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model-sanitization | model-sanitization-master/backdoor/prune_svhn/main_B.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import models
from compute_flops import print... | 8,464 | 43.552632 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/compute_flops.py |
import numpy as np
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
def print_model_param_nums(model=None, multiply_adds=True):
if model == None:
model = torchvision.models.alexnet()
total = sum([param.nelement() for param in model.parameters()])
print(' ... | 3,980 | 33.921053 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/res110prune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 7,297 | 37.209424 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/main_finetune.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torchvision
import models
import utils... | 6,367 | 37.829268 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/models/resnet.py | from __future__ import absolute_import
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from torch.autograd import Variable
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes... | 4,156 | 29.123188 | 87 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/models/vgg.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
__all__ = ['vgg']
defaultcfg = {
11 : [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
13 : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
16 : [64, 64, 'M', 128, 128, 'M', 256, 256, 256... | 2,599 | 31.5 | 108 | py |
model-sanitization | model-sanitization-master/Hessian/evalacc.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from tqdm import tqdm
from models.vgg import *
from models.c1 import *
from models.convfc import... | 7,114 | 31.340909 | 108 | py |
model-sanitization | model-sanitization-master/Hessian/train_resnet.py | from __future__ import print_function
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from models.resnet import resnet
from utils import *
import hessianflo... | 5,085 | 38.123077 | 129 | py |
model-sanitization | model-sanitization-master/Hessian/hessian_eig_driver.py | from __future__ import print_function
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from utils import *
from models.c... | 3,697 | 37.123711 | 138 | py |
model-sanitization | model-sanitization-master/Hessian/utils.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import os
def getData(name = 'cifar10', train_bs = 128, test_bs = 1000):
if name == 'svhn':
train_loader = ... | 4,650 | 40.159292 | 141 | py |
model-sanitization | model-sanitization-master/Hessian/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(
... | 12,321 | 37.626959 | 100 | py |
model-sanitization | model-sanitization-master/Hessian/train.py |
from __future__ import print_function
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from models.c1 import *
from models.resnet import *
from models.vgg im... | 4,039 | 36.757009 | 156 | py |
model-sanitization | model-sanitization-master/Hessian/models/c1.py |
from __future__ import print_function
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
class c1_model(nn.Module):
def __init__(self, num_classes=1... | 2,119 | 31.615385 | 95 | py |
model-sanitization | model-sanitization-master/Hessian/models/resnet.py | from __future__ import absolute_import
import torch.nn as nn
import math
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.... | 4,537 | 28.467532 | 77 | py |
model-sanitization | model-sanitization-master/Hessian/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]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(conf... | 4,937 | 29.481481 | 100 | py |
model-sanitization | model-sanitization-master/Hessian/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 |
model-sanitization | model-sanitization-master/Hessian/hessianflow/eigen.py | import torch
import math
from torch.autograd import Variable
import numpy as np
from .utils import *
def get_eigen(model, inputs, targets, criterion, cuda = True, maxIter = 50, tol = 1e-3):
"""
compute the top eigenvalues of model parameters and
the corresponding eigenvectors.
