mirror of
https://github.com/open-mmlab/mmdeploy.git
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308e28fcb0
* support cascade (mask) rcnn * fix docstring * support SwinTransformer * move dense_head support to this branch * fix function names * fix part of uts of mmdet * fix for mmdet ut * fix det model cfg for ut * fix test_object_detection.py * fix mmdet object_detection_model.py * fix mmdet yolov3 ort ut * fix part of uts * fix cascade bbox head ut * fix cascade bbox head ut * remove useless ssd ncnn test * fix ncnn wrapper * fix openvino ut for reppoint head * fix openvino cascade mask rcnn * sync codes * support roll * remove unused pad * fix yolox * fix isort * fix lint * fix flake8 * reply for comments and fix failed ut * fix sdk_export in dump_info * fix temp hidden xlsx bugs * fix mmdet regression test * fix lint * fix timer * fix timecount side-effect * adapt profile.py for mmdet 2.0 * hardcode report.txt for T4 benchmark test: temp version * fix no-visualizer case * fix backend_model * fix android build * adapt new mmdet 2.0 0825 * fix new 2.0 * fix test_mmdet_structures * fix test_object_detection * fix codebase import * fix ut * fix all mmdet uts * fix det * fix mmdet trt * fix ncnn onnx optimize
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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import pytest
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import torch
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from mmengine import Config
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Backend, Codebase
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from mmdeploy.utils.test import (WrapFunction, check_backend,
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get_rewrite_outputs)
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import_codebase(Codebase.MMDET)
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@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
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def test_distance2bbox(backend_type: Backend):
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check_backend(backend_type)
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deploy_cfg = Config(
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dict(
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onnx_config=dict(output_names=None, input_shape=None),
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backend_config=dict(type=backend_type.value, model_inputs=None),
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codebase_config=dict(type='mmdet', task='ObjectDetection')))
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# wrap function to enable rewrite
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def distance2bbox(*args, **kwargs):
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import mmdet.structures.bbox.transforms
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return mmdet.structures.bbox.transforms.distance2bbox(*args, **kwargs)
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points = torch.rand(3, 2)
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distance = torch.rand(3, 4)
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original_outputs = distance2bbox(points, distance)
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# wrap function to nn.Module, enable torch.onnx.export
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wrapped_func = WrapFunction(distance2bbox)
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_func,
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model_inputs={
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'points': points,
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'distance': distance
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},
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deploy_cfg=deploy_cfg)
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if is_backend_output:
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model_output = original_outputs.squeeze().cpu().numpy()
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rewrite_output = rewrite_outputs[0].squeeze()
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assert np.allclose(
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model_output, rewrite_output, rtol=1e-03, atol=1e-05)
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else:
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assert rewrite_outputs is not None
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