From 6fb2aa602373e88999896f5a7f6226cec3e8b40b Mon Sep 17 00:00:00 2001 From: zhangz0911gm Date: Fri, 6 Nov 2020 23:04:42 -0500 Subject: [PATCH] Updating Notes for py files --- mindspore/ops/composite/random_ops.py | 1 + mindspore/ops/op_selector.py | 4 +- mindspore/ops/operations/_inner_ops.py | 4 ++ mindspore/ops/operations/_quant_ops.py | 10 ++-- mindspore/ops/operations/_thor_ops.py | 2 +- mindspore/ops/operations/array_ops.py | 64 ++++++++++++++++++++++++-- mindspore/ops/operations/math_ops.py | 11 ++++- mindspore/ops/operations/nn_ops.py | 20 +++++++- mindspore/ops/operations/other_ops.py | 2 +- mindspore/ops/operations/random_ops.py | 2 + mindspore/ops/primitive.py | 6 +-- 11 files changed, 106 insertions(+), 20 deletions(-) diff --git a/mindspore/ops/composite/random_ops.py b/mindspore/ops/composite/random_ops.py index 066ab69841..50302c8beb 100644 --- a/mindspore/ops/composite/random_ops.py +++ b/mindspore/ops/composite/random_ops.py @@ -50,6 +50,7 @@ def normal(shape, mean, stddev, seed=None): >>> mean = Tensor(1.0, mstype.float32) >>> stddev = Tensor(1.0, mstype.float32) >>> output = C.normal(shape, mean, stddev, seed=5) + >>> print(output) [[1.0996436 0.44371283 0.11127508 -0.48055804] [0.31989878 -1.0644426 1.5076542 1.2290289 ]] """ diff --git a/mindspore/ops/op_selector.py b/mindspore/ops/op_selector.py index bdd00ac7f1..2020a161d2 100644 --- a/mindspore/ops/op_selector.py +++ b/mindspore/ops/op_selector.py @@ -40,7 +40,7 @@ class _OpSelector: Examples: >>> class A: pass >>> selected_op = _OpSelector(A, "GraphKernel", - >>> "graph_kernel.ops.pkg", "primitive.ops.pkg") + ... "graph_kernel.ops.pkg", "primitive.ops.pkg") >>> # selected_op() will call graph_kernel.ops.pkg.A() """ GRAPH_KERNEL = "GraphKernel" @@ -92,7 +92,7 @@ def new_ops_selector(primitive_pkg, graph_kernel_pkg): Examples: >>> op_selector = new_ops_selector("primitive_pkg.some.path", - >>> "graph_kernel_pkg.some.path") + ... "graph_kernel_pkg.some.path") >>> @op_selector >>> class ReduceSum: pass """ diff --git a/mindspore/ops/operations/_inner_ops.py b/mindspore/ops/operations/_inner_ops.py index 05989b9cd0..c81d3db579 100644 --- a/mindspore/ops/operations/_inner_ops.py +++ b/mindspore/ops/operations/_inner_ops.py @@ -294,6 +294,7 @@ class LinSpace(PrimitiveWithInfer): >>> stop = Tensor(10, mindspore.float32) >>> num = Tensor(5, mindspore.int32) >>> output = linspace(assist, start, stop, num) + >>> print(output) [12.25, 13.375] """ @@ -329,6 +330,7 @@ class MatrixDiag(PrimitiveWithInfer): >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32) >>> matrix_diag = P.MatrixDiag() >>> result = matrix_diag(x, assist) + >>> print(result) [[[-12. 11.] [-10. 9.]] [[ -8. 7.] @@ -382,6 +384,7 @@ class MatrixDiagPart(PrimitiveWithInfer): >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32) >>> matrix_diag_part = P.MatrixDiagPart() >>> result = matrix_diag_part(x, assist) + >>> print(result) [[12., -9.], [8., -5.], [4., -1.]] """ @@ -424,6 +427,7 @@ class MatrixSetDiag(PrimitiveWithInfer): >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32) >>> matrix_set_diag = P.MatrixSetDiag() >>> result = matrix_set_diag(x, diagonal) + >>> print(result) [[[-1, 0], [0, 2]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]] """ diff --git a/mindspore/ops/operations/_quant_ops.py b/mindspore/ops/operations/_quant_ops.py index b960bb3605..78375abb33 100644 --- a/mindspore/ops/operations/_quant_ops.py +++ b/mindspore/ops/operations/_quant_ops.py @@ -187,7 +187,7 @@ class