diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.cc index 61661ce684..6e262ad976 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.cc @@ -45,8 +45,12 @@ bool ResizeNearestNeighborCPUKernel::Launch(const std::vector(inputs, outputs); } else if (dtype_ == kNumberTypeFloat32) { LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeFloat64) { + LaunchKernel(inputs, outputs); } else if (dtype_ == kNumberTypeInt32) { LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeInt64) { + LaunchKernel(inputs, outputs); } return true; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.h index 4f83b002da..2841e3d645 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_cpu_kernel.h @@ -61,8 +61,15 @@ MS_REG_CPU_KERNEL(ResizeNearestNeighbor, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), ResizeNearestNeighborCPUKernel); +MS_REG_CPU_KERNEL(ResizeNearestNeighbor, + KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64), + ResizeNearestNeighborCPUKernel); + MS_REG_CPU_KERNEL(ResizeNearestNeighbor, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), ResizeNearestNeighborCPUKernel); + +MS_REG_CPU_KERNEL(ResizeNearestNeighbor, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), + ResizeNearestNeighborCPUKernel); } // namespace kernel } // namespace mindspore #endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RESIZE_NEAREST_NEIGHBOR_CPU_KERNEL_H_ diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.cc index 4d20c01d3b..bc456b3cd6 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.cc @@ -44,8 +44,12 @@ bool ResizeNearestNeighborGradCPUKernel::Launch(const std::vector(inputs, outputs); } else if (dtype_ == kNumberTypeFloat32) { LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeFloat64) { + LaunchKernel(inputs, outputs); } else if (dtype_ == kNumberTypeInt32) { LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeInt64) { + LaunchKernel(inputs, outputs); } return true; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.h index f2a2e89ebd..9256fb2345 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/resize_nearest_neighbor_grad_cpu_kernel.h @@ -60,9 +60,17 @@ MS_REG_CPU_KERNEL(ResizeNearestNeighborGrad, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), ResizeNearestNeighborGradCPUKernel); +MS_REG_CPU_KERNEL(ResizeNearestNeighborGrad, + KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64), + ResizeNearestNeighborGradCPUKernel); + MS_REG_CPU_KERNEL(ResizeNearestNeighborGrad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), ResizeNearestNeighborGradCPUKernel); + +MS_REG_CPU_KERNEL(ResizeNearestNeighborGrad, + KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), + ResizeNearestNeighborGradCPUKernel); } // namespace kernel } // namespace mindspore #endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RESIZE_NEAREST_NEIGHBOR_GRAD_CPU_KERNEL_H_ diff --git a/tests/st/ops/cpu/test_resize_nearest_neighbor_op.py b/tests/st/ops/cpu/test_resize_nearest_neighbor_op.py index 0e3c45d31a..39cd518d0d 100755 --- a/tests/st/ops/cpu/test_resize_nearest_neighbor_op.py +++ b/tests/st/ops/cpu/test_resize_nearest_neighbor_op.py @@ -105,7 +105,6 @@ def resize_nn_grayscale_integer_ratio(datatype): output = resize_nn(input_tensor) np.testing.assert_array_equal(output.asnumpy(), input_tensor.asnumpy()) - def resize_nn_grayscale_not_integer_ratio(datatype): input_tensor = Tensor(np.array([[[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], @@ -187,12 +186,11 @@ def resize_nn_grayscale_not_integer_ratio(datatype): output = resize_nn(input_tensor) np.testing.assert_array_equal(output.asnumpy(), input_tensor.asnumpy()) - -def