst_dir, exist_ok=True) + print(f"Pre-processing {dataset} dataset...") + + if dataset in ["sha", "shb"]: + _shanghaitech(data_src_dir, data_dst_dir, min_size, max_size) + + elif dataset == "nwpu": + _nwpu(data_src_dir, data_dst_dir, min_size, max_size) + + elif dataset == "qnrf": + _qnrf(data_src_dir, data_dst_dir, min_size, max_size) + + else: # dataset == "jhu" + _jhu(data_src_dir, data_dst_dir, min_size, max_size) + + +def _resize_and_save( + image: np.ndarray, + name: str, + image_dst_dir: str, + label: Optional[np.ndarray] = None, + label_dst_dir: Optional[str] = None, + min_size: Optional[int] = None, + max_size: Optional[int] = None, +) -> None: + os.makedirs(image_dst_dir, exist_ok=True) + + if label is not None: + assert label_dst_dir is not None, "label_dst_dir must be provided if label is provided" + os.makedirs(label_dst_dir, exist_ok=True) + + image_dst_path = os.path.join(image_dst_dir, f"{name}.jpg") + + if label is not None: + label_dst_path = os.path.join(label_dst_dir, f"{name}.npy") + else: + label = np.array([]) + label_dst_path = None + + if min_size is not None: + assert max_size is not None, f"max_size must be provided if min_size is provided, got {max_size}" + image, label, success = _resize(image, label, min_size, max_size) + if not success: + print(f"image: {image_dst_path} is not resized") + + cv2.imwrite(image_dst_path, image) + + if label_dst_path is not None: + np.save(label_dst_path, label) + + +def _shanghaitech( + data_src_dir: str, + data_dst_dir: str, + min_size: int, + max_size: int, +) -> None: + for split in ["train", "val"]: + print(f"Processing {split}...") + if split == "train": + image_src_dir = os.path.join(data_src_dir, "train_data", "images") + label_src_dir = os.path.join(data_src_dir, "train_data", "ground-truth") + image_src_paths = glob(os.path.join(image_src_dir, "*.jpg")) + label_src_paths = glob(os.path.join(label_src_dir, "*.mat")) + assert len(image_src_paths) == len(label_src_paths) in [300, 400], f"Expected 300 (part_A) or 400 (part_B) images and labels, got {len(image_src_paths)} images and {len(label_src_paths)} labels" + else: + image_src_dir = os.path.join(data_src_dir, "test_data", "images") + label_src_dir = os.path.join(data_src_dir, "test_data", "ground-truth") + image_src_paths = glob(os.path.join(image_src_dir, "*.jpg")) + label_src_paths = glob(os.path.join(label_src_dir, "*.mat")) + assert len(image_src_paths) == len(label_src_paths) in [182, 316], f"Expected 182 (part_A) or 316 (part_B) images and labels, got {len(image_src_paths)} images and {len(label_src_paths)} labels" + + sort_key = lambda x: int((os.path.basename(x).split(".")[0]).split("_")[-1]) + image_src_paths.sort(key=sort_key) + label_src_paths.sort(key=sort_key) + + image_dst_dir = os.path.join(data_dst_dir, split, "images") + label_dst_dir = os.path.join(data_dst_dir, split, "labels") + os.makedirs(image_dst_dir, exist_ok=True) + os.makedirs(label_dst_dir, exist_ok=True) + + size = len(str(len(image_src_paths))) + for i, (image_src_path, label_src_path) in tqdm(enumerate(zip(image_src_paths, label_src_paths)), total=len(image_src_paths)): + image_id = int((os.path.basename(image_src_path).split(".")[0]).split("_")[-1]) + label_id = int((os.path.basename(label_src_path).split(".")[0]).split("_")[-1]) + assert image_id == label_id, f"Expected image id {image_id} to match label id {label_id}" + name = f"{(i + 1):0{size}d}" + image = cv2.imread(image_src_path) + label = loadmat(label_src_path)["image_info"][0][0][0][0][0] + _resize_and_save( + image=image, + label=label, + name=name, + image_dst_dir=image_dst_dir, + label_dst_dir=label_dst_dir, + min_size=min_size, + max_size=max_size + ) + +def _nwpu( + data_src_dir: str, + data_dst_dir: str, + min_size: int, + max_size: int, +) -> None: + for split in ["train", "val"]: + print(f"Processing {split}...") + with open(os.path.join(data_src_dir, f"{split}.txt"), "r") as f: + indices = f.read().splitlines() + indices = [idx.split(" ")[0] for idx in indices] + image_src_paths = [os.path.join(data_src_dir, f"images_part{min(5, (int(idx) - 1) // 1000 + 1)}", f"{idx}.jpg") for idx in indices] + label_src_paths = [os.path.join(data_src_dir, "mats", f"{idx}.mat") for idx in indices] + + image_dst_dir = os.path.join(data_dst_dir, split, "images") + label_dst_dir = os.path.join(data_dst_dir, split, "labels") + os.makedirs(image_dst_dir, exist_ok=True) + os.makedirs(label_dst_dir, exist_ok=True) + + size = len(str(len(image_src_paths))) + for i, (image_src_path, label_src_path) in tqdm(enumerate(zip(image_src_paths, label_src_paths)), total=len(image_src_paths)): + image_id = os.path.basename(image_src_path).split(".")[0] + label_id = os.path.basename(label_src_path).split(".")[0] + assert image_id == label_id, f"Expected image id {image_id} to match label id {label_id}" + name = f"{(i + 1):0{size}d}" + image = cv2.imread(image_src_path) + label = loadmat(label_src_path)["annPoints"] + _resize_and_save( + image=image, + label=label, + name=name, + image_dst_dir=image_dst_dir, + label_dst_dir=label_dst_dir, + min_size=min_size, + max_size=max_size + ) + + # preprocess the test set + split = "test" + print(f"Processing {split}...") + with open(os.path.join(data_src_dir, f"{split}.txt"), "r") as f: + indices = f.read().splitlines() + indices = [idx.split(" ")[0] for idx in indices] + image_src_paths = [os.path.join(data_src_dir, f"images_part{min(5, (int(idx) - 1) // 1000 + 1)}", f"{idx}.jpg") for idx in indices] + + image_dst_dir = os.path.join(data_dst_dir, split, "images") + os.makedirs(image_dst_dir, exist_ok=True) + + for image_src_path in tqdm(image_src_paths): + image_id = os.path.basename(image_src_path).split(".")[0] + image = cv2.imread(image_src_path) + _resize_and_save( + image=image, + label=None, + name=image_id, + image_dst_dir=image_dst_dir, + label_dst_dir=None, + min_size=min_size, + max_size=max_size + ) + + +def _qnrf( + data_src_dir: str, + data_dst_dir: str, + min_size: int, + max_size: int, +) -> None: + for split in ["train", "val"]: + print(f"Processing {split}...") + if split == "train": + image_src_dir = os.path.join(data_src_dir, "Train") + label_src_dir = os.path.join(data_src_dir, "Train") + image_src_paths = glob(os.path.join(image_src_dir, "*.jpg")) + label_src_paths = glob(os.path.join(label_src_dir, "*.mat")) + assert len(image_src_paths) == len(label_src_paths) == 1201, f"Expected 1201 images and labels, got {len(image_src_paths)} images and {len(label_src_paths)} labels" + else: + image_src_dir = os.path.join(data_src_dir, "Test") + label_src_dir = os.path.join(data_src_dir, "Test") + image_src_paths = glob(os.path.join(image_src_dir, "*.jpg")) + label_src_paths = glob(os.path.join(label_src_dir, "*.mat")) + assert len(image_src_paths) == len(label_src_paths) == 334, f"Expected 334 images and labels, got {len(image_src_paths)} images and {len(label_src_paths)} labels" + + sort_key = lambda x: int((os.path.basename(x).split(".")[0]).split("_")[1]) + image_src_paths.sort(key=sort_key) + label_src_paths.sort(key=sort_key) + + image_dst_dir = os.path.join(data_dst_dir, split, "images") + label_dst_dir = os.path.join(data_dst_dir, split, "labels") + os.makedirs(image_dst_dir, exist_ok=True) + os.makedirs(label_dst_dir, exist_ok=True) + + size = len(str(len(image_src_paths))) + for i, (image_src_path, label_src_path) in tqdm(enumerate(zip(image_src_paths, label_src_paths)), total=len(image_src_paths)): + image_id = int((os.path.basename(image_src_path).split(".")[0]).split("_")[1]) + label_id = int((os.path.basename(label_src_path).split(".")[0]).split("_")[1]) + assert image_id == label_id, f"Expected image id {image_id} to match label id {label_id}" + name = f"{(i + 1):0{size}d}" + image = cv2.imread(image_src_path) + label = loadmat(label_src_path)["annPoints"] + _resize_and_save( + image=image, + label=label, + name=name, + image_dst_dir=image_dst_dir, + label_dst_dir=label_dst_dir, + min_size=min_size, + max_size=max_size + ) + +def _jhu( + data_src_dir: str, + data_dst_dir: str, + min_size: int, + max_size: int, +) -> None: + for split in ["train", "val", "test"]: + with open(os.path.join(data_src_dir, split, "image_labels.txt"), "r") as f: + image_names = f.read().splitlines() + image_names = [name.split(",")[0] for name in image_names] + image_src_paths = [os.path.join(data_src_dir, split, "images", f"{name}.jpg") for name in image_names] + label_src_paths = [os.path.join(data_src_dir, split, "gt", f"{name}.txt") for name in image_names] + + image_dst_dir = os.path.join(data_dst_dir, split, "images") + label_dst_dir = os.path.join(data_dst_dir, split, "labels") + os.makedirs(image_dst_dir, exist_ok=True) + os.makedirs(label_dst_dir, exist_ok=True) + + size = len(str(len(image_src_paths))) + for i, (image_src_path, label_src_path) in tqdm(enumerate(zip(image_src_paths, label_src_paths)), total=len(image_src_paths)): + image_id = int(os.path.basename(image_src_path).split(".")[0]) + label_id = int(os.path.basename(label_src_path).split(".")