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"""
from https://github.com/LikeLy-Journey/SegmenTron/blob/
4bc605eedde7d680314f63d329277b73f83b1c5f/segmentron/modules/basic.py#L34
"""
from collections import OrderedDict
from pathlib import Path
import torch
import torch.nn as nn
from climategan.blocks import InterpolateNearest2d
class SeparableConv2d(nn.Module):
def __init__(
self,
inplanes,
planes,
kernel_size=3,
stride=1,
dilation=1,
relu_first=True,
bias=False,
norm_layer=nn.BatchNorm2d,
):
super().__init__()
depthwise = nn.Conv2d(
inplanes,
inplanes,
kernel_size,
stride=stride,
padding=dilation,
dilation=dilation,
groups=inplanes,
bias=bias,
)
bn_depth = norm_layer(inplanes)
pointwise = nn.Conv2d(inplanes, planes, 1, bias=bias)
bn_point = norm_layer(planes)
if relu_first:
self.block = nn.Sequential(
OrderedDict(
[
("relu", nn.ReLU()),
("depthwise", depthwise),
("bn_depth", bn_depth),
("pointwise", pointwise),
("bn_point", bn_point),
]
)
)
else:
self.block = nn.Sequential(
OrderedDict(
[
("depthwise", depthwise),
("bn_depth", bn_depth),
("relu1", nn.ReLU(inplace=True)),
("pointwise", pointwise),
("bn_point", bn_point),
("relu2", nn.ReLU(inplace=True)),
]
)
)
def forward(self, x):
return self.block(x)
class _ConvBNReLU(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
relu6=False,
norm_layer=nn.BatchNorm2d,
):
super(_ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias=False,
)
self.bn = norm_layer(out_channels)
self.relu = nn.ReLU6(True) if relu6 else nn.ReLU(True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class _DepthwiseConv(nn.Module):
"""conv_dw in MobileNet"""
def __init__(
self, in_channels, out_channels, stride, norm_layer=nn.BatchNorm2d, **kwargs
):
super(_DepthwiseConv, self).__init__()
self.conv = nn.Sequential(
_ConvBNReLU(
in_channels,
in_channels,
3,
stride,
1,
groups=in_channels,
norm_layer=norm_layer,
),
_ConvBNReLU(in_channels, out_channels, 1, norm_layer=norm_layer),
)
def forward(self, x):
return self.conv(x)
class InvertedResidual(nn.Module):
def __init__(
self,
in_channels,
out_channels,
stride,
expand_ratio,
dilation=1,
norm_layer=nn.BatchNorm2d,
):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
self.use_res_connect = stride == 1 and in_channels == out_channels
layers = list()
inter_channels = int(round(in_channels * expand_ratio))
if expand_ratio != 1:
# pw
layers.append(
_ConvBNReLU(
in_channels, inter_channels, 1, relu6=True, norm_layer=norm_layer
)
)
layers.extend(
[
# dw
_ConvBNReLU(
inter_channels,
inter_channels,
3,
stride,
dilation,
dilation,
groups=inter_channels,
relu6=True,
norm_layer=norm_layer,
),
# pw-linear
nn.Conv2d(inter_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
]
)
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, norm_layer=nn.BatchNorm2d, pretrained_path=None, no_init=False):
super(MobileNetV2, self).__init__()
output_stride = 16
self.multiplier = 1.0
if output_stride == 32:
dilations = [1, 1]
elif output_stride == 16:
dilations = [1, 2]
elif output_stride == 8:
dilations = [2, 4]
else:
raise NotImplementedError
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# building first layer
input_channels = int(32 * self.multiplier) if self.multiplier > 1.0 else 32
# last_channels = int(1280 * multiplier) if multiplier > 1.0 else 1280
self.conv1 = _ConvBNReLU(
3, input_channels, 3, 2, 1, relu6=True, norm_layer=norm_layer
)
# building inverted residual blocks
self.planes = input_channels
self.block1 = self._make_layer(
InvertedResidual,
self.planes,
inverted_residual_setting[0:1],
norm_layer=norm_layer,
)
self.block2 = self._make_layer(
InvertedResidual,
self.planes,
inverted_residual_setting[1:2],
norm_layer=norm_layer,
)
self.block3 = self._make_layer(
InvertedResidual,
self.planes,
inverted_residual_setting[2:3],
norm_layer=norm_layer,
)
self.block4 = self._make_layer(
InvertedResidual,
self.planes,
inverted_residual_setting[3:5],
dilations[0],
norm_layer=norm_layer,
)
self.block5 = self._make_layer(
InvertedResidual,
self.planes,
inverted_residual_setting[5:],
dilations[1],
norm_layer=norm_layer,
)
self.last_inp_channels = self.planes
self.up2 = InterpolateNearest2d()
# weight initialization
if not no_init:
self.pretrained_path = pretrained_path
if pretrained_path is not None:
self._load_pretrained_model()
else:
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out")
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.zeros_(m.bias)
def _make_layer(
self,
block,
planes,
inverted_residual_setting,
dilation=1,
norm_layer=nn.BatchNorm2d,
):
features = list()
for t, c, n, s in inverted_residual_setting:
out_channels = int(c * self.multiplier)
stride = s if dilation == 1 else 1
features.append(
block(planes, out_channels, stride, t, dilation, norm_layer)
)
planes = out_channels
for i in range(n - 1):
features.append(
block(planes, out_channels, 1, t, norm_layer=norm_layer)
)
planes = out_channels
self.planes = planes
return nn.Sequential(*features)
def forward(self, x):
x = self.conv1(x)
x = self.block1(x)
c1 = self.block2(x)
c2 = self.block3(c1)
c3 = self.block4(c2)
c4 = self.up2(self.block5(c3))
# x = self.features(x)
# x = self.classifier(x.view(x.size(0), x.size(1)))
return c4, c1
def _load_pretrained_model(self):
assert self.pretrained_path is not None
assert Path(self.pretrained_path).exists()
pretrain_dict = torch.load(self.pretrained_path)
pretrain_dict = {k.replace("encoder.", ""): v for k, v in pretrain_dict.items()}
model_dict = {}
state_dict = self.state_dict()
ignored = []
for k, v in pretrain_dict.items():
if k in state_dict:
model_dict[k] = v
else:
ignored.append(k)
state_dict.update(model_dict)
self.load_state_dict(state_dict)
self.loaded_pre_trained = True
print(
" - Loaded pre-trained MobileNetV2: ignored {}/{} keys".format(
len(ignored), len(pretrain_dict)
)
)
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