"""
if cuda:
... | 10,771 | 33.636656 | 112 | py |
model-sanitization | model-sanitization-master/Hessian/hessianflow/utils.py |
import torch
import math
from torch.autograd import Variable
import numpy as np
def group_product(xs, ys):
"""
the inner product of two lists of variables xs,ys
:param xs:
:param ys:
:return:
"""
return sum([torch.sum(x * y) for (x, y) in zip(xs, ys)])
def group_add(params, update, alph... | 2,220 | 21.663265 | 103 | py |
model-sanitization | model-sanitization-master/Hessian/hessianflow/optimizer/absa.py |
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from .progressbar import progress_bar
from .optm_utils import fgsm, exp_lr_scheduler... | 7,080 | 35.127551 | 138 | py |
model-sanitization | model-sanitization-master/Hessian/hessianflow/optimizer/optm_utils.py |
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from .progressbar import progress_bar
def fgsm(model, data, target, eps, cuda = True... | 1,796 | 27.983871 | 79 | py |
model-sanitization | model-sanitization-master/Hessian/hessianflow/optimizer/baseline.py | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from .progressbar import progress_bar
from .optm_utils import exp_lr_scheduler, test
... | 2,352 | 34.651515 | 107 | py |
textClassifier | textClassifier-master/textClassifierRNN.py | # author - Richard Liao
# Dec 26 2016
import numpy as np
import pandas as pd
import cPickle
from collections import defaultdict
import re
from bs4 import BeautifulSoup
import sys
import os
os.environ['KERAS_BACKEND']='theano'
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pa... | 5,840 | 31.631285 | 98 | py |
textClassifier | textClassifier-master/textClassifierHATT.py | # author - Richard Liao
# Dec 26 2016
import numpy as np
import pandas as pd
import cPickle
from collections import defaultdict
import re
from bs4 import BeautifulSoup
import sys
import os
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
from keras.preprocessing.sequence import pad_sequences
fr... | 6,416 | 31.246231 | 115 | py |
textClassifier | textClassifier-master/textClassifierConv.py | import numpy as np
import pandas as pd
import pickle
from collections import defaultdict
import re
from bs4 import BeautifulSoup
import sys
import os
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
from keras.layer... | 5,469 | 31.951807 | 90 | py |
cvdn | cvdn-master/scripts/precompute_img_features.py | #!/usr/bin/env python
''' Script to precompute image features using a Caffe ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import numpy as np
import cv2
import json
import math
import base64
import csv
import sy... | 5,485 | 32.656442 | 120 | py |
cvdn | cvdn-master/tasks/NDH/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
attention methods) and a d... | 5,708 | 38.923077 | 100 | py |
cvdn | cvdn-master/tasks/NDH/agent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from util... | 12,142 | 36.594427 | 114 | py |
cvdn | cvdn-master/tasks/NDH/train.py | import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import os
import time
import numpy as np
import pandas as pd
from collections import defaultdict
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,ti... | 12,106 | 45.926357 | 133 | py |
cvdn | cvdn-master/tasks/NDH_NavHistConcat/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import numpy as np
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
attenti... | 9,969 | 41.067511 | 109 | py |
cvdn | cvdn-master/tasks/NDH_NavHistConcat/agent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from util... | 13,968 | 37.482094 | 119 | py |
cvdn | cvdn-master/tasks/NDH_NavHistConcat/train.py | import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import os
import time
import numpy as np
import pandas as pd
from collections import defaultdict
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,ti... | 12,041 | 45.315385 | 133 | py |
CosmoGraphNet | CosmoGraphNet-master/main.py | #----------------------------------------------------
# Main routine for training and testing GNN models
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------
import time, datetime, psutil
from Source.metalayer import *
from Source.training import *
from Source.pl... | 3,217 | 31.18 | 109 | py |
CosmoGraphNet | CosmoGraphNet-master/ps_test.py | #----------------------------------------------------
# Compute the power spectrum of different point distirbutions with the GNN trained in CAMELS
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------
import time, datetime
from Source.metalayer import *
from Sourc... | 9,026 | 29.393939 | 125 | py |
CosmoGraphNet | CosmoGraphNet-master/hyperparams_optimization.py | #----------------------------------------------------------------------
# Script for optimizing the hyperparameters of the network using optuna
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------------------------
import optuna
from main import *
from optuna.vis... | 4,243 | 34.07438 | 129 | py |
CosmoGraphNet | CosmoGraphNet-master/visualize_graphs.py | #----------------------------------------------------------------------
# Script to visualize galaxy catalogues as graphs
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------------------------
import time, datetime
from Source.plotting import *
from Source.load_d... | 4,525 | 31.561151 | 113 | py |
CosmoGraphNet | CosmoGraphNet-master/Source/constants.py | #----------------------------------------------------------------------
# List of constants and some common functions
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------------------------
