repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/__init__.py | # -*- coding: utf-8 -*-
from . import common
from . import cwt_utils
from . import stft_utils
from . import fft_utils
from . import backend
from .common import *
from .cwt_utils import *
from .stft_utils import *
from .fft_utils import *
from .backend import *
| 262 | 19.230769 | 25 | 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 |
rnlps | rnlps-master/rnlps/__init__.py | 0 | 0 | 0 | py | |
rnlps | rnlps-master/rnlps/policies/base.py | """
Defines the interaction between policy and bandit.
Also includes the base policies for the different types of problems -
non-contextual, contextual and linear bandits.
"""
import numpy as np
import tensorflow as tf
import contextlib
from termcolor import cprint
@contextlib.contextmanager
def _printo... | 17,699 | 33.038462 | 114 | py |
rnlps | rnlps-master/rnlps/policies/contextual_policies.py | """
Policies for contextual bandit problems.
"""
import numpy as np
import tensorflow as tf
import contextlib
from termcolor import cprint
from rnlps.policies.base import Trial, Policy
from rnlps.policies.base import BaseOracle, BaseFixed, BaseRandom
from rnlps.policies.base import BaseThompsonRecurrentNetwork, ... | 5,628 | 33.115152 | 127 | py |
rnlps | rnlps-master/rnlps/policies/non_contextual_policies.py | """
Policies for non-contextual bandit problems.
"""
import numpy as np
import tensorflow as tf
import contextlib
from termcolor import cprint
from rnlps.policies.base import Trial, Policy
from rnlps.policies.base import BaseOracle, BaseFixed, BaseRandom
from rnlps.policies.base import BaseThompsonRecurrentNetwo... | 9,757 | 31.098684 | 112 | py |
rnlps | rnlps-master/rnlps/policies/contextual_linear_policies.py | """
Policies for linear bandit problems.
"""
import numpy as np
import tensorflow as tf
import contextlib
from termcolor import cprint
from math import log
from rnlps.policies.base import Trial, Policy
from rnlps.policies.base import BaseOracle, BaseFixed, BaseRandom
from rnlps.policies.base import BaseThompsonR... | 17,762 | 36.161088 | 185 | py |
rnlps | rnlps-master/rnlps/policies/__init__.py | 0 | 0 | 0 | py | |
rnlps | rnlps-master/rnlps/examples/example_hgrids/hgrid_linear.py | """
Creates config files for experiments and hyperparamter grid search.
"""
import os
import argparse
import json
import numpy as np
from itertools import product
from collections import namedtuple
# Configuration templates with different arguments
CfgOracle = namedtuple('Oracle', [])
CfgRandom = namedtuple('Rando... | 4,560 | 34.632813 | 101 | py |
rnlps | rnlps-master/rnlps/examples/example_hgrids/hgrid_contextual.py | """
Creates config files for experiments and hyperparamter grid search.
"""
import os
import argparse
import json
import numpy as np
from itertools import product
from collections import namedtuple
# Configuration templates with different arguments
CfgOracle = namedtuple('Oracle', [])
CfgRandom = namedtuple('Rando... | 3,770 | 32.972973 | 90 | py |
rnlps | rnlps-master/rnlps/scripts/hgrid.py | """
Creates config files for experiments and hyperparamter grid search.
"""
import os
import argparse
import json
import numpy as np
from itertools import product
from collections import namedtuple
# Configuration templates with different arguments
CfgOracle = namedtuple('Oracle', [])
CfgRandom = namedtuple('Rando... | 4,355 | 32.507692 | 118 | py |
rnlps | rnlps-master/rnlps/scripts/hp_sensitivity_plot.py | """
Generates the hyperparameter sensitivity plot. Takes as an argument the
directory that contains the summary file (policy_mean_perf.csv) which is
generated by create_summary.py
"""
import matplotlib
matplotlib.use('Agg')
import numpy as np
import pandas as pd
import os
import argparse
import seaborn as... | 2,301 | 28.139241 | 88 | py |
rnlps | rnlps-master/rnlps/scripts/run.py | """
Runs a single experiment for a particular configuration of bandit and
policy settings. Saves results in trial.csv.
"""
import os
import argparse
import json
import numpy as np
import pandas as pd
from rnlps.environments.non_contextual_bandits import non_contextual_bandits
from rnlps.environments.contextu... | 2,371 | 34.939394 | 100 | py |
rnlps | rnlps-master/rnlps/scripts/create_summary.py | """
Creates 2 csv files summarising the performance of all the policies
on an experiment.
analysis.csv - return (cumulative reward) of every run from each individual
experiment.
policy_mean_perf.csv - aggregates performance across random seeds to
provide the mean and standard deviation of the ... | 1,784 | 27.790323 | 82 | py |
rnlps | rnlps-master/rnlps/scripts/regret_analysis.py | """
Generates the regret plot for an experiment. Includes the regret curves for
the random policy, conventional algorithms like SW-UCB, and the default and
best neural bandits.
