--- a
+++ b/tools/onnx2tensorrt.py
@@ -0,0 +1,276 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+from typing import Iterable, Optional, Union
+
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+import onnxruntime as ort
+import torch
+from mmcv.ops import get_onnxruntime_op_path
+from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt,
+                           save_trt_engine)
+
+from mmseg.apis.inference import LoadImage
+from mmseg.datasets import DATASETS
+from mmseg.datasets.pipelines import Compose
+
+
+def get_GiB(x: int):
+    """return x GiB."""
+    return x * (1 << 30)
+
+
+def _prepare_input_img(img_path: str,
+                       test_pipeline: Iterable[dict],
+                       shape: Optional[Iterable] = None,
+                       rescale_shape: Optional[Iterable] = None) -> dict:
+    # build the data pipeline
+    if shape is not None:
+        test_pipeline[1]['img_scale'] = (shape[1], shape[0])
+    test_pipeline[1]['transforms'][0]['keep_ratio'] = False
+    test_pipeline = [LoadImage()] + test_pipeline[1:]
+    test_pipeline = Compose(test_pipeline)
+    # prepare data
+    data = dict(img=img_path)
+    data = test_pipeline(data)
+    imgs = data['img']
+    img_metas = [i.data for i in data['img_metas']]
+
+    if rescale_shape is not None:
+        for img_meta in img_metas:
+            img_meta['ori_shape'] = tuple(rescale_shape) + (3, )
+
+    mm_inputs = {'imgs': imgs, 'img_metas': img_metas}
+
+    return mm_inputs
+
+
+def _update_input_img(img_list: Iterable, img_meta_list: Iterable):
+    # update img and its meta list
+    N = img_list[0].size(0)
+    img_meta = img_meta_list[0][0]
+    img_shape = img_meta['img_shape']
+    ori_shape = img_meta['ori_shape']
+    pad_shape = img_meta['pad_shape']
+    new_img_meta_list = [[{
+        'img_shape':
+        img_shape,
+        'ori_shape':
+        ori_shape,
+        'pad_shape':
+        pad_shape,
+        'filename':
+        img_meta['filename'],
+        'scale_factor':
+        (img_shape[1] / ori_shape[1], img_shape[0] / ori_shape[0]) * 2,
+        'flip':
+        False,
+    } for _ in range(N)]]
+
+    return img_list, new_img_meta_list
+
+
+def show_result_pyplot(img: Union[str, np.ndarray],
+                       result: np.ndarray,
+                       palette: Optional[Iterable] = None,
+                       fig_size: Iterable[int] = (15, 10),
+                       opacity: float = 0.5,
+                       title: str = '',
+                       block: bool = True):
+    img = mmcv.imread(img)
+    img = img.copy()
+    seg = result[0]
+    seg = mmcv.imresize(seg, img.shape[:2][::-1])
+    palette = np.array(palette)
+    assert palette.shape[1] == 3
+    assert len(palette.shape) == 2
+    assert 0 < opacity <= 1.0
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    img = img * (1 - opacity) + color_seg * opacity
+    img = img.astype(np.uint8)
+
+    plt.figure(figsize=fig_size)
+    plt.imshow(mmcv.bgr2rgb(img))
+    plt.title(title)
+    plt.tight_layout()
+    plt.show(block=block)
+
+
+def onnx2tensorrt(onnx_file: str,
+                  trt_file: str,
+                  config: dict,
+                  input_config: dict,
+                  fp16: bool = False,
+                  verify: bool = False,
+                  show: bool = False,
+                  dataset: str = 'CityscapesDataset',
+                  workspace_size: int = 1,
+                  verbose: bool = False):
+    import tensorrt as trt
+    min_shape = input_config['min_shape']
+    max_shape = input_config['max_shape']
+    # create trt engine and wrapper
+    opt_shape_dict = {'input': [min_shape, min_shape, max_shape]}
+    max_workspace_size = get_GiB(workspace_size)
+    trt_engine = onnx2trt(
+        onnx_file,
+        opt_shape_dict,
+        log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
+        fp16_mode=fp16,
+        max_workspace_size=max_workspace_size)
+    save_dir, _ = osp.split(trt_file)
+    if save_dir:
+        os.makedirs(save_dir, exist_ok=True)
+    save_trt_engine(trt_engine, trt_file)
+    print(f'Successfully created TensorRT engine: {trt_file}')
+
+    if verify:
+        inputs = _prepare_input_img(
+            input_config['input_path'],
+            config.data.test.pipeline,
+            shape=min_shape[2:])
+
+        imgs = inputs['imgs']
+        img_metas = inputs['img_metas']
+        img_list = [img[None, :] for img in imgs]
+        img_meta_list = [[img_meta] for img_meta in img_metas]
+        # update img_meta
+        img_list, img_meta_list = _update_input_img(img_list, img_meta_list)
+
