init

app.py

@@ -18,6 +18,8 @@ from io import BytesIO
 from fastai.vision import *
 from fastai.vision import load_learner
 
+from core import FeatureLoss
+
 ORIGINAL_REPO_URL = 'https://github.com/vijishmadhavan/ArtLine'
 TITLE = 'vijishmadhavan/ArtLine'
 DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
@@ -66,7 +68,7 @@ def run(
 
  p, img_hr, b = learn.predict(img_fast)
  r = PIL.Image(img_hr)
- 
+
  return r
 
 learn = None

core/__init__.py

@@ -0,0 +1,30 @@
+from fastai.vision import *
+
+
+class FeatureLoss(nn.Module):
+ def __init__(self, m_feat, layer_ids, layer_wgts):
+ super().__init__()
+ self.m_feat = m_feat
+ 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 = [base_loss(input, target)]
+ self.feat_losses += [base_loss(f_in, f_out) * w
+ for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
+ self.feat_losses += [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()
+