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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|---|---|---|---|---|---|---|
muisc | muisc-main/transformers/tests/test_modeling_auto.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. 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 requir... | 12,065 | 42.876364 | 119 | py |
muisc | muisc-main/transformers/tests/test_modeling_funnel.py | # coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# 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... | 19,285 | 36.741683 | 119 | py |
muisc | muisc-main/transformers/tests/test_modeling_pegasus.py | # coding=utf-8
# Copyright 2021, The HuggingFace Inc. team. 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 ... | 23,326 | 42.683521 | 1,802 | py |
muisc | muisc-main/transformers/tests/test_tokenization_fsmt.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. 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 requir... | 6,444 | 37.136095 | 141 | py |
muisc | muisc-main/transformers/tests/test_modeling_albert.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. 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 requir... | 12,943 | 40.620579 | 119 | py |
muisc | muisc-main/transformers/tests/deepspeed/test_deepspeed.py | # Copyright 2020 The HuggingFace Team. 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 required by applicabl... | 38,102 | 39.92696 | 126 | py |
muisc | muisc-main/transformers/tests/deepspeed/test_model_zoo.py | # Copyright 2020 The HuggingFace Team. 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 required by applicabl... | 7,943 | 29.553846 | 113 | py |
muisc | muisc-main/transformers/tests/sagemaker/conftest.py | # we define a fixture function below and it will be "used" by
# referencing its name from tests
import os
import pytest
from attr import dataclass
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # defaults region
@dataclass
class SageMakerTestEnvironment:
framework: str
role = "arn:aws:iam::558105141721:... | 2,183 | 32.090909 | 148 | py |
muisc | muisc-main/transformers/tests/sagemaker/test_single_node_gpu.py | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFac... | 3,554 | 35.649485 | 117 | py |
muisc | muisc-main/transformers/tests/sagemaker/test_multi_node_data_parallel.py | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface im... | 4,260 | 37.387387 | 118 | py |
muisc | muisc-main/transformers/tests/sagemaker/test_multi_node_model_parallel.py | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface im... | 4,521 | 35.176 | 117 | py |
muisc | muisc-main/transformers/tests/sagemaker/scripts/pytorch/run_ddp.py | import json
import logging
import os
import subprocess
from argparse import ArgumentParser
logger = logging.getLogger(__name__)
def parse_args():
parser = ArgumentParser()
parsed, unknown = parser.parse_known_args()
for arg in unknown:
if arg.startswith(("-", "--")):
parser.add_argum... | 1,468 | 26.716981 | 102 | py |
muisc | muisc-main/transformers/tests/sagemaker/scripts/tensorflow/run_tf.py | import argparse
import logging
import sys
import time
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Hyperparameters sent by the client are passed as comm... | 3,690 | 39.119565 | 112 | py |
muisc | muisc-main/transformers/tests/sagemaker/scripts/tensorflow/run_tf_dist.py | import argparse
import logging
import os
import sys
import time
import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.file_utils import is_sagemaker_dp_enabled
if os.environ.get("SDP_ENABLED") or... | 7,331 | 36.6 | 110 | py |
muisc | muisc-main/transformers/tests/extended/test_trainer_ext.py | # Copyright 2020 The HuggingFace Team. 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 required by applicabl... | 11,054 | 34.892857 | 119 | py |
muisc | muisc-main/transformers/utils/tests_fetcher.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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... | 17,434 | 39.358796 | 118 | py |
