94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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sys.path.insert(0, ".")
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import tools.infer.utils as utils
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import numpy as np
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import cv2
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import time
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def predict(args, predictor):
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input_names = predictor.get_input_names()
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input_tensor = predictor.get_input_handle(input_names[0])
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output_names = predictor.get_output_names()
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output_tensor = predictor.get_output_handle(output_names[0])
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test_num = 500
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test_time = 0.0
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if not args.enable_benchmark:
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# for PaddleHubServing
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if args.hubserving:
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img = args.image_file
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# for predict only
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else:
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img = cv2.imread(args.image_file)[:, :, ::-1]
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assert img is not None, "Error in loading image: {}".format(
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args.image_file)
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inputs = utils.preprocess(img, args)
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inputs = np.expand_dims(
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inputs, axis=0).repeat(
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args.batch_size, axis=0).copy()
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input_tensor.copy_from_cpu(inputs)
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predictor.run()
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output = output_tensor.copy_to_cpu()
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classes, scores = utils.postprocess(output, args)
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if args.hubserving:
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return classes, scores
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print("Current image file: {}".format(args.image_file))
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print("\ttop-1 class: {0}".format(classes[0]))
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print("\ttop-1 score: {0}".format(scores[0]))
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else:
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for i in range(0, test_num + 10):
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inputs = np.random.rand(args.batch_size, 3, 224,
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224).astype(np.float32)
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start_time = time.time()
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input_tensor.copy_from_cpu(inputs)
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predictor.run()
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output = output_tensor.copy_to_cpu()
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output = output.flatten()
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if i >= 10:
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test_time += time.time() - start_time
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time.sleep(0.01) # sleep for T4 GPU
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fp_message = "FP16" if args.use_fp16 else "FP32"
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trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt"
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print("{0}\t{1}\t{2}\tbatch size: {3}\ttime(ms): {4}".format(
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args.model, trt_msg, fp_message, args.batch_size, 1000 * test_time
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/ test_num))
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def main(args):
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if not args.enable_benchmark:
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assert args.batch_size == 1
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else:
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assert args.model is not None
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# HALF precission predict only work when using tensorrt
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if args.use_fp16 is True:
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assert args.use_tensorrt is True
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predictor = utils.create_paddle_predictor(args)
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predict(args, predictor)
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if __name__ == "__main__":
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args = utils.parse_args()
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main(args)
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