update

benchmark/fluid/fluid_benchmark.py

@@ -266,7 +266,10 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
             # FIXME(wuyi): For use_reader_op, if the current
             # pass is not the last, the last batch of this pass
             # is also equal to args.batch_size.
-            num_samples += len(args.batch_size)
+            if args.use_reader_op:
+                num_samples += args.batch_size
+            else:
+                num_samples += len(data)
             train_losses.append(loss)
             print("Pass: %d, Iter: %d, Loss: %f\n" %
                   (pass_id, iters, np.mean(train_losses)))
@@ -350,9 +353,6 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
             if iters == args.skip_batch_num:
                 start_time = time.time()
                 num_samples = 0
-            # NOTE: if use reader ops, the input data is not splited to multiple cards
-            if args.use_reader_op and iters >= args.iterations / args.gpus:
-                break
             if args.use_fake_data or args.use_reader_op:
                 try:
                     loss, = exe.run([avg_loss.name])
@@ -362,7 +362,10 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
                 loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
             if args.update_method == "pserver":
                 exe.bcast_params()
-            num_samples += len(data)
+            if args.use_reader_op:
+                num_samples += args.batch_size
+            else:
+                num_samples += len(data)
             iters += 1
             if batch_id % 1 == 0:
                 print("Pass %d, batch %d, loss %s" %