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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TTS | TTS-master/tests/test_tacotron2_tf_model.py | import os
import unittest
import numpy as np
import tensorflow as tf
import torch
from tests import get_tests_input_path
from TTS.tts.tf.models.tacotron2 import Tacotron2
from TTS.tts.tf.utils.tflite import (convert_tacotron2_to_tflite,
load_tflite_model)
from TTS.utils.io import l... | 5,947 | 42.101449 | 121 | py |
TTS | TTS-master/tests/test_vocoder_pqmf.py | import os
import torch
import soundfile as sf
from librosa.core import load
from tests import get_tests_path, get_tests_input_path
from TTS.vocoder.layers.pqmf import PQMF
TESTS_PATH = get_tests_path()
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
def test_pqmf():
w, sr = load(WAV_FILE)
... | 626 | 21.392857 | 64 | py |
TTS | TTS-master/tests/test_vocoder_gan_datasets.py | import os
import numpy as np
from tests import get_tests_path, get_tests_input_path, get_tests_output_path
from torch.utils.data import DataLoader
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.vocoder.datasets.gan_dataset import GANDataset
from TTS.vocoder.datasets.preproces... | 4,221 | 42.979167 | 140 | py |
TTS | TTS-master/tests/test_tacotron2_model.py | import copy
import os
import unittest
import torch
from tests import get_tests_input_path
from torch import nn, optim
from TTS.tts.layers.losses import MSELossMasked
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.utils.io import load_config
from TTS.utils.audio import AudioProcessor
#pylint: disable=unused-... | 14,763 | 49.047458 | 299 | py |
TTS | TTS-master/tests/test_vocoder_melgan_discriminator.py | import numpy as np
import torch
from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
from TTS.vocoder.models.melgan_multiscale_discriminator import MelganMultiscaleDiscriminator
def test_melgan_discriminator():
model = MelganDiscriminator()
print(model)
dummy_input = torch.rand((4, 1, ... | 882 | 31.703704 | 92 | py |
TTS | TTS-master/tests/test_speedy_speech_layers.py | import torch
from TTS.tts.layers.speedy_speech.encoder import Encoder
from TTS.tts.layers.speedy_speech.decoder import Decoder
from TTS.tts.layers.speedy_speech.duration_predictor import DurationPredictor
from TTS.tts.utils.generic_utils import sequence_mask
from TTS.tts.models.speedy_speech import SpeedySpeech
use_... | 5,880 | 34.005952 | 79 | py |
TTS | TTS-master/tests/test_vocoder_parallel_wavegan_generator.py | import numpy as np
import torch
from TTS.vocoder.models.parallel_wavegan_generator import ParallelWaveganGenerator
def test_pwgan_generator():
model = ParallelWaveganGenerator(
in_channels=1,
out_channels=1,
kernel_size=3,
num_res_blocks=30,
stacks=3,
res_channels=... | 767 | 26.428571 | 82 | py |
TTS | TTS-master/tests/test_wavegrad_train.py | import unittest
import numpy as np
import torch
from torch import optim
from TTS.vocoder.models.wavegrad import Wavegrad
#pylint: disable=unused-variable
torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class WavegradTrainTest(unittes... | 2,498 | 38.666667 | 82 | py |
TTS | TTS-master/tests/test_vocoder_wavernn.py | import numpy as np
import torch
import random
from TTS.vocoder.models.wavernn import WaveRNN
def test_wavernn():
model = WaveRNN(
rnn_dims=512,
fc_dims=512,
mode=10,
mulaw=False,
pad=2,
use_aux_net=True,
use_upsample_net=True,
upsample_factors=[4, 8,... | 850 | 25.59375 | 67 | py |
TTS | TTS-master/tests/test_vocoder_wavernn_datasets.py | import os
import shutil
import numpy as np
from tests import get_tests_path, get_tests_input_path, get_tests_output_path
from torch.utils.data import DataLoader
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
from TTS.voco... | 3,538 | 37.053763 | 101 | py |
ssqueezepy | ssqueezepy-master/tests/fft_test.py | # -*- coding: utf-8 -*-
"""Fast Fourier Transform, CPU parallelization, and GPU execution tests:
- multi-thread CPU & GPU outputs match that of single-thread CPU
- batched (multi-input) outputs match single for-looped
- `ssqueezepy.FFT` outputs match `scipy`'s
- unified synchrosqueezing pipelines outputs ma... | 24,286 | 35.195231 | 81 | py |
ssqueezepy | ssqueezepy-master/tests/test_signals_test.py | # -*- coding: utf-8 -*-
"""Test ssqueezepy/_test_signals.py"""
import os
import pytest
import warnings
import numpy as np
import scipy.signal as sig
from ssqueezepy import Wavelet, TestSignals
from ssqueezepy.utils import window_resolution
VIZ = 0
os.environ['SSQ_GPU'] = '0' # in case concurrent tests set it to '1'
... | 3,282 | 26.358333 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/_cwt.py | # -*- coding: utf-8 -*-
import numpy as np
from .utils import fft, ifft, ifftshift, FFT_GLOBAL
from .utils import WARN, adm_cwt, adm_ssq, _process_fs_and_t
from .utils import padsignal, process_scales, logscale_transition_idx
from .utils import backend as S
from .utils.backend import Q
from .algos import replace_at_inf... | 23,966 | 39.691002 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/_gmw.py | # -*- coding: utf-8 -*-
"""Generalized Morse Wavelets.
