Add SrPixLoss, which focuses pixel-based losses on high-frequency regions

of the image.

codes/trainer/losses.py

@@ -7,6 +7,8 @@ import random
 import functools
 import torch.nn.functional as F
 
+from utils.util import opt_get
+
 
 def create_loss(opt_loss, env):
     type = opt_loss['type']
@@ -23,6 +25,8 @@ def create_loss(opt_loss, env):
         return CrossEntropy(opt_loss, env)
     elif type == 'pix':
         return PixLoss(opt_loss, env)
+    elif type == 'sr_pix':
+        return SrPixLoss(opt_loss, env)
     elif type == 'direct':
         return DirectLoss(opt_loss, env)
     elif type == 'feature':
@@ -143,6 +147,29 @@ class PixLoss(ConfigurableLoss):
         return self.criterion(fake.float(), real.float())
 
 
+class SrPixLoss(ConfigurableLoss):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.opt = opt
+        self.base_loss = opt_get(opt, ['base_loss'], .2)
+        self.exp = opt_get(opt, ['exp'], 2)
+        self.scale = opt['scale']
+
+    def forward(self, _, state):
+        real = state[self.opt['real']]
+        fake = state[self.opt['fake']]
+        l2 = (fake - real) ** 2
+        self.metrics.append(("l2_loss", l2.mean()))
+        # Adjust loss by prioritizing reconstruction of HF details.
+        no_hf = F.interpolate(F.interpolate(real, scale_factor=1/self.scale, mode="area"), scale_factor=self.scale, mode="nearest")
+        weights = (torch.abs(real - no_hf) + self.base_loss) ** self.exp
+        weights = weights / weights.mean()
+        loss = l2*weights
+        # Preserve the intensity of the loss, just adjust the weighting.
+        loss = loss*l2.mean()/loss.mean()
+        return loss.mean()
+
+
 # Loss defined by averaging the input tensor across all dimensions an optionally inverting it.
 class DirectLoss(ConfigurableLoss):
     def __init__(self, opt, env):