[Unittest]Add ops test (#108)
* add ncnn test exporter in test_ops.py * add ncnn test exporter in utils.py * add onnxruntime and tensorrt ops test * fix blank line * fix comment add nms ops test * remove nms test * add test sample add dockerstring * remove nms test * fix grid_sample add type hind * fix problem * fix dockerstring * add nms batch_nms multi_level_roi_align * add test data * fix problem * rm pkl file dependent * rm file * add docstring * remove multi_level_dependce * Update test_ops.pypull/12/head
VVsssssk
GitHub
parent
de9498a8f2
commit
43eee77fdd
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@ -57,8 +57,8 @@ class WrapModel(nn.Module):
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return func(*args, **kwargs)
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def assert_allclose(actual: List[Union[torch.Tensor, np.ndarray]],
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expected: List[Union[torch.Tensor, np.ndarray]],
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def assert_allclose(expected: List[Union[torch.Tensor, np.ndarray]],
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actual: List[Union[torch.Tensor, np.ndarray]],
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tolerate_small_mismatch: bool = False):
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"""Determine whether all actual values are closed with the expected values.
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@ -195,6 +195,236 @@ def test_instance_norm(backend,
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save_dir=save_dir)
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@pytest.mark.parametrize('backend', [TEST_TENSORRT])
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@pytest.mark.parametrize(
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'iou_threshold, score_threshold,max_output_boxes_per_class',
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[(0.6, 0.2, 3), (0.4, 0, 4)])
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def test_nms(backend,
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iou_threshold,
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score_threshold,
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max_output_boxes_per_class,
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input_list=None,
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save_dir=None):
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backend.check_env()
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if input_list is None:
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boxes = torch.tensor([[[291.1746, 316.2263, 343.5029, 347.7312],
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[288.4846, 315.0447, 343.7267, 346.5630],
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[288.5307, 318.1989, 341.6425, 349.7222],
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[918.9102, 83.7463, 933.3920, 164.9041],
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[895.5786, 78.2361, 907.8049, 172.0883],
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[292.5816, 316.5563, 340.3462, 352.9989],
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[609.4592, 83.5447, 631.2532, 144.0749],
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[917.7308, 85.5870, 933.2839, 168.4530],
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[895.5138, 79.3596, 908.2865, 171.0418],
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[291.4747, 318.6987, 347.1208, 349.5754]]])
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scores = torch.tensor([[[
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0.1790, 0.6798, 0.1875, 0.2358, 0.7146, 0.8325, 0.0760, 0.5339,
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0.0892, 0.9320
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],
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[
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0.0823, 0.1769, 0.4965, 0.8658, 0.0522,
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0.5388, 0.0811, 0.2998, 0.6442, 0.4870
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],
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[
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0.4649, 0.2912, 0.1123, 0.9007, 0.1675,
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0.7509, 0.7241, 0.8785, 0.7636, 0.3442
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],
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[
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0.7217, 0.0010, 0.0481, 0.9762, 0.7573,
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0.2049, 0.4464, 0.3340, 0.2695, 0.6959
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],
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[
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0.5959, 0.8608, 0.0664, 0.0709, 0.0453,
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0.8023, 0.5779, 0.0068, 0.8733, 0.7946
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]]])
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else:
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boxes = torch.tensor(input_list[0], dtype=torch.float32)
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scores = torch.tensor(input_list[1], dtype=torch.float32)
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cfg = dict()
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register_extra_symbolics(cfg=cfg, backend='tensorrt', opset=11)
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from mmdeploy.mmcv.ops import DummyONNXNMSop
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def wrapped_function(torch_bboxes, torch_scores):
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return DummyONNXNMSop.apply(torch_bboxes, torch_scores,
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max_output_boxes_per_class, iou_threshold,
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score_threshold)
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wrapped_model = WrapFunction(wrapped_function).eval()
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backend.run_and_validate(
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wrapped_model, [boxes, scores],
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'nms',
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input_names=['boxes', 'scores'],
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output_names=['nms_bboxes'],
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save_dir=save_dir)
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@pytest.mark.parametrize('backend', [TEST_TENSORRT])
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@pytest.mark.parametrize('num_classes,pre_topk,after_topk,iou_threshold,'
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'score_threshold,background_label_id',
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[(5, 6, 3, 0.7, 0.1, -1)])
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def test_batched_nms(backend,
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num_classes,
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pre_topk,
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after_topk,
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iou_threshold,
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score_threshold,
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background_label_id,
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input_list=None,
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save_dir=None):
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backend.check_env()
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if input_list is None:
