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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os | |
| import warnings | |
| from functools import partial | |
| import numpy as np | |
| import onnx | |
| import onnxruntime as rt | |
| import pytest | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from packaging import version | |
| onnx_file = 'tmp.onnx' | |
| if torch.__version__ == 'parrots': | |
| pytest.skip('not supported in parrots now', allow_module_level=True) | |
| def run_before_and_after_test(): | |
| # clear onnx_file before test | |
| if os.path.exists(onnx_file): | |
| os.remove(onnx_file) | |
| yield | |
| # clear onnx_file after test | |
| if os.path.exists(onnx_file): | |
| os.remove(onnx_file) | |
| class WrapFunction(nn.Module): | |
| def __init__(self, wrapped_function): | |
| super().__init__() | |
| self.wrapped_function = wrapped_function | |
| def forward(self, *args, **kwargs): | |
| return self.wrapped_function(*args, **kwargs) | |
| def process_grid_sample(func, input, grid, ort_custom_op_path=''): | |
| wrapped_model = WrapFunction(func).eval() | |
| input_names = ['input', 'grid'] | |
| output_names = ['output'] | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (input, grid), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=input_names, | |
| output_names=output_names, | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| session_options = rt.SessionOptions() | |
| if ort_custom_op_path: | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # get onnx output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [node.name for node in onnx_model.graph.initializer] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| ort_result = sess.run(None, { | |
| 'input': input.detach().numpy(), | |
| 'grid': grid.detach().numpy() | |
| }) | |
| pytorch_results = wrapped_model(input.clone(), grid.clone()) | |
| assert np.allclose(pytorch_results, ort_result, atol=1e-3) | |
| def test_grid_sample(mode, padding_mode, align_corners): | |
| from mmcv.onnx.symbolic import register_extra_symbolics | |
| opset_version = 11 | |
| register_extra_symbolics(opset_version) | |
| from mmcv.ops import get_onnxruntime_op_path | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| input = torch.rand(1, 1, 10, 10) | |
| grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]]) | |
| grid = F.affine_grid( | |
| grid, (1, 1, 15, 15), align_corners=align_corners).type_as(input) | |
| def func(input, grid): | |
| return F.grid_sample( | |
| input, | |
| grid, | |
| mode=mode, | |
| padding_mode=padding_mode, | |
| align_corners=align_corners) | |
| return process_grid_sample(func, input, grid, ort_custom_op_path) | |
| def test_bilinear_grid_sample(align_corners): | |
| from mmcv.ops.point_sample import bilinear_grid_sample | |
| # only support pytorch >= 1.5.0 | |
| if version.parse(torch.__version__) < version.parse('1.5.0'): | |
| pytest.skip('Only support PyTorch >= 1.5.0') | |
| input = torch.rand(1, 1, 10, 10) | |
| grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]]) | |
| grid = F.affine_grid( | |
| grid, (1, 1, 15, 15), align_corners=align_corners).type_as(input) | |
| def func(input, grid): | |
| return bilinear_grid_sample(input, grid, align_corners=align_corners) | |
| return process_grid_sample(func, input, grid) | |
| def test_nms(): | |
| from mmcv.ops import get_onnxruntime_op_path, nms | |
| np_boxes = np.array([[6.0, 3.0, 8.0, 7.0], [3.0, 6.0, 9.0, 11.0], | |
| [3.0, 7.0, 10.0, 12.0], [1.0, 4.0, 13.0, 7.0]], | |
| dtype=np.float32) | |
| np_scores = np.array([0.6, 0.9, 0.7, 0.2], dtype=np.float32) | |
| boxes = torch.from_numpy(np_boxes) | |
| scores = torch.from_numpy(np_scores) | |
| nms = partial( | |
| nms, iou_threshold=0.3, offset=0, score_threshold=0, max_num=0) | |
| pytorch_dets, _ = nms(boxes, scores) | |
| pytorch_score = pytorch_dets[:, 4] | |
| wrapped_model = WrapFunction(nms) | |
