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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BraVL | BraVL-master/BraVL_fMRI/brain_image_text/networks/MLP_Image.py |
import torch
import torch.nn as nn
class EncoderImage(nn.Module):
def __init__(self, flags):
super(EncoderImage, self).__init__()
self.flags = flags;
self.hidden_dim = 2048;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m2_dim, self.hidden_dim), nn.ReLU(True))... | 1,982 | 37.134615 | 108 | py |
BraVL | BraVL-master/BraVL_fMRI/brain_image_text/networks/QNET.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class QNet(nn.Module):
def __init__(self, input_dim,latent_dim):
super(QNet, self).__init__()
self.fc1 = nn.Linear(input_dim,512)
self.fc21 = nn.Linear(512, latent_dim)
self.fc22 = nn.Linear(512, latent_dim)
def ... | 504 | 30.5625 | 46 | py |
BraVL | BraVL-master/BraVL_fMRI/brain_image_text/networks/MLP_Brain.py |
import torch
import torch.nn as nn
class EncoderBrain(nn.Module):
def __init__(self, flags):
super(EncoderBrain, self).__init__()
self.flags = flags;
self.hidden_dim = 512;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m1_dim, self.hidden_dim), nn.ReLU(True)))... | 2,002 | 35.418182 | 108 | py |
BraVL | BraVL-master/BraVL_fMRI/divergence_measures/mm_div.py |
import torch
import torch.nn as nn
from divergence_measures.kl_div import calc_kl_divergence
from divergence_measures.kl_div import calc_kl_divergence_lb_gauss_mixture
from divergence_measures.kl_div import calc_kl_divergence_ub_gauss_mixture
from divergence_measures.kl_div import calc_entropy_gauss
from utils.utils... | 5,927 | 38 | 110 | py |
BraVL | BraVL-master/BraVL_fMRI/divergence_measures/kl_div.py | import math
import torch
from utils.utils import reweight_weights
def calc_kl_divergence(mu0, logvar0, mu1=None, logvar1=None, norm_value=None):
if mu1 is None or logvar1 is None:
KLD = -0.5 * torch.sum(1 - logvar0.exp() - mu0.pow(2) + logvar0)
else:
KLD = -0.5 * (torch.sum(1 - logvar0.exp()/... | 4,561 | 40.099099 | 128 | py |
BraVL | BraVL-master/BraVL_fMRI/utils/BaseMMVae.py | from abc import ABC, abstractmethod
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.distributions as dist
from divergence_measures.mm_div import calc_alphaJSD_modalities
from divergence_measures.mm_div import calc_group_divergence_moe
from divergence_measures.mm_div impor... | 14,033 | 41.017964 | 121 | py |
BraVL | BraVL-master/BraVL_fMRI/utils/utils.py | import os
import torch
# Print iterations progress
def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required ... | 4,100 | 32.892562 | 106 | py |
BraVL | BraVL-master/BraVL_fMRI/utils/BaseFlags.py | import os
import argparse
import torch
import scipy.io as sio
parser = argparse.ArgumentParser()
# TRAINING
parser.add_argument('--batch_size', type=int, default=512, help="batch size for training")
parser.add_argument('--initial_learning_rate', type=float, default=0.0001, help="starting learning rate")
parser.add_arg... | 4,581 | 62.638889 | 129 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Neural Rendering Network/models/NeuralTexture.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from PIL import Image
from util import util
from torchvision import models
from collections import n... | 6,109 | 46 | 181 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Neural Rendering Network/models/UNET.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from PIL import Image
from util import util
from torchvision import models
from collections import n... | 17,496 | 54.370253 | 177 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Neural Rendering Network/models/VGG_LOSS.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from torchvision import models
from collections import namedtuple
class VGG16(torch.nn.Module):
... | 2,718 | 33.417722 | 97 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Neural Rendering Network/models/DynamicNeuralTextures_model.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from PIL import Image
from util import util
from torchvision import models
from collections import n... | 11,642 | 39.996479 | 186 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/test.py | import os
