166 lines
6.2 KiB
Python
166 lines
6.2 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 sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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import cv2
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import numpy as np
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from utils import logger
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from utils import config
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from utils.predictor import Predictor
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from utils.get_image_list import get_image_list
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from python.preprocess import create_operators
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from python.postprocess import build_postprocess
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class ClsPredictor(Predictor):
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def __init__(self, config):
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super().__init__(config["Global"])
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self.preprocess_ops = []
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self.postprocess = None
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if "PreProcess" in config:
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if "transform_ops" in config["PreProcess"]:
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self.preprocess_ops = create_operators(config["PreProcess"][
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"transform_ops"])
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if "PostProcess" in config:
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self.postprocess = build_postprocess(config["PostProcess"])
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# for whole_chain project to test each repo of paddle
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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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import os
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pid = os.getpid()
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size = config["PreProcess"]["transform_ops"][1]["CropImage"][
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"size"]
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if config["Global"].get("use_int8", False):
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precision = "int8"
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elif config["Global"].get("use_fp16", False):
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precision = "fp16"
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else:
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precision = "fp32"
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self.auto_logger = auto_log.AutoLogger(
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model_name=config["Global"].get("model_name", "cls"),
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model_precision=precision,
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batch_size=config["Global"].get("batch_size", 1),
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data_shape=[3, size, size],
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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):
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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 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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cls_predictor = ClsPredictor(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 = cls_predictor.predict(batch_imgs)
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for number, result_dict in enumerate(batch_results):
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if "PersonAttribute" in config[
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"PostProcess"] or "VehicleAttribute" in config[
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"PostProcess"] or "TableAttribute" in config["PostProcess"]:
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filename = batch_names[number]
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print("{}:\t {}".format(filename, result_dict))
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else:
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filename = batch_names[number]
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clas_ids = result_dict["class_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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label_names = result_dict["label_names"]
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print(
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"{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
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format(filename, clas_ids, scores_str, label_names))
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batch_imgs = []
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batch_names = []
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if cls_predictor.benchmark:
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cls_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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