repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
ssl-chewing | ssl-chewing-master/src/dataset/template/basesubset.py | from abc import ABC, abstractmethod
from typing import Union, List, Tuple, NoReturn, Optional
from warnings import warn
import numpy as np
from tensorflow.keras.utils import Sequence
from dataset.commons import SubsetType
from dataset.template.commons import PureAbstractError
from utilities.matlabutils import strcmp,... | 9,286 | 43.014218 | 120 | py |
ssl-chewing | ssl-chewing-master/src/utilities/kerasutils.py | from typing import List
from tensorflow import Tensor, is_tensor
from tensorflow.python.keras.engine.base_layer import Layer
from utilities.typingutils import is_typed_list
def apply_block(block: List[Layer], input_tensor: Tensor) -> Tensor:
"""
Apply a block of layers to an input tensor.
:param block:... | 682 | 26.32 | 79 | py |
ssl-chewing | ssl-chewing-master/src/simclr/simclrhelper.py | from pathlib import Path
from typing import List, NoReturn, Optional
from tensorflow import is_tensor
from tensorflow.keras.layers import Layer, Input
from tensorflow.keras.models import Model
import globalconfig as g_conf
from utilities.kerasutils import apply_block
from utilities.typingutils import is_typed_list
... | 3,841 | 39.020833 | 111 | py |
ssl-chewing | ssl-chewing-master/src/simclr/warmupandcosinedecay.py | import math
import tensorflow as tf
from tensorflow import Tensor
from tensorflow.keras.experimental import CosineDecay
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
class WarmUpAndCosineDecay(LearningRateSchedule):
"""Applies a warmup schedule on a given learning rate decay schedule."""... | 2,250 | 42.288462 | 110 | py |
ssl-chewing | ssl-chewing-master/src/simclr/contrastiveloss.py | import tensorflow as tf
from tensorflow import Tensor
from tensorflow.keras.losses import categorical_crossentropy
LARGE_NUM = 1e9
def __contrastive_loss(hidden, hidden_norm: bool = True, temperature: float = 1.0, weights: float = 1.0):
"""
Notes on original method:
- hidden: Tensor, shape is (1024, 128)... | 2,789 | 40.641791 | 116 | py |
ssl-chewing | ssl-chewing-master/src/optimizer/larsoptimizer.py | # coding=utf-8
# Copyright 2020 The SimCLR Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | 6,911 | 39.658824 | 88 | py |
ssl-chewing | ssl-chewing-master/src/model/papapanagiotou2017convolutional.py | """
Models from the paper: https://ieeexplore.ieee.org/document/8037060/
"""
from typing import List
from tensorflow.keras.activations import relu, sigmoid, softmax
from tensorflow.keras.layers import Layer, Conv1D, MaxPool1D, Dense, Dropout, Flatten
from utilities.typingutils import is_typed_list
def _is_list_of_... | 2,257 | 30.361111 | 112 | py |
ssl-chewing | ssl-chewing-master/src/model/simclrprojectionhead.py | """
A few projection head templates from: https://github.com/google-research/simclr
"""
from typing import List
from tensorflow.keras.activations import relu, linear
from tensorflow.keras.layers import Layer, Dense
def linear_projection_head(projection_dim: int) -> List[Layer]:
"""
A dense layer with no bias... | 1,709 | 37.863636 | 118 | py |
FogRemoval | FogRemoval-main/losses.py | import torch
from torch import nn
from torchvision.models.vgg import vgg16, vgg19
from modules import SimpleGray
class PixelwiseLoss(nn.Module):
"""
It is just a simple MSE loss
assuming input in range [-1, 1]
"""
def __init__(self, is_gray=False):
super(PixelwiseLoss, self).__init__()
... | 18,620 | 36.242 | 133 | py |
FogRemoval | FogRemoval-main/test.py | import argparse
import os
from os import makedirs, listdir
from os.path import join, isfile, basename, exists
from math import ceil
from PIL import Image
import PIL
import torch
import torchvision.transforms as transforms
from tqdm import tqdm
from networks import GenerativeModel
from utils import get_config
from modul... | 3,733 | 27.287879 | 140 | py |
FogRemoval | FogRemoval-main/modules.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
def CircularGaussKernel(kernlen=None, circ_zeros=False, sigma=None, norm=True):
assert ((kernlen is not None) or sigma is not None)
if kernlen is None:
kernlen = int(2.0 * 3.0 * sigm... | 2,391 | 30.064935 | 88 | py |
FogRemoval | FogRemoval-main/SRDefog_test.py | import time, itertools
