decodervggmodify.py

        for i, layer in enumerate(features):
            if isinstance(layer, nn.MaxPool2d):
                features[i] = nn.Upsample(scale_factor = (2, 2), mode = 'nearest')
            elif isinstance(layer, nn.Conv2d):
                conv2d = nn.Conv2d(layer.out_channels, layer.in_channels, \
                                   kernel_size = layer.kernel_size, stride = layer.stride, \
                                   padding = layer.padding, padding_mode = 'reflect')
                with torch.no_grad():
                    conv2d.weight[...] = layer.weight.transpose(0, 1)
                features[i] = conv2d
            elif isinstance(layer, nn.ReLU):
                layer.inplace = False