test_resize_nn_rgb_integer_ratio(): +def resize_nn_rgb_integer_ratio(datatype): input_tensor = Tensor(np.array( [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[11, 12, 13], [14, 15, 16], [17, 18, 19]], - [[111, 112, 113], [114, 115, 116], [117, 118, 119]]]]).astype(np.int32)) + [[111, 112, 113], [114, 115, 116], [117, 118, 119]]]]).astype(datatype)) # larger h and w resize_nn = NetResizeNearestNeighbor((9, 9)) @@ -224,7 +222,7 @@ def test_resize_nn_rgb_integer_ratio(): [117, 117, 117, 118, 118, 118, 119, 119, 119], [117, 117, 117, 118, 118, 118, 119, 119, 119], [117, 117, 117, 118, 118, 118, 119, 119, 119]]]]) - expected_output = Tensor(np.array(expected_output_array).astype(np.int32)) + expected_output = Tensor(np.array(expected_output_array).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) @@ -232,7 +230,7 @@ def test_resize_nn_rgb_integer_ratio(): resize_nn = NetResizeNearestNeighbor((1, 1)) output = resize_nn(input_tensor) expected_output = Tensor( - np.array([[[[1]], [[11]], [[111]]]]).astype(np.int32)) + np.array([[[[1]], [[11]], [[111]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # smaller h, larger w @@ -240,7 +238,7 @@ def test_resize_nn_rgb_integer_ratio(): output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1, 1, 2, 2, 3, 3]], [[11, 11, 12, 12, 13, 13]], - [[111, 111, 112, 112, 113, 113]]]]).astype(np.int32)) + [[111, 111, 112, 112, 113, 113]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # larger h, smaller w @@ -248,7 +246,7 @@ def test_resize_nn_rgb_integer_ratio(): output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1], [1], [4], [4], [7], [7]], [[11], [11], [14], [14], [17], [17]], - [[111], [111], [114], [114], [117], [117]]]]).astype(np.int32)) + [[111], [111], [114], [114], [117], [117]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # smaller h, same w @@ -256,7 +254,7 @@ def test_resize_nn_rgb_integer_ratio(): output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1, 2, 3]], [[11, 12, 13]], - [[111, 112, 113]]]]).astype(np.int32)) + [[111, 112, 113]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # larger h, same w @@ -279,7 +277,7 @@ def test_resize_nn_rgb_integer_ratio(): [114, 115, 116], [114, 115, 116], [117, 118, 119], - [117, 118, 119]]]]).astype(np.int32)) + [117, 118, 119]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same h, smaller w @@ -287,7 +285,7 @@ def test_resize_nn_rgb_integer_ratio(): output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1], [4], [7]], [[11], [14], [17]], - [[111], [114], [117]]]]).astype(np.int32)) + [[111], [114], [117]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same h, larger w @@ -301,7 +299,7 @@ def test_resize_nn_rgb_integer_ratio(): [17, 17, 18, 18, 19, 19]], [[111, 111, 112, 112, 113, 113], [114, 114, 115, 115, 116, 116], - [117, 117, 118, 118, 119, 119]]]]).astype(np.int32)) + [117, 117, 118, 118, 119, 119]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same w, same h (identity) @@ -309,8 +307,7 @@ def test_resize_nn_rgb_integer_ratio(): output = resize_nn(input_tensor) np.testing.assert_array_equal(output.asnumpy(), input_tensor.asnumpy()) - -def test_resize_nn_rgb_not_integer_ratio(): +def resize_nn_rgb_not_integer_ratio(datatype): input_tensor = Tensor(np.array([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 0, 1, 2]], @@ -319,7 +316,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [19, 10, 11, 12]], [[111, 112, 113, 114], [115, 116, 117, 118], - [119, 110, 111, 112]]]]).astype(np.int32)) + [119, 110, 111, 112]]]]).astype(datatype)) # larger h and w resize_nn = NetResizeNearestNeighbor((7, 7)) @@ -344,14 +341,14 @@ def test_resize_nn_rgb_not_integer_ratio(): [115, 115, 116, 116, 117, 117, 118], [115, 115, 116, 116, 117, 117, 118], [119, 119, 110, 110, 111, 111, 112], - [119, 119, 110, 110, 111, 111, 112]]]]).astype(np.int32)) + [119, 119, 110, 110, 111, 111, 112]]]]).astype(datatype)) # smaller h and w resize_nn = NetResizeNearestNeighbor((2, 3)) output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1, 2, 3], [5, 6, 7]], [[11, 12, 13], [15, 16, 17]], - [[111, 112, 113], [115, 116, 117]]]]).astype(np.int32)) + [[111, 112, 113], [115, 116, 117]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # smaller h, larger w @@ -362,7 +359,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [[11, 11, 12, 12, 13, 13, 14], [15, 15, 16, 16, 17, 17, 18]], [[111, 111, 112, 112, 113, 113, 114], - [115, 115, 116, 116, 117, 117, 118]]]]).astype(np.int32)) + [115, 115, 116, 116, 117, 117, 118]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # larger h, smaller w @@ -382,7 +379,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [111, 112, 113], [115, 116, 117], [115, 116, 117], - [119, 110, 111]]]]).astype(np.int32)) + [119, 110, 111]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # smaller h, same w @@ -393,7 +390,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [[11, 12, 13, 14], [15, 16, 17, 18]], [[111, 112, 113, 114], - [115, 116, 117, 118]]]]).astype(np.int32)) + [115, 116, 117, 118]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # larger h, same w @@ -422,7 +419,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [115, 116, 117, 118], [115, 116, 117, 118], [119, 110, 111, 112], - [119, 110, 111, 112]]]]).astype(np.int32)) + [119, 110, 111, 112]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same h, smaller w @@ -430,7 +427,7 @@ def test_resize_nn_rgb_not_integer_ratio(): output = resize_nn(input_tensor) expected_output = Tensor(np.array([[[[1, 3], [5, 7], [9, 1]], [[11, 13], [15, 17], [19, 11]], - [[111, 113], [115, 117], [119, 111]]]]).astype(np.int32)) + [[111, 113], [115, 117], [119, 111]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same h, larger w @@ -444,7 +441,7 @@ def test_resize_nn_rgb_not_integer_ratio(): [19, 19, 10, 11, 11, 12]], [[111, 111, 112, 113, 113, 114], [115, 115, 116, 117, 117, 118], - [119, 119, 110, 111, 111, 112]]]]).astype(np.int32)) + [119, 119, 110, 111, 111, 112]]]]).astype(datatype)) np.testing.assert_array_equal(expected_output.asnumpy(), output.asnumpy()) # same w, same h (identity) @@ -452,47 +449,7 @@ def test_resize_nn_rgb_not_integer_ratio(): output = resize_nn(input_tensor) np.testing.assert_array_equal(output.asnumpy(), input_tensor.asnumpy()) - -def resize_nn_grayscale_multiple_images(datatype): - input_tensor = Tensor(np.array([[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]], - [[[0.4, 0.5, 0.6], [0.7, 0.8, 0.9], [0.1, 0.2, 0.3]]], - [[[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]]]).astype(datatype)) - - resize_nn = NetResizeNearestNeighbor((2, 6)) - output = resize_nn(input_tensor) - - expected_output = Tensor(np.array([[[[0.1, 0.1, 0.2, 0.2, 0.3, 0.3], - [0.4, 0.4, 0.5, 0.5, 0.6, 0.6]]], - [[[0.4, 0.4, 0.5, 0.5, 0.6, 0.6], - [0.7, 0.7, 0.8, 0.8, 0.9, 0.9]]], - [[[0.7, 0.7, 0.8, 0.8, 0.9, 0.9], - [0.1, 0.1, 0.2, 0.2, 0.3, 0.3]]]]).astype(datatype)) - - np.testing.assert_array_equal(output.asnumpy(), expected_output.asnumpy()) - - -def resize_nn_grayscale_align_corners(datatype): - input_tensor = Tensor( - np.array([[[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]]]).astype(datatype)) - - resize_nn_corners_aligned = NetResizeNearestNeighbor( - (3, 