FakeQuantWithMinMaxVars(PrimitiveWithInfer): >>> min_tensor = Tensor(np.array([-6]), mstype.float32) >>> max_tensor = Tensor(np.array([6]), mstype.float32) >>> output_tensor = FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False)( - >>> input_tensor, min_tensor, max_tensor) + ... input_tensor, min_tensor, max_tensor) >>> output_tensor shape: (3, 16, 5, 5) data type: mstype.float32 """ @@ -249,7 +249,7 @@ class FakeQuantWithMinMaxVarsGradient(PrimitiveWithInfer): >>> min_tensor = Tensor(np.array([-6]), mstype.float32) >>> max_tensor = Tensor(np.array([6]), mstype.float32) >>> x_gradient, min_gradient, max_gradient = FakeQuantWithMinMaxVarsGradient(num_bits=8,narrow_range=False) - >>> (gradients, input_tensor, min_tensor, max_tensor) + ... (gradients, input_tensor, min_tensor, max_tensor) >>> x_gradient shape: (3, 16, 5, 5) data type: mstype.float32 >>> min_gradient shape: (1,) data type: mstype.float32 >>> max_gradient shape: (1,) data type: mstype.float32 @@ -310,7 +310,7 @@ class FakeQuantWithMinMaxVarsPerChannel(PrimitiveWithInfer): >>> min_tensor = Tensor(np.array([-6, -1, -2, -3]), mstype.float32) >>> max_tensor = Tensor(np.array([6, 1, 2, 3]), mstype.float32) >>> output_tensor = FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False)( - >>> input_tensor, min_tensor, max_tensor) + ... input_tensor, min_tensor, max_tensor) >>> output_tensor shape: (3, 16, 3, 4) data type: mstype.float32 """ @@ -365,8 +365,8 @@ class FakeQuantWithMinMaxVarsPerChannelGradient(PrimitiveWithInfer): >>> min_tensor = Tensor(np.array([-6, -1, -2, -3]), mstype.float32) >>> max_tensor = Tensor(np.array([6, 1, 2, 3]), mstype.float32) >>> x_gradient, min_gradient, max_gradient = FakeQuantWithMinMaxVarsPerChannelGradient( - >>> num_bits=8, narrow_range=False)( - >>> gradients, input_tensor, min_tensor, max_tensor) + ... num_bits=8, narrow_range=False)( + ... gradients, input_tensor, min_tensor, max_tensor) >>> x_gradient shape: (3, 16, 3, 4) data type: mstype.float32 >>> min_gradient shape: (4,) data type: mstype.float32 >>> max_gradient shape: (4,) data type: mstype.float32 diff --git a/mindspore/ops/operations/_thor_ops.py b/mindspore/ops/operations/_thor_ops.py index 86260dcdb5..4de955e1da 100644 --- a/mindspore/ops/operations/_thor_ops.py +++ b/mindspore/ops/operations/_thor_ops.py @@ -585,7 +585,7 @@ class UpdateThorGradient(PrimitiveWithInfer): >>> temp_x3 = np.random.rand(8, 128, 128).astype(np.float32) >>> input_x3 = np.zeros(16,8,128,128).astype(np.float32) >>> for i in range(16): - >>> input_x3[i,:,:,:] = temp_x3 + ... input_x3[i,:,:,:] = temp_x3 >>> input_x3 = Tensor(input_x3) >>> update_thor_gradient = P.UpdateThorGradient(split_dim=128) >>> output = update_thor_gradient(input_x1, input_x2, input_x3) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 10753f4a0f..2373552cc3 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -147,6 +147,7 @@ class ExpandDims(PrimitiveWithInfer): >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> expand_dims = P.ExpandDims() >>> output = expand_dims(input_tensor, 0) + >>> print(output) [[[2.0, 2.0], [2.0, 2.0]]] """ @@ -230,6 +231,7 @@ class SameTypeShape(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> out = P.SameTypeShape()(input_x, input_y) + >>> print(out) [[2. 2.] [2. 