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nms_boxes = torch.tensor([[[291.1746, 316.2263, 343.5029, 347.7312],
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[288.4846, 315.0447, 343.7267, 346.5630],
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[288.5307, 318.1989, 341.6425, 349.7222],
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[918.9102, 83.7463, 933.3920, 164.9041],
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[895.5786, 78.2361, 907.8049, 172.0883],
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[292.5816, 316.5563, 340.3462, 352.9989],
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[609.4592, 83.5447, 631.2532, 144.0749],
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[917.7308, 85.5870, 933.2839, 168.4530],
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[895.5138, 79.3596, 908.2865, 171.0418],
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[291.4747, 318.6987, 347.1208, 349.5754]]])
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scores = torch.tensor([[[0.9577, 0.9745, 0.3030, 0.6589, 0.2742],
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[0.1618, 0.7963, 0.5124, 0.6964, 0.6850],
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[0.8425, 0.4843, 0.9489, 0.8068, 0.7340],
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[0.7337, 0.4340, 0.9923, 0.0704, 0.4506],
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[0.3090, 0.5606, 0.6939, 0.3764, 0.6920],
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[0.0044, 0.7986, 0.2221, 0.2782, 0.4378],
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[0.7293, 0.2735, 0.8381, 0.0264, 0.6278],
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[0.7144, 0.1066, 0.4125, 0.4041, 0.8819],
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[0.4963, 0.7891, 0.6908, 0.1499, 0.5584],
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[0.4385, 0.6035, 0.0508, 0.0662, 0.5938]]])
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else:
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nms_boxes = torch.tensor(input_list[0], dtype=torch.float32)
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scores = torch.tensor(input_list[1], dtype=torch.float32)
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from mmdeploy.mmdet.core.post_processing.bbox_nms import _multiclass_nms
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expected_result = _multiclass_nms(
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nms_boxes,
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scores,
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iou_threshold=iou_threshold,
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score_threshold=score_threshold,
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pre_top_k=pre_topk + 1,
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keep_top_k=after_topk + 1)
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boxes = nms_boxes.unsqueeze(2).tile(num_classes, 1)
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from mmdeploy.mmcv.ops.nms import TRTBatchedNMSop
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batched_nms = TRTBatchedNMSop.apply
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def wrapped_function(boxes, scores):
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return batched_nms(boxes, scores, num_classes, pre_topk, after_topk,
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iou_threshold, score_threshold, background_label_id)
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wrapped_model = WrapFunction(wrapped_function)
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backend.run_and_validate(
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wrapped_model, [boxes, scores],
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'batched_nms',
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input_names=['boxes', 'scores'],
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output_names=['batched_nms_bboxes', 'inds'],
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expected_result=expected_result,
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save_dir=save_dir)
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@pytest.mark.parametrize('backend', [TEST_TENSORRT])
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@pytest.mark.parametrize('out_size, sampling_ratio,roi_scale_factor,'
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' finest_scale,featmap_strides, aligned',
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[(tuple([2, 2]), 2, 1.0, 2, list([2.0, 4.0]), 1)])
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def test_multi_level_roi_align(backend,
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out_size,
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sampling_ratio,
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roi_scale_factor,
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finest_scale,
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featmap_strides,
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aligned,
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input_list=None,
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save_dir=None):
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backend.check_env()
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if input_list is None:
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input = [
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torch.tensor([[[[0.3014, 0.7334, 0.6502, 0.1689],
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[0.3031, 0.3735, 0.6032, 0.1644],
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[0.0393, 0.4415, 0.3858, 0.2657],
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[0.5766, 0.0211, 0.6384, 0.0016]],
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[[0.0811, 0.6255, 0.0247, 0.3471],
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[0.1390, 0.9298, 0.6178, 0.6636],
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[0.2243, 0.2024, 0.2366, 0.3660],
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[0.1050, 0.2301, 0.7489, 0.7506]],
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[[0.3868, 0.1706, 0.2390, 0.8494],
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[0.2643, 0.9347, 0.0412, 0.5790],
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[0.6202, 0.0682, 0.0390, 0.5296],
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[0.5383, 0.1221, 0.6344, 0.1514]]]]),
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torch.tensor([[[[0.1939, 0.9983, 0.4031, 0.2712],
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[0.7929, 0.1504, 0.0946, 0.5030],
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[0.1421, 0.7908, 0.9595, 0.4198],
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[0.6880, 0.4722, 0.9896, 0.2266]],
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[[0.0778, 0.4232, 0.0736, 0.0168],
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[0.2887, 0.8461, 0.1140, 0.9582],
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[0.5169, 0.4924, 0.8275, 0.5530],
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[0.8961, 0.7466, 0.5976, 0.3760]],
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[[0.1542, 0.5028, 0.8412, 0.6617],
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[0.3751, 0.2798, 0.3835, 0.8640],
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[0.5821, 0.6588, 0.1324, 0.7619],
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[0.9178, 0.7282, 0.0291, 0.3028]]]])
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]
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rois = torch.tensor([[0., 0., 0., 4., 4.]])