For complete functionality, utility functions have been ported from jLab, and
largely validated to match jLab's behavior. jLab tests not ported.
"""
import numpy as np
from numpy.fft import ifft
from numba import jit
from scipy.special import (gamma as gamma_fn,... | 29,146 | 36.657623 | 83 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/_ssq_cwt.py | # -*- coding: utf-8 -*-
import numpy as np
from .utils import EPS32, EPS64, pi, p2up, adm_ssq, process_scales
from .utils import trigdiff, _process_fs_and_t
from .utils import backend as S
from .algos import replace_under_abs, phase_cwt_cpu, phase_cwt_gpu
from .ssqueezing import ssqueeze, _check_ssqueezing_args
from .w... | 24,265 | 40.059222 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/wavelets.py | # -*- coding: utf-8 -*-
import numpy as np
import gc
from numba import jit
from types import FunctionType
from scipy import integrate
from .algos import find_maximum
from .configs import gdefaults, USE_GPU, IS_PARALLEL
from .utils import backend as S
from .utils.fft_utils import ifft, fftshift, ifftshift
from .utils.ba... | 38,840 | 38.154234 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/_stft.py | # -*- coding: utf-8 -*-
import numpy as np
import scipy.signal as sig
from .utils import WARN, padsignal, buffer, unbuffer, window_norm
from .utils import _process_fs_and_t
from .utils.fft_utils import fft, ifft, rfft, irfft, fftshift, ifftshift
from .utils.backend import torch, is_tensor
from .algos import zero_denorm... | 13,033 | 39.858934 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/_ssq_stft.py | # -*- coding: utf-8 -*-
import numpy as np
from ._stft import stft, get_window, _check_NOLA
from ._ssq_cwt import _invert_components, _process_component_inversion_args
from .utils.cwt_utils import _process_fs_and_t, infer_scaletype
from .utils.common import WARN, EPS32, EPS64
from .utils import backend as S
from .utils... | 8,660 | 34.207317 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/configs.py | # -*- coding: utf-8 -*-
"""
Contains `GDEFAULTS`, global defaults dictionary, set in `ssqueezepy.configs.ini`.
The .ini is parsed into a dict, then values are retrieved internally by functions
via `gdefaults()`, which sets default values if keyword arguments weren't set
to original functions (or were set to `None`).
... | 4,853 | 31.145695 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/ssqueezing.py | # -*- coding: utf-8 -*-
import numpy as np
from types import FunctionType
from .algos import indexed_sum_onfly, ssqueeze_fast
from .utils import p2up, process_scales, infer_scaletype, _process_fs_and_t
from .utils import NOTE, pi, logscale_transition_idx, assert_is_one_of
from .utils.backend import Q
from .utils.common... | 15,556 | 41.159892 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/algos.py | # -*- coding: utf-8 -*-
"""CPU- & GPU-accelerated routines, and few neat algorithms.
