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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nuts-ml | nuts-ml-master/nutsml/examples/keras_/autoencoder/conv_autoencoder.py | """
A simple convolutional autoencoder adapted from
https://blog.keras.io/building-autoencoders-in-keras.html
"""
from nutsml import KerasNetwork
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras.models import Model
from runner import INPUT_SHAPE
def create_network()... | 1,312 | 36.514286 | 77 | py |
nuts-ml | nuts-ml-master/nutsml/examples/keras_/autoencoder/runner.py | """
Runs training and prediction.
Trains an autoencoder on MNIST and in the prediction phase shows
the original image, the decoded images and the difference.
"""
from __future__ import print_function
import numpy as np
from six.moves import zip, range
from nutsflow import *
from nutsml import *
NUM_EPOCHS = 10 #... | 2,640 | 26.8 | 78 | py |
nuts-ml | nuts-ml-master/nutsml/examples/keras_/mnist/cnn_train.py | """
.. module:: cnn_train
:synopsis: Example nuts-ml pipeline for training a CNN on MNIST
This is code is based on a Keras example (see here)
https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
to train a Multi-layer perceptron on the MNIST data and modified to
use nuts for the data-preprocessing.
"... | 3,378 | 34.568421 | 78 | py |
nuts-ml | nuts-ml-master/nutsml/examples/keras_/mnist/write_images.py | """
.. module:: write_images
:synopsis: Example for writing of image data
"""
from six.moves import zip
from nutsflow import Take, Consume, Enumerate, Zip, Format, Get, Print
from nutsml import WriteImage
def load_samples():
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.loa... | 681 | 28.652174 | 80 | py |
nuts-ml | nuts-ml-master/nutsml/examples/keras_/mnist/mlp_train.py | """
.. module:: mlp_train
:synopsis: Example nuts-ml pipeline for training and evaluation
This is code is based on a Keras example (see here)
https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py
to train a Multi-layer perceptron on the MNIST data and modified to
use nuts for the data-preprocessing.
"... | 2,820 | 31.056818 | 73 | py |
nuts-ml | nuts-ml-master/nutsml/examples/keras_/cifar/cnn_train.py | """
.. module:: mlp_view_misclassified
:synopsis: Example for showing misclassified examples
"""
from __future__ import print_function
import pickle
import os.path as osp
from six.moves import zip, map, range
from nutsflow import PrintProgress, Zip, Unzip, Pick, Shuffle, Mean
from nutsml import (KerasNetwork, Tr... | 4,395 | 33.614173 | 76 | py |
Excessive-Invariance | Excessive-Invariance-master/l0/invariant_l0_attack.py | import tensorflow as tf
import random
import time
import numpy as np
from keras.datasets import mnist
import sys
import os
import itertools
import sklearn.cluster
import scipy.misc
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers import Con... | 14,333 | 36.03876 | 152 | py |
irbl | irbl-master/src/main.py | import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
from sklearn.calib... | 67,075 | 35.673592 | 199 | py |
mooc_knowledge_gain | mooc_knowledge_gain-main/feature_extraction/embedding.py | from sentence_transformers import SentenceTransformer, util
from numpy import add
from torch import Tensor
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
def get_model(name):
""" Loads the SentenceTransformer for the specific model. If the model is not stored it will be dow... | 5,370 | 42.314516 | 125 | py |
mooc_knowledge_gain | mooc_knowledge_gain-main/feature_extraction/files.py | import os
import sys
import csv
import fitz
import processor
import client
import embedding
import re
import scipy
import numpy
import torch
from operator import itemgetter
from SortedCollection import SortedCollection
def load_stop_words(path):
""" Loads stopwords of the stopwords.txt file to use them later
... | 34,651 | 50.642325 | 119 | py |
GoogleScraper | GoogleScraper-master/GoogleScraper/parsing-new-version.py | # -*- coding: utf-8 -*-
import sys
import os
import re
import lxml.html
from lxml.html.clean import Cleaner
