diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py index d66a44239a..b12f0b4a5b 100644 --- a/mindspore/ops/__init__.py +++ b/mindspore/ops/__init__.py @@ -18,17 +18,7 @@ Operators can be used in the construct function of Cell. Examples: - >>> from mindspore.ops import operations as P - >>> from mindspore.ops import composite as C - >>> from mindspore.ops import functional as F >>> import mindspore.ops as ops - -Note: - - The Primitive operators in operations need to be instantiated before being used. - - The composite operators are the pre-defined combination of operators. - - The functional operators are the pre-instantiated Primitive operators, which can be used directly as a function. - - For functional operators usage, please refer to - https://gitee.com/mindspore/mindspore/blob/master/mindspore/ops/functional.py """ from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register diff --git a/mindspore/ops/operations/_cache_ops.py b/mindspore/ops/operations/_cache_ops.py index 5e36031fe0..749924e877 100644 --- a/mindspore/ops/operations/_cache_ops.py +++ b/mindspore/ops/operations/_cache_ops.py @@ -94,7 +94,7 @@ class SearchCacheIdx(PrimitiveWithInfer): [21, 9, -5, 1]], np.int32)), name="hashmap") >>> indices = Tensor(np.array([10, 2, 25, 5, 3], np.int32)) >>> step = 0, emb_max_num = 25, cache_max_num = 10 - >>> ops = P.SearchCacheIdx() + >>> ops = ops.SearchCacheIdx() >>> cache_idx, miss_idx, miss_emb_idx = ops(hashmap, indices, step, emb_max_num, cache_max_num) cache_idx : [5, 1, 10, -1, 3] miss_idx : [-1, -1, -1, 3, -1] diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 486cf2e6d7..5395eefa74 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -496,7 +496,7 @@ class DropoutGrad(PrimitiveWithInfer): Tensor, the value of generated mask for input shape. Examples: - >>> dropout_grad = P.DropoutGrad(keep_prob=0.5) + >>> dropout_grad = ops.DropoutGrad(keep_prob=0.5) >>> in = Tensor((20, 16, 50, 50)) >>> out = dropout_grad(in) """ diff --git a/mindspore/ops/operations/_inner_ops.py b/mindspore/ops/operations/_inner_ops.py index 1f8da40198..e7685c7e4b 100644 --- a/mindspore/ops/operations/_inner_ops.py +++ b/mindspore/ops/operations/_inner_ops.py @@ -130,7 +130,7 @@ class Range(PrimitiveWithInfer): Tensor, has the same shape and dtype as `input_x`. Examples: - >>> range = P.Range(1.0, 8.0, 2.0) + >>> range = ops.Range(1.0, 8.0, 2.0) >>> x = Tensor(np.array([1, 2, 3, 2]), mindspore.int32) >>> output = range(x) >>> print(output) @@ -199,7 +199,7 @@ class Quant(PrimitiveWithInfer): Examples: >>> input_x = Tensor([100.0, 150.0], mstype.float32) - >>> quant = P.Quant(80.0, 0.0, False, "Round") + >>> quant = ops.Quant(80.0, 0.0, False, "Round") >>> y = quant(input_x) """ @@ -253,7 +253,7 @@ class Dequant(PrimitiveWithInfer): Examples: >>> input_x = Tensor([100.0, 150.0], mstype.float32) - >>> dequant = P.Dequant(False, False) + >>> dequant = ops.Dequant(False, False) >>> y = dequant(input_x) """ @@ -289,7 +289,7 @@ class LinSpace(PrimitiveWithInfer): Tensor, has the same shape as `assist`. Examples: - >>> linspace = P.LinSpace() + >>> linspace = ops.LinSpace() >>> assist = Tensor([5, 5.5], mindspore.float32) >>> start = Tensor(1, mindspore.float32) >>> stop = Tensor(10, mindspore.float32) @@ -329,7 +329,7 @@ class MatrixDiag(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([1, -1]), mstype.float32) >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32) - >>> matrix_diag = P.MatrixDiag() + >>> matrix_diag = ops.MatrixDiag() >>> result = matrix_diag(x, assist) >>> print(result) [[[-12. 11.] @@ -383,7 +383,7 @@ class MatrixDiagPart(PrimitiveWithInfer): Examples: >>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32) >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32) - >>> matrix_diag_part = P.MatrixDiagPart() + >>> matrix_diag_part = ops.MatrixDiagPart() >>> result = matrix_diag_part(x, assist) >>> print(result) [[12., -9.], [8., -5.], [4., -1.]] @@ -426,7 +426,7 @@ class MatrixSetDiag(PrimitiveWithInfer): Examples: >>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32) >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32) - >>> matrix_set_diag = P.MatrixSetDiag() + >>> matrix_set_diag = ops.MatrixSetDiag() >>> result = matrix_set_diag(x, diagonal) >>> print(result) [[[-1, 0], [0, 2]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]] @@ -523,7 +523,7 @@ class DynamicGRUV2(PrimitiveWithInfer): >>> bias_i = Tensor(np.random.rand(48).astype(np.float16)) >>> bias_h = Tensor(np.random.rand(48).astype(np.float16)) >>> init_h = Tensor(np.random.rand(8, 16).astype(np.float16)) - >>> dynamic_gru_v2 = P.DynamicGRUV2() + >>> dynamic_gru_v2 = ops.DynamicGRUV2() >>> output = dynamic_gru_v2(x, weight_i, weight_h, bias_i, bias_h, None, init_h) >>> result = output[0].shape >>> print(result) @@ -640,7 +640,7 @@ class ConfusionMulGrad(PrimitiveWithInfer): the shape of output is :math:`(x_1,x_4,...x_R)`. Examples: - >>> confusion_mul_grad = P.ConfusionMulGrad() + >>> confusion_mul_grad = ops.ConfusionMulGrad() >>> input_0 = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32) >>> input_1 = Tensor(np.random.randint(0, 4, (2, 3)), mindspore.float32) >>> input_2 = Tensor(np.random.randint(-4, 0, (2, 3)), mindspore.float32) diff --git a/mindspore/ops/operations/_quant_ops.py b/mindspore/ops/operations/_quant_ops.py index 70b591d77c..cb10f8c19b 100644 --- a/mindspore/ops/operations/_quant_ops.py +++ b/mindspore/ops/operations/_quant_ops.py @@ -752,7 +752,7 @@ class BatchNormFoldGrad(PrimitiveWithInfer): Performs grad of BatchNormFold operation. Examples: - >>> batch_norm_fold_grad = P.BatchNormFoldGrad() + >>> batch_norm_fold_grad = ops.BatchNormFoldGrad() >>> d_batch_mean = Tensor(np.random.randint(-2., 2., (1, 2, 2, 3)), mindspore.float32) >>> d_batch_std = Tensor(np.random.randn(1, 2, 2, 3), mindspore.float32) >>> input_x = Tensor(np.random.randint(0, 256, (4, 1, 4, 6)), mindspore.float32) @@ -809,7 +809,7 @@ class CorrectionMul(PrimitiveWithInfer): - **out** (Tensor) - Tensor has the same shape as x. Examples: - >>> correction_mul = P.CorrectionMul() + >>> correction_mul = ops.CorrectionMul() >>> input_x = Tensor(np.random.randint(-8, 12, (3, 4)), mindspore.float32) >>> batch_std = Tensor(np.array([1.5, 3, 2]), mindspore.float32) >>> running_std = Tensor(np.array([2, 1.2, 0.5]), mindspore.float32) @@ -842,7 +842,7 @@ class CorrectionMulGrad(PrimitiveWithInfer): Performs grad of CorrectionMul operation. Examples: - >>> correction_mul_grad = P.CorrectionMulGrad() + >>> correction_mul_grad = ops.CorrectionMulGrad() >>> dout = Tensor(np.array([1.5, -2.2, 0.7, -3, 1.6, 2.8]).reshape(2, 1, 1, 3), mindspore.float32) >>> input_x = Tensor(np.random.randint(0, 256, (2, 1, 1, 3)), mindspore.float32) >>> gamma = Tensor(np.array([0.2, -0.2, 2.5, -1.]).reshape(2, 1, 2), mindspore.float32) @@ -882,7 +882,7 @@ class CorrectionMulGradReduce(PrimitiveWithInfer): Performs grad reduce of CorrectionMul operation. Examples: - >>> correction_mul_grad_rd = P.CorrectionMulGradReduce() + >>> correction_mul_grad_rd = ops.CorrectionMulGradReduce() >>> dout = Tensor(np.array([1.5, -2.2, 0.7, -3, 1.6, 2.8]).reshape(2, 1, 1, 3), mindspore.float32) >>> input_x = Tensor(np.random.randint(0, 256, (2, 1, 1, 3)), mindspore.float32) >>> gamma = Tensor(np.array([0.2, -0.2, 2.5, -1.]).reshape(2, 1, 2), mindspore.float32) @@ -926,7 +926,7 @@ class BatchNormFold2(PrimitiveWithInfer): - **y** (Tensor) - Tensor has the same shape as x. Examples: - >>> batch_norm_fold2 = P.BatchNormFold2() + >>> batch_norm_fold2 = ops.BatchNormFold2() >>> input_x = Tensor(np.random.randint(-6, 6, (4, 3)), mindspore.float32) >>> beta = Tensor(np.array([0.2, -0.1, 0.25]), mindspore.float32) >>> gamma = Tensor(np.array([-0.1, -0.25, 0.1]), mindspore.float32) @@ -974,7 +974,7 @@ class BatchNormFold2Grad(PrimitiveWithInfer): Performs grad of CorrectionAddGrad operation. Examples: - >>> bnf2_grad = P.BatchNormFold2Grad() + >>> bnf2_grad = ops.BatchNormFold2Grad() >>> input_x = Tensor(np.arange(3*3*12*12).reshape(6, 3, 6, 12), mindspore.float32) >>> dout = Tensor(np.random.randint(-32, 32, (6, 3, 6, 12)), mindspore.float32) >>> gamma = Tensor(np.random.randint(-4, 4, (3, 1, 1, 2)), mindspore.float32) diff --git a/mindspore/ops/operations/_thor_ops.py b/mindspore/ops/operations/_thor_ops.py index 4de955e1da..bb3bc3aa5f 100644 --- a/mindspore/ops/operations/_thor_ops.py +++ b/mindspore/ops/operations/_thor_ops.py @@ -82,7 +82,7 @@ class CusBatchMatMul(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32) - >>> cus_batch_matmul = P.CusBatchMatMul() + >>> cus_batch_matmul = ops.CusBatchMatMul() >>> output = cus_batch_matmul(input_x, input_y) """ @@ -115,7 +115,7 @@ class