Usage:
$ python3 regret_analysis.py experiment_folder/
With no additional flag this takes the 'best' rnn and ffnn po... | 11,381 | 38.520833 | 199 | py |
rnlps | rnlps-master/rnlps/scripts/__init__.py | 0 | 0 | 0 | py | |
rnlps | rnlps-master/rnlps/scripts/multirun.py | """
Runs multiple jobs in parallel in a tmux session.
"""
import os
import time
import datetime
import argparse
def main():
if 'TMUX' not in os.environ:
raise Exception('This script should be called from a tmux session.')
parser = argparse.ArgumentParser()
parser.add_argument('directory')
... | 1,803 | 26.333333 | 76 | py |
rnlps | rnlps-master/rnlps/environments/contextual_bandits.py | """
Contextual bandit environments to evaluate performance.
"""
import numpy as np
import os
class StationaryContextualBandit:
def __init__(self, dataset, seed, err_sigma = 0.05):
# Can also be used for real-world non-stationary problems
# as it doesn't shuffle the data.
self.random... | 3,790 | 27.503759 | 102 | py |
rnlps | rnlps-master/rnlps/environments/non_contextual_bandits.py | """
Non-contextual bandit environments to evaluate performance.
"""
import numpy as np
class StationaryBernoulliBandit:
def __init__(self, means, seed):
self.means = np.array(means)
self.random_state = np.random.RandomState(seed)
if (max(self.means) > 1.) or (min(self.means) < 0.):
... | 10,137 | 32.458746 | 92 | py |
rnlps | rnlps-master/rnlps/environments/__init__.py | 0 | 0 | 0 | py | |
rnlps | rnlps-master/rnlps/environments/linear_bandits.py | """
Linear bandit environments to evaluate performance.
"""
import numpy as np
import os
class StationaryLinearBandit:
def __init__(self, n_arms, dimension, seed, arm_pool_size = 2000, err_sigma = 0.05):
self.n_arms = n_arms
self.dimension = dimension
self.arm_pool_size = arm_pool_s... | 7,215 | 32.719626 | 132 | 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/utils.py | import sys
import os, errno
import logging
#-----------------------------------------------------------------------------------------------------------#
def set_logger(out_dir=None):
console_format = BColors.OKBLUE + '[%(levelname)s]' + BColors.ENDC + ' (%(name)s) %(message)s'
#datefmt='%Y-%m-%d %Hh-%Mm-%Ss'
logge... | 4,263 | 24.686747 | 109 | 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/config/config_FP32.py | from easydict import EasyDict as edict
config = edict()
config.dataset = "emoreIresNet" # training dataset
config.embedding_size = 512 # embedding size of model
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128 # batch size per GPU
config.lr = 0.1
config.output = "output/MobileFaceNet_fp32" # tr... | 1,734 | 35.145833 | 106 | py |
QuantFace | QuantFace-master/config/config_Quantization.py | from easydict import EasyDict as edict
config = edict()
config.dataset = "emoreIresNetTunning" # training dataset
config.embedding_size = 512 # embedding size of model
config.momentum = 0.9
config.weight_decay =5e-4
config.batch_size = 128
# batch size per GPU
config.lr = 0.1
config.output = "output/output_r50_w8_a8" ... | 1,435 | 30.217391 | 101 | py |
QuantFace | QuantFace-master/config/config_Quantization_Synthetic.py | from easydict import EasyDict as edict
config = edict()
config.dataset = "emoreIresNetTunningSyntheticFP32" # training dataset
config.embedding_size = 512 # embedding size of model
config.momentum = 0.9
config.weight_decay =5e-4
config.batch_size = 128
# batch size per GPU
config.lr = 0.1
config.output = "output/outpu... | 1,459 | 32.181818 | 97 | 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/eval/__init__.py | 0 | 0 | 0 | py | |
QuantFace | QuantFace-master/plots/plot_param.py | import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
fontP = FontProperties()
dbs=["agedb", "lfw", "calfw", "cplfw", "cfp","IJB-B", "IJB-C"]
for db ... | 4,907 | 31.078431 | 105 | 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/utils/utils_logging.py | import logging
import os
import sys
class AverageMeter(object):
"""Computes and stores the average and current value
"""
def __init__(self):
self.val = None
self.avg = None
self.sum = None
self.count = None
self.reset()
def reset(self):
self.val = 0
... | 1,157 | 26.571429 | 80 | py |
QuantFace | QuantFace-master/utils/__init__.py | 0 | 0 | 0 | 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 |
QuantFace | QuantFace-master/backbones/__init__.py | 1 | 0 | 0 | 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/pruner/__init__.py | from pruner.pruner import *
| 28 | 13.5 | 27 | 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/__init__.py | from models.ResNet import *
from models.ResNets import *
from models.VGG import *
model_dict = {
"resnet18": resnet18,
"resnet34": resnet34,
"wide_resnet50_2": wide_resnet50_2,
"wide_resnet101_2": wide_resnet101_2,
"resnet101": resnet101,
"resnet50": resnet50,
"resnet20s": resnet20s,
"r... | 398 | 22.470588 | 41 | 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 |
Progressive-Pruning | Progressive-Pruning-main/wb/__init__.py | from .wandb_logger import *
| 28 | 13.5 | 27 | 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/run_regression.py | import os
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides results for Figure~3 and Table~5.