from urllib.parse import unquote
import pprint
import logging
from cssselect import HTMLTranslator
logger = logging.getLogger(__name__)
class InvalidSearchTypeException(Exception):
pass
class UnknowUrlE... | 38,678 | 34.355576 | 151 | py |
GoogleScraper | GoogleScraper-master/GoogleScraper/parsing.py | # -*- coding: utf-8 -*-
import sys
import os
import re
import lxml.html
from lxml.html.clean import Cleaner
from urllib.parse import unquote
import pprint
from GoogleScraper.database import SearchEngineResultsPage
import logging
from cssselect import HTMLTranslator
logger = logging.getLogger(__name__)
class Invalid... | 39,004 | 34.203069 | 153 | py |
GoogleScraper | GoogleScraper-master/GoogleScraper/selenium_mode.py | # -*- coding: utf-8 -*-
import tempfile
import threading
from urllib.parse import quote
import json
import datetime
import time
import math
import random
import re
import sys
import os
try:
from selenium import webdriver
from selenium.common.exceptions import TimeoutException, WebDriverException
from sele... | 36,591 | 37.845011 | 147 | py |
GoogleScraper | GoogleScraper-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,975 | 29.181818 | 79 | py |
crnn-pytorch | crnn-pytorch-master/utils.py | #!/usr/bin/python
# encoding: utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import collections
class strLabelConverter(object):
"""Convert between str and label.
NOTE:
Insert `blank` to the alphabet for CTC.
Args:
alphabet (str): set of the possible charac... | 4,860 | 28.107784 | 136 | py |
crnn-pytorch | crnn-pytorch-master/dataset.py | #!/usr/bin/python
# encoding: utf-8
import random
import torch
from torch.utils.data import Dataset
from torch.utils.data import sampler
import torchvision.transforms as transforms
import lmdb
import six
import sys
from PIL import Image
import numpy as np
class lmdbDataset(Dataset):
def __init__(self, root=None... | 4,008 | 28.262774 | 78 | py |
crnn-pytorch | crnn-pytorch-master/demo.py | import torch
from torch.autograd import Variable
import utils
import dataset
from PIL import Image
import models.crnn as crnn
import params
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model_path', type = str, required = True, help = 'crnn model path')
parser.add_argument('-i', '--i... | 1,455 | 27.54902 | 96 | py |
crnn-pytorch | crnn-pytorch-master/train.py | from __future__ import print_function
from __future__ import division
import argparse
import random
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import numpy as np
# from warpctc_pytorch import CTCLoss
from torch.nn import CTC... | 8,227 | 29.587361 | 133 | py |
crnn-pytorch | crnn-pytorch-master/models/crnn.py | import torch.nn as nn
import params
import torch.nn.functional as F
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
... | 2,865 | 30.494505 | 78 | py |
crnn-pytorch | crnn-pytorch-master/tool/convert_t7.py | import torchfile
import argparse
import torch
from torch.nn.parameter import Parameter
import numpy as np
import models.crnn as crnn
layer_map = {
'SpatialConvolution': 'Conv2d',
'SpatialBatchNormalization': 'BatchNorm2d',
'ReLU': 'ReLU',
'SpatialMaxPooling': 'MaxPool2d',
'SpatialAveragePooling': ... | 5,075 | 29.214286 | 76 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/parser.py |
import os
import torch
import argparse
def parse_arguments():
parser = argparse.ArgumentParser(description="Benchmarking Visual Geolocalization",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Training parameters
parser.add_argument("--train_batch_size", ty... | 9,823 | 70.188406 | 142 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/test.py |
import faiss
import torch
import logging
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
def test_efficient_ram_usage(args, eval_ds, model, test_method="hard_resize"):
"""This function gives the same output as test(), but uses much less... | 14,018 | 53.761719 | 121 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/commons.py |
"""
This file contains some functions and classes which can be useful in very diverse projects.