CusCholeskyTrsm(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float32) - >>> cus_choleskytrsm = P.CusCholeskyTrsm() + >>> cus_choleskytrsm = ops.CusCholeskyTrsm() >>> output = matmul(input_x) """ @@ -151,7 +151,7 @@ class CusFusedAbsMax1(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32) - >>> cus_fused_abs_max1 = P.CusFusedAbsMax1() + >>> cus_fused_abs_max1 = ops.CusFusedAbsMax1() >>> output = cus_fused_abs_max1(input_x) """ @@ -187,7 +187,7 @@ class CusImg2Col(PrimitiveWithInfer): Tensor, the shape of the output tensor is :math:`(N * H_O * W_O, C1 * K_W * K_H * C0)`. Examples: >>> input_x = Tensor(np.ones(shape=[32, 3, 224, 224]), mindspore.float16) - >>> cusimg2col = P.CusImg2Col() + >>> cusimg2col = ops.CusImg2Col() >>> output = cusimg2col(input_x) """ @@ -233,7 +233,7 @@ class CusMatMulCubeDenseLeft(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16) >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16) - >>> matmulcubedenseleft = P.CusMatMulCubeDenseLeft() + >>> matmulcubedenseleft = ops.CusMatMulCubeDenseLeft() >>> output = matmulcubedenseleft(input_x, input_y) """ @@ -268,7 +268,7 @@ class CusMatMulCubeFraczRightMul(PrimitiveWithInfer): >>> input_x1 = Tensor(np.ones(shape=[256, 256]), mindspore.float16) >>> input_x2 = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16) >>> input_x3 = Tensor(np.ones(shape=[1, ]), mindspore.float16) - >>> cusmatmulfraczrightmul = P.CusMatMulCubeFraczRightMul() + >>> cusmatmulfraczrightmul = ops.CusMatMulCubeFraczRightMul() >>> output = cusmatmulfraczrightmul(input_x1, input_x2, input_x3) """ @@ -307,7 +307,7 @@ class CusMatMulCube(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16) >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16) - >>> cusmatmulcube = P.CusMatMulCube() + >>> cusmatmulcube = ops.CusMatMulCube() >>> output = matmul(input_x, input_y) """ @@ -349,7 +349,7 @@ class CusMatrixCombine(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32) - >>> cusmatrixcombine = P.CusMatrixCombine() + >>> cusmatrixcombine = ops.CusMatrixCombine() >>> output = cusmatrixcombine(input_x) """ @@ -383,7 +383,7 @@ class CusTranspose02314(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[32, 1, 224, 224, 16]), mindspore.float16) - >>> custranspose02314 = P.CusTranspose02314() + >>> custranspose02314 = ops.CusTranspose02314() >>> output = custranspose02314(input_x) """ @@ -429,7 +429,7 @@ class CusMatMulCubeDenseRight(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16) >>> input_y = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16) - >>> cusmatmulcubedenseright = P.CusMatMulCubeDenseRight() + >>> cusmatmulcubedenseright = ops.CusMatMulCubeDenseRight() >>> output = cusmatmulcubedenseright(input_x, input_y) """ @@ -464,7 +464,7 @@ class CusMatMulCubeFraczLeftCast(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16) >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16) - >>> cusmatmulcubefraczleftcast = P.CusMatMulCubeFraczLeftCast() + >>> cusmatmulcubefraczleftcast = ops.CusMatMulCubeFraczLeftCast() >>> output = cusmatmulcubefraczleftcast(input_x, input_y) """ @@ -494,7 +494,7 @@ class Im2Col(PrimitiveWithInfer): Tensor. Examples: >>> input_x = Tensor(np.random.rand(32, 3, 224, 224).astype(np.float16)) - >>> img2col = P.CusMatMulCubeDenseLeft(kernel_size=7, pad=3, stride=2) + >>> img2col = ops.CusMatMulCubeDenseLeft(kernel_size=7, pad=3, stride=2) >>> output = img2col(input_x) """ @@ -587,7 +587,7 @@ class UpdateThorGradient(PrimitiveWithInfer): >>> for i in range(16): ... input_x3[i,:,:,:] = temp_x3 >>> input_x3 = Tensor(input_x3) - >>> update_thor_gradient = P.UpdateThorGradient(split_dim=128) + >>> update_thor_gradient = ops.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 1265f6656b..271b82571d 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -148,7 +148,7 @@ class ExpandDims(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> expand_dims = P.ExpandDims() + >>> expand_dims = ops.ExpandDims() >>> output = expand_dims(input_tensor, 0) >>> print(output) [[[2. 2.] @@ -200,7 +200,7 @@ class DType(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> output = P.DType()(input_tensor) + >>> output = ops.DType()(input_tensor) >>> print(output) Float32 """ @@ -239,7 +239,7 @@ class SameTypeShape(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> output = P.SameTypeShape()(input_x, input_y) + >>> output = ops.SameTypeShape()(input_x, input_y) >>> print(output) [[2. 2.] [2. 2.]] @@ -284,7 +284,7 @@ class Cast(PrimitiveWithInfer): >>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32) >>> input_x = Tensor(input_np) >>> type_dst = mindspore.float16 - >>> cast = P.Cast() + >>> cast = ops.Cast() >>> output = cast(input_x, type_dst) >>> print(output.dtype) Float16 @@ -357,7 +357,7 @@ class IsSubClass(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> output = P.IsSubClass()(mindspore.int32, mindspore.intc) + >>> output = ops.IsSubClass()(mindspore.int32, mindspore.intc) >>> print(output) True """ @@ -397,7 +397,7 @@ class IsInstance(PrimitiveWithInfer): Examples: >>> a = 1 - >>> output = P.IsInstance()(a, mindspore.int32) + >>> output = ops.IsInstance()(a, mindspore.int32) >>> print(output) False """ @@ -447,7 +447,7 @@ class Reshape(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) - >>> reshape = P.Reshape() + >>> reshape = ops.Reshape() >>> output = reshape(input_tensor, (3, 2)) >>> print(output) [[-0.1 0.3] @@ -539,7 +539,7 @@ class Shape(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) - >>> shape = P.Shape() + >>> shape = ops.Shape() >>> output = shape(input_tensor) >>> print(output) (3, 2, 1) @@ -572,7 +572,7 @@ class DynamicShape(Primitive): Examples: >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) - >>> shape = P.DynamicShape() + >>> shape = ops.DynamicShape() >>> output = shape(input_tensor) """ @@ -610,7 +610,7 @@ class Squeeze(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) - >>> squeeze = P.Squeeze(2) + >>> squeeze = ops.Squeeze(2) >>> output = squeeze(input_tensor) >>> print(output) [[1. 1.] @@ -669,7 +669,7 @@ class Transpose(PrimitiveWithCheck): Examples: >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32) >>> perm = (0, 2, 1) - >>> transpose = P.Transpose() + >>> transpose = ops.Transpose() >>> output = transpose(input_tensor, perm) >>> print(output) [[[ 1. 4.] @@ -711,7 +711,7 @@ class Unique(Primitive): Examples: >>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32) - >>> output = P.Unique()(x) + >>> output = ops.Unique()(x) >>> print(output) (Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1])) """ @@ -743,7 +743,7 @@ class GatherV2(PrimitiveWithCheck): >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32) >>> axis = 1 - >>> output = P.GatherV2()(input_params, input_indices, axis) + >>> output = ops.GatherV2()(input_params, input_indices, axis) >>> print(output) [[ 2. 7.] [ 4. 54.] @@ -796,7 +796,7 @@ class SparseGatherV2(GatherV2): >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32) >>> axis = 1 - >>> out = P.SparseGatherV2()(input_params, input_indices, axis) + >>> out = ops.SparseGatherV2()(input_params, input_indices, axis) """ @@ -820,7 +820,7 @@ class Padding(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([[8], [10]]), mindspore.float32) >>> pad_dim_size = 4 - >>> output = P.Padding(pad_dim_size)(x) + >>> output = ops.Padding(pad_dim_size)(x) >>> print(output) [[ 8. 0. 0. 0.] [10. 0. 0. 0.]] @@ -865,7 +865,7 @@ class UniqueWithPad(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([1, 1, 5, 5, 4, 4, 3, 3, 2, 2,]), mindspore.int32) >>> pad_num = 8 - >>> output = P.UniqueWithPad()(x, pad_num) + >>> output = ops.UniqueWithPad()(x, pad_num) >>> print(output) (Tensor(shape=[10], dtype=Int32, value= [1, 5, 4, 3, 2, 8, 8, 8, 8, 8]), Tensor(shape=[10], dtype=Int32, value= [0, 0, 1, 1, 2, 2, 3, 3, 4, 4])) @@ -911,7 +911,7 @@ class Split(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> split = P.Split(1, 2) + >>> split = ops.Split(1, 2) >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]), mindspore.int32) >>> output = split(x) >>> print(output) @@ -973,7 +973,7 @@ class Rank(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> rank = P.Rank() + >>> rank = ops.Rank() >>> output = rank(input_tensor) >>> print(output) 2 @@ -1009,7 +1009,7 @@ class TruncatedNormal(PrimitiveWithInfer): Examples: >>> shape = (1, 2, 3) - >>> truncated_normal = P.TruncatedNormal() + >>> truncated_normal = ops.TruncatedNormal() >>> output = truncated_normal(shape) """ @@ -1048,7 +1048,7 @@ class Size(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> size = P.Size() + >>> size = ops.Size() >>> output = size(input_tensor) >>> print(output) 4 @@ -1090,7 +1090,7 @@ class Fill(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> fill = P.Fill() + >>> fill = ops.Fill() >>> output = fill(mindspore.float32, (2, 2), 1) >>> print(output) [[1. 