from dataset import ImageFolder
from torchvision import transforms
from torch.utils.data import DataLoader
from networks import *
from utils import *
from glob import glob
from PIL import Image
from cv2 import resize
class SRDefog(object) :
def __init__(self, args):
self.mode... | 4,584 | 44.39604 | 148 | py |
FogRemoval | FogRemoval-main/utils.py | import os, cv2, torch
import random
import shutil
import torch
import torchvision
import yaml
import ramps
from scipy import misc
import numpy as np
def get_config(config):
with open(config, 'r') as stream:
return yaml.load(stream)
def write_grid_grid(list_of_tensor, grid_batch_size=None, filename=None,
... | 7,119 | 31.66055 | 111 | py |
FogRemoval | FogRemoval-main/data_utils.py | from os import listdir
from os.path import join, isfile
import numbers, random
from PIL import Image
import numpy as np
import torch
from torch.utils.data import DataLoader, Subset
from torch.utils.data.dataset import Dataset, ConcatDataset
from torch.utils.data.sampler import BatchSampler, Sampler
from torchvision.tra... | 8,521 | 34.656904 | 129 | py |
FogRemoval | FogRemoval-main/dataset.py | import torch.utils.data as data
from PIL import Image
import os
import os.path
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
Returns:
bool: True if the filename ends with a known image extensio... | 3,479 | 30.926606 | 110 | py |
FogRemoval | FogRemoval-main/networks.py | import torch
import torch.nn as nn
from torch.nn import init
from torch.optim import lr_scheduler
from torch.nn.parameter import Parameter
import functools
from random import getrandbits
from modules import SimpleGray, GaussianBlur, RGB2Saturation
from losses import GANLoss
import torch.nn.functional as F
############... | 64,397 | 42.133289 | 175 | py |
FogRemoval | FogRemoval-main/metrics.py | from math import exp, log10
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
from losses import PerceptualLoss
class ImageReconstructionError(nn.Module):
def __init__(self, metrics=['psnr', 'ssim']):
super(ImageReconstructionError, self).__init__()
... | 4,497 | 35.274194 | 114 | py |
FogRemoval | FogRemoval-main/model_vit/contra_loss.py | import torch
from torch import nn
from torch.nn import functional as F
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.... | 2,904 | 33.176471 | 95 | py |
FogRemoval | FogRemoval-main/model_vit/model.py | import torch
from . import networks
class Model(torch.nn.Module):
def __init__(self, cfg):
super().__init__()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.netG = networks.define_G(cfg['init_type'], cfg['init_gain']).to(device)
self.cfg = cfg
def fo... | 820 | 33.208333 | 98 | py |
FogRemoval | FogRemoval-main/model_vit/networks.py | from torch.nn import init
from torch.optim import lr_scheduler
from models.unet.skip import skip
def get_scheduler(optimizer, opt):
if opt.lr_policy == 'linear':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
retu... | 2,636 | 43.694915 | 116 | py |
FogRemoval | FogRemoval-main/model_vit/extractor.py | import torch
def attn_cosine_sim(x, eps=1e-08):
x = x[0]
norm1 = x.norm(dim=2, keepdim=True)
factor = torch.clamp(norm1 @ norm1.permute(0, 2, 1), min=eps)
sim_matrix = (x @ x.permute(0, 2, 1)) / factor
return sim_matrix
class VitExtractor:
BLOCK_KEY = 'block'
ATTN_KEY = 'attn'
PATCH_IM... | 6,535 | 38.373494 | 112 | py |
FogRemoval | FogRemoval-main/model_vit/loss_vit.py | from torchvision.transforms import Resize
from torchvision import transforms
import torch
import torch.nn.functional as F
import torch.nn as nn
from model_vit.extractor import VitExtractor
from model_vit.contra_loss import PatchLoss,ConstLoss
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class... | 5,711 | 44.333333 | 112 | py |
FogRemoval | FogRemoval-main/model_vit/unet/downsampler.py | import numpy as np
import torch
import torch.nn as nn
class Downsampler(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
'''
def __init__(self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False):
... | 5,379 | 30.83432 | 129 | py |
FogRemoval | FogRemoval-main/model_vit/unet/common.py | import torch
import torch.nn as nn
import numpy as np
from .downsampler import Downsampler
def add_module(self, module):
self.add_module(str(len(self) + 1), module)
