857 lines
32 KiB
Python
857 lines
32 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import onnx
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import pytest
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import torch
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import torch.nn as nn
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from mmcv import Config
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from onnx.helper import (make_graph, make_model, make_node,
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make_tensor_value_info)
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from mmdeploy.core import RewriterContext
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from mmdeploy.utils.test import WrapFunction, assert_allclose
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from .utils import TestNCNNExporter, TestOnnxRTExporter, TestTensorRTExporter
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TEST_ONNXRT = TestOnnxRTExporter()
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TEST_TENSORRT = TestTensorRTExporter()
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TEST_NCNN = TestNCNNExporter()
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@pytest.mark.parametrize('backend', [TEST_TENSORRT])
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@pytest.mark.parametrize('pool_h,pool_w,spatial_scale,sampling_ratio',
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[(2, 2, 1.0, 2), (4, 4, 2.0, 4)])
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def test_roi_align(backend,
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pool_h,
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pool_w,
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spatial_scale,
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sampling_ratio,
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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 = torch.rand(1, 1, 16, 16, dtype=torch.float32)
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single_roi = torch.tensor([[0, 0, 0, 4, 4]], dtype=torch.float32)
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else:
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input = torch.tensor(input_list[0], dtype=torch.float32)
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single_roi = torch.tensor(input_list[1], dtype=torch.float32)
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from mmcv.ops import roi_align
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def wrapped_function(torch_input, torch_rois):
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return roi_align(torch_input, torch_rois, (pool_w, pool_h),
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spatial_scale, sampling_ratio, 'avg', True)
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wrapped_model = WrapFunction(wrapped_function).eval()
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with RewriterContext(
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Config({'backend_config': {
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'type': backend.backend_name
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}}),
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backend=backend.backend_name,
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opset=11):
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backend.run_and_validate(
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wrapped_model, [input, single_roi],
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'roi_align',
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input_names=['input', 'rois'],
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output_names=['roi_feat'],
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save_dir=save_dir)
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@pytest.mark.parametrize('backend', [TEST_TENSORRT, TEST_ONNXRT])
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@pytest.mark.parametrize('mode', ['bilinear', 'nearest'])
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@pytest.mark.parametrize('padding_mode', ['zeros', 'border', 'reflection'])
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@pytest.mark.parametrize('align_corners', [True, False])
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def test_grid_sample(backend,
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mode,
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padding_mode,
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align_corners,
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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 = torch.rand(1, 1, 10, 10)
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else:
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input = torch.tensor(input_list[0])
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grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]])
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grid = nn.functional.affine_grid(
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grid, (1, 1, input.shape[2] * 2, input.shape[3] * 2)).type_as(input)
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def wrapped_function(inputs, grid):
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return nn.functional.grid_sample(
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inputs,
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grid,
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mode=mode,
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padding_mode=padding_mode,
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align_corners=align_corners)
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wrapped_model = WrapFunction(wrapped_function).eval()
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with RewriterContext(
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Config({'backend_config': {
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'type': backend.backend_name
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}}),
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backend=backend.backend_name,
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opset=11):
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backend.run_and_validate(
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wrapped_model, [input, grid],
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'grid_sampler',
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input_names=['input', 'grid'],
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output_names=['output'],
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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('dynamic_export', [True, False])
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@pytest.mark.parametrize('mode', ['bicubic', 'nearest'])
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@pytest.mark.parametrize('align_corners', [True, False])
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@pytest.mark.parametrize('output_size', [[10, 20], None])
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@pytest.mark.parametrize('scale_factor', [2])
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@pytest.mark.parametrize('n, c, h, w', [(2, 3, 5, 10)])
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def test_bicubic_interpolate(backend,
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dynamic_export,
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mode,
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align_corners,
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output_size,
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scale_factor,
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n,