| wrapped_model.cpu().eval() | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (boxes, scores), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['boxes', 'scores'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # get onnx output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [node.name for node in onnx_model.graph.initializer] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_dets, _ = sess.run(None, { | |
| 'scores': scores.detach().numpy(), | |
| 'boxes': boxes.detach().numpy() | |
| }) | |
| onnx_score = onnx_dets[:, 4] | |
| assert np.allclose(pytorch_score, onnx_score, atol=1e-3) | |
| def test_softnms(): | |
| from mmcv.ops import get_onnxruntime_op_path, soft_nms | |
| # only support pytorch >= 1.7.0 | |
| if version.parse(torch.__version__) < version.parse('1.7.0'): | |
| warnings.warn('test_softnms should be ran with pytorch >= 1.7.0') | |
| return | |
| # only support onnxruntime >= 1.5.1 | |
| assert version.parse(rt.__version__) >= version.parse( | |
| '1.5.1'), 'test_softnms should be ran with onnxruntime >= 1.5.1' | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('softnms for onnxruntime is not compiled.') | |
| np_boxes = np.array([[6.0, 3.0, 8.0, 7.0], [3.0, 6.0, 9.0, 11.0], | |
| [3.0, 7.0, 10.0, 12.0], [1.0, 4.0, 13.0, 7.0]], | |
| dtype=np.float32) | |
| np_scores = np.array([0.6, 0.9, 0.7, 0.2], dtype=np.float32) | |
| boxes = torch.from_numpy(np_boxes) | |
| scores = torch.from_numpy(np_scores) | |
| configs = [[0.3, 0.5, 0.01, 'linear'], [0.3, 0.5, 0.01, 'gaussian'], | |
| [0.3, 0.5, 0.01, 'naive']] | |
| session_options = rt.SessionOptions() | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| for _iou_threshold, _sigma, _min_score, _method in configs: | |
| pytorch_dets, pytorch_inds = soft_nms( | |
| boxes, | |
| scores, | |
| iou_threshold=_iou_threshold, | |
| sigma=_sigma, | |
| min_score=_min_score, | |
| method=_method) | |
| nms = partial( | |
| soft_nms, | |
| iou_threshold=_iou_threshold, | |
| sigma=_sigma, | |
| min_score=_min_score, | |
| method=_method) | |
| wrapped_model = WrapFunction(nms) | |
| wrapped_model.cpu().eval() | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (boxes, scores), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['boxes', 'scores'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| # get onnx output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [ | |
| node.name for node in onnx_model.graph.initializer | |
| ] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_dets, onnx_inds = sess.run(None, { | |
| 'scores': scores.detach().numpy(), | |
| 'boxes': boxes.detach().numpy() | |
| }) | |
| assert np.allclose(pytorch_dets, onnx_dets, atol=1e-3) | |
| assert np.allclose(onnx_inds, onnx_inds, atol=1e-3) | |
| def test_roialign(): | |
| try: | |
| from mmcv.ops import get_onnxruntime_op_path, roi_align | |
| except (ImportError, ModuleNotFoundError): | |
| pytest.skip('roi_align op is not successfully compiled') | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| # roi align config | |
| pool_h = 2 | |
| pool_w = 2 | |
| spatial_scale = 1.0 | |
| sampling_ratio = 2 | |
| inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0., 0., 1., 1.]]), | |
| ([[[[1., 2.], [3., 4.]], [[4., 3.], | |
| [2., 1.]]]], [[0., 0., 0., 1., 1.]]), | |
| ([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.], | |
| [11., 12., 15., 16.]]]], [[0., 0., 0., 3., 3.]])] | |
| def warpped_function(torch_input, torch_rois): | |
| return roi_align(torch_input, torch_rois, (pool_w, pool_h), | |
| spatial_scale, sampling_ratio, 'avg', True) | |
| for case in inputs: | |
| np_input = np.array(case[0], dtype=np.float32) | |
| np_rois = np.array(case[1], dtype=np.float32) | |
| input = torch.from_numpy(np_input) | |
| rois = torch.from_numpy(np_rois) | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = roi_align(input, rois, (pool_w, pool_h), | |