Example usage python run.py
The results are saved under ./results folder.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,369 | 70.818182 | 172 | 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/setup.py | from setuptools import setup
setup(
name="dccp",
version="1.0.3",
author="Xinyue Shen, Steven Diamond, Stephen Boyd",
author_email="xinyues@stanford.edu, diamond@cs.stanford.edu, boyd@stanford.edu",
packages=["dccp"],
license="GPLv3",
zip_safe=False,
install_requires=["cvxpy >= 0.3.5"],... | 456 | 27.5625 | 84 | 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/run_benchmark_MMD_simple.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 10,851 | 49.240741 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/MMD_fair_run.py | import models
import fairness_metrics
import data_loader
import MMD_fair
import argparse
import pandas as pd
import numpy as np
import time
import pickle
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides an implementation of... | 6,679 | 44.753425 | 158 | 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/run.py | import os
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides results for Figure~4 and Table~6.
Example usage python run.py
The results are saved under ./results folder.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 1,453 | 54.923077 | 148 | 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/load_data.py | import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from collections import namedtuple
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # for plotting stuff
import os
import collections
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 11,005 | 46.034188 | 207 | 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/run_benchmark.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 7,511 | 46.544304 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/run_benchmark_regression.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 6,191 | 45.208955 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/objective.py | __author__ = "Xinyue"
from dccp.linearize import linearize, linearize_para
import cvxpy as cvx
# from linearize import linearize_para
def convexify_para_obj(obj):
"""
input:
obj: an objective of a problem
return:
if the objective is dcp,
return the cost function (an expression);
... | 1,445 | 23.931034 | 66 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/problem.py | __author__ = "Xinyue"
import numpy as np
import cvxpy as cvx
import logging
from dccp.objective import convexify_obj
from dccp.objective import convexify_para_obj
from dccp.constraint import convexify_para_constr
from dccp.constraint import convexify_constr
logger = logging.getLogger("dccp")
logger.addHandler(loggin... | 12,115 | 35.059524 | 101 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/constraint.py | __author__ = "Xinyue"
from dccp.linearize import linearize, linearize_para
import cvxpy as cvx
# from dccp.linearize import linearize_para
def convexify_para_constr(self):
"""
input:
self: a constraint of a problem
return:
if the constraint is dcp, return itself;
otherwise, return
... | 2,925 | 32.632184 | 109 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/linearize.py | __author__ = "Xinyue"
import numpy as np
import cvxpy as cvx
def linearize_para(expr):
"""
input:
expr: an expression
return:
linear_expr: linearized expression
zero_order: zero order parameter
linear_dictionary: {variable: [value parameter, [gradient parameter]]}
d... | 3,432 | 36.725275 | 117 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/test/test_example.py | """
Copyright 2013 Steven Diamond
This file is part of CVXPY.
CVXPY is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CVXPY is distributed i... | 3,862 | 32.301724 | 81 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/dccp/test/base_test.py | # Base class for unit tests.
import unittest
import numpy as np
class BaseTest(unittest.TestCase):
# AssertAlmostEqual for lists.
def assertItemsAlmostEqual(self, a, b, places=5):
a = self.mat_to_list(a)
b = self.mat_to_list(b)
for i in range(len(a)):
self.assertAlmostEqual... | 1,417 | 34.45 | 123 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/build/lib/dccp/objective.py | __author__ = "Xinyue"
from dccp.linearize import linearize, linearize_para
import cvxpy as cvx
# from linearize import linearize_para
def convexify_para_obj(obj):
"""
input:
obj: an objective of a problem
return:
if the objective is dcp,
return the cost function (an expression);
... | 1,445 | 23.931034 | 66 | py |
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