optgs/model/backbones/unimatch/backbone.py

import torch.nn as nn


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,
        return_quarter=False,  # return 1/4 resolution feature
        lowest_scale=8,  # lowest resolution, 1/8 or 1/4
        return_all_scales=False,
        **kwargs,
    ):
        super(CNNEncoder, self).__init__()
        self.num_scales = num_output_scales
        self.return_quarter = return_quarter
        self.lowest_scale = lowest_scale
        self.return_all_scales = return_all_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

        if self.lowest_scale == 4:
            stride = 1
        else:
            stride = 2
        self.layer2 = self._make_layer(
            feature_dims[1], stride=stride, norm_layer=norm_layer
        )  # 1/2 or 1/4

        # lowest resolution 1/4 or 1/8
        self.layer3 = self._make_layer(
            feature_dims[2],
            stride=2,
            norm_layer=norm_layer,
        )  # 1/4 or 1/8

        self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)

        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):
        output_all_scales = []
        output = []
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)

        x = self.layer1(x)  # 1/2

        if self.return_all_scales:
            output_all_scales.append(x)

        if self.num_scales >= 3:
            output.append(x)

        x = self.layer2(x)  # 1/2 or 1/4
        if self.return_quarter:
            output.append(x)

        if self.return_all_scales:
            output_all_scales.append(x)

        if self.num_scales >= 2:
            output.append(x)

        x = self.layer3(x)  # 1/4 or 1/8
        x = self.conv2(x)

        if self.return_all_scales:
            output_all_scales.append(x)

        if self.return_all_scales:
            return output_all_scales

        if self.return_quarter:
            output.append(x)
            return output

        if self.num_scales >= 1:
            output.append(x)
            return output

        out = [x]

        return out