update demo

demo/nas/sa_nas_mobilenetv2.py

@@ -120,26 +120,24 @@ def search_mobilenetv2(config, args, image_size):
         train_loader.set_sample_list_generator(
             train_reader,
             places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
-        test_loader.set_sample_list_generator(
-            test_reader,
-            places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
+        test_loader.set_sample_list_generator(test_reader, places=place)
 
-        build_strategy = fluid.BuildStrategy()
-        train_compiled_program = fluid.CompiledProgram(
-            train_program).with_data_parallel(
-                loss_name=avg_cost.name, build_strategy=build_strategy)
-        for epoch_id in range(args.retain_epoch):
-            for batch_id, data in enumerate(train_loader()):
-                fetches = [avg_cost.name]
-                s_time = time.time()
-                outs = exe.run(train_compiled_program,
-                               feed=data,
-                               fetch_list=fetches)[0]
-                batch_time = time.time() - s_time
-                if batch_id % 10 == 0:
-                    _logger.info(
-                        'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
-                        format(step, epoch_id, batch_id, outs[0], batch_time))
+        #build_strategy = fluid.BuildStrategy()
+        #train_compiled_program = fluid.CompiledProgram(
+        #    train_program).with_data_parallel(
+        #        loss_name=avg_cost.name, build_strategy=build_strategy)
+        #for epoch_id in range(args.retain_epoch):
+        #    for batch_id, data in enumerate(train_loader()):
+        #        fetches = [avg_cost.name]
+        #        s_time = time.time()
+        #        outs = exe.run(train_compiled_program,
+        #                       feed=data,
+        #                       fetch_list=fetches)[0]
+        #        batch_time = time.time() - s_time
+        #        if batch_id % 10 == 0:
+        #            _logger.info(
+        #                'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
+        #                format(step, epoch_id, batch_id, outs[0], batch_time))
 
         reward = []
         for batch_id, data in enumerate(test_loader()):
@@ -154,7 +152,8 @@ def search_mobilenetv2(config, args, image_size):
 
             _logger.info(
                 'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
-                format(step, test_outs[0], test_outs[1], test_outs[2]))
+                format(step, batch_id, batch_reward[0], batch_reward[1],
+                       batch_reward[2]))
 
         finally_reward = np.mean(np.array(reward), axis=0)
         _logger.info(