from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import save_images
from util import html
from util import util
from scipy.misc import imresize
import torch
import numpy as np
from PIL import Image
import time
import cv2
def... | 6,888 | 39.523529 | 163 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/train.py | import time
import copy
import torch
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
# training dataset
opt = TrainOptions().parse()
data_loader = CreateDataLoader(opt)
dat... | 4,758 | 39.675214 | 142 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/options/base_options.py | import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, ... | 8,300 | 58.719424 | 223 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/models/base_model.py | import os
import torch
import torch.nn as nn
from collections import OrderedDict
from . import networks
import numpy as np
from PIL import Image
def save_tensor_image(input_image, image_path):
if isinstance(input_image, torch.Tensor):
image_tensor = input_image.data
image_numpy = image_tensor[0].cp... | 9,458 | 41.227679 | 110 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
def get_norm_layer(norm... | 15,748 | 40.013021 | 151 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/models/audio2ExpressionsAttentionTMP4_model.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from BaselModel.basel_model import *
################
### HELPER ###
################
INVALID_U... | 16,798 | 46.860399 | 172 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/util/image_pool.py | import random
import torch
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
return images
return_images = ... | 1,072 | 31.515152 | 93 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/util/util.py | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import sys
import array
import OpenEXR
import Imath
def load_exr(image_path):
# Open the input file
file = OpenEXR.InputFile(image_path)
# Compute the size
dw = file.header()['dataWindow']
w, h = ... | 2,454 | 28.578313 | 127 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/BaselModel/basel_model.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import numpy as np
import soft_renderer as sr
N_EXPRESSIONS=76
class MorphableModel(nn.Module):
def __init__(self, filename_average=''):
super(MorphableModel, self).__init__()
print('Load Morphable Model (Ba... | 4,607 | 42.065421 | 221 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/facetmp_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
from util import util
#def make_dataset(dir):
# images = []
# assert os... | 15,143 | 37.339241 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/audio_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
def make_dataset(dir):
images = []
ids = []
assert os.path.isdir(... | 5,048 | 33.582192 | 88 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
@staticmethod
def modify_commandline_options(parser, is_trai... | 3,469 | 31.735849 | 91 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
#def make_dataset(dir):
# images = []
# assert os.path.isdir(dir), '%s ... | 19,436 | 43.580275 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_cached_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
import progressbar
#def make_dataset(dir):
# images = []
# assert os.pa... | 20,838 | 44.400871 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/audio.py | import time
import random
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchaudio
import torchaudio.transforms
import librosa
import scipy.signal
import librosa.display
import matplotlib.pyplot as plt
class Aud... | 3,047 | 38.584416 | 218 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/face_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
from util import util
#def make_dataset(dir):
# images = []
# assert os... | 14,941 | 38.013055 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/aligned_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
#def make_dataset(dir):
# images = []
# assert os.path.isdir(dir), '%s ... | 7,488 | 34.832536 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Training Code/data/__init__.py | import importlib
