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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
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