snapshot...

modules/GSOC/E2_ESRGAN/lib/train.py

@@ -134,8 +134,7 @@ class Trainer(object):
                 "warmup_loss", metric.result(), step=G_optimizer.iterations)
             tf.summary.scalar("mean_psnr", psnr_metric.result(), G_optimizer.iterations)
 
-          # if not num_steps % self.settings["print_step"]: # test
-          if True:
+          if not num_steps % self.settings["print_step"]:
             logging.info(
                 "[WARMUP] Step: {}\tGenerator Loss: {}"
                 "\tPSNR: {}\tTime Taken: {} sec".format(
@@ -302,8 +301,7 @@ class Trainer(object):
             tf.summary.scalar("mean_psnr", psnr_metric.result(), step=D_optimizer.iterations)
 
           # Logging and Checkpointing
-          # if not num_step % self.settings["print_step"]: # testing
-          if True:
+          if not num_step % self.settings["print_step"]:
             logging.info(
                 "Step: {}\tGen Loss: {}\tDisc Loss: {}"
                 "\tPSNR: {}\tTime Taken: {} sec".format(
@@ -312,7 +310,7 @@ class Trainer(object):
                     disc_metric.result(),
                     psnr_metric.result(),
                     time.time() - start))
-            # if psnr_metric.result() > last_psnr:
-            last_psnr = psnr_metric.result()
-            utils.save_checkpoint(checkpoint, "phase_2", self.model_dir)
+            if psnr_metric.result() > last_psnr:
+                last_psnr = psnr_metric.result()
+                utils.save_checkpoint(checkpoint, "phase_2", self.model_dir)
             start = time.time()