"""
import numpy as np
from numba import jit, prange
from functools import reduce
from .utils.backend import asnumpy, cp, torch
from .utils.gpu_utils import _run_on_gpu, _get_kernel_params
from .utils import backend as S
from .configs ... | 46,480 | 34.78214 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/cwt_utils.py | # -*- coding: utf-8 -*-
import numpy as np
from scipy import integrate
from .common import WARN, assert_is_one_of, p2up
from .backend import torch, asnumpy
from ..configs import gdefaults
pi = np.pi
__all__ = [
'adm_ssq',
'adm_cwt',
'cwt_scalebounds',
'process_scales',
'infer_scaletype',
'make... | 29,511 | 39.650138 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/common.py | # -*- coding: utf-8 -*-
import numpy as np
import logging
from textwrap import wrap
from .fft_utils import fft, ifft
logging.basicConfig(format='')
WARN = lambda msg: logging.warning("WARNING: %s" % msg)
NOTE = lambda msg: logging.warning("NOTE: %s" % msg) # else it's mostly ignored
pi = np.pi
EPS32 = np.finfo(np.fl... | 10,463 | 32.43131 | 81 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/gpu_utils.py | # -*- coding: utf-8 -*-
import numpy as np
from collections import namedtuple
from string import Template
from .backend import torch, cp
Stream = namedtuple('Stream', ['ptr'])
def _run_on_gpu(kernel, grid, block, *args, **kwargs):
kernel_name = kernel.split('void ')[1].split('(')[0]
fn = load_kernel(kernel_n... | 1,432 | 33.95122 | 74 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/backend.py | # -*- coding: utf-8 -*-
import numpy as np
# torch & cupy imported at bottom
def allclose(a, b, device='cuda'):
"""`numpy.allclose` or `torch.allclose`, latter if input(s) are Tensor."""
if is_tensor(a, b, mode='any'):
a, b = asarray(a, device=device), asarray(b, device=device)
return torch.al... | 3,656 | 24.573427 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/fft_utils.py | # -*- coding: utf-8 -*-
import numpy as np
import multiprocessing
from scipy.fft import fftshift as sfftshift, ifftshift as sifftshift
from scipy.fft import fft as sfft, rfft as srfft, ifft as sifft, irfft as sirfft
from pathlib import Path
from . import backend as S
from ..configs import IS_PARALLEL
try:
from tor... | 14,188 | 37.142473 | 82 | py |
ssqueezepy | ssqueezepy-master/ssqueezepy/utils/stft_utils.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy.fft import fft, fftshift
from numba import jit, prange
from scipy import integrate
from .gpu_utils import _run_on_gpu, _get_kernel_params
from ..configs import IS_PARALLEL
from .backend import torch
from . import backend as S
__all__ = [
"buffer",
"unbuffer... | 7,677 | 30.991667 | 82 | py |
DAS | DAS-master/code/my_layers.py | import keras.backend as K
from keras.engine.topology import Layer
from keras.layers.convolutional import Conv1D
from keras import initializers
from keras import regularizers
from keras import constraints
import tensorflow as tf
import numpy as np
#######################################################################... | 4,951 | 26.359116 | 80 | py |
DAS | DAS-master/code/optimizers.py | import keras.optimizers as opt
def get_optimizer(args):
clipvalue = 0
clipnorm = 10
if args.algorithm == 'rmsprop':
optimizer = opt.RMSprop(lr=0.0005, rho=0.9, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue)
elif args.algorithm == 'sgd':
optimizer = opt.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=F... | 943 | 41.909091 | 115 | py |
DAS | DAS-master/code/train_batch.py | import argparse
import logging
import numpy as np
from time import time
import utils as U
logging.basicConfig(
# filename='out.log',
level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
logger = logging.getLogger(__name__)
###################... | 14,860 | 49.037037 | 205 | py |
DAS | DAS-master/code/models.py | import numpy as np
import logging
import codecs
from keras.layers import Dense, Dropout, Activation, Embedding, Input
from keras.models import Model
import keras.backend as K
from my_layers import Conv1DWithMasking, Max_over_time, KL_loss, Ensemble_pred_loss, mmd_loss
from keras.constraints import maxnorm
logging.bas... | 6,134 | 39.629139 | 130 | py |
DAS | DAS-master/code/read.py | import codecs
import operator
import numpy as np
import re
from keras.preprocessing import sequence
from keras.utils.np_utils import to_categorical
num_regex = re.compile('^[+-]?[0-9]+\.?[0-9]*$')
def create_vocab(file_list, vocab_size, skip_len):
print 'Creating vocab ...'
