No batch factors for eval

codes/train.py

@@ -221,7 +221,7 @@ class Trainer:
         if opt_get(opt, ['eval', 'pure'], False) and self.current_step % opt['train']['val_freq'] == 0:
             metrics = []
             for val_data in tqdm(self.val_loader):
-                self.model.feed_data(val_data, self.current_step)
+                self.model.feed_data(val_data, self.current_step, perform_micro_batching=False)
                 metrics.append(self.model.test())
             reduced_metrics = {}
             for metric in metrics:

codes/trainer/ExtensibleTrainer.py

@@ -157,7 +157,7 @@ class ExtensibleTrainer(BaseModel):
         # Setting this to false triggers SRGAN to call the models update_model() function on the first iteration.
         self.updated = True
 
-    def feed_data(self, data, step, need_GT=True):
+    def feed_data(self, data, step, need_GT=True, perform_micro_batching=True):
         self.env['step'] = step
         self.batch_factor = self.mega_batch_factor
         self.opt['checkpointing_enabled'] = self.checkpointing_cache
@@ -174,10 +174,11 @@ class ExtensibleTrainer(BaseModel):
             o.zero_grad()
         torch.cuda.empty_cache()
 
+        batch_factor = self.batch_factor if perform_micro_batching else 1
         self.dstate = {}
         for k, v in data.items():
             if isinstance(v, torch.Tensor):
-                self.dstate[k] = [t.to(self.device) for t in torch.chunk(v, chunks=self.batch_factor, dim=0)]
+                self.dstate[k] = [t.to(self.device) for t in torch.chunk(v, chunks=batch_factor, dim=0)]
 
     def optimize_parameters(self, step):
         # Some models need to make parametric adjustments per-step. Do that here.