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c,
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h,
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w,
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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 = torch.randn(n, c, h, w)
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if dynamic_export:
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dynamic_axes = {
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'input': {
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0: 'n',
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2: 'h',
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3: 'w',
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},
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'output': {
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0: 'n',
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2: 'h',
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3: 'w',
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},
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}
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else:
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dynamic_axes = None
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if mode == 'nearest':
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align_corners = None
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if output_size is None:
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resize = nn.Upsample(
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scale_factor=scale_factor, mode=mode, align_corners=align_corners)
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else:
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resize = nn.Upsample(
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size=output_size, mode=mode, align_corners=align_corners)
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expected_result = resize(input).cuda()
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wrapped_model = WrapFunction(resize).eval()
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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
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backend.run_and_validate(
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wrapped_model, [input],
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'bicubic_interpolate',
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input_names=['input'],
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dynamic_axes=dynamic_axes,
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output_names=['output'],
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save_dir=save_dir,
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expected_result=expected_result)
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@pytest.mark.parametrize('backend', [TEST_TENSORRT, TEST_ONNXRT])
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@pytest.mark.parametrize('in_channels,out_channels,stride,padding,'
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'dilation,groups,deform_groups,kernel_size',
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[(3, 64, 1, 0, 1, 1, 1, 3),
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(1, 32, 3, 2, 1, 1, 1, 3)])
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@pytest.mark.parametrize('bias', [True, False])
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def test_modulated_deform_conv(backend,
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in_channels,
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out_channels,
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stride,
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padding,
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dilation,
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groups,
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deform_groups,
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kernel_size,
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bias,
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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 = torch.rand(
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1, in_channels, 28, 28, requires_grad=False) # (n, c, h, w)
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else:
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input = torch.tensor(input_list[0])
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conv_offset = nn.Conv2d(
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in_channels=in_channels,
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out_channels=deform_groups * 3 * kernel_size * kernel_size,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=True)
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out = conv_offset(input)
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o1, o2, mask = torch.chunk(out, 3, dim=1)
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offset = torch.cat((o1, o2), dim=1)
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mask = torch.sigmoid(mask)
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from mmcv.ops import ModulatedDeformConv2d
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model = ModulatedDeformConv2d(in_channels, out_channels, kernel_size,
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stride, padding, dilation, groups,
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deform_groups, bias).eval()
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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
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backend.run_and_validate(
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model, [input, offset, mask],
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'modulated_deform_conv',
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input_names=['input', 'offset', 'mask'],
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output_names=['output'],
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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('in_channels,out_channels,stride,padding,'
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'dilation,groups,deform_groups,kernel_size',
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[(3, 64, 1, 0, 1, 1, 1, 3),
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(1, 32, 3, 2, 1, 1, 1, 3)])
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def test_deform_conv(backend,
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in_channels,
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out_channels,
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stride,
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padding,
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dilation,
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groups,
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deform_groups,
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kernel_size,
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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 = torch.rand(
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1, in_channels, 28, 28, requires_grad=False) # (n, c, h, w)
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else:
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input = torch.tensor(input_list[0])
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conv_offset = nn.Conv2d(
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in_channels=in_channels,
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out_channels=deform_groups * 2 * kernel_size * kernel_size,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=True)