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expected_result = torch.tensor([[[[0.1939, 0.3950], [0.3437, 0.4543]],
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[[0.0778, 0.1641], [0.1305, 0.2301]],
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[[0.1542, 0.2413], [0.2094,
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0.2688]]]])
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else:
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input = input_list[0]
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rois = input_list[1]
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expected_result = input_list[2]
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input_name = [('input_' + str(i)) for i in range(len(featmap_strides))]
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input_name.insert(0, 'rois')
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inputs = [
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onnx.helper.make_tensor_value_info(
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input_name[i + 1], onnx.TensorProto.FLOAT, shape=input[i].shape)
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for i in range(len(input_name) - 1)
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]
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inputs.append(
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onnx.helper.make_tensor_value_info(
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'rois', onnx.TensorProto.FLOAT, shape=rois.shape))
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outputs = [
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onnx.helper.make_tensor_value_info(
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'bbox_feats', onnx.TensorProto.FLOAT, shape=expected_result.shape)
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]
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node = onnx.helper.make_node(
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'MMCVMultiLevelRoiAlign',
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input_name, ['bbox_feats'],
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'MMCVMultiLevelRoiAlign_0',
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None,
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'mmlab',
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aligned=aligned,
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featmap_strides=featmap_strides,
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finest_scale=finest_scale,
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output_height=out_size[0],
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output_width=out_size[1],
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roi_scale_factor=roi_scale_factor,
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sampling_ratio=sampling_ratio)
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graph = onnx.helper.make_graph([node], 'torch-jit-export', inputs, outputs)
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onnx_model = onnx.helper.make_model(
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graph, producer_name='pytorch', producer_version='1.8')
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onnx_model.opset_import[0].version = 11
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onnx_model.opset_import.append(
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onnx.onnx_ml_pb2.OperatorSetIdProto(domain='mmlab', version=1))
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backend.run_and_validate(
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onnx_model, [rois, *input],
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'multi_level_roi_align',
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input_names=input_name,
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output_names=['bbox_feats'],
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expected_result=expected_result,
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save_dir=save_dir)
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@pytest.mark.parametrize('backend', [TEST_NCNN])
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@pytest.mark.parametrize('k', [1, 3, 5])
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@pytest.mark.parametrize('dim', [1, 2, 3])
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@ -32,6 +32,7 @@ class TestOnnxRTExporter:
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dynamic_axes=None,
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output_names=None,
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input_names=None,
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expected_result=None,
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save_dir=None):
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if save_dir is None:
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@ -51,10 +52,11 @@ class TestOnnxRTExporter:
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do_constant_folding=do_constant_folding,
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dynamic_axes=dynamic_axes,
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opset_version=11)
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with torch.no_grad():
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model_outputs = model(*input_list)
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if expected_result is None:
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with torch.no_grad():
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model_outputs = model(*input_list)
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else:
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model_outputs = expected_result
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if isinstance(model_outputs, torch.Tensor):
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model_outputs = [model_outputs]
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else:
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@ -90,6 +92,7 @@ class TestTensorRTExporter:
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dynamic_axes=None,
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output_names=None,
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input_names=None,
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expected_result=None,
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save_dir=None):
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if save_dir is None:
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onnx_file_path = tempfile.NamedTemporaryFile().name
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@ -97,18 +100,21 @@ class TestTensorRTExporter:
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else:
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onnx_file_path = os.path.join(save_dir, model_name + '.onnx')
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trt_file_path = os.path.join(save_dir, model_name + '.trt')
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with torch.no_grad():
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torch.onnx.export(
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model,
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tuple(input_list),
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onnx_file_path,
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export_params=True,
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keep_initializers_as_inputs=True,
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input_names=input_names,
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output_names=output_names,
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do_constant_folding=do_constant_folding,
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dynamic_axes=dynamic_axes,
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opset_version=11)
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if isinstance(model, onnx.onnx_ml_pb2.ModelProto):
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onnx.save(model, onnx_file_path)
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else:
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with torch.no_grad():
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torch.onnx.export(
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model,
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tuple(input_list),
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onnx_file_path,
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export_params=True,
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keep_initializers_as_inputs=True,
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input_names=input_names,
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output_names=output_names,
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do_constant_folding=do_constant_folding,
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dynamic_axes=dynamic_axes,
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opset_version=11)
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deploy_cfg = mmcv.Config(
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dict(
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@ -135,18 +141,20 @@ class TestTensorRTExporter:
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0,
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deploy_cfg=deploy_cfg,
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onnx_model=onnx_model)
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with torch.no_grad():
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model_outputs = model(*input_list)
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if expected_result is None and not isinstance(
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model, onnx.onnx_ml_pb2.ModelProto):
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with torch.no_grad():
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model_outputs = model(*input_list)
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else:
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model_outputs = expected_result
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if isinstance(model_outputs, torch.Tensor):
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model_outputs = [model_outputs.cpu().float()]
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else:
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model_outputs = [data.cpu().float() for data in model_outputs]
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trt_model = trt_apis.TRTWrapper(trt_file_path)
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inputs_list = [data.cuda() for data in input_list]
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trt_outputs = trt_model(dict(zip(input_names, inputs_list)))
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input_list = [data.cuda() for data in input_list]
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trt_outputs = trt_model(dict(zip(input_names, input_list)))
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trt_outputs = [
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trt_outputs[name].cpu().float() for name in output_names
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]
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