2.]] """ @@ -341,6 +343,7 @@ class IsSubClass(PrimitiveWithInfer): Examples: >>> result = P.IsSubClass()(mindspore.int32, mindspore.intc) + >>> print(result) True """ @@ -377,6 +380,7 @@ class IsInstance(PrimitiveWithInfer): Examples: >>> a = 1 >>> result = P.IsInstance()(a, mindspore.int32) + >>> print(result) True """ @@ -424,6 +428,7 @@ class Reshape(PrimitiveWithInfer): >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> reshape = P.Reshape() >>> output = reshape(input_tensor, (3, 2)) + >>> print(output) [[-0.1 0.3] [3.6 0.4 ] [0.5 -3.2]] @@ -490,6 +495,7 @@ class Shape(PrimitiveWithInfer): >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> shape = P.Shape() >>> output = shape(input_tensor) + >>> print(output) (3, 2, 1) """ @@ -554,6 +560,7 @@ class Squeeze(PrimitiveWithInfer): >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> squeeze = P.Squeeze(2) >>> output = squeeze(input_tensor) + >>> print(output) [[1. 1.] [1. 1.] [1. 1.]] @@ -609,6 +616,7 @@ class Transpose(PrimitiveWithCheck): >>> perm = (0, 2, 1) >>> transpose = P.Transpose() >>> output = transpose(input_tensor, perm) + >>> print(output) [[[1. 4.] [2. 5.] [3. 6.]] @@ -673,6 +681,7 @@ class GatherV2(PrimitiveWithCheck): >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32) >>> axis = 1 >>> out = P.GatherV2()(input_params, input_indices, axis) + >>> print(out) [[2.0, 7.0], [4.0, 54.0], [2.0, 55.0]] @@ -746,6 +755,7 @@ class Padding(PrimitiveWithInfer): >>> x = Tensor(np.array([[8], [10]]), mindspore.float32) >>> pad_dim_size = 4 >>> out = P.Padding(pad_dim_size)(x) + >>> print(out) [[8, 0, 0, 0], [10, 0, 0, 0]] """ @@ -786,6 +796,7 @@ class UniqueWithPad(PrimitiveWithInfer): >>> x = Tensor(np.array([1, 1, 5, 5, 4, 4, 3, 3, 2, 2,]), mindspore.int32) >>> pad_num = 8 >>> out = P.UniqueWithPad()(x, pad_num) + >>> print(out) ([1, 5, 4, 3, 2, 8, 8, 8, 8, 8], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]) """ @@ -829,6 +840,7 @@ class Split(PrimitiveWithInfer): >>> split = P.Split(1, 2) >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> output = split(x) + >>> print(output) ([[1, 1], [2, 2]], [[1, 1], @@ -884,7 +896,8 @@ class Rank(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> rank = P.Rank() - >>> rank(input_tensor) + >>> output = rank(input_tensor) + >>> print(output) 2 """ @@ -956,6 +969,7 @@ class Size(PrimitiveWithInfer): >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> size = P.Size() >>> output = size(input_tensor) + >>> print(output) 4 """ @@ -993,7 +1007,8 @@ class Fill(PrimitiveWithInfer): Examples: >>> fill = P.Fill() - >>> fill(mindspore.float32, (2, 2), 1) + >>> output = fill(mindspore.float32, (2, 2), 1) + >>> print(output) [[1.0, 1.0], [1.0, 1.0]] """ @@ -1124,6 +1139,7 @@ class OnesLike(PrimitiveWithInfer): >>> oneslike = P.OnesLike() >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> output = oneslike(x) + >>> print(output) [[1, 1], [1, 1]] """ @@ -1156,6 +1172,7 @@ class ZerosLike(PrimitiveWithCheck): >>> zeroslike = P.ZerosLike() >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32)) >>> output = zeroslike(x) + >>> print(output) [[0.0, 0.0], [0.0, 0.0]] """ @@ -1184,6 +1201,7 @@ class TupleToArray(PrimitiveWithInfer): Examples: >>> type = P.TupleToArray()((1,2,3)) + >>> print(type) [1 2 3] """ @@ -1228,6 +1246,7 @@ class ScalarToArray(PrimitiveWithInfer): >>> op = P.ScalarToArray() >>> data = 1.0 >>> output = op(data) + >>> print(output) 1.0 """ @@ -1260,6 +1279,7 @@ class ScalarToTensor(PrimitiveWithInfer): >>> op = P.ScalarToTensor() >>> data = 1 >>> output = op(data, mindspore.float32) + >>> print(output) 1.0 """ @@ -1365,6 +1385,7 @@ class Argmax(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32) >>> index = P.Argmax(output_type=mindspore.int32)(input_x) + >>> print(index) 1 """ @@ -1523,6 +1544,7 @@ class ArgMinWithValue(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.random.rand(5), mindspore.float32) >>> index, output = P.ArgMinWithValue()(input_x) + >>> print((index, output)) 0 0.0496291 """ @@ -1579,6 +1601,7 @@ class Tile(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.float32) >>> multiples = (2, 3) >>> result = tile(input_x, multiples) + >>> print(result) [[1. 