1.] @@ -1139,8 +1139,8 @@ class Ones(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> from mindspore.ops import operations as P - >>> ones = P.Ones() + >>> from mindspore.ops import operations as ops + >>> ones = ops.Ones() >>> output = ones((2, 2), mindspore.float32) >>> print(output) [[1.0, 1.0], @@ -1192,8 +1192,8 @@ class Zeros(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> from mindspore.ops import operations as P - >>> zeros = P.Zeros() + >>> from mindspore.ops import operations as ops + >>> zeros = ops.Zeros() >>> output = zeros((2, 2), mindspore.float32) >>> print(output) [[0.0, 0.0], @@ -1243,7 +1243,7 @@ class OnesLike(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> oneslike = P.OnesLike() + >>> oneslike = ops.OnesLike() >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> output = oneslike(x) >>> print(output) @@ -1279,7 +1279,7 @@ class ZerosLike(PrimitiveWithCheck): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> zeroslike = P.ZerosLike() + >>> zeroslike = ops.ZerosLike() >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32)) >>> output = zeroslike(x) >>> print(output) @@ -1313,7 +1313,7 @@ class TupleToArray(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> type = P.TupleToArray()((1,2,3)) + >>> type = ops.TupleToArray()((1,2,3)) >>> print(type) [1 2 3] """ @@ -1359,7 +1359,7 @@ class ScalarToArray(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> op = P.ScalarToArray() + >>> op = ops.ScalarToArray() >>> data = 1.0 >>> output = op(data) >>> print(output) @@ -1395,7 +1395,7 @@ class ScalarToTensor(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> op = P.ScalarToTensor() + >>> op = ops.ScalarToTensor() >>> data = 1 >>> output = op(data, mindspore.float32) >>> print(output) @@ -1441,7 +1441,7 @@ class InvertPermutation(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> invert = P.InvertPermutation() + >>> invert = ops.InvertPermutation() >>> input_data = (3, 4, 0, 2, 1) >>> output = invert(input_data) >>> print(output) @@ -1510,7 +1510,7 @@ class Argmax(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32) - >>> output = P.Argmax(output_type=mindspore.int32)(input_x) + >>> output = ops.Argmax(output_type=mindspore.int32)(input_x) >>> print(output) 1 """ @@ -1562,7 +1562,7 @@ class Argmin(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32) - >>> index = P.Argmin()(input_x) + >>> index = ops.Argmin()(input_x) >>> print(index) 2 """ @@ -1623,7 +1623,7 @@ class ArgMaxWithValue(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.random.rand(5), mindspore.float32) - >>> index, output = P.ArgMaxWithValue()(input_x) + >>> index, output = ops.ArgMaxWithValue()(input_x) """ @prim_attr_register @@ -1678,7 +1678,7 @@ class ArgMinWithValue(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.random.rand(5), mindspore.float32) - >>> output = P.ArgMinWithValue()(input_x) + >>> output = ops.ArgMinWithValue()(input_x) >>> print(output) (Tensor(shape=[], dtype=Int32, value= 2), Tensor(shape=[], dtype=Float32, value= 0.0595638)) """ @@ -1735,7 +1735,7 @@ class Tile(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> tile = P.Tile() + >>> tile = ops.Tile() >>> input_x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.float32) >>> multiples = (2, 3) >>> output = tile(input_x, multiples) @@ -1816,7 +1816,7 @@ class UnsortedSegmentSum(PrimitiveWithInfer): >>> input_x = Tensor([1, 2, 3, 4], mindspore.float32) >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32) >>> num_segments = 4 - >>> output = P.UnsortedSegmentSum()(input_x, segment_ids, num_segments) + >>> output = ops.UnsortedSegmentSum()(input_x, segment_ids, num_segments) >>> print(output) [3. 3. 4. 0.] """ @@ -1894,7 +1894,7 @@ class UnsortedSegmentMin(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32)) >>> segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32)) >>> num_segments = 2 - >>> unsorted_segment_min = P.UnsortedSegmentMin() + >>> unsorted_segment_min = ops.UnsortedSegmentMin() >>> output = unsorted_segment_min(input_x, segment_ids, num_segments) >>> print(output) [[1. 2. 3.] @@ -1953,7 +1953,7 @@ class UnsortedSegmentMax(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32)) >>> segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32)) >>> num_segments = 2 - >>> unsorted_segment_max = P.UnsortedSegmentMax() + >>> unsorted_segment_max = ops.UnsortedSegmentMax() >>> output = unsorted_segment_max(input_x, segment_ids, num_segments) >>> print(output) [[1. 2. 3.] @@ -2009,7 +2009,7 @@ class UnsortedSegmentProd(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32)) >>> segment_ids = Tensor(np.array([0, 1, 0]).astype(np.int32)) >>> num_segments = 2 - >>> unsorted_segment_prod = P.UnsortedSegmentProd() + >>> unsorted_segment_prod = ops.UnsortedSegmentProd() >>> output = unsorted_segment_prod(input_x, segment_ids, num_segments) >>> print(output) [[4. 4. 3.] @@ -2075,7 +2075,7 @@ class Concat(PrimitiveWithInfer): Examples: >>> data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) - >>> op = P.Concat() + >>> op = ops.Concat() >>> output = op((data1, data2)) >>> print(output) [[0 1] @@ -2129,7 +2129,7 @@ class ParallelConcat(PrimitiveWithInfer): Examples: >>> data1 = Tensor(np.array([[0, 1]]).astype(np.int32)) >>> data2 = Tensor(np.array([[2, 1]]).astype(np.int32)) - >>> op = P.ParallelConcat() + >>> op = ops.ParallelConcat() >>> output = op((data1, data2)) >>> print(output) [[0 1] @@ -2216,7 +2216,7 @@ class Pack(PrimitiveWithInfer): Examples: >>> data1 = Tensor(np.array([0, 1]).astype(np.float32)) >>> data2 = Tensor(np.array([2, 3]).astype(np.float32)) - >>> pack = P.Pack() + >>> pack = ops.Pack() >>> output = pack([data1, data2]) >>> print(output) [[0. 1.] @@ -2269,7 +2269,7 @@ class Unpack(PrimitiveWithInfer): ``Ascend`` Examples: - >>> unpack = P.Unpack() + >>> unpack = ops.Unpack() >>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> output = unpack(input_x) >>> print(output) @@ -2330,7 +2330,7 @@ class Slice(PrimitiveWithInfer): >>> 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)) - >>> slice = P.Slice() + >>> slice = ops.Slice() >>> output = slice(data, (1, 0, 0), (1, 1, 3)) >>> print(output) [[[3 3 3]]] @@ -2385,7 +2385,7 @@ class ReverseV2(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]), mindspore.int32) - >>> op = P.ReverseV2(axis=[1]) + >>> op = ops.ReverseV2(axis=[1]) >>> output = op(input_x) >>> print(output) [[4 3 2 1] @@ -2427,7 +2427,7 @@ class Rint(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([-1.6, -0.1, 1.5, 2.0]), mindspore.float32) - >>> op = P.Rint() + >>> op = ops.Rint() >>> output = op(input_x) >>> print(output) [-2. 0. 2. 2.] @@ -2489,7 +2489,7 @@ class Select(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> select = P.Select() + >>> select = ops.Select() >>> input_cond = Tensor([True, False]) >>> input_x = Tensor([2,3], mindspore.float32) >>> input_y = Tensor([1,2], mindspore.float32) @@ -2628,7 +2628,7 @@ 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) - >>> slice = P.StridedSlice() + >>> slice = ops.StridedSlice() >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1)) >>> print(output) [[[3. 3. 3.]]] @@ -2773,7 +2773,7 @@ class Diag(PrimitiveWithInfer): Examples: >>> input_x = Tensor([1, 2, 3, 4]) - >>> diag = P.Diag() + >>> diag = ops.Diag() >>> output = diag(input_x) >>> print(output) [[1, 0, 0, 0], @@ -2826,7 +2826,7 @@ class DiagPart(PrimitiveWithInfer): ... [0, 2, 0, 0], ... [0, 0, 3, 0], ... [0, 0, 0, 4]]) - >>> diag_part = P.DiagPart() + >>> diag_part = ops.DiagPart() >>> output = diag_part(input_x) >>> print(output) [1 2 3 4] @@ -2879,7 +2879,7 @@ class Eye(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> eye = P.Eye() + >>> eye = ops.Eye() >>> output = eye(2, 2, mindspore.int32) >>> print(output) [[1 0] @@ -2918,7 +2918,7 @@ class ScatterNd(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> op = P.ScatterNd() + >>> op = ops.ScatterNd() >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32) >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32) >>> shape = (3, 3) @@ -2975,7 +2975,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[[[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]]]), mindspore.float32) - >>> resize = P.ResizeNearestNeighbor((2, 2)) + >>> resize = ops.ResizeNearestNeighbor((2, 2)) >>> output = resize(input_tensor) >>> print(output) [[[[-0.1 0.3] @@ -3020,7 +3020,7 @@ class GatherNd(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) - >>> op = P.GatherNd() + >>> op = ops.GatherNd() >>> output = op(input_x, indices) >>> print(output) [-0.1 0.5] @@ -3061,7 +3061,7 @@ class TensorScatterUpdate(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) - >>> op = P.TensorScatterUpdate() + >>> op = ops.TensorScatterUpdate() >>> output = op(input_x, indices, update) >>> print(output) [[ 1. 