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
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Oort | Oort-master/training/utils/transforms_wav.py | """Transforms on raw wav samples."""
__author__ = 'Yuan Xu'
import random
import numpy as np
import librosa
import torch
from torch.utils.data import Dataset
random.seed(233)
def should_apply_transform(prob=0.5):
"""Transforms are only randomly applied with the given probability."""
return random.random() ... | 5,000 | 29.87037 | 129 | py |
Oort | Oort-master/training/utils/voice_model.py | import math
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
supported_rnns = {
'lstm': nn.LSTM,
'rnn': nn.RNN,
'gru': nn.GRU
}
supported_rnns_inv = dict((v, k) for k, v in supported_rnns.items())
class SequenceWise(nn.Module):
def __init__(self,... | 9,585 | 36.299611 | 120 | py |
E2FGVI | E2FGVI-master/test.py | # -*- coding: utf-8 -*-
import cv2
from PIL import Image
import numpy as np
import importlib
import os
import argparse
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib import animation
import torch
from core.utils import to_tensors
parser = argparse.ArgumentParser(description="E2FGVI")
parser.add... | 7,775 | 33.56 | 89 | py |
E2FGVI | E2FGVI-master/evaluate.py | # -*- coding: utf-8 -*-
import cv2
import numpy as np
import importlib
import os
import argparse
from PIL import Image
import torch
from torch.utils.data import DataLoader
from core.dataset import TestDataset
from core.metrics import calc_psnr_and_ssim, calculate_i3d_activations, calculate_vfid, init_i3d_model
# glo... | 6,741 | 37.090395 | 119 | py |
E2FGVI | E2FGVI-master/train.py | import os
import json
import argparse
from shutil import copyfile
import torch
import torch.multiprocessing as mp
from core.trainer import Trainer
from core.dist import (
get_world_size,
get_local_rank,
get_global_rank,
get_master_ip,
)
parser = argparse.ArgumentParser(description='E2FGVI')
parser.ad... | 3,169 | 34.222222 | 79 | py |
E2FGVI | E2FGVI-master/core/lr_scheduler.py | """
LR scheduler from BasicSR https://github.com/xinntao/BasicSR
"""
import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
class MultiStepRestartLR(_LRScheduler):
""" MultiStep with restarts learning rate scheme.
Args:
optimizer (torch.nn.optimizer): Torch o... | 4,386 | 37.823009 | 79 | py |
E2FGVI | E2FGVI-master/core/loss.py | import torch
import torch.nn as nn
class AdversarialLoss(nn.Module):
r"""
Adversarial loss
https://arxiv.org/abs/1711.10337
"""
def __init__(self,
type='nsgan',
target_real_label=1.0,
target_fake_label=0.0):
r"""
type = nsgan | lsg... | 1,279 | 29.47619 | 75 | py |
E2FGVI | E2FGVI-master/core/utils.py | import os
import io
import cv2
import random
import numpy as np
from PIL import Image, ImageOps
import zipfile
import torch
import matplotlib
import matplotlib.patches as patches
from matplotlib.path import Path
from matplotlib import pyplot as plt
from torchvision import transforms
# matplotlib.use('agg')
# #######... | 12,064 | 35.450151 | 85 | py |
E2FGVI | E2FGVI-master/core/dataset.py | import os
import json
import random
import cv2
from PIL import Image
import numpy as np
import torch
import torchvision.transforms as transforms
from core.utils import (TrainZipReader, TestZipReader,
create_random_shape_with_random_motion, Stack,
ToTorchFormatTensor, G... | 4,958 | 35.463235 | 79 | py |
E2FGVI | E2FGVI-master/core/dist.py | import os
import torch
def get_world_size():
"""Find OMPI world size without calling mpi functions
:rtype: int
"""
if os.environ.get('PMI_SIZE') is not None:
return int(os.environ.get('PMI_SIZE') or 1)
elif os.environ.get('OMPI_COMM_WORLD_SIZE') is not None:
return int(os.environ.g... | 1,459 | 29.416667 | 69 | py |
E2FGVI | E2FGVI-master/core/metrics.py | import numpy as np
from skimage import measure
from scipy import linalg
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.utils import to_tensors
def calculate_epe(flow1, flow2):
"""Calculate End point errors."""
