DeOldify/fasterai/loss.py

from fastai import *
from fastai.core import *
from fastai.torch_core import *
from fastai.callbacks  import hook_outputs
import torchvision.models as models


class FeatureLoss(nn.Module):
    def __init__(self, layer_wgts:[float]=[5.0,15.0,2.0], gram_wgt:float=5e3):
        super().__init__()
        self.gram_wgt = gram_wgt
        self.base_loss = F.l1_loss
        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
        requires_grad(self.m_feat, False)
        blocks = [i-1 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
        layer_ids = blocks[2:5]
        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 _gram_matrix(self, x:torch.Tensor):
        n,c,h,w = x.size()
        x = x.view(n, c, -1)
        return (x @ x.transpose(1,2))/(c*h*w)

    def make_features(self, x:torch.Tensor, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input:torch.Tensor, target:torch.Tensor):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        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(input,target)]

        self.feat_losses += [self.base_loss(self._gram_matrix(f_in), self._gram_matrix(f_out))*w**2 * self.gram_wgt
                             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()



class FeatureLoss2(nn.Module):
    def __init__(self, layer_wgts:[float]=[20.0,70.0,10.0], gram_wgt:float=5e3):
        super().__init__()
        self.gram_wgt = gram_wgt
        self.base_loss = F.l1_loss
        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
        requires_grad(self.m_feat, False)
        blocks = [i-1 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
        layer_ids = blocks[2:5]
        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 _gram_matrix(self, x:torch.Tensor):
        n,c,h,w = x.size()
        x = x.view(n, c, -1)
        return (x @ x.transpose(1,2))/(c*h*w)

    def make_features(self, x:torch.Tensor, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input:torch.Tensor, target:torch.Tensor):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        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(input,target)*100]

        self.feat_losses += [self.base_loss(self._gram_matrix(f_in), self._gram_matrix(f_out))*w**2 * self.gram_wgt
                             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()


#Includes wasserstein loss
class FeatureLoss3(nn.Module):
    def __init__(self, layer_wgts=[5,15,2], wass_wgts=[3.0,0.7,0.01]):
        super().__init__()

        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
        requires_grad(self.m_feat, False)
        blocks = [i-1 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
        layer_ids = blocks[2:5]
        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.wass_wgts = wass_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'wass_{i}' for i in range(len(layer_ids))]
        self.base_loss = F.l1_loss

    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 _calc_2_moments(self, tensor):
        chans = tensor.shape[1]
        tensor = tensor.view(1, chans, -1)
        n = tensor.shape[2] 
        mu = tensor.mean(2)
        tensor = (tensor - mu[:,:,None]).squeeze(0)
        cov = torch.mm(tensor, tensor.t()) / float(n)   
        return mu, cov

    def _get_style_vals(self, tensor):
        mean, cov = self._calc_2_moments(tensor) 
        eigvals, eigvects = torch.symeig(cov, eigenvectors=True)
        eigroot_mat = torch.diag(torch.sqrt(eigvals.clamp(min=0)))     
        root_cov = torch.mm(torch.mm(eigvects, eigroot_mat), eigvects.t())  
        tr_cov = eigvals.clamp(min=0).sum() 
        return mean, tr_cov, root_cov

    def _calc_l2wass_dist(self, mean_stl, tr_cov_stl, root_cov_stl, mean_synth, cov_synth):
        tr_cov_synth = torch.symeig(cov_synth, eigenvectors=True)[0].clamp(min=0).sum()
        mean_diff_squared = (mean_stl - mean_synth).pow(2).sum()
        cov_prod = torch.mm(torch.mm(root_cov_stl, cov_synth), root_cov_stl)
        var_overlap = torch.sqrt(torch.symeig(cov_prod, eigenvectors=True)[0].clamp(min=0)+1e-8).sum()
        dist = mean_diff_squared + tr_cov_stl + tr_cov_synth - 2*var_overlap
        return dist

    def _single_wass_loss(self, pred, targ):
        mean_test, tr_cov_test, root_cov_test = targ
        mean_synth, cov_synth = self._calc_2_moments(pred)
        loss = self._calc_l2wass_dist(mean_test, tr_cov_test, root_cov_test, mean_synth, cov_synth)
        return loss
    
    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)]
        
        styles = [self._get_style_vals(i) for i in out_feat]
        self.feat_losses += [self._single_wass_loss(f_pred, f_targ)*w
                            for f_pred, f_targ, w in zip(in_feat, styles, self.wass_wgts)]
        
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()


#"Before activations" in ESRGAN paper
class FeatureLoss4(nn.Module):
    def __init__(self, layer_wgts=[5,15,2]):
        super().__init__()

        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
        requires_grad(self.m_feat, False)
        blocks = [i-2 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
        layer_ids = blocks[2:5]
        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))] 
        self.base_loss = F.l1_loss

    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.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()