import numpy as np
import torch
import os
import random
# Random seeds
to... | 1,121 | 21 | 71 | py |
CosmoGraphNet | CosmoGraphNet-master/Source/training.py | #----------------------------------------------------------------------
# Routines for training and testing the GNNs
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------------------------
from Source.constants import *
# Training step
def train(loader, model, h... | 5,629 | 42.984375 | 163 | py |
CosmoGraphNet | CosmoGraphNet-master/Source/metalayer.py | #----------------------------------------------------
# Graph Neural Network architecture
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------
import torch
import torch.nn.functional as F
from torch_cluster import knn_graph, radius_graph
from torch.nn import Sequ... | 7,375 | 34.980488 | 122 | py |
CosmoGraphNet | CosmoGraphNet-master/Source/load_data.py | #----------------------------------------------------
# Routine for loading the CAMELS galaxy catalogues
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------
import h5py
from torch_geometric.data import Data, DataLoader
from Source.constants import *
from Source.... | 9,832 | 35.018315 | 169 | py |
JoJoGAN | JoJoGAN-main/model.py | import math
import random
import functools
import operator
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from op import conv2d_gradfix
if torch.cuda.is_available():
from op.fused_act import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn2d import u... | 18,279 | 26.242921 | 100 | py |
JoJoGAN | JoJoGAN-main/e4e_projection.py | import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
from argparse import Namespace
from e4e.models.psp import pSp
from util import *
@ torch.no_grad()
def projection(img, name, device='cuda'):
model_path = 'models/e4e_ffhq_encode.pt'
ckpt = to... | 980 | 26.25 | 73 | py |
JoJoGAN | JoJoGAN-main/util.py | from matplotlib import pyplot as plt
import torch
import torch.nn.functional as F
import os
import cv2
import dlib
from PIL import Image
import numpy as np
import math
import torchvision
import scipy
import scipy.ndimage
import torchvision.transforms as transforms
google_drive_paths = {
"models/stylegan2-ffhq-conf... | 8,678 | 39.180556 | 123 | py |
JoJoGAN | JoJoGAN-main/predict.py | # Prediction interface for Cog ⚙️
# Reference: https://github.com/replicate/cog/blob/main/docs/python.md
import os
import tempfile
from copy import deepcopy
from pathlib import Path
import cog
import numpy as np
import torch
from PIL import Image
from torch import optim
from torch.nn import functional as F
from torch... | 6,805 | 31.255924 | 119 | py |
JoJoGAN | JoJoGAN-main/op/conv2d_gradfix.py | import contextlib
import warnings
import torch
from torch import autograd
from torch.nn import functional as F
enabled = True
weight_gradients_disabled = False
@contextlib.contextmanager
def no_weight_gradients():
global weight_gradients_disabled
old = weight_gradients_disabled
weight_gradients_disable... | 6,394 | 27.048246 | 117 | py |
JoJoGAN | JoJoGAN-main/op/upfirdn2d.py | import os
import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
'upfirdn2d',
sources=[
os.path.join(module_path, 'upfirdn2d.cpp'),
os.path.join(module_path, 'upfirdn2d_kernel.cu'),
],
)
cl... | 5,186 | 26.590426 | 108 | py |
JoJoGAN | JoJoGAN-main/op/fused_act_cpu.py | import os
import torch
from torch import nn
from torch.autograd import Function
from torch.nn import functional as F
module_path = os.path.dirname(__file__)
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
super().__init__()
self.bias = nn.Param... | 1,135 | 26.047619 | 86 | py |
JoJoGAN | JoJoGAN-main/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.joi... | 3,277 | 24.609375 | 86 | py |
JoJoGAN | JoJoGAN-main/op/upfirdn2d_cpu.py | import os
import torch
from torch.autograd import Function
from torch.nn import functional as F
module_path = os.path.dirname(__file__)
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
out = upfirdn2d_native(
input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
)
return out... | 1,695 | 26.803279 | 83 | py |
JoJoGAN | JoJoGAN-main/e4e/training/coach.py | import os
import random
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
import torch
from torch import nn, autograd
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
from utils import common, train_utils
from criteria imp... | 19,841 | 44.30137 | 120 | py |
JoJoGAN | JoJoGAN-main/e4e/training/ranger.py | # Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
# Ranger has now been used to capture 12 records on the FastAI leaderboard.
... | 5,899 | 34.97561 | 169 | py |
JoJoGAN | JoJoGAN-main/e4e/models/psp.py | import matplotlib
matplotlib.use('Agg')
import torch
from torch import nn
from e4e.models.encoders import psp_encoders
from e4e.models.stylegan2.model import Generator
from e4e.configs.paths_config import model_paths
def get_keys(d, name):
if 'state_dict' in d:
d = d['state_dict']
d_filt = {k[len(nam... | 4,003 | 39.04 | 109 | py |
JoJoGAN | JoJoGAN-main/e4e/models/discriminator.py | from torch import nn
class LatentCodesDiscriminator(nn.Module):
def __init__(self, style_dim, n_mlp):
super().__init__()
self.style_dim = style_dim
layers = []
for i in range(n_mlp-1):
layers.append(
nn.Linear(style_dim, style_dim)
)
... | 496 | 22.666667 | 47 | py |
JoJoGAN | JoJoGAN-main/e4e/models/latent_codes_pool.py | import random
import torch
class LatentCodesPool:
"""This class implements latent codes buffer that stores previously generated w latent codes.