epe = torch.sum((flow1 - flow2)**2, dim=1).sqrt()
epe = epe.vie... | 20,663 | 35.189142 | 86 | py |
E2FGVI | E2FGVI-master/core/trainer.py | import os
import glob
import logging
import importlib
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWrit... | 16,590 | 40.4775 | 79 | py |
E2FGVI | E2FGVI-master/model/e2fgvi_hq.py | ''' Towards An End-to-End Framework for Video Inpainting
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.modules.flow_comp import SPyNet
from model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment
from model.modules.tfocal_transformer_hq import Tempor... | 14,282 | 39.692308 | 132 | py |
E2FGVI | E2FGVI-master/model/e2fgvi.py | ''' Towards An End-to-End Framework for Video Inpainting
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.modules.flow_comp import SPyNet
from model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment
from model.modules.tfocal_transformer import TemporalF... | 14,229 | 39.541311 | 132 | py |
E2FGVI | E2FGVI-master/model/modules/tfocal_transformer_hq.py | """
This code is based on:
[1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021
https://github.com/ruiliu-ai/FuseFormer
[2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021
https://github.com/yitu-opensource/T2T-... | 24,390 | 42.09364 | 131 | py |
E2FGVI | E2FGVI-master/model/modules/spectral_norm.py | """
Spectral Normalization from https://arxiv.org/abs/1802.05957
"""
import torch
from torch.nn.functional import normalize
class SpectralNorm(object):
# Invariant before and after each forward call:
# u = normalize(W @ v)
# NB: At initialization, this invariant is not enforced
_version = 1
# ... | 12,357 | 41.909722 | 118 | py |
E2FGVI | E2FGVI-master/model/modules/tfocal_transformer.py | """
This code is based on:
[1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021
https://github.com/ruiliu-ai/FuseFormer
[2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021
https://github.com/yitu-opensource/T2T-... | 23,112 | 42.040968 | 168 | py |
E2FGVI | E2FGVI-master/model/modules/feat_prop.py | """
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022
"""
import torch
import torch.nn as nn
from mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d
from mmcv.cnn import constant_init
from model.modules.flow_comp import flow_warp
class SecondOrderDeforma... | 6,004 | 39.033333 | 99 | py |
E2FGVI | E2FGVI-master/model/modules/flow_comp.py | import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
from mmcv.cnn import ConvModule
from mmcv.runner import load_checkpoint
class FlowCompletionLoss(nn.Module):
"""Flow completion loss"""
def __init__(self):
super().__init__()
self.fix_spynet = SPyNet()
... | 16,931 | 36.543237 | 157 | py |
returnn-experiments | returnn-experiments-master/2020-TTS-LJSpeech/decoder.py | import argparse
from num2words import num2words
import numpy
import os
import subprocess
import sys
import torch
import yaml
import wave
sys.path.append("returnn")
sys.path.append("ParallelWaveGAN")
from parallel_wavegan import models as pwg_models
from parallel_wavegan.layers import PQMF
from returnn import rnn
from... | 4,301 | 37.070796 | 133 | py |
returnn-experiments | returnn-experiments-master/2020-rnn-transducer/configs/code/rna_tf_impl.py | #!/usr/bin/env python3
# vim: sw=2
"""
Implementation of the RNA loss in pure TF,
plus comparisons against reference implementations.