muisc | muisc-main/transformers/utils/check_copies.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 13,736 | 37.695775 | 119 | py |
muisc | muisc-main/transformers/utils/notification_service.py | # Copyright 2020 The HuggingFace Team. 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 required by applicabl... | 7,977 | 39.090452 | 127 | py |
muisc | muisc-main/transformers/utils/check_repo.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 23,065 | 39.824779 | 141 | py |
muisc | muisc-main/transformers/utils/check_dummies.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 6,848 | 33.943878 | 118 | py |
muisc | muisc-main/transformers/utils/check_table.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 7,578 | 39.747312 | 116 | py |
voicefixer | voicefixer-main/setup.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
# python3 setup.py sdist bdist_wheel
"""
@File : setup.py.py
@Contact : haoheliu@gmail.com
@License : (C)Copyright 2020-2100
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
9/6/21 5:16 PM Haohe Liu ... | 4,334 | 27.708609 | 86 | py |
voicefixer | voicefixer-main/test/test.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@File : test.py
@Contact : haoheliu@gmail.com
@License : (C)Copyright 2020-2100
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
9/14/21 11:02 AM Haohe Liu 1.0 None
"""
import git
impor... | 3,219 | 29.666667 | 88 | py |
voicefixer | voicefixer-main/test/streamlit.py | import os
import argparse
import time
import librosa
import soundfile
import streamlit as st
import torch
from io import BytesIO
from voicefixer import VoiceFixer
@st.experimental_singleton
def init_voicefixer():
return VoiceFixer()
# init with global shared singleton instance
voice_fixer = init_voicefixer()
... | 1,581 | 21.927536 | 140 | py |
voicefixer | voicefixer-main/voicefixer/base.py | import librosa.display
from voicefixer.tools.pytorch_util import *
from voicefixer.tools.wav import *
from voicefixer.restorer.model import VoiceFixer as voicefixer_fe
import os
EPS = 1e-8
class VoiceFixer(nn.Module):
def __init__(self):
super(VoiceFixer, self).__init__()
self._model = voicefixer... | 6,280 | 42.317241 | 166 | py |
voicefixer | voicefixer-main/voicefixer/__main__.py | #!/usr/bin/python3
from genericpath import exists
import os.path
import argparse
from voicefixer import VoiceFixer
import torch
import os
def writefile(infile, outfile, mode, append_mode, cuda, verbose=False):
if append_mode is True:
outbasename, outext = os.path.splitext(os.path.basename(outfile))
... | 5,899 | 33.502924 | 180 | py |
voicefixer | voicefixer-main/voicefixer/tools/pytorch_util.py | import torch
import torch.nn as nn
import numpy as np
def check_cuda_availability(cuda):
if cuda and not torch.cuda.is_available():
raise RuntimeError("Error: You set cuda=True but no cuda device found.")
def try_tensor_cuda(tensor, cuda):
if cuda and torch.cuda.is_available():
return tensor... | 5,357 | 28.60221 | 84 | py |
voicefixer | voicefixer-main/voicefixer/tools/base.py | import math
import numpy as np
import torch
import os
import torch.fft
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
def get_window(window_size, window_type, square_root_window=True):
"""Return the window"""
window = {
"hamming": torch.hamming_window(window_size),
"hanning": torch.hann_window(... | 7,591 | 29.987755 | 105 | py |
voicefixer | voicefixer-main/voicefixer/tools/random_.py | import random
import torch
RANDOM_RESOLUTION = 2**31
def random_torch(high, to_int=True):
if to_int:
return int((torch.rand(1)) * high) # do not use numpy.random.random
else:
return (torch.rand(1)) * high # do not use numpy.random.random
def shuffle_torch(list):
length = len(list)
... | 1,225 | 22.132075 | 76 | py |
voicefixer | voicefixer-main/voicefixer/tools/mel_scale.py | import torch
from torch import Tensor
from typing import Optional
import math
import warnings
class MelScale(torch.nn.Module):
r"""Turn a normal STFT into a mel frequency STFT, using a conversion
matrix. This uses triangular filter banks.