"""
import os
import sys
import torch
import random
import logging
import traceback
import numpy as np
from os.path import join
def make_deterministic(seed=0):
"""Make results deterministic. If seed == -1, do not mak... | 2,811 | 36.493333 | 91 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/util.py |
import re
import torch
import shutil
import logging
import torchscan
import numpy as np
from collections import OrderedDict
from os.path import join
from sklearn.decomposition import PCA
import datasets_ws
def get_flops(model, input_shape=(480, 640)):
"""Return the FLOPs as a string, such as '22.33 GFLOPs'"""
... | 3,201 | 40.584416 | 102 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/datasets_ws.py |
import os
import torch
import faiss
import logging
import numpy as np
from glob import glob
from tqdm import tqdm
from PIL import Image
from os.path import join
import torch.utils.data as data
import torchvision.transforms as T
from torch.utils.data.dataset import Subset
from sklearn.neighbors import NearestNeighbors
... | 23,388 | 56.750617 | 138 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/eval.py |
"""
With this script you can evaluate checkpoints or test models from two popular
landmark retrieval github repos.
The first is https://github.com/naver/deep-image-retrieval from Naver labs,
provides ResNet-50 and ResNet-101 trained with AP on Google Landmarks 18 clean.
$ python eval.py --off_the_shelf=naver --l2=none... | 5,209 | 46.363636 | 146 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/train.py |
import math
import torch
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import multiprocessing
from os.path import join
from datetime import datetime
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
import util
import test
import parser
impo... | 10,186 | 45.729358 | 133 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/aggregation.py |
import math
import torch
import faiss
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.utils.data import DataLoader, SubsetRandomSampler
import model.functional as LF
import model.normalization as normaliz... | 10,963 | 41.007663 | 132 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/network.py |
import os
import torch
import logging
import torchvision
from torch import nn
from os.path import join
from transformers import ViTModel
from google_drive_downloader import GoogleDriveDownloader as gdd
from model.cct import cct_14_7x2_384
from model.aggregation import Flatten
from model.normalization import L2Norm
im... | 9,160 | 43.687805 | 137 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/functional.py |
import math
import torch
import torch.nn.functional as F
def sare_ind(query, positive, negative):
'''all 3 inputs are supposed to be shape 1xn_features'''
dist_pos = ((query - positive)**2).sum(1)
dist_neg = ((query - negative)**2).sum(1)
dist = - torch.cat((dist_pos, dist_neg))
dist = F.log_... | 3,170 | 36.305882 | 105 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/normalization.py |
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
| 238 | 18.916667 | 48 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 768 | 24.633333 | 76 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 16,465 | 38.869249 | 135 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,385 | 30.813333 | 95 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/transformers.py | import torch
from torch.nn import Module, ModuleList, Linear, Dropout, LayerNorm, Identity, Parameter, init
import torch.nn.functional as F
from .stochastic_depth import DropPath
class Attention(Module):
"""
Obtained from timm: github.com:rwightman/pytorch-image-models
"""
def __init__(self, dim, num... | 13,211 | 38.088757 | 112 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/embedder.py | import torch.nn as nn