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offset = conv_offset(input)
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from mmcv.ops import DeformConv2d
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model = DeformConv2d(in_channels, out_channels, kernel_size, stride,
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padding, dilation, groups, deform_groups).eval()
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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
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backend.run_and_validate(
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model, [input, offset],
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'deform_conv',
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input_names=['input', 'offset'],
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output_names=['output'],
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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('dynamic_export', [True, False])
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@pytest.mark.parametrize('fp16_mode', [True, False])
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@pytest.mark.parametrize('n, c, h, w', [(2, 3, 10, 10)])
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def test_instance_norm(backend,
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dynamic_export,
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fp16_mode,
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n,
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c,
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h,
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w,
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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 = torch.randn(n, c, h, w)
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if dynamic_export:
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dynamic_axes = {
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'input': {
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0: 'n',
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2: 'h',
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3: 'w',
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},
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'output': {
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0: 'n',
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2: 'h',
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3: 'w',
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},
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}
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else:
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dynamic_axes = None
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norm = nn.InstanceNorm2d(c, affine=True)
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wrapped_model = WrapFunction(norm).eval()
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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
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backend.run_and_validate(
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wrapped_model, [input],
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'instance_norm',
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input_names=['input'],
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dynamic_axes=dynamic_axes,
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output_names=['output'],
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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.codebase.mmdet.core.post_processing 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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expected_result = (expected_result[0][:,
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0:-1, :], expected_result[1][:,
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0:-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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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
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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(
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'out_size, pool_mode, sampling_ratio,roi_scale_factor,'
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' finest_scale,featmap_strides, aligned',
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[(tuple([2, 2]), 0, 2, 1.0, 2, list([2.0, 4.0]), 1),
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(tuple([2, 2]), 1, 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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pool_mode,
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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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if pool_mode == 1:
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expected_result = torch.tensor([[[[0.1939, 0.3950],
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[0.3437, 0.4543]],
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[[0.0778, 0.1641],
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[0.1305, 0.2301]],
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[[0.1542, 0.2413],
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[0.2094, 0.2688]]]])
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else:
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expected_result = torch.tensor([[[[0.1939, 0.4956],
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[0.4185, 0.5167]],
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[[0.0778, 0.2073],
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[0.1569, 0.3162]],
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[[0.1542, 0.2849],
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[0.2370, 0.3053]]]])
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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(
|
|
'rois', onnx.TensorProto.FLOAT, shape=rois.shape))
|
|
outputs = [
|
|
onnx.helper.make_tensor_value_info(
|
|
'bbox_feats', onnx.TensorProto.FLOAT, shape=expected_result.shape)
|
|
]
|
|
node = onnx.helper.make_node(
|
|
'MMCVMultiLevelRoiAlign',
|
|
input_name, ['bbox_feats'],
|
|
'MMCVMultiLevelRoiAlign_0',
|
|
None,
|
|
'mmdeploy',
|
|
pool_mode=pool_mode,
|
|
aligned=aligned,
|
|
featmap_strides=featmap_strides,
|
|
finest_scale=finest_scale,
|
|
output_height=out_size[0],
|
|
output_width=out_size[1],
|
|
roi_scale_factor=roi_scale_factor,
|
|
sampling_ratio=sampling_ratio)
|
|
graph = onnx.helper.make_graph([node], 'torch-jit-export', inputs, outputs)
|
|
onnx_model = onnx.helper.make_model(
|
|
graph, producer_name='pytorch', producer_version='1.8')
|
|
onnx_model.opset_import[0].version = 11
|
|
onnx_model.opset_import.append(
|
|
onnx.onnx_ml_pb2.OperatorSetIdProto(domain='mmdeploy', version=1))
|
|
|
|
backend.run_and_validate(
|
|
onnx_model, [rois, *input],
|
|
'multi_level_roi_align',
|
|
input_names=input_name,
|
|
output_names=['bbox_feats'],
|
|
expected_result=expected_result,
|
|
save_dir=save_dir)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('k', [1, 3, 5])
|
|
@pytest.mark.parametrize('dim', [1, 2, 3])
|
|
@pytest.mark.parametrize('largest', [True, False])
|
|
@pytest.mark.parametrize('sorted', [True, False])
|
|
def test_topk(backend,
|
|
k,
|
|
dim,
|
|
largest,
|
|
sorted,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
|
|
if input_list is None:
|
|
input = torch.rand(1, 8, 12, 17)
|
|
else:
|
|
input = input_list[0]
|
|
assert input.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but got {input.shape[0]}')
|
|
|
|
def topk_function(inputs):
|
|
return torch.Tensor.topk(inputs, k, dim, largest, sorted)
|
|
|
|
wrapped_model = WrapFunction(topk_function)
