or4cl3ai/Aiden_t5
Text Generation • Updated • 1.12k • 20
python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
from setuptools import setup, find_packages
setup(
name = 'Mega-pytorch',
packages = find_packages(exclude=[]),
version = '0.1.0',
license='MIT',
description = 'Mega - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markdown',
url = 'https:... | Mega-pytorch-main | setup.py |
from mega_pytorch.mega_pytorch import Mega
from mega_pytorch.autoregressive_wrapper import AutoregressiveWrapper
import argparse
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# co... | Mega-pytorch-main | train.py |
import math
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.fft import rfft, irfft
from einops import rearrange
from einops.layers.torch import Rearrange
from scipy.fftpack import next_fast_len
# functions
def exists(val):
return val is not Non... | Mega-pytorch-main | mega_pytorch/mega_pytorch.py |
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
# helper function
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwar... | Mega-pytorch-main | mega_pytorch/autoregressive_wrapper.py |
from mega_pytorch.mega_pytorch import MegaLayer, Mega, MultiHeadedEMA
| Mega-pytorch-main | mega_pytorch/__init__.py |
import sys
from setuptools import setup, find_packages
sys.path[0:0] = ['deep_daze']
from version import __version__
setup(
name = 'deep-daze',
packages = find_packages(),
include_package_data = True,
entry_points={
'console_scripts': [
'imagine = deep_daze.cli:main',
],
},
version = __versi... | deep-daze-main | setup.py |
__version__ = '0.11.1'
| deep-daze-main | deep_daze/version.py |
from deep_daze.deep_daze import DeepDaze, Imagine
| deep-daze-main | deep_daze/__init__.py |
import sys
import fire
from deep_daze import Imagine
def train(
text=None,
img=None,
learning_rate=1e-5,
num_layers=16,
hidden_size=256,
batch_size=4,
gradient_accumulate_every=4,
epochs=20,
iterations=1050,
save_every=100,
imag... | deep-daze-main | deep_daze/cli.py |
import os
import subprocess
import sys
import random
from datetime import datetime
from pathlib import Path
import torch
import torch.nn.functional as F
from siren_pytorch import SirenNet, SirenWrapper
from torch import nn
from torch.cuda.amp import GradScaler, autocast
from torch_optimizer import DiffGrad, AdamP
impo... | deep-daze-main | deep_daze/deep_daze.py |
from collections import OrderedDict
from typing import Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from pathlib import Path
import hashlib
import os
import urllib
import warnings
from typing import Union, List
from torchvision.transforms import Compose, Normalize
from tqdm import tq... | deep-daze-main | deep_daze/clip.py |
from setuptools import setup, find_packages
setup(
name = 'reformer_pytorch',
packages = find_packages(exclude=['examples', 'pretraining']),
version = '1.4.4',
license='MIT',
description = 'Reformer, the Efficient Transformer, Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url =... | reformer-pytorch-master | setup.py |
from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from reformer_pytorch.reformer_pytorch import ReformerLM
from reformer_pytorch.autopadder import Autopadder
def top_p(logits, thres = 0.9):
sorted_logits, sorted_indices = tor... | reformer-pytorch-master | reformer_pytorch/generative_tools.py |
import math
import torch
from torch import nn
import torch.nn.functional as F
from reformer_pytorch.reformer_pytorch import Reformer, ReformerLM, LSHSelfAttention
def pad_to_multiple(tensor, seqlen, multiple, dim=-1):
m = seqlen / multiple
if m.is_integer():
return tensor
remainder = math.ceil(m) ... | reformer-pytorch-master | reformer_pytorch/autopadder.py |
import re
from torch import nn
from reformer_pytorch.reformer_pytorch import ReformerLM
from reformer_pytorch.generative_tools import TrainingWrapper
ENC_PREFIX = 'enc_'
DEC_PREFIX = 'dec_'
def group_dict_by_key(cond, d):
return_val = [dict(),dict()]
for key in d.keys():
match = bool(cond(key))
... | reformer-pytorch-master | reformer_pytorch/reformer_enc_dec.py |
import torch
import torch.nn as nn
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# following example for saving and setting rng here https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html
class Deterministic(nn.Module):
def __init... | reformer-pytorch-master | reformer_pytorch/reversible.py |
from torch import nn
from reformer_pytorch.reformer_pytorch import LSHAttention, LSHSelfAttention
from collections import defaultdict
class Recorder(nn.Module):
def __init__(self, net):
super().__init__()
self.iter = 0
self.recordings = defaultdict(list)
self.net = net
self.... | reformer-pytorch-master | reformer_pytorch/recorder.py |
from reformer_pytorch.reformer_pytorch import LSHAttention, LSHSelfAttention, Reformer, ReformerLM