total_words, unique_words = 0, 0
... | 7,918 | 32.273109 | 124 | py |
DAS | DAS-master/code/read_amazon.py | import codecs
import operator
import numpy as np
import re
from keras.preprocessing import sequence
from keras.utils.np_utils import to_categorical
from read import create_vocab
num_regex = re.compile('^[+-]?[0-9]+\.?[0-9]*$')
def create_data(vocab, file_path, skip_top, skip_len, replace_non_vocab):
data = []
... | 4,704 | 40.27193 | 127 | py |
QuantFace | QuantFace-master/train_quantization_synthetic.py | import argparse
import logging
import os
import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel.distributed import DistributedDataParallel
import torch.utils.data.distributed
from torch.nn.utils import clip_grad_norm_
from backbones.mobilefacenet import Mobile... | 5,889 | 37.496732 | 104 | py |
QuantFace | QuantFace-master/train_quantization.py | import argparse
import logging
import os
import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel.distributed import DistributedDataParallel
import torch.utils.data.distributed
from torch.nn.utils import clip_grad_norm_
from backbones.mobilefacenet import Mobile... | 5,795 | 37.64 | 104 | py |
QuantFace | QuantFace-master/train_fp32.py | import argparse
import logging
import os
import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel.distributed import DistributedDataParallel
import torch.utils.data.distributed
from torch.nn.utils import clip_grad_norm_
from torch.nn import CrossEntropyLoss
from... | 7,876 | 39.394872 | 156 | py |
QuantFace | QuantFace-master/eval/verification.py | """Helper for evaluation on the Labeled Faces in the Wild dataset
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# 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 restricti... | 16,187 | 38.579462 | 152 | py |
QuantFace | QuantFace-master/quantization_utils/quant_modules.py | # *
# @file Different utility functions
# Copyright (c) Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami
# All rights reserved.
# This file is part of ZeroQ repository.
# https://github.com/amirgholami/ZeroQ
# ZeroQ is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public Li... | 7,593 | 28.320463 | 121 | py |
QuantFace | QuantFace-master/quantization_utils/quant_utils.py | #*
# @file Different utility functions
# Copyright (c) Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami
# All rights reserved.
# This file is part of ZeroQ repository.
# https://github.com/amirgholami/ZeroQ
# ZeroQ is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public Lic... | 5,114 | 35.535714 | 100 | py |
QuantFace | QuantFace-master/utils/losses.py | import torch
from torch import nn
import math
import numpy as np
import torch.nn.functional as F
def l2_norm(input, axis = 1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output
class MLLoss(nn.Module):
def __init__(self, s=64.0):
super(MLLoss, self).__ini... | 8,386 | 40.315271 | 211 | py |
QuantFace | QuantFace-master/utils/countFLOPS.py | from torch.autograd import Variable
import numpy as np
import torch
def count_model_flops(model, input_res=[112, 112], multiply_adds=True):
list_conv = []
def conv_hook(self, input, output):
batch_size, input_channels, input_height, input_width = input[0].size()
output_channels, output_height... | 4,062 | 36.275229 | 112 | py |
QuantFace | QuantFace-master/utils/modelFLOPS.py | import logging
from pytorch_model_summary import summary
import torch
from utils.countFLOPS import count_model_flops
from backbones.iresnet import iresnet100
from config.config_FP32 import config as cfg
if __name__ == "__main__":
# load model
if cfg.network == "iresnet100":
backbone = iresnet100(... | 813 | 21.611111 | 77 | py |
QuantFace | QuantFace-master/utils/dataset.py | import numbers
import os
import queue as Queue
import random
import threading
import mxnet as mx
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
import cv2
class BackgroundGenerator(threading.Thread):
def __init__(self, generator, local_rank, m... | 4,790 | 30.728477 | 82 | py |
QuantFace | QuantFace-master/utils/utils_amp.py | from typing import Dict, List
import torch
from torch._six import container_abcs
from torch.cuda.amp import GradScaler
class _MultiDeviceReplicator(object):
"""
Lazily serves copies of a tensor to requested devices. Copies are cached per-device.
"""
def __init__(self, master_tensor: torch.Tensor) -... | 3,187 | 37.878049 | 109 | py |
QuantFace | QuantFace-master/utils/utils_callbacks.py | import logging
import os
import time
from typing import List
import torch
from eval import verification
from utils.utils_logging import AverageMeter
class CallBackVerification(object):
def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)):
self.frequent: int = frequent
... | 4,819 | 43.220183 | 120 | py |
QuantFace | QuantFace-master/backbones/vggface.py | import torch
from torchvision import datasets, transforms, models
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import Dataset, DataLoader
from skimage import io, transform
from PIL import Image
import torchvision.transform... | 3,350 | 29.189189 | 67 | py |
QuantFace | QuantFace-master/backbones/activation.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from inspect import isfunction
class Identity(nn.Module):
"""
Identity block.
