update train size

utils_nlp/models/transformers/common.py

@@ -224,6 +224,7 @@ class Transformer:
         # init training
         tr_loss = 0.0
         accum_loss = 0
+        train_size = 0
         self.model.train()
         self.model.zero_grad()
 
@@ -254,7 +255,7 @@ class Transformer:
 
                 tr_loss += loss.item()
                 accum_loss += loss.item()
-
+                train_size += list(inputs.values())[0].size()[0] 
                 if (step + 1) % gradient_accumulation_steps == 0:
 
                     global_step += 1
@@ -274,13 +275,14 @@ class Transformer:
                         endtime_string = datetime.datetime.fromtimestamp(end).strftime(
                             "%d/%m/%Y %H:%M:%S"
                         )
-                        log_line = """timestamp: {0:s}, loss: {1:.6f}, time duration: {2:f},
-                            number of examples in current step: {3:.0f}, step {4:.0f}
+                        log_line = """timestamp: {0:s}, average loss: {1:.6f}, time duration: {2:f},
+                            number of examples in current reporting: {3:.0f}, step {4:.0f}
                             out of total {5:.0f}""".format(
                             endtime_string,
                             accum_loss / report_every,
                             end - start,
-                            list(inputs.values())[0].size()[0],
+                            #list(inputs.values())[0].size()[0],
+                            train_size,
                             global_step,
                             max_steps,
 
@@ -288,6 +290,7 @@ class Transformer:
                         logger.info(log_line)
                         print(log_line)
                         accum_loss = 0
+                        train_size = 0
                         start = end
                     if type(optimizer) == list:
                         for o in optimizer: