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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__() | |
# change | |
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 | |
# change | |
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 | |
) # changed padding from 1 to 0 | |
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 | |