torch.nn.Module.add = add_module
class Concat(nn.Module):
def __init__(self, dim, *args):
super(Concat, self).__init__()
sel... | 3,531 | 27.483871 | 128 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/test.py | import argparse
import os
import numpy as np
import math
import itertools
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
#from mod... | 8,612 | 34.8875 | 139 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/favtgan-noisy-label.py | import argparse
import os
import numpy as np
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from datasets import *
import torch.nn as ... | 15,858 | 36.849642 | 125 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/favtgan-gaussian.py | import argparse
import os
import numpy as np
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from datasets import *
import torch.nn as ... | 17,383 | 37.375276 | 179 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/datasets.py | import glob
import random
import os
import numpy as np
import torch
import pandas as pd
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
#I added attributes=None
class ImageDataset(Dataset):
def __init__(self, annots_csv, root, transforms_=None, mode="train"):... | 2,587 | 34.452055 | 85 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/favtgan-no-noise.py | import argparse
import os
import numpy as np
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from datasets import *
import torch.nn as ... | 17,779 | 38.599109 | 179 | py |
favtGAN | favtGAN-main/favtGAN/favtGAN/favtgan-baseline.py | import argparse
import os
import numpy as np
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from datasets import *
import torch.nn as ... | 16,687 | 37.809302 | 125 | py |
favtGAN | favtGAN-main/favtGAN/pix2pix/test.py | import argparse
import os
import numpy as np
import math
import itertools
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from mode... | 4,027 | 39.686869 | 128 | py |
favtGAN | favtGAN-main/favtGAN/pix2pix/datasets.py | import glob
import random
import os
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
class ImageDataset(Dataset):
def __init__(self, root, transforms_=None, mode="train"):
self.transform = transforms.Compose(transforms_)
se... | 1,669 | 27.793103 | 85 | py |
favtGAN | favtGAN-main/favtGAN/pix2pix/models.py | import torch.nn as nn
import torch.nn.functional as F
import torch
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.... | 4,712 | 32.190141 | 81 | py |
favtGAN | favtGAN-main/favtGAN/pix2pix/pix2pix-smooth.py | import argparse
import os
import numpy as np
import math
import itertools
import time
import datetime
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from model... | 8,284 | 33.957806 | 142 | py |
LightSKD | LightSKD-master/main.py | import os
from torch import nn, optim
from datasets import get_trainloader as tl, get_testloader as tel
from backbone.resnet import ResNet18,ResNet50,ResNet101
from backbone.adapter import adapter_2
from backbone.mobilenetv2 import *
import torch.nn.functional as F
import argparse
parser = argparse.ArgumentParser(des... | 5,324 | 31.87037 | 135 | py |
LightSKD | LightSKD-master/utils.py | import torch
from backbone.resnet import *
from backbone.ResNeXt import resnext50_32x4d
from backbone.densenet import densenet121 as densenet121
def output_process(output):
return torch.sort(output)[0]
def params_detection(net):
stat = net.state_dict()
for k, v in stat.items():
try:
pr... | 1,951 | 29.030769 | 61 | py |
LightSKD | LightSKD-master/datasets.py | import random
from collections import defaultdict
import torch
import matplotlib.pyplot as plt
import torchvision
import numpy as np
import seaborn as sns
import pandas as pd
import numpy
import math
from backbone.resnet import *
import torchvision.transforms as transforms, torchvision.datasets as datasets
from torchvi... | 9,393 | 36.277778 | 116 | py |
LightSKD | LightSKD-master/train.py | import os
import time
import numpy as np
import torch
torch.set_printoptions(threshold=np.inf)
from torch.nn import DataParallel
from torch import nn, optim
from datasets import get_trainloader as tl, get_testloader as tel, get_single_val_loader as svl
from backbone.adapter import adapter_2
from backbone.mobilenetv2 i... | 6,844 | 36.404372 | 124 | py |