This is very similar to RNN-T loss, but restricts
the paths to be strictly monotonic.
references:
* recurrent neural aligner:
https://pdfs.semanticscholar.org/7703/a2c5468ecbee5... | 48,807 | 40.222973 | 139 | py |
hsoftmax | hsoftmax-master/wenet/dataset/wav_distortion.py | import sys
import random
import math
import torchaudio
import torch
torchaudio.set_audio_backend("sox_io")
def db2amp(db):
return pow(10, db / 20)
def amp2db(amp):
return 20 * math.log10(amp)
def make_poly_distortion(conf):
"""Generate a db-domain ploynomial distortion function
f(x) = a * x^m ... | 8,784 | 27.247588 | 81 | py |
hsoftmax | hsoftmax-master/wenet/dataset/dataset.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Chao Yang)
# Copyright (c) 2021 Jinsong Pan
#
# 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/LIC... | 19,933 | 36.329588 | 87 | py |
hsoftmax | hsoftmax-master/wenet/bin/export_jit.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 2,252 | 36.55 | 79 | py |
hsoftmax | hsoftmax-master/wenet/bin/average_model.py | # Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
import os
import argparse
import glob
import yaml
import numpy as np
import torch
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='average model')
parser.add_argument('--dst_model', required=True, help... | 2,924 | 36.025316 | 76 | py |
hsoftmax | hsoftmax-master/wenet/bin/alignment.py | # Copyright (c) 2021 Mobvoi Inc. (authors: Di Wu)
#
# 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 a... | 8,176 | 36.682028 | 79 | py |
hsoftmax | hsoftmax-master/wenet/bin/recognize.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Xiaoyu Chen, Di Wu)
#
# 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 requ... | 9,875 | 42.315789 | 105 | py |
hsoftmax | hsoftmax-master/wenet/bin/train.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Xiaoyu Chen)
#
# 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... | 12,809 | 40.189711 | 100 | py |
hsoftmax | hsoftmax-master/wenet/utils/checkpoint.py | # Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: binbinzhang@mobvoi.com (Binbin Zhang)
import logging
import os
import re
import yaml
import torch
def load_checkpoint(model: torch.nn.Module, path: str) -> dict:
if torch.cuda.is_available():
logging.info('Checkpoint: loading from checkpoint %s... | 1,473 | 30.361702 | 77 | py |
hsoftmax | hsoftmax-master/wenet/utils/ctc_util.py | # Copyright 2021 Mobvoi Inc. All Rights Reserved.
# Author: binbinzhang@mobvoi.com (Di Wu)
import numpy as np
import torch
def insert_blank(label, blank_id=0):
"""Insert blank token between every two label token."""
label = np.expand_dims(label, 1)
blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + ... | 2,668 | 35.561644 | 85 | py |
hsoftmax | hsoftmax-master/wenet/utils/executor.py | # Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: binbinzhang@mobvoi.com (Binbin Zhang)
import logging
from contextlib import nullcontext
# if your python version < 3.7 use the below one
# from contextlib import suppress as nullcontext
import torch
from torch.nn.utils import clip_grad_norm_
from tqdm import ... | 8,091 | 44.206704 | 89 | py |
hsoftmax | hsoftmax-master/wenet/utils/hsoftmax_processpool.py |
import numpy as np
import torch
import torch.multiprocessing as mp
from torch.multiprocessing import Process
# purpose of this class is to save function context to subprocess
import time
class Worker(Process):
"""Process executing tasks from shared memories"""
def __init__(self, workerid,
... | 4,436 | 34.214286 | 107 | py |
hsoftmax | hsoftmax-master/wenet/utils/scheduler.py | from typing import Union
import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types
class WarmupLR(_LRScheduler):
"""The WarmupLR scheduler
This scheduler is almost same as NoamLR Scheduler except for following
difference:
NoamLR:
lr = optimi... | 3,726 | 27.234848 | 77 | py |
hsoftmax | hsoftmax-master/wenet/utils/common.py | """Unility functions for Transformer."""
import multiprocessing
import math
from typing import Tuple, List
import torch
from torch.nn.utils.rnn import pad_sequence
IGNORE_ID = -1
def pad_list(xs: List[torch.Tensor], pad_value: int):
"""Perform padding for the list of tensors.
Args:
xs (List): List... | 6,689 | 30.261682 | 79 | py |
hsoftmax | hsoftmax-master/wenet/utils/mask.py | # -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import torch
def subsequent_mask(
size: int,
device: torch.device = torch.device("cpu"),
) -> torch.Tensor:
"""Create mask for subsequent steps (size, size).