+        if max_shape[0] > 1:
+            # concate flip image for batch test
+            flip_img_list = [_.flip(-1) for _ in img_list]
+            img_list = [
+                torch.cat((ori_img, flip_img), 0)
+                for ori_img, flip_img in zip(img_list, flip_img_list)
+            ]
+
+        # Get results from ONNXRuntime
+        ort_custom_op_path = get_onnxruntime_op_path()
+        session_options = ort.SessionOptions()
+        if osp.exists(ort_custom_op_path):
+            session_options.register_custom_ops_library(ort_custom_op_path)
+        sess = ort.InferenceSession(onnx_file, session_options)
+        sess.set_providers(['CPUExecutionProvider'], [{}])  # use cpu mode
+        onnx_output = sess.run(['output'],
+                               {'input': img_list[0].detach().numpy()})[0][0]
+
+        # Get results from TensorRT
+        trt_model = TRTWraper(trt_file, ['input'], ['output'])
+        with torch.no_grad():
+            trt_outputs = trt_model({'input': img_list[0].contiguous().cuda()})
+        trt_output = trt_outputs['output'][0].cpu().detach().numpy()
+
+        if show:
+            dataset = DATASETS.get(dataset)
+            assert dataset is not None
+            palette = dataset.PALETTE
+
+            show_result_pyplot(
+                input_config['input_path'],
+                (onnx_output[0].astype(np.uint8), ),
+                palette=palette,
+                title='ONNXRuntime',
+                block=False)
+            show_result_pyplot(
+                input_config['input_path'], (trt_output[0].astype(np.uint8), ),
+                palette=palette,
+                title='TensorRT')
+
+        np.testing.assert_allclose(
+            onnx_output, trt_output, rtol=1e-03, atol=1e-05)
+        print('TensorRT and ONNXRuntime output all close.')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert MMSegmentation models from ONNX to TensorRT')
+    parser.add_argument('config', help='Config file of the model')
+    parser.add_argument('model', help='Path to the input ONNX model')
+    parser.add_argument(
+        '--trt-file', type=str, help='Path to the output TensorRT engine')
+    parser.add_argument(
+        '--max-shape',
+        type=int,
+        nargs=4,
+        default=[1, 3, 400, 600],
+        help='Maximum shape of model input.')
+    parser.add_argument(
+        '--min-shape',
+        type=int,
+        nargs=4,
+        default=[1, 3, 400, 600],
+        help='Minimum shape of model input.')
+    parser.add_argument('--fp16', action='store_true', help='Enable fp16 mode')
+    parser.add_argument(
+        '--workspace-size',
+        type=int,
+        default=1,
+        help='Max workspace size in GiB')
+    parser.add_argument(
+        '--input-img', type=str, default='', help='Image for test')
+    parser.add_argument(
+        '--show', action='store_true', help='Whether to show output results')
+    parser.add_argument(
+        '--dataset',
+        type=str,
+        default='CityscapesDataset',
+        help='Dataset name')
+    parser.add_argument(
+        '--verify',
+        action='store_true',
+        help='Verify the outputs of ONNXRuntime and TensorRT')
+    parser.add_argument(
+        '--verbose',
+        action='store_true',
+        help='Whether to verbose logging messages while creating \
+                TensorRT engine.')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+
+    assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
+    args = parse_args()
+
+    if not args.input_img:
+        args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.png')
+
+    # check arguments
+    assert osp.exists(args.config), 'Config {} not found.'.format(args.config)
+    assert osp.exists(args.model), \
+        'ONNX model {} not found.'.format(args.model)
+    assert args.workspace_size >= 0, 'Workspace size less than 0.'
+    assert DATASETS.get(args.dataset) is not None, \
+        'Dataset {} does not found.'.format(args.dataset)
+    for max_value, min_value in zip(args.max_shape, args.min_shape):
+        assert max_value >= min_value, \
+            'max_shape should be larger than min shape'
+
+    input_config = {
+        'min_shape': args.min_shape,
+        'max_shape': args.max_shape,
+        'input_path': args.input_img
+    }
+
+    cfg = mmcv.Config.fromfile(args.config)
+    onnx2tensorrt(
+        args.model,
+        args.trt_file,
+        cfg,
+        input_config,
+        fp16=args.fp16,
+        verify=args.verify,
+        show=args.show,
+        dataset=args.dataset,
+        workspace_size=args.workspace_size,
+        verbose=args.verbose)