fix benchmark

tools/infer/predict.py

@@ -15,13 +15,10 @@
 import argparse
 import utils
 import numpy as np
-import logging
 import time
 
 from paddle.fluid.core import AnalysisConfig
 from paddle.fluid.core import create_paddle_predictor
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
 
 
 def parse_args():
@@ -101,7 +98,6 @@ def main():
     else:
         assert args.use_gpu is True
         assert args.model_name is not None
-        assert args.use_tensorrt is True
     # HALF precission predict only work when using tensorrt
     if args.use_fp16 is True:
         assert args.use_tensorrt is True
@@ -130,8 +126,9 @@ def main():
         output = output.flatten()
         cls = np.argmax(output)
         score = output[cls]
-        logger.info("class: {0}".format(cls))
-        logger.info("score: {0}".format(score))
+        print("Current image file: {}".format(args.image_file))
+        print("\ttop-1 class: {0}".format(cls))
+        print("\ttop-1 score: {0}".format(score))
     else:
         for i in range(0, test_num + 10):
             inputs = np.random.rand(args.batch_size, 3, 224,
@@ -145,11 +142,13 @@ def main():
             output = output.flatten()
             if i >= 10:
                 test_time += time.time() - start_time
+            time.sleep(0.01)  # sleep for T4 GPU
 
         fp_message = "FP16" if args.use_fp16 else "FP32"
-        logger.info("{0}\t{1}\tbatch size: {2}\ttime(ms): {3}".format(
-            args.model_name, fp_message, args.batch_size, 1000 * test_time /
-            test_num))
+        trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt"
+        print("{0}\t{1}\t{2}\tbatch size: {3}\ttime(ms): {4}".format(
+            args.model_name, trt_msg, fp_message, args.batch_size, 1000 *
+            test_time / test_num))
 
 
 if __name__ == "__main__":