|
|
|
|
# when the 'sorted' attribute is False, pytorch will return
|
|
# a hard to expect result, which only features that the topk
|
|
# number is right. So the Topk unittest only check whether the
|
|
# topk elements are right, all the possible order will be accepted.
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
if not sorted:
|
|
backend.run_and_validate(
|
|
wrapped_model, [input.float()],
|
|
'topk' + f'_no_sorted_dim_{dim}',
|
|
input_names=['inputs'],
|
|
output_names=['data', 'index'],
|
|
save_dir=save_dir)
|
|
else:
|
|
backend.run_and_validate(
|
|
wrapped_model, [input.float()],
|
|
'topk',
|
|
input_names=['inputs'],
|
|
output_names=['data', 'index'],
|
|
save_dir=save_dir)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('dim, n, c, h, w', [(1, 1, 1, 1, 8), (2, 1, 1, 5, 7),
|
|
(3, 1, 3, 10, 15)])
|
|
def test_shape(backend,
|
|
dim,
|
|
n,
|
|
c,
|
|
h,
|
|
w,
|
|
input_names=['input'],
|
|
output_names=['output'],
|
|
tolerate_small_mismatch=False,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
|
|
orig_shape = (n, c, h, w)[-dim - 1:]
|
|
if input_list is None:
|
|
input = torch.rand(orig_shape)
|
|
else:
|
|
input = input_list[0]
|
|
assert input.dim() == dim + 1, 'input.dim() must equal to dim + 1'
|
|
assert tuple(input.shape) == orig_shape, 'input.shape must the \
|
|
same as orig_shape'
|
|
|
|
assert input.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but got {input.shape[0]}')
|
|
|
|
shape_node = make_node('Shape', input_names, output_names)
|
|
assert len(input_names) == 1, 'length of input_names must be 1'
|
|
assert len(output_names) == 1, 'length of output_names must be 1'
|
|
shape_graph = make_graph([shape_node], 'shape_graph', [
|
|
make_tensor_value_info(input_names[0], onnx.TensorProto.FLOAT,
|
|
orig_shape)
|
|
], [
|
|
make_tensor_value_info(output_names[0], onnx.TensorProto.FLOAT,
|
|
(dim + 1, ))
|
|
])
|
|
shape_model = make_model(shape_graph)
|
|
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
ncnn_model = backend.onnx2ncnn(shape_model, 'shape', output_names,
|
|
save_dir)
|
|
|
|
# ncnn mat has implicit batch for mat, the ncnn_output is a mat,
|
|
# so the ncnn_outputs has 2 dimensions, not 1.
|
|
model_outputs = [torch.tensor(orig_shape).unsqueeze(0).float()]
|
|
ncnn_outputs = ncnn_model(dict(zip(input_names, [input])))
|
|
ncnn_outputs = [ncnn_outputs[name] for name in output_names]
|
|
assert_allclose(model_outputs, ncnn_outputs, tolerate_small_mismatch)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('dim, n, c, h, w', [(1, 1, 1, 1, 8), (2, 1, 1, 5, 7),
|
|
(3, 1, 3, 10, 15)])
|
|
@pytest.mark.parametrize('val', [0., 1., -3, 4.25])
|
|
def test_constantofshape(backend,
|
|
dim,
|
|
n,
|
|
c,
|
|
h,
|
|
w,
|
|
val,
|
|
input_names=['input'],
|
|
output_names=['output'],
|
|
tolerate_small_mismatch=False,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
if input_list is None:
|
|
input = torch.tensor((n, c, h, w)[-dim - 1:]).unsqueeze(0)
|
|
else:
|
|
input = input_list[0]
|
|
assert input.dim() == dim + 1, 'input.dim() must equal to dim + 1'
|
|
assert tuple(input.shape) == (n, c, h,
|
|
w)[-dim - 1:], 'input.shape must the \
|
|
same as orig_shape'
|
|
|
|
assert input.shape[0] == 1, (f'ncnn input batch must be 1, \
|
|
got {input.shape[0]}')
|
|
assert input[0][0] == 1, (f'ncnn output mat batch must be 1, \
|
|
got {input[0][0]}')
|
|
|
|
constantofshape_node = make_node(
|
|
'ConstantOfShape', input_names, output_names, value=float(val))
|
|
assert len(input_names) == 1, 'length of input_names must be 1'
|
|
assert len(output_names) == 1, 'length of output_names must be 1'
|
|
constantofshape_graph = make_graph(
|
|
[constantofshape_node], 'constantofshape_graph', [
|
|
make_tensor_value_info(input_names[0], onnx.TensorProto.FLOAT,
|
|
input.shape)
|
|
], [
|
|
make_tensor_value_info(output_names[0], onnx.TensorProto.FLOAT,
|
|
torch.Size(input[0]))
|
|
])
|
|
constantofshape_model = make_model(constantofshape_graph)
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
ncnn_model = backend.onnx2ncnn(constantofshape_model,
|
|
'constantofshape', output_names,
|
|
save_dir)