7), align_corners=True) - output_corners_aligned = resize_nn_corners_aligned(input_tensor) - - resize_nn = NetResizeNearestNeighbor((3, 7)) - output = resize_nn(input_tensor) - - expected_output = Tensor(np.array([[[[0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4], - [0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8], - [0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8]]]]).astype(datatype)) - - np.testing.assert_array_equal( - output_corners_aligned.asnumpy(), expected_output.asnumpy()) - np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, - output.asnumpy(), expected_output.asnumpy()) - - -def test_resize_nn_rgb_multiple(): +def resize_nn_rgb_multiple(datatype): input_tensor = Tensor(np.array([[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]], [[111, 112, 113, 114, 115], [116, 117, 118, 119, 120]]], @@ -501,7 +458,7 @@ def test_resize_nn_rgb_multiple(): [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]], [[[111, 112, 113, 114, 115], [116, 117, 118, 119, 120]], [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], - [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]]).astype(np.int32)) + [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]]).astype(datatype)) resize_nn = NetResizeNearestNeighbor((5, 2)) output = resize_nn(input_tensor) @@ -514,15 +471,14 @@ def test_resize_nn_rgb_multiple(): [[1, 3], [1, 3], [1, 3], [6, 8], [6, 8]]], [[[111, 113], [111, 113], [111, 113], [116, 118], [116, 118]], [[1, 3], [1, 3], [1, 3], [6, 8], [6, 8]], - [[11, 13], [11, 13], [11, 13], [16, 18], [16, 18]]]]).astype(np.int32)) + [[11, 13], [11, 13], [11, 13], [16, 18], [16, 18]]]]).astype(datatype)) np.testing.assert_array_equal(output.asnumpy(), expected_output.asnumpy()) - -def test_resize_nn_rgb_align_corners(): +def resize_nn_rgb_align_corners(datatype): input_tensor = Tensor(np.array([[[[1, 2, 3, 4], [5, 6, 7, 8]], [[11, 12, 13, 14], [15, 16, 17, 18]], - [[21, 22, 23, 24], [25, 26, 27, 28]]]]).astype(np.int32)) + [[21, 22, 23, 24], [25, 26, 27, 28]]]]).astype(datatype)) resize_nn_corners_aligned = NetResizeNearestNeighbor( (5, 2), align_corners=True) @@ -534,56 +490,128 @@ def test_resize_nn_rgb_align_corners(): expected_output = Tensor(np.array([[[[1, 4], [1, 4], [5, 8], [5, 8], [5, 8]], [[11, 14], [11, 14], [15, 18], [15, 18], [15, 18]], - [[21, 24], [21, 24], [25, 28], [25, 28], [25, 28]]]]).astype(np.int32)) + [[21, 24], [21, 24], [25, 28], [25, 28], [25, 28]]]]).astype(datatype)) np.testing.assert_array_equal( output_corners_aligned.asnumpy(), expected_output.asnumpy()) np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, output.asnumpy(), expected_output.asnumpy()) +def resize_nn_grayscale_multiple_images(datatype): + input_tensor = Tensor(np.array([[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]], + [[[0.4, 0.5, 0.6], [0.7, 0.8, 0.9], [0.1, 0.2, 0.3]]], + [[[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]]]).astype(datatype)) + + resize_nn = NetResizeNearestNeighbor((2, 6)) + output = resize_nn(input_tensor) + + expected_output = Tensor(np.array([[[[0.1, 0.1, 0.2, 0.2, 0.3, 0.3], + [0.4, 0.4, 0.5, 0.5, 0.6, 0.6]]], + [[[0.4, 0.4, 0.5, 0.5, 0.6, 0.6], + [0.7, 0.7, 0.8, 0.8, 0.9, 0.9]]], + [[[0.7, 0.7, 0.8, 0.8, 0.9, 0.9], + [0.1, 0.1, 0.2, 0.2, 0.3, 0.3]]]]).astype(datatype)) + + np.testing.assert_array_equal(output.asnumpy(), expected_output.asnumpy()) + +def resize_nn_grayscale_align_corners(datatype): + input_tensor = Tensor( + np.array([[[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]]]).astype(datatype)) + + resize_nn_corners_aligned = NetResizeNearestNeighbor( + (3, 