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): """5x5 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride, padding=2, bias=False) 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 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:])))) ''' 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, 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']