| spatial_scale, sampling_ratio, 'avg', | |
| True) | |
| # export and load onnx model | |
| wrapped_model = WrapFunction(warpped_function) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (input, rois), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input', 'rois'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # compute onnx_output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [ | |
| node.name for node in onnx_model.graph.initializer | |
| ] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_output = sess.run(None, { | |
| 'input': input.detach().numpy(), | |
| 'rois': rois.detach().numpy() | |
| }) | |
| onnx_output = onnx_output[0] | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |
| def test_roialign_rotated(): | |
| try: | |
| from mmcv.ops import get_onnxruntime_op_path, roi_align_rotated | |
| except (ImportError, ModuleNotFoundError): | |
| pytest.skip('roi_align_aligned op is not successfully compiled') | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| # roi align config | |
| pool_h = 2 | |
| pool_w = 2 | |
| spatial_scale = 1.0 | |
| sampling_ratio = 2 | |
| inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., 0]]), | |
| ([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., np.pi / 2]]), | |
| ([[[[1., 2.], [3., 4.]], | |
| [[4., 3.], [2., 1.]]]], [[0., 0.5, 0.5, 1., 1., 0]]), | |
| ([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.], | |
| [11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3., 0]]), | |
| ([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.], | |
| [11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3., | |
| np.pi / 2]])] | |
| def warpped_function(torch_input, torch_rois): | |
| return roi_align_rotated(torch_input, torch_rois, (pool_w, pool_h), | |
| spatial_scale, sampling_ratio, True, False) | |
| for case in inputs: | |
| np_input = np.array(case[0], dtype=np.float32) | |
| np_rois = np.array(case[1], dtype=np.float32) | |
| input = torch.from_numpy(np_input) | |
| rois = torch.from_numpy(np_rois) | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = roi_align_rotated(input, rois, (pool_w, pool_h), | |
| spatial_scale, sampling_ratio, | |
| True, False) | |
| # export and load onnx model | |
| wrapped_model = WrapFunction(warpped_function) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (input, rois), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['features', 'rois'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # compute onnx_output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [ | |
| node.name for node in onnx_model.graph.initializer | |
| ] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_output = sess.run(None, { | |
| 'features': input.detach().numpy(), | |
| 'rois': rois.detach().numpy() | |
| }) | |
| onnx_output = onnx_output[0] | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |
| def test_roipool(): | |
| from mmcv.ops import roi_pool | |
| # roi pool config | |
| pool_h = 2 | |
| pool_w = 2 | |
| spatial_scale = 1.0 | |
| inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0., 0., 1., 1.]]), | |
| ([[[[1., 2.], [3., 4.]], [[4., 3.], | |
| [2., 1.]]]], [[0., 0., 0., 1., 1.]]), | |
| ([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.], | |
| [11., 12., 15., 16.]]]], [[0., 0., 0., 3., 3.]])] | |
| def warpped_function(torch_input, torch_rois): | |
| return roi_pool(torch_input, torch_rois, (pool_w, pool_h), | |
| spatial_scale) | |
| for case in inputs: | |
| np_input = np.array(case[0], dtype=np.float32) | |
| np_rois = np.array(case[1], dtype=np.float32) | |
| input = torch.from_numpy(np_input).cuda() | |
| rois = torch.from_numpy(np_rois).cuda() | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = roi_pool(input, rois, (pool_w, pool_h), | |
| spatial_scale) | |
| pytorch_output = pytorch_output.cpu() | |
| # export and load onnx model | |