|
|
|
|
# ncnn mat has implicit batch for mat, the ncnn_output is a mat,
|
|
# so the ncnn_outputs has 2 dimensions, not 1.
|
|
model_outputs = [torch.fill_(torch.rand(tuple(input[0])), val)]
|
|
ncnn_outputs = ncnn_model(dict(zip(input_names, [input.float()])))
|
|
ncnn_outputs = [ncnn_outputs[name] for name in output_names]
|
|
assert_allclose(model_outputs, ncnn_outputs, tolerate_small_mismatch)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('axis, data_dims, indice_dims', [(0, 1, 1), (0, 2, 1),
|
|
(1, 2, 1), (0, 3, 1),
|
|
(1, 3, 1),
|
|
(2, 3, 1)])
|
|
def test_gather(backend,
|
|
axis,
|
|
data_dims,
|
|
indice_dims,
|
|
input_names=['input', 'indices'],
|
|
output_names=['output'],
|
|
tolerate_small_mismatch=False,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
|
|
if input_list is None:
|
|
# the real data dims is data_dims + 1
|
|
data = torch.rand((8, 12, 17)[-data_dims:]).unsqueeze(0)
|
|
indice = torch.randint(0, 8, (3, 4, 5)[-indice_dims:]).unsqueeze(0)
|
|
else:
|
|
data = input_list[0]
|
|
indice = input_list[1]
|
|
assert data.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but got {data.shape[0]}')
|
|
assert indice.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but got {indice.shape[0]}')
|
|
|
|
gather_node = make_node('Gather', input_names, output_names, axis=axis + 1)
|
|
gather_graph = make_graph([gather_node], 'gather_graph', [
|
|
make_tensor_value_info(input_names[0], onnx.TensorProto.FLOAT, None),
|
|
make_tensor_value_info(input_names[1], onnx.TensorProto.INT64, None)
|
|
], [make_tensor_value_info(output_names[0], onnx.TensorProto.FLOAT, None)])
|
|
gather_model = make_model(gather_graph)
|
|
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
ncnn_model = backend.onnx2ncnn(gather_model, 'gather', output_names,
|
|
save_dir)
|
|
|
|
# ncnn mat has implicit batch for mat, the ncnn_output is a mat,
|
|
# so the ncnn_outputs has 2 dimensions, not 1.
|
|
import importlib
|
|
|
|
import onnxruntime
|
|
assert importlib.util.find_spec('onnxruntime') is not None, 'onnxruntime \
|
|
not installed.'
|
|
|
|
import numpy as np
|
|
session = onnxruntime.InferenceSession(gather_model.SerializeToString())
|
|
model_outputs = session.run(
|
|
output_names,
|
|
dict(
|
|
zip(input_names, [
|
|
np.array(data, dtype=np.float32),
|
|
np.array(indice[0], dtype=np.int64)
|
|
])))
|
|
model_outputs = [model_output for model_output in model_outputs]
|
|
|
|
ncnn_outputs = ncnn_model(
|
|
dict(zip(input_names, [data.float(), indice.float()])))
|
|
ncnn_outputs = [ncnn_outputs[name] for name in output_names]
|
|
assert_allclose(model_outputs, ncnn_outputs, tolerate_small_mismatch)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('dim', [1, 2, 3])
|
|
def test_tensorslice(backend, dim, input_list=None, save_dir=None):
|
|
backend.check_env()
|
|
|
|
if input_list is None:
|
|
input = torch.rand((8, 12, 17)[-dim:]).unsqueeze(0)
|
|
else:
|
|
input = input_list[0]
|
|
assert input.dim() == dim + 1, f'input.dim() must equal to \
|
|
dim + 1, expected: {dim + 1}, got: {input.dim()}'
|
|
|
|
assert input.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but got {input.shape[0]}')
|
|
|
|
def tensorslice_function(inputs):
|
|
if dim == 1:
|
|
return inputs[:, 2:17:7]
|
|
if dim == 2:
|
|
return inputs[:, 3:12:4, 2:15:3]
|
|
if dim == 3:
|
|
return inputs[:, 0:8:2, 2:12:4, 2:17:7]
|
|
|
|
wrapped_model = WrapFunction(tensorslice_function)
|
|
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
backend.run_and_validate(
|
|
wrapped_model, [input.float()],
|
|
'tensorslice',
|
|
input_names=['inputs'],
|
|
output_names=['outputs'],
|
|
save_dir=save_dir)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_NCNN])