class Embedder(nn.Module):
def __init__(self,
word_embedding_dim=300,
vocab_size=100000,
padding_idx=1,
pretrained_weight=None,
embed_freeze=False,
*args, **kwargs):
super(Embedder, ... | 1,332 | 34.078947 | 96 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/stochastic_depth.py | # Thanks to rwightman's timm package
# github.com:rwightman/pytorch-image-models
import torch
import torch.nn as nn
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""
Obtained from: github.com:rwightman/pytorch-image-models
Drop paths (Stochastic Depth) per sample (when applied in main ... | 1,586 | 38.675 | 108 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/cct.py | from torch.hub import load_state_dict_from_url
import torch.nn as nn
import torch
import torch.nn.functional as F
from .transformers import TransformerClassifier
from .tokenizer import Tokenizer
from .helpers import pe_check
from timm.models.registry import register_model
model_urls = {
'cct_7_3x1_32':
'... | 15,794 | 42.753463 | 114 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/tokenizer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Tokenizer(nn.Module):
def __init__(self,
kernel_size, stride, padding,
pooling_kernel_size=3, pooling_stride=2, pooling_padding=1,
n_conv_layers=1,
n_input_channels=3,
... | 4,035 | 35.690909 | 95 | py |
deep-visual-geo-localization-benchmark | deep-visual-geo-localization-benchmark-main/model/cct/helpers.py | import math
import torch
import torch.nn.functional as F
def resize_pos_embed(posemb, posemb_new, num_tokens=1):
# Copied from `timm` by Ross Wightman:
# github.com/rwightman/pytorch-image-models
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/... | 1,573 | 46.69697 | 132 | py |
anomaly-seg | anomaly-seg-master/dataset.py | import os
import json
import torch
from torchvision import transforms
import numpy as np
from PIL import Image
def imresize(im, size, interp='bilinear'):
if interp == 'nearest':
resample = Image.NEAREST
elif interp == 'bilinear':
resample = Image.BILINEAR
elif interp == 'bicubic':
... | 11,901 | 39.074074 | 108 | py |
anomaly-seg | anomaly-seg-master/eval_ood.py | # System libs
import os
import time
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
# Our libs
from config import cfg
from dataset import ValDataset
from models import ModelBuilder, SegmentationModule
from uti... | 10,392 | 34.35034 | 116 | py |
fmriprep | fmriprep-master/docs/conf.py | # fmriprep documentation build configuration file, created by
# sphinx-quickstart on Mon May 9 09:04:25 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values h... | 11,942 | 32.642254 | 89 | py |
PIBConv | PIBConv-main/cnn/complexity.py | import torch
from model import *
from genotypes import *
from ptflops import get_model_complexity_info
def print_complexity(network):
macs, params = get_model_complexity_info(network, (3, 32, 32), as_strings=True,
print_per_layer_stat=True, verbose=True)
print('{:<30}... | 788 | 31.875 | 127 | py |
PIBConv | PIBConv-main/cnn/train_cpath.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import genotypes
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkADP as... | 13,664 | 43.366883 | 118 | py |
PIBConv | PIBConv-main/cnn/apply_gradcam.py | import argparse
import cv2
import numpy as np
import torch
from torchvision import models
from pytorch_grad_cam import GradCAM, \
HiResCAM, \
ScoreCAM, \
GradCAMPlusPlus, \
AblationCAM, \
XGradCAM, \
EigenCAM, \
EigenGradCAM, \
LayerCAM, \
FullGrad, \
GradCAMElementWise
from mode... | 5,853 | 34.26506 | 100 | py |
PIBConv | PIBConv-main/cnn/test_cpath.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkADP as Network
# for ADP dataset
fr... | 7,551 | 42.154286 | 141 | py |
PIBConv | PIBConv-main/cnn/train_search_rmsgd.py | from operator import index