User can control which device the filter bank (`fb`) is (e.g. fb... | 7,799 | 31.635983 | 113 | py |
voicefixer | voicefixer-main/voicefixer/tools/modules/pqmf.py | """
@File : subband_util.py
@Contact : liu.8948@buckeyemail.osu.edu
@License : (C)Copyright 2020-2021
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
2020/4/3 4:54 PM Haohe Liu 1.0 None
"""
import torch
import torch.nn.functional as F
im... | 3,962 | 32.871795 | 82 | py |
voicefixer | voicefixer-main/voicefixer/tools/modules/fDomainHelper.py | from torchlibrosa.stft import STFT, ISTFT, magphase
import torch
import torch.nn as nn
import numpy as np
from voicefixer.tools.modules.pqmf import PQMF
class FDomainHelper(nn.Module):
def __init__(
self,
window_size=2048,
hop_size=441,
center=True,
pad_mode="reflect",
... | 8,508 | 35.208511 | 202 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/base.py | from voicefixer.vocoder.model.generator import Generator
from voicefixer.tools.wav import read_wave, save_wave
from voicefixer.tools.pytorch_util import *
from voicefixer.vocoder.model.util import *
from voicefixer.vocoder.config import Config
import os
import numpy as np
class Vocoder(nn.Module):
def __init__(se... | 3,713 | 41.689655 | 186 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/config.py | import torch
import numpy as np
import os
from voicefixer.tools.path import root_path
class Config:
@classmethod
def refresh(cls, sr):
if sr == 44100:
Config.ckpt = os.path.join(
os.path.expanduser("~"),
".cache/voicefixer/synthesis_module/44100/model.ckpt-1... | 7,953 | 24.091483 | 88 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/model/pqmf.py | import os
import sys
import torch
import torch.nn as nn
import numpy as np
import scipy.io.wavfile
class PQMF(nn.Module):
def __init__(self, N, M, file_path="utils/pqmf_hk_4_64.dat"):
super().__init__()
self.N = N # nsubband
self.M = M # nfilter
self.ana_conv_filter = nn.Conv1d(
... | 2,146 | 33.629032 | 99 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/model/modules.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from voicefixer.vocoder.config import Config
# From xin wang of nii
class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.00... | 32,158 | 32.922996 | 92 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/model/res_msd.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
LRELU_SLOPE = 0.1
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname... | 2,128 | 28.569444 | 79 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/model/util.py | from voicefixer.vocoder.config import Config
from voicefixer.tools.pytorch_util import try_tensor_cuda, check_cuda_availability
import torch
import librosa
import numpy as np
def tr_normalize(S):
if Config.allow_clipping_in_normalization:
if Config.symmetric_mels:
return torch.clip(
... | 4,034 | 28.669118 | 85 | py |
voicefixer | voicefixer-main/voicefixer/vocoder/model/generator.py | import torch
import torch.nn as nn
import numpy as np
from voicefixer.vocoder.model.modules import UpsampleNet, ResStack
from voicefixer.vocoder.config import Config
from voicefixer.vocoder.model.pqmf import PQMF
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
class Generator(nn.Module):
def __init__(
... | 6,076 | 34.95858 | 87 | py |
voicefixer | voicefixer-main/voicefixer/restorer/modules.py | import torch.nn as nn
import torch
import torch.nn.functional as F
import math
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, size, activation, momentum):
super(ConvBlockRes, self).__init__()
self.activation = activation
if type(size) == type((3, 4)):
... | 6,535 | 28.981651 | 103 | py |
voicefixer | voicefixer-main/voicefixer/restorer/model.py | # import pytorch_lightning as pl
import torch.utils
from voicefixer.tools.mel_scale import MelScale
import torch.utils.data
import matplotlib.pyplot as plt
import librosa.display
from voicefixer.vocoder.base import Vocoder
from voicefixer.tools.pytorch_util import *
from voicefixer.restorer.model_kqq_bn import UNetRes... | 22,571 | 32.145374 | 99 | py |
voicefixer | voicefixer-main/voicefixer/restorer/__init__.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@File : __init__.py.py
@Contact : haoheliu@gmail.com
@License : (C)Copyright 2020-2100
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
9/14/21 12:31 AM Haohe Liu 1.0 None
"""
impor... | 1,409 | 30.333333 | 132 | py |
voicefixer | voicefixer-main/voicefixer/restorer/model_kqq_bn.py | from voicefixer.restorer.modules import *
from voicefixer.tools.pytorch_util import *
class UNetResComplex_100Mb(nn.Module):
def __init__(self, channels, nsrc=1):
super(UNetResComplex_100Mb, self).__init__()
activation = "relu"
momentum = 0.01
self.nsrc = nsrc
self.channe... | 5,881 | 30.454545 | 88 | py |
dcdi | dcdi-master/main.py | # coding=utf-8
"""
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limita... | 8,288 | 54.630872 | 119 | py |
dcdi | dcdi-master/gies/gies.py | """GIES algorithm.
