SPIGA/spiga/models/cnn/layers.py

from torch import nn


class Conv(nn.Module):
    def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True):
        super(Conv, self).__init__()
        self.inp_dim = inp_dim
        self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride, padding=(kernel_size - 1) // 2, bias=False)
        self.relu = None
        self.bn = None
        if relu:
            self.relu = nn.ReLU()
        if bn:
            self.bn = nn.BatchNorm2d(out_dim)

    def forward(self, x):
        assert x.size()[1] == self.inp_dim, "{} {}".format(x.size()[1], self.inp_dim)
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class Deconv(nn.Module):
    def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True):
        super(Deconv, self).__init__()
        self.inp_dim = inp_dim
        self.deconv = nn.ConvTranspose2d(inp_dim, out_dim, kernel_size=kernel_size, stride=stride, bias=False)
        self.relu = None
        self.bn = None
        if relu:
            self.relu = nn.ReLU()
        if bn:
            self.bn = nn.BatchNorm2d(out_dim)

    def forward(self, x):
        assert x.size()[1] == self.inp_dim, "{} {}".format(x.size()[1], self.inp_dim)
        x = self.deconv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class Residual(nn.Module):
    def __init__(self, inp_dim, out_dim, kernel=3):
        super(Residual, self).__init__()
        self.relu = nn.ReLU()
        self.bn1 = nn.BatchNorm2d(inp_dim)
        self.conv1 = Conv(inp_dim, int(out_dim / 2), 1, relu=False)
        self.bn2 = nn.BatchNorm2d(int(out_dim / 2))
        self.conv2 = Conv(int(out_dim / 2), int(out_dim / 2), kernel, relu=False)
        self.bn3 = nn.BatchNorm2d(int(out_dim / 2))
        self.conv3 = Conv(int(out_dim / 2), out_dim, 1, relu=False)
        self.skip_layer = Conv(inp_dim, out_dim, 1, relu=False)
        if inp_dim == out_dim:
            self.need_skip = False
        else:
            self.need_skip = True

    def forward(self, x):
        if self.need_skip:
            residual = self.skip_layer(x)
        else:
            residual = x
        out = self.bn1(x)
        out = self.relu(out)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn3(out)
        out = self.relu(out)
        out = self.conv3(out)
        out += residual
        return out