[0]) + assert image_id == label_id, f"Expected image id {image_id} to match label id {label_id}" + name = f"{(i + 1):0{size}d}" + image = cv2.imread(image_src_path) + with open(label_src_path, "r") as f: + label = f.read().splitlines() + label = np.array([list(map(float, line.split(" ")[0: 2])) for line in label]) + _resize_and_save( + image=image, + label=label, + name=name, + image_dst_dir=image_dst_dir, + label_dst_dir=label_dst_dir, + min_size=min_size, + max_size=max_size + ) + + +def parse_args(): + parser = ArgumentParser(description="Pre-process datasets to resize images and labeled into a given range.") + parser.add_argument( + "--dataset", + type=str, + choices=["nwpu", "ucf_qnrf", "jhu", "shanghaitech_a", "shanghaitech_b"], + required=True, + help="The dataset to pre-process." + ) + parser.add_argument("--src_dir", type=str, required=True, help="The root directory of the source dataset.") + parser.add_argument("--dst_dir", type=str, required=True, help="The root directory of the destination dataset.") + parser.add_argument("--min_size", type=int, default=448, help="The minimum size of the shorter side of the image.") + parser.add_argument("--max_size", type=int, default=None, help="The maximum size of the longer side of the image.") + + args = parser.parse_args() + args.src_dir = os.path.abspath(args.src_dir) + args.dst_dir = os.path.abspath(args.dst_dir) + args.max_size = float("inf") if args.max_size is None else args.max_size + return args + + +if __name__ == "__main__": + args = parse_args() + _preprocess( + dataset=args.dataset, + data_src_dir=args.src_dir, + data_dst_dir=args.dst_dir, + min_size=args.min_size, + max_size=args.max_size, + ) + + +# python preprocess.py --dataset shanghaitech_a --src_dir ./data/ShanghaiTech/part_A --dst_dir ./data/sha --min_size 448. +# python preprocess.py --dataset shanghaitech_b --src_dir ./data/ShanghaiTech/part_B --dst_dir ./data/shb --min_size 448 +# python preprocess.py --dataset nwpu --src_dir ./data/NWPU-Crowd --dst_dir ./data/nwpu --min_size 448 --max_size 2048 +# python preprocess.py --dataset ucf_qnrf --src_dir ./data/UCF-QNRF --dst_dir ./data/qnrf --min_size 448 --max_size 3072 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..2420e2dfc6188288afed6d65b7b05af3568e8187 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +einops==0.8.1 +numpy==2.3.1 +peft==0.15.2 +pyturbojpeg==1.8.0 +PyYAML==6.0.2 +scipy==1.16.0 +tensorboardX==2.6.4 +timm==1.0.16 +torch==2.7.1 +torchvision==0.22.0 +tqdm==4.67.1 diff --git a/run.sh b/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..2374b8a2867e633a2e15227a73c21ab2a00b5189 --- /dev/null +++ b/run.sh @@ -0,0 +1,20 @@ +# Train the base model on ShanghaiTech A +python trainer.py \ + --dataset sha --input_size 224 --block_size 16 \ + --model_name ebc_b --num_vpt 96 --sliding_window --warmup_lr 1e-3 \ + --amp --num_workers 8 + +# Train the base model on ShanghaiTech B. You can also try block_size 32. +python trainer.py \ + --dataset sha --input_size 448 --block_size 16 \ + --model_name ebc_b --amp --num_workers 8 + +# Train the base model on UCF-QRNF. +python trainer.py \ + --dataset qnrf --input_size 672 --block_size 32 \ + --model_name ebc_b --amp --num_workers 8 + +# Train the base model on NWPU-Crowd. You can also try block_size 16 or 32. +python trainer.py \ + --dataset sha --input_size 672 --block_size 8 \ + --model_name ebc_b --amp --num_workers 8 \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 0000000000000000000000000000000000000000..bfd4d2d5c609ea48433f0488b6426bb73d37c9c0 --- /dev/null +++ b/test.py @@ -0,0 +1,97 @@ +import torch +from torch import nn +import torch.multiprocessing as mp +from torch.nn.parallel import DistributedDataParallel as DDP +from argparse import ArgumentParser +import os + +current_dir = os.path.abspath(os.path.dirname(__file__)) + +from datasets import standardize_dataset_name +from models import get_model +from utils import get_config, get_dataloader, setup, cleanup +from evaluate import evaluate + + +parser = ArgumentParser(description="Test a trained model on a dataset.") +# Parameters for model +parser.add_argument("--weight_path", type=str, required=True, help="The name of the weight to use.") +parser.add_argument("--output_filename", type=str, default=None, help="The name of the result file.") + +# Parameters for evaluation +parser.add_argument("--dataset", type=str, required=True, help="The dataset to evaluate on.") +parser.add_argument("--split", type=str, default="val", choices=["val", "test"], help="The split to evaluate on.") +parser.add_argument("--input_size", type=int, default=224, help="The size of the input image.") +parser.add_argument("--sliding_window", action="store_true", help="Use sliding window strategy for evaluation.") +parser.add_argument("--max_input_size", type=int, default=4096, help="The maximum size of the input image in evaluation. Images larger than this will be processed using sliding window by force to avoid OOM.") +parser.add_argument("--max_num_windows", type=int, default=8, help="The maximum number of windows to be simultaneously processed.") +parser.add_argument("--resize_to_multiple", action="store_true", help="Resize the image to the nearest multiple of the input size.") +parser.add_argument("--stride", type=int, default=None, help="The stride for sliding window strategy.") +parser.add_argument("--amp", action="store_true", help="Use automatic mixed precision for evaluation.") +parser.add_argument("--device", type=str, default="cuda", help="The device to use for evaluation.") +parser.add_argument("--num_workers", type=int, default=8, help="The number of workers for the data loader.") +parser.add_argument("--local_rank", type=int, default=-1, help="The local rank for distributed training.") + + +def run(local_rank: int, nprocs: int, args: ArgumentParser): + print(f"Rank {local_rank} process among {nprocs} processes.") + setup(local_rank, nprocs) + print(f"Initialized successfully. Training with {nprocs} GPUs.") + device = f"cuda:{local_rank}" if local_rank != -1 else "cuda:0" + print(f"Using device: {device}.") + + ddp = nprocs > 1 + _ = get_config(vars(args).copy(), mute=False) + + model = get_model(model_info_path=args.weight_path).to(device) + model = DDP(nn.SyncBatchNorm.convert_sync_batchnorm(model), device_ids=[local_rank], output_device=local_rank) if ddp else model + model = model.to(device) + model.eval() + + args.output_filename = f"{model.model_name}_{args.weight_path.split('/')[-1].split('.')[0]}" if args.output_filename is None else args.output_filename + + dataloader = get_dataloader(args, split=args.split) + scores = evaluate( + model=model, + data_loader=dataloader, + sliding_window=args.sliding_window, + max_input_size=args.max_input_size, + window_size=args.input_size, + stride=args.stride, + max_num_windows=args.max_num_windows, + amp=args.amp, + local_rank=local_rank, + nprocs=nprocs, + ) + + if local_rank == 0: + for k, v in scores.items(): + print(f"{k}: {v}") + + result_dir = os.path.join(current_dir, "results", args.dataset, args.split) + os.makedirs(result_dir, exist_ok=True) + with open(os.path.join(result_dir, f"{args.output_filename}.txt"), "w") as f: + for k, v in scores.items(): + f.write(f"{k}: {v}\n") + + cleanup(ddp) + + +if __name__ == "__main__": + args = parser.parse_args() + args.dataset = standardize_dataset_name(args.dataset) + + if args.dataset in ["sha", "shb", "qnrf", "nwpu"]: + assert args.split == "val", f"Split {args.split} is not available for dataset {args.dataset}." + + # Sliding window prediction will be used if args.sliding_window is True, or when the image size is larger than args.max_input_size + args.stride = args.stride or args.input_size + assert os.path.exists(args.weight_path), f"Weight path {args.weight_path} does not exist." + args.in_memory_dataset = False + + args.nprocs = torch.cuda.device_count() + print(f"Using {args.nprocs} GPUs.") + if args.nprocs > 1: + mp.spawn(run, nprocs=args.nprocs, args=(args.nprocs, args)) + else: + run(0, 1, args) diff --git a/test.sh b/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..7629fd511b3167b645addd7db6ce31ba5ba27dd5 --- /dev/null +++ b/test.sh @@ -0,0 +1,38 @@ +# # ShanghaiTech A +# python test.py --dataset sha --weight_path checkpoints/sha/ebc_p_best/best_mae.pth --output_filename ebc_p_best_mae --amp + +# python test.py --dataset sha --weight_path checkpoints/sha/ebc_n_best/best_mae.pth --output_filename ebc_n_best_mae --amp + +# python test.py --dataset sha --weight_path checkpoints/sha/ebc_t_best/best_mae.pth --output_filename ebc_t_best_mae --amp + +# python test.py --dataset sha --weight_path checkpoints/sha/ebc_s_best/best_mae.pth --output_filename ebc_s_best_mae --amp + +# python test.py --dataset sha --weight_path checkpoints/sha/ebc_b_vit_best/best_mae.pth --output_filename ebc_b_best_mae --sliding_window --input_size 224 --amp + + +# # ShanghaiTech B +# python test.py --dataset shb --weight_path checkpoints/shb/ebc_p_best/best_mae.pth --output_filename ebc_p_best_mae --amp + +# python test.py --dataset shb --weight_path checkpoints/shb/ebc_n_best/best_mae.pth --output_filename ebc_n_best_mae --amp + +# python test.py --dataset shb --weight_path checkpoints/shb/ebc_t_best/best_mae.pth --output_filename ebc_t_best_mae --amp + +# python test.py --dataset shb --weight_path checkpoints/shb/ebc_s_best/best_mae.pth --output_filename ebc_s_best_mae --amp + +# python test.py --dataset shb --weight_path checkpoints/shb/ebc_b_best/best_mae.pth --output_filename ebc_b_best_mae --amp + + +# # UCF-QNRF +# python test.py --dataset qnrf --weight_path checkpoints/qnrf/ebc_p_best/best_mae.pth --input_size 672 --output_filename ebc_p_best_mae --amp + +# python test.py --dataset qnrf --weight_path checkpoints/qnrf/ebc_n_best/best_mae.pth --input_size 672 --output_filename ebc_n_best_mae --amp + +# python test.py --dataset qnrf --weight_path checkpoints/qnrf/ebc_t_best/best_mae.pth --input_size 672 --output_filename ebc_t_best_mae --amp + +# python test.py --dataset qnrf --weight_path checkpoints/qnrf/ebc_s_best/best_mae.pth --input_size 672 --output_filename ebc_s_best_mae --amp + +# python test.py --dataset qnrf --weight_path checkpoints/qnrf/ebc_b_best/best_mae.pth --input_size 672 --output_filename ebc_b_best_mae --amp + + +# NWPU +python test.py --dataset nwpu --weight_path checkpoints/nwpu/ebc_b_best/best_mae.pth --input_size 672 --output_filename ebc_b_best_mae --amp --device mps \ No newline at end of file diff --git a/test_nwpu.py b/test_nwpu.py new file mode 100644 index 0000000000000000000000000000000000000000..150ebfda7c473673335e080260750732fe2f95fd --- /dev/null +++ b/test_nwpu.py @@ -0,0 +1,94 @@ +import torch +import torch.nn.functional as F +from argparse import ArgumentParser +import os +from tqdm import tqdm + +current_dir = os.path.abspath(os.path.dirname(__file__)) + +from datasets import NWPUTest, Resize2Multiple +from models import get_model +from utils import get_config, sliding_window_predict + +parser = ArgumentParser(description="Generate the test result of a trained model on the NWPU-Crowd test set.") +# Parameters for model +parser.add_argument("--weight_path", type=str, required=True, help="The directory to the checkpoints. This should also include the model_info.pkl file.") +parser.add_argument("--output_filename", type=str, default="test_results", help="The name of the output file.") + +# Parameters for evaluation +parser.add_argument("--input_size", type=int, default=224, help="The size of the input image.") +parser.add_argument("--sliding_window", action="store_true", help="Use sliding window strategy for evaluation.") +parser.add_argument("--max_input_size", type=int, default=4096, help="The maximum size of the input image in evaluation. Images larger than this will be processed using sliding window by force to avoid OOM.") +parser.add_argument("--max_num_windows", type=int, default=8, help="The maximum number of windows to be simultaneously processed.") +parser.add_argument("--resize_to_multiple", action="store_true", help="Resize the image to the nearest multiple of the input size.") +parser.add_argument("--stride", type=int, default=None, help="The stride for sliding window strategy.") +parser.add_argument("--amp", action="store_true", help="Use automatic mixed precision for evaluation.") +parser.add_argument("--device", type=str, default="cuda", help="The device to use for evaluation.") + + +def main(args: ArgumentParser): + print("Testing a trained model on the NWPU-Crowd test set.") + device = torch.device(args.device) + _ = get_config(vars(args).copy(), mute=False) + + model = get_model(model_info_path=args.weight_path).to(device) + model.eval() + + sliding_window = args.sliding_window + if args.resize_to_multiple: + transforms = Resize2Multiple(base=args.input_size) + else: + transforms = None + + dataset = NWPUTest(transforms=transforms, return_filename=True) + + image_ids = [] + preds = [] + input_size = args.input_size + + for idx in tqdm(range(len(dataset)), desc="Testing on NWPU"): + image, image_path = dataset[idx] + image = image.unsqueeze(0) # add batch dimension + image = image.to(device) # add batch dimension + image_height, image_width = image.shape[-2:] + + # Resize image if it's smaller than the window size + aspect_ratio = image_width / image_height + if image_height < input_size: + new_height = input_size + new_width = int(new_height * aspect_ratio) + image = F.interpolate(image, size=(new_height, new_width), mode="bicubic", align_corners=False) + image_height, image_width = new_height, new_width + if image_width < input_size: + new_width = input_size + new_height = int(new_width / aspect_ratio) + image = F.interpolate(image, size=(new_height, new_width), mode="bicubic", align_corners=False) + image_height, image_width = new_height, new_width + + with torch.set_grad_enabled(False), torch.autocast(device_type="cuda", enabled=args.amp): + if sliding_window or (args.max_input_size is not None and (image_height * image_width) > args.max_input_size ** 2): + pred_den_map = sliding_window_predict(model, image, input_size, args.stride, args.max_num_windows) + else: + pred_den_map = model(image) + + pred_count = pred_den_map.sum(dim=(1, 2, 3)).item() + + image_ids.append(os.path.basename(image_path).split(".")[0]) + preds.append(pred_count) + + result_dir = os.path.join(current_dir, "nwpu_test_results") + os.makedirs(result_dir, exist_ok=True) + with open(os.path.join(result_dir, f"{args.output_filename}.txt"), "w") as f: + for idx, (image_id, pred) in enumerate(zip(image_ids, preds)): + if idx != len(image_ids) - 1: + f.write(f"{image_id} {pred}\n") + else: + f.write(f"{image_id} {pred}") # no newline at the end of the file + + +if __name__ == "__main__": + args = parser.parse_args() + # Sliding window prediction will be used if args.sliding_window is True, or when the image size is larger than args.max_input_size + args.stride = args.stride or args.input_size + assert os.path.exists(args.weight_path), f"Checkpoint directory {args.weight_path} does not exist." + main(args) diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..76c3b826ce7b2287d4fa11c92ae65bc60402fcb4 --- /dev/null +++ b/train.py @@ -0,0 +1,120 @@ +import torch +from torch import nn +from torch.optim import Optimizer +from torch.utils.data import DataLoader +from torch.amp import GradScaler, autocast +import numpy as np +from tqdm import tqdm +from typing import Dict, Tuple, Union +from copy import deepcopy + +from utils import barrier, reduce_mean, update_loss_info +from evaluate import evaluate + + +def train( + model: nn.Module, + data_loader: DataLoader, + loss_fn: nn.Module, + optimizer: Optimizer, + grad_scaler: Union[GradScaler, None], + device: torch.device = torch.device("cuda"), + rank: int = 0, + nprocs: int = 1, + **kwargs, +) -> Tuple[nn.Module, Optimizer, GradScaler, Dict[str, float]]: + info = None + data_iter = tqdm(data_loader) if rank == 0 else data_loader + ddp = nprocs > 1 + + if "eval_data_loader" in kwargs: # we are evaluting the model withing one training epoch + assert "eval_freq" in kwargs and 0 < kwargs["eval_freq"] < 1, f"eval_freq should be a float between 0 and 1, but got {kwargs['eval_freq']}" + assert "sliding_window" in kwargs, "sliding_window should be provided in kwargs" + assert "max_input_size" in kwargs, "max_input_size should be provided in kwargs" + assert "window_size" in kwargs, "window_size should be provided in kwargs" + assert "stride" in kwargs, "stride should be provided in kwargs" + assert "max_num_windows" in kwargs, "max_num_windows should be provided in kwargs" + + eval_within_epoch = True + eval_data_loader = kwargs["eval_data_loader"] + eval_freq = int(kwargs["eval_freq"] * len(data_loader)) + sliding_window = kwargs["sliding_window"] + max_input_size = kwargs["max_input_size"] + window_size = kwargs["window_size"] + stride = kwargs["stride"] + max_num_windows = kwargs["max_num_windows"] + + best_scores = {} + best_weights = {} + + else: + eval_within_epoch = False + best_scores = None + best_weights = None + + for batch_idx, (image, gt_points, gt_den_map) in enumerate(data_iter): + image = image.to(device) + gt_points = [p.to(device) for p in gt_points] + gt_den_map = gt_den_map.to(device) + model.train() + with torch.set_grad_enabled(True): + with autocast(device_type="cuda", enabled=grad_scaler is not None and grad_scaler.is_enabled()): + if (model.module.zero_inflated if ddp else model.zero_inflated): + pred_logit_pi_map, pred_logit_map, pred_lambda_map, pred_den_map = model(image) + total_loss, total_loss_info = loss_fn( + pred_logit_pi_map=pred_logit_pi_map, + pred_logit_map=pred_logit_map, + pred_lambda_map=pred_lambda_map, + pred_den_map=pred_den_map, + gt_den_map=gt_den_map, + gt_points=gt_points, + ) + else: + pred_logit_map, pred_den_map = model(image) + total_loss, total_loss_info = loss_fn( + pred_logit_map=pred_logit_map, + pred_den_map=pred_den_map, + gt_den_map=gt_den_map, + gt_points=gt_points, + ) + + optimizer.zero_grad() + if grad_scaler is not None: + grad_scaler.scale(total_loss).backward() + grad_scaler.step(optimizer) + grad_scaler.update() + else: + total_loss.backward() + optimizer.step() + + total_loss_info = {k: reduce_mean(v.detach(), nprocs).item() if ddp else v.detach().item() for k, v in total_loss_info.items()} + info = update_loss_info(info, total_loss_info) + barrier(ddp) + + if eval_within_epoch and ((batch_idx + 1) % eval_freq == 0 or batch_idx == len(data_loader) - 1): + batch_scores = evaluate( + model=model, + data_loader=eval_data_loader, + sliding_window=sliding_window, + max_input_size=max_input_size, + window_size=window_size, + stride=stride, + max_num_windows=max_num_windows, + device=device, + amp=grad_scaler is not None and grad_scaler.is_enabled(), + local_rank=rank, + nprocs=nprocs, + progress_bar=False, + ) + for k, v in batch_scores.items(): + if k not in best_scores: + best_scores[k] = v + best_weights[k] = deepcopy(model.module.state_dict() if ddp else model.state_dict()) + elif v < best_scores[k]: # smaller is better + best_scores[k] = v + best_weights[k] = deepcopy(model.module.state_dict() if ddp else model.state_dict()) + + barrier(ddp) + + torch.cuda.empty_cache() + return model, optimizer, grad_scaler, {k: np.mean(v) for k, v in info.items()}, best_scores, best_weights diff --git a/trainer.py b/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea1f87031b7e453be799cb45de7f6141cedb4a3 --- /dev/null +++ b/trainer.py @@ -0,0 +1,489 @@ +import torch +from torch import nn +import torch.multiprocessing as mp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.amp import GradScaler +import numpy as np +from copy import deepcopy +from argparse import ArgumentParser +import os, json, hashlib, yaml + +current_dir = os.path.abspath(os.path.dirname(__file__)) + +from datasets import standardize_dataset_name +from models import get_model + +from utils import setup, cleanup, init_seeds, get_logger, get_config, barrier +from utils import get_dataloader, get_loss_fn, get_optimizer, load_checkpoint, save_checkpoint +from utils import get_writer, update_train_result, update_eval_result, log, calc_bin_center +from train import train +from evaluate import evaluate + + +parser = ArgumentParser(description="Train an EBC model.") + +# Parameters for model +parser.add_argument("--model_name", type=str, default="CLIP_RN50", help="The model to train.") +parser.add_argument("--block_size", type=int, default=16, choices=[7, 8, 14, 16, 28, 32], help="The block sizes for the model.") +parser.add_argument("--clip_weight_name", type=str, default=None, help="The weight name for CLIP models.") +parser.add_argument("--norm", type=str, default="none", choices=["none", "bn", "ln"], help="The normalization layer to use. 