.. MIT License
..
.. Copyright (c) 2018 Diviyan Kalainathan
..
.. Permission is hereby granted, free of charge, to any person obtaining a copy
.. of this software and associated documentation files (the "Software"), to deal
.. in the Software without restriction, including without limitation the rig... | 10,676 | 37.545126 | 229 | py |
dcdi | dcdi-master/jci/pc.py | """
Modified from:
.. MIT License
..
.. Copyright (c) 2018 Diviyan Kalainathan
..
.. Permission is hereby granted, free of charge, to any person obtaining a copy
.. of this software and associated documentation files (the "Software"), to deal
.. in the Software without restriction, including without limitation the righ... | 4,455 | 37.747826 | 169 | py |
dcdi | dcdi-master/dcdi/main.py | # coding=utf-8
"""
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limita... | 9,590 | 49.21466 | 119 | py |
dcdi | dcdi-master/dcdi/torchkit.py | """
Copyright Chin-Wei Huang
"""
import numpy as np
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
delta = 1e-6
c = - 0.5 * np.log(2 * np.pi)
def log(x):
return torch.log(x * 1e2) - np.log(1e2)
def log_normal(x, mean, log_var, eps=0.00001):
retur... | 3,495 | 24.151079 | 91 | py |
dcdi | dcdi-master/dcdi/data.py | """
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights... | 9,536 | 42.747706 | 118 | py |
dcdi | dcdi-master/dcdi/dag_optim.py | """
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights... | 5,015 | 37.290076 | 118 | py |
dcdi | dcdi-master/dcdi/plot.py | # coding=utf-8
"""
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limita... | 14,509 | 40.457143 | 139 | py |
dcdi | dcdi-master/dcdi/train.py | """
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights... | 28,724 | 46.715947 | 146 | py |
dcdi | dcdi-master/dcdi/prox.py | import math
import torch
def monkey_patch_RMSprop(RMSProp_class):
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
... | 2,283 | 35.253968 | 94 | py |
dcdi | dcdi-master/dcdi/models/base_model.py | """
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights... | 12,935 | 43.30137 | 125 | py |
dcdi | dcdi-master/dcdi/models/learnables.py | """
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights... | 5,233 | 46.581818 | 119 | py |
dcdi | dcdi-master/dcdi/models/flows.py | import torch
from torch.autograd import Variable
from ..torchkit import log_normal, SigmoidFlow
from .base_model import BaseModel
class FlowModel(BaseModel):
"""
Abstract class for normalizing flow model
"""
def __init__(self, num_vars, num_layers, hid_dim, num_params, nonlin="leaky-relu",
... | 7,309 | 52.357664 | 120 | py |
dcdi | dcdi-master/dcdi/utils/penalty.py | import torch
def compute_penalty(list_, p=2, target=0.):
penalty = 0
for m in list_:
penalty += torch.norm(m - target, p=p) ** p
return penalty
| 165 | 19.75 | 51 | py |
dcdi | dcdi-master/dcdi/utils/gumbel.py | import torch
def sample_logistic(shape, uniform):
u = uniform.sample(shape)
return torch.log(u) - torch.log(1 - u)
def gumbel_sigmoid(log_alpha, uniform, bs, tau=1, hard=False):
shape = tuple([bs] + list(log_alpha.size()))
logistic_noise = sample_logistic(shape, uniform)
y_soft = torch.sigmoid((l... | 675 | 26.04 | 63 | py |
dcdi | dcdi-master/cam/cam.py | """CAM algorithm.
Imported from the Pcalg package.
Adapted from:
Author: Diviyan Kalainathan
.. MIT License
..
.. Copyright (c) 2018 Diviyan Kalainathan
..
.. Permission is hereby granted, free of charge, to any person obtaining a copy
.. of this software and associated documentation files (the "Software"), to deal
.... | 10,249 | 38.883268 | 156 | py |
dcdi | dcdi-master/data/generation/causal_mechanisms.py | """Defining a set of classes that represent causal functions/ mechanisms.
