118 lines
4.4 KiB
Python
118 lines
4.4 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 os
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import numpy as np
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import cv2
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import time
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import sys
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sys.path.insert(0, ".")
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from ppcls.utils import logger
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from tools.infer.utils import parse_args, get_image_list, create_paddle_predictor, preprocess, postprocess
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class Predictor(object):
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def __init__(self, args):
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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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self.args = args
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self.paddle_predictor = create_paddle_predictor(args)
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input_names = self.paddle_predictor.get_input_names()
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self.input_tensor = self.paddle_predictor.get_input_handle(input_names[
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0])
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output_names = self.paddle_predictor.get_output_names()
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self.output_tensor = self.paddle_predictor.get_output_handle(
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output_names[0])
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def predict(self, batch_input):
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self.input_tensor.copy_from_cpu(batch_input)
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self.paddle_predictor.run()
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batch_output = self.output_tensor.copy_to_cpu()
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return batch_output
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def normal_predict(self):
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image_list = get_image_list(self.args.image_file)
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batch_input_list = []
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img_name_list = []
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cnt = 0
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for idx, img_path in enumerate(image_list):
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img = cv2.imread(img_path)
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if img is None:
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logger.warning(
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"Image file failed to read and has been skipped. The path: {}".
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format(img_path))
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continue
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else:
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img = img[:, :, ::-1]
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img = preprocess(img, args)
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batch_input_list.append(img)
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img_name = img_path.split("/")[-1]
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img_name_list.append(img_name)
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cnt += 1
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if cnt % args.batch_size == 0 or (idx + 1) == len(image_list):
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batch_outputs = self.predict(np.array(batch_input_list))
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batch_result_list = postprocess(batch_outputs, self.args.top_k)
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for number, result_dict in enumerate(batch_result_list):
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filename = img_name_list[number]
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clas_ids = result_dict["clas_ids"]
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scores_str = "[{}]".format(", ".join("{:.2f}".format(
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r) for r in result_dict["scores"]))
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print(
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"File:{}, Top-{} result: class id(s): {}, score(s): {}".
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format(filename, self.args.top_k, clas_ids,
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scores_str))
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batch_input_list = []
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img_name_list = []
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def benchmark_predict(self):
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test_num = 500
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test_time = 0.0
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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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batch_output = self.predict(inputs).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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if __name__ == "__main__":
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args = parse_args()
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assert os.path.exists(
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args.model_file), "The path of 'model_file' does not exist: {}".format(
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args.model_file)
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assert os.path.exists(
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args.params_file
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), "The path of 'params_file' does not exist: {}".format(args.params_file)
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predictor = Predictor(args)
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if not args.enable_benchmark:
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predictor.normal_predict()
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else:
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assert args.model is not None
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predictor.benchmark_predict()
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