"""
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
def __repr__(self):
return '{name}... | 2,439 | 27.045977 | 106 | py |
QuantFace | QuantFace-master/backbones/countFLOPS.py | from torch.autograd import Variable
import numpy as np
import torch
def count_model_flops(model, input_res=[112, 112], multiply_adds=True):
list_conv = []
def conv_hook(self, input, output):
batch_size, input_channels, input_height, input_width = input[0].size()
output_channels, output_height... | 4,062 | 36.275229 | 112 | py |
QuantFace | QuantFace-master/backbones/mobilefacenet.py | import copy
from torch.nn import (
Linear,
Conv2d,
BatchNorm1d,
BatchNorm2d,
PReLU,
ReLU,
Sigmoid,
Dropout2d,
Dropout,
AvgPool2d,
MaxPool2d,
AdaptiveAvgPool2d,
Sequential,
Module,
Parameter,
)
import torch.nn.functional as F
import torch
import torch.nn as nn... | 10,285 | 28.13881 | 120 | py |
QuantFace | QuantFace-master/backbones/utils.py | import torch
from torch import nn
import torch.nn.functional as F
from backbones.activation import get_activation_layer
class DropBlock2D(nn.Module):
r"""Randomly zeroes 2D spatial blocks of the input tensor.
As described in the paper
`DropBlock: A regularization method for convolutional networks`_ ,
... | 15,256 | 29.211881 | 120 | py |
QuantFace | QuantFace-master/backbones/senet.py | import torch.nn as nn
import math
import torch.nn.functional as F
__all__ = ['SENet', 'senet50']
from backbones.countFLOPS import count_model_flops
from backbones.utils import _calc_width
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes,... | 15,709 | 33.679912 | 107 | py |
QuantFace | QuantFace-master/backbones/iresnet.py | import copy
from collections import OrderedDict
import torch
from torch import nn
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100']
from backbones.countFLOPS import _calc_width, count_model_flops
from quantization_utils.quant_modules import QuantAct, Quant_Linear, Quant_Conv2d, QuantActPreLu
def conv... | 11,550 | 36.141479 | 142 | py |
Progressive-Pruning | Progressive-Pruning-main/main_anytime_train.py | import argparse
import os
import pdb
import pickle
import random
import shutil
import time
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.multiprocessing
import torch.nn as nn
import torch.nn.functional as F
import torch.optim... | 19,993 | 31.777049 | 107 | py |
Progressive-Pruning | Progressive-Pruning-main/main_anytime_baseline.py | import argparse
import os
import pdb
import pickle
import random
import shutil
import time
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.multiprocessing
import torch.nn as nn
import torch.nn.functional as F
import torch.optim... | 16,789 | 31.041985 | 107 | py |
Progressive-Pruning | Progressive-Pruning-main/utils.py | """
setup model and datasets
"""
import torch
import torch.nn as nn
from advertorch.utils import NormalizeByChannelMeanStd
# from advertorch.utils import NormalizeByChannelMeanStd
from torch.autograd.variable import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvisio... | 3,323 | 27.904348 | 75 | py |
Progressive-Pruning | Progressive-Pruning-main/dataset.py | """
function for loading datasets
contains:
CIFAR-10
CIFAR-100
"""
import os
import random
import numpy as np
import torch
import torchvision
from torch.utils.data import DataLoader, Subset
from torchvision import transforms
from torchvision.datasets import CIFAR10, CIFAR100
__all__ = [
... | 21,039 | 28.928876 | 99 | py |
Progressive-Pruning | Progressive-Pruning-main/main_anytime_one.py | import argparse
import os
import pdb
import pickle
import random
import shutil
import time
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.multiprocessing
import torch.nn as nn
import torch.nn.functional as F
import torch.optim... | 19,758 | 31.767828 | 107 | py |
Progressive-Pruning | Progressive-Pruning-main/generate_mask.py | import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torchvision
from advertorch.utils import NormalizeByChannelMeanStd
from torch.utils.data import DataLoader, Subset
from torchvision import transforms
from torchvision.datasets import CIFAR10, CIFAR100
from models.ResNets import res... | 3,926 | 26.270833 | 81 | py |
Progressive-Pruning | Progressive-Pruning-main/tools/layers.py | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(i... | 1,785 | 25.656716 | 86 | py |
Progressive-Pruning | Progressive-Pruning-main/tools/pruning_utils.py | import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.utils.prune as prune
from tools.layers import Conv2d, Linear
__all__ = [
"masked_parameters",
"SynFlow",
"Mag",
"Taylor1ScorerAbs",
"Rand",
"SNIP",
"GraSP",
"check_sparsity_dict",
"extract_mask",
... | 11,187 | 31.618076 | 97 | py |
Progressive-Pruning | Progressive-Pruning-main/pruner/pruner.py | import copy
import torch
import torch.nn as nn
import torch.nn.utils.prune as prune
__all__ = [
"pruning_model",
"pruning_model_random",
"prune_model_custom",
"remove_prune",
"extract_mask",
"reverse_mask",
"check_sparsity",
"check_sparsity_dict",
]
# Pruning operation
def pruning_mo... | 3,421 | 24.537313 | 79 | py |
Progressive-Pruning | Progressive-Pruning-main/models/ResNet.py | import torch
import torch.nn as nn
from advertorch.utils import NormalizeByChannelMeanStd
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = [
