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import torch
import torch.nn as nn
import torch.nn.functional as F
from climategan.blocks import InterpolateNearest2d
from climategan.utils import find_target_size
class _ASPPModule(nn.Module):
# https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/aspp.py
def __init__(
self, inplanes, planes, kernel_size, padding, dilation, BatchNorm, no_init
):
super().__init__()
self.atrous_conv = nn.Conv2d(
inplanes,
planes,
kernel_size=kernel_size,
stride=1,
padding=padding,
dilation=dilation,
bias=False,
)
self.bn = BatchNorm(planes)
self.relu = nn.ReLU()
if not no_init:
self._init_weight()
def forward(self, x):
x = self.atrous_conv(x)
x = self.bn(x)
return self.relu(x)
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class ASPP(nn.Module):
# https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/aspp.py
def __init__(self, backbone, output_stride, BatchNorm, no_init):
super().__init__()
if backbone == "mobilenet":
inplanes = 320
else:
inplanes = 2048
if output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError
self.aspp1 = _ASPPModule(
inplanes,
256,
1,
padding=0,
dilation=dilations[0],
BatchNorm=BatchNorm,
no_init=no_init,
)
self.aspp2 = _ASPPModule(
inplanes,
256,
3,
padding=dilations[1],
dilation=dilations[1],
BatchNorm=BatchNorm,
no_init=no_init,
)
self.aspp3 = _ASPPModule(
inplanes,
256,
3,
padding=dilations[2],
dilation=dilations[2],
BatchNorm=BatchNorm,
no_init=no_init,
)
self.aspp4 = _ASPPModule(
inplanes,
256,
3,
padding=dilations[3],
dilation=dilations[3],
BatchNorm=BatchNorm,
no_init=no_init,
)
self.global_avg_pool = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
BatchNorm(256),
nn.ReLU(),
)
self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
self.bn1 = BatchNorm(256)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.5)
if not no_init:
self._init_weight()
def forward(self, x):
x1 = self.aspp1(x)
x2 = self.aspp2(x)
x3 = self.aspp3(x)
x4 = self.aspp4(x)
x5 = self.global_avg_pool(x)
x5 = F.interpolate(x5, size=x4.size()[2:], mode="bilinear", align_corners=True)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
return self.dropout(x)
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class DeepLabV2Decoder(nn.Module):
# https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/decoder.py
# https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/deeplab.py
def __init__(self, opts, no_init=False):
super().__init__()
self.aspp = ASPP("resnet", 16, nn.BatchNorm2d, no_init)
self.use_dada = ("d" in opts.tasks) and opts.gen.s.use_dada
conv_modules = [
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Dropout(0.1),
]
if opts.gen.s.upsample_featuremaps:
conv_modules = [InterpolateNearest2d(scale_factor=2)] + conv_modules
conv_modules += [
nn.Conv2d(256, opts.gen.s.output_dim, kernel_size=1, stride=1),
]
self.conv = nn.Sequential(*conv_modules)
self._target_size = find_target_size(opts, "s")
print(
" - {}: setting target size to {}".format(
self.__class__.__name__, self._target_size
)
)
def set_target_size(self, size):
"""
Set final interpolation's target size
Args:
size (int, list, tuple): target size (h, w). If int, target will be (i, i)
"""
if isinstance(size, (list, tuple)):
self._target_size = size[:2]
else:
self._target_size = (size, size)
def forward(self, z, z_depth=None):
if self._target_size is None:
error = "self._target_size should be set with self.set_target_size()"
error += "to interpolate logits to the target seg map's size"
raise Exception(error)
if isinstance(z, (list, tuple)):
z = z[0]
if z.shape[1] != 2048:
raise Exception(
"Segmentation decoder will only work with 2048 channels for z"
)
if z_depth is not None and self.use_dada:
z = z * z_depth
y = self.aspp(z)
y = self.conv(y)
return F.interpolate(y, self._target_size, mode="bilinear", align_corners=True)
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