|
|
@pytest.mark.parametrize('input_dim, output_dim', [(1, 1), (1, 2), (1, 3),
|
|
(2, 2), (2, 3), (3, 3)])
|
|
def test_expand(backend,
|
|
input_dim,
|
|
output_dim,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
if input_list is None:
|
|
input = torch.rand((1, 12, 1)[-input_dim:]).unsqueeze(0)
|
|
target = torch.rand((8, 12, 17)[-output_dim:]).unsqueeze(0)
|
|
else:
|
|
input = input_list[0]
|
|
target = input_list[1]
|
|
assert input.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but not {input.shape[0]}')
|
|
assert target.shape[0] == 1, (f'ncnn batch must be 1, \
|
|
but not {target.shape[0]}')
|
|
|
|
def expand_function(input, target):
|
|
return input.expand_as(target)
|
|
|
|
wrapped_model = WrapFunction(expand_function)
|
|
with RewriterContext(cfg={}, backend=backend.backend_name, opset=11):
|
|
backend.run_and_validate(
|
|
wrapped_model, [input.float(), target.float()],
|
|
'expand',
|
|
input_names=['input', 'shape'],
|
|
output_names=['output'],
|
|
save_dir=save_dir)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_ONNXRT])
|
|
@pytest.mark.parametrize('iou_threshold', [0.1, 0.3])
|
|
@pytest.mark.parametrize('score_threshold', [0., 0.1])
|
|
def test_nms_rotated(backend, iou_threshold, score_threshold, save_dir=None):
|
|
backend.check_env()
|
|
|
|
boxes = torch.tensor(
|
|
[[[60, 75, 20, 50, 0], [65, 80, 10, 40, 0], [30, 30, 40, 40, 0]],
|
|
[[60, 75, 20, 50, 0], [65, 80, 10, 40, 0], [30, 30, 40, 40, 0]]],
|
|
dtype=torch.float32)
|
|
scores = torch.tensor(
|
|
[[[0.5, 0.1, 0.1], [0.1, 0.6, 0.1], [0.1, 0.1, 0.7], [0.1, 0.1, 0.1]],
|
|
[[0.1, 0.1, 0.1], [0.7, 0.1, 0.1], [0.1, 0.6, 0.1], [0.1, 0.1, 0.5]]],
|
|
dtype=torch.float32)
|
|
|
|
from mmdeploy.mmcv.ops import ONNXNMSRotatedOp
|
|
|
|
def wrapped_function(torch_boxes, torch_scores):
|
|
return ONNXNMSRotatedOp.apply(torch_boxes, torch_scores, iou_threshold,
|
|
score_threshold)
|
|
|
|
wrapped_model = WrapFunction(wrapped_function).eval()
|
|
|
|
with RewriterContext(
|
|
Config({'backend_config': {
|
|
'type': backend.backend_name
|
|
}}),
|
|
backend=backend.backend_name,
|
|
opset=11):
|
|
backend.run_and_validate(
|
|
wrapped_model, [boxes, scores],
|
|
'nms_rotated',
|
|
input_names=['boxes', 'scores'],
|
|
output_names=['keep_inds'],
|
|
save_dir=save_dir)
|
|
|
|
|
|
@pytest.mark.parametrize('backend', [TEST_ONNXRT])
|
|
@pytest.mark.parametrize('pool_h,pool_w,spatial_scale,sampling_ratio',
|
|
[(2, 2, 1.0, 2), (4, 4, 2.0, 4)])
|
|
def test_roi_align_rotated(backend,
|
|
pool_h,
|
|
pool_w,
|
|
spatial_scale,
|
|
sampling_ratio,
|
|
input_list=None,
|
|
save_dir=None):
|
|
backend.check_env()
|
|
|
|
if input_list is None:
|
|
# input = torch.rand(1, 1, 16, 16, dtype=torch.float32)
|
|
input = torch.tensor([[[[1., 2.], [3., 4.]]]], dtype=torch.float32)
|
|
single_roi = torch.tensor([[0., 0.5, 0.5, 1., 1., 0]],
|
|
dtype=torch.float32)
|
|
else:
|
|
input = torch.tensor(input_list[0], dtype=torch.float32)
|
|
single_roi = torch.tensor(input_list[1], dtype=torch.float32)
|
|
|
|
from mmcv.ops import roi_align_rotated
|
|
|
|
def wrapped_function(torch_input, torch_rois):
|
|
return roi_align_rotated(torch_input, torch_rois, (pool_w, pool_h),
|
|
spatial_scale, sampling_ratio, True, False)
|
|
|
|
wrapped_model = WrapFunction(wrapped_function).eval()
|
|
|
|
with RewriterContext(
|
|
Config({'backend_config': {
|
|
'type': backend.backend_name
|
|
}}),
|
|
backend=backend.backend_name,
|
|
opset=11):
|
|
backend.run_and_validate(
|
|
wrapped_model, [input, single_roi],
|
|
'roi_align_rotated',
|
|
input_names=['input', 'rois'],
|
|
output_names=['roi_feat'],
|
|
save_dir=save_dir)
|