This buffer enables us to update discriminators using a history of generated w's
rather than the ones produced by the latest encoder.
"""
def __init__(se... | 2,349 | 40.964286 | 141 | py |
JoJoGAN | JoJoGAN-main/e4e/models/stylegan2/model.py | import math
import random
import torch
from torch import nn
from torch.nn import functional as F
if torch.cuda.is_available():
from op.fused_act import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn2d import upfirdn2d
else:
from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn... | 18,729 | 26.584683 | 100 | py |
JoJoGAN | JoJoGAN-main/e4e/models/stylegan2/op/upfirdn2d.py | import os
import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
'upfirdn2d',
sources=[
os.path.join(module_path, 'upfirdn2d.cpp'),
os.path.join(module_path, 'upfirdn2d_kernel.cu'),
],
)
cla... | 5,203 | 27.12973 | 108 | py |
JoJoGAN | JoJoGAN-main/e4e/models/stylegan2/op/fused_act.py | import os
import torch
from torch import nn
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
'fused',
sources=[
os.path.join(module_path, 'fused_bias_act.cpp'),
os.path.join(module_path, 'fused_bias_act_kernel.... | 2,378 | 26.662791 | 83 | py |
JoJoGAN | JoJoGAN-main/e4e/models/encoders/psp_encoders.py | from enum import Enum
import math
import numpy as np
import torch
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from e4e.models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add
from e4e.models.stylegan2.model import EqualLinear
class Progre... | 7,188 | 34.766169 | 98 | py |
JoJoGAN | JoJoGAN-main/e4e/models/encoders/model_irse.py | from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
from e4e.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbon... | 3,245 | 37.188235 | 101 | py |
JoJoGAN | JoJoGAN-main/e4e/models/encoders/helpers.py | from collections import namedtuple
import torch
import torch.nn.functional as F
from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
"""
ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Flatten(Module):
def... | 4,882 | 33.631206 | 112 | py |
JoJoGAN | JoJoGAN-main/e4e/scripts/inference.py | import argparse
import torch
import numpy as np
import sys
import os
import dlib
sys.path.append(".")
sys.path.append("..")
from configs import data_configs, paths_config
from datasets.inference_dataset import InferenceDataset
from torch.utils.data import DataLoader
from utils.model_utils import setup_model
from uti... | 4,919 | 35.716418 | 117 | py |
JoJoGAN | JoJoGAN-main/e4e/scripts/calc_losses_on_images.py | from argparse import ArgumentParser
import os
import json
import sys
from tqdm import tqdm
import numpy as np
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
sys.path.append(".")
sys.path.append("..")
from criteria.lpips.lpips import LPIPS
from datasets.gt_res_dataset ... | 2,904 | 32.011364 | 92 | py |
JoJoGAN | JoJoGAN-main/e4e/scripts/train.py | """
This file runs the main training/val loop
"""
import os
import json
import math
import sys
import pprint
import torch
from argparse import Namespace
sys.path.append(".")
sys.path.append("..")
from options.train_options import TrainOptions
from training.coach import Coach
def main():
opts = TrainOptions().parse... | 2,504 | 27.146067 | 95 | py |
JoJoGAN | JoJoGAN-main/e4e/metrics/LEC.py | import sys
import argparse
import torch
import numpy as np
from torch.utils.data import DataLoader
sys.path.append(".")
sys.path.append("..")