2. 1. 2. 1. 2.] [3. 4. 3. 4. 3. 4.] [1. 2. 1. 2. 1. 2.] @@ -1884,6 +1907,7 @@ class Concat(PrimitiveWithInfer): >>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> op = P.Concat() >>> output = op((data1, data2)) + >>> print(output) [[0, 1], [2, 1], [0, 1], @@ -1931,6 +1955,7 @@ class ParallelConcat(PrimitiveWithInfer): >>> data2 = Tensor(np.array([[2, 1]]).astype(np.int32)) >>> op = P.ParallelConcat() >>> output = op((data1, data2)) + >>> print(output) [[0, 1], [2, 1]] """ @@ -2013,6 +2038,7 @@ class Pack(PrimitiveWithInfer): >>> data2 = Tensor(np.array([2, 3]).astype(np.float32)) >>> pack = P.Pack() >>> output = pack([data1, data2]) + >>> print(output) [[0, 1], [2, 3]] """ @@ -2062,6 +2088,7 @@ class Unpack(PrimitiveWithInfer): >>> unpack = P.Unpack() >>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> output = unpack(input_x) + >>> print(output) ([1, 1, 1, 1], [2, 2, 2, 2]) """ @@ -2113,9 +2140,10 @@ class Slice(PrimitiveWithInfer): Examples: >>> data = Tensor(np.array([[[1, 1, 1], [2, 2, 2]], - >>> [[3, 3, 3], [4, 4, 4]], - >>> [[5, 5, 5], [6, 6, 6]]]).astype(np.int32)) + ... [[3, 3, 3], [4, 4, 4]], + ... [[5, 5, 5], [6, 6, 6]]]).astype(np.int32)) >>> type = P.Slice()(data, (1, 0, 0), (1, 1, 3)) + >>> print(type) [[[3 3 3]]] """ @@ -2164,6 +2192,7 @@ class ReverseV2(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]), mindspore.int32) >>> op = P.ReverseV2(axis=[1]) >>> output = op(input_x) + >>> print(output) [[4, 3, 2, 1], [8, 7, 6, 5]] """ @@ -2201,6 +2230,7 @@ class Rint(PrimitiveWithInfer): >>> input_x = Tensor(np.array([-1.6, -0.1, 1.5, 2.0]), mindspore.float32) >>> op = P.Rint() >>> output = op(input_x) + >>> print(output) [-2., 0., 2., 2.] """ @@ -2391,7 +2421,7 @@ class StridedSlice(PrimitiveWithInfer): Examples >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], - >>> [[5, 5, 5], [6, 6, 6]]], mindspore.float32) + ... [[5, 5, 5], [6, 6, 6]]], mindspore.float32) >>> slice = P.StridedSlice() >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1)) >>> output.shape @@ -2643,6 +2673,7 @@ class Eye(PrimitiveWithInfer): Examples: >>> eye = P.Eye() >>> out_tensor = eye(2, 2, mindspore.int32) + >>> print(out_tensor) [[1, 0], [0, 1]] """ @@ -2681,6 +2712,7 @@ class ScatterNd(PrimitiveWithInfer): >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32) >>> shape = (3, 3) >>> output = op(indices, update, shape) + >>> print(output) [[0. 3.2 0.] [0. 1.1 0.] [0. 0. 0. ]] @@ -2731,6 +2763,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer): >>> input_tensor = Tensor(np.array([[[[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]]]), mindspore.float32) >>> resize = P.ResizeNearestNeighbor((2, 2)) >>> output = resize(input_tensor) + >>> print(output) [[[[-0.1 0.3] [0.4 0.5 ]]]] """ @@ -2772,6 +2805,7 @@ class GatherNd(PrimitiveWithInfer): >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> op = P.GatherNd() >>> output = op(input_x, indices) + >>> print(output) [-0.1, 0.5] """ @@ -2863,6 +2897,7 @@ class ScatterUpdate(_ScatterOp_Dynamic): >>> updates = Tensor(np_updates, mindspore.float32) >>> op = P.ScatterUpdate() >>> output = op(input_x, indices, updates) + >>> print(output) [[2.0, 1.2, 1.0], [3.0, 1.2, 1.0]] """ @@ -2901,6 +2936,7 @@ class ScatterNdUpdate(_ScatterNdOp): >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) >>> op = P.ScatterNdUpdate() >>> output = op(input_x, indices, update) + >>> print(output) [[1. 