| import torch.nn as nn |
| from climategan.blocks import ResBlocks |
|
|
| affine_par = True |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None): |
| super(Bottleneck, self).__init__() |
| |
| self.conv1 = nn.Conv2d( |
| inplanes, planes, kernel_size=1, stride=stride, bias=False |
| ) |
| self.bn1 = nn.BatchNorm2d(planes, affine=affine_par) |
| for i in self.bn1.parameters(): |
| i.requires_grad = False |
| padding = dilation |
| |
| self.conv2 = nn.Conv2d( |
| planes, |
| planes, |
| kernel_size=3, |
| stride=1, |
| padding=padding, |
| bias=False, |
| dilation=dilation, |
| ) |
| self.bn2 = nn.BatchNorm2d(planes, affine=affine_par) |
| for i in self.bn2.parameters(): |
| i.requires_grad = False |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(planes * 4, affine=affine_par) |
| for i in self.bn3.parameters(): |
| i.requires_grad = False |
| self.relu = nn.ReLU(inplace=True) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| residual = x |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu(out) |
| out = self.conv2(out) |
| out = self.bn2(out) |
| out = self.relu(out) |
| out = self.conv3(out) |
| out = self.bn3(out) |
| if self.downsample is not None: |
| residual = self.downsample(x) |
| out += residual |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class ResNetMulti(nn.Module): |
| def __init__( |
| self, |
| layers, |
| n_res=4, |
| res_norm="instance", |
| activ="lrelu", |
| pad_type="reflect", |
| ): |
| self.inplanes = 64 |
| block = Bottleneck |
| super(ResNetMulti, self).__init__() |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) |
| self.bn1 = nn.BatchNorm2d(64, affine=affine_par) |
| for i in self.bn1.parameters(): |
| i.requires_grad = False |
| self.relu = nn.ReLU(inplace=True) |
| self.maxpool = nn.MaxPool2d( |
| kernel_size=3, stride=2, padding=0, ceil_mode=True |
| ) |
| self.layer1 = self._make_layer(block, 64, layers[0]) |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4) |
|
|
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| m.weight.data.normal_(0, 0.01) |
| elif isinstance(m, nn.BatchNorm2d): |
| m.weight.data.fill_(1) |
| m.bias.data.zero_() |
| self.layer_res = ResBlocks( |
| n_res, 2048, norm=res_norm, activation=activ, pad_type=pad_type |
| ) |
|
|
| def _make_layer(self, block, planes, blocks, stride=1, dilation=1): |
| downsample = None |
| if ( |
| stride != 1 |
| or self.inplanes != planes * block.expansion |
| or dilation == 2 |
| or dilation == 4 |
| ): |
| downsample = nn.Sequential( |
| nn.Conv2d( |
| self.inplanes, |
| planes * block.expansion, |
| kernel_size=1, |
| stride=stride, |
| bias=False, |
| ), |
| nn.BatchNorm2d(planes * block.expansion, affine=affine_par), |
| ) |
| for i in downsample._modules["1"].parameters(): |
| i.requires_grad = False |
| layers = [] |
| layers.append( |
| block( |
| self.inplanes, planes, stride, dilation=dilation, downsample=downsample |
| ) |
| ) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes, dilation=dilation)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def forward(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| x = self.maxpool(x) |
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| x = self.layer_res(x) |
| return x |
|
|
