DL-Art-School/codes/models/image_generation/ResGen_arch.py

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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
__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 conv5x5(in_planes, out_planes, stride=1):
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"""5x5 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
padding=2, bias=False)
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def conv7x7(in_planes, out_planes, stride=1):
"""7x7 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride,
padding=3, 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, conv_create=conv3x3):
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 = conv_create(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 = conv_create(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 FixupResNet(nn.Module):
def __init__(self, block, layers, upscale_applications=2, num_filters=64, inject_noise=False):
super(FixupResNet, self).__init__()
self.inject_noise = inject_noise
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self.num_layers = sum(layers) + layers[-1] * (upscale_applications - 1) # The last layer is applied repeatedly to achieve high level SR.
self.inplanes = num_filters
self.upscale_applications = upscale_applications
# Part 1 - Process raw input image. Most denoising should appear here and this should be the most complicated
# part of the block.
self.conv1 = nn.Conv2d(3, num_filters, kernel_size=5, stride=1, padding=2,
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, kernel_size=5, stride=1, padding=2, bias=False)
self.skip1_bias = nn.Parameter(torch.zeros(1))
# Part 2 - This is the upsampler core. It consists of a normal multiplicative conv followed by several residual
# convs which are intended to repair artifacts caused by 2x interpolation.
# This core layer should by itself accomplish 2x super-resolution. We use it in repeat to do the
# requested SR.
self.nf2 = int(num_filters/4)
# This part isn't repeated. It de-filters the output from the previous step to fit the filter size used in the
# upsampler-conv.
self.upsampler_conv = nn.Conv2d(num_filters, self.nf2, kernel_size=3, stride=1, padding=1, bias=False)
self.uc_bias = nn.Parameter(torch.zeros(1))
self.inplanes = self.nf2
if layers[1] > 0:
# This is the repeated part.
self.layer2 = self._make_layer(block, int(self.nf2), layers[1], stride=1, conv_type=conv5x5)
self.skip2 = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=False)
self.skip2_bias = nn.Parameter(torch.zeros(1))
self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=True)
self.bias2 = nn.Parameter(torch.zeros(1))
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:]))))
def _make_layer(self, block, planes, blocks, stride=1, conv_type=conv3x3):
defilter = None
if self.inplanes != planes * block.expansion:
defilter = conv1x1(self.inplanes, planes * block.expansion, stride)
layers = []
layers.append(block(self.inplanes, planes, stride, defilter))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, conv_create=conv_type))
return nn.Sequential(*layers)
def forward(self, x):
if self.inject_noise:
rand_feature = torch.randn_like(x)
x = x + rand_feature * .1
x = self.conv1(x)
x = self.lrelu(x + self.bias1)
x = self.layer1(x)
skip_lo = self.skip1(x) + self.skip1_bias
x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
if self.upscale_applications > 0:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
skip_med = self.skip2(x) + self.skip2_bias
else:
skip_med = skip_lo
if self.upscale_applications > 1:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
x = self.final_defilter(x) + self.bias2
return x, skip_med, skip_lo
class FixupResNetV2(FixupResNet):
def __init__(self, **kwargs):
super(FixupResNetV2, self).__init__(**kwargs)
# Use one unified filter-to-image stack, not the previous skip stacks.
self.skip1 = None
self.skip1_bias = None
self.skip2 = None
self.skip2_bias = None
# The new filter-to-image stack will be 2 conv layers deep, not 1.
self.final_process = nn.Conv2d(self.nf2, self.nf2, kernel_size=5, stride=1, padding=2, bias=True)
self.bias2 = nn.Parameter(torch.zeros(1))
self.fp_bn = nn.BatchNorm2d(self.nf2)
self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=3, stride=1, padding=1, bias=True)
self.bias3 = nn.Parameter(torch.zeros(1))
def filter_to_image(self, filter):
x = self.final_process(filter) + self.bias2
x = self.lrelu(self.fp_bn(x))
x = self.final_defilter(x) + self.bias3
return x
def forward(self, x):
if self.inject_noise:
rand_feature = torch.randn_like(x)
x = x + rand_feature * .1
x = self.conv1(x)
x = self.lrelu(x + self.bias1)
x = self.layer1(x)
x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
skip_lo = self.filter_to_image(x)
if self.upscale_applications > 0:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
skip_med = self.filter_to_image(x)
if self.upscale_applications > 1:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
if self.upscale_applications == 2:
x = self.filter_to_image(x)
elif self.upscale_applications == 1:
x = skip_med
skip_med = skip_lo
skip_lo = None
elif self.upscale_applications == 0:
x = skip_lo
skip_lo = None
skip_med = None
return x, skip_med, skip_lo
def fixup_resnet34(nb_denoiser=20, nb_upsampler=10, **kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
model = FixupResNet(FixupBasicBlock, [nb_denoiser, nb_upsampler], **kwargs)
return model
def fixup_resnet34_v2(nb_denoiser=20, nb_upsampler=10, **kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
kwargs['block'] = FixupBasicBlock
kwargs['layers'] = [nb_denoiser, nb_upsampler]
model = FixupResNetV2(**kwargs)
return model
__all__ = ['FixupResNet', 'fixup_resnet34', 'fixup_resnet34_v2']