Merge pull request #11859 from luotao1/fluid_demo

add no_random args in fluid_benchmark.py

Author: luotao1

benchmark/fluid/args.py

@@ -125,6 +125,10 @@ def parse_args():
     parser.add_argument(
         '--use_inference_transpiler',
         action='store_true',
-        help='If set, uses inference transpiler to optimize the program.')
+        help='If set, use inference transpiler to optimize the program.')
+    parser.add_argument(
+        '--no_random',
+        action='store_true',
+        help='If set, keep the random seed and do not shuffle the data.')
     args = parser.parse_args()
     return args

benchmark/fluid/fluid_benchmark.py

@@ -132,10 +132,6 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
     exe.run(startup_prog)
 
     # Use inference_transpiler to speedup
-    if args.use_inference_transpiler:
-        t = fluid.InferenceTranspiler()
-        t.transpile(infer_prog, place)
-
     if not args.use_reader_op:
         feed_var_list = [
             var for var in train_prog.global_block().vars.itervalues()
@@ -186,6 +182,10 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
         print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))),
         # evaluation
         if not args.no_test and batch_acc and not args.use_reader_op:
+            if args.use_inference_transpiler:
+                t = fluid.InferenceTranspiler()
+                t.transpile(infer_prog, place)
+
             pass_test_acc = test(exe, infer_prog, test_reader, feeder,
                                  batch_acc)
             print(", Test Accuracy: %f" % pass_test_acc)
@@ -316,6 +316,8 @@ def main():
     args = parse_args()
     print_arguments(args)
     print_paddle_envs()
+    if args.no_random:
+        fluid.default_startup_program().random_seed = 1
 
     # the unique trainer id, starting from 0, needed by trainer
     # only

benchmark/fluid/models/resnet.py

@@ -197,12 +197,12 @@ def get_model(args):
     optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
 
     batched_train_reader = paddle.batch(
-        paddle.reader.shuffle(
+        train_reader if args.no_random else paddle.reader.shuffle(
             train_reader, buf_size=5120),
         batch_size=args.batch_size * args.gpus,
         drop_last=True)
     batched_test_reader = paddle.batch(
-        train_reader, batch_size=args.batch_size, drop_last=True)
+        test_reader, batch_size=args.batch_size, drop_last=True)
 
     return avg_cost, inference_program, optimizer, batched_train_reader,\
                    batched_test_reader, batch_acc