import torch.utils.data
from data.base_data_loader import BaseDataLoader
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
# Given the option --dataset_mode [datasetname],
# the file "data/datasetname_dataset.py"
# will be imported.
dataset_filename ... | 3,157 | 33.703297 | 168 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/transfer.py | import os
import os.path
from options.transfer_options import TransferOptions
from data import CreateDataLoader
from data.face_dataset import FaceDataset
from data.audio_dataset import AudioDataset
from models import create_model
from util.visualizer import save_images
from util import html
import torch
import torch.nn... | 9,356 | 36.729839 | 104 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/options/base_options.py | import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, ... | 8,300 | 58.719424 | 223 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/models/base_model.py | import os
import torch
import torch.nn as nn
from collections import OrderedDict
from . import networks
import numpy as np
from PIL import Image
def save_tensor_image(input_image, image_path):
if isinstance(input_image, torch.Tensor):
image_tensor = input_image.data
image_numpy = image_tensor[0].cp... | 9,458 | 41.227679 | 110 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
def get_norm_layer(norm... | 15,748 | 40.013021 | 151 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/models/audio2ExpressionsAttentionTMP4_model.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import functools
from BaselModel.basel_model import *
INVALID_UV = -1.0
from torchvision import models
from collec... | 16,974 | 47.5 | 172 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/util/image_pool.py | import random
import torch
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
return images
return_images = ... | 1,072 | 31.515152 | 93 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/util/util.py | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import sys
import array
import OpenEXR
import Imath
def load_exr(image_path):
# Open the input file
file = OpenEXR.InputFile(image_path)
# Compute the size
dw = file.header()['dataWindow']
w, h = ... | 2,454 | 28.578313 | 127 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/BaselModel/basel_model.py | import os
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import numpy as np
#########################
N_EXPRESSIONS=76 # <<<<<< NEEDS TO BE SPECIFIED ACCORDING TO THE USED FACE MODEL
#########################
#import soft_renderer as sr
#
#class MorphableModel(nn.Module):
# def __i... | 4,171 | 43.860215 | 222 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/audio_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
def make_dataset(dir):
images = []
ids = []
assert os.path.isdir(... | 5,048 | 33.582192 | 88 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
@staticmethod
def modify_commandline_options(parser, is_trai... | 3,469 | 31.735849 | 91 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/multi_face_audio_eq_tmp_cached_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
#from data.image_folder import make_dataset
from PIL import Image
import progressbar
#def make_dataset(dir):
# images = []
# assert os.pa... | 20,838 | 44.400871 | 160 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/audio.py | import time
import random
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchaudio
import torchaudio.transforms
import librosa
import scipy.signal
import librosa.display
import matplotlib.pyplot as plt
class Aud... | 3,047 | 38.584416 | 218 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/face_dataset.py | import os.path
import random
import torchvision.transforms as transforms
import torch
import numpy as np
from data.base_dataset import BaseDataset
from data.audio import Audio
from PIL import Image
from util import util
def make_dataset(dir):
images = []
ids = []
assert os.path.isdir(dir), '%s is not a val... | 7,029 | 34.505051 | 103 | py |
NeuralVoicePuppetry | NeuralVoicePuppetry-master/Audio2ExpressionNet/Inference/data/__init__.py | import importlib
import torch.utils.data
from data.base_data_loader import BaseDataLoader
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
# Given the option --dataset_mode [datasetname],
# the file "data/datasetname_dataset.py"
# will be imported.
dataset_filename ... | 3,157 | 33.703297 | 168 | py |
z2fsl | z2fsl-main/setup.py | '''Setup script
Usage: pip install .
To install development dependencies too, run: pip install .[dev]
'''
from setuptools import setup, find_packages
setup(
name='z2fsl',
version='v1',
packages=find_packages(),
url = 'https://github.com/gchochla/z2fsl',
author='Georgios Chochlakis',
scripts=[],... | 495 | 19.666667 | 64 | py |
z2fsl | z2fsl-main/z2fsl/z2fsl_vaegan.py | """Z2FSL(VAEGAN, PN) trainer class and script."""
import os
import argparse
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import ParameterGrid
from z2fsl.modules.classifiers import PrototypicalNet
from z2fsl.utils.losses import EpisodeCrossEntr... | 57,772 | 36.248872 | 100 | py |
z2fsl | z2fsl-main/z2fsl/modules/feature_extractors.py | """Pre-trained visual feature extractors."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import (
googlenet,
inception_v3,
resnet18,
resnet34,
resnet50,
resnet101,
resnet152,
vgg16_bn,
vgg11_bn,
vgg13_bn,
vgg19_bn,
)
class Im... | 7,014 | 27.40081 | 89 | py |
z2fsl | z2fsl-main/z2fsl/modules/classifiers.py | """Supervised Classifiers."""
import torch
import torch.nn as nn
from z2fsl.utils.submodules import MLP
class PrototypicalNet(nn.Module):
"""Classifies examples based on distance metric
from class prototypes. FSL setting.
