144 lines
5.2 KiB
Python
144 lines
5.2 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 cv2
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import numpy as np
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from paddleclas.deploy.utils import logger, config
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from paddleclas.deploy.utils.predictor import Predictor
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from paddleclas.deploy.utils.get_image_list import get_image_list
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from paddleclas.deploy.python.preprocess import create_operators
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from paddleclas.deploy.python.postprocess import build_postprocess
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class RecPredictor(Predictor):
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def __init__(self, config):
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super().__init__(config["Global"],
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config["Global"]["rec_inference_model_dir"])
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self.preprocess_ops = create_operators(config["RecPreProcess"][
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"transform_ops"])
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self.postprocess = build_postprocess(config["RecPostProcess"])
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self.benchmark = config["Global"].get("benchmark", False)
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if self.benchmark:
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import auto_log
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pid = os.getpid()
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self.auto_logger = auto_log.AutoLogger(
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model_name=config["Global"].get("model_name", "rec"),
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model_precision='fp16'
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if config["Global"]["use_fp16"] else 'fp32',
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batch_size=config["Global"].get("batch_size", 1),
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data_shape=[3, 224, 224],
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save_path=config["Global"].get("save_log_path",
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"./auto_log.log"),
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inference_config=self.config,
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pids=pid,
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process_name=None,
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gpu_ids=None,
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time_keys=[
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'preprocess_time', 'inference_time', 'postprocess_time'
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],
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warmup=2)
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def predict(self, images, feature_normalize=True):
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use_onnx = self.args.get("use_onnx", False)
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if not use_onnx:
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input_names = self.predictor.get_input_names()
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input_tensor = self.predictor.get_input_handle(input_names[0])
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output_names = self.predictor.get_output_names()
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output_tensor = self.predictor.get_output_handle(output_names[0])
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else:
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input_names = self.predictor.get_inputs()[0].name
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output_names = self.predictor.get_outputs()[0].name
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if self.benchmark:
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self.auto_logger.times.start()
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if not isinstance(images, (list, )):
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images = [images]
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for idx in range(len(images)):
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for ops in self.preprocess_ops:
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images[idx] = ops(images[idx])
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image = np.array(images)
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if self.benchmark:
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self.auto_logger.times.stamp()
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if not use_onnx:
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input_tensor.copy_from_cpu(image)
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self.predictor.run()
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batch_output = output_tensor.copy_to_cpu()
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else:
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batch_output = self.predictor.run(
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output_names=[output_names],
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input_feed={input_names: image})[0]
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if self.benchmark:
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self.auto_logger.times.stamp()
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if feature_normalize:
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feas_norm = np.sqrt(
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np.sum(np.square(batch_output), axis=1, keepdims=True))
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batch_output = np.divide(batch_output, feas_norm)
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if self.postprocess is not None:
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batch_output = self.postprocess(batch_output)
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if self.benchmark:
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self.auto_logger.times.end(stamp=True)
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return batch_output
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def main(config):
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rec_predictor = RecPredictor(config)
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image_list = get_image_list(config["Global"]["infer_imgs"])
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batch_imgs = []
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batch_names = []
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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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else:
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img = img[:, :, ::-1]
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batch_imgs.append(img)
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img_name = os.path.basename(img_path)
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batch_names.append(img_name)
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cnt += 1
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if cnt % config["Global"]["batch_size"] == 0 or (idx + 1
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) == len(image_list):
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if len(batch_imgs) == 0:
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continue
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batch_results = rec_predictor.predict(batch_imgs)
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for number, result_dict in enumerate(batch_results):
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filename = batch_names[number]
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print("{}:\t{}".format(filename, result_dict))
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batch_imgs = []
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batch_names = []
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if rec_predictor.benchmark:
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rec_predictor.auto_logger.report()
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return
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(args.config, overrides=args.override, show=True)
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main(config)
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