'none' means no normalization layer will be detected automatically, 'bn' means batch normalization, 'ln' means layer normalization.") +parser.add_argument("--act", type=str, default="none", choices=["none", "relu", "gelu"], help="The activation function to use. 'none' means no activation function will be detected automatically, 'relu' means ReLU, 'gelu' means GELU.") + +parser.add_argument("--num_vpt", type=int, default=96, help="The number of visual prompt tokens.") +parser.add_argument("--vpt_drop", type=float, default=0.0, help="The dropout rate for visual prompt tokens.") + +parser.add_argument("--adapter", action="store_true", help="Use adapter for the model. This will freeze the backbone and only train the adapter layers and newly added layers.") +parser.add_argument("--adapter_reduction", type=int, default=4, help="The reduction ratio for the adapter layers. This will be used to reduce the number of parameters in the adapter layers.") + +parser.add_argument("--lora", action="store_true", help="Use LoRA for the model. This will freeze the backbone and only train the LoRA layers and newly added layers.") +parser.add_argument("--lora_rank", type=int, default=16, help="The rank for the LoRA layers. This will be used to reduce the number of parameters in the LoRA layers.") +parser.add_argument("--lora_alpha", type=float, default=32.0, help="The alpha for the LoRA layers. This will be used to scale the LoRA layers.") +parser.add_argument("--lora_dropout", type=float, default=0.0, help="The dropout rate for the LoRA layers.") + +# Parameters for dataset +parser.add_argument("--dataset", type=str, required=True, help="The dataset to train on.") +parser.add_argument("--in_memory_dataset", action="store_true", help="Load the dataset into memory. This will speed up training but requires more memory.") +parser.add_argument("--input_size", type=int, default=None, help="The size of the input image.") +parser.add_argument("--batch_size", type=int, default=None, help="The training batch size.") +parser.add_argument("--num_crops", type=int, default=None, help="The number of crops for multi-crop training.") +parser.add_argument("--aug_min_scale", type=float, default=None, help="The minimum scale for random scale augmentation.") +parser.add_argument("--aug_max_scale", type=float, default=None, help="The maximum scale for random scale augmentation.") +parser.add_argument("--aug_brightness", type=float, default=None, help="The brightness factor for random color jitter augmentation.") +parser.add_argument("--aug_contrast", type=float, default=None, help="The contrast factor for random color jitter augmentation.") +parser.add_argument("--aug_saturation", type=float, default=None, help="The saturation factor for random color jitter augmentation.") +parser.add_argument("--aug_hue", type=float, default=None, help="The hue factor for random color jitter augmentation.") +parser.add_argument("--aug_kernel_size", type=int, default=None, help="The kernel size for Gaussian blur augmentation.") +parser.add_argument("--aug_saltiness", type=float, default=None, help="The saltiness for pepper salt noise augmentation.") +parser.add_argument("--aug_spiciness", type=float, default=None, help="The spiciness for pepper salt noise augmentation.") +parser.add_argument("--aug_blur_prob", type=float, default=None, help="The probability for Gaussian blur augmentation.") + +# Parameters for evaluation +parser.add_argument("--sliding_window", action="store_true", help="Use sliding window strategy for evaluation.") +parser.add_argument("--stride", type=int, default=None, help="The stride for sliding window strategy.") +parser.add_argument("--max_input_size", type=int, default=4096, help="The maximum size of the input image in evaluation. Images larger than this will be processed using sliding window by force to avoid OOM.") +parser.add_argument("--max_num_windows", type=int, default=64, help="The maximum number of windows to be simultaneously processed.") +parser.add_argument("--resize_to_multiple", action="store_true", help="Resize the image to a multiple of the input size.") + +# Parameters for loss function +parser.add_argument("--reg_loss", type=str, default="zipnll", choices=["zipnll", "pnll", "dm", "msmae", "mae", "mse"], help="The regression loss function.") +parser.add_argument("--aux_loss", type=str, default="msmae", choices=["zipnll", "pnll", "dm", "msmae", "mae", "mse", "none"], help="The auxiliary loss function.") +parser.add_argument("--weight_cls", type=float, default=1.0, help="The weight for classification loss.") +parser.add_argument("--weight_reg", type=float, default=1.0, help="The weight for regression loss.") +parser.add_argument("--weight_aux", type=float, default=1.0, help="The weight for auxiliary loss.") +parser.add_argument("--numItermax", type=int, default=100, help="The maximum number of iterations for the OT/POT solver.") +parser.add_argument("--regularization", type=float, default=10.0, help="The regularization term for the OT/POT loss.") +parser.add_argument("--scales", type=int, nargs="+", default=[1, 2, 4], help="The scales for multi-scale mae loss.") +parser.add_argument("--min_scale_weight", type=float, default=0.0, help="The minimum weight for multi-scale mae loss.") +parser.add_argument("--max_scale_weight", type=float, default=1.0, help="The maximum weight for multi-scale mae loss.") +parser.add_argument("--alpha", type=float, default=0.5, help="The alpha for multi-scale mae loss.") + +# Parameters for optimizer +parser.add_argument("--optimizer", type=str, default="adam", choices=["sgd", "adam", "adamw", "radam"], help="The optimizer to use.") +parser.add_argument("--lr", type=float, default=None, help="The learning rate for untrained modules.") +parser.add_argument("--vpt_lr", type=float, default=None, help="The learning rate for the visual prompt tokens.") +parser.add_argument("--adapter_lr", type=float, default=None, help="The learning rate for the adapter layers. If None, it will be set to the same as lr.") +parser.add_argument("--lora_lr", type=float, default=None, help="The learning rate for the LoRA layers. If None, it will be set to the same as lr.") +parser.add_argument("--backbone_lr", type=float, default=None, help="The learning rate for the pretrained backbone.") +parser.add_argument("--weight_decay", type=float, default=None, help="The weight decay for untrained modules.") +parser.add_argument("--vpt_weight_decay", type=float, default=None, help="The weight decay for the visual prompt tokens.") +parser.add_argument("--adapter_weight_decay", type=float, default=None, help="The weight decay for the adapter layers. If None, it will be set to the same as weight_decay.") +parser.add_argument("--lora_weight_decay", type=float, default=None, help="The weight decay for the LoRA layers. If None, it will be set to the same as weight_decay.") +parser.add_argument("--backbone_weight_decay", type=float, default=None, help="The weight decay for the pretrained backbone.") + +# Parameters for learning rate scheduler +parser.add_argument("--scheduler", type=str, default="cos_restarts", choices=["step", "cos", "cos_restarts"], help="The learning rate scheduler.") +parser.add_argument("--warmup_epochs", type=int, default=25, help="Number of epochs for warmup. The learning rate will linearly change from warmup_lr to lr.") +parser.add_argument("--warmup_lr", type=float, default=1e-5, help="Learning rate for warmup.") +parser.add_argument("--eta_min", type=float, default=1e-6, help="Minimum learning rate.") +# Step Decay parameters +parser.add_argument("--gamma", type=float, default=0.925, help="The decay factor for step scheduler.") +parser.add_argument("--step_size", type=int, default=20, help="The step size for step scheduler.") +# Cosine Annealing with Warm Restarts parameters +parser.add_argument("--T_0", type=int, default=5, help="Number of epochs for the first restart.") +parser.add_argument("--T_mult", type=int, default=2, help="A factor increases T_0 after a restart.") +# Cosine Annealing parameters +parser.add_argument("--T_max", type=int, default=20, help="The maximum number of epochs for the cosine annealing scheduler.") + +# Parameters for training +parser.add_argument("--ckpt_dir_name", type=str, default=None, help="The name of the checkpoint folder.") +parser.add_argument("--total_epochs", type=int, default=1300, help="Number of epochs to train.") +parser.add_argument("--eval_start", type=int, default=None, help="Start to evaluate after this number of epochs.") +parser.add_argument("--eval_freq", type=float, default=None, help="Evaluate every this number of epochs. If < 1, evaluate every this fraction of an epoch.") +parser.add_argument("--save_freq", type=int, default=50, help="Save checkpoint every this number of epochs. Could help reduce I/O.") +parser.add_argument("--save_best_k", type=int, default=5, help="Save the best k checkpoints.") +parser.add_argument("--amp", action="store_true", help="Use automatic mixed precision training.") +parser.add_argument("--num_workers", type=int, default=os.cpu_count(), help="Number of workers for data loading.") +parser.add_argument("--local_rank", type=int, default=-1, help="Local rank for distributed training.") +parser.add_argument("--seed", type=int, default=42, help="Random seed for initialization.") + + +def run(local_rank: int, nprocs: int, args: ArgumentParser) -> None: + print(f"Rank {local_rank} process among {nprocs} processes.") + init_seeds(args.seed + local_rank) + setup(local_rank, nprocs) + args.local_rank = local_rank + print(f"Initialized successfully. Training with {nprocs} GPUs.") + device = f"cuda:{local_rank}" if local_rank != -1 else "cuda:0" + print(f"Using device: {device}.") + + ddp = nprocs > 1 + + # Define the bins and bin centers + with open(os.path.join(current_dir, "configs", "bin_config.json"), "r") as f: + bins = json.load(f)[args.dataset][str(args.block_size)] + bins = [(float(b[0]), float(b[1])) for b in bins] + + with open(os.path.join(current_dir, "counts", f"{args.dataset}.json"), "r") as f: + count_stats = json.load(f)[str(args.block_size)] + count_stats = {int(k): int(v) for k, v in count_stats.items()} + bin_centers, bin_counts = calc_bin_center(bins, count_stats) + + args.bins = bins + args.bin_centers = bin_centers + args.bin_counts = bin_counts + + model = get_model( + model_info_path=os.path.join(args.ckpt_dir, "model_info.pth"), + model_name=args.model_name, + block_size=args.block_size, + bins=bins, + bin_centers=bin_centers, + zero_inflated=args.reg_loss == "zipnll" or args.aux_loss == "zipnll", + clip_weight_name=args.clip_weight_name, + num_vpt=args.num_vpt, + vpt_drop=args.vpt_drop, + adapter=args.adapter, + adapter_reduction=args.adapter_reduction, + lora=args.lora, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + input_size=args.input_size, + norm=args.norm, + act=args.act, + ).to(device) + + total_params = sum(p.numel() for p in model.parameters()) + total_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + total_nontrainable_params = total_params - total_trainable_params + + grad_scaler = GradScaler(device=device) if args.amp else None + + loss_fn = get_loss_fn(args) + optimizer, scheduler = get_optimizer(args, model) + + model, optimizer, scheduler, grad_scaler, start_epoch, loss_info, hist_val_scores, best_val_scores = load_checkpoint(args, model, optimizer, scheduler, grad_scaler) + model = DDP(nn.SyncBatchNorm.convert_sync_batchnorm(model), device_ids=[local_rank], output_device=local_rank) if ddp else model + + if local_rank == 0: + writer = get_writer(args.ckpt_dir) + logger = get_logger(os.path.join(args.ckpt_dir, "train.log")) + logger.info(get_config(vars(args), mute=False)) + logger.info(f"Total parameters: {total_params:,}\nTrainable parameters: {total_trainable_params:,}\nNon-trainable parameters: {total_nontrainable_params:,}\n") + + train_loader, sampler = get_dataloader(args, split="train") + val_loader = get_dataloader(args, split="val") + + for epoch in range(start_epoch, args.total_epochs + 1): # start from 1 + if local_rank == 0: + message = f"\tlr: {optimizer.param_groups[0]['lr']:.3e}" + log(logger, epoch, args.total_epochs, message=message) + + if sampler is not None: + sampler.set_epoch(epoch) + + if args.eval_freq < 1: + eval_model = epoch >= args.eval_start + + if eval_model: + model, optimizer, grad_scaler, loss_info, curr_val_scores, curr_weights = train( + model=model, + data_loader=train_loader, + loss_fn=loss_fn, + optimizer=optimizer, + grad_scaler=grad_scaler, + device=device, + rank=local_rank, + nprocs=nprocs, + eval_data_loader=val_loader, + eval_freq=args.eval_freq, + sliding_window=args.sliding_window, + max_input_size=args.max_input_size, + window_size=args.input_size, + stride=args.stride, + max_num_windows=args.max_num_windows, + ) + scheduler.step() + barrier(ddp) + + else: + model, optimizer, grad_scaler, loss_info, _, _ = train( + model=model, + data_loader=train_loader, + loss_fn=loss_fn, + optimizer=optimizer, + grad_scaler=grad_scaler, + device=device, + rank=local_rank, + nprocs=nprocs, + ) + scheduler.step() + barrier(ddp) + + else: + model, optimizer, grad_scaler, loss_info, _, _ = train( + model=model, + data_loader=train_loader, + loss_fn=loss_fn, + optimizer=optimizer, + grad_scaler=grad_scaler, + device=device, + rank=local_rank, + nprocs=nprocs, + ) + + scheduler.step() + barrier(ddp) + + eval_model = (epoch >= args.eval_start) and ((epoch - args.eval_start) % args.eval_freq == 0) + if eval_model: + curr_val_scores = evaluate( + model=model, + data_loader=val_loader, + sliding_window=args.sliding_window, + max_input_size=args.max_input_size, + window_size=args.input_size, + stride=args.stride, + max_num_windows=args.max_num_windows, + device=device, + amp=args.amp, + local_rank=local_rank, + nprocs=nprocs + ) + + state_dict = deepcopy(model.module.state_dict() if ddp else model.state_dict()) + curr_weights = {k: state_dict for k in curr_val_scores.keys()} # copy the state_dict + + if local_rank == 0: + update_train_result(epoch, loss_info, writer) + log(logger, None, None, loss_info=loss_info, message="\n" * 2 if not eval_model else None) + + if eval_model: + hist_val_scores, best_val_scores = update_eval_result( + epoch=epoch, + curr_scores=curr_val_scores, + hist_scores=hist_val_scores, + best_scores=best_val_scores, + model_info={"config": model.module.config if ddp else model.config, "weights": curr_weights}, + writer=writer, + ckpt_dir=args.ckpt_dir, + ) + + log(logger, None, None, None, curr_val_scores, best_val_scores, message="\n" * 3) + + if local_rank == 0 and (epoch % args.save_freq == 0): + save_checkpoint( + epoch + 1, + model.module.state_dict() if ddp else model.state_dict(), + optimizer.state_dict(), + scheduler.state_dict() if scheduler is not None else None, + grad_scaler.state_dict() if grad_scaler is not None else None, + loss_info, + hist_val_scores, + best_val_scores, + args.ckpt_dir, + ) + + barrier(ddp) + + if local_rank == 0: + writer.close() + print("Training completed. Best scores:") + for k in best_val_scores.keys(): + scores = " ".join([f"{best_val_scores[k][i]:.4f};" for i in range(len(best_val_scores[k]))]) + print(f" {k}: {scores}. \t Mean: {np.mean(best_val_scores[k]):.4f}") + + cleanup(ddp) + + +def main(): + args = parser.parse_args() + args.dataset = standardize_dataset_name(args.dataset) + + dataset_config_path = os.path.join(current_dir, "configs", f"{args.dataset}.yaml") + with open(dataset_config_path, "r") as f: + dataset_config = yaml.safe_load(f) + for k, v in dataset_config.items(): + if k in vars(args) and vars(args)[k] is None: + vars(args)[k] = v + + # Sliding window prediction will be used if args.sliding_window is True, or when the image size is larger than args.max_input_size + args.stride = args.stride or args.input_size + + assert args.model_name in ["ebc_p", "ebc_n", "ebc_t", "ebc_s", "ebc_b"], f"Expected model_name to be one of ['ebc_p', 'ebc_n', 'ebc_t', 'ebc_s', 'ebc_b'], got {args.model_name}." + + if args.model_name == "ebc_p": # pico + args.model_name = "mobilenetv4_conv_small_050" + + elif args.model_name == "ebc_n": # nano + args.model_name = "mobilenetv4_conv_small" + + elif args.model_name == "ebc_t": # tiny + args.model_name = "mobilenetv4_conv_medium" + + elif args.model_name == "ebc_s": + args.model_name = "CLIP_MobileCLIP_S1" + args.clip_weight_name = "datacompdr" + + else: # args.model_name == "ebc_b": + if args.dataset == "sha": + args.model_name = "CLIP_ViT_B_16" + args.clip_weight_name = "openai" + args.num_vpt = args.num_vpt or 96 + elif args.dataset == "shb": + args.model_name = "CLIP_RN50x4" + args.clip_weight_name = "openai" + else: + args.model_name = "CLIP_convnext_base_w_320" + args.clip_weight_name = "laion_aesthetic_s13b_b82k_augreg" + + if "CLIP_" not in args.model_name: + args.clip_weight_name = None + + if args.adapter: + assert not args.lora, "Cannot use both adapter and LoRA at the same time." + + args.num_vpt = None + args.vpt_drop = None + args.vpt_lr = None + args.vpt_weight_decay = None + args.lora_rank = None + args.lora_alpha = None + args.lora_dropout = None + args.lora_lr = None + args.lora_weight_decay = None + args.backbone_lr = None + args.backbone_weight_decay = None + + assert args.adapter_lr > 0, f"Expected adapter_lr to be greater than 0, got {args.adapter_lr}" + assert args.adapter_weight_decay > 0, f"Expected adapter_weight_decay to be greater than 0, got {args.adapter_weight_decay}" + assert args.adapter_reduction > 0, f"Expected adapter_reduction to be greater than 0, got {args.adapter_reduction}" + + else: + args.adapter_reduction = None + args.adapter_lr = None + args.adapter_weight_decay = None + + if args.lora: + assert not args.adapter, "Cannot use both adapter and LoRA at the same time." + + args.num_vpt = None + args.vpt_drop = None + args.vpt_lr = None + args.vpt_weight_decay = None + args.adapter_reduction = None + args.adapter_lr = None + args.adapter_weight_decay = None + + assert args.lora_rank > 0, f"Expected lora_rank to be greater than 0, got {args.lora_rank}" + assert args.lora_alpha > 0, f"Expected lora_alpha to be greater than 0, got {args.lora_alpha}" + assert 0 <= args.lora_dropout < 1, f"Expected lora_dropout to be between 0 and 1, got {args.lora_dropout}" + assert args.lora_lr > 0, f"Expected lora_lr to be greater than 0, got {args.lora_lr}" + assert args.lora_weight_decay > 0, f"Expected lora_weight_decay to be greater than or equal to 0, got {args.lora_weight_decay}" + else: + args.lora_rank = None + args.lora_alpha = None + args.lora_dropout = None + args.lora_lr = None + args.lora_weight_decay = None + + + if "vit" not in args.model_name.lower(): + args.num_vpt = None + args.vpt_drop = None + args.vpt_lr = None + args.vpt_weight_decay = None + else: + args.backbone_lr = None + args.backbone_weight_decay = None + + if not (args.lora or args.adapter): # Use VPT only if not using LoRA or adapter + assert args.num_vpt > 0, f"Expected num_vpt to be greater than 0, got {args.num_vpt}" + assert 0 <= args.vpt_drop < 1, f"Expected vpt_drop to be between 0 and 1, got {args.vpt_drop}" + assert args.vpt_lr > 0, f"Expected vpt_lr to be greater than 0, got {args.vpt_lr}" + assert args.vpt_weight_decay >= 0, f"Expected vpt_weight_decay to be greater than or equal to 0, got {args.vpt_weight_decay}" + else: + args.num_vpt = None + args.vpt_drop = None + args.vpt_lr = None + args.vpt_weight_decay = None + + if args.reg_loss != "dm" and args.aux_loss != "dm": + args.numItermax = None + args.regularization = None + + if args.reg_loss != "msmae" and args.aux_loss != "msmae": + args.scales = None + args.min_scale_weight = None + args.max_scale_weight = None + args.alpha = None + else: + assert args.max_scale_weight >= args.min_scale_weight >= 0, f"Expected