Author: Diviyan Kalainathan
Modified by Philippe Brouillard, July 24th 2019
.. MIT License
..
.. Copyright (c) 2018 Diviyan Kalainathan
..
.. Permission is hereby granted, free of charge, to any person obtaining a copy
.. of this software and a... | 32,235 | 32.790356 | 98 | py |
WSPLIN | WSPLIN-main/main.py | import os
from collections import OrderedDict
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import time
import argparse
import datetime
import numpy as np
from copy import deepcopy
import random
import torch
import torch.backends.cudnn as cudnn
from torch.nn.parallel import DistributedDataParallel as NativeDDP
import torc... | 24,756 | 46.701349 | 236 | py |
WSPLIN | WSPLIN-main/lr_scheduler.py | import torch
from torch.optim.lr_scheduler import MultiStepLR
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.step_lr import StepLRScheduler
from timm.scheduler.scheduler import Scheduler
import math
from typing import Dict, Any
def build_scheduler(config, optimizer, n_iter_per_epoch):
n... | 6,167 | 38.793548 | 180 | py |
WSPLIN | WSPLIN-main/utils.py | from collections import OrderedDict
import csv
import os
from cv2 import norm
from sklearn import metrics
import torch
import torch.distributed as dist
import shutil
from copy import deepcopy
import math
import torch.nn.functional as F
import torch.nn as nn
from timm.utils.clip_grad import dispatch_clip_grad
from torch... | 25,558 | 43.527875 | 187 | py |
WSPLIN | WSPLIN-main/config.py | import os
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
import yaml
from yacs.config import CfgNode as CN
_C = CN()
# Base config files
_C.BASE = ['']
# -----------------------------------------------------------------------------
# Data settings
# --------------------------------------... | 13,497 | 32.493797 | 187 | py |
WSPLIN | WSPLIN-main/criterion.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
import numpy as np
def _build_criterion(name,config):
if name == 'crossentropy':
if config.AUG.MIXUP > 0.:
# smoothing is ... | 5,862 | 40.58156 | 235 | py |
WSPLIN | WSPLIN-main/models/build_models.py | import torch
import torch.nn as nn
from timm.models import create_model
from .wsplin import *
from .stn import *
from .dino import *
from .simsiam import *
from .ioplin import *
from .utils import LinearProbWrapper
# from ._vit import *
ONE_BACKBONE_GROUP = ('wsplin','stn','ioplin','simsiam','pict','clmim')
TWO_BACKB... | 4,089 | 37.584906 | 109 | py |
WSPLIN | WSPLIN-main/models/wsplin.py | import math
import os
import torch
import torch.nn as nn
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
class ClassifierNetwork(nn.Module):
def __init__(self, num_classes,patches,dp_rate=0.5):
super().__init__()
self.cls_head = nn.Sequential(
... | 2,760 | 33.08642 | 168 | py |
WSPLIN | WSPLIN-main/models/stn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models import create_model
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
class STN(nn.Module):
def __init__(self, backbone,type=1):
super().__init__()
self.backbone = backbon... | 5,834 | 32.342857 | 97 | py |
WSPLIN | WSPLIN-main/models/simsiam.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_
from timm.models.registry import register_model
import os
from .utils import get_num_features
# copyright simsiam@facebook. ref: https://arxiv.org/abs/2011.10566
# Comple from: https://github.com/PatrickHua... | 4,828 | 32.303448 | 109 | py |
WSPLIN | WSPLIN-main/models/utils.py | import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from itertools import chain
import numpy as np
from timm.models.layers import trunc_normal_
from torch.nn import Parameter
import random
def patchify(imgs,patch_size):
"""
imgs: (N, 3, H, W)
x: (N, L, patch_size**... | 16,834 | 37.174603 | 140 | py |
WSPLIN | WSPLIN-main/models/dino.py | import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_
from timm.models.registry import register_model
from .utils import MultiCropWrapper
sys.path.append(os.path.dirname(__file__) + os.sep + '../')
from utils import ModelEmaV3,cosine_schedu... | 3,565 | 36.145833 | 124 | py |
WSPLIN | WSPLIN-main/models/ioplin.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
import os
class IOPLIN(nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
def forward_features(se... | 1,862 | 30.576271 | 114 | py |
WSPLIN | WSPLIN-main/engine/base.py | from contextlib import suppress
from copy import deepcopy
import time
from cv2 import reduce
import numpy as np
from numpy import ndarray
import datetime
from collections import OrderedDict
try:
import wandb
has_wandb = True
except ImportError:
has_wandb = False
import sys,os
sys.path.append(os.path.dir... | 16,504 | 43.013333 | 196 | py |
WSPLIN | WSPLIN-main/engine/wsplin.py | import numpy as np
from collections import OrderedDict
from sklearn.metrics import roc_auc_score,precision_recall_curve,f1_score
import math
import torch
import random as native_random
from timm.utils import *
from .iNet_cls import INetClsEngine
import sys
sys.path.append("..")
