更换文档检测模型

paddle_detection/deploy/third_engine/onnx/infer.py

@@ -0,0 +1,150 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import glob
+import os
+
+import numpy as np
+import yaml
+from onnxruntime import InferenceSession
+
+from .preprocess import Compose
+
+# Global dictionary
+SUPPORT_MODELS = {
+    'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
+    'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
+    'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet'
+}
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml")
+parser.add_argument(
+    '--onnx_file', type=str, default="model.onnx", help="onnx model file path")
+parser.add_argument("--image_dir", type=str)
+parser.add_argument("--image_file", type=str)
+
+
+def get_test_images(infer_dir, infer_img):
+    """
+    Get image path list in TEST mode
+    """
+    assert infer_img is not None or infer_dir is not None, \
+        "--image_file or --image_dir should be set"
+    assert infer_img is None or os.path.isfile(infer_img), \
+        "{} is not a file".format(infer_img)
+    assert infer_dir is None or os.path.isdir(infer_dir), \
+        "{} is not a directory".format(infer_dir)
+
+    # infer_img has a higher priority
+    if infer_img and os.path.isfile(infer_img):
+        return [infer_img]
+
+    images = set()
+    infer_dir = os.path.abspath(infer_dir)
+    assert os.path.isdir(infer_dir), \
+        "infer_dir {} is not a directory".format(infer_dir)
+    exts = ['jpg', 'jpeg', 'png', 'bmp']
+    exts += [ext.upper() for ext in exts]
+    for ext in exts:
+        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
+    images = list(images)
+
+    assert len(images) > 0, "no image found in {}".format(infer_dir)
+    print("Found {} inference images in total.".format(len(images)))
+
+    return images
+
+
+class PredictConfig(object):
+    """set config of preprocess, postprocess and visualize
+    Args:
+        infer_config (str): path of infer_cfg.yml
+    """
+
+    def __init__(self, infer_config):
+        # parsing Yaml config for Preprocess
+        with open(infer_config) as f:
+            yml_conf = yaml.safe_load(f)
+        self.check_model(yml_conf)
+        self.arch = yml_conf['arch']
+        self.preprocess_infos = yml_conf['Preprocess']
+        self.min_subgraph_size = yml_conf['min_subgraph_size']
+        self.label_list = yml_conf['label_list']
+        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
+        self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
+        self.mask = yml_conf.get("mask", False)
+        self.tracker = yml_conf.get("tracker", None)
+        self.nms = yml_conf.get("NMS", None)
+        self.fpn_stride = yml_conf.get("fpn_stride", None)
+        if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
+            print(
+                'The RCNN export model is used for ONNX and it only supports batch_size = 1'
+            )
+        self.print_config()
+
+    def check_model(self, yml_conf):
+        """
+        Raises:
+            ValueError: loaded model not in supported model type
+        """
+        for support_model in SUPPORT_MODELS:
+            if support_model in yml_conf['arch']:
+                return True
+        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
+                                                                      'arch'], SUPPORT_MODELS))
+
+    def print_config(self):
+        print('-----------  Model Configuration -----------')
+        print('%s: %s' % ('Model Arch', self.arch))
+        print('%s: ' % ('Transform Order'))
+        for op_info in self.preprocess_infos:
+            print('--%s: %s' % ('transform op', op_info['type']))
+        print('--------------------------------------------')
+
+
+def predict_image(infer_config, predictor, img_list):
+    # load preprocess transforms
+    transforms = Compose(infer_config.preprocess_infos)
+    # predict image
+    for img_path in img_list:
+        inputs = transforms(img_path)
+        inputs["image"] = np.array(inputs["image"]).astype('float32')
+        inputs_name = [var.name for var in predictor.get_inputs()]
+        inputs = {k: inputs[k][None,] for k in inputs_name}
+
+        outputs = predictor.run(output_names=None, input_feed=inputs)
+
+        print("ONNXRuntime predict: ")
+        if infer_config.arch in ["HRNet"]:
+            print(np.array(outputs[0]))
+        else:
+            bboxes = np.array(outputs[0])
+            for bbox in bboxes:
+                if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
+                    print(f"{int(bbox[0])} {bbox[1]} "
+                          f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
+
+
+if __name__ == '__main__':
+    FLAGS = parser.parse_args()
+    # load image list
+    img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
+    # load predictor
+    predictor = InferenceSession(FLAGS.onnx_file)
+    # load infer config
+    infer_config = PredictConfig(FLAGS.infer_cfg)
+
+    predict_image(infer_config, predictor, img_list)