Attributes:
mapper (`nn.Module`): mapper from feature to
embe... | 5,942 | 34.586826 | 90 | py |
z2fsl | z2fsl-main/z2fsl/pretraining/fsl_diagonal.py | """Prototypical Net training."""
import os
import argparse
import torch
import torch.optim as optim
import torch.nn as nn
from sklearn.model_selection import ParameterGrid
from z2fsl.utils.losses import EpisodeCrossEntropyLoss
from z2fsl.modules.classifiers import PrototypicalNet
from z2fsl.utils.datasets import Fea... | 16,610 | 34.267516 | 100 | py |
z2fsl | z2fsl-main/z2fsl/utils/losses.py | """Losses."""
import torch
import torch.nn as nn
import torch.autograd as autograd
class EpisodeCrossEntropyLoss(nn.Module):
"""FSL episode classification loss.
Attributes:
criterion (`nn.Module`): module that computes loss.
reduction (`str`): how to reduce vector results.
"""
def _... | 3,204 | 28.953271 | 99 | py |
z2fsl | z2fsl-main/z2fsl/utils/datasets.py | """Dataset."""
import os
import torch
from z2fsl.utils.conf import TRAIN_SPLIT, TEST_SPLIT
from z2fsl.utils.general import configuration_filename, slice_fn, dataset_name
class FeatureEpisodeFactory:
"""Factory of episodes/minibatches in a ZSL setting
with extracted features instead of images.
Attribut... | 8,594 | 39.352113 | 100 | py |
z2fsl | z2fsl-main/z2fsl/utils/submodules.py | """Various major components."""
import torch
import torch.nn as nn
class MLP(nn.Module):
"""Simple MLP.
Attributes:
layers (`nn.Module`): sequence of layers.
"""
def __init__(
self,
in_features,
out_features,
hidden_layers=None,
dropout=0,
hid... | 7,552 | 29.954918 | 91 | py |
z2fsl | z2fsl-main/z2fsl/utils/general.py | """General utilities."""
import argparse
import os
import random
import numpy as np
import torch
def str2bool(arg):
"""CL bool arguments to bools"""
if arg.lower() == 'true':
return True
if arg.lower() == 'false':
return False
raise argparse.ArgumentTypeError('Boolean value expected.... | 3,130 | 23.653543 | 89 | py |
FSAD-Net | FSAD-Net-master/main.py | import torch
from models import *
from torch.utils.data import DataLoader
from Recorder import Recorder
from tqdm import tqdm
from pathlib import Path
import torch.nn.init as init
import os
import torchvision
from complex_2d_my_data_loader import MyDataLoader
import numpy
from sampler import BalancedBatchSampler
recor... | 6,705 | 35.053763 | 151 | py |
FSAD-Net | FSAD-Net-master/sampler.py | import torch
is_torchvision_installed = True
try:
import torchvision
except:
is_torchvision_installed = False
import torch.utils.data
import random
class BalancedBatchSampler(torch.utils.data.sampler.Sampler):
def __init__(self, dataset, labels=None):
self.labels = labels
self.dataset = dic... | 2,195 | 40.433962 | 109 | py |
FSAD-Net | FSAD-Net-master/Helper.py | from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy
import os
import torchvision
from sklearn.metrics import roc_curve, auc
def poly_lr_scheduler(my_optimizer, init_lr, epoch,
lr_decay_iter=1,
max_iter=100,
... | 5,905 | 32.556818 | 89 | py |
FSAD-Net | FSAD-Net-master/Recorder.py | import os
import numpy as np
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from IPython import display
from matplotlib import pyplot as plt
import torch
'''
TensorBoard Data will be stored in './runs' path
'''
class Recorder:
def __init__(self, model_name, data_name):
sel... | 3,894 | 32.008475 | 119 | py |
FSAD-Net | FSAD-Net-master/models.py | from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn as nn
class Encoder(nn.Module):
def __init__(self, rep_dim=64):
super().__init__()
self.rep_dim = rep_dim
self.c0 = nn.Conv2d(3, 64, kernel_size=4, stride=1)
self.c1 =... | 7,387 | 28.31746 | 159 | py |
FSAD-Net | FSAD-Net-master/complex_2d_my_data_loader.py | import os
import torch.utils.data
from PIL import Image
class MyDataLoader(torch.utils.data.Dataset):
# constructor of the class
def __init__(self, normal_path, abnormal_path, test_path, transform=None, train=False, validate=False, test=False):
assert (train is True and test is False and validate is ... | 3,082 | 40.662162 | 119 | py |