LightSKD | LightSKD-master/backbone/ResNeXt.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
from torchvision.models import resnet18
__all__ = ['ResNet', 'resnet18', 'resnext50_32x4d']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pyto... | 6,835 | 34.978947 | 106 | py |
LightSKD | LightSKD-master/backbone/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet50
__all__ = ['Re... | 4,398 | 34.475806 | 116 | py |
LightSKD | LightSKD-master/backbone/mobilenetv2.py | from typing import List
import torch.nn as nn
import torch
def _make_divisible(v: float, divisor: int, min_value=None) -> int:
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensor... | 5,906 | 35.018293 | 121 | py |
LightSKD | LightSKD-master/backbone/adapter.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
... | 6,546 | 32.747423 | 134 | py |
LightSKD | LightSKD-master/backbone/mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self,num_classes=100):
super(MLP, self).__init__()
self.model = nn.Sequential(
nn.Linear(num_classes,256),
nn.BatchNorm1d(256),
nn.LeakyReLU(),
nn.Linear(256,128),
nn.Ba... | 537 | 24.619048 | 39 | py |
LightSKD | LightSKD-master/backbone/densenet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
# from .utils import load_state_dict_from_url
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as... | 11,265 | 44.983673 | 118 | py |
irfu-python | irfu-python-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# Built-in imports
import os
import shutil
import sys
# 3rd party imports
import pydat... | 8,698 | 28.68942 | 83 | py |
side | side-main/projects/verify_wikipedia/evaluation/retrievers/reranker.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os.path
from collections import OrderedDict
from pathlib import Path
import torch
import json
import ... | 13,001 | 40.407643 | 126 | py |
side | side-main/projects/verify_wikipedia/dpr/options.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Command line arguments utils
"""
import json
import logging
import random
import numpy as np
im... | 2,292 | 26.626506 | 108 | py |
side | side-main/projects/verify_wikipedia/dpr/dense_retriever.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Command line tool to get dense results and validate them
"""
import logging
import pickle
impo... | 10,487 | 33.162866 | 119 | py |
side | side-main/projects/verify_wikipedia/dpr/dataset/training.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import random
import socket
import tempfile
from typing import List, Union
import filelock
import... | 12,547 | 35.905882 | 118 | py |
side | side-main/projects/verify_wikipedia/dpr/dataset/retrieval.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import csv
import logging
import os
import pickle
import sys
import zlib
from pathlib import Path
from t... | 7,955 | 30.951807 | 108 | py |
side | side-main/projects/verify_wikipedia/dpr/dataset/input_transform.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import json
import logging
from typing import List
import torch
from dpr.dataset.utils import normaliz... | 12,425 | 36.092537 | 120 | py |
side | side-main/projects/verify_wikipedia/dpr/models/hf_models.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Encoder model wrappers based on HuggingFace code
"""
import logging
from typing import Tuple, L... | 23,576 | 38.426421 | 120 | py |
side | side-main/projects/verify_wikipedia/dpr/models/reranker.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
BiEncoder component + loss function for 'all-in-batch' training
"""
import logging
from typing ... | 6,486 | 32.096939 | 113 | py |
side | side-main/projects/verify_wikipedia/dpr/models/ranker_loss.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Tuple
import torch
import torch.nn.functional as F
from torch imp... | 12,576 | 41.063545 | 125 | py |
side | side-main/projects/verify_wikipedia/dpr/models/biencoder.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
BiEncoder component + loss function for 'all-in-batch' training
"""
import collections
import lo... | 5,024 | 30.21118 | 113 | py |
side | side-main/projects/verify_wikipedia/dpr/utils/model_utils.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import glob
import logging
import os
from typing import List
import torch