max_scale_weight to be greater than or equal to min_scale_weight, got {args.min_scale_weight} and {args.max_scale_weight}" + assert 1 >= args.alpha > 0, f"Expected alpha to be between 0 and 1, got {args.alpha}" + + if args.scheduler == "step": + args.T_0 = None + args.T_mult = None + args.T_max = None + elif args.scheduler == "cos": + args.step_size = None + args.gamma = None + args.T_0 = None + args.T_mult = None + else: + args.step_size = None + args.gamma = None + args.T_max = None + + args.nprocs = torch.cuda.device_count() + args.batch_size = int(args.batch_size / args.nprocs) + args.num_workers = int(args.num_workers / args.nprocs) + + if args.ckpt_dir_name is None: + hyperparams_dict = (vars(args)).copy() + hyperparams_dict.pop("save_freq") + hyperparams_dict.pop("save_best_k") + hyperparams_dict.pop("local_rank") + hyperparams_dict.pop("num_workers") + hyperparams_dict.pop("nprocs") + hyperparams_dict.pop("ckpt_dir_name") + hyperparams_dict = json.dumps(hyperparams_dict, sort_keys=True) + args.hash = hashlib.sha256(hyperparams_dict.encode("utf-8")).hexdigest() + + if "CLIP_" in args.model_name: + ckpt_dir_name = f"{args.model_name}_{args.clip_weight_name}_" + if "ViT" in args.model_name: + ckpt_dir_name += f"{args.num_vpt}_{args.vpt_drop}_" + else: + ckpt_dir_name = f"{args.model_name}_{args.block_size}_" + ckpt_dir_name += f"{args.weight_cls}+{args.weight_reg}x{(args.reg_loss)}+{args.weight_aux}{(args.aux_loss)}_" + ckpt_dir_name += f"{args.optimizer}_{args.scheduler}_{args.hash[:8]}" + + else: + ckpt_dir_name = args.ckpt_dir_name + + args.ckpt_dir = os.path.join(current_dir, "checkpoints", args.dataset, ckpt_dir_name) + os.makedirs(args.ckpt_dir, exist_ok=True) + + print(f"Using {args.nprocs} GPUs.") + if args.nprocs > 1: + if args.in_memory_dataset: + print("In-memory dataset is not supported for distributed training. Using disk-based dataset instead.") + args.in_memory_dataset = False + mp.spawn(run, nprocs=args.nprocs, args=(args.nprocs, args)) + else: + run(0, 1, args) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..137d1fc6f08f9341f531f68da1def14d8a05212a --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,14 @@ +from .ddp_utils import reduce_mean, setup, cleanup, init_seeds, barrier +from .eval_utils import calculate_errors, resize_density_map, sliding_window_predict +from .log_utils import get_logger, get_config, get_writer, print_epoch, print_train_result, print_eval_result, update_train_result, update_eval_result, log, update_loss_info +from .train_utils import step_decay, cosine_annealing, cosine_annealing_warm_restarts, get_loss_fn, get_optimizer, load_checkpoint, save_checkpoint +from .data_utils import get_dataloader, calc_bin_center + + +__all__ = [ + "reduce_mean", "setup", "cleanup", "init_seeds", "barrier", + "calculate_errors", "resize_density_map", "sliding_window_predict", + "get_logger", "get_config", "get_writer", "print_epoch", "print_train_result", "print_eval_result", "update_train_result", "update_eval_result", "log", "update_loss_info", + "get_dataloader", "get_loss_fn", "get_optimizer", "load_checkpoint", "save_checkpoint", "cosine_annealing", "cosine_annealing_warm_restarts", "step_decay", + "calc_bin_center", +] diff --git a/utils/data_utils.py b/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f1ea1014ba317da5f26ca1bc83a5f239ef1b63a7 --- /dev/null +++ b/utils/data_utils.py @@ -0,0 +1,134 @@ +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from torchvision.transforms.v2 import Compose +import os, sys +from argparse import ArgumentParser +from typing import Union, Tuple, List, Dict + +parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.append(parent_dir) + +import datasets + + +def calc_bin_center( + bins: List[Tuple[float, float]], + count_stats: Dict[int, int], +) -> Tuple[List[float], List[int]]: + """ + Calculate the representative value for each bin based on the count statistics. + + `bins` may look like: [(0, 0), (1, 1), (2, 3), (4, 6), (7, float('inf'))] + `count_stats` may look like: {0: 10, 1: 20, 2: 30, 3: 40, 4: 50, 5: 60, 6: 70, 7: 80, 8: 90, 9: 100} + In this example, for bin (2, 3), we have 30 samples of 2 and 40 samples of 3 that fall into this bin. + The representative value for this bin is (30 * 2 + 40 * 3) / (30 + 40) = 2.6. + + The returned list will have the same length as `bins`, and each element is the representative value for the corresponding bin. + """ + bin_counts = [0] * len(bins) + bin_sums = [0] * len(bins) + for k, v in count_stats.items(): + for i, (start, end) in enumerate(bins): + if start <= int(k) <= end: + bin_counts[i] += int(v) + bin_sums[i] += int(v) * int(k) + break + assert all(c > 0 for c in bin_counts), f"Expected all bin_counts to be greater than 0, got {bin_counts}. Consider to re-design the bins {bins}." + bin_centers = [s / c for s, c in zip(bin_sums, bin_counts)] + return bin_centers, bin_counts + + +def get_dataloader(args: ArgumentParser, split: str = "train") -> Union[Tuple[DataLoader, Union[DistributedSampler, None]], DataLoader]: + ddp = args.nprocs > 1 + if split == "train": # train, strong augmentation + transforms = [ + datasets.RandomResizedCrop((args.input_size, args.input_size), scale=(args.aug_min_scale, args.aug_max_scale)), + datasets.RandomHorizontalFlip(), + ] + if args.aug_brightness > 0 or args.aug_contrast > 0 or args.aug_saturation > 0 or args.aug_hue > 0: + transforms.append(datasets.ColorJitter( + brightness=args.aug_brightness, contrast=args.aug_contrast, saturation=args.aug_saturation, hue=args.aug_hue + )) + if args.aug_blur_prob > 0 and args.aug_kernel_size > 0: + transforms.append(datasets.RandomApply([ + datasets.GaussianBlur(kernel_size=args.aug_kernel_size), + ], p=args.aug_blur_prob)) + if args.aug_saltiness > 0 or args.aug_spiciness > 0: + transforms.append(datasets.PepperSaltNoise( + saltiness=args.aug_saltiness, spiciness=args.aug_spiciness, + )) + transforms = Compose(transforms) + + elif args.sliding_window and args.resize_to_multiple: + transforms = datasets.Resize2Multiple(args.window_size, stride=args.stride) + + else: + transforms = None + + dataset_class = datasets.InMemoryCrowd if args.in_memory_dataset else datasets.Crowd + prefetch_factor = None if args.num_workers == 0 else 3 + persistent_workers = False if args.num_workers == 0 else True + + dataset = dataset_class( + dataset=args.dataset, + split=split, + transforms=transforms, + sigma=None, + return_filename=False, + num_crops=args.num_crops if split == "train" else 1, + ) + + if ddp and split == "train": # data_loader for training in DDP + sampler = DistributedSampler(dataset, num_replicas=args.nprocs, rank=args.local_rank, shuffle=True, seed=args.seed+args.local_rank) + data_loader = DataLoader( + dataset, + batch_size=args.batch_size, + sampler=sampler, + num_workers=args.num_workers, + pin_memory=True, + collate_fn=datasets.collate_fn, + prefetch_factor=prefetch_factor, + persistent_workers=persistent_workers, + ) + return data_loader, sampler + + elif (not ddp) and split == "train": # data_loader for training + data_loader = DataLoader( + dataset, + batch_size=args.batch_size, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + collate_fn=datasets.collate_fn, + prefetch_factor=prefetch_factor, + persistent_workers=persistent_workers, + ) + return data_loader, None + + elif ddp and split == "val": + sampler = DistributedSampler(dataset, num_replicas=args.nprocs, rank=args.local_rank, shuffle=False) + data_loader = DataLoader( + dataset, + batch_size=1, # Use batch size 1 for evaluation + sampler=sampler, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + collate_fn=datasets.collate_fn, + prefetch_factor=prefetch_factor, + persistent_workers=persistent_workers, + ) + return data_loader + + else: # (not ddp) and split == "val" + data_loader = DataLoader( + dataset, + batch_size=1, # Use batch size 1 for evaluation + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + collate_fn=datasets.collate_fn, + prefetch_factor=prefetch_factor, + persistent_workers=persistent_workers, + ) + return data_loader diff --git a/utils/ddp_utils.py b/utils/ddp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b8a95a8b90640c8752ca64d83d3d6ed3165e3af6 --- /dev/null +++ b/utils/ddp_utils.py @@ -0,0 +1,44 @@ +import torch +from torch import Tensor +import torch.distributed as dist +import numpy as np +import random +import os + + +def reduce_mean(tensor: Tensor, nprocs: int) -> Tensor: + rt = tensor.clone() + dist.all_reduce(rt, op=dist.ReduceOp.SUM) + rt /= nprocs + return rt + + +def setup(local_rank: int, nprocs: int) -> None: + if nprocs > 1: + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12355" + dist.init_process_group("nccl", rank=local_rank, world_size=nprocs) + else: + print("Single process. No need to setup dist.") + + +def cleanup(ddp: bool = True) -> None: + if ddp: + dist.destroy_process_group() + + +def init_seeds(seed: int, cuda_deterministic: bool = True) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if cuda_deterministic: # slower, but reproducible + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + else: # faster, not reproducible + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + + +def barrier(ddp: bool = True) -> None: + if ddp: + dist.barrier() diff --git a/utils/eval_utils.py b/utils/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..37afa50e92b64cc3813caf293c0e6f6e5a3ff6bc --- /dev/null +++ b/utils/eval_utils.py @@ -0,0 +1,111 @@ +import torch +from torch import Tensor, nn +import torch.nn.functional as F +import numpy as np +from typing import Dict, Tuple, Union + + +def calculate_errors(pred_counts: np.ndarray, gt_counts: np.ndarray) -> Dict[str, float]: + assert isinstance(pred_counts, np.ndarray), f"Expected numpy.ndarray, got {type(pred_counts)}" + assert isinstance(gt_counts, np.ndarray), f"Expected numpy.ndarray, got {type(gt_counts)}" + assert len(pred_counts) == len(gt_counts), f"Length