glide_text2im/vgg.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from .nn import mean_flat

# input image range [-1,1]
class VGG(nn.Module):
    def __init__(self, conv_index='22', rgb_range=1):
        super(VGG, self).__init__()
        vgg_features = models.vgg19(pretrained=True).features
        modules = [m for m in vgg_features]
        if conv_index.find('22') >= 0:
            self.vgg = nn.Sequential(*modules[:8])
        elif conv_index.find('54') >= 0:
            self.vgg = nn.Sequential(*modules[:35])

        vgg_mean = (0.485, 0.456, 0.406)
        vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
        self.sub_mean =  MeanShift(rgb_range, vgg_mean, vgg_std)
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, sr, hr):
        def _forward(x):
            x = self.sub_mean(x)
            x = self.vgg(x)
            return x
            
        sr = (sr + 1.)/2.
        hr = (hr + 1.)/2.

        vgg_sr = _forward(sr)
        with torch.no_grad():
            vgg_hr = _forward(hr.detach())
 
        loss = mean_flat((vgg_sr - vgg_hr) ** 2)

        return loss

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
        for p in self.parameters():
            p.requires_grad = False