This mask is used... | 8,920 | 34.400794 | 78 | py |
hsoftmax | hsoftmax-master/wenet/utils/optimizer.py | import torch.optim as optim
OPTIMIZER_DICT = {'adam': optim.Adam, 'sgd': optim.SGD}
def init_optimizer(parameters, configs):
assert configs['optim'] in OPTIMIZER_DICT
optim = OPTIMIZER_DICT[configs['optim']]
return optim(parameters, **configs['optim_conf']) | 271 | 33 | 55 | py |
hsoftmax | hsoftmax-master/wenet/transformer/embedding.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Positonal Encoding Module."""
import math
from typing import Tuple
import torch
class PositionalEncoding(torch.nn.Module):
"""Positional encoding.
:param int d_model: embe... | 4,668 | 33.330882 | 79 | py |
hsoftmax | hsoftmax-master/wenet/transformer/encoder_layer.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Encoder self-attention layer definition."""
from typing import Optional, Tuple
import torch
from torch import nn
class TransformerEncoderLayer(nn.Module):
"""Encoder layer modu... | 10,193 | 36.895911 | 79 | py |
hsoftmax | hsoftmax-master/wenet/transformer/label_smoothing_loss.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Label smoothing module."""
import torch
from torch import nn
class LabelSmoothingLoss(nn.Module):
"""Label-smoothing loss.
In a standard CE loss, the label's data di... | 3,046 | 32.855556 | 77 | py |
hsoftmax | hsoftmax-master/wenet/transformer/positionwise_feed_forward.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Positionwise feed forward layer definition."""
import torch
class PositionwiseFeedForward(torch.nn.Module):
"""Positionwise feed forward layer.
FeedForward are appie... | 1,399 | 30.818182 | 68 | py |
hsoftmax | hsoftmax-master/wenet/transformer/ctc.py | import torch
import torch.nn.functional as F
from typeguard import check_argument_types
class CTC(torch.nn.Module):
"""CTC module"""
def __init__(
self,
odim: int,
encoder_output_size: int,
dropout_rate: float = 0.0,
reduce: bool = True,
):
""" Construct CTC... | 2,415 | 33.514286 | 77 | py |
hsoftmax | hsoftmax-master/wenet/transformer/subsampling.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Subsampling layer definition."""
from typing import Tuple
import torch
class BaseSubsampling(torch.nn.Module):
def __init__(self):
super().__init__()
self.right... | 7,611 | 32.240175 | 74 | py |
hsoftmax | hsoftmax-master/wenet/transformer/encoder.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Encoder definition."""
from typing import Tuple, List, Optional
import torch
from typeguard import check_argument_types
from wenet.transformer.attention import MultiHeadedAttention
f... | 19,676 | 42.629712 | 82 | py |
hsoftmax | hsoftmax-master/wenet/transformer/convolution.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2021 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""ConvolutionModule definition."""
from typing import Optional, Tuple
import torch
from torch import nn
from typeguard import check_argument_types
class ConvolutionModule(nn.Module):... | 4,512 | 32.42963 | 78 | py |
hsoftmax | hsoftmax-master/wenet/transformer/cmvn.py | #!/usr/bin/env python3
# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 a... | 1,510 | 30.479167 | 74 | py |
hsoftmax | hsoftmax-master/wenet/transformer/decoder.py | # Copyright 2021 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Decoder definition."""
from typing import Tuple, List, Optional
import torch
from typeguard import check_argument_types
from wenet.transformer.attention import MultiHeade... | 13,287 | 40.917981 | 83 | py |
hsoftmax | hsoftmax-master/wenet/transformer/hsoftmax_layer.py | from multiprocessing import dummy
from typing import List
import torch
from torch import nn
import math
from wenet.utils.huffman_tree import HuffmanTree
from wenet.utils.hsoftmax_processpool import ProcessPool, Worker
from queue import PriorityQueue
import heapq
import time
import logging
class HSoftmaxLayer(nn.Modu... | 12,322 | 40.076667 | 153 | py |
hsoftmax | hsoftmax-master/wenet/transformer/subsampling.bak.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Subsampling layer definition."""