import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = "1,2,3"
print("bruv")
import sys
import time
import glob
import utils
import logging
import argparse
import numpy as np
import pandas as pd
import pickle
import torch
import torch.nn as nn
import t... | 24,566 | 42.713523 | 153 | py |
PIBConv | PIBConv-main/cnn/architect.py | import torch
import numpy as np
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from collections import OrderedDict
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Architect(object):
def __init__(self, model, criterion, args):
gpus = [... | 11,819 | 48.456067 | 133 | py |
PIBConv | PIBConv-main/cnn/train_imagenet.py | import os
import sys
import numpy as np
import time
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torc... | 8,636 | 34.9875 | 106 | py |
PIBConv | PIBConv-main/cnn/train_search_adas.py | import os
import sys
import time
import glob
import utils
import logging
import argparse
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import pickle
import gc
from cop... | 22,044 | 43.445565 | 153 | py |
PIBConv | PIBConv-main/cnn/utils.py | import os
import numpy as np
import pandas as pd
import torch
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
from torchvision.datasets.utils import check_integrity,\
extract_archive, verify_str_arg, download_and_extract_archive
from torchvision.datasets.folder import d... | 25,313 | 33.161943 | 111 | py |
PIBConv | PIBConv-main/cnn/model.py | import torch
import torch.nn as nn
from operations import *
from torch.autograd import Variable
from utils import drop_path
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
print(C_prev_prev, C_prev, C)
i... | 10,318 | 33.627517 | 90 | py |
PIBConv | PIBConv-main/cnn/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype
class MixedOp(nn.Module):
def __init__(self, C, stride, learnable_bn):
super(MixedOp, self).__init__()
self... | 7,707 | 36.057692 | 144 | py |
PIBConv | PIBConv-main/cnn/test_cifar.py | import os
import sys
import glob
import numpy as np
import pandas as pd
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import Netwo... | 4,431 | 34.456 | 106 | py |
PIBConv | PIBConv-main/cnn/test_imagenet.py | import os
import sys
import numpy as np
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torch.autograd i... | 4,020 | 33.663793 | 104 | py |
PIBConv | PIBConv-main/cnn/train_cifar.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkCIFAR ... | 7,720 | 37.412935 | 117 | py |
PIBConv | PIBConv-main/cnn/operations.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
OPS = {
'none': lambda C, stride, affine: Zero(stride),
'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stri... | 6,930 | 37.082418 | 115 | py |
PIBConv | PIBConv-main/cnn/adas/Adas.py | """
"""
from torch.optim.optimizer import Optimizer, required
import sys
import numpy as np
import torch
mod_name = vars(sys.modules[__name__])['__name__']
if 'adas.' in mod_name:
from .metrics import Metrics
else:
from .optim.metrics import Metrics
class Adas(Optimizer):
"""
Vectorized SGD from t... | 5,240 | 34.174497 | 78 | py |
PIBConv | PIBConv-main/cnn/adas/metrics.py | """
"""
from typing import List, Union, Tuple
import sys
import numpy as np
import torch
mod_name = vars(sys.modules[__name__])['__name__']
if 'adas.' in mod_name:
from .components import LayerMetrics, ConvLayerMetrics
from .matrix_factorization import EVBMF
else:
from optim.components import LayerMetric... | 7,146 | 43.391304 | 85 | py |
PIBConv | PIBConv-main/cnn/adas/matrix_factorization.py | from __future__ import division
import numpy as np
# from scipy.sparse.linalg import svds
from scipy.optimize import minimize_scalar
import torch
def EVBMF(Y, sigma2=None, H=None):
"""Implementation of the analytical solution to Empirical Variational
Bayes Matrix Factorization.
