[Project] Add pp_mobileseg onnx inference demo (#3268)

## Motivation Add a model deployment example. ## Modification Add an inference script and update the README. ## BC-breaking (Optional) None ## Use cases (Optional) In README.
2025-06-03 22:03:48 +08:00 · 2023-08-16 18:01:22 +08:00 · 2023-08-16 18:01:22 +08:00 · 92774182ba
commit 92774182ba
parent 0391bc4998
2 changed files with 260 additions and 0 deletions
--- a/projects/pp_mobileseg/README.md
+++ b/projects/pp_mobileseg/README.md
@ -43,6 +43,63 @@ Same as other models in MMsegmentation, you can run the following command to tes
 ./tools/dist_test.sh projects/pp_mobileseg/configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_base.py checkpoints/pp_mobileseg_mobilenetv3_2xb16_3rdparty-base_512x512-ade20k-f12b44f3.pth 8
 ```

+## Inference with ONNXRuntime
+
+### Prerequisites
+
+**1. Install onnxruntime inference engine.**
+
+Choose one of the following ways to install onnxruntime.
+
+- CPU version
+
+```shell
+pip install onnxruntime==1.15.1
+wget https://github.com/microsoft/onnxruntime/releases/download/v1.15.1/onnxruntime-linux-x64-1.15.1.tgz
+tar -zxvf onnxruntime-linux-x64-1.15.1.tgz
+export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-1.15.1
+export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
+```
+
+**2. Convert model to onnx file**
+
+- Install `mim` and `mmdeploy`.
+
+```shell
+pip install openmim
+mim install mmdeploy
+git clone https://github.com/open-mmlab/mmdeploy.git
+```
+
+- Download pp_mobileseg model.
+
+```shell
+wget https://download.openmmlab.com/mmsegmentation/v0.5/pp_mobileseg/pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth
+```
+
+- Convert model to onnx files.
+
+```shell
+python mmdeploy/tools/deploy.py mmdeploy/configs/mmseg/segmentation_onnxruntime_dynamic.py \
+    configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py \
+    pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth \
+    ../../demo/demo.png \
+    --work-dir mmdeploy_model/mmseg/ort \
+    --show
+```
+
+**3. Run demo**
+
+```shell
+python inference_onnx.py ${ONNX_FILE_PATH} ${IMAGE_PATH} [${MODEL_INPUT_SIZE} ${DEVICE} ${OUTPUT_IMAGE_PATH}]
+```
+
+Example:
+
+```shell
+python inference_onnx.py mmdeploy_model/mmseg/ort/end2end.onnx ../../demo/demo.png
+```
+
 ## Citation

 If you find our project useful in your research, please consider citing:
--- a/projects/pp_mobileseg/inference_onnx.py
+++ b/projects/pp_mobileseg/inference_onnx.py
@ -0,0 +1,203 @@
+import argparse
+import time
+from typing import List, Tuple
+
+import cv2
+import loguru
+import numpy as np
+import onnxruntime as ort
+
+logger = loguru.logger
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='PP_Mobileseg ONNX inference demo.')
+    parser.add_argument('onnx_file', help='ONNX file path')
+    parser.add_argument('image_file', help='Input image file path')
+    parser.add_argument(
+        '--input-size',
+        type=int,
+        nargs='+',
+        default=[512, 512],
+        help='input image size')
+    parser.add_argument(
+        '--device', help='device type for inference', default='cpu')
+    parser.add_argument(
+        '--save-path',
+        help='path to save the output image',
+        default='output.jpg')
+    args = parser.parse_args()
+    return args
+
+
+def preprocess(
+    img: np.ndarray, input_size: Tuple[int, int] = (512, 512)
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Preprocess image for inference."""
+    img_shape = img.shape[:2]
+    # Resize
+    resized_img = cv2.resize(img, input_size)
+
+    # Normalize
+    mean = np.array([123.575, 116.28, 103.53], dtype=np.float32)
+    std = np.array([58.395, 57.12, 57.375], dtype=np.float32)
+    resized_img = (resized_img - mean) / std
+
+    return resized_img, img_shape
+
+
+def build_session(onnx_file: str, device: str = 'cpu') -> ort.InferenceSession:
+    """Build onnxruntime session.