| wrapped_model = WrapFunction(warpped_function) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (input, rois), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input', 'rois'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| # compute onnx_output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [ | |
| node.name for node in onnx_model.graph.initializer | |
| ] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file) | |
| onnx_output = sess.run( | |
| None, { | |
| 'input': input.detach().cpu().numpy(), | |
| 'rois': rois.detach().cpu().numpy() | |
| }) | |
| onnx_output = onnx_output[0] | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |
| def test_interpolate(): | |
| from mmcv.onnx.symbolic import register_extra_symbolics | |
| opset_version = 11 | |
| register_extra_symbolics(opset_version) | |
| def func(feat, scale_factor=2): | |
| out = F.interpolate(feat, scale_factor=scale_factor) | |
| return out | |
| net = WrapFunction(func) | |
| net = net.cpu().eval() | |
| dummy_input = torch.randn(2, 4, 8, 8).cpu() | |
| torch.onnx.export( | |
| net, | |
| dummy_input, | |
| onnx_file, | |
| input_names=['input'], | |
| opset_version=opset_version) | |
| sess = rt.InferenceSession(onnx_file) | |
| onnx_result = sess.run(None, {'input': dummy_input.detach().numpy()}) | |
| pytorch_result = func(dummy_input).detach().numpy() | |
| assert np.allclose(pytorch_result, onnx_result, atol=1e-3) | |
| def test_rotated_feature_align(): | |
| if torch.__version__ == 'parrots': | |
| pytest.skip('onnx is not supported in parrots directly') | |
| try: | |
| from mmcv.ops import get_onnxruntime_op_path, rotated_feature_align | |
| except (ImportError, ModuleNotFoundError): | |
| pytest.skip('rotated_feature_align op is not successfully compiled') | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| spatial_scale = 1.0 / 8 | |
| points = 1 | |
| def warpped_function(feature, bbox): | |
| return rotated_feature_align( | |
| feature, bbox, spatial_scale=spatial_scale, points=points) | |
| feature = torch.tensor([[[[1.2924, -0.2172, -0.5222, 0.1172], | |
| [0.9144, 1.2248, 1.3115, -0.9690], | |
| [-0.8949, -1.1797, -0.9093, -0.3961], | |
| [-0.4586, 0.5062, -0.7947, -0.7397]], | |
| [[-1.0943, -0.7495, 1.3461, -1.1652], | |
| [0.2034, 0.6763, -1.2357, 0.5231], | |
| [-1.0062, 1.2592, 1.4225, -0.3951], | |
| [-0.1242, -1.6240, 0.1932, 2.7181]], | |
| [[-1.6271, -1.0276, 0.0578, -0.2997], | |
| [-0.9684, -1.6946, -1.3188, -1.1938], | |
| [-1.6744, -0.8917, -0.6556, 1.0073], | |
| [-0.1205, 0.3671, -0.3731, -0.5347]]], | |
| [[[0.7035, 0.2089, -0.1774, 3.4670], | |
| [-0.8505, -0.9278, 1.4714, 0.1644], | |
| [0.0898, 0.3531, -0.4007, 0.1927], | |
| [1.2569, -0.2636, -0.5223, 0.0616]], | |
| [[0.1760, -0.7639, -0.4600, -1.3260], | |
| [-0.9921, -0.2970, -0.8955, 1.0508], | |
| [1.3515, -0.1641, 1.9679, 1.1986], | |
| [-0.3616, 0.6287, 0.4933, 0.3360]], | |
| [[-0.5860, 0.2124, -0.8700, 2.4200], | |
| [-0.0551, -1.5103, -1.6779, 0.8399], | |
| [0.8431, 1.2414, -1.1243, -0.3887], | |
| [-2.1254, 0.6047, -0.3515, 0.7254]]]]) | |
| bbox = torch.tensor( | |
| [[[[1.3080e+01, 1.2688e+01, 1.1214e+01, 9.3944e+01, -9.1905e-01], | |
| [3.8104e+01, 1.0134e+01, 1.4659e+02, 9.0306e+01, -9.8211e-01], | |
| [-5.3213e+01, 4.9508e+01, 5.1513e+01, 3.2055e+01, -3.1954e-01], | |
| [2.6974e+01, 2.5248e+01, 5.4495e+01, 3.1083e+00, -6.2127e-01]], | |
| [[-1.5604e+01, -5.1908e+01, 2.3998e+02, 1.5008e+01, -1.2546e+00], | |
| [3.1354e+01, -7.3635e+00, 6.7879e+01, 3.5081e+01, -3.3851e-01], | |
| [-5.3292e+00, 9.1946e+00, 1.2834e+01, 1.0485e+01, -1.3039e+00], | |
| [-2.3925e+01, 3.6623e+01, 3.9875e+01, 7.2009e+01, -6.5934e-01]], | |
| [[7.2114e+01, -2.3781e+01, 2.9106e+01, 8.4501e+01, -1.1340e+00], | |
| [2.6258e+01, -7.7034e+00, 1.7629e+02, 1.0615e+02, -1.2156e+00], | |
| [3.8057e+01, 4.6016e+01, 1.2965e+01, 6.9384e+00, -1.0855e+00], | |
| [2.4428e+01, -1.6189e+01, 2.0572e+02, 3.1622e+01, -1.5719e-01]], | |