recsim | recsim-master/recsim/agents/dopamine/dqn_agent.py | # coding=utf-8
# coding=utf-8
# Copyright 2019 The RecSim Authors.
#
# 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 ap... | 7,883 | 38.42 | 80 | py |
MCGR | MCGR-main/eval_ensemble.py | import argparse
import numpy as np
import os
import sys
import torch
import torch.nn.functional as F
import data
import models
import utils
parser = argparse.ArgumentParser(description='Ensemble evaluation')
parser.add_argument('--dataset', type=str, default='CIFAR10', metavar='DATASET',
help='da... | 2,287 | 33.149254 | 90 | py |
MCGR | MCGR-main/fge.py | import argparse
import numpy as np
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import data
import models
import utils
parser = argparse.ArgumentParser(description='FGE training')
parser.add_argument('--dir', type=str, default='/tmp/fge/', metavar='DIR',
... | 4,796 | 36.476563 | 99 | py |
MCGR | MCGR-main/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='/tmp/eval', metavar='DIR',
... | 6,194 | 31.952128 | 100 | py |
MCGR | MCGR-main/test.py | import argparse
import torch
import curves
import d
import models
import attack.pgd as pgd
import attack.pgd2 as pgd2
from tqdm import tqdm
from attack.autopgd_train import apgd_train,pgd_1
from attack.att import msd_v1,msd_v0,l1_dir_topk,pgd_l1_topk
parser = argparse.ArgumentParser(description='DNN curve training')
... | 5,523 | 35.826667 | 131 | py |
MCGR | MCGR-main/curve_test.py | import torch
import argparse
import numpy as np
import os
import tabulate
import pgdtest2
import models
import curves
import utils
import data
import csv
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cost = torch.nn.CrossEntropyLoss()
parser = argparse.Arg... | 4,043 | 34.787611 | 131 | py |
MCGR | MCGR-main/d.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import random_split
class Data:
def __init__(self):
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
... | 755 | 36.8 | 82 | py |
MCGR | MCGR-main/other_utils.py | import os
import torch
class Logger():
def __init__(self, log_path):
self.log_path = log_path
def log(self, str_to_log):
print(str_to_log)
if not self.log_path is None:
with open(self.log_path, 'a') as f:
f.write(str_to_log + '\n')
f.... | 1,284 | 25.22449 | 102 | py |
MCGR | MCGR-main/train_.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='/tmp/curve/', metavar='DIR',
... | 7,504 | 37.096447 | 106 | py |
MCGR | MCGR-main/utils.py | import numpy as np
import os
import torch
import curves
import attack.pgd as pgd
import attack.pgd2 as pgd2
from attack.att import *
from attack.autopgd_train import apgd_train,pgd_1
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 += t... | 6,292 | 29.697561 | 140 | py |
MCGR | MCGR-main/merge.py | import torch
import argparse
import numpy as np
import os
import tabulate
import models
import curves
import utils
import data
import csv
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cost = torch.nn.CrossEntropyLoss()
parser = argparse.ArgumentParser(desc... | 4,198 | 32.592 | 110 | py |
MCGR | MCGR-main/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
class Transforms:
class CIFAR10:
class VGG:
train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.... | 2,753 | 33.425 | 100 | py |
MCGR | MCGR-main/pgdtest.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import random_split
import attack.pgd as pgd
import attack.pgd2 as pgd2
from attack.att import *
from tqdm import tqdm
class PGDTest():
def __init__(self,dataset):
if dataset=='CIFAR100':
self.data_train = datasets.... | 4,567 | 50.909091 | 125 | py |
MCGR | MCGR-main/connect.py | import argparse
import numpy as np
import os
import sys
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import utils
parser = argparse.ArgumentParser(description='Connect models with polychain')
parser.add_argument('--dir', type=str, default='/tmp/chain/', metavar='DIR',
... | 4,742 | 31.486301 | 98 | py |
MCGR | MCGR-main/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,379 | 37.566978 | 100 | py |