"ResNet",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"resnext50_32x4d",
"resnext101_32x8d",
... | 14,716 | 32.754587 | 107 | py |
Progressive-Pruning | Progressive-Pruning-main/models/VGG.py | import torch
import torch.nn as nn
from advertorch.utils import NormalizeByChannelMeanStd
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = [
"VGG",
"vgg11",
"vgg11_bn",
"vgg13",
"vgg13_bn",
"vgg16",
"vgg16_bn",
"vgg19_bn",
"vgg19",
]
model_urls = {
... | 7,591 | 32.59292 | 113 | py |
Progressive-Pruning | Progressive-Pruning-main/models/ResNets.py | """
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the implementations on the web is copy-paste from
torchvision's resnet and has wr... | 5,404 | 30.794118 | 85 | py |
Progressive-Pruning | Progressive-Pruning-main/wb/wandb_logger.py | """
Utilities for Weights & Biases logging.
"""
from pathlib import Path
from typing import Union
import PIL
from matplotlib.pyplot import Figure
from PIL.Image import Image
from torch import Tensor
__all__ = ["WandBLogger"]
class WandBLogger:
"""
The `WandBLogger` provides an easy integration with
We... | 4,146 | 30.416667 | 87 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/baseline_convex_fair_regression.py | import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
import fairness_metrics
import data_loader
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
... | 10,008 | 39.522267 | 145 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/fair_training.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides imple... | 5,881 | 45.314961 | 189 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/fairness_metrics.py | import torch
import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverge... | 9,848 | 34.428058 | 133 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/benchmark.py | # benchmark.py
# file with functions for running experiment
import fair_training
import numpy as np
import torch
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull
import time
def convergence_plotter(regloss, fairloss, lambda_):
plt.figure(figsize=(16,5))
plt.subplot(131)
plt.plot(regloss... | 7,129 | 40.213873 | 206 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/data_loader.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn import preprocessing
"""
% Metrizi... | 16,841 | 39.681159 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_classification.py | # Baseline 1: https://arxiv.org/pdf/1706.02409.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
from zafar_method import funcs_disp_mist
from zafar_method.utils import *
import fairness_metrics
import data_loader
from zafar_method import utils
import numpy as np
from tqdm impor... | 8,066 | 42.139037 | 195 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/MMD_fair.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
import time
from tqdm import tqdm
# +---------------------------------+
# | Algorithm 1: Gradient Descent |
# +---------------------------------+
"""
% Metrizing Fairness
%%%%%%%%%%%%... | 6,629 | 44.102041 | 187 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/models.py | # models.py
# models for regression
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides models for MFL and Oneta et al.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,012 | 29.5 | 97 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/fair_KDE.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 14,239 | 40.037464 | 153 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/fairness_metrics.py | import torch
import cvxpy as cp
import numpy as np
# +------------------------------------------+
# | Metric 1: Energy Distance |
# +------------------------------------------+
def energy_distance(y1, y2):
'''
Compute energy distance between empirical distance y1 and y2, each 1 dimensional
... | 8,196 | 30.771318 | 100 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/algorithm.py | import random
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import CustomDataset
from utils import measures_from_Yhat
tau = 0.5
# Approximation of Q-function given by López-Benítez & Cas... | 7,375 | 41.390805 | 141 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/dataloader.py | import os
import copy
import torch
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import data_loader
from tempeh.configurations import datasets
from sklearn.datasets import make_moons
from sklearn.preprocessing import LabelEncoder, StandardScaler
def arrays_to_tensor(X, Y, Z, XZ,... | 11,649 | 40.459075 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/data_loader_or.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
# TODO: possibly some form of (cross) validation
def to_tensor(data, device):
D = data
if type(data) == pd.core... | 11,586 | 38.546075 | 121 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/models.py | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_layers, n_inputs, n_hidden_units):
super(Classifier, self).__init__()
layers = []
if n_layers == 1: # Logistic Regression
layers.append(nn.Linear(n_inputs, 1))
