3b4e54c4c5
Add RRDBNetXL, which performs processing at multiple image sizes. Add DiscResnet_passthrough, which allows passthrough of image at different sizes for discrimination. Adjust the rest of the repo to allow generators that return more than just a single image.
207 lines
7.8 KiB
Python
207 lines
7.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import numpy as np
|
|
|
|
|
|
__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
|
|
|
|
|
|
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)
|
|
|
|
|
|
def conv1x1(in_planes, out_planes, stride=1):
|
|
"""1x1 convolution"""
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
|
|
|
|
|
class FixupBasicBlock(nn.Module):
|
|
expansion = 1
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
|
super(FixupBasicBlock, self).__init__()
|
|
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
|
|
self.bias1a = nn.Parameter(torch.zeros(1))
|
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
|
self.bias1b = nn.Parameter(torch.zeros(1))
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
|
self.bias2a = nn.Parameter(torch.zeros(1))
|
|
self.conv2 = conv3x3(planes, planes)
|
|
self.scale = nn.Parameter(torch.ones(1))
|
|
self.bias2b = nn.Parameter(torch.zeros(1))
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
identity = x
|
|
|
|
out = self.conv1(x + self.bias1a)
|
|
out = self.lrelu(out + self.bias1b)
|
|
|
|
out = self.conv2(out + self.bias2a)
|
|
out = out * self.scale + self.bias2b
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x + self.bias1a)
|
|
|
|
out += identity
|
|
out = self.lrelu(out)
|
|
|
|
return out
|
|
|
|
class FixupBottleneck(nn.Module):
|
|
expansion = 4
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
|
super(FixupBottleneck, self).__init__()
|
|
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
|
|
self.bias1a = nn.Parameter(torch.zeros(1))
|
|
self.conv1 = conv1x1(inplanes, planes)
|
|
self.bias1b = nn.Parameter(torch.zeros(1))
|
|
self.bias2a = nn.Parameter(torch.zeros(1))
|
|
self.conv2 = conv3x3(planes, planes, stride)
|
|
self.bias2b = nn.Parameter(torch.zeros(1))
|
|
self.bias3a = nn.Parameter(torch.zeros(1))
|
|
self.conv3 = conv1x1(planes, planes * self.expansion)
|
|
self.scale = nn.Parameter(torch.ones(1))
|
|
self.bias3b = nn.Parameter(torch.zeros(1))
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
identity = x
|
|
|
|
out = self.conv1(x + self.bias1a)
|
|
out = self.lrelu(out + self.bias1b)
|
|
|
|
out = self.conv2(out + self.bias2a)
|
|
out = self.lrelu(out + self.bias2b)
|
|
|
|
out = self.conv3(out + self.bias3a)
|
|
out = out * self.scale + self.bias3b
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x + self.bias1a)
|
|
|
|
out += identity
|
|
out = self.lrelu(out)
|
|
|
|
return out
|
|
|
|
|
|
class FixupResNet(nn.Module):
|
|
|
|
def __init__(self, block, layers, num_filters=64, num_classes=1000, input_img_size=64):
|
|
super(FixupResNet, self).__init__()
|
|
self.num_layers = sum(layers)
|
|
self.inplanes = num_filters
|
|
self.conv1 = nn.Conv2d(3, num_filters, kernel_size=7, stride=2, padding=3,
|
|
bias=False)
|
|
self.bias1 = nn.Parameter(torch.zeros(1))
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
self.layer1 = self._make_layer(block, num_filters, layers[0], stride=1)
|
|
self.skip1 = nn.Conv2d(num_filters + 3, num_filters, kernel_size=5, stride=1, padding=2, bias=False)
|
|
self.skip1_bias = nn.Parameter(torch.zeros(1))
|
|
self.layer2 = self._make_layer(block, num_filters*2, layers[1], stride=2)
|
|
self.skip2 = nn.Conv2d(num_filters*2 + 3, num_filters*2, kernel_size=5, stride=1, padding=2, bias=False)
|
|
self.skip2_bias = nn.Parameter(torch.zeros(1))
|
|
self.layer3 = self._make_layer(block, num_filters*4, layers[2], stride=2)
|
|
self.layer4 = self._make_layer(block, num_filters*8, layers[3], stride=2)
|
|
self.layer5 = self._make_layer(block, num_filters*16, layers[4], stride=2)
|
|
self.bias2 = nn.Parameter(torch.zeros(1))
|
|
reduced_img_sz = int(input_img_size / 32)
|
|
self.fc1 = nn.Linear(num_filters * 16 * reduced_img_sz * reduced_img_sz, 100)
|
|
self.fc2 = nn.Linear(100, num_classes)
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, FixupBasicBlock):
|
|
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
|
|
nn.init.constant_(m.conv2.weight, 0)
|
|
if m.downsample is not None:
|
|
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
|
|
elif isinstance(m, FixupBottleneck):
|
|
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
|
|
nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
|
|
nn.init.constant_(m.conv3.weight, 0)
|
|
if m.downsample is not None:
|
|
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
|
|
'''
|
|
elif isinstance(m, nn.Linear):
|
|
nn.init.constant_(m.weight, 0)
|
|
nn.init.constant_(m.bias, 0)'''
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1):
|
|
downsample = None
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = conv1x1(self.inplanes, planes * block.expansion, stride)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample))
|
|
self.inplanes = planes * block.expansion
|
|
for _ in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def forward(self, x):
|
|
# This class expects a medium skip (half-res) and low skip (quarter-res) provided as a tuple in the input.
|
|
hi, med_skip, lo_skip = x
|
|
|
|
x = self.conv1(hi)
|
|
x = self.lrelu(x + self.bias1)
|
|
x = self.layer1(x)
|
|
x = self.lrelu(self.skip1(torch.cat([x, med_skip], dim=1)) + self.skip1_bias)
|
|
|
|
x = self.layer2(x)
|
|
x = self.lrelu(self.skip2(torch.cat([x, lo_skip], dim=1)) + self.skip2_bias)
|
|
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
x = self.layer5(x)
|
|
|
|
x = x.view(x.size(0), -1)
|
|
x = self.lrelu(self.fc1(x))
|
|
x = self.fc2(x + self.bias2)
|
|
|
|
return x
|
|
|
|
|
|
def fixup_resnet18(**kwargs):
|
|
"""Constructs a Fixup-ResNet-18 model.2
|
|
"""
|
|
model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2, 2], **kwargs)
|
|
return model
|
|
|
|
|
|
def fixup_resnet34(**kwargs):
|
|
"""Constructs a Fixup-ResNet-34 model.
|
|
"""
|
|
model = FixupResNet(FixupBasicBlock, [5, 4, 3, 3, 2], **kwargs)
|
|
return model
|
|
|
|
|
|
def fixup_resnet50(**kwargs):
|
|
"""Constructs a Fixup-ResNet-50 model.
|
|
"""
|
|
model = FixupResNet(FixupBottleneck, [3, 4, 6, 3, 2], **kwargs)
|
|
return model
|
|
|
|
|
|
def fixup_resnet101(**kwargs):
|
|
"""Constructs a Fixup-ResNet-101 model.
|
|
"""
|
|
model = FixupResNet(FixupBottleneck, [3, 4, 23, 3, 2], **kwargs)
|
|
return model
|
|
|
|
|
|
def fixup_resnet152(**kwargs):
|
|
"""Constructs a Fixup-ResNet-152 model.
|
|
"""
|
|
model = FixupResNet(FixupBottleneck, [3, 8, 36, 3, 2], **kwargs)
|
|
return model
|
|
|
|
|
|
__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152'] |