151 lines
5.4 KiB
Python
151 lines
5.4 KiB
Python
# Copyright (c) 2022 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 argparse
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import glob
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import os
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import numpy as np
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import yaml
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from onnxruntime import InferenceSession
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from .preprocess import Compose
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# Global dictionary
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SUPPORT_MODELS = {
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'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
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'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
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'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet'
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}
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml")
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parser.add_argument(
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'--onnx_file', type=str, default="model.onnx", help="onnx model file path")
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--image_file", type=str)
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def get_test_images(infer_dir, infer_img):
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"""
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Get image path list in TEST mode
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"""
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assert infer_img is not None or infer_dir is not None, \
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"--image_file or --image_dir should be set"
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assert infer_img is None or os.path.isfile(infer_img), \
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"{} is not a file".format(infer_img)
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assert infer_dir is None or os.path.isdir(infer_dir), \
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"{} is not a directory".format(infer_dir)
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# infer_img has a higher priority
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if infer_img and os.path.isfile(infer_img):
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return [infer_img]
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images = set()
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infer_dir = os.path.abspath(infer_dir)
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assert os.path.isdir(infer_dir), \
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"infer_dir {} is not a directory".format(infer_dir)
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exts = ['jpg', 'jpeg', 'png', 'bmp']
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exts += [ext.upper() for ext in exts]
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for ext in exts:
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images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
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images = list(images)
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assert len(images) > 0, "no image found in {}".format(infer_dir)
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print("Found {} inference images in total.".format(len(images)))
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return images
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class PredictConfig(object):
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"""set config of preprocess, postprocess and visualize
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Args:
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infer_config (str): path of infer_cfg.yml
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"""
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def __init__(self, infer_config):
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# parsing Yaml config for Preprocess
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with open(infer_config) as f:
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yml_conf = yaml.safe_load(f)
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self.check_model(yml_conf)
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self.arch = yml_conf['arch']
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self.preprocess_infos = yml_conf['Preprocess']
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self.min_subgraph_size = yml_conf['min_subgraph_size']
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self.label_list = yml_conf['label_list']
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self.use_dynamic_shape = yml_conf['use_dynamic_shape']
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self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
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self.mask = yml_conf.get("mask", False)
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self.tracker = yml_conf.get("tracker", None)
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self.nms = yml_conf.get("NMS", None)
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self.fpn_stride = yml_conf.get("fpn_stride", None)
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if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
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print(
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'The RCNN export model is used for ONNX and it only supports batch_size = 1'
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)
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self.print_config()
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def check_model(self, yml_conf):
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"""
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Raises:
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ValueError: loaded model not in supported model type
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"""
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for support_model in SUPPORT_MODELS:
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if support_model in yml_conf['arch']:
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return True
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raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
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'arch'], SUPPORT_MODELS))
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def print_config(self):
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print('----------- Model Configuration -----------')
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print('%s: %s' % ('Model Arch', self.arch))
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print('%s: ' % ('Transform Order'))
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for op_info in self.preprocess_infos:
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print('--%s: %s' % ('transform op', op_info['type']))
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print('--------------------------------------------')
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def predict_image(infer_config, predictor, img_list):
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# load preprocess transforms
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transforms = Compose(infer_config.preprocess_infos)
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# predict image
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for img_path in img_list:
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inputs = transforms(img_path)
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inputs["image"] = np.array(inputs["image"]).astype('float32')
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inputs_name = [var.name for var in predictor.get_inputs()]
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inputs = {k: inputs[k][None,] for k in inputs_name}
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outputs = predictor.run(output_names=None, input_feed=inputs)
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print("ONNXRuntime predict: ")
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if infer_config.arch in ["HRNet"]:
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print(np.array(outputs[0]))
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else:
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bboxes = np.array(outputs[0])
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for bbox in bboxes:
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if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
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print(f"{int(bbox[0])} {bbox[1]} "
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f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
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if __name__ == '__main__':
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FLAGS = parser.parse_args()
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# load image list
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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# load predictor
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predictor = InferenceSession(FLAGS.onnx_file)
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# load infer config
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infer_config = PredictConfig(FLAGS.infer_cfg)
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predict_image(infer_config, predictor, img_list)
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