from utils import getDataByStick
class ... | 7,067 | 36.2 | 179 | py |
WSPLIN | WSPLIN-main/engine/iNet_cls.py | from ast import Or
import numpy as np
from collections import OrderedDict
from sklearn.metrics import roc_auc_score,precision_recall_curve,f1_score
import torch
from timm.utils import *
import sys
sys.path.append("..")
from utils import getDataByStick
class INetClsEngine:
def __init__(self,config,**kwargs):
... | 5,013 | 32.651007 | 131 | py |
WSPLIN | WSPLIN-main/engine/dino.py | import numpy as np
import sys,os
from collections import OrderedDict
from timm.utils import *
import torch
import torch.nn as nn
from .iNet_cls import INetClsEngine
sys.path.append(os.path.dirname(__file__) + os.sep + '../')
from utils import cosine_scheduler
class DINOEngine(INetClsEngine):
def __init__(self,c... | 3,108 | 38.35443 | 136 | py |
WSPLIN | WSPLIN-main/engine/ioplin.py | import numpy as np
from collections import OrderedDict
from sklearn.metrics import roc_auc_score,precision_recall_curve,f1_score
import torch
import torch.nn as nn
from timm.utils import *
from .iNet_cls import INetClsEngine
import sys
sys.path.append("..")
from utils import getDataByStick
class PIC:
thr=0.5 #阈值... | 13,111 | 37.005797 | 150 | py |
WSPLIN | WSPLIN-main/optimizer/optimizer.py | #from torch import optim as optim
from timm.optim import create_optimizer_v2
from .ranger import RangerLars
import torch.nn as nn
def build_optimizer(config, model):
"""
Use the timm optimizer
"""
param,weight_decay = get_param(config,model)
if config.TRAIN.OPTIMIZER.NAME.lower() == 'rangerlars':
... | 2,832 | 34.860759 | 171 | py |
WSPLIN | WSPLIN-main/optimizer/ranger/over9000.py | import torch, math
from torch.optim.optimizer import Optimizer
import itertools as it
from .lookahead import *
from .ralamb import *
# RAdam + LARS + LookAHead
# Lookahead implementation from https://github.com/lonePatient/lookahead_pytorch/blob/master/optimizer.py
# RAdam + LARS implementation from https://gist.git... | 539 | 32.75 | 105 | py |
WSPLIN | WSPLIN-main/optimizer/ranger/radam.py | # from https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam.py
import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight... | 8,099 | 37.755981 | 185 | py |
WSPLIN | WSPLIN-main/optimizer/ranger/ranger.py | import math
import torch
from torch.optim.optimizer import Optimizer, required
import itertools as it
from lookahead import *
from radam import *
def Ranger(params, alpha=0.5, k=6, *args, **kwargs):
radam = RAdam(params, *args, **kwargs)
return Lookahead(radam, alpha, k) | 283 | 27.4 | 53 | py |
WSPLIN | WSPLIN-main/optimizer/ranger/ralamb.py | import torch, math
from torch.optim.optimizer import Optimizer
# RAdam + LARS
class Ralamb(Optimizer):
# def __init__(self, params, lr=8e-4, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, bet... | 4,119 | 39.792079 | 181 | py |
WSPLIN | WSPLIN-main/optimizer/ranger/lookahead.py | # Lookahead implementation from https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lookahead.py
""" Lookahead Optimizer Wrapper.
