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jblitzar
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github-python-corpus

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def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)
def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)
def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)
# <FILESEP>
import os
import time
import utils
import torch
import dataloader
import torchvision
from utils import *
from torch.nn import BCELoss
from torch.autograd import grad
import torchvision.utils as tvls
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from discri import DGWGAN
from generator import Generator
def freeze(net):
for p in net.parameters():
p.requires_grad_(False)
def unfreeze(net):
for p in net.parameters():
p.requires_grad_(True)
def gradient_penalty(x, y):
# interpolation
shape = [x.size(0)] + [1] * (x.dim() - 1)
alpha = torch.rand(shape).cuda()
z = x + alpha * (y - x)
z = z.cuda()
z.requires_grad = True
o = DG(z)
g = grad(o, z, grad_outputs = torch.ones(o.size()).cuda(), create_graph = True)[0].view(z.size(0), -1)
gp = ((g.norm(p = 2, dim = 1) - 1) ** 2).mean()
return gp
save_img_dir = "./binaryGAN/imgs_celeba_gan"
save_model_dir= "./binaryGAN/"
os.makedirs(save_model_dir, exist_ok=True)
os.makedirs(save_img_dir, exist_ok=True)
dataset_name = "celeba"
log_path = "./attack_logs"
os.makedirs(log_path, exist_ok=True)
log_file = "binaryGAN_celeba.txt"
utils.Tee(os.path.join(log_path, log_file), 'w')