[MMSIG] Support the deployment of SparseInst on TensorRT

[Boomerl]

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Motivation

Support the deployment of real-time instance segmentation algorithm SparseInst on TensorRT backend.

Modification

Support the deployment of real-time instance segmentation algorithm SparseInst on TensorRT backend.

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repositories?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

Checklist

1. Pre-commit or other linting tools are used to fix the potential lint issues.
2. The modification is covered by complete unit tests. If not, please add more unit tests to ensure the correctness.
3. If the modification has a dependency on downstream projects of a newer version, this PR should be tested with all supported versions of downstream projects.
4. The documentation has been modified accordingly, like docstring or example tutorials.

[Boomerl]

Because of the lack of sparseinst metafile, I didn't finish the regression test.

[mchaniotakis]

Great work! Could we also include queryInst in this PR since they are very similar?

[Boomerl]

Great work! Could we also include queryInst in this PR since they are very similar?

OK, I will do it.

[RunningLeon]

could we post the evaluation results of trt model and check if it's aligned with pytorch model?

[RunningLeon]

when build trt engine, there's warning

Profile kMIN values are not self-consistent. /encoder/ppm/Resize_1: IResizeLayer requires that if input dimension is zero, output dimension must be zero too (axis = 3 input dimension = (+ (CEIL_DIV (+ width -800) 416) 1) output dimension = (+ (CEIL_DIV (+ width -32) 32) 1))
Condition '==' violated: 0 != 10. Instruction: CHECK_EQUAL 0 10.

This might lead to failure in some cases.
Running test.py failed after some iterations

Epoch(test) [  50/5000]    eta: 0:03:57  time: 0.0470  data_time: 0.0014  memory: 495  
12/15 02:59:38 - mmengine - INFO - Epoch(test) [ 100/5000]    eta: 0:03:35  time: 0.0413  data_time: 0.0013  memory: 506  
12/15 02:59:40 - mmengine - INFO - Epoch(test) [ 150/5000]    eta: 0:03:24  time: 0.0349  data_time: 0.0014  memory: 501  
12/15 02:59:42 - mmengine - INFO - Epoch(test) [ 200/5000]    eta: 0:03:16  time: 0.0358  data_time: 0.0014  memory: 501  
12/15 02:59:44 - mmengine - INFO - Epoch(test) [ 250/5000]    eta: 0:03:10  time: 0.0344  data_time: 0.0013  memory: 505  
12/15 02:59:46 - mmengine - INFO - Epoch(test) [ 300/5000]    eta: 0:03:07  time: 0.0371  data_time: 0.0013  memory: 501  
12/15 02:59:48 - mmengine - INFO - Epoch(test) [ 350/5000]    eta: 0:03:06  time: 0.0420  data_time: 0.0015  memory: 508  
12/15 02:59:50 - mmengine - INFO - Epoch(test) [ 400/5000]    eta: 0:03:03  time: 0.0340  data_time: 0.0013  memory: 501  
12/15 02:59:52 - mmengine - INFO - Epoch(test) [ 450/5000]    eta: 0:03:01  time: 0.0420  data_time: 0.0014  memory: 508  
12/15 02:59:54 - mmengine - INFO - Epoch(test) [ 500/5000]    eta: 0:02:59  time: 0.0390  data_time: 0.0014  memory: 502  
[12/15/2023-02:59:56] [TRT] [E] 7: [shapeMachine.cpp::executeContinuation::864] Error Code 7: Internal Error (/encoder/ppm/Resize_1: IResizeLayer requires that if input dimension is zero, output dimension must be zero too (axis = 3 input dimension = (+ (CEIL_DIV (+ width -800) 416) 1) output dimension = (+ (CEIL_DIV (+ width -32) 32) 1))
 Condition '==' violated: 0 != 12. Instruction: CHECK_EQUAL 0 12.)
Traceback (most recent call last):
  File "tools/test.py", line 159, in <module>
    main()
  File "tools/test.py", line 153, in main
    runner.test()
  File "/usr/local/lib/python3.8/dist-packages/mmengine/runner/runner.py", line 1791, in test
    metrics = self.test_loop.run()  # type: ignore
  File "/usr/local/lib/python3.8/dist-packages/mmengine/runner/loops.py", line 435, in run
    self.run_iter(idx, data_batch)
  File "/usr/local/lib/python3.8/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/mmengine/runner/loops.py", line 454, in run_iter
    outputs = self.runner.model.test_step(data_batch)
  File "/usr/local/lib/python3.8/dist-packages/mmengine/model/base_model/base_model.py", line 145, in test_step
    return self._run_forward(data, mode='predict')  # type: ignore
  File "/usr/local/lib/python3.8/dist-packages/mmengine/model/base_model/base_model.py", line 340, in _run_forward
    results = self(**data, mode=mode)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/deploy/object_detection_model.py", line 296, in forward
    outputs = self.predict(inputs)
  File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/deploy/object_detection_model.py", line 313, in predict
    outputs = self.wrapper({self.input_name: imgs})
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/workspace/mmdeploy/mmdeploy/backend/tensorrt/wrapper.py", line 167, in forward
    shape = tuple(self.context.get_binding_shape(idx))
ValueError: __len__() should return >= 0

[RunningLeon]

@Boomerl From my experience, tensorrt have not supported nn.AdaptiveAvgPool2d which is used in here. This means sparseinst model can only accept static shape for tensorrt backends, which should use configs like configs/mmdet/instance-seg/instance-seg_tensorrt_static-800x1344.py.
You could include this note in docs like https://github.com/open-mmlab/mmdeploy/blob/main/docs/en/04-supported-codebases/mmdet.md#reminder

[Boomerl]

Thank u for the guidance, I will check it immediately.

[Boomerl]

When I use static deploy config to convert this model to trt, I met an error:
[TRT] [E] 4: [network.cpp::operator()::3088] Error Code 4: Internal Error (input: kMIN dimensions in profile 0 are [1,3,640,853] but input has static dimensions [1,3,576,864].)
How can I solve this error?

[RunningLeon]

When I use static deploy config to convert this model to trt, I met an error: [TRT] [E] 4: [network.cpp::operator()::3088] Error Code 4: Internal Error (input: kMIN dimensions in profile 0 are [1,3,640,853] but input has static dimensions [1,3,576,864].) How can I solve this error?

@Boomerl could you provide the full script you are running?

[Boomerl]

python3 tools/deploy.py \ configs/mmdet/instance-seg/instance-seg_sparseinst_tensorrt_static-640x640.py \ sparseinst/sparseinst_r50_iam_8xb8-ms-270k_coco.py \ sparseinst/sparseinst_r50_iam_8xb8-ms-270k_coco_20221111_181051-72c711cd.pth \ ./demo/resources/det.jpg \ --work-dir sparseinst/workdir/trt \ --device cuda \ --show \ --dump-info
This is the script I used to convert model.

[Boomerl]

I used the deploy config below:
base = [
'../base/base_instance-seg_static.py',
'../../base/backends/tensorrt.py'
]
onnx_config = dict(input_shape=None)
codebase_config = dict(post_processing=dict(export_postprocess_mask=True))
backend_config = dict(
common_config=dict(max_workspace_size=1 << 32),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 640, 853],
opt_shape=[1, 3, 640, 853],
max_shape=[1, 3, 640, 853])))
])