from typing import Tuple
import torch
class BaseSubsampling(torch.nn.Module):
def __init__(self):
super().__init__()
self.right... | 7,611 | 32.240175 | 74 | py |
hsoftmax | hsoftmax-master/wenet/transformer/swish.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Swish() activation function for Conformer."""
import torch
class Swish(torc... | 511 | 29.117647 | 70 | py |
hsoftmax | hsoftmax-master/wenet/transformer/attention.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Multi-Head Attention layer definition."""
import math
from typing import Optional, Tuple
import torch
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Mult... | 9,149 | 40.03139 | 80 | py |
hsoftmax | hsoftmax-master/wenet/transformer/decoder_layer.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Decoder self-attention layer definition."""
from typing import Optional, Tuple
import torch
from torch import nn
class DecoderLayer(nn.Module):
"""Single decoder layer mo... | 5,170 | 35.160839 | 79 | py |
hsoftmax | hsoftmax-master/wenet/transformer/asr_model.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 31,951 | 41.376658 | 80 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/fairness_test_Celeba_timm.py | import argparse
import torch
import os
import torch.nn as nn
print('Imported torch')
from util.fairness_utils import evaluate, most_least_variant_classes
from util.data_utils_balanced import load_dict_as_str
from util.data_utils_balanced import ImageFolderWithProtectedAttributes
import numpy as np
import torchvision.tr... | 7,492 | 42.563953 | 121 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/fairness_test_Celeba.py | import argparse
import torch
import os
import torch.nn as nn
print('Imported torch')
from util.fairness_utils import evaluate, most_least_variant_classes
from util.data_utils_balanced import load_dict_as_str
from util.data_utils_balanced import ImageFolderWithProtectedAttributes
from backbone.model_resnet import ResNet... | 9,029 | 48.615385 | 121 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/config.py | import torch
configurations = {
1: dict(
SEED = 1337, # random seed for reproduce results
DATA_ROOT = '/home/peter/Project/face.evoLVe.PyTorch/data', # the parent root where your train/val/test data are stored
MODEL_ROOT = '/home/peter/Project/face.evoLVe.PyTorch/model', # the root to buf... | 2,645 | 40.34375 | 194 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/fairness_train_timm.py | from pathlib import Path
from comet_ml import Experiment
import argparse
from tqdm import tqdm
from config import user_configs
import os
import torch
import torch.nn as nn
import torch.optim as optim
from head.metrics import CosFace
from loss.focal import FocalLoss
from util.utils import separate_resnet_bn_paras, warm... | 10,167 | 42.639485 | 229 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/train.py | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from config import configurations
from backbone.model_resnet import ResNet_50, ResNet_101, ResNet_152
from backbone.model_irse import IR_50, IR_101, IR_152, IR_SE_50, IR_SE_... | 14,081 | 52.340909 | 290 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/model_resnet.py | import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, ReLU, Dropout, MaxPool2d, Sequential, Module
# Support: ['ResNet_50', 'ResNet_101', 'ResNet_152']
def conv3x3(in_planes, out_planes, stride = 1):
"""3x3 convolution with padding"""
return Conv2d(in_planes, out_planes, kern... | 5,756 | 29.460317 | 107 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/MobileFaceNets.py | # based on:
# https://github.com/TreB1eN/InsightFace_Pytorch/blob/master/model.py
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
import torch
class Flatten(Module):
def forward(self, input):
return input.view(input.size(0), -1)
class Conv_block(Module):
def _... | 4,399 | 44.833333 | 131 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/EfficientNets.py | # based on:
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo
from torch.nn import Sequential, BatchNorm1d, BatchNorm2d, Dropout... | 42,605 | 40.165217 | 130 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/model_irse.py | import torch
import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
AdaptiveAvgPool2d, Sequential, Module
from collections import namedtuple
# Support: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
class Flatten(Modu... | 7,418 | 30.172269 | 112 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/GhostNet.py |
# based on:
# https://github.com/huawei-noah/ghostnet/blob/master/ghostnet_pytorch/ghostnet.py
# 2020.06.09-Changed for building GhostNet
# Huawei Technologies Co., Ltd. <foss@huawei.com>
"""
Creates a GhostNet Model as defined in:
GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Ti... | 8,416 | 33.495902 | 115 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backbone/AttentionNets.py | # based on:
