[Feature] Update deploy test tools (#553)

* add trt test tool

* create deploy_test, update document

* fix with isort

* move import inside __init__

* remove comment, fix doc

* update document

docs/useful_tools.md

@@ -76,9 +76,9 @@ Description of arguments:
 
 **Note**: This tool is still experimental. Some customized operators are not supported for now.
 
-### Evaluate ONNX model with ONNXRuntime
+### Evaluate ONNX model
 
-We provide `tools/ort_test.py` to evaluate ONNX model with ONNXRuntime backend.
+We provide `tools/deploy_test.py` to evaluate ONNX model with different backend.
 
 #### Prerequisite
 
@@ -88,12 +88,15 @@ We provide `tools/ort_test.py` to evaluate ONNX model with ONNXRuntime backend.
   pip install onnx onnxruntime-gpu
   ```
 
+- Install TensorRT following [how-to-build-tensorrt-plugins-in-mmcv](https://mmcv.readthedocs.io/en/latest/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv)(optional)
+
 #### Usage
 
 ```bash
-python tools/ort_test.py \
+python tools/deploy_test.py \
     ${CONFIG_FILE} \
-    ${ONNX_FILE} \
+    ${MODEL_FILE} \
+    ${BACKEND} \
     --out ${OUTPUT_FILE} \
     --eval ${EVALUATION_METRICS} \
     --show \
@@ -106,7 +109,8 @@ python tools/ort_test.py \
 Description of all arguments
 
 - `config`: The path of a model config file.
-- `model`: The path of a ONNX model file.
+- `model`: The path of a converted model file.
+- `backend`: Backend of the inference, options: `onnxruntime`, `tensorrt`.
 - `--out`: The path of output result file in pickle format.
 - `--format-only` : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to `False`. Note that this argument is **mutually exclusive** with `--eval`.
 - `--eval`: Evaluation metrics, which depends on the dataset, e.g., "mIoU" for generic datasets, and "cityscapes" for Cityscapes. Note that this argument is **mutually exclusive** with `--format-only`.
@@ -118,12 +122,17 @@ Description of all arguments
 
 #### Results and Models
 
-|   Model    |                     Config                     |  Dataset   | Metric | PyTorch | ONNXRuntime |
+|   Model    |                     Config                      |  Dataset   | Metric | PyTorch | ONNXRuntime | TensorRT-fp32 | TensorRT-fp16 |
-| :--------: | :--------------------------------------------: | :--------: | :----: | :-----: | :---------: |
+| :--------: | :---------------------------------------------: | :--------: | :----: | :-----: | :---------: | :-----------: | :-----------: |
-|    FCN     |     fcn_r50-d8_512x1024_40k_cityscapes.py      | cityscapes |  mIoU  |  72.2   |    72.2     |
+|    FCN     |      fcn_r50-d8_512x1024_40k_cityscapes.py      | cityscapes |  mIoU  |  72.2   |    72.2     |     72.2      |     72.2      |
-|   PSPNet   |    pspnet_r50-d8_769x769_40k_cityscapes.py     | cityscapes |  mIoU  |  78.2   |    78.1     |
+|   PSPNet   |    pspnet_r50-d8_512x1024_40k_cityscapes.py     | cityscapes |  mIoU  |  77.8   |    77.8     |     77.8      |     77.8      |
-| deeplabv3  |   deeplabv3_r50-d8_769x769_40k_cityscapes.py   | cityscapes |  mIoU  |  78.5   |    78.3     |
+| deeplabv3  |   deeplabv3_r50-d8_512x1024_40k_cityscapes.py   | cityscapes |  mIoU  |  79.0   |    79.0     |     79.0      |     79.0      |
-| deeplabv3+ | deeplabv3plus_r50-d8_769x769_40k_cityscapes.py | cityscapes |  mIoU  |  78.9   |    78.7     |
+| deeplabv3+ | deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py | cityscapes |  mIoU  |  79.6   |    79.5     |     79.5      |     79.5      |
+|   PSPNet   |     pspnet_r50-d8_769x769_40k_cityscapes.py     | cityscapes |  mIoU  |  78.2   |    78.1     |               |               |
+| deeplabv3  |   deeplabv3_r50-d8_769x769_40k_cityscapes.py    | cityscapes |  mIoU  |  78.5   |    78.3     |               |               |
+| deeplabv3+ | deeplabv3plus_r50-d8_769x769_40k_cityscapes.py  | cityscapes |  mIoU  |  78.9   |    78.7     |               |               |
+
+**Note**: TensorRT is only available on configs with `whole mode`.
 