0.3 3.6] @@ -3120,7 +3120,7 @@ class ScatterUpdate(_ScatterOp_Dynamic): >>> indices = Tensor(np.array([0, 1]), mindspore.int32) >>> np_updates = np.array([[2.0, 1.2, 1.0], [3.0, 1.2, 1.0]]) >>> updates = Tensor(np_updates, mindspore.float32) - >>> op = P.ScatterUpdate() + >>> op = ops.ScatterUpdate() >>> output = op(input_x, indices, updates) >>> print(output) [[2. 1.2 1. ] @@ -3164,7 +3164,7 @@ class ScatterNdUpdate(_ScatterNdOp): >>> input_x = mindspore.Parameter(Tensor(np_x, mindspore.float32), name="x") >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) - >>> op = P.ScatterNdUpdate() + >>> op = ops.ScatterNdUpdate() >>> output = op(input_x, indices, update) >>> print(output) [[ 1. 0.3 3.6] @@ -3215,7 +3215,7 @@ class ScatterMax(_ScatterOp): >>> input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32), name="input_x") >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> update = Tensor(np.ones([2, 2, 3]) * 88, mindspore.float32) - >>> scatter_max = P.ScatterMax() + >>> scatter_max = ops.ScatterMax() >>> output = scatter_max(input_x, indices, update) >>> print(output) [[88. 88. 88.] @@ -3260,7 +3260,7 @@ class ScatterMin(_ScatterOp): >>> input_x = Parameter(Tensor(np.array([[0.0, 1.0, 2.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="input_x") >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> update = Tensor(np.ones([2, 2, 3]), mindspore.float32) - >>> scatter_min = P.ScatterMin() + >>> scatter_min = ops.ScatterMin() >>> output = scatter_min(input_x, indices, update) >>> print(output) [[0. 1. 1.] @@ -3299,7 +3299,7 @@ class ScatterAdd(_ScatterOp_Dynamic): >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32) >>> updates = Tensor(np.ones([2, 2, 3]), mindspore.float32) - >>> scatter_add = P.ScatterAdd() + >>> scatter_add = ops.ScatterAdd() >>> output = scatter_add(input_x, indices, updates) >>> print(output) [[1. 1. 1.] @@ -3345,7 +3345,7 @@ class ScatterSub(_ScatterOp): >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[0, 1]]), mindspore.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]), mindspore.float32) - >>> scatter_sub = P.ScatterSub() + >>> scatter_sub = ops.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) >>> print(output) [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1.0]] @@ -3383,7 +3383,7 @@ class ScatterMul(_ScatterOp): >>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x") >>> indices = Tensor(np.array([0, 1]), mindspore.int32) >>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32) - >>> scatter_mul = P.ScatterMul() + >>> scatter_mul = ops.ScatterMul() >>> output = scatter_mul(input_x, indices, updates) >>> print(output) [[2. 2. 2.] @@ -3422,7 +3422,7 @@ class ScatterDiv(_ScatterOp): >>> input_x = Parameter(Tensor(np.array([[6.0, 6.0, 6.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x") >>> indices = Tensor(np.array([0, 1]), mindspore.int32) >>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32) - >>> scatter_div = P.ScatterDiv() + >>> scatter_div = ops.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) >>> print(output) [[3. 3. 3.] @@ -3461,7 +3461,7 @@ class ScatterNdAdd(_ScatterNdOp): >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32) >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) - >>> scatter_nd_add = P.ScatterNdAdd() + >>> scatter_nd_add = ops.ScatterNdAdd() >>> output = scatter_nd_add(input_x, indices, updates) >>> print(output) [ 1. 10. 9. 4. 12. 6. 7. 17.] @@ -3499,7 +3499,7 @@ class ScatterNdSub(_ScatterNdOp): >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32) >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) - >>> scatter_nd_sub = P.ScatterNdSub() + >>> scatter_nd_sub = ops.ScatterNdSub() >>> output = scatter_nd_sub(input_x, indices, updates) >>> print(output) [ 1. -6. -3. 4. -2. 6. 7. -1.] @@ -3534,7 +3534,7 @@ class ScatterNonAliasingAdd(_ScatterNdOp): >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32) >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) - >>> scatter_non_aliasing_add = P.ScatterNonAliasingAdd() + >>> scatter_non_aliasing_add = ops.ScatterNonAliasingAdd() >>> output = scatter_non_aliasing_add(input_x, indices, updates) >>> print(output) [ 1. 10. 9. 4. 12. 6. 7. 17.] @@ -3580,7 +3580,7 @@ class SpaceToDepth(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1,3,2,2), mindspore.float32) >>> block_size = 2 - >>> space_to_depth = P.SpaceToDepth(block_size) + >>> space_to_depth = ops.SpaceToDepth(block_size) >>> output = space_to_depth(x) >>> print(output) (1, 12, 1, 1) @@ -3641,7 +3641,7 @@ class DepthToSpace(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1,12,1,1), mindspore.float32) >>> block_size = 2 - >>> depth_to_space = P.DepthToSpace(block_size) + >>> depth_to_space = ops.DepthToSpace(block_size) >>> output = depth_to_space(x) >>> print(output.shape) (1, 3, 2, 2) @@ -3710,7 +3710,7 @@ class SpaceToBatch(PrimitiveWithInfer): Examples: >>> block_size = 2 >>> paddings = [[0, 0], [0, 0]] - >>> space_to_batch = P.SpaceToBatch(block_size, paddings) + >>> space_to_batch = ops.SpaceToBatch(block_size, paddings) >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) >>> output = space_to_batch(input_x) >>> print(output) @@ -3787,7 +3787,7 @@ class BatchToSpace(PrimitiveWithInfer): Examples: >>> block_size = 2 >>> crops = [[0, 0], [0, 0]] - >>> batch_to_space = P.BatchToSpace(block_size, crops) + >>> batch_to_space = ops.BatchToSpace(block_size, crops) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = batch_to_space(input_x) >>> print(output) @@ -3868,7 +3868,7 @@ class SpaceToBatchND(PrimitiveWithInfer): Examples: >>> block_shape = [2, 2] >>> paddings = [[0, 0], [0, 0]] - >>> space_to_batch_nd = P.SpaceToBatchND(block_shape, paddings) + >>> space_to_batch_nd = ops.SpaceToBatchND(block_shape, paddings) >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) >>> output = space_to_batch_nd(input_x) >>> print(output) @@ -3967,7 +3967,7 @@ class BatchToSpaceND(PrimitiveWithInfer): Examples: >>> block_shape = [2, 2] >>> crops = [[0, 0], [0, 0]] - >>> batch_to_space_nd = P.BatchToSpaceND(block_shape, crops) + >>> batch_to_space_nd = ops.BatchToSpaceND(block_shape, crops) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = batch_to_space_nd(input_x) >>> print(output) @@ -4050,7 +4050,7 @@ class BroadcastTo(PrimitiveWithInfer): Examples: >>> shape = (2, 3) >>> input_x = Tensor(np.array([1, 2, 3]).astype(np.float32)) - >>> broadcast_to = P.BroadcastTo(shape) + >>> broadcast_to = ops.BroadcastTo(shape) >>> output = broadcast_to(input_x) >>> print(output) [[1. 2. 3.] @@ -4107,7 +4107,7 @@ class Meshgrid(PrimitiveWithInfer): >>> y = np.array([5, 6, 7]).astype(np.int32) >>> z = np.array([8, 9, 0, 1, 2]).astype(np.int32) >>> inputs = (x, y, z) - >>> meshgrid = P.Meshgrid(indexing="xy") + >>> meshgrid = ops.Meshgrid(indexing="xy") >>> meshgrid(inputs) (Tensor(shape=[3, 4, 6], dtype=UInt32, value= [[[1, 1, 1, 1, 1], @@ -4203,7 +4203,7 @@ class InplaceUpdate(PrimitiveWithInfer): >>> indices = (0, 1) >>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32) >>> v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32) - >>> inplace_update = P.InplaceUpdate(indices) + >>> inplace_update = ops.InplaceUpdate(indices) >>> output = inplace_update(x, v) >>> print(output) [[0.5 1. ] @@ -4262,7 +4262,7 @@ class ReverseSequence(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32) >>> seq_lengths = Tensor(np.array([1, 2, 3])) - >>> reverse_sequence = P.ReverseSequence(seq_dim=1) + >>> reverse_sequence = ops.ReverseSequence(seq_dim=1) >>> output = reverse_sequence(x, seq_lengths) >>> print(output) [[1. 2. 3.] @@ -4322,7 +4322,7 @@ class EditDistance(PrimitiveWithInfer): >>> from mindspore import context >>> from mindspore import Tensor >>> import mindspore.nn as nn - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> context.set_context(mode=context.GRAPH_MODE) >>> class EditDistance(nn.Cell): ... def __init__(self, hypothesis_shape, truth_shape, normalize=True): @@ -4437,7 +4437,7 @@ class Sort(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mindspore.float16) - >>> sort = P.Sort() + >>> sort = ops.Sort() >>> output = sort(x) >>> print(output) (Tensor(shape=[3, 3], dtype=Float16, value= @@ -4489,7 +4489,7 @@ class EmbeddingLookup(PrimitiveWithInfer): >>> input_params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mindspore.float32) >>> input_indices = Tensor(np.array([[5, 2], [8, 5]]), mindspore.int32) >>> offset = 4 - >>> output = P.EmbeddingLookup()(input_params, input_indices, offset) + >>> output = ops.EmbeddingLookup()(input_params, input_indices, offset) >>> print(output) [[[10. 