common.py

import math

import torch
import torch.nn as nn
import torch.nn.functional as F


def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(
        in_channels, out_channels, kernel_size,
        padding=(kernel_size//2), bias=bias)


class MeanShift(nn.Conv2d):
    def __init__(self, rgb_range, rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):

        super(MeanShift, self).__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
        self.requires_grad = False


class BasicBlock(nn.Sequential):
    def __init__(self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False, bn=True, act=nn.ReLU(True)):

        m = [conv(in_channels, out_channels, kernel_size, bias=bias)]
        if bn: m.append(nn.BatchNorm2d(out_channels))
        if act is not None: m.append(act)
        super(BasicBlock, self).__init__(*m)


class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
                nn.Linear(channel, channel // reduction),
                nn.ReLU(inplace=True),
                nn.Linear(channel // reduction, channel),
                nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


class ResBlock(nn.Module):
    def __init__(self, conv, n_feats, kernel_size,bias=True, bn=False, act=nn.ReLU(True), res_scale=1):

        super(ResBlock, self).__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        self.res_scale = res_scale
        self.squeeze = SEBlock(n_feats)

    def forward(self, x):
        res = self.body(x).mul(self.res_scale)
        res = self.squeeze(res)
        res += x

        return res


class Upsampler(nn.Sequential):
    def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):

        m = []
        if (scale & (scale - 1)) == 0:    # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feats, 4 * n_feats, 3, bias))
                m.append(nn.PixelShuffle(2))
                if bn:
                    m.append(nn.BatchNorm2d(n_feats))

                if act == 'relu':
                    m.append(nn.ReLU())
                elif act == 'prelu':
                    m.append(nn.PReLU())
                elif act == 'lrelu':
                    m.append(nn.LeakyReLU(0.2, inplace=True))
        else:
            raise NotImplementedError

        super(Upsampler, self).__init__(*m)