of predictions and ground truths should be equal, but got {len(pred_counts)} and {len(gt_counts)}" + indices = gt_counts > 0 + errors = { + "mae": np.mean(np.abs(pred_counts - gt_counts)), + "rmse": np.sqrt(np.mean((pred_counts - gt_counts) ** 2)), + "nae": np.mean(np.abs(pred_counts[indices] - gt_counts[indices]) / gt_counts[indices]) + } + return errors + + +def resize_density_map(x: Tensor, size: Tuple[int, int]) -> Tensor: + x_sum = torch.sum(x, dim=(-1, -2)) + x = F.interpolate(x, size=size, mode="bilinear") + scale_factor = torch.nan_to_num(torch.sum(x, dim=(-1, -2)) / x_sum, nan=0.0, posinf=0.0, neginf=0.0) + return x * scale_factor + + +def sliding_window_predict( + model: nn.Module, + image: Tensor, + window_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]], + max_num_windows: int, +) -> Tensor: + """ + Generate the density map for an image using the sliding window method. Overlapping regions will be averaged. + + Args: + model (nn.Module): The model to use. + image (Tensor): The image (1, c, h, w) to generate the density map for. The batch size must be 1 due to varying image sizes. + window_size (Union[int, Tuple[int, int]]): The size of the window. + stride (Union[int, Tuple[int, int]]): The step size of the window. + """ + assert len(image.shape) == 4, f"Image must be a 4D tensor (1, c, h, w), got {image.shape}" + window_size = (int(window_size), int(window_size)) if isinstance(window_size, (int, float)) else window_size + stride = (int(stride), int(stride)) if isinstance(stride, (int, float)) else stride + window_size = tuple(window_size) + stride = tuple(stride) + assert isinstance(window_size, tuple) and len(window_size) == 2 and window_size[0] > 0 and window_size[1] > 0, f"Window size must be a positive integer tuple (h, w), got {window_size}" + assert isinstance(stride, tuple) and len(stride) == 2 and stride[0] > 0 and stride[1] > 0, f"Stride must be a positive integer tuple (h, w), got {stride}" + assert stride[0] <= window_size[0] and stride[1] <= window_size[1], f"Stride must be smaller than window size, got {stride} and {window_size}" + + image_height, image_width = image.shape[-2:] + window_height, window_width = window_size + assert image_height >= window_height and image_width >= window_width, f"Image size must be larger than window size, got image size {image.shape} and window size {window_size}" + stride_height, stride_width = stride + + num_rows = int(np.ceil((image_height - window_height) / stride_height) + 1) + num_cols = int(np.ceil((image_width - window_width) / stride_width) + 1) + + if hasattr(model, "block_size"): + block_size = model.block_size + elif hasattr(model, "module") and hasattr(model.module, "block_size"): + block_size = model.module.block_size + else: + raise ValueError("Model must have block_size attribute") + assert window_height % block_size == 0 and window_width % block_size == 0, f"Window size must be divisible by block size, got {window_size} and {block_size}" + + windows = [] + for i in range(num_rows): + for j in range(num_cols): + x_start, y_start = i * stride_height, j * stride_width + x_end, y_end = x_start + window_height, y_start + window_width + if x_end > image_height: + x_start, x_end = image_height - window_height, image_height + if y_end > image_width: + y_start, y_end = image_width - window_width, image_width + + window = image[:, :, x_start:x_end, y_start:y_end] + windows.append(window) + + windows = torch.cat(windows, dim=0).to(image.device) # batched windows, shape: (num_windows, c, h, w) + + model.eval() + preds = [] + for i in range(0, len(windows), max_num_windows): + with torch.no_grad(): + preds_ = model(windows[i: min(i + max_num_windows, len(windows))]) + preds.append(preds_.cpu().numpy()) + preds = np.concatenate(preds, axis=0) # shape: (num_windows, 1, h // block_size, w // block_size) + + # assemble the density map + pred_map = np.zeros((preds.shape[1], image_height // block_size, image_width // block_size), dtype=np.float32) + count_map = np.zeros((preds.shape[1], image_height // block_size, image_width // block_size), dtype=np.float32) + idx = 0 + for i in range(num_rows): + for j in range(num_cols): + x_start, y_start = i * stride_height, j * stride_width + x_end, y_end = x_start + window_height, y_start + window_width + if x_end > image_height: + x_start, x_end = image_height - window_height, image_height + if y_end > image_width: + y_start, y_end = image_width - window_width, image_width + + pred_map[:, (x_start // block_size): (x_end // block_size), (y_start // block_size): (y_end // block_size)] += preds[idx, :, :, :] + count_map[:, (x_start // block_size): (x_end // block_size), (y_start // block_size): (y_end // block_size)] += 1. + idx += 1 + + pred_map /= count_map # average the overlapping regions + preds = torch.tensor(pred_map).unsqueeze(0) # shape: (1, 1, h // block_size, w // block_size) + return preds \ No newline at end of file diff --git a/utils/log_utils.py b/utils/log_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..807e0c1d25766acfe3af48120b6a36dadaa0e9d0 --- /dev/null +++ b/utils/log_utils.py @@ -0,0 +1,151 @@ +import torch +from torch import Tensor +from tensorboardX import SummaryWriter +import logging +import os +from typing import Dict, Union, Optional, List, Tuple +from collections import OrderedDict + + +def get_logger(log_file: str) -> logging.Logger: + logger = logging.getLogger(log_file) + logger.setLevel(logging.DEBUG) + logger.propagate = False + fh = logging.FileHandler(log_file) + fh.setLevel(logging.DEBUG) + ch = logging.StreamHandler() + ch.setLevel(logging.INFO) + formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") + ch.setFormatter(formatter) + fh.setFormatter(formatter) + logger.addHandler(ch) + logger.addHandler(fh) + return logger + + +def get_config(config: Dict, mute: bool = False) -> str: + config = config.copy() + config = "\n".join([f"{k.ljust(15)}:\t{v}" for k, v in config.items()]) + if not mute: + print(config) + return config + + +def get_writer(ckpt_dir: str) -> SummaryWriter: + return SummaryWriter(log_dir=os.path.join(ckpt_dir, "logs")) + + +def print_epoch(epoch: int, total_epochs: int, mute: bool = False) -> Union[str, None]: + digits = len(str(total_epochs)) + info = f"Epoch: {(epoch):0{digits}d} / {total_epochs:0{digits}d}" + if mute: + return info + print(info) + + +def print_train_result(loss_info: Dict[str, float], mute: bool = False) -> Union[str, None]: + loss_info = [f"{k}: {v};" for k, v in loss_info.items()] + info = "Training: " + " ".join(loss_info) + if mute: + return info + print(info) + + +def print_eval_result(curr_scores: Dict[str, float], best_scores: Dict[str, float], mute: bool = False) -> Union[str, None]: + scores = [] + for k in curr_scores.keys(): + info = f"Curr {k}: {curr_scores[k]:.4f}; \t Best {k}: " + info += " ".join([f"{best_scores[k][i]:.4f};" for i in range(len(best_scores[k]))]) + scores.append(info) + + info = "Evaluation:\n" + "\n".join(scores) + if mute: + return info + print(info) + + +def update_train_result(epoch: int, loss_info: Dict[str, float], writer: SummaryWriter) -> None: + for k, v in loss_info.items(): + writer.add_scalar(f"train/{k}", v, epoch) + + +def update_eval_result( + epoch: int, + curr_scores: Dict[str, float], + hist_scores: Dict[str, List[float]], + best_scores: Dict[str, List[float]], + model_info: Dict[str, Tensor], + writer: SummaryWriter, + ckpt_dir: str, +) -> Tuple[Dict[str, List[float]], Dict[str, float]]: + os.makedirs(ckpt_dir, exist_ok=True) + model_config = model_info["config"] + state_dict = model_info["weights"] + + for k, v in curr_scores.items(): + hist_scores[k].append(v) + writer.add_scalar(f"val/{k}", v, epoch) + + # best_scores[k][0] is the best score. Smaller is better. + # Find the location idx where the new score v should be inserted + loc = None + for i in range(len(best_scores[k])): + if v < best_scores[k][i]: + best_scores[k].insert(i, v) # Add the new best score to the location i + loc = i + break + + # If the new score is better than the worst best score + if loc is not None: + # Update the best scores + best_scores[k] = best_scores[k][:len(best_scores[k]) - 1] + + # Rename the best_{k}_{i}.pth to best_{k}_{i+1}.pth, best_{k}_{i+1}.pth to best_{k}_{i+2}.pth ... + for i in range(len(best_scores[k]) - 1, loc, -1): + if os.path.exists(os.path.join(ckpt_dir, f"best_{k}_{i-1}.pth")): + os.rename(os.path.join(ckpt_dir, f"best_{k}_{i-1}.pth"), os.path.join(ckpt_dir, f"best_{k}_{i}.pth")) + + # Save the best checkpoint + torch.save({"config": model_config, "weights": state_dict[k]}, os.path.join(ckpt_dir, f"best_{k}_{loc}.pth")) + + return hist_scores, best_scores + + +def update_loss_info(hist_scores: Union[Dict[str, List[float]], None], curr_scores: Dict[str, float]) -> Dict[str, List[float]]: + assert all([isinstance(v, float) for v in curr_scores.values()]), f"Expected all values to be float, got {curr_scores}" + if hist_scores is None or len(hist_scores) == 0: + hist_scores = {k: [v] for k, v in curr_scores.items()} + else: + for k, v in curr_scores.items(): + hist_scores[k].append(v) + return hist_scores + + +def log( + logger: logging.Logger, + epoch: Union[int, None], + total_epochs: int, + loss_info: Optional[Dict[str, float]] = None, + curr_scores: Optional[Dict[str, float]] = None, + best_scores: Optional[Dict[str, float]] = None, + message: Optional[str] = None, +) -> None: + if epoch is None: + assert total_epochs is None, f"Expected total_epochs to be None when epoch is None, got {total_epochs}" + msg = "" + else: + assert total_epochs is not None, f"Expected total_epochs to be not None when epoch is not None, got {total_epochs}" + msg = print_epoch(epoch, total_epochs, mute=True) + + if loss_info is not None: + msg += "\n" if len(msg) > 0 else "" + msg += print_train_result(loss_info, mute=True) + + if curr_scores is not None: + assert best_scores is not None, f"Expected