+
+    Args:
+        onnx_file (str): ONNX file path.
+        device (str): Device type for inference.
+
+    Returns:
+        sess (ort.InferenceSession): ONNXRuntime session.
+    """
+    providers = ['CPUExecutionProvider'
+                 ] if device == 'cpu' else ['CUDAExecutionProvider']
+    sess = ort.InferenceSession(path_or_bytes=onnx_file, providers=providers)
+
+    return sess
+
+
+def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
+    """Inference RTMPose model.
+
+    Args:
+        sess (ort.InferenceSession): ONNXRuntime session.
+        img (np.ndarray): Input image in shape.
+
+    Returns:
+        outputs (np.ndarray): Output of RTMPose model.
+    """
+    # build input
+    input_img = [img.transpose(2, 0, 1).astype(np.float32)]
+
+    # build output
+    sess_input = {sess.get_inputs()[0].name: input_img}
+    sess_output = []
+    for out in sess.get_outputs():
+        sess_output.append(out.name)
+
+    # inference
+    outputs = sess.run(output_names=sess_output, input_feed=sess_input)
+
+    return outputs
+
+
+def postprocess(outputs: List[np.ndarray],
+                origin_shape: Tuple[int, int]) -> np.ndarray:
+    """Postprocess outputs of PP_Mobileseg model.
+
+    Args:
+        outputs (List[np.ndarray]): Outputs of PP_Mobileseg model.
+        origin_shape (Tuple[int, int]): Input size of PP_Mobileseg model.
+
+    Returns:
+        seg_map (np.ndarray): Segmentation map.
+    """
+    seg_map = outputs[0][0][0]
+    seg_map = cv2.resize(seg_map.astype(np.float32), origin_shape)
+    return seg_map
+
+
+def visualize(img: np.ndarray,
+              seg_map: np.ndarray,
+              filename: str = 'output.jpg',
+              opacity: float = 0.8) -> np.ndarray:
+    assert 0.0 <= opacity <= 1.0, 'opacity should be in range [0, 1]'
+    palette = np.array(PALETTE)
+    color_seg = np.zeros((seg_map.shape[0], seg_map.shape[1], 3),
+                         dtype=np.uint8)
+    for label, color in enumerate(palette):
+        color_seg[seg_map == label, :] = color
+    # convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    img = img * (1 - opacity) + color_seg * opacity
+    cv2.imwrite(filename, img)
+
+    return img
+
+
+def main():
+    args = parse_args()
+    logger.info('Start running model inference...')
+
+    # read image from file
+    logger.info(f'1. Read image from file {args.image_file}...')
+    img = cv2.imread(args.image_file)
+
+    # build onnx model
+    logger.info(f'2. Build onnx model from {args.onnx_file}...')
+    sess = build_session(args.onnx_file, args.device)
+
+    # preprocess
+    logger.info('3. Preprocess image...')
+    model_input_size = tuple(args.input_size)
+    assert len(model_input_size) == 2
+    resized_img, origin_shape = preprocess(img, model_input_size)
+
+    # inference
+    logger.info('4. Inference...')
+    start = time.time()
+    outputs = inference(sess, resized_img)
+    logger.info(f'Inference time: {time.time() - start:.4f}s')
+
+    # postprocess
+    logger.info('5. Postprocess...')
+    h, w = origin_shape
+    seg_map = postprocess(outputs, (w, h))
+
+    # visualize
+    logger.info('6. Visualize...')
+    visualize(img, seg_map, args.save_path)
+
+    logger.info('Done...')
+
+
+PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+           [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+           [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+           [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+           [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+           [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+           [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+           [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+           [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+           [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+           [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+           [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+           [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+           [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+           [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+           [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+           [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+           [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+           [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+           [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+           [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+           [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+           [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+           [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+           [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+           [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+           [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+           [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+           [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+           [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+           [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+           [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+           [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+           [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+           [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+           [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+           [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+           [102, 255, 0], [92, 0, 255]]
+
+if __name__ == '__main__':
+    main()