7), align_corners=True) + output_corners_aligned = resize_nn_corners_aligned(input_tensor) + + resize_nn = NetResizeNearestNeighbor((3, 7)) + output = resize_nn(input_tensor) + + expected_output = Tensor(np.array([[[[0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4], + [0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8], + [0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8]]]]).astype(datatype)) + + np.testing.assert_array_equal( + output_corners_aligned.asnumpy(), expected_output.asnumpy()) + np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, + output.asnumpy(), expected_output.asnumpy()) def test_resize_nn_grayscale_integer_ratio_half(): resize_nn_grayscale_integer_ratio(np.float16) - def test_resize_nn_grayscale_integer_ratio_float(): resize_nn_grayscale_integer_ratio(np.float32) +def test_resize_nn_grayscale_integer_ratio_double(): + resize_nn_grayscale_integer_ratio(np.float64) def test_resize_nn_grayscale_not_integer_ratio_half(): resize_nn_grayscale_not_integer_ratio(np.float16) - def test_resize_nn_grayscale_not_integer_ratio_float(): resize_nn_grayscale_not_integer_ratio(np.float32) +def test_resize_nn_grayscale_not_integer_ratio_double(): + resize_nn_grayscale_not_integer_ratio(np.float64) def test_resize_nn_grayscale_multiple_half(): resize_nn_grayscale_multiple_images(np.float16) - def test_resize_nn_grayscale_multiple_float(): resize_nn_grayscale_multiple_images(np.float32) +def test_resize_nn_grayscale_multiple_double(): + resize_nn_grayscale_multiple_images(np.float64) def test_resize_nn_grayscale_align_corners_half(): resize_nn_grayscale_align_corners(np.float16) - def test_resize_nn_grayscale_align_corners_float(): resize_nn_grayscale_align_corners(np.float32) +def test_resize_nn_grayscale_align_corners_double(): + resize_nn_grayscale_align_corners(np.float64) + +def test_resize_nn_rgb_integer_ratio_int32(): + resize_nn_rgb_integer_ratio(np.int32) + +def test_resize_nn_rgb_integer_ratio_int64(): + resize_nn_rgb_integer_ratio(np.int64) + +def test_resize_nn_rgb_not_integer_ratio_int32(): + resize_nn_rgb_not_integer_ratio(np.int32) + +def test_resize_nn_rgb_not_integer_ratio_int64(): + resize_nn_rgb_not_integer_ratio(np.int64) + +def test_resize_nn_rgb_multiple_int32(): + resize_nn_rgb_multiple(np.int32) + +def test_resize_nn_rgb_multiple_int64(): + resize_nn_rgb_multiple(np.int64) + +def test_resize_nn_rgb_align_corners_int32(): + resize_nn_rgb_align_corners(np.int32) + +def test_resize_nn_rgb_align_corners_int64(): + resize_nn_rgb_align_corners(np.int64) if __name__ == "__main__": test_resize_nn_grayscale_integer_ratio_half() test_resize_nn_grayscale_integer_ratio_float() + test_resize_nn_grayscale_integer_ratio_double() test_resize_nn_grayscale_not_integer_ratio_half() test_resize_nn_grayscale_not_integer_ratio_float() + test_resize_nn_grayscale_not_integer_ratio_double() test_resize_nn_grayscale_multiple_half() test_resize_nn_grayscale_multiple_float() + test_resize_nn_grayscale_multiple_double() test_resize_nn_grayscale_align_corners_half() test_resize_nn_grayscale_align_corners_float() - test_resize_nn_rgb_integer_ratio() - test_resize_nn_rgb_not_integer_ratio() - test_resize_nn_rgb_multiple() - test_resize_nn_rgb_align_corners() + test_resize_nn_grayscale_align_corners_double() + test_resize_nn_rgb_integer_ratio_int32() + test_resize_nn_rgb_integer_ratio_int64() + test_resize_nn_rgb_not_integer_ratio_int32() + test_resize_nn_rgb_not_integer_ratio_int64() + test_resize_nn_rgb_multiple_int32() + test_resize_nn_rgb_multiple_int64() + test_resize_nn_rgb_align_corners_int32() + test_resize_nn_rgb_align_corners_int64()