# https://github.com/tengshaofeng/ResidualAttentionNetwork-pytorch/tree/master/Residual-Attention-Network/model
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
import numpy as np
class Flatten(nn.Module):
def forward(self, x):
r... | 10,698 | 44.52766 | 111 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/data_pipe.py | from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import numpy as np
import cv2
import bcolz
import pickle
import mxnet as mx
from tqdm import tqdm
def load_bin(path, rootdir, transform, image_size = [112, 112]):
if not rootdir.exists():
rootdir.mkdir()
bins, issame_list = pickle... | 1,671 | 34.574468 | 118 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/prepare_data.py | from config import get_config
from data.data_pipe import load_bin, load_mx_rec
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'for extracting faces_emore data')
parser.add_argument("-r", "--rec_path", help="mxnet record file path",default = 'faces_emore', type = str)
... | 670 | 40.9375 | 110 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/utils.py | import torch
import torchvision.transforms as transforms
import torch.nn.functional as F
from .verification import evaluate
from datetime import datetime
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import numpy as np
from PIL import Image
import bcolz
import io
import os
# Support: ['get_time', 'l2_no... | 7,584 | 30.869748 | 306 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/config.py | import torch
configurations = {
1: dict(
SEED = 1337, # random seed for reproduce results
DATA_ROOT = '/media/pc/6T/jasonjzhao/data/faces_emore', # the parent root where your train/val/test data are stored
MODEL_ROOT = '/media/pc/6T/jasonjzhao/buffer/model_ir152', # the root to buffer you... | 1,963 | 52.081081 | 194 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/metrics.py | from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
# Support: ['Softmax', 'ArcFace', 'CosFace', 'SphereFace', 'Am_softmax']
class Softmax(nn.Module):
r"""Implement of Softmax (normal ... | 9,015 | 37.365957 | 121 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/backup/train.py | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from config import configurations
from backbone.model_resnet import ResNet_50, ResNet_101, ResNet_152
from backbone.model_irse import IR_50, IR_101, IR_152, IR_SE_50, IR_SE... | 16,788 | 55.911864 | 318 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/head/metrics.py | from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
# Support: ['Softmax', 'ArcFace', 'CosFace', 'SphereFace', 'Am_softmax']
# Support: ['AdaCos','AdaM_Softmax','ArcFace','ArcNegFace','Circl... | 30,133 | 42.609262 | 138 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/util/data_utils_balanced.py | import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
import random
import json
import os
from PIL import Image
import numpy as np
torch.manual_seed(222)
torch.cuda.manual_seed_all(222)
np.random.seed(222)
#r... | 16,509 | 42.677249 | 151 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/util/extract_feature_v1.py | # Helper function for extracting features from pre-trained models
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
import os
def l2_norm(input, axis = 1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return out... | 3,253 | 34.369565 | 258 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/util/utils.py | import torch
import torchvision.transforms as transforms
import torch.nn.functional as F
import torchvision.datasets as datasets
from .verification import evaluate
from sklearn.model_selection import train_test_split
from torch.utils.data import Subset
from datetime import datetime
import matplotlib.pyplot as plt
plt.s... | 9,632 | 31.434343 | 165 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/util/extract_feature_v2.py | # Helper function for extracting features from pre-trained models
import torch
import cv2
import numpy as np
import os
import matplotlib.pyplot as plt
def l2_norm(input, axis = 1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output
def extract_feature(img_root, backbo... | 2,206 | 29.652778 | 137 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/util/fairness_utils.py | import torch
from tqdm import tqdm
import numpy as np
import random
import os
import pandas as pd
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(222)
torch.cuda.manual_seed_all(222)
np.random.seed(222)
random.seed(222)
torch.backends.cudnn.deterministic = True
torch.backends.cu... | 9,000 | 37.965368 | 183 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/data_processing/randaugment.py | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageEnhance, ImageOps
import numpy as np
import random