| [[3.8226e+00, 2.9608e+01, 1.4457e+01, 6.8179e+01, -9.1997e-01], | |
| [2.5003e+01, -4.2490e+01, 9.6007e+01, 4.9086e+01, -1.4786e+00], | |
| [8.5983e+01, 5.4980e+01, 7.8080e+01, 1.0003e+02, -1.0926e+00], | |
| [9.9065e+00, 4.1457e+01, 5.9799e+00, 1.7973e+01, -5.6313e-01]]], | |
| [[[-1.8244e+01, 4.6309e+00, 5.3010e+01, 2.4310e+01, -7.0345e-01], | |
| [1.9419e+01, 3.6704e+01, 5.2390e+01, 5.4133e+01, -3.7730e-01], | |
| [5.6387e+01, 2.3752e+01, 9.0441e+00, 1.7792e+01, -1.5583e+00], | |
| [3.6303e+01, 1.6396e+01, 2.0283e+01, 1.9148e+01, -8.3419e-01]], | |
| [[3.2169e+01, 3.0521e+01, 2.6283e+01, 1.9680e+02, -3.0454e-01], | |
| [2.5788e+01, -3.2189e+01, 8.8882e+01, 1.0207e+02, -1.5328e+00], | |
| [8.4676e+00, -1.6668e+01, 2.4657e+01, 1.1275e+02, -4.0388e-01], | |
| [-1.0799e+01, 6.0422e+00, 9.5807e+00, 3.3677e+01, -3.5438e-01]], | |
| [[6.9363e+01, 1.0850e+01, 2.5968e+01, 2.2311e+01, -1.6408e-01], | |
| [2.8140e+00, 4.6843e+00, 3.1289e+00, 2.1480e+01, -6.7583e-01], | |
| [2.6661e+01, 4.5290e+01, 6.1679e+00, 3.0005e+01, -8.9806e-01], | |
| [5.0871e+00, 1.3234e+01, 9.2087e+01, 4.9622e+01, -2.8020e-01]], | |
| [[-1.2643e+01, 2.5176e+01, 5.0488e+01, 5.4246e+01, -4.4840e-01], | |
| [-3.4521e+01, 9.8435e-01, 5.2413e+01, 9.7996e+00, -8.4218e-01], | |
| [4.9829e+01, -1.0808e+01, 2.9848e+01, 7.3579e+01, -6.2672e-01], | |
| [8.0446e+01, 2.8064e+01, 4.5273e+01, 5.3809e+01, -1.2359e+00]]]]) | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = rotated_feature_align( | |
| feature, bbox, spatial_scale=spatial_scale, points=points) | |
| # export and load onnx model | |
| wrapped_model = WrapFunction(warpped_function) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, (feature, bbox), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['feature', 'bbox'], | |
| opset_version=11) | |
| onnx_model = onnx.load(onnx_file) | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # compute onnx_output | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [node.name for node in onnx_model.graph.initializer] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 2) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_output = sess.run(None, { | |
| 'feature': feature.detach().numpy(), | |
| 'bbox': bbox.detach().numpy() | |
| }) | |
| onnx_output = onnx_output[0] | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |
| def test_corner_pool(mode, opset=11): | |
| from mmcv.ops import get_onnxruntime_op_path | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| from mmcv.ops.corner_pool import CornerPool | |
| def corner_pool_func(input): | |
| corner_pool_module = CornerPool(mode) | |
| return corner_pool_module.corner_pool.apply(input) | |
| wrapped_model = WrapFunction(corner_pool_func).eval() | |
| input = torch.rand((2, 3, 9, 12)) # (n,c,h,w) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, | |
| input, | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input'], | |
| output_names=['output'], | |
| opset_version=opset) | |
| onnx_model = onnx.load(onnx_file) | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [node.name for node in onnx_model.graph.initializer] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 1) | |
| session_options = rt.SessionOptions() | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| ort_result = sess.run(None, {'input': input.detach().numpy()}) | |
| pytorch_results = wrapped_model(input.clone()) | |
| assert np.allclose(pytorch_results, ort_result, atol=1e-5) | |
| def test_cummax_cummin(key, opset=11): | |
| # Note generally `cummax` or `cummin` is exportable to ONNX | |
| # as long as the pytorch version >= 1.5.0, since `torch.cummax` | |
| # is only supported with torch >= 1.5.0. | |
| # But when `cummax` or `cummin` serves as an intermediate component | |
| # whose outputs is used as inputs for another modules, it's expected | |
| # that pytorch version must be >= 1.7.0. Otherwise error appears like: | |
