forked from mindspore-Ecosystem/mindspore
!7482 roialign gpu operator output is zero
Merge pull request !7482 from JonathanY/roialign_zero
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commit
6cc37db833
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@ -91,7 +91,7 @@ __device__ void bin_box(int thread_idx, const T *roi_boxes, int roi_cols, const
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}
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// Scale and shift ROI
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T roi_offset = roi_end_mode == 1 ? static_cast<T>(0.5) : static_cast<T>(.0);
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T roi_offset = roi_end_mode == 0 ? static_cast<T>(0.5) : static_cast<T>(.0);
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*roi_start_w = roi_box[0] * spatial_scale - roi_offset;
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*roi_start_h = roi_box[1] * spatial_scale - roi_offset;
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T roi_end_w = roi_box[2] * spatial_scale - roi_offset;
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@ -121,10 +121,9 @@ __global__ void ROIAlignKernel(size_t size, const T *input, const T *roi_boxes,
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thread_idx += blockDim.x * gridDim.x) {
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int n = thread_idx / pooled_width / pooled_height / channels;
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const T *roi_box = roi_boxes + n * roi_cols;
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if (roi_box[0] < static_cast<T>(0.001) && roi_box[1] < static_cast<T>(0.001) &&
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roi_box[2] < static_cast<T>(0.001) && roi_box[3] < static_cast<T>(0.001) &&
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roi_box[0] > static_cast<T>(-0.001) && roi_box[1] > static_cast<T>(-0.001) &&
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roi_box[2] > static_cast<T>(-0.001) && roi_box[3] > static_cast<T>(-0.001)) {
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// Skip if roi box is a line
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if (roi_box[1] < static_cast<T>(0.001) && roi_box[3] < static_cast<T>(0.001) &&
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roi_box[1] > static_cast<T>(-0.001) && roi_box[3] > static_cast<T>(-0.001)) {
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continue;
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}
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@ -136,8 +135,6 @@ __global__ void ROIAlignKernel(size_t size, const T *input, const T *roi_boxes,
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pooled_height, pooled_width, &offset, &n, &c, &ph, &pw, &roi_bin_grid_h, &roi_bin_grid_w, &bin_size_h,
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&bin_size_w, &roi_start_h, &roi_start_w);
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if (offset < 0 || offset >= size) continue;
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// (n, c, ph, pw) is the base param of pooled map
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const T count_points_in_grid_cell = roi_bin_grid_h * roi_bin_grid_w;
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@ -209,10 +206,8 @@ __global__ void ROIAlignGradKernel(size_t size, const T *dy, const T *roi_boxes,
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thread_idx += blockDim.x * gridDim.x) {
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int n = thread_idx / pooled_width / pooled_height / channels;
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const T *roi_box = roi_boxes + n * roi_cols;
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if (roi_box[0] < static_cast<T>(0.001) && roi_box[1] < static_cast<T>(0.001) &&
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roi_box[2] < static_cast<T>(0.001) && roi_box[3] < static_cast<T>(0.001) &&
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roi_box[0] > static_cast<T>(-0.001) && roi_box[1] > static_cast<T>(-0.001) &&
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roi_box[2] > static_cast<T>(-0.001) && roi_box[3] > static_cast<T>(-0.001)) {
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if (roi_box[1] < static_cast<T>(0.001) && roi_box[3] < static_cast<T>(0.001) &&
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roi_box[1] > static_cast<T>(-0.001) && roi_box[3] > static_cast<T>(-0.001)) {
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continue;
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}
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@ -224,8 +219,6 @@ __global__ void ROIAlignGradKernel(size_t size, const T *dy, const T *roi_boxes,
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pooled_height, pooled_width, &offset, &n, &c, &ph, &pw, &roi_bin_grid_h, &roi_bin_grid_w, &bin_size_h,
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&bin_size_w, &roi_start_h, &roi_start_w);
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if (offset < 0 || offset >= size) continue;
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// (n, c, ph, pw) is the base param of pooled map
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const T count_points_in_grid_cell = roi_bin_grid_h * roi_bin_grid_w;
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@ -62,10 +62,17 @@ def test_roi_align_grad_half():
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sample_num)
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output = roi_align_grad(dy, rois)
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print(output)
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expect = ([[[[0.0563, 0.0563, 0.0750, 0.0938, 0.1125, 0.0563],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0188, 0.0188, 0.0250, 0.0312, 0.0375, 0.0188]]]])
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# the out if aligned is True
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# expect = ([[[[0.0563, 0.0563, 0.0750, 0.0938, 0.1125, 0.0563],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0188, 0.0188, 0.0250, 0.0312, 0.0375, 0.0188]]]])
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expect = ([[[[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075]]]])
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np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)
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@ -62,10 +62,17 @@ def test_roi_align_grad():
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sample_num)
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output = roi_align_grad(dy, rois)
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print(output)
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expect = ([[[[0.0563, 0.0563, 0.0750, 0.0938, 0.1125, 0.0563],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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[0.0188, 0.0188, 0.0250, 0.0312, 0.0375, 0.0188]]]])
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# the out if aligned is True
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# expect = ([[[[0.0563, 0.0563, 0.0750, 0.0938, 0.1125, 0.0563],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0375, 0.0375, 0.0500, 0.0625, 0.0750, 0.0375],
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# [0.0188, 0.0188, 0.0250, 0.0312, 0.0375, 0.0188]]]])
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expect = ([[[[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075],
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[0.025, 0.025, 0.05, 0.05, 0.075, 0.075]]]])
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np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)
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@ -39,7 +39,7 @@ def test_roi_align_half():
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# test case 1
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pooled_height, pooled_width, spatial_scale, sample_num = 4, 4, 0.2, 3
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[1.2333, 2.1000, 3.3000, 4.5000],
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@ -39,7 +39,7 @@ def test_roi_align():
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# test case 1
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pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[2.75, 4.5, 6.5],
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@ -49,7 +49,7 @@ def test_roi_align():
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# test case 2
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pooled_height, pooled_width, spatial_scale, sample_num = 4, 4, 0.2, 3
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[1.2333, 2.1000, 3.3000, 4.5000],
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@ -63,7 +63,7 @@ def test_roi_align():
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rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0],
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[0, 1.0, 0.0, 19.0, 18.0]],
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np.float32))
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[3.3333, 5.5000, 7.6667],
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@ -77,7 +77,7 @@ def test_roi_align():
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# test case 4
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pooled_height, pooled_width, spatial_scale, sample_num = 2, 2, 1.0, -1
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rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], np.float32))
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
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roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[8.2222, 0.],
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