forked from nelson1425/EfficientAD
/
common.py
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common.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
from torch import nn
from torchvision.datasets import ImageFolder
def get_autoencoder(out_channels=384):
return nn.Sequential(
# encoder
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=8),
# decoder
nn.Upsample(size=3, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=8, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=15, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=32, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=63, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=127, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1,
padding=2),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Upsample(size=56, mode='bilinear'),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1,
padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=out_channels, kernel_size=3,
stride=1, padding=1)
)
def get_pdn_small(out_channels=384, padding=False):
pad_mult = 1 if padding else 0
return nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=128, kernel_size=4,
padding=3 * pad_mult),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1 * pad_mult),
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4,
padding=3 * pad_mult),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1 * pad_mult),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3,
padding=1 * pad_mult),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=out_channels, kernel_size=4)
)
def get_pdn_medium(out_channels=384, padding=False):
pad_mult = 1 if padding else 0
return nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=256, kernel_size=4,
padding=3 * pad_mult),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1 * pad_mult),
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4,
padding=3 * pad_mult),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1 * pad_mult),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3,
padding=1 * pad_mult),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=out_channels, kernel_size=4),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=out_channels, out_channels=out_channels,
kernel_size=1)
)
class ImageFolderWithoutTarget(ImageFolder):
def __getitem__(self, index):
sample, target = super().__getitem__(index)
return sample
class ImageFolderWithPath(ImageFolder):
def __getitem__(self, index):
path, target = self.samples[index]
sample, target = super().__getitem__(index)
return sample, target, path
def InfiniteDataloader(loader):
iterator = iter(loader)
while True:
try:
yield next(iterator)
except StopIteration:
iterator = iter(loader)