snapshot...

modules/GSOC/E2_ESRGAN/lib/train.py

@@ -217,21 +217,30 @@ class Trainer(object):
       with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
         fake = generator.unsigned_call(image_lr)
         logging.debug("Fetched Generator Fake")
-        fake = utils.preprocess_input(fake)
-        image_lr = utils.preprocess_input(image_lr)
-        image_hr = utils.preprocess_input(image_hr)
+
+        # TDR, not sure about these...
+        # fake = utils.preprocess_input(fake)
+        # image_lr = utils.preprocess_input(image_lr)
+        # image_hr = utils.preprocess_input(image_hr)
+        # ------------------------------------------------
+        # TDR, not using perceptual loss with CLD OPD
         # percep_loss = tf.reduce_mean(perceptual_loss(image_hr, fake))
         # logging.debug("Calculated Perceptual Loss")
+
         l1_loss = utils.pixel_loss(image_hr, fake)
         logging.debug("Calculated Pixel Loss")
+
         loss_RaG = ra_gen(image_hr, fake)
         logging.debug("Calculated Relativistic"
                       "Average (RA) Loss for Generator")
+
         disc_loss = ra_disc(image_hr, fake)
         logging.debug("Calculated RA Loss Discriminator")
+        # TDR, we don't have percep_loss
         # gen_loss = percep_loss + lambda_ * loss_RaG + eta * l1_loss
         gen_loss = lambda_ * loss_RaG + eta * l1_loss
         logging.debug("Calculated Generator Loss")
+
         disc_metric(disc_loss)
         gen_metric(gen_loss)
         gen_loss = gen_loss * (1.0 / self.batch_size)