This function can be ... | 5,693 | 30.458564 | 91 | py |
PIBConv | PIBConv-main/cnn/ADP_utils/thresholded_metrics.py | import os
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from .classesADP import classesADP
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
class Thresholded_Metrics:
def __init__(self, targets, predictions, level, network, epoch):
se... | 6,205 | 46.738462 | 146 | py |
clx-branch-23.04 | clx-branch-23.04/examples/run_dga_training.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 2,926 | 31.522222 | 119 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/tests/test_dga_detector.py | # Copyright (c) 2019, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 4,063 | 32.586777 | 97 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/tests/test_asset_classification.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 4,887 | 38.104 | 202 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/tests/test_binary_sequence_classifier.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 3,113 | 32.12766 | 96 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/tests/test_multiclass_sequence_classifier.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 3,132 | 32.329787 | 96 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/tests/test_cybert.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 5,066 | 43.447368 | 88 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/asset_classification.py | import cudf
from cuml.model_selection import train_test_split
import torch
import torch.optim as torch_optim
import torch.nn.functional as F
import logging
from torch.utils.dlpack import from_dlpack
from clx.analytics.model.tabular_model import TabularModel
log = logging.getLogger(__name__)
class AssetClassification... | 9,402 | 34.217228 | 154 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/binary_sequence_classifier.py | import logging
import cudf
from cudf.core.subword_tokenizer import SubwordTokenizer
import torch
import torch.nn as nn
from torch.utils.dlpack import to_dlpack
from clx.analytics.sequence_classifier import SequenceClassifier
from clx.utils.data.dataloader import DataLoader
from clx.utils.data.dataset import Dataset
fr... | 3,848 | 37.878788 | 256 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/sequence_classifier.py | import logging
import os
import cudf
from cudf.core.subword_tokenizer import SubwordTokenizer
import cupy
import torch
from clx.utils.data.dataloader import DataLoader
from clx.utils.data.dataset import Dataset
from torch.utils.dlpack import to_dlpack
from tqdm import trange
from torch.optim import AdamW
from abc imp... | 8,757 | 35.953586 | 256 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/cybert.py | # Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 9,636 | 36.644531 | 99 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/detector.py | import logging
import torch
import torch.nn as nn
from abc import ABC, abstractmethod
log = logging.getLogger(__name__)
GPU_COUNT = torch.cuda.device_count()
class Detector(ABC):
def __init__(self, lr=0.001):
self.lr = lr
self._model = None
self._optimizer = None
self._criterion ... | 2,728 | 25.495146 | 92 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/dga_detector.py | import cudf
import torch
import logging
from tqdm import trange
from torch.utils.dlpack import from_dlpack
from clx.utils.data import utils
from clx.analytics.detector import Detector
from clx.utils.data.dataloader import DataLoader
from clx.analytics.dga_dataset import DGADataset
from clx.analytics.model.rnn_classifie... | 10,504 | 38.197761 | 189 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/multiclass_sequence_classifier.py | import logging
import cudf
from cudf.core.subword_tokenizer import SubwordTokenizer
import cupy
import torch
import torch.nn as nn
from torch.utils.dlpack import to_dlpack
from clx.analytics.sequence_classifier import SequenceClassifier
from clx.utils.data.dataloader import DataLoader
from clx.utils.data.dataset impor... | 3,867 | 38.876289 | 256 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/model/rnn_classifier.py | # Original code at https://github.com/spro/practical-pytorch
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence
DROPOUT = 0.0
class RNNClassifier(nn.Module):
def __init__(
self, input_size, hidden_size, output_size, n_layers, bidirectional=True
):
super(RNN... | 2,067 | 31.3125 | 82 | py |
clx-branch-23.04 | clx-branch-23.04/python/clx/analytics/model/tabular_model.py | # Original code at https://github.com/spro/practical-pytorch
import torch
import torch.nn as nn
class TabularModel(nn.Module):
"Basic model for tabular data"
def __init__(self, emb_szs, n_cont, out_sz, layers, drops,
emb_drop, use_bn, is_reg, is_multi):
super().__init__()
se... | 1,858 | 38.553191 | 116 | py |
pdarts | pdarts-master/test.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from model import NetworkCIFAR as Network
parser = argparse.ArgumentParser("c... | 3,279 | 31.475248 | 100 | py |
pdarts | pdarts-master/train_imagenet.py | import os
import sys
import numpy as np
import time
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torc... | 10,818 | 38.922509 | 127 | py |
pdarts | pdarts-master/utils.py | import os
import numpy as np
import torch
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum... | 3,652 | 24.907801 | 105 | py |
pdarts | pdarts-master/model.py | import torch
import torch.nn as nn
from operations import *
from torch.autograd import Variable
from utils import drop_path