from configs import data_configs
from datasets.images_dataset import ImagesDataset
from utils.model_utils import setup_model
class LEC:
def __init__(self, net, is_cars=Fa... | 5,528 | 39.955556 | 120 | py |
JoJoGAN | JoJoGAN-main/e4e/datasets/gt_res_dataset.py | #!/usr/bin/python
# encoding: utf-8
import os
from torch.utils.data import Dataset
from PIL import Image
import torch
class GTResDataset(Dataset):
def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None):
self.pairs = []
for f in os.listdir(root_path):
image_path = os.path.join(root_pa... | 890 | 26 | 82 | py |
JoJoGAN | JoJoGAN-main/e4e/datasets/inference_dataset.py | from torch.utils.data import Dataset
from PIL import Image
from utils import data_utils
class InferenceDataset(Dataset):
def __init__(self, root, opts, transform=None, preprocess=None):
self.paths = sorted(data_utils.make_dataset(root))
self.transform = transform
self.preprocess = preprocess
self.opts = opt... | 639 | 23.615385 | 65 | py |
JoJoGAN | JoJoGAN-main/e4e/datasets/images_dataset.py | from torch.utils.data import Dataset
from PIL import Image
from utils import data_utils
class ImagesDataset(Dataset):
def __init__(self, source_root, target_root, opts, target_transform=None, source_transform=None):
self.source_paths = sorted(data_utils.make_dataset(source_root))
self.target_paths = sorted(data... | 928 | 26.323529 | 98 | py |
JoJoGAN | JoJoGAN-main/e4e/configs/transforms_config.py | from abc import abstractmethod
import torchvision.transforms as transforms
class TransformsConfig(object):
def __init__(self, opts):
self.opts = opts
@abstractmethod
def get_transforms(self):
pass
class EncodeTransforms(TransformsConfig):
def __init__(self, opts):
super(EncodeTransforms, self).__init__... | 1,766 | 27.047619 | 61 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/moco_loss.py | import torch
from torch import nn
import torch.nn.functional as F
from configs.paths_config import model_paths
class MocoLoss(nn.Module):
def __init__(self, opts):
super(MocoLoss, self).__init__()
print("Loading MOCO model from path: {}".format(model_paths["moco"]))
self.model = self.__l... | 2,705 | 36.583333 | 92 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/id_loss.py | import torch
from torch import nn
from configs.paths_config import model_paths
from models.encoders.model_irse import Backbone
class IDLoss(nn.Module):
def __init__(self):
super(IDLoss, self).__init__()
print('Loading ResNet ArcFace')
self.facenet = Backbone(input_size=112, num_layers=50, ... | 1,805 | 36.625 | 92 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/w_norm.py | import torch
from torch import nn
class WNormLoss(nn.Module):
def __init__(self, start_from_latent_avg=True):
super(WNormLoss, self).__init__()
self.start_from_latent_avg = start_from_latent_avg
def forward(self, latent, latent_avg=None):
if self.start_from_latent_avg:
latent = latent - latent_avg
retu... | 379 | 24.333333 | 64 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/lpips/lpips.py | import torch
import torch.nn as nn
from criteria.lpips.networks import get_network, LinLayers
from criteria.lpips.utils import get_state_dict
class LPIPS(nn.Module):
r"""Creates a criterion that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
net_type (str): the network typ... | 1,203 | 32.444444 | 71 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/lpips/utils.py | from collections import OrderedDict
import torch
def normalize_activation(x, eps=1e-10):
norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
return x / (norm_factor + eps)
def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
# build url
url = 'https://raw.githubusercontent... | 885 | 27.580645 | 79 | py |
JoJoGAN | JoJoGAN-main/e4e/criteria/lpips/networks.py | from typing import Sequence
from itertools import chain
import torch
import torch.nn as nn
from torchvision import models
from criteria.lpips.utils import normalize_activation
def get_network(net_type: str):
if net_type == 'alex':
return AlexNet()
elif net_type == 'squeeze':
return SqueezeN... | 2,667 | 26.791667 | 79 | py |
JoJoGAN | JoJoGAN-main/e4e/utils/model_utils.py | import torch
import argparse
from models.psp import pSp
from models.encoders.psp_encoders import Encoder4Editing
def setup_model(checkpoint_path, device='cuda'):
ckpt = torch.load(checkpoint_path, map_location='cpu')
opts = ckpt['opts']
opts['checkpoint_path'] = checkpoint_path
opts['device'] = devic... | 1,063 | 28.555556 | 112 | py |
JoJoGAN | JoJoGAN-main/e4e/editings/ganspace.py | import torch
def edit(latents, pca, edit_directions):
edit_latents = []
for latent in latents:
for pca_idx, start, end, strength in edit_directions:
delta = get_delta(pca, latent, pca_idx, strength)
delta_padded = torch.zeros(latent.shape).to('cuda')
delta_padded[st... | 843 | 35.695652 | 91 | py |
JoJoGAN | JoJoGAN-main/e4e/editings/sefa.py | import torch
import numpy as np
from tqdm import tqdm
def edit(generator, latents, indices, semantics=1, start_distance=-15.0, end_distance=15.0, num_samples=1, step=11):
layers, boundaries, values = factorize_weight(generator, indices)
codes = latents.detach().cpu().numpy() # (1,18,512)
# Generate vis... | 1,724 | 35.702128 | 116 | py |
JoJoGAN | JoJoGAN-main/e4e/editings/latent_editor.py | import torch
import sys
sys.path.append(".")