class Rand_Augment():
def __init__(self, Numbers=None, max_Magnitude=None):
self.transforms = ['autocontrast', 'equalize', 'rotate', 'solarize', 'color', ... | 5,693 | 45.292683 | 120 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/loss/focal.py | import torch
import torch.nn as nn
# Support: ['FocalLoss']
class FocalLoss(nn.Module):
def __init__(self, elementwise = False, gamma = 2, eps = 1e-7):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.elementwise = elementwise
if self.elementwise:
... | 689 | 23.642857 | 67 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/paddle/mult_gpu_training.py | import paddle
import paddle.nn as nn
import paddle.optimizer as optim
from paddle.vision import transforms
from config import configurations
from dataload import NormalDataset,BalancingClassDataset
from backbone.model_resnet import ResNet_50, ResNet_101, ResNet_152
from backbone.model_irse import IR_50, IR_101, IR_152,... | 12,632 | 49.939516 | 171 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/paddle/dataload.py | import paddle
import os, tqdm,cv2
import numpy as np
from paddle.vision import transforms
import paddle.vision as vision
class BalancingClassDataset(paddle.io.Dataset):
def __init__(self, data_root, input_size, mean, std):
super(BalancingClassDataset, self).__init__()
self.input_size = input_size
... | 4,753 | 38.616667 | 110 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/paddle/head/metrics.py | from __future__ import print_function
from __future__ import division
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import math
# Support: ['Softmax', 'ArcFace', 'CosFace', 'SphereFace', 'Am_softmax']
class Softmax(nn.Layer):
"""Implement of Softmax (normal classification head):
Ar... | 9,710 | 38.47561 | 110 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/paddle/align/get_nets.py | import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from collections import OrderedDict
import numpy as np
class Flatten(nn.Layer):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [bat... | 5,081 | 28.546512 | 65 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/paddle/align/detector.py | import numpy as np
import paddle
from get_nets import PNet, RNet, ONet
from box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from first_stage import run_first_stage
def detect_faces(image, min_face_size = 20.0,
thresholds=[0.6, 0.7, 0.8],
nms_thresholds=[0.7, 0... | 4,350 | 33.259843 | 95 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/applications/align/get_nets.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch... | 4,864 | 27.786982 | 65 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/applications/align/first_stage.py | import torch
from torch.autograd import Variable
import math
from PIL import Image
import numpy as np
from box_utils import nms, _preprocess
def run_first_stage(image, net, scale, threshold):
"""Run P-Net, generate bounding boxes, and do NMS.
Arguments:
image: an instance of PIL.Image.
net: a... | 3,012 | 30.061856 | 76 | py |
FR-NAS | FR-NAS-main/face.evoLVe.PyTorch/applications/align/detector.py | import numpy as np
import torch
from torch.autograd import Variable
from get_nets import PNet, RNet, ONet
from box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from first_stage import run_first_stage
def detect_faces(image, min_face_size = 20.0,
thresholds=[0.6, 0.7, 0.8],
... | 4,186 | 32.496 | 95 | py |
catastrophic-overfitting | catastrophic-overfitting-main/eval.py | from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import torch
from torchattacks import FGSM, PGD, MultiAttack
from defenses.loader import base_loader
from defenses.model import get_model
def run(name, model, root, data_path, gpu, method, eps, alpha, steps, restart):
torch.cuda.set_device(gpu... | 2,537 | 36.323529 | 113 | py |
catastrophic-overfitting | catastrophic-overfitting-main/train.py | from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import numpy as np
import random
import torch
import torch.optim as optim
from defenses.loader import base_loader
from defenses.model import get_model
from defenses.trainer import COAdvTrainer
from defenses.trainer import FastAdvTrainer
def run(name... | 3,697 | 36.734694 | 135 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/model.py | import torch.nn as nn
from .models.preact_resnet import PreActBlock, PreActResNet
from .models.wide_resnet import WideResNet
from .models.normalize import Normalize, Identity
def get_model(name, num_classes, fc_input_dim_scale=1):
# CIFAR10 w/ Normalize Layer
norm = Normalize(mean=[0.4914, 0.4822, 0.4465... | 1,768 | 45.552632 | 131 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/loaders/base_loader.py | import torchvision.utils
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from .datasets import Datasets
r"""
Arguments:
data_name (str): model to train.