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
if reduction_prev:
self.prep... | 7,284 | 34.710784 | 95 | py |
pdarts | pdarts-master/model_search.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype
class MixedOp(nn.Module):
def __init__(self, C, stride, switch, p):
super(MixedOp, self).__ini... | 6,003 | 34.738095 | 147 | py |
pdarts | pdarts-master/train_search.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import copy
from model_search import Network
from gen... | 19,015 | 39.545842 | 215 | py |
pdarts | pdarts-master/test_imagenet.py | import os
import sys
import numpy as np
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from model import Net... | 3,334 | 31.378641 | 116 | py |
pdarts | pdarts-master/train_cifar.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkCIFAR ... | 7,688 | 39.68254 | 113 | py |
pdarts | pdarts-master/operations.py | import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect' : lambd... | 4,144 | 32.97541 | 129 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/run_exp.py | ##########################################################
# pytorch-kaldi-gan
# Walter Heymans
# North West University
# 2020
# Adapted from:
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
from __futur... | 33,246 | 35.216776 | 152 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/quaternion_neural_networks.py | ##########################################################
# Quaternion Neural Networks
# Titouan Parcollet, Xinchi Qiu, Mirco Ravanelli
# University of Oxford and Mila, University of Montreal
# May 2020
##########################################################
import torch
import torch.nn.functional as F
import torc... | 24,754 | 37.20216 | 135 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/resample_files.py | import torch
import torchaudio
import numpy as np
import matplotlib.pyplot as plt
import configparser
import os
import sys
import random
import shutil
# Reading global cfg file (first argument-mandatory file)
cfg_file = sys.argv[1]
if not (os.path.exists(cfg_file)):
sys.stderr.write("ERROR: The config file %s does... | 1,528 | 26.303571 | 107 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/core.py | ##########################################################
# pytorch-kaldi-gan
# Walter Heymans
# North West University
# 2020
# Adapted from:
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import sys
... | 22,371 | 33.793157 | 138 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/neural_networks.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from dist... | 73,602 | 34.049048 | 226 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/multistyle_training.py | from augmentation_utils import *
import configparser
import sox
import logging
logging.getLogger('sox').setLevel(logging.ERROR)
# Reading global cfg file (first argument-mandatory file)
cfg_file = sys.argv[1]
if not (os.path.exists(cfg_file)):
sys.stderr.write("ERROR: The config file %s does not exist!\n" % (cfg_... | 4,581 | 34.796875 | 111 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/utils.py | ##########################################################
# pytorch-kaldi-gan
# Walter Heymans
# North West University
# 2020
# Adapted from:
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import conf... | 110,615 | 36.598912 | 206 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/train_gan.py | ##########################################################
# pytorch-kaldi-gan
# Walter Heymans
# North West University
# 2020
##########################################################
import sys
import configparser
import os
import time
import numpy
import numpy as np
import random
import torch
import torch.nn.func... | 44,242 | 36.621599 | 191 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/gan_networks.py | import torch
import torch.nn as nn
from distutils.util import strtobool
from torch.nn.utils import spectral_norm
import math
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.... | 20,229 | 32.001631 | 111 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/tune_hyperparameters.py | #!/usr/bin/env python
##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
#
# Description:
# This scripts generates config files with the random hyperparamters specified by the user.
# python tune_hyperparame... | 3,100 | 35.916667 | 229 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/weights_and_biases.py | ##########################################################
# pytorch-kaldi-gan v.1.0
# Walter Heymans
# North West University
# 2020
##########################################################
import wandb
import yaml
import os
from sys import exit
def initialize_wandb(project, config, directory, resume, identity = ""... | 1,414 | 22.983051 | 83 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/audio_processing.py | import torch
import torchaudio
import numpy as np
import matplotlib.pyplot as plt
import configparser
import os
import sys
import random
import shutil
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as spectral_norm
# Reading global cfg file (first argument-mandatory file)
cf... | 18,066 | 35.061876 | 144 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/data_io.py | ##########################################################
# pytorch-kaldi-gan
# Walter Heymans
# North West University
# 2020
# Adapted from:
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import nump... | 55,933 | 36.767725 | 142 | py |
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