11.] [ 0. 0.]] @@ -4545,7 +4545,7 @@ class GatherD(PrimitiveWithInfer): >>> x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.int32) >>> index = Tensor(np.array([[0, 0], [1, 0]]), mindspore.int32) >>> dim = 1 - >>> output = P.GatherD()(x, dim, index) + >>> output = ops.GatherD()(x, dim, index) >>> print(output) [[1 1] [4 3]] @@ -4594,7 +4594,7 @@ class Identity(PrimitiveWithInfer): Examples: >>> x = Tensor(np.array([1, 2, 3, 4]), mindspore.int64) - >>> output = P.Identity()(x) + >>> output = ops.Identity()(x) >>> print(output) [1 2 3 4] """ diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py index 5413af8f41..1cf5a61299 100644 --- a/mindspore/ops/operations/comm_ops.py +++ b/mindspore/ops/operations/comm_ops.py @@ -78,13 +78,13 @@ class AllReduce(PrimitiveWithInfer): >>> from mindspore import Tensor >>> from mindspore.ops.operations.comm_ops import ReduceOp >>> import mindspore.nn as nn - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> >>> init() >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.allreduce_sum = P.AllReduce(ReduceOp.SUM, group="nccl_world_group") + ... self.allreduce_sum = ops.AllReduce(ReduceOp.SUM, group="nccl_world_group") ... ... def construct(self, x): ... return self.allreduce_sum(x) @@ -134,7 +134,7 @@ class Send(PrimitiveWithInfer): - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. Examples: - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> import mindspore.nn as nn >>> from mindspore.communication import init >>> from mindspore import Tensor @@ -144,8 +144,8 @@ class Send(PrimitiveWithInfer): >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.depend = P.Depend() - >>> self.send = P.Send(st_tag=0, dest_rank=8, group="hccl_world_group") + >>> self.depend = ops.Depend() + >>> self.send = ops.Send(st_tag=0, dest_rank=8, group="hccl_world_group") >>> >>> def construct(self, x): >>> out = self.depend(x, self.send(x)) @@ -191,7 +191,7 @@ class Receive(PrimitiveWithInfer): - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. Examples: - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> import mindspore.nn as nn >>> from mindspore.communication import init >>> from mindspore import Tensor @@ -201,7 +201,7 @@ class Receive(PrimitiveWithInfer): >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.recv = P.Receive(st_tag=0, src_rank=0, shape=[2, 8], dtype=np.float32, + >>> self.recv = ops.Receive(st_tag=0, src_rank=0, shape=[2, 8], dtype=np.float32, >>> group="hccl_world_group") >>> >>> def construct(self, x): @@ -253,7 +253,7 @@ class AllGather(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> import mindspore.nn as nn >>> from mindspore.communication import init >>> from mindspore import Tensor @@ -262,7 +262,7 @@ class AllGather(PrimitiveWithInfer): ... class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.allgather = P.AllGather(group="nccl_world_group") + ... self.allgather = ops.AllGather(group="nccl_world_group") ... ... def construct(self, x): ... return self.allgather(x) @@ -373,14 +373,14 @@ class ReduceScatter(PrimitiveWithInfer): >>> from mindspore.communication import init >>> from mindspore.ops.operations.comm_ops import ReduceOp >>> import mindspore.nn as nn - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> import numpy as np >>> >>> init() >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.reducescatter = P.ReduceScatter(ReduceOp.SUM) + ... self.reducescatter = ops.ReduceScatter(ReduceOp.SUM) ... ... def construct(self, x): ... return self.reducescatter(x) @@ -493,14 +493,14 @@ class Broadcast(PrimitiveWithInfer): >>> from mindspore import Tensor >>> from mindspore.communication import init >>> import mindspore.nn as nn - >>> import mindspore.ops.operations as P + >>> import mindspore.ops.operations as ops >>> import numpy as np >>> >>> init() >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.broadcast = P.Broadcast(1) + ... self.broadcast = ops.Broadcast(1) ... ... def construct(self, x): ... return self.broadcast((x,)) diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py index 77cd418ffc..d7eb00c6e1 100644 --- a/mindspore/ops/operations/control_ops.py +++ b/mindspore/ops/operations/control_ops.py @@ -57,7 +57,7 @@ class ControlDepend(Primitive): ... def __init__(self): ... super(Net, self).__init__() ... self.control_depend = P.ControlDepend() - ... self.softmax = P.Softmax() + ... self.softmax = ops.Softmax() ... ... def construct(self, x, y): ... mul = x * y @@ -104,12 +104,12 @@ class GeSwitch(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.square = P.Square() - ... self.add = P.TensorAdd() + ... self.square = ops.Square() + ... self.add = ops.TensorAdd() ... self.value = Tensor(np.full((1), 3), mindspore.float32) - ... self.switch = P.GeSwitch() - ... self.merge = P.Merge() - ... self.less = P.Less() + ... self.switch = ops.GeSwitch() + ... self.merge = ops.Merge() + ... self.less = ops.Less() ... ... def construct(self, x, y): ... cond = self.less(x, y) @@ -159,7 +159,7 @@ class Merge(PrimitiveWithInfer): tuple. Output is tuple(`data`, `output_index`). The `data` has the same shape of `inputs` element. Examples: - >>> merge = P.Merge() + >>> merge = ops.Merge() >>> input_x = Tensor(np.linspace(0, 8, 8).reshape(2, 4), mindspore.float32) >>> input_y = Tensor(np.random.randint(-4, 4, (2, 4)), mindspore.float32) >>> result = merge((input_x, input_y)) diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py index 30ce75358e..df7cfe0ddd 100644 --- a/mindspore/ops/operations/debug_ops.py +++ b/mindspore/ops/operations/debug_ops.py @@ -55,8 +55,8 @@ class ScalarSummary(PrimitiveWithInfer): >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() - ... self.summary = P.ScalarSummary() - ... self.add = P.TensorAdd() + ... self.summary = ops.ScalarSummary() + ... self.add = ops.TensorAdd() ... ... def construct(self, x, y): ... name = "x" @@ -97,7 +97,7 @@ class ImageSummary(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.summary = P.ImageSummary() + ... self.summary = ops.ImageSummary() ... ... def construct(self, x): ... name = "image" @@ -138,8 +138,8 @@ class TensorSummary(PrimitiveWithInfer): >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() - ... self.summary = P.TensorSummary() - ... self.add = P.TensorAdd() + ... self.summary = ops.TensorSummary() + ... self.add = ops.TensorAdd() ... ... def construct(self, x, y): ... x = self.add(x, y) @@ -180,8 +180,8 @@ class HistogramSummary(PrimitiveWithInfer): >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() - ... self.summary = P.HistogramSummary() - ... self.add = P.TensorAdd() + ... self.summary = ops.HistogramSummary() + ... self.add = ops.TensorAdd() ... ... def construct(self, x, y): ... x = self.add(x, y) @@ -234,8 +234,8 @@ class InsertGradientOf(PrimitiveWithInfer): ... ... return ret ... - >>> clip = P.InsertGradientOf(clip_gradient) - >>> grad_all = C.GradOperation(get_all=True) + >>> clip = ops.InsertGradientOf(clip_gradient) + >>> grad_all = ops.GradOperation(get_all=True) >>> def InsertGradientOfClipDemo(): ... def clip_test(x, y): ... x = clip(x) @@ -289,7 +289,7 @@ class HookBackward(PrimitiveWithInfer): ... print(grad_out) ... >>> grad_all = GradOperation(get_all=True) - >>> hook = P.HookBackward(hook_fn) + >>> hook = ops.HookBackward(hook_fn) >>> def hook_test(x, y): ... z = x * y ... z = hook(z) @@ -341,7 +341,7 @@ class Print(PrimitiveWithInfer): >>> class PrintDemo(nn.Cell): ... def __init__(self): ... super(PrintDemo, self).__init__() - ... self.print = P.Print() + ... self.print = ops.Print() ... ... def construct(self, x, y): ... self.print('Print Tensor x and Tensor y:', x, y) @@ -382,8 +382,8 @@ class Assert(PrimitiveWithInfer): >>> class AssertDemo(nn.Cell): ... def __init__(self): ... super(AssertDemo, self).__init__() - ... self.assert1 = P.Assert(summarize=10) - ... self.add = P.TensorAdd() + ... self.assert1 = ops.Assert(summarize=10) + ... self.add = ops.TensorAdd() ... ... def construct(self, x, y): ... data = self.add(x, y) diff --git a/mindspore/ops/operations/image_ops.py b/mindspore/ops/operations/image_ops.py index 2e913ce24e..de5c253e6b 100644 --- a/mindspore/ops/operations/image_ops.py +++ b/mindspore/ops/operations/image_ops.py @@ -60,7 +60,7 @@ class CropAndResize(PrimitiveWithInfer): >>> class CropAndResizeNet(nn.Cell): ... def __init__(self, crop_size): ... super(CropAndResizeNet, self).