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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 | |