from t... | 4,681 | 28.821656 | 110 | py |
side | side-main/projects/verify_wikipedia/dpr/utils/dist_utils.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Utilities for distributed model training
"""
import logging
import os
import pickle
import socke... | 7,902 | 33.662281 | 119 | py |
side | side-main/projects/verify_wikipedia/dpr/utils/data_utils.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Utilities for general purpose data processing
"""
import itertools
import json
import logging
im... | 15,101 | 33.717241 | 117 | py |
DMCMC | DMCMC-main/losses.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 8,332 | 38.492891 | 115 | py |
DMCMC | DMCMC-main/utils.py | import torch
import tensorflow as tf
import os
import logging
def restore_checkpoint(ckpt_dir, state, device):
if not tf.io.gfile.exists(ckpt_dir):
tf.io.gfile.makedirs(os.path.dirname(ckpt_dir))
logging.warning(f"No checkpoint found at {ckpt_dir}. "
f"Returned the same state as input")
... | 909 | 30.37931 | 71 | py |
DMCMC | DMCMC-main/sampling.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 17,416 | 35.134855 | 116 | py |
DMCMC | DMCMC-main/datasets.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 7,244 | 35.77665 | 99 | py |
DMCMC | DMCMC-main/sde_lib.py | """Abstract SDE classes, Reverse SDE, and VE/VP SDEs."""
import abc
import torch
import numpy as np
class SDE(abc.ABC):
"""SDE abstract class. Functions are designed for a mini-batch of inputs."""
def __init__(self, N):
"""Construct an SDE.
Args:
N: number of discretization time steps.
"""
... | 7,637 | 28.719844 | 153 | py |
DMCMC | DMCMC-main/models/up_or_down_sampling.py | """Layers used for up-sampling or down-sampling images.
Many functions are ported from https://github.com/NVlabs/stylegan2.
"""
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
from op import upfirdn2d
# Function ported from StyleGAN2
def get_weight(module,
shape,... | 8,900 | 33.5 | 91 | py |
DMCMC | DMCMC-main/models/utils.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 5,695 | 29.297872 | 105 | py |
DMCMC | DMCMC-main/models/layers.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 22,687 | 33.271903 | 112 | py |
DMCMC | DMCMC-main/models/ddpm.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 6,082 | 32.423077 | 113 | py |
DMCMC | DMCMC-main/models/ncsnv2.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 16,043 | 37.567308 | 120 | py |
DMCMC | DMCMC-main/models/normalization.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 7,657 | 34.453704 | 106 | py |
DMCMC | DMCMC-main/models/ema.py | # Modified from https://raw.githubusercontent.com/fadel/pytorch_ema/master/torch_ema/ema.py
from __future__ import division
from __future__ import unicode_literals
import torch
# Partially based on: https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
class ExponentialMo... | 3,414 | 33.846939 | 119 | py |
DMCMC | DMCMC-main/models/ncsnpp.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 13,653 | 34.743455 | 113 | py |
DMCMC | DMCMC-main/models/layerspp.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 9,001 | 31.734545 | 99 | py |
DMCMC | DMCMC-main/configs/default_cifar10_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 50000
training.log_freq = 50
training.eval_freq = ... | 1,975 | 26.068493 | 94 | py |
DMCMC | DMCMC-main/configs/default_celeba_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 50000
training.log_freq = 50
training.eval_freq = ... | 1,947 | 26.055556 | 94 | py |
DMCMC | DMCMC-main/configs/default_lsun_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 64
training.n_iters = 2400001
training.snapshot_freq = 50000
training.log_freq = 50
training.eval_freq = 1... | 1,944 | 26.013889 | 94 | py |
DMCMC | DMCMC-main/configs/ve/ffhq_ncsnpp_continuous.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 3,229 | 28.099099 | 94 | py |
DMCMC | DMCMC-main/configs/ve/celebahq_ncsnpp_continuous.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 3,184 | 27.693694 | 94 | py |
DMCMC | DMCMC-main/op/losses.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 8,332 | 38.492891 | 115 | py |
DMCMC | DMCMC-main/op/upfirdn2d.py | import os
import torch
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
"upfirdn2d",
sources=[