__init__() - ... self.crop_and_resize = P.CropAndResize() + ... self.crop_and_resize = ops.CropAndResize() ... self.crop_size = crop_size ... ... def construct(self, x, boxes, box_index): diff --git a/mindspore/ops/operations/inner_ops.py b/mindspore/ops/operations/inner_ops.py index b0552a984f..8e2cc5b3a1 100644 --- a/mindspore/ops/operations/inner_ops.py +++ b/mindspore/ops/operations/inner_ops.py @@ -36,7 +36,7 @@ class ScalarCast(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> scalar_cast = P.ScalarCast() + >>> scalar_cast = ops.ScalarCast() >>> output = scalar_cast(255.0, mindspore.int32) >>> print(output) 255 diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 87f898af2f..a19ed5e4be 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -139,7 +139,7 @@ class TensorAdd(_MathBinaryOp): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> add = P.TensorAdd() + >>> add = ops.TensorAdd() >>> input_x = Tensor(np.array([1,2,3]).astype(np.float32)) >>> input_y = Tensor(np.array([4,5,6]).astype(np.float32)) >>> output = add(input_x, input_y) @@ -180,7 +180,7 @@ class AssignAdd(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.AssignAdd = P.AssignAdd() + ... self.AssignAdd = ops.AssignAdd() ... self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step") ... ... def construct(self, x): @@ -235,7 +235,7 @@ class AssignSub(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.AssignSub = P.AssignSub() + ... self.AssignSub = ops.AssignSub() ... self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step") ... ... def construct(self, x): @@ -358,7 +358,7 @@ class ReduceMean(_Reduce): Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = P.ReduceMean(keep_dims=True) + >>> op = ops.ReduceMean(keep_dims=True) >>> output = op(input_x, 1) >>> result = output.shape >>> print(result) @@ -396,7 +396,7 @@ class ReduceSum(_Reduce): Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = P.ReduceSum(keep_dims=True) + >>> op = ops.ReduceSum(keep_dims=True) >>> output = op(input_x, 1) >>> output.shape (3, 1, 5, 6) @@ -440,7 +440,7 @@ class ReduceAll(_Reduce): Examples: >>> input_x = Tensor(np.array([[True, False], [True, True]])) - >>> op = P.ReduceAll(keep_dims=True) + >>> op = ops.ReduceAll(keep_dims=True) >>> output = op(input_x, 1) >>> print(output) [[False] @@ -482,7 +482,7 @@ class ReduceAny(_Reduce): Examples: >>> input_x = Tensor(np.array([[True, False], [True, True]])) - >>> op = P.ReduceAny(keep_dims=True) + >>> op = ops.ReduceAny(keep_dims=True) >>> output = op(input_x, 1) >>> print(output) [[ True] @@ -524,7 +524,7 @@ class ReduceMax(_Reduce): Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = P.ReduceMax(keep_dims=True) + >>> op = ops.ReduceMax(keep_dims=True) >>> output = op(input_x, 1) >>> result = output.shape >>> print(result) @@ -572,7 +572,7 @@ class ReduceMin(_Reduce): Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = P.ReduceMin(keep_dims=True) + >>> op = ops.ReduceMin(keep_dims=True) >>> output = op(input_x, 1) >>> result = output.shape >>> print(result) @@ -611,7 +611,7 @@ class ReduceProd(_Reduce): Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = P.ReduceProd(keep_dims=True) + >>> op = ops.ReduceProd(keep_dims=True) >>> output = op(input_x, 1) >>> reuslt = output.shape >>> print(result) @@ -641,13 +641,13 @@ class CumProd(PrimitiveWithInfer): Examples: >>> a, b, c, = 1, 2, 3 >>> input_x = Tensor(np.array([a, b, c]).astype(np.float32)) - >>> op0 = P.CumProd() + >>> op0 = ops.CumProd() >>> output0 = op0(input_x, 0) # output=[a, a * b, a * b * c] - >>> op1 = P.CumProd(exclusive=True) + >>> op1 = ops.CumProd(exclusive=True) >>> output1 = op1(input_x, 0) # output=[1, a, a * b] - >>> op2 = P.CumProd(reverse=True) + >>> op2 = ops.CumProd(reverse=True) >>> output2 = op2(input_x, 0) # output=[a * b * c, b * c, c] - >>> op3 = P.CumProd(exclusive=True, reverse=True) + >>> op3 = ops.CumProd(exclusive=True, reverse=True) >>> output3 = op3(input_x, 0) # output=[b * c, c, 1] >>> print(output0) [1. 2. 6.] @@ -705,7 +705,7 @@ class MatMul(PrimitiveWithInfer): Examples: >>> input_x1 = Tensor(np.ones(shape=[1, 3]), mindspore.float32) >>> input_x2 = Tensor(np.ones(shape=[3, 4]), mindspore.float32) - >>> matmul = P.MatMul() + >>> matmul = ops.MatMul() >>> output = matmul(input_x1, input_x2) """ @@ -787,7 +787,7 @@ class BatchMatMul(MatMul): Examples: >>> input_x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32) - >>> batmatmul = P.BatchMatMul() + >>> batmatmul = ops.BatchMatMul() >>> output = batmatmul(input_x, input_y) >>> print(output) [[[[3. 3. 3. 3.]] @@ -801,7 +801,7 @@ class BatchMatMul(MatMul): >>> >>> input_x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32) - >>> batmatmul = P.BatchMatMul(transpose_a=True) + >>> batmatmul = ops.BatchMatMul(transpose_a=True) >>> output = batmatmul(input_x, input_y) >>> print(output) [[[[3. 3. 3. 3.]] @@ -848,7 +848,7 @@ class CumSum(PrimitiveWithInfer): Examples: >>> input = Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float32)) - >>> cumsum = P.CumSum() + >>> cumsum = ops.CumSum() >>> output = cumsum(input, 1) >>> print(output) [[ 3. 7. 13. 23.] @@ -898,7 +898,7 @@ class AddN(PrimitiveWithInfer): >>> class NetAddN(nn.Cell): ... def __init__(self): ... super(NetAddN, self).__init__() - ... self.addN = P.AddN() + ... self.addN = ops.AddN() ... ... def construct(self, *z): ... return self.addN(z) @@ -984,7 +984,7 @@ class AccumulateNV2(PrimitiveWithInfer): >>> class NetAccumulateNV2(nn.Cell): ... def __init__(self): ... super(NetAccumulateNV2, self).__init__() - ... self.accumulateNV2 = P.AccumulateNV2() + ... self.accumulateNV2 = ops.AccumulateNV2() ... ... def construct(self, *z): ... return self.accumulateNV2(z) @@ -1043,7 +1043,7 @@ class Neg(PrimitiveWithInfer): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> neg = P.Neg() + >>> neg = ops.Neg() >>> input_x = Tensor(np.array([1, 2, -1, 2, 0, -3.5]), mindspore.float32) >>> output = neg(input_x) >>> print(output) @@ -1094,7 +1094,7 @@ class InplaceAdd(PrimitiveWithInfer): >>> indices = (0, 1) >>> input_x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32) >>> input_v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32) - >>> inplaceAdd = P.InplaceAdd(indices) + >>> inplaceAdd = ops.InplaceAdd(indices) >>> output = inplaceAdd(input_x, input_v) >>> print(output) [[1.5 3. ] @@ -1156,7 +1156,7 @@ class InplaceSub(PrimitiveWithInfer): >>> indices = (0, 1) >>> input_x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32) >>> input_v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32) - >>> inplaceSub = P.InplaceSub(indices) + >>> inplaceSub = ops.InplaceSub(indices) >>> output = inplaceSub(input_x, input_v) >>> print(output) [[0.5 1. ] @@ -1222,7 +1222,7 @@ class Sub(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32) - >>> sub = P.Sub() + >>> sub = ops.Sub() >>> output = sub(input_x, input_y) >>> print(output) [-3 -3 -3] @@ -1265,7 +1265,7 @@ class Mul(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) - >>> mul = P.Mul() + >>> mul = ops.Mul() >>> output = mul(input_x, input_y) >>> print(output) [ 4. 10. 18.] @@ -1308,7 +1308,7 @@ class SquaredDifference(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([2.0, 4.0, 6.0]), mindspore.float32) - >>> squared_difference = P.SquaredDifference() + >>> squared_difference = ops.SquaredDifference() >>> output = squared_difference(input_x, input_y) >>> print(output) [1. 4. 9.] @@ -1334,7 +1334,7 @@ class Square(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) - >>> square = P.Square() + >>> square = ops.Square() >>> output = square(input_x) >>> print(output) [1. 4. 9.] @@ -1376,7 +1376,7 @@ class Rsqrt(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor([[4, 4], [9, 9]], mindspore.float32) - >>> rsqrt = P.Rsqrt() + >>> rsqrt = ops.Rsqrt() >>> output = rsqrt(input_tensor) >>> print(output) [[0.5 0.5 ] @@ -1419,7 +1419,7 @@ class Sqrt(PrimitiveWithCheck): Examples: >>> input_x = Tensor(np.array([1.0, 4.0, 9.0]), mindspore.float32) - >>> sqrt = P.Sqrt() + >>> sqrt = ops.Sqrt() >>> output = sqrt(input_x) >>> print(output) [1. 2. 3.] @@ -1457,7 +1457,7 @@ class Reciprocal(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> reciprocal = P.Reciprocal() + >>> reciprocal = ops.Reciprocal() >>> output = reciprocal(input_x) >>> print(output) [1. 0.5 0.25] @@ -1515,14 +1515,14 @@ class Pow(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> input_y = 3.0 - >>> pow = P.Pow() + >>> pow = ops.Pow() >>> output = pow(input_x, input_y) >>> print(output) [ 1. 8. 64.] >>> >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> input_y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32) - >>> pow = P.Pow() + >>> pow = ops.Pow() >>> output = pow(input_x, input_y) >>> print(output) [ 1. 16. 