diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h index aa37e8e6f94..6d5e506782c 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h @@ -59,6 +59,7 @@ class StridedSliceGpuKernel : public GpuKernel { ParseMasks(kernel_node); FillOutputDim(); null_output_ = IsNullOutput(); + InitSizeLists(); return true; } @@ -86,14 +87,15 @@ class StridedSliceGpuKernel : public GpuKernel { for (size_t i = 0; i < MAX_DIMS; i++) { if (i < begin_.size()) { - begin_[i] = - std::min(begin_[i] < 0 ? SizeToInt(begin_[i] + input_shape_[i]) : begin_[i], SizeToInt(input_shape_[i] - 1)); + int dim = SizeToInt(input_shape_[i]); + begin_[i] = std::min(begin_[i] < 0 ? std::max(begin_[i] + dim, 0) : begin_[i], dim - 1); } else { begin_.push_back(0); } if (i < end_.size()) { - end_[i] = std::max(end_[i] < 0 ? end_[i] + SizeToInt(input_shape_[i]) : end_[i], -1); + int dim = SizeToInt(input_shape_[i]); + end_[i] = std::max(end_[i] < 0 ? end_[i] + dim : std::min(end_[i], dim), -1); } else { end_.push_back(i < input_shape_.size() ? input_shape_[i] : 1); } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h index f9cc3bcbfd8..737dcdb3e3d 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h @@ -87,14 +87,15 @@ class StridedSliceGradGpuKernel : public GpuKernel { for (size_t i = 0; i < MAX_DIMS; i++) { if (i < begin_.size()) { - begin_[i] = - std::min(begin_[i] < 0 ? SizeToInt(begin_[i] + input_shape_[i]) : begin_[i], SizeToInt(input_shape_[i] - 1)); + int dim = SizeToInt(input_shape_[i]); + begin_[i] = std::min(begin_[i] < 0 ? std::max(begin_[i] + dim, 0) : begin_[i], dim - 1); } else { begin_.push_back(0); } if (i < end_.size()) { - end_[i] = std::max(end_[i] < 0 ? end_[i] + SizeToInt(input_shape_[i]) : end_[i], -1); + int dim = SizeToInt(input_shape_[i]); + end_[i] = std::max(end_[i] < 0 ? end_[i] + dim : std::min(end_[i], dim), -1); } else { end_.push_back(i < input_shape_.size() ? input_shape_[i] : 1); } diff --git a/tests/st/ops/gpu/test_stridedslice_grad_op.py b/tests/st/ops/gpu/test_stridedslice_grad_op.py index 2faa32c706b..17ad80d00aa 100644 --- a/tests/st/ops/gpu/test_stridedslice_grad_op.py +++ b/tests/st/ops/gpu/test_stridedslice_grad_op.py @@ -150,73 +150,6 @@ def strided_slice_grad(nptype): [0., 0., 0., 0., 0.]]]]).astype(nptype) assert np.allclose(dx[0].asnumpy(), expect) - # ME infer fault - # y = GradData()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2)) - # expect = np.array([[[[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]]], - - - # [[[0., 0., 0., 0., 0.], - # [0., 1., 0., 1., 0.], - # [0., 1., 0., 1., 0.], - # [0., 1., 0., 1., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 1., 0., 1., 0.], - # [0., 1., 0., 1., 0.], - # [0., 1., 0., 1., 0.]],begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100 - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]]]]) - # assert np.allclose(y.asnumpy(), expect) - - # y = Grad(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1)) - # expect = np.array([[[[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]]], - - - # [[[0., 0., 1., 1., 0.], - # [0., 0., 1., 1., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 1., 1., 0.], - # [0., 0., 1., 1., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]], - - # [[0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.], - # [0., 0., 0., 0., 0.]]]]) - # assert np.allclose(y.asnumpy(), expect) - net = StridedSliceNet((1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1), begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100) diff --git a/tests/st/ops/gpu/test_stridedslice_op.py b/tests/st/ops/gpu/test_stridedslice_op.py index 61f7e479c15..5e88b744fa5 100644 --- a/tests/st/ops/gpu/test_stridedslice_op.py +++ b/tests/st/ops/gpu/test_stridedslice_op.py @@ -45,23 +45,23 @@ def strided_slice(nptype): [89, 88, 87]]]]).astype(nptype) assert np.allclose(y.asnumpy(), expect) - # ME infer fault - # y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2)) - # expect = np.array([[[[78, 76], - # [73, 71], - # [68, 66]], - # [[98, 96], - # [93, 91], - # [88, 86]]]]) - # assert np.allclose(y.asnumpy(), expect) + y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2)) + expect = np.array([[[[78, 76], + [73, 71], + [68, 66]], + [[98, 96], + [93, 91], + [88, 86]]]]).astype(nptype) + assert np.allclose(y.asnumpy(), expect) + # ME Infer fault # y = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1)) - # expect = np.array([[[[ 62, 63], - # [ 67, 68]], - # [[ 82, 83], - # [ 87, 88]], + # expect = np.array([[[[62, 63], + # [67, 68]], + # [[82, 83], + # [87, 88]], # [[102, 103], - # [107, 108]]]]) + # [107, 108]]]]).astype(nptype) # assert np.allclose(y.asnumpy(), expect) op = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100) @@ -125,3 +125,25 @@ def test_strided_slice_uint8(): @pytest.mark.env_onecard def test_strided_slice_bool(): strided_slice(np.bool) + x = Tensor(np.arange(0, 4*4*4).reshape(4, 4, 4).astype(np.float32)) + y = x[-8:, :8] + expect = np.array([[[0., 1., 2., 3.], + [4., 5., 6., 7.], + [8., 9., 10., 11.], + [12., 13., 14., 15.]], + + [[16., 17., 18., 19.], + [20., 21., 22., 23.], + [24., 25., 26., 27.], + [28., 29., 30., 31.]], + + [[32., 33., 34., 35.], + [36., 37., 38., 39.], + [40., 41., 42., 43.], + [44., 45., 46., 47.]], + + [[48., 49., 50., 51.], + [52., 53., 54., 55.], + [56., 57., 58., 59.], + [60., 61., 62., 63.]]]) + assert np.allclose(y.asnumpy(), expect)