from reformer_pytorch.reformer_enc_dec import ReformerEncDec
from reformer_pytorch.recorder import Recorder
from reformer_pytorch.autopadder import Autopadder
| reformer-pytorch-master | reformer_pytorch/__init__.py |
import math
import torch
import torch.nn as nn
from torch.nn import Identity
import torch.nn.functional as F
from torch.autograd import Function
from functools import partial, reduce, wraps
from itertools import chain
from operator import mul
from local_attention import LocalAttention
from axial_positional_embedding i... | reformer-pytorch-master | reformer_pytorch/reformer_pytorch.py |
import deepspeed
from reformer_pytorch import ReformerLM
from reformer_pytorch.generative_tools import TrainingWrapper
import argparse
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset... | reformer-pytorch-master | examples/enwik8_deepspeed/train.py |
from reformer_pytorch import ReformerLM
from reformer_pytorch.generative_tools import TrainingWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1... | reformer-pytorch-master | examples/enwik8_simple/train.py |
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
from reformer_pytorch import Reformer, ReformerLM
from transformers import BertTokenizer, PreTrainedTokenizer
from fairseq.optim.adafactor import Adafactor
... | reformer-pytorch-master | pretraining/self-supervised.py |
from setuptools import setup, find_packages
setup(
name = 'tranception-pytorch',
packages = find_packages(exclude=[]),
version = '0.0.8',
license='MIT',
description = 'Tranception - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markdown',
... | tranception-pytorch-main | setup.py |
from tranception_pytorch.tranception_pytorch import Tranception
| tranception-pytorch-main | tranception_pytorch/__init__.py |
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
from einops_exts import rearrange_many
from einops.layers.torch import Rearrange
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
# relat... | tranception-pytorch-main | tranception_pytorch/tranception_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'g-mlp-pytorch',
packages = find_packages(),
version = '0.1.5',
license='MIT',
description = 'gMLP - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/g-mlp-pytorch',
keywords = [
'... | g-mlp-pytorch-main | setup.py |
from g_mlp_pytorch import gMLP
from g_mlp_pytorch.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1... | g-mlp-pytorch-main | train.py |
import torch
from torch import nn
import torch.nn.functional as F
# helper function
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_training)
return out
return inner
... | g-mlp-pytorch-main | g_mlp_pytorch/autoregressive_wrapper.py |
from g_mlp_pytorch.g_mlp_pytorch import gMLP, gMLPVision, gMLPBlock, SpatialGatingUnit
| g-mlp-pytorch-main | g_mlp_pytorch/__init__.py |
from random import randrange
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from einops.layers.torch import Rearrange, Reduce
# functions
def exists(val):
return val is not None
def pair(val):
return (val, val) if not isinstance(val, tuple) els... | g-mlp-pytorch-main | g_mlp_pytorch/g_mlp_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'charformer-pytorch',
packages = find_packages(),
version = '0.0.4',
license='MIT',
description = 'Charformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/charformer-pytorch',
ke... | charformer-pytorch-main | setup.py |
from charformer_pytorch.charformer_pytorch import GBST
| charformer-pytorch-main | charformer_pytorch/__init__.py |
import math
from math import gcd
import functools
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange
# helpers
def exists(val):
return val is not None
def lcm(*numbers):
return int(functools.reduce(... | charformer-pytorch-main | charformer_pytorch/charformer_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'retrieval-augmented-ddpm',
packages = find_packages(exclude=[]),
version = '0.0.1',
license='MIT',
description = 'Retrieval-Augmented Denoising Diffusion Probabilistic Models',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = ... | retrieval-augmented-ddpm-main | setup.py |
retrieval-augmented-ddpm-main | retrieval_augmented_ddpm/retrieval_augmented_ddpm.py | |
retrieval-augmented-ddpm-main | retrieval_augmented_ddpm/__init__.py | |
import argparse
from pathlib import Path
from tqdm import tqdm
# torch
import torch
from einops import repeat
# vision imports
from PIL import Image
from torchvision.utils import make_grid, save_image
# dalle related classes and utils
from dalle_pytorch import __version__
from dalle_pytorch import DiscreteVAE, O... | DALLE-pytorch-main | generate.py |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 6
Models trained or fine-tuned on kye/all-lucidrain-python-3