sys.path.append("..")
from editings import ganspace, sefa
from utils.common import tensor2im
class LatentEditor(object):
def __init__(self, stylegan_generator, is_cars=False):
self.generator = stylegan_generator
self.is_cars = is_cars # Since the cars ... | 1,931 | 41 | 120 | py |
inverse-online | inverse-online-master/setup.py | from setuptools import find_packages, setup
setup(
name="iol",
version="0.0.1",
author="Alex",
author_email="alexjameschan@gmail.com",
description="Treatment effect beliefs",
url="url-to-github-page",
packages=find_packages(),
test_suite="iol.tests.test_all.suite",
install_requires=... | 463 | 22.2 | 43 | py |
inverse-online | inverse-online-master/iol/analysis_over_time.py | from iol.models import AdaptiveLinearModel
from iol.data_loading import generate_linear_dataset, get_centre_data
import matplotlib.pyplot as plt
import numpy as np
import torch
torch.manual_seed(41310)
hyperparams = {
"covariate_size": 63,
"action_size": 2,
"outcome_size": 1,
"memory_hidden_size": 32... | 1,773 | 22.972973 | 84 | py |
inverse-online | inverse-online-master/iol/benchmarks.py | from iol.models import (
BehaviouralCloning,
BehaviouralCloningLSTM,
BehaviouralCloningDeep,
CIRL,
AdaptiveLinearModel,
RCAL,
) # noqa: F401
from iol.data_loading import generate_linear_dataset, get_centre_data
import numpy as np
import pickle
import torch
torch.manual_seed(41310)
hyperparams... | 2,463 | 23.39604 | 93 | py |
inverse-online | inverse-online-master/iol/get_cancer_data.py | from iol.data_loading import get_processed_data, get_cancer_sim_data
from iol.constants import CHEMO_COEFF, RADIO_COEFF
from pkg_resources import resource_filename
import numpy as np
import torch
seed = 41310
torch.manual_seed(seed)
np.random.seed(seed)
pickle_map = get_cancer_sim_data(
chemo_coeff=CHEMO_COEFF, ... | 1,947 | 28.515152 | 88 | py |
inverse-online | inverse-online-master/iol/models/base_model.py | import torch
import time
import datetime
from pkg_resources import resource_filename
class BaseModel(torch.nn.Module):
def __init__(self):
super(BaseModel, self).__init__()
self.name = "Base"
def fit(
self,
dataset,
epochs=10,
batch_size=128,
learning_... | 2,513 | 27.896552 | 84 | py |
inverse-online | inverse-online-master/iol/models/mlp_model.py | import torch
from iol.models import BaseModel, MLPNetwork
import torch.nn.functional as F
class MLPModel(BaseModel):
def __init__(
self,
covariate_size: int,
action_size: int,
outcome_size: int,
hidden_size: int,
num_layers: int,
**kwargs,
):
su... | 1,198 | 23.979167 | 70 | py |
inverse-online | inverse-online-master/iol/models/rcal.py | import torch
from iol.models import BaseModel
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, roc_auc_score, average_precision_score
class RCAL(BaseModel):
def __init__(
self,
covariate_size: int,
action_size: int,
hidden_size: int,
**kwargs,
... | 2,462 | 27.639535 | 82 | py |
inverse-online | inverse-online-master/iol/models/optimal_treatment_rules.py | import torch
class OTR():
def __init__(self):
self.name = "name"
class NormalisedRatio(OTR):
def __init__(self):
super(NormalisedRatio, self).__init__()
def __call__(self, y_1, y_0):
ratio = (y_1 - y_0) / y_0
return torch.sigmoid(ratio, 2)
| 291 | 15.222222 | 47 | py |
inverse-online | inverse-online-master/iol/models/utils.py | import torch
def reverse_sequence(seqs, mask):
batch_size, max_seq_len, dim = seqs.size()
rev_seqs = seqs.new_zeros(seqs.size())
seq_lens = mask.sum(axis=1)
for b in range(batch_size):
T = seq_lens[b].int()
time_slice = torch.arange(T - 1, -1, -1, device=seqs.device)
rev_seq =... | 445 | 26.875 | 82 | py |
inverse-online | inverse-online-master/iol/models/bc.py | import torch
from iol.models import BaseModel
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, roc_auc_score, average_precision_score
class BehaviouralCloning(BaseModel):
def __init__(
self,
covariate_size: int,
action_size: int,
**kwargs,
):
... | 4,658 | 26.568047 | 82 | py |
inverse-online | inverse-online-master/iol/models/cate_nets.py | import torch
from iol.models import BaseModel
