<reponame>henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders<filename>resnet_autoencoder_training/featureLoss_function.py
from fastai.callbacks import *
from fastai.utils.mem import *
from torchvision.models import vgg16_bn
import torch
import torch.nn.functional as F
from fastai.torch_core import requires_grad, children

def gram_matrix(x):
    n,c,h,w = x.size()
    x = x.view(n, c, -1)
    return (x @ x.transpose(1,2))/(c*h*w)


class FeatureLoss(nn.Module):
    def __init__(self, m_feat, layer_ids, layer_wgts, base_loss):
        super().__init__()
        self.m_feat = m_feat
        self.base_loss = base_loss
        self.loss_features = [self.m_feat[i] for i in layer_ids]
        self.hooks = hook_outputs(self.loss_features, detach=False)
        self.wgts = layer_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'gram_{i}' for i in range(len(layer_ids))]

    def make_features(self, x, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]

    def forward(self, input, target):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [self.base_loss(input,target)]
        self.feat_losses += [self.base_loss(f_in, f_out)*w
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.feat_losses += [self.base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)

    def __del__(self): self.hooks.remove()