| # `RuntimeError: tuple appears in op that does not forward tuples, | |
| # unsupported 'kind: prim::PythonOp`. | |
| if version.parse(torch.__version__) < version.parse('1.7.0'): | |
| pytest.skip('test_cummax_cummin should be ran with pytorch >= 1.7.0') | |
| # register custom op `mmcv::cummax` and `mmcv::cummin` | |
| from mmcv.onnx.symbolic import register_extra_symbolics | |
| register_extra_symbolics(opset) | |
| from mmcv.ops import get_onnxruntime_op_path | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| input_list = [ | |
| # arbitrary shape, e.g. 1-D, 2-D, 3-D, ... | |
| torch.rand((2, 3, 4, 1, 5)), | |
| torch.rand(1), | |
| torch.rand((2, 0, 1)), # tensor.numel() is 0 | |
| torch.FloatTensor(), # empty tensor | |
| ] | |
| cummax_cummin_funcs = {'cummax': torch.cummax, 'cummin': torch.cummin} | |
| for input in input_list: | |
| ndims = input.dim() | |
| # valid dim range is [-ndims, ndims-1] | |
| # test for all `dim` value which is valid | |
| for dim in range(-ndims, ndims): | |
| cummax_func = partial(cummax_cummin_funcs[key], dim=dim) | |
| wrapped_model = WrapFunction(cummax_func).eval() | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, | |
| input, | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input'], | |
| output_names=['output', 'indices'], | |
| opset_version=opset) | |
| onnx_model = onnx.load(onnx_file) | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [ | |
| node.name for node in onnx_model.graph.initializer | |
| ] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 1) | |
| session_options = rt.SessionOptions() | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| ort_output, ort_inds = sess.run(None, | |
| {'input': input.detach().numpy()}) | |
| pytorch_output, pytorch_inds = wrapped_model(input.clone()) | |
| pytorch_output = pytorch_output.detach().numpy() | |
| pytorch_inds = pytorch_inds.detach().numpy() | |
| assert np.allclose(pytorch_output, ort_output, atol=1e-5) | |
| assert np.all(pytorch_inds == ort_inds) | |
| def test_roll(shifts_dims_pair): | |
| opset = 11 | |
| from mmcv.onnx.symbolic import register_extra_symbolics | |
| register_extra_symbolics(opset) | |
| input = torch.arange(0, 4 * 5 * 6, dtype=torch.float32).view(4, 5, 6) | |
| shifts, dims = shifts_dims_pair | |
| func = partial(torch.roll, shifts=shifts, dims=dims) | |
| wrapped_model = WrapFunction(func).eval() | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| wrapped_model, | |
| input, | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input'], | |
| output_names=['output'], | |
| opset_version=opset) | |
| onnx_model = onnx.load(onnx_file) | |
| input_all = [node.name for node in onnx_model.graph.input] | |
| input_initializer = [node.name for node in onnx_model.graph.initializer] | |
| net_feed_input = list(set(input_all) - set(input_initializer)) | |
| assert (len(net_feed_input) == 1) | |
| sess = rt.InferenceSession(onnx_file) | |
| ort_output = sess.run(None, {'input': input.detach().numpy()})[0] | |
| with torch.no_grad(): | |
| pytorch_output = wrapped_model(input.clone()) | |
| torch.testing.assert_allclose(ort_output, pytorch_output) | |
| def test_modulated_deform_conv2d(): | |
| try: | |
| from mmcv.ops import ModulatedDeformConv2d, get_onnxruntime_op_path | |
| except (ImportError, ModuleNotFoundError): | |
| pytest.skip('modulated_deform_conv op is not successfully compiled') | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| # modulated deform conv config | |
| in_channels = 3 | |
| out_channels = 64 | |
| stride = 1 | |
| padding = 0 | |
| dilation = 1 | |
| groups = 1 | |
| deform_groups = 1 | |
| kernel_size = 3 | |
| input = torch.rand(1, in_channels, 28, 28).cuda() # (n, c, h, w) | |
| conv_offset = nn.Conv2d( | |
| in_channels=3, | |
| out_channels=deform_groups * 3 * kernel_size * kernel_size, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| dilation=dilation, | |
