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[Feature] Add Ascend support for RoIAlign op #2638

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merged 4 commits into from
Mar 20, 2023
Merged

[Feature] Add Ascend support for RoIAlign op #2638

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a384964
add roi_algin op
momo609 Mar 3, 2023
b6834bd
add unit test
Mar 20, 2023
01be0e6
update ops.md
Mar 20, 2023
c3ef4c0
fix typo
zhouzaida Mar 20, 2023
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2 changes: 1 addition & 1 deletion docs/en/understand_mmcv/ops.md
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ We implement common ops used in detection, segmentation, etc.
| RoIPool | | √ | √ | | √ |
| RoIAlignRotated | √ | √ | √ | | |
| RiRoIAlignRotated | | √ | | | |
| RoIAlign | √ | √ | √ | | |
| RoIAlign | √ | √ | √ | | √ |
| RoIAwarePool3d | | √ | √ | | |
| SAConv2d | | √ | | | |
| SigmoidFocalLoss | | √ | √ | | √ |
Expand Down
2 changes: 1 addition & 1 deletion docs/zh_cn/understand_mmcv/ops.md
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Expand Up @@ -46,7 +46,7 @@ MMCV 提供了检测、分割等任务中常用的算子
| RoIPool | | √ | √ | | √ |
| RoIAlignRotated | √ | √ | √ | | |
| RiRoIAlignRotated | | √ | | | |
| RoIAlign | √ | √ | √ | | |
| RoIAlign | √ | √ | √ | | √ |
| RoIAwarePool3d | | √ | √ | | |
| SAConv2d | | √ | | | |
| SigmoidFocalLoss | | √ | √ | | √ |
Expand Down
68 changes: 68 additions & 0 deletions mmcv/ops/csrc/pytorch/npu/roi_align_npu.cpp
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@@ -0,0 +1,68 @@
#include "pytorch_npu_helper.hpp"

using namespace NPU_NAME_SPACE;
using namespace std;

void roi_align_forward_npu(Tensor input, Tensor rois, Tensor output,
Tensor argmax_y, Tensor argmax_x, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, int pool_mode, bool aligned) {
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if (!aligned) {
LOG(WARNING) << "The [aligned] attr in roi_align op is false";
}
int64_t aligned_height_64 = aligned_height;
int64_t aligned_width_64 = aligned_width;
int64_t sampling_ratio_64 = sampling_ratio;
int64_t roi_end_mode = 0;
OpCommand cmd;
cmd.Name("ROIAlign")
.Input(input)
.Input(rois)
.Output(output)
.Attr("spatial_scale", spatial_scale)
.Attr("pooled_height", aligned_height_64)
.Attr("pooled_width", aligned_width_64)
.Attr("sample_num", sampling_ratio_64)
.Attr("roi_end_mode", roi_end_mode)
.Run();
}

void roi_align_backward_npu(Tensor grad_output, Tensor rois, Tensor argmax_y,
Tensor argmax_x, Tensor grad_input,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio,
int pool_mode, bool aligned) {
int64_t aligned_height_64 = aligned_height;
int64_t aligned_width_64 = aligned_width;
int64_t sampling_ratio_64 = sampling_ratio;
int64_t roi_end_mode = 0;
c10::SmallVector<int64_t, SIZE> xdiff_shape =
at_npu::native::array_to_small_vector(grad_input.sizes());
OpCommand cmd;
cmd.Name("ROIAlignGrad")
.Input(grad_output)
.Input(rois)
.Output(grad_input)
.Attr("xdiff_shape", xdiff_shape)
.Attr("pooled_width", aligned_width_64)
.Attr("pooled_height", aligned_height_64)
.Attr("spatial_scale", spatial_scale)
.Attr("sample_num", sampling_ratio_64)
.Attr("roi_end_mode", roi_end_mode)
.Run();
}

void roi_align_forward_impl(Tensor input, Tensor rois, Tensor output,
Tensor argmax_y, Tensor argmax_x,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio,
int pool_mode, bool aligned);

void roi_align_backward_impl(Tensor grad_output, Tensor rois, Tensor argmax_y,
Tensor argmax_x, Tensor grad_input,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio,
int pool_mode, bool aligned);

REGISTER_NPU_IMPL(roi_align_forward_impl, roi_align_forward_npu);
REGISTER_NPU_IMPL(roi_align_backward_impl, roi_align_backward_npu);
12 changes: 8 additions & 4 deletions tests/test_ops/test_roi_align.py
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Expand Up @@ -3,7 +3,7 @@
import pytest
import torch

from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE

_USING_PARROTS = True
try:
Expand Down Expand Up @@ -102,15 +102,19 @@ def _test_roialign_allclose(device, dtype):
pytest.param(
'mlu',
marks=pytest.mark.skipif(
not IS_MLU_AVAILABLE, reason='requires MLU support'))
not IS_MLU_AVAILABLE, reason='requires MLU support')),
pytest.param(
'npu',
marks=pytest.mark.skipif(
not IS_NPU_AVAILABLE, reason='requires NPU support'))
])
@pytest.mark.parametrize('dtype', [
torch.float,
pytest.param(
torch.double,
marks=pytest.mark.skipif(
IS_MLU_AVAILABLE,
reason='MLU does not support for 64-bit floating point')),
IS_MLU_AVAILABLE or IS_NPU_AVAILABLE,
reason='MLU and NPU do not support for 64-bit floating point')),
torch.half
])
def test_roialign(device, dtype):
Expand Down