MCGR | MCGR-main/eval_curve_pgd.py | import torch
import argparse
import numpy as np
import os
import tabulate
import pgdtest
import models
import curves
import utils
import data
import csv
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cost = torch.nn.CrossEntropyLoss()
parser = argparse.Argu... | 4,961 | 38.070866 | 181 | py |
MCGR | MCGR-main/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='/tmp/... | 8,033 | 39.989796 | 128 | py |
MCGR | MCGR-main/attack/pgd.py | import torch
import torch.nn as nn
class PGD:
def __init__(self, eps=8 / 255., step_size=2 / 255., max_iter=10, random_init=True,
targeted=False, loss_fn=nn.CrossEntropyLoss(), batch_size=64):
self.eps = eps
self.step_size = step_size
self.max_iter = max_iter
self.... | 2,353 | 38.233333 | 109 | py |
MCGR | MCGR-main/attack/att.py | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader, TensorDataset
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch
#import ipdb
import random
def norms_l0(Z):
... | 22,195 | 40.027726 | 219 | py |
MCGR | MCGR-main/attack/slide.py | """
Implementation of attack methods. Running this file as a program will
apply the attack to the model specified by the config file and store
the examples in an .npy file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
f... | 13,090 | 40.55873 | 117 | py |
MCGR | MCGR-main/attack/autopgd_train.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import random
#from autopgd_base import L1_projection
from other_utils import L1_norm, L2_norm, L0_norm
def L1_projection(x2, y2, eps1):
'''
x2: center of the L1 ball (bs x input_dim)
y2: current perturbation (x2 + y2 is the p... | 13,887 | 35.643799 | 108 | py |
MCGR | MCGR-main/attack/pgd2.py | import torch
import torch.nn as nn
class PGD:
def __init__(self, eps=1.0, step_size=0.2, max_iter=10, random_init=True,
targeted=False, loss_fn=nn.CrossEntropyLoss(), batch_size=64,eps_for_division=1e-10):
self.eps = eps
self.step_size = step_size
self.max_iter = max_iter
... | 3,000 | 39.013333 | 109 | py |
MCGR | MCGR-main/models/preresnet.py | """
PreResNet model definition
ported from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/preresnet.py
"""
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out... | 10,221 | 32.080906 | 103 | py |
MCGR | MCGR-main/models/vgg.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
... | 5,029 | 29.670732 | 100 | py |
MCGR | MCGR-main/models/wide_resnet.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes... | 6,272 | 36.562874 | 100 | py |
MCGR | MCGR-main/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
Conformer-RLpatching | Conformer-RLpatching-main/RLpatching/main_agent_traing.py | from ast import arg
from Re_buffer import *
import os
import critic_q
import argparse
from critic_q import Critic
import numpy as np
import torch
import torch.optim as optim
import torch.nn.functional as F
from numpy import *
from actor_pre_training.actor_pre_training import Agent_Supervised_Actor as Model_p
from criti... | 30,268 | 46.742902 | 270 | py |
Conformer-RLpatching | Conformer-RLpatching-main/RLpatching/critic_pre_training/critic_pre_training.py | import csv
import random
import torch.utils.data as Data
import numpy
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from collections import OrderedDict
from torch import optim
from torch.autograd import Variable
import os
import sys
sys.path.append(r'~/lixinhang/RLpatching/')
BA... | 17,230 | 39.735225 | 285 | py |
Conformer-RLpatching | Conformer-RLpatching-main/RLpatching/actor_pre_training/actor_pre_training.py | import csv
import random
import torch.utils.data as Data
import numpy
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from collections import OrderedDict
from torch import optim
from torch.autograd import Variable
import os
import sys
sys.path.append(r'~/lixinhang/RLpatching/')
BA... | 15,929 | 38.626866 | 275 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/Conformer.py | import argparse
import math
import numpy as np
import torch
import exp.exp_informer as EI