| bias=True).cuda() | |
| conv_offset.cuda() | |
| out = conv_offset(input) | |
| o1, o2, mask = torch.chunk(out, 3, dim=1) | |
| offset = torch.cat((o1, o2), dim=1) | |
| mask = torch.sigmoid(mask) | |
| model_with_bias = ModulatedDeformConv2d( | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| stride, | |
| padding, | |
| dilation, | |
| groups, | |
| deform_groups, | |
| bias=True) | |
| model_without_bias = ModulatedDeformConv2d( | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| stride, | |
| padding, | |
| dilation, | |
| groups, | |
| deform_groups, | |
| bias=False) | |
| models = [model_with_bias.cuda(), model_without_bias.cuda()] | |
| for model in models: | |
| # export and load onnx model | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| model, (input, offset, mask), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input', 'offset', 'mask'], | |
| opset_version=11) | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # compute onnx_output | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_output = sess.run( | |
| None, { | |
| 'input': input.cpu().detach().numpy(), | |
| 'offset': offset.cpu().detach().numpy(), | |
| 'mask': mask.cpu().detach().numpy() | |
| })[0] | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = model(input, offset, mask).cpu() | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |
| def test_deform_conv2d(threshold=1e-3): | |
| try: | |
| from mmcv.ops import DeformConv2d, get_onnxruntime_op_path | |
| except (ImportError, ModuleNotFoundError): | |
| pytest.skip('deform_conv op is not successfully compiled') | |
| ort_custom_op_path = get_onnxruntime_op_path() | |
| if not os.path.exists(ort_custom_op_path): | |
| pytest.skip('custom ops for onnxruntime are not compiled.') | |
| # deform conv config | |
| # modulated deform conv config | |
| in_channels = 1 | |
| out_channels = 64 | |
| stride = 1 | |
| padding = 0 | |
| dilation = 1 | |
| groups = 1 | |
| deform_groups = 1 | |
| kernel_size = 2 | |
| input = [[[[1., 2., 3.], [0., 1., 2.], [3., 5., 2.]]]] | |
| offset_weight = [[[0.1, 0.4, 0.6, 0.1]], [[0.3, 0.2, 0.1, 0.3]], | |
| [[0.5, 0.5, 0.2, 0.8]], [[0.8, 0.3, 0.9, 0.1]], | |
| [[0.3, 0.1, 0.2, 0.5]], [[0.3, 0.7, 0.5, 0.3]], | |
| [[0.6, 0.2, 0.5, 0.3]], [[0.4, 0.1, 0.8, 0.4]]] | |
| offset_bias = [0.7, 0.1, 0.8, 0.5, 0.6, 0.5, 0.4, 0.7] | |
| deform_weight = [[[0.4, 0.2, 0.1, 0.9]]] | |
| x = torch.tensor(input) | |
| conv_offset = nn.Conv2d( | |
| in_channels=in_channels, | |
| out_channels=deform_groups * 2 * kernel_size * kernel_size, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| dilation=dilation, | |
| bias=True) | |
| conv_offset.weight.data = torch.nn.Parameter( | |
| torch.Tensor(offset_weight).reshape(8, 1, 2, 2)) | |
| conv_offset.bias.data = torch.nn.Parameter( | |
| torch.Tensor(offset_bias).reshape(8)) | |
| offset = conv_offset(x) | |
| model = DeformConv2d(in_channels, out_channels, kernel_size, stride, | |
| padding, dilation, groups, deform_groups) | |
| model.weight.data = torch.nn.Parameter( | |
| torch.Tensor(deform_weight).reshape(1, 1, 2, 2)) | |
| with torch.no_grad(): | |
| torch.onnx.export( | |
| model, (x, offset), | |
| onnx_file, | |
| export_params=True, | |
| keep_initializers_as_inputs=True, | |
| input_names=['input', 'offset'], | |
| opset_version=11) | |
| session_options = rt.SessionOptions() | |
| if os.path.exists(ort_custom_op_path): | |
| session_options.register_custom_ops_library(ort_custom_op_path) | |
| # compute onnx_output | |
| sess = rt.InferenceSession(onnx_file, session_options) | |
| onnx_output = sess.run( | |
| None, { | |
| 'input': x.cpu().detach().numpy(), | |
| 'offset': offset.cpu().detach().numpy(), | |
| })[0] | |
| # compute pytorch_output | |
| with torch.no_grad(): | |
| pytorch_output = model(x, offset).cpu() | |
| # allclose | |
| assert np.allclose(pytorch_output, onnx_output, atol=1e-3) | |