layers.append... | 829 | 33.583333 | 72 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/fair_KDE_.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 9,960 | 34.830935 | 136 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/fair_training.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides imple... | 5,881 | 45.314961 | 189 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/fairness_metrics.py | import torch
#import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverg... | 10,438 | 34.266892 | 133 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/data_loader.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn import preprocessing
"""
% Metrizi... | 16,842 | 39.585542 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/bias_eval.py | import torch
import data_loader
import models
import fairness_metrics
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
def find_batchsize(N_target, A):
candidate_1 = torch.argmax((A.flatten().cumsum(0)==2).int()).item() + 1
candidate_0 = torch.argmax(((1-A).flatten().cumsum(0)==2).int()).item(... | 9,003 | 36.991561 | 150 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/models.py | # models.py
# models for regression
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides models for MFL and Oneta et al.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,012 | 29.5 | 97 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fair_training.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides imple... | 6,302 | 45.688889 | 189 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fairness_metrics.py | import torch
import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverge... | 10,671 | 34.221122 | 133 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/benchmark.py | # benchmark.py
# file with functions for running experiment
import fair_training
import numpy as np
import torch
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull
import time
def convergence_plotter(regloss, fairloss, lambda_):
plt.figure(figsize=(16,5))
plt.subplot(131)
plt.plot(regloss... | 7,159 | 40.149425 | 206 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/data_loader.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn import preprocessing
"""
% Metrizi... | 16,841 | 39.681159 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_classification.py | # Baseline 1: https://arxiv.org/pdf/1706.02409.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
from zafar_method import funcs_disp_mist
from zafar_method.utils import *
import fairness_metrics
import data_loader
from zafar_method import utils
import numpy as np
from tqdm impor... | 8,147 | 42.340426 | 195 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/MMD_fair.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
import time
from tqdm import tqdm
# +---------------------------------+
# | Algorithm 1: Gradient Descent |
# +---------------------------------+
"""
% Metrizing Fairness
%%%%%%%%%%%%... | 6,671 | 44.387755 | 187 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/models.py | # models.py
# models for regression
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides models for MFL and Oneta et al.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,012 | 29.5 | 97 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fair_KDE.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 14,571 | 40.280453 | 153 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/fairness_metrics.py | import torch
import cvxpy as cp
import numpy as np
# +------------------------------------------+
# | Metric 1: Energy Distance |
# +------------------------------------------+
def energy_distance(y1, y2):
'''
Compute energy distance between empirical distance y1 and y2, each 1 dimensional
... | 8,196 | 30.771318 | 100 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/algorithm.py | import random
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import CustomDataset
from utils import measures_from_Yhat
tau = 0.5
# Approximation of Q-function given by López-Benítez & Cas... | 7,375 | 41.390805 | 141 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/dataloader.py | import os
import copy
import torch
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import data_loader
#from tempeh.configurations import datasets
from sklearn.datasets import make_moons
from sklearn.preprocessing import LabelEncoder, StandardScaler
def arrays_to_tensor(X, Y, Z, XZ... | 11,650 | 40.462633 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/data_loader_or.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
# TODO: possibly some form of (cross) validation
def to_tensor(data, device):
D = data
if type(data) == pd.core... | 11,586 | 38.546075 | 121 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/models.py | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_layers, n_inputs, n_hidden_units):
super(Classifier, self).__init__()
layers = []
if n_layers == 1: # Logistic Regression
layers.append(nn.Linear(n_inputs, 1))
layers.append... | 829 | 33.583333 | 72 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/fair_KDE_.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 9,960 | 34.830935 | 136 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_regression/fairness_metrics.py | import torch
import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverge... | 11,133 | 34.012579 | 133 | py |
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