from utils.metrics import metric,MSE,RMSE
parser = argparse.ArgumentParser(description='[Informer] Long Sequences Forecasting')
parser.add_argument('--model', type=str, required=False, default='Informer',help='model of experim... | 9,348 | 46.94359 | 237 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/models/embed.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_mode... | 4,135 | 36.944954 | 202 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/models/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.masking import TriangularCausalMask, ProbMask
from models.encoder import Encoder, EncoderLayer, ConvLayer, EncoderStack
from models.decoder import Decoder, DecoderLayer
from models.attn import FullAttention, ProbAttention, AttentionLayer
fro... | 7,066 | 43.446541 | 122 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/models/encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvLayer(nn.Module):
def __init__(self, c_in):
super(ConvLayer, self).__init__()
padding = 1 if torch.__version__>='1.5.0' else 2
self.downConv = nn.Conv1d(in_channels=c_in,
out_chann... | 3,555 | 34.919192 | 90 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/models/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class DecoderLayer(nn.Module):
def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
dropout=0.1, activation="relu"):
super(DecoderLayer, self).__init__()
d_ff = d_ff or 4*d_model
self.self... | 1,772 | 33.764706 | 85 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/models/attn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from math import sqrt
from utils.masking import TriangularCausalMask, ProbMask
class FullAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
sup... | 6,179 | 36.682927 | 108 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/utils/tools.py | import numpy as np
import torch
import pandas as pd
import os
def adjust_learning_rate(optimizer, epoch, args):
# lr = args.learning_rate * (0.2 ** (epoch // 2))
if args.lradj=='type1':
lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch-1) // 1))}
elif args.lradj=='type2':
lr_adjust =... | 3,260 | 36.056818 | 112 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/utils/masking.py | import torch
class TriangularCausalMask():
def __init__(self, B, L, device="cpu"):
mask_shape = [B, 1, L, L]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
@property
def mask(self):
return self._mask
class... | 851 | 34.5 | 100 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/data/data_loader.py | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
# from sklearn.preprocessing import StandardScaler
from utils.tools import StandardScaler
from utils.timefeatures import time_features
import warnings
warnings.filterwarnings('ignore')
class Dataset_ETT_ho... | 13,665 | 34.496104 | 127 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/exp/exp_informer.py | from data.data_loader import Dataset_ETT_hour, Dataset_ETT_minute, Dataset_Custom, Dataset_Pred
from exp.exp_basic import Exp_Basic
from models.model import Informer, InformerStack
from utils.metrics import MSE
from utils.tools import EarlyStopping, adjust_learning_rate
from utils.metrics import metric
from utils.tools... | 13,055 | 37.627219 | 112 | py |
Conformer-RLpatching | Conformer-RLpatching-main/Conformer/exp/exp_basic.py | import os
import torch
import numpy as np
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.device = self._acquire_device()
self.model = self._build_model().to(self.device)
def _build_model(self):
raise NotImplementedError
return None
def... | 875 | 23.333333 | 121 | py |
DiffMIC | DiffMIC-main/main.py | import argparse
import traceback
import shutil
import logging
import yaml
import sys
import os
import time
import torch
import numpy as np
import random
torch.set_printoptions(sci_mode=False)
parser = argparse.ArgumentParser(description=globals()["__doc__"])
parser.add_argument(
"--config", type=str, required=Tr... | 10,762 | 31.914373 | 115 | py |
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