os.path.join(module_path, "upfirdn2d.cpp"),
os.path.join(module_path, ... | 5,672 | 27.223881 | 108 | py |
DMCMC | DMCMC-main/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.joi... | 2,690 | 26.459184 | 83 | py |
Merak | Merak-main/setup.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com), Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy o... | 4,168 | 32.894309 | 112 | py |
Merak | Merak-main/tools/convert_megatron_gpt2_checkpoint.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http:... | 30,784 | 41.57953 | 172 | py |
Merak | Merak-main/Merak/merak_trainer.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com), TXacs (txacs1993@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy o... | 30,587 | 43.266281 | 169 | py |
Merak | Merak-main/Merak/__init__.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com), TXacs (txacs1993@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy o... | 3,218 | 33.612903 | 120 | py |
Merak | Merak-main/Merak/train_func.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com), Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy o... | 14,314 | 41.731343 | 147 | py |
Merak | Merak-main/Merak/modules/mp_attrs.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 6,480 | 40.812903 | 184 | py |
Merak | Merak-main/Merak/modules/mp_layers.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 17,194 | 38.079545 | 129 | py |
Merak | Merak-main/Merak/modules/utils.py | # coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 5,836 | 31.071429 | 153 | py |
Merak | Merak-main/Merak/modules/module.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 27,014 | 44.403361 | 167 | py |
Merak | Merak-main/Merak/modules/layer_proxy.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 9,716 | 38.987654 | 122 | py |
Merak | Merak-main/Merak/modules/transformer_blocks.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 27,453 | 38.616162 | 144 | py |
Merak | Merak-main/Merak/runtime/engine.py | '''
Copyright 2019 The Microsoft DeepSpeed Team
'''
# https://github.com/microsoft/DeepSpeed/blob/85acf14c58658964a796c5c901b58123f99fb1df/deepspeed/runtime/engine.py
import os
import stat
import math
import torch
import warnings
import hashlib
import torch.distributed as dist
from collections import OrderedDict
from ... | 32,047 | 37.799031 | 178 | py |
Merak | Merak-main/Merak/runtime/utils.py | '''
Copyright 2019 The Microsoft DeepSpeed Team
Copyright NVIDIA/Megatron
Helper functions and classes from multiple sources.
'''
# https://github.com/microsoft/DeepSpeed/blob/85acf14c58658964a796c5c901b58123f99fb1df/deepspeed/runtime/utils.py
import os
import psutil
import gc
import torch
from torch._six import inf... | 18,063 | 38.87638 | 115 | py |
Merak | Merak-main/Merak/runtime/config.py | """
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
# https://github.com/microsoft/DeepSpeed/blob/85acf14c58658964a796c5c901b58123f99fb1df/deepspeed/runtime/config.py
import os
from typing import Union
import torch
import json
import copy
from .config_utils import get_scalar_param, dict_raise... | 20,321 | 33.620102 | 128 | py |
Merak | Merak-main/Merak/runtime/pipe_engine.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com), TXacs (txacs1993@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy o... | 57,499 | 40.307471 | 159 | py |
Merak | Merak-main/Merak/runtime/schedule.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 35,103 | 37.874862 | 173 | py |
Merak | Merak-main/Merak/runtime/checkpointing.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 23,186 | 35.630332 | 186 | py |
Merak | Merak-main/Merak/mpu/mappings.py | # coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 11,917 | 29.020151 | 110 | py |
Merak | Merak-main/Merak/mpu/topology.py | # Copyright 2019 The Microsoft DeepSpeed Team
# https://github.com/microsoft/DeepSpeed/blob/85acf14c58658964a796c5c901b58123f99fb1df/deepspeed/runtime/pipe/topology.py
from ..utils import logger
import torch.distributed as dist
import sys
from collections import namedtuple
from itertools import product as cartesian... | 17,384 | 36.875817 | 121 | py |
Merak | Merak-main/Merak/mpu/initialize.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | 5,420 | 29.116667 | 152 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.