114 lines
3.9 KiB
Python
114 lines
3.9 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 sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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from tools.infer.utils import parse_args, get_image_list, preprocess, np_to_b64
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from ppcls.utils import logger
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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 requests
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import json
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import base64
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def main(args):
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image_path_list = get_image_list(args.image_file)
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headers = {"Content-type": "application/json"}
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cnt = 0
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predict_time = 0
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all_score = 0.0
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start_time = time.time()
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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_path_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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data = preprocess(img, args)
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batch_input_list.append(data)
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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_path_list):
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batch_input = np.array(batch_input_list)
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b64str, revert_shape = np_to_b64(batch_input)
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data = {
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"images": b64str,
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"revert_params": {
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"shape": revert_shape,
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"dtype": str(batch_input.dtype)
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},
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"top_k": args.top_k
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}
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try:
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r = requests.post(
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url=args.server_url,
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headers=headers,
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data=json.dumps(data))
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r.raise_for_status
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if r.json()["status"] != "000":
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msg = r.json()["msg"]
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raise Exception(msg)
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except Exception as e:
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logger.error("{}, in file(s): {} etc.".format(e, img_name_list[
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0]))
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continue
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else:
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results = r.json()["results"]
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batch_result_list = results["prediction"]
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elapse = results["elapse"]
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cnt += len(batch_result_list)
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predict_time += elapse
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for number, result_list in enumerate(batch_result_list):
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all_score += result_list["scores"][0]
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result_str = ""
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for i in range(len(result_list["clas_ids"])):
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result_str += "{}: {:.2f}\t".format(
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result_list["clas_ids"][i],
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result_list["scores"][i])
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logger.info("File:{}, The top-{} result(s): {}".format(
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img_name_list[number], args.top_k, result_str))
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finally:
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batch_input_list = []
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img_name_list = []
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total_time = time.time() - start_time
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logger.info("The average time of prediction cost: {:.3f} s/image".format(
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predict_time / cnt))
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logger.info("The average time cost: {:.3f} s/image".format(total_time /
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cnt))
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logger.info("The average top-1 score: {:.3f}".format(all_score / cnt))
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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