Implementation modified from: https://github.com/alphadl/lookahead.pytorch
Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610... | 4,075 | 41.020619 | 117 | py |
WSPLIN | WSPLIN-main/dataloader/build_loader.py | from timm.data import Mixup
from dataloader.ioplin import ioplin_dataloader
from .iNet_torch import *
def build_loader(config,is_train):
mixup_fn = None
mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
name = config.DATA.DATALOADER_NAME.lower().spl... | 1,128 | 39.321429 | 113 | py |
WSPLIN | WSPLIN-main/dataloader/iNet_torch.py | from timm.data import create_loader
import torch
from .dataset import build_dataset
def timm_dataloader(config,is_train):
if is_train:
dataset_train,dataset_val = build_dataset(config,'train_val')
loader_train = create_loader(
dataset_train,
input_size=config.DATA.IMG_SIZE,... | 3,797 | 44.214286 | 246 | py |
WSPLIN | WSPLIN-main/dataloader/ioplin.py | import torch
from typing import TypeVar, Optional, Iterator
from torch.utils.data import Sampler,Dataset
import torch.distributed as dist
import math
from .dataset import build_dataset
class SubsetRandomSampler(torch.utils.data.Sampler):
r"""Samples elements randomly from a given list of indices, without replaceme... | 8,495 | 44.924324 | 248 | py |
WSPLIN | WSPLIN-main/dataloader/dataset/transform.py | from timm.data import create_transform
from timm.data.transforms import str_to_interp_mode
import albumentations as A
from albumentations.pytorch import ToTensorV2
from torchvision import transforms
import torch
import numpy as np
from .transforms import *
from .utils import TransformCompatWrapper,MultiViewWarper
def ... | 6,610 | 43.972789 | 162 | py |
WSPLIN | WSPLIN-main/dataloader/dataset/utils.py | import os
import torch
import numpy as np
import random
from PIL import ImageFilter, ImageOps
from timm.data.parsers.parser import Parser
from copy import deepcopy
import torch.utils.data as data
class SubsetRandomSampler(torch.utils.data.Sampler):
r"""Samples elements randomly from a given list of indices, withou... | 5,081 | 28.375723 | 98 | py |
WSPLIN | WSPLIN-main/dataloader/dataset/datasets.py | '''
Note: Please implements the target transform in the custom dataset!!!
Note: Please implements the target transform in the custom dataset!!!
Note: Please implements the target transform in the custom dataset!!!
'''
import torch.utils.data as data
from timm.data import create_parser
from timm.data.transforms import... | 12,567 | 38.031056 | 148 | py |
WSPLIN | WSPLIN-main/dataloader/dataset/transforms.py | import torchvision.transforms as transforms
import numpy as np
from PIL import Image
from .utils import GaussianBlur,Solarization
class DataAugmentationDINO(object):
def __init__(self, global_crops_scale, local_crops_scale, local_crops_number,img_size):
flip_and_color_jitter = transforms.Compose([
... | 3,409 | 39.595238 | 122 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/eval_retrieval_video.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... | 9,531 | 37.128 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/pretrain.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... | 6,666 | 37.537572 | 148 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/eval_nocaps.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... | 4,249 | 35.016949 | 119 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/Winoground.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from data.utils import pre_caption
from datasets import load_dataset
from... | 6,426 | 43.944056 | 143 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/utils.py | import math
def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
"""Decay the learning rate"""
lr = (init_lr - min_lr) * 0.5 * (1. + math.cos(math.pi * epoch / max_epoch)) + min_lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def warmup_lr_schedule(opti... | 8,474 | 29.485612 | 94 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/train_vqa.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... | 7,751 | 37.376238 | 128 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/VSR.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from data.utils import pre_caption
from models.blip_pretr... | 4,825 | 37.608 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/train_nlvr.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... | 8,060 | 36.84507 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/train_retrieval.py | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel.yaml as yaml
import numpy as np
imp... | 14,183 | 39.876081 | 141 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP/VALSE.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from data.utils import pre_caption
from models.blip_pretrain import blip_p... | 6,007 | 39.870748 | 148 | py |
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