 ### Convert to TorchScript (experimental)
 

tools/deploy_test.py

@@ -2,10 +2,10 @@ import argparse
 import os
 import os.path as osp
 import warnings
+from typing import Any, Iterable
 
 import mmcv
 import numpy as np
-import onnxruntime as ort
 import torch
 from mmcv.parallel import MMDataParallel
 from mmcv.runner import get_dist_info
@@ -18,8 +18,10 @@ from mmseg.models.segmentors.base import BaseSegmentor
 
 class ONNXRuntimeSegmentor(BaseSegmentor):
 
-    def __init__(self, onnx_file, cfg, device_id):
+    def __init__(self, onnx_file: str, cfg: Any, device_id: int):
         super(ONNXRuntimeSegmentor, self).__init__()
+        import onnxruntime as ort
+
         # get the custom op path
         ort_custom_op_path = ''
         try:
@@ -60,7 +62,8 @@ class ONNXRuntimeSegmentor(BaseSegmentor):
     def forward_train(self, imgs, img_metas, **kwargs):
         raise NotImplementedError('This method is not implemented.')
 
-    def simple_test(self, img, img_meta, **kwargs):
+    def simple_test(self, img: torch.Tensor, img_meta: Iterable,
+                    **kwargs) -> list:
         device_type = img.device.type
         self.io_binding.bind_input(
             name='input',
@@ -87,11 +90,63 @@ class ONNXRuntimeSegmentor(BaseSegmentor):
         raise NotImplementedError('This method is not implemented.')
 
 
-def parse_args():
+class TensorRTSegmentor(BaseSegmentor):
+
+    def __init__(self, trt_file: str, cfg: Any, device_id: int):
+        super(TensorRTSegmentor, self).__init__()
+        from mmcv.tensorrt import TRTWraper, load_tensorrt_plugin
+        try:
+            load_tensorrt_plugin()
+        except (ImportError, ModuleNotFoundError):
+            warnings.warn('If input model has custom op from mmcv, \
+                you may have to build mmcv with TensorRT from source.')
+        model = TRTWraper(
+            trt_file, input_names=['input'], output_names=['output'])
+
+        self.model = model
+        self.device_id = device_id
+        self.cfg = cfg
+        self.test_mode = cfg.model.test_cfg.mode
+
+    def extract_feat(self, imgs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def encode_decode(self, img, img_metas):
+        raise NotImplementedError('This method is not implemented.')
+
+    def forward_train(self, imgs, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def simple_test(self, img: torch.Tensor, img_meta: Iterable,
+                    **kwargs) -> list:
+        with torch.cuda.device(self.device_id), torch.no_grad():
+            seg_pred = self.model({'input': img})['output']
+        seg_pred = seg_pred.detach().cpu().numpy()
+        # whole might support dynamic reshape
+        ori_shape = img_meta[0]['ori_shape']
+        if not (ori_shape[0] == seg_pred.shape[-2]
+                and ori_shape[1] == seg_pred.shape[-1]):
+            seg_pred = torch.from_numpy(seg_pred).float()
+            seg_pred = torch.nn.functional.interpolate(
+                seg_pred, size=tuple(ori_shape[:2]), mode='nearest')
+            seg_pred = seg_pred.long().detach().cpu().numpy()
+        seg_pred = seg_pred[0]
+        seg_pred = list(seg_pred)
+        return seg_pred
+
+    def aug_test(self, imgs, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+
+def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description='mmseg onnxruntime backend test (and eval) a model')
+        description='mmseg backend test (and eval)')
     parser.add_argument('config', help='test config file path')
     parser.add_argument('model', help='Input model file')
+    parser.add_argument(
+        '--backend',
+        help='Backend of the model.',
+        choices=['onnxruntime', 'tensorrt'])
     parser.add_argument('--out', help='output result file in pickle format')
     parser.add_argument(
         '--format-only',
@@ -163,7 +218,12 @@ def main():
 
     # load onnx config and meta
     cfg.model.train_cfg = None
+
+    if args.backend == 'onnxruntime':
         model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0)
+    elif args.backend == 'tensorrt':
+        model = TensorRTSegmentor(args.model, cfg=cfg, device_id=0)
+
     model.CLASSES = dataset.CLASSES
     model.PALETTE = dataset.PALETTE