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 conv5x5(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride, padding=2, 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, use_bn=False, 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 self.use_bn = use_bn if use_bn: self.bn1 = nn.BatchNorm2d(planes) self.bn2 = nn.BatchNorm2d(planes) def forward(self, x): identity = x out = self.conv1(x + self.bias1a) if self.use_bn: out = self.bn1(out) out = self.lrelu(out + self.bias1b) out = self.conv2(out + self.bias2a) if self.use_bn: out = self.bn2(out) 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, number_skips=2, use_bn=False, disable_passthrough=False): super(FixupResNet, self).__init__() self.num_layers = sum(layers) self.inplanes = 3 self.number_skips = number_skips self.disable_passthrough = disable_passthrough self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.layer0 = self._make_layer(block, num_filters*2, layers[0], stride=2, use_bn=use_bn, conv_type=conv5x5) if number_skips > 0: self.inplanes = self.inplanes + 3 # Accomodate a skip connection from the generator. self.layer1 = self._make_layer(block, num_filters*4, layers[1], stride=2, use_bn=use_bn, conv_type=conv5x5) if number_skips > 1: self.inplanes = self.inplanes + 3 # Accomodate a second skip connection from the generator. self.layer2 = self._make_layer(block, num_filters*8, layers[2], stride=2, use_bn=use_bn) # SRGAN already has a feature loss tied to a separate VGG discriminator. We really don't care about features. # Therefore, level off the filter count from this block forwards. self.layer3 = self._make_layer(block, num_filters*8, layers[3], stride=2, use_bn=use_bn) self.layer4 = self._make_layer(block, num_filters*8, layers[4], stride=2, use_bn=use_bn) self.bias2 = nn.Parameter(torch.zeros(1)) reduced_img_sz = int(input_img_size / 32) self.fc1 = nn.Linear(num_filters * 8 * 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:])))) def _make_layer(self, block, outplanes, blocks, stride=1, use_bn=False, conv_type=conv3x3): layers = [] for _ in range(1, blocks): layers.append(block(self.inplanes, self.inplanes)) downsample = None if stride != 1 or self.inplanes != outplanes * block.expansion: downsample = conv1x1(self.inplanes, outplanes * block.expansion, stride) layers.append(block(self.inplanes, outplanes, stride, downsample, use_bn=use_bn, conv_create=conv_type)) self.inplanes = outplanes * block.expansion return nn.Sequential(*layers) def forward(self, x): if len(x) == 3: # This class can take a medium skip (half-res) and low skip (quarter-res) provided as a tuple in the input. x, med_skip, lo_skip = x else: # Or just a tuple with only the high res input (this assumes number_skips was set right). x = x[0] if self.disable_passthrough: if self.number_skips > 0: med_skip = torch.zeros_like(med_skip) if self.number_skips > 1: lo_skip = torch.zeros_like(lo_skip) x = self.layer0(x) if self.number_skips > 0: x = torch.cat([x, med_skip], dim=1) x = self.layer1(x) if self.number_skips > 1: x = torch.cat([x, lo_skip], dim=1) x = self.layer2(x) x = self.layer3(x) x = self.layer4(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, 5, 3, 3, 3], **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']