root (str): strength of the attack or maximum perturbation.
val_info (int or float or list): ratio or index of the v... | 2,563 | 36.15942 | 94 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/loaders/datasets.py | import random
import torch
from torch.utils.data import DataLoader, Subset
from torch.utils.data.sampler import SubsetRandomSampler
import torchvision.utils
import torchvision.datasets as dsets
import torchvision.transforms as transforms
class Datasets() :
def __init__(self, data_name, root='./data',
... | 20,157 | 38.52549 | 114 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/models/preact_resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# Modified from https://github.com/kuangliu/pytorch-cifar/tree/master/models
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1):
s... | 3,781 | 38.810526 | 102 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/models/wide_resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# Modified from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/wrn.py
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
... | 3,921 | 44.08046 | 116 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/models/normalize.py | # Modified from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/wrn.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Normalize(nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.register_buffer('mean', ... | 967 | 28.333333 | 97 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/trainers/adv_trainer.py | import os
import torch
# from torchhk import Trainer
from .trainer import Trainer
from torchattacks.attack import Attack
from torchattacks import VANILA, FGSM, PGD, GN
r"""
Trainer for Adversarial Training.
Attributes:
self.model : model.
self.device : device where model is.
self.optimizer : optimizer.
... | 2,877 | 29.946237 | 83 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/trainers/co_adv_trainer.py | import torch
import torch.nn as nn
from torchattacks.attack import Attack
from .adv_trainer import AdvTrainer
r"""
'Understanding Catastrophic Overfitting in Single-step Adversarial Training'
[https://arxiv.org/abs/2010.01799]
Attributes:
self.model : model.
self.device : device where model is.
self.opt... | 5,929 | 35.832298 | 118 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/trainers/fast_adv_trainer.py | import torch
import torch.nn as nn
from torchattacks import FFGSM
from .adv_trainer import AdvTrainer
r"""
'Fast is better than free: Revisiting adversarial training'
[https://arxiv.org/abs/2001.03994]
Attributes:
self.model : model.
self.device : device where model is.
self.optimizer : optimizer.
s... | 1,627 | 26.133333 | 79 | py |
catastrophic-overfitting | catastrophic-overfitting-main/defenses/trainers/trainer.py | import os
import torch
from torch.optim import *
from torch.optim.lr_scheduler import *
from torchhk import RecordManager
r"""
Trainer.
Attributes:
self.model : model.
self.device : device where model is.
self.optimizer : optimizer.
self.scheduler : scheduler (* Automatically Updated).
self.... | 9,113 | 35.166667 | 119 | py |
urnng | urnng-master/data.py | #!/usr/bin/env python3
import numpy as np
import torch
import pickle
class Dataset(object):
def __init__(self, data_file):
data = pickle.load(open(data_file, 'rb')) #get text data
self.sents = self._convert(data['source']).long()
self.other_data = data['other_data']
self.sent_lengths = self._convert(... | 1,585 | 35.883721 | 90 | py |
urnng | urnng-master/eval_ppl.py | #!/usr/bin/env python3
import sys
import os
import argparse
import json
import random
import shutil
import copy
import torch
from torch import cuda
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import numpy as np
import time
import ... | 3,753 | 35.446602 | 116 | py |
urnng | urnng-master/TreeCRF.py | #!/usr/bin/env python3
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import itertools
import utils
import random
class ConstituencyTreeCRF(nn.Module):
def __init__(self):
super(ConstituencyTreeCRF, self).__init__()
self.huge = 1e9
def logadd(self, x, y):
d = ... | 6,934 | 32.995098 | 97 | py |
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