best_scores to be not None when curr_scores is not None, got {best_scores}" + msg += "\n" if len(msg) > 0 else "" + msg += print_eval_result(curr_scores, best_scores, mute=True) + + msg += message if message is not None else "" + + logger.info(msg) diff --git a/utils/train_utils.py b/utils/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f86449bc9e31b8b472fcaf4d07896ef9bb91d3b1 --- /dev/null +++ b/utils/train_utils.py @@ -0,0 +1,306 @@ +import torch +from torch import nn, Tensor + +from torch.optim import SGD, Adam, AdamW, RAdam +from torch.amp import GradScaler +from torch.optim.lr_scheduler import LambdaLR + +from functools import partial +from argparse import ArgumentParser + +import os, sys, math +from typing import Union, Tuple, Dict, List, Optional +from collections import OrderedDict + +parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.append(parent_dir) + +import losses + + +def _check_lr(lr: float, eta_min: float) -> None: + assert lr > eta_min > 0, f"lr and eta_min must satisfy 0 < eta_min < lr, got lr={lr} and eta_min={eta_min}." + + +def _check_warmup(warmup_epochs: int, warmup_lr: float) -> None: + assert warmup_epochs >= 0, f"warmup_epochs must be non-negative, got {warmup_epochs}." + assert warmup_lr > 0, f"warmup_lr must be positive, got {warmup_lr}." + + +def _warmup_lr( + epoch: int, + base_lr: float, + warmup_epochs: int, + warmup_lr: float, +) -> float: + """ + Linear Warmup + """ + base_lr, warmup_lr = float(base_lr), float(warmup_lr) + assert epoch >= 0, f"epoch must be non-negative, got {epoch}." + _check_warmup(warmup_epochs, warmup_lr) + + if epoch < warmup_epochs: + # Compute the current learning rate in log-linear scale + lr = math.exp(math.log(warmup_lr) + epoch * (math.log(base_lr) - math.log(warmup_lr)) / warmup_epochs) + else: + lr = base_lr + + return lr + + +def step_decay( + epoch: int, + base_lr: float, + warmup_epochs: int, + warmup_lr: float, + step_size: int, + gamma: float, + eta_min: float, +) -> float: + """ + Warmup + Step Decay + """ + base_lr, warmup_lr, eta_min = float(base_lr), float(warmup_lr), float(eta_min) + assert epoch >= 0, f"epoch must be non-negative, got {epoch}." + assert step_size >= 1, f"step_size must be greater than or equal to 1, got {step_size}." + assert 0 < gamma < 1, f"gamma must be in the range (0, 1), got {gamma}." + _check_lr(base_lr, eta_min) + _check_warmup(warmup_epochs, warmup_lr) + + if epoch < warmup_epochs: + lr = _warmup_lr(epoch, base_lr, warmup_epochs, warmup_lr) + else: + epoch -= warmup_epochs + lr = base_lr * (gamma ** (epoch // step_size)) + lr = max(lr, eta_min) + + return lr / base_lr + + +def cosine_annealing( + epoch: int, + base_lr: float, + warmup_epochs: int, + warmup_lr: float, + T_max: int, + eta_min: float, +) -> float: + """ + Warmup + Cosine Annealing + """ + base_lr, warmup_lr, eta_min = float(base_lr), float(warmup_lr), float(eta_min) + assert epoch >= 0, f"epoch must be non-negative, got {epoch}." + assert T_max >= 1, f"T_max must be greater than or equal to 1, got {T_max}." + _check_lr(base_lr, eta_min) + _check_warmup(warmup_epochs, warmup_lr) + + if epoch < warmup_epochs: + lr = _warmup_lr(epoch, base_lr, warmup_epochs, warmup_lr) + else: + epoch -= warmup_epochs + lr = eta_min + (base_lr - eta_min) * (1 + math.cos(math.pi * epoch / T_max)) / 2 + + return lr / base_lr + + +def cosine_annealing_warm_restarts( + epoch: int, + base_lr: float, + warmup_epochs: int, + warmup_lr: float, + T_0: int, + T_mult: int, + eta_min: float, +) -> float: + """ + Warmup + Cosine Annealing with Warm Restarts + """ + base_lr, warmup_lr, eta_min = float(base_lr), float(warmup_lr), float(eta_min) + assert epoch >= 0, f"epoch must be non-negative, got {epoch}." + assert isinstance(T_0, int) and T_0 >= 1, f"T_0 must be greater than or equal to 1, got {T_0}." + assert isinstance(T_mult, int) and T_mult >= 1, f"T_mult must be greater than or equal to 1, got {T_mult}." + _check_lr(base_lr, eta_min) + _check_warmup(warmup_epochs, warmup_lr) + + if epoch < warmup_epochs: + lr = _warmup_lr(epoch, base_lr, warmup_epochs, warmup_lr) + else: + epoch -= warmup_epochs + if T_mult == 1: + T_cur = epoch % T_0 + T_i = T_0 + else: + n = int(math.log((epoch / T_0 * (T_mult - 1) + 1), T_mult)) + T_cur = epoch - T_0 * (T_mult ** n - 1) / (T_mult - 1) + T_i = T_0 * T_mult ** (n) + + lr = eta_min + (base_lr - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2 + + return lr / base_lr + + +def get_loss_fn(args: ArgumentParser) -> nn.Module: + return losses.QuadLoss( + input_size=args.input_size, + block_size=args.block_size, + bins=args.bins, + reg_loss=args.reg_loss, + aux_loss=args.aux_loss, + weight_cls=args.weight_cls, + weight_reg=args.weight_reg, + weight_aux=args.weight_aux, + numItermax=args.numItermax, + regularization=args.regularization, + scales=args.scales, + min_scale_weight=args.min_scale_weight, + max_scale_weight=args.max_scale_weight, + alpha=args.alpha, + ) + + +def get_optimizer( + args: ArgumentParser, + model: nn.Module +) -> Tuple[Union[SGD, Adam, AdamW, RAdam], LambdaLR]: + backbone_params = [] + new_params = [] + vpt_params = [] + adpater_params = [] + + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + + if "vpt" in name: + vpt_params.append(param) + elif "adapter" in name: + adpater_params.append(param) + elif "backbone" not in name or ("refiner" in name or "decoder" in name): + new_params.append(param) + else: + backbone_params.append(param) + + if args.num_vpt is not None: # using VTP to tune ViT-based model + assert len(backbone_params) == 0, f"Expected backbone_params to be empty when using VTP, got {len(backbone_params)}" + assert len(adpater_params) == 0, f"Expected adpater_params to be empty when using VTP, got {len(adpater_params)}" + param_groups = [ + {"params": vpt_params,"lr": args.vpt_lr, "weight_decay": args.vpt_weight_decay}, + {"params": new_params, "lr": args.lr, "weight_decay": args.weight_decay}, + ] + elif args.adapter: # using adapter to tune CLIP-based model + assert len(backbone_params) == 0, f"Expected backbone_params to be empty when using adapter, got {len(backbone_params)}" + assert len(vpt_params) == 0, f"Expected vpt_params to be empty when using adapter, got {len(vpt_params)}" + param_groups = [ + {"params": adpater_params, "lr": args.adapter_lr, "weight_decay": args.adapter_weight_decay}, + {"params": new_params, "lr": args.lr, "weight_decay": args.weight_decay}, + ] + else: + param_groups = [ + {"params": new_params, "lr": args.lr, "weight_decay": args.weight_decay}, + {"params": backbone_params, "lr": args.backbone_lr, "weight_decay": args.backbone_weight_decay} + ] + if args.optimizer == "adam": + optimizer = Adam(param_groups) + elif args.optimizer == "adamw": + optimizer = AdamW(param_groups) + elif args.optimizer == "sgd": + optimizer = SGD(param_groups, momentum=0.9) + else: + assert args.optimizer == "radam", f"Expected optimizer to be one of ['adam', 'adamw', 'sgd', 'radam'], got {args.optimizer}." + optimizer = RAdam(param_groups, decoupled_weight_decay=True) + + if args.scheduler == "step": + lr_lambda = partial( + step_decay, + base_lr=args.lr, + warmup_epochs=args.warmup_epochs, + warmup_lr=args.warmup_lr, + step_size=args.step_size, + eta_min=args.eta_min, + gamma=args.gamma, + ) + elif args.scheduler == "cos": + lr_lambda = partial( + cosine_annealing, + base_lr=args.lr, + warmup_epochs=args.warmup_epochs, + warmup_lr=args.warmup_lr, + T_max=args.T_max, + eta_min=args.eta_min, + ) + elif args.scheduler == "cos_restarts": + lr_lambda = partial( + cosine_annealing_warm_restarts, + warmup_epochs=args.warmup_epochs, + warmup_lr=args.warmup_lr, + T_0=args.T_0, + T_mult=args.T_mult, + eta_min=args.eta_min, + base_lr=args.lr + ) + + scheduler = LambdaLR( + optimizer=optimizer, + lr_lambda=[lr_lambda for _ in range(len(param_groups))] + ) + + return optimizer, scheduler + + +def load_checkpoint( + args: ArgumentParser, + model: nn.Module, + optimizer: Union[SGD, Adam, AdamW, RAdam], + scheduler: LambdaLR, + grad_scaler: GradScaler, + ckpt_dir: Optional[str] = None, +) -> Tuple[nn.Module, Union[SGD, Adam, AdamW, RAdam], Union[LambdaLR, None], GradScaler, int, Union[Dict[str, float], None], Dict[str, List[float]], Dict[str, float]]: + ckpt_path = os.path.join(args.ckpt_dir if ckpt_dir is None else ckpt_dir, "ckpt.pth") + if os.path.exists(ckpt_path): + ckpt = torch.load(ckpt_path, weights_only=False) + model.load_state_dict(ckpt["model_state_dict"]) + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + start_epoch = ckpt["epoch"] + loss_info = ckpt["loss_info"] + hist_scores = ckpt["hist_scores"] + best_scores = ckpt["best_scores"] + + if scheduler is not None: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if grad_scaler is not None: + grad_scaler.load_state_dict(ckpt["grad_scaler_state_dict"]) + + print(f"Loaded checkpoint from {ckpt_path}.") + + else: + start_epoch = 1 + loss_info, hist_scores = None, {"mae": [], "rmse": [], "nae": []} + best_scores = {k: [torch.inf] * args.save_best_k for k in hist_scores.keys()} + print(f"Checkpoint not found at {ckpt_path}.") + + return model, optimizer, scheduler, grad_scaler, start_epoch, loss_info, hist_scores, best_scores + + +def save_checkpoint( + epoch: int, + model_state_dict: OrderedDict[str, Tensor], + optimizer_state_dict: OrderedDict[str, Tensor], + scheduler_state_dict: OrderedDict[str, Tensor], + grad_scaler_state_dict: OrderedDict[str, Tensor], + loss_info: Dict[str, List[float]], + hist_scores: Dict[str, List[float]], + best_scores: Dict[str, float], + ckpt_dir: str, +) -> None: + ckpt = { + "epoch": epoch, + "model_state_dict": model_state_dict, + "optimizer_state_dict": optimizer_state_dict, + "scheduler_state_dict": scheduler_state_dict, + "grad_scaler_state_dict": grad_scaler_state_dict, + "loss_info": loss_info, + "hist_scores": hist_scores, + "best_scores": best_scores, + } + torch.save(ckpt, os.path.join(ckpt_dir, "ckpt.pth"))