0.3 3.6] [0.4 2.2 -3.2]] """ @@ -2948,6 +2984,7 @@ class ScatterMax(_ScatterOp): >>> update = Tensor(np.ones([2, 2, 3]) * 88, mindspore.float32) >>> scatter_max = P.ScatterMax() >>> output = scatter_max(input_x, indices, update) + >>> print(output) [[88.0, 88.0, 88.0], [88.0, 88.0, 88.0]] """ @@ -2988,6 +3025,7 @@ class ScatterMin(_ScatterOp): >>> update = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> scatter_min = P.ScatterMin() >>> output = scatter_min(input_x, indices, update) + >>> print(output) [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0]] """ @@ -3022,6 +3060,7 @@ class ScatterAdd(_ScatterOp_Dynamic): >>> updates = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> scatter_add = P.ScatterAdd() >>> output = scatter_add(input_x, indices, updates) + >>> print(output) [[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]] """ @prim_attr_register @@ -3062,6 +3101,7 @@ class ScatterSub(_ScatterOp): >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]), mindspore.float32) >>> scatter_sub = P.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) + >>> print(output) [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1.0]] """ @@ -3096,6 +3136,7 @@ class ScatterMul(_ScatterOp): >>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32) >>> scatter_mul = P.ScatterMul() >>> output = scatter_mul(input_x, indices, updates) + >>> print(output) [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]] """ @@ -3130,6 +3171,7 @@ class ScatterDiv(_ScatterOp): >>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32) >>> scatter_div = P.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) + >>> print(output) [[3.0, 3.0, 3.0], [1.0, 1.0, 1.0]] """ @@ -3164,6 +3206,7 @@ class ScatterNdAdd(_ScatterNdOp): >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) >>> scatter_nd_add = P.ScatterNdAdd() >>> output = scatter_nd_add(input_x, indices, updates) + >>> print(output) [1, 10, 9, 4, 12, 6, 7, 17] """ @@ -3198,6 +3241,7 @@ class ScatterNdSub(_ScatterNdOp): >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) >>> scatter_nd_sub = P.ScatterNdSub() >>> output = scatter_nd_sub(input_x, indices, updates) + >>> print(output) [1, -6, -3, 4, -2, 6, 7, -1] """ @@ -3229,6 +3273,7 @@ class ScatterNonAliasingAdd(_ScatterNdOp): >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) >>> scatter_non_aliasing_add = P.ScatterNonAliasingAdd() >>> output = scatter_non_aliasing_add(input_x, indices, updates) + >>> print(output) [1, 10, 9, 4, 12, 6, 7, 17] """ @@ -3466,6 +3511,7 @@ class BatchToSpace(PrimitiveWithInfer): >>> op = P.BatchToSpace(block_size, crops) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = op(input_x) + >>> print(output) [[[[1., 2.], [3., 4.]]]] """ @@ -3635,6 +3681,7 @@ class BatchToSpaceND(PrimitiveWithInfer): >>> batch_to_space_nd = P.BatchToSpaceND(block_shape, crops) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = batch_to_space_nd(input_x) + >>> print(output) [[[[1., 2.], [3., 4.]]]] """ @@ -3860,6 +3907,7 @@ class InplaceUpdate(PrimitiveWithInfer): >>> v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32) >>> inplace_update = P.InplaceUpdate(indices) >>> result = inplace_update(x, v) + >>> print(result) [[0.5, 1.0], [1.0, 1.5], [5.0, 6.0]] @@ -3915,6 +3963,7 @@ class ReverseSequence(PrimitiveWithInfer): >>> seq_lengths = Tensor(np.array([1, 2, 3])) >>> reverse_sequence = P.ReverseSequence(seq_dim=1) >>> output = reverse_sequence(x, seq_lengths) + >>> print(output) [[1 2 3] [5 4 6] [9 8 7]] @@ -3993,6 +4042,7 @@ class EditDistance(PrimitiveWithInfer): >>> truth_shape = Tensor(np.array([2, 2, 2]).astype(np.int64)) >>> edit_distance = EditDistance(hypothesis_shape, truth_shape) >>> out = edit_distance(hypothesis_indices, hypothesis_values, truth_indices, truth_values) + >>> print(out) >>> [[1.0, 1.0], [1.0, 1.0]] """ @@ -4126,6 +4176,7 @@ class EmbeddingLookup(PrimitiveWithInfer): >>> input_indices = Tensor(np.array([[5, 2], [8, 5]]), mindspore.int32) >>> offset = 4 >>> out = P.EmbeddingLookup()(input_params, input_indices, offset) + >>> print(out) [[[10, 11], [0 ,0]], [[0, 0], [10, 11]]] """ @@ -4168,6 +4219,7 @@ class GatherD(PrimitiveWithInfer): >>> index = Tensor(np.array([[0, 0], [1, 0]]), mindspore.int32) >>> dim = 1 >>> out = P.GatherD()(x, dim, index) + >>> print(out) [[1, 1], [4, 3]] """ @@ -4212,6 +4264,7 @@ class Identity(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([1, 2, 3, 4]), mindspore.int64) >>> y = P.Identity()(x) + >>> print(y) [1, 2, 3, 4] """ @@ -4246,6 +4299,7 @@ class RepeatElements(PrimitiveWithInfer): >>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32) >>> repeat_elements = P.RepeatElements(rep = 2, axis = 0) >>> output = repeat_elements(x) + >>> print(output) [[0, 1, 2], [0, 1, 2], [3, 4, 5], diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index d00f4448c3..9e5d7b9520 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -460,6 +460,7 @@ class ReduceAny(_Reduce): >>> input_x = Tensor(np.array([[True, False], [True, True]])) >>> op = P.ReduceAny(keep_dims=True) >>> output = op(input_x, 1) + >>> print(output) [[True], [True]] """ @@ -983,6 +984,7 @@ class Neg(PrimitiveWithInfer): >>> neg = P.Neg() >>> input_x = Tensor(np.array([1, 2, -1, 2, 0, -3.5]), mindspore.float32) >>> result = neg(input_x) + >>> print(result) [-1. -2. 1. -2. 0. 3.5] """ @@ -2893,7 +2895,8 @@ class NPUClearFloatStatus(PrimitiveWithInfer): >>> init = alloc_status() >>> flag = get_status(init) >>> clear = clear_status(init) - Tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=(8,), dtype=mindspore.float32) + >>> print(clear) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] """ @prim_attr_register @@ -3144,6 +3147,7 @@ class Sign(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[2.0, 0.0, -1.0]]), mindspore.float32) >>> sign = P.Sign() >>> output = sign(input_x) + >>> print(output) [[1.0, 0.0, -1.0]] """ @@ -3440,6 +3444,7 @@ class BesselI0e(PrimitiveWithInfer): >>> bessel_i0e = P.BesselI0e() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = bessel_i0e(input_x) + >>> print(output) [0.7979961, 0.5144438, 0.75117415, 0.9157829] """ @@ -3470,6 +3475,7 @@ class BesselI1e(PrimitiveWithInfer): >>> bessel_i1e = P.BesselI1e() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = bessel_i1e(input_x) + >>> print(output) [0.09507662, 0.19699717, 0.11505538, 0.04116856] """ @@ -3500,6 +3506,7 @@ class Inv(PrimitiveWithInfer): >>> inv = P.Inv() >>> input_x = Tensor(np.array([0.25, 0.4, 0.31, 0.52]), mindspore.float32) >>> output = inv(input_x) + >>> print(output) [4., 2.5, 3.2258065, 1.923077] """ @@ -3530,6 +3537,7 @@ class Invert(PrimitiveWithInfer): >>> invert = P.Invert() >>> input_x = Tensor(np.array([25, 4, 13, 9]), mindspore.int16) >>> output = invert(input_x) + >>> print(output) [-26, -5, -14, -10] """ @@ -3558,6 +3566,7 @@ class Eps(PrimitiveWithInfer): Examples: >>> input_x = Tensor([4, 1, 2, 3], mindspore.float32) >>> out = P.Eps()(input_x) + >>> print(out) [1.52587891e-05, 1.52587891e-05, 1.52587891e-05, 1.52587891e-05] """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index dc641d0321..0e0a2b9718 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -288,6 +288,7 @@ class ReLU(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> relu = P.ReLU() >>> result = relu(input_x) + >>> print(result) [[0, 4.0, 0.0], [2.0, 0.0, 9.0]] """ @@ -320,6 +321,7 @@ class ReLU6(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> relu6 = P.ReLU6() >>> result = relu6(input_x) + >>> print(result) [[0. 4. 0.] [2. 0. 6.]] """ @@ -413,8 +415,9 @@ class Elu(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> elu = P.Elu() >>> result = elu(input_x) - Tensor([[-0.632 4.0 -0.999] - [2.0 -0.993 9.0 ]], shape=(2, 3), dtype=mindspore.float32) + >>> print(result) + [[-0.632 4.0 -0.999] + [2.0 -0.993 9.0 ]] """ @prim_attr_register @@ -1558,6 +1561,7 @@ class AvgPool(_Pool): >>> input_x = Tensor(np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4), mindspore.float32) >>> net = Net() >>> result = net(input_x) + >>> print(result) [[[[ 2.5 3.5 4.5] [ 6.5 7.5 8.5]] [[ 14.5 15.5 16.5] @@ -1829,6 +1833,7 @@ class SoftmaxCrossEntropyWithLogits(PrimitiveWithInfer): >>> labels = Tensor([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]], mindspore.float32) >>> softmax_cross = P.SoftmaxCrossEntropyWithLogits() >>> loss, backprop = softmax_cross(logits, labels) + >>> print((loss, backprop)) ([0.5899297, 0.52374405], [[0.02760027, 0.20393994, 0.01015357, 0.20393994, -0.44563377], [0.08015892, 0.02948882, 0.08015892, -0.4077012, 0.21789455]]) """ @@ -2851,6 +2856,7 @@ class PReLU(PrimitiveWithInfer): >>> weight = Tensor(np.array([0.1, 0.6, -0.3]), mindspore.float32) >>> net = Net() >>> result = net(input_x, weight) + >>> print(result) [[[-0.1, 1.0], [0.0, 2.0], [0.0, 0.0]], @@ -3107,6 +3113,7 @@ class MirrorPad(PrimitiveWithInfer): >>> paddings = Tensor([[1,1],[2,2]]) >>> pad = Net() >>> ms_output = pad(Tensor(x), paddings) + >>> print(ms_output) [[0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144] [0.31417271 0.96308136 0.934709 0.96308136 0.31417271 0.96308136 0.934709 ] [0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144] @@ -3177,6 +3184,7 @@ class ROIAlign(PrimitiveWithInfer): >>> rois = Tensor(np.array([[0, 0.2, 0.3, 0.2, 0.3]]), mindspore.float32) >>> roi_align = P.ROIAlign(2, 2, 0.5, 2) >>> output_tensor = roi_align(input_tensor, rois) + >>> print(output_tensor) [[[[1.77499998e+00, 2.02500010e+00], [2.27500010e+00, 2.52500010e+00]]]] """ @@ -3880,6 +3888,7 @@ class BinaryCrossEntropy(PrimitiveWithInfer): >>> input_y = Tensor(np.array([0., 1., 0.]), mindspore.float32) >>> weight = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> result = net(input_x, input_y, weight) + >>> print(result) 0.38240486 """ @@ -4364,6 +4373,7 @@ class SparseApplyAdagrad(PrimitiveWithInfer): >>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32)) >>> indices = Tensor([0], mstype.int32) >>> result = net(grad, indices) + >>> print(result) ([[[1.0]]], [[[1.0]]]) """ @@ -4453,6 +4463,7 @@ class SparseApplyAdagradV2(PrimitiveWithInfer): >>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32)) >>> indices = Tensor([0], mstype.int32) >>> result = net(grad, indices) + >>> print(result) ([[[1.0]]], [[[1.67194188]]]) """ @@ -4650,6 +4661,7 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck): >>> grad = Tensor(np.random.rand(1, 3).astype(np.float32)) >>> indices = Tensor(np.ones((1,), np.int32)) >>> output = net(grad, indices) + >>> print(output) ([[6.94971561e-01, 5.24479389e-01, 5.52502394e-01]], [[1.69961065e-01, 9.21632349e-01, 7.83344746e-01]]) """ @@ -5179,6 +5191,7 @@ class ApplyFtrl(PrimitiveWithInfer): >>> net = ApplyFtrlNet() >>> input_x = Tensor(np.random.randint(-4, 4, (3, 3)), mindspore.float32) >>> result = net(input_x) + >>> print(result) [[0.67455846 0.14630564 0.160499 ] [0.16329421 0.00415689 0.05202988] [0.18672481 0.17418946 0.36420345]] @@ -5266,6 +5279,7 @@ class SparseApplyFtrl(PrimitiveWithCheck): >>> grad = Tensor(np.random.rand(1, 1).astype(np.float32)) >>> indices = Tensor(np.ones([1]), mindspore.int32) >>> output = net(grad, indices) + >>> print(output) ([[1.02914639e-01]], [[7.60280550e-01]], [[7.64630079e-01]]) """ @@ -5364,6 +5378,7 @@ class SparseApplyFtrlV2(PrimitiveWithInfer): >>> grad = Tensor(np.random.rand(1, 3).astype(np.float32)) >>> indices = Tensor(np.ones([1]), mindspore.int32) >>> output = net(grad, indices) + >>> print(output) ([[3.98493223e-02, 4.38684933e-02, 8.25387388e-02]], [[6.40987396e-01, 7.19417334e-01, 1.52606890e-01]], [[7.43463933e-01, 2.92334408e-01, 6.81572020e-01]]) @@ -5877,6 +5892,7 @@ class InTopK(PrimitiveWithInfer): >>> x2 = Tensor(np.array([1, 3]), mindspore.int32) >>> in_top_k = P.InTopK(3) >>> result = in_top_k(x1, x2) + >>> print(result) [True False] """ diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py index 25c39f1572..5b86152a22 100644 --- a/mindspore/ops/operations/other_ops.py +++ b/mindspore/ops/operations/other_ops.py @@ -169,7 +169,7 @@ class BoundingBoxDecode(PrimitiveWithInfer): >>> anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) >>> deltas = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32) >>> boundingbox_decode = P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), - >>> max_shape=(768, 1280), wh_ratio_clip=0.016) + ... max_shape=(768, 1280), wh_ratio_clip=0.016) >>> boundingbox_decode(anchor_box, deltas) [[4.1953125 0. 0. 5.1953125] [2.140625 0. 3.859375 60.59375]] diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index 033aae1c4f..d07f4fd4fc 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -250,6 +250,7 @@ class UniformInt(PrimitiveWithInfer): >>> maxval = Tensor(5, mstype.int32) >>> uniform_int = P.UniformInt(seed=10) >>> output = uniform_int(shape, minval, maxval) + >>> print(output) [[4 2 1 3] [4 3 4 5]] """ @@ -299,6 +300,7 @@ class UniformReal(PrimitiveWithInfer): >>> shape = (2, 2) >>> uniformreal = P.UniformReal(seed=2) >>> output = uniformreal(shape) + >>> print(output) [[0.4359949 0.18508208] [0.02592623 0.93154085]] """ diff --git a/mindspore/ops/primitive.py b/mindspore/ops/primitive.py index a1e20e5a43..a30600f53b 100644 --- a/mindspore/ops/primitive.py +++ b/mindspore/ops/primitive.py @@ -477,13 +477,13 @@ def constexpr(fn=None, get_instance=True, name=None): >>> # make an operator to calculate tuple len >>> @constexpr >>> def tuple_len(x): - >>> return len(x) + ... return len(x) >>> assert tuple_len(a) == 2 - >>> + ... >>> # make a operator class to calculate tuple len >>> @constexpr(get_instance=False, name="TupleLen") >>> def tuple_len_class(x): - >>> return len(x) + ... return len(x) >>> assert tuple_len_class()(a) == 2 """