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, roc_auc_score, average_precision_score
class CIRL(BaseModel):
def __init__(
self,
covariate_size: int,
action_size: int,
hidden_size: int,
**kwargs,
... | 2,210 | 26.296296 | 82 | py |
inverse-online | inverse-online-master/iol/models/inverse_online.py | import torch
from iol.models import BaseModel
from iol.models.utils import reverse_sequence
import torch.nn.functional as F
from torch.distributions.kl import kl_divergence
from sklearn.metrics import accuracy_score, roc_auc_score, average_precision_score
class AdaptiveLinearModel(BaseModel):
def __init__(
... | 20,815 | 30.022355 | 88 | py |
inverse-online | inverse-online-master/iol/data_loading/data_loader.py | import torch
import numpy as np
from pkg_resources import resource_filename
class CancerDataset(torch.utils.data.Dataset):
def __init__(self, load_prerun=True, fold="training"):
super(CancerDataset, self).__init__()
if load_prerun:
path_tail = f"data_loading/data/prerun_cancer_{fold}... | 1,335 | 28.043478 | 73 | py |
inverse-online | inverse-online-master/iol/data_loading/organ_donation.py | import numpy as np
import torch
import pickle
from pkg_resources import resource_filename
class OrganDataset(torch.utils.data.Dataset):
def __init__(self):
super(OrganDataset, self).__init__()
self.covariates = None
self.actions = None
self.outcomes = None
self.mask = None... | 2,110 | 24.743902 | 70 | py |
inverse-online | inverse-online-master/iol/data_loading/basic_simulation.py | import numpy as np
import torch
from scipy.special import expit
EPS = 0.00001
def simulate_x_and_pos(
n,
d: int = 5,
covariate_model=None,
covariate_model_params: dict = None,
mu_0_model=None,
mu_0_model_params: dict = None,
mu_1_model=None,
mu_1_model_params: dict = None,
error_... | 6,676 | 25.923387 | 88 | py |
inverse-online | inverse-online-master/iol/data_loading/sup_datasets.py | import torch
import numpy as np
import pandas as pd
from medkit.domains import CFDomain, ICUDomain, WardDomain
from medkit.bases import standard_dataset
class BaseDataset(torch.utils.data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
self.covariates = None
self.action... | 1,830 | 25.536232 | 76 | py |
pymusepipe | pymusepipe-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# 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 have a default; values that are commented out
# serve to show the default.
import os
impo... | 9,325 | 32.789855 | 85 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/main.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import config as cf
import torchvision
import torchvision.transforms as transforms
import os
import sys
import time
import argparse
import datetime... | 8,463 | 35.17094 | 113 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/config.py | ############### Pytorch CIFAR configuration file ###############
import math
start_epoch = 1
num_epochs = 200
batch_size = 128
optim_type = 'SGD'
mean = {
'cifar10': (0.4914, 0.4822, 0.4465),
'cifar100': (0.5071, 0.4867, 0.4408),
}
std = {
'cifar10': (0.2023, 0.1994, 0.2010),
'cifar100': (0.2675, 0.2... | 787 | 19.736842 | 90 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/networks/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_init(m):
classname = m.__class__.__name__
if... | 4,054 | 32.237705 | 101 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/networks/wide_resnet.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_init... | 3,079 | 33.222222 | 98 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/networks/vggnet.py | import torch
import torch.nn as nn
from torch.autograd import Variable
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
def cfg(depth):
depth_lst = [11, 13, 16, 19]
assert (depth ... | 2,214 | 26.345679 | 95 | py |
wide-resnet.pytorch | wide-resnet.pytorch-master/networks/lenet.py | import torch.nn as nn
import torch.nn.functional as F
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
class LeNet(nn.Module):
def __init__(self, num_classes):
super(LeNet, se... | 880 | 28.366667 | 54 | py |
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