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import torch.nn as nn
from .trident_conv import MultiScaleTridentConv
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1,
):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
dilation=dilation, padding=dilation, stride=stride, bias=False)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
dilation=dilation, padding=dilation, bias=False)
self.relu = nn.ReLU(inplace=True)
self.norm1 = norm_layer(planes)
self.norm2 = norm_layer(planes)
if not stride == 1 or in_planes != planes:
self.norm3 = norm_layer(planes)
if stride == 1 and in_planes == planes:
self.downsample = None
else:
self.downsample = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
def forward(self, x):
y = x
y = self.relu(self.norm1(self.conv1(y)))
y = self.relu(self.norm2(self.conv2(y)))
if self.downsample is not None:
x = self.downsample(x)
return self.relu(x + y)
class CNNEncoder(nn.Module):
def __init__(self, output_dim=128,
norm_layer=nn.InstanceNorm2d,
num_output_scales=1,
**kwargs,
):
super(CNNEncoder, self).__init__()
self.num_branch = num_output_scales
feature_dims = [64, 96, 128]
self.conv1 = nn.Conv2d(3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False) # 1/2
self.norm1 = norm_layer(feature_dims[0])
self.relu1 = nn.ReLU(inplace=True)
self.in_planes = feature_dims[0]
self.layer1 = self._make_layer(feature_dims[0], stride=1, norm_layer=norm_layer) # 1/2
self.layer2 = self._make_layer(feature_dims[1], stride=2, norm_layer=norm_layer) # 1/4
# highest resolution 1/4 or 1/8
stride = 2 if num_output_scales == 1 else 1
self.layer3 = self._make_layer(feature_dims[2], stride=stride,
norm_layer=norm_layer,
) # 1/4 or 1/8
self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)
if self.num_branch > 1:
if self.num_branch == 4:
strides = (1, 2, 4, 8)
elif self.num_branch == 3:
strides = (1, 2, 4)
elif self.num_branch == 2:
strides = (1, 2)
else:
raise ValueError
self.trident_conv = MultiScaleTridentConv(output_dim, output_dim,
kernel_size=3,
strides=strides,
paddings=1,
num_branch=self.num_branch,
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d):
layer1 = ResidualBlock(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation)
layer2 = ResidualBlock(dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation)
layers = (layer1, layer2)
self.in_planes = dim
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
x = self.layer1(x) # 1/2
x = self.layer2(x) # 1/4
x = self.layer3(x) # 1/8 or 1/4
x = self.conv2(x)
if self.num_branch > 1:
out = self.trident_conv([x] * self.num_branch) # high to low res
else:
out = [x]
return out
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