64.] @@ -1553,7 +1553,7 @@ class Exp(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> exp = P.Exp() + >>> exp = ops.Exp() >>> output = exp(input_x) >>> print(output) [ 2.718282 7.389056 54.598152] @@ -1595,7 +1595,7 @@ class Expm1(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([0.0, 1.0, 2.0, 4.0]), mindspore.float32) - >>> expm1 = P.Expm1() + >>> expm1 = ops.Expm1() >>> output = expm1(input_x) >>> print(output) [ 0. 1.718282 6.389056 53.598152] @@ -1637,7 +1637,7 @@ class HistogramFixedWidth(PrimitiveWithInfer): Examples: >>> x = Tensor([-1.0, 0.0, 1.5, 2.0, 5.0, 15], mindspore.float16) >>> range = Tensor([0.0, 5.0], mindspore.float16) - >>> hist = P.HistogramFixedWidth(5) + >>> hist = ops.HistogramFixedWidth(5) >>> output = hist(x, range) >>> print(output) [2 1 1 0 2] @@ -1677,7 +1677,7 @@ class Log(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> log = P.Log() + >>> log = ops.Log() >>> output = log(input_x) >>> print(output) [0. 0.6931472 1.38629444] @@ -1718,7 +1718,7 @@ class Log1p(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> log1p = P.Log1p() + >>> log1p = ops.Log1p() >>> output = log1p(input_x) >>> print(output) [0.6931472 1.0986123 1.609438 ] @@ -1752,7 +1752,7 @@ class Erf(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([-1, 0, 1, 2, 3]), mindspore.float32) - >>> erf = P.Erf() + >>> erf = ops.Erf() >>> output = erf(input_x) >>> print(output) [-0.8427168 0. 0.8427168 0.99530876 0.99997765] @@ -1786,7 +1786,7 @@ class Erfc(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([-1, 0, 1, 2, 3]), mindspore.float32) - >>> erfc = P.Erfc() + >>> erfc = ops.Erfc() >>> output = erfc(input_x) >>> print(output) [1.8427168e+00 1.0000000e+00 1.5728319e-01 4.6912432e-03 2.2351742e-05] @@ -1832,7 +1832,7 @@ class Minimum(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32) - >>> minimum = P.Minimum() + >>> minimum = ops.Minimum() >>> output = minimum(input_x, input_y) >>> print(output) [1. 2. 3.] @@ -1875,7 +1875,7 @@ class Maximum(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32) - >>> maximum = P.Maximum() + >>> maximum = ops.Maximum() >>> output = maximum(input_x, input_y) >>> print(output) [4. 5. 6.] @@ -1918,7 +1918,7 @@ class RealDiv(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) - >>> realdiv = P.RealDiv() + >>> realdiv = ops.RealDiv() >>> output = realdiv(input_x, input_y) >>> print(output) [0.25 0.4 0.5 ] @@ -1962,7 +1962,7 @@ class Div(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) - >>> div = P.Div() + >>> div = ops.Div() >>> output = div(input_x, input_y) >>> print(output) [-1.3333334 2.5 2. ] @@ -2004,7 +2004,7 @@ class DivNoNan(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([-1.0, 0., 1.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([0., 0., 0., 2.0, 3.0]), mindspore.float32) - >>> div_no_nan = P.DivNoNan() + >>> div_no_nan = ops.DivNoNan() >>> output = div_no_nan(input_x, input_y) >>> print(output) [0. 0. 0. 2.5 2. ] @@ -2053,7 +2053,7 @@ class FloorDiv(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> floor_div = P.FloorDiv() + >>> floor_div = ops.FloorDiv() >>> output = floor_div(input_x, input_y) >>> print(output) [ 0 1 -1] @@ -2088,7 +2088,7 @@ class TruncateDiv(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> truncate_div = P.TruncateDiv() + >>> truncate_div = ops.TruncateDiv() >>> output = truncate_div(input_x, input_y) >>> print(output) [0 1 0] @@ -2122,7 +2122,7 @@ class TruncateMod(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> truncate_mod = P.TruncateMod() + >>> truncate_mod = ops.TruncateMod() >>> output = truncate_mod(input_x, input_y) >>> print(output) [ 2 1 -1] @@ -2157,7 +2157,7 @@ class Mod(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) - >>> mod = P.Mod() + >>> mod = ops.Mod() >>> output = mod(input_x, input_y) >>> print(output) [-1. 1. 0.] @@ -2186,7 +2186,7 @@ class Floor(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.1, 2.5, -1.5]), mindspore.float32) - >>> floor = P.Floor() + >>> floor = ops.Floor() >>> output = floor(input_x) >>> print(output) [ 1. 2. -2.] @@ -2231,7 +2231,7 @@ class FloorMod(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> floor_mod = P.FloorMod() + >>> floor_mod = ops.FloorMod() >>> output = floor_mod(input_x, input_y) >>> print(output) [2 1 2] @@ -2253,7 +2253,7 @@ class Ceil(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.1, 2.5, -1.5]), mindspore.float32) - >>> ceil_op = P.Ceil() + >>> ceil_op = ops.Ceil() >>> output = ceil_op(input_x) >>> print(output) [ 2. 3. -1.] @@ -2298,7 +2298,7 @@ class Xdivy(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.float32) >>> input_y = Tensor(np.array([2, 2, 2]), mindspore.float32) - >>> xdivy = P.Xdivy() + >>> xdivy = ops.Xdivy() >>> output = xdivy(input_x, input_y) >>> print(output) [ 1. 2. -0.5] @@ -2337,7 +2337,7 @@ class Xlogy(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([-5, 0, 4]), mindspore.float32) >>> input_y = Tensor(np.array([2, 2, 2]), mindspore.float32) - >>> xlogy = P.Xlogy() + >>> xlogy = ops.Xlogy() >>> output = xlogy(input_x, input_y) >>> print(output) [-3.465736 0. 2.7725887] @@ -2361,7 +2361,7 @@ class Acosh(PrimitiveWithInfer): ``Ascend`` Examples: - >>> acosh = P.Acosh() + >>> acosh = ops.Acosh() >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) >>> output = acosh(input_x) """ @@ -2392,7 +2392,7 @@ class Cosh(PrimitiveWithInfer): ``Ascend`` Examples: - >>> cosh = P.Cosh() + >>> cosh = ops.Cosh() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = cosh(input_x) >>> print(output) @@ -2425,7 +2425,7 @@ class Asinh(PrimitiveWithInfer): ``Ascend`` Examples: - >>> asinh = P.Asinh() + >>> asinh = ops.Asinh() >>> input_x = Tensor(np.array([-5.0, 1.5, 3.0, 100.0]), mindspore.float32) >>> output = asinh(input_x) >>> print(output) @@ -2458,7 +2458,7 @@ class Sinh(PrimitiveWithInfer): ``Ascend`` Examples: - >>> sinh = P.Sinh() + >>> sinh = ops.Sinh() >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32) >>> output = sinh(input_x) >>> print(output) @@ -2515,13 +2515,13 @@ class Equal(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) - >>> equal = P.Equal() + >>> equal = ops.Equal() >>> equal(input_x, 2.0) [False, True, False] >>> >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> equal = P.Equal() + >>> equal = ops.Equal() >>> output = equal(input_x, input_y) >>> print(output) [ True True False] @@ -2565,7 +2565,7 @@ class ApproximateEqual(_LogicBinaryOp): Examples: >>> x1 = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> x2 = Tensor(np.array([2, 4, 6]), mindspore.float32) - >>> approximate_equal = P.ApproximateEqual(2.) + >>> approximate_equal = ops.ApproximateEqual(2.) >>> output = approximate_equal(x1, x2) >>> print(output) [ True True False] @@ -2606,7 +2606,7 @@ class EqualCount(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> equal_count = P.EqualCount() + >>> equal_count = ops.EqualCount() >>> output = equal_count(input_x, input_y) >>> print(output) [2] @@ -2651,14 +2651,14 @@ class NotEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) - >>> not_equal = P.NotEqual() + >>> not_equal = ops.NotEqual() >>> output = not_equal(input_x, 2.0) >>> print(output) [ True False True] >>> >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> not_equal = P.NotEqual() + >>> not_equal = ops.NotEqual() >>> output = not_equal(input_x, input_y) >>> print(output) [False False True] @@ -2694,7 +2694,7 @@ class Greater(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> greater = P.Greater() + >>> greater = ops.Greater() >>> output = greater(input_x, input_y) >>> print(output) [False True False] @@ -2735,7 +2735,7 @@ class GreaterEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> greater_equal = P.GreaterEqual() + >>> greater_equal = ops.GreaterEqual() >>> output = greater_equal(input_x, input_y) >>> print(output) [ True True False] @@ -2776,7 +2776,7 @@ class Less(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> less = P.Less() + >>> less = ops.Less() >>> output = less(input_x, input_y) >>> print(output) [False False True] @@ -2817,7 +2817,7 @@ class LessEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> less_equal = P.LessEqual() + >>> less_equal = ops.LessEqual() >>> output = less_equal(input_x, input_y) >>> print(output) [ True False True] @@ -2847,7 +2847,7 @@ class LogicalNot(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) - >>> logical_not = P.LogicalNot() + >>> logical_not = ops.LogicalNot() >>> output = logical_not(input_x) >>> print(output) [False True False] @@ -2891,7 +2891,7 @@ class LogicalAnd(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_) - >>> logical_and = P.LogicalAnd() + >>> logical_and = ops.LogicalAnd() >>> output = logical_and(input_x, input_y) >>> print(output) [ True False False] @@ -2926,7 +2926,7 @@ class LogicalOr(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_) - >>> logical_or = P.LogicalOr() + >>> logical_or = ops.LogicalOr() >>> output = logical_or(input_x, input_y) >>> print(output) [ True True True] @@ -2950,7 +2950,7 @@ class IsNan(PrimitiveWithInfer): ``GPU`` Examples: - >>> is_nan = P.IsNan() + >>> is_nan = ops.IsNan() >>> input_x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mindspore.float32) >>> result = is_nan(input_x) """ @@ -2981,7 +2981,7 @@ class IsInf(PrimitiveWithInfer): ``GPU`` Examples: - >>> is_inf = P.IsInf() + >>> is_inf = ops.IsInf() >>> input_x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mindspore.float32) >>> result = is_inf(input_x) """ @@ -3012,7 +3012,7 @@ class IsFinite(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> is_finite = P.IsFinite() + >>> is_finite = ops.IsFinite() >>> input_x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mindspore.float32) >>> output = is_finite(input_x) >>> print(output) @@ -3047,7 +3047,7 @@ class FloatStatus(PrimitiveWithInfer): ``GPU`` Examples: - >>> float_status = P.FloatStatus() + >>> float_status = ops.FloatStatus() >>> input_x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mindspore.float32) >>> result = float_status(input_x) >>> print(result) @@ -3083,7 +3083,7 @@ class NPUAllocFloatStatus(PrimitiveWithInfer): ``Ascend`` Examples: - >>> alloc_status = P.NPUAllocFloatStatus() + >>> alloc_status = ops.NPUAllocFloatStatus() >>> output = alloc_status() >>> print(output) [0. 0. 0. 0. 0. 0. 0. 0.] @@ -3120,8 +3120,8 @@ class NPUGetFloatStatus(PrimitiveWithInfer): ``Ascend`` Examples: - >>> alloc_status = P.NPUAllocFloatStatus() - >>> get_status = P.NPUGetFloatStatus() + >>> alloc_status = ops.NPUAllocFloatStatus() + >>> get_status = ops.NPUGetFloatStatus() >>> init = alloc_status() >>> output = get_status(init) >>> print(output) @@ -3165,9 +3165,9 @@ class NPUClearFloatStatus(PrimitiveWithInfer): ``Ascend`` Examples: - >>> alloc_status = P.NPUAllocFloatStatus() - >>> get_status = P.NPUGetFloatStatus() - >>> clear_status = P.NPUClearFloatStatus() + >>> alloc_status = ops.NPUAllocFloatStatus() + >>> get_status = ops.NPUGetFloatStatus() + >>> clear_status = ops.NPUClearFloatStatus() >>> init = alloc_status() >>> flag = get_status(init) >>> output = clear_status(init) @@ -3205,7 +3205,7 @@ class Cos(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> cos = P.Cos() + >>> cos = ops.Cos() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = cos(input_x) >>> print(output) @@ -3238,7 +3238,7 @@ class ACos(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> acos = P.ACos() + >>> acos = ops.ACos() >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) >>> output = acos(input_x) """ @@ -3269,7 +3269,7 @@ class Sin(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> sin = P.Sin() + >>> sin = ops.Sin() >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32) >>> output = sin(input_x) >>> print(output) @@ -3302,7 +3302,7 @@ class Asin(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> asin = P.Asin() + >>> asin = ops.Asin() >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) >>> output = asin(input_x) >>> print(output) @@ -3359,7 +3359,7 @@ class NMSWithMask(PrimitiveWithInfer): >>> bbox[:, 2] += bbox[:, 0] >>> bbox[:, 3] += bbox[:, 1] >>> inputs = Tensor(bbox, mindspore.float32) - >>> nms = P.NMSWithMask(0.5) + >>> nms = ops.NMSWithMask(0.5) >>> output_boxes, indices, mask = nms(inputs) """ @@ -3398,7 +3398,7 @@ class Abs(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([-1.0, 1.0, 0.0]), mindspore.float32) - >>> abs = P.Abs() + >>> abs = ops.Abs() >>> output = abs(input_x) >>> print(output) [1. 1. 0.] @@ -3445,7 +3445,7 @@ class Sign(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[2.0, 0.0, -1.0]]), mindspore.float32) - >>> sign = P.Sign() + >>> sign = ops.Sign() >>> output = sign(input_x) >>> print(output) [[ 1. 0. -1.]] @@ -3478,7 +3478,7 @@ class Round(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]), mindspore.float32) - >>> round = P.Round() + >>> round = ops.Round() >>> output = round(input_x) >>> print(output) [ 1. 2. 2. 2. -4.] @@ -3512,7 +3512,7 @@ class Tan(PrimitiveWithInfer): ``Ascend`` Examples: - >>> tan = P.Tan() + >>> tan = ops.Tan() >>> input_x = Tensor(np.array([-1.0, 0.0, 1.0]), mindspore.float32) >>> output = tan(input_x) [-1.5574081 0. 1.5574081] @@ -3546,9 +3546,9 @@ class Atan(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.047, 0.785]), mindspore.float32) - >>> tan = P.Tan() + >>> tan = ops.Tan() >>> output_y = tan(input_x) - >>> atan = P.Atan() + >>> atan = ops.Atan() >>> output = atan(output_y) >>> print(output) [1.047 0.7850001] @@ -3581,7 +3581,7 @@ class Atanh(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.047, 0.785]), mindspore.float32) - >>> atanh = P.Atanh() + >>> atanh = ops.Atanh() >>> output = atanh(input_x) >>> print(output) [1.8869909 1.058268 ] @@ -3624,7 +3624,7 @@ class Atan2(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([0, 1]), mindspore.float32) >>> input_y = Tensor(np.array([1, 1]), mindspore.float32) - >>> atan2 = P.Atan2() + >>> atan2 = ops.Atan2() >>> output = atan2(input_x, input_y) >>> print(output) [0. 0.7853982] @@ -3652,7 +3652,7 @@ class SquareSumAll(PrimitiveWithInfer): Examples: >>> input_x1 = Tensor(np.array([0, 0, 2, 0]), mindspore.float32) >>> input_x2 = Tensor(np.array([0, 0, 2, 4]), mindspore.float32) - >>> square_sum_all = P.SquareSumAll() + >>> square_sum_all = ops.SquareSumAll() >>> output = square_sum_all(input_x1, input_x2) >>> print(output) (Tensor(shape=[], dtype=Float32, value= 4), @@ -3697,7 +3697,7 @@ class BitwiseAnd(_BitwiseBinaryOp): Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16) >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16) - >>> bitwise_and = P.BitwiseAnd() + >>> bitwise_and = ops.BitwiseAnd() >>> output = bitwise_and(input_x1, input_x2) >>> print(output) [ 0 0 1 -1 1 0 1] @@ -3727,7 +3727,7 @@ class BitwiseOr(_BitwiseBinaryOp): Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16) >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16) - >>> bitwise_or = P.BitwiseOr() + >>> bitwise_or = ops.BitwiseOr() >>> boutput = itwise_or(input_x1, input_x2) >>> print(output) [ 0 1 1 -1 -1 3 3] @@ -3757,7 +3757,7 @@ class BitwiseXor(_BitwiseBinaryOp): Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16) >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16) - >>> bitwise_xor = P.BitwiseXor() + >>> bitwise_xor = ops.BitwiseXor() >>> output = bitwise_xor(input_x1, input_x2) >>> print(output) [ 0 1 0 0 -2 3 2] @@ -3779,7 +3779,7 @@ class BesselI0e(PrimitiveWithInfer): ``Ascend`` Examples: - >>> bessel_i0e = P.BesselI0e() + >>> bessel_i0e = ops.BesselI0e() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = bessel_i0e(input_x) >>> print(output) @@ -3813,7 +3813,7 @@ class BesselI1e(PrimitiveWithInfer): ``Ascend`` Examples: - >>> bessel_i1e = P.BesselI1e() + >>> bessel_i1e = ops.BesselI1e() >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = bessel_i1e(input_x) >>> print(output) @@ -3847,7 +3847,7 @@ class Inv(PrimitiveWithInfer): ``Ascend`` Examples: - >>> inv = P.Inv() + >>> inv = ops.Inv() >>> input_x = Tensor(np.array([0.25, 0.4, 0.31, 0.52]), mindspore.float32) >>> output = inv(input_x) >>> print(output) @@ -3881,7 +3881,7 @@ class Invert(PrimitiveWithInfer): ``Ascend`` Examples: - >>> invert = P.Invert() + >>> invert = ops.Invert() >>> input_x = Tensor(np.array([25, 4, 13, 9]), mindspore.int16) >>> output = invert(input_x) >>> print(output) @@ -3915,7 +3915,7 @@ class Eps(PrimitiveWithInfer): Examples: >>> input_x = Tensor([4, 1, 2, 3], mindspore.float32) - >>> output = P.Eps()(input_x) + >>> output = ops.Eps()(input_x) >>> print(output) [1.5258789e-05 1.5258789e-05 1.5258789e-05 1.5258789e-05] """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 82a409196d..863a29b98b 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -91,7 +91,7 @@ class Flatten(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32) - >>> flatten = P.Flatten() + >>> flatten = ops.Flatten() >>> output = flatten(input_tensor) >>> print(output.shape) (1, 24) @@ -138,7 +138,7 @@ class Softmax(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) - >>> softmax = P.Softmax() + >>> softmax = ops.Softmax() >>> output = softmax(input_x) >>> print(output) [0.01165623 0.03168492 0.08612854 0.23412167 0.6364086 ] @@ -192,7 +192,7 @@ class LogSoftmax(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) - >>> log_softmax = P.LogSoftmax() + >>> log_softmax = ops.LogSoftmax() >>> output = log_softmax(input_x) >>> print(output) [-4.4519143 -3.4519143 -2.4519143 -1.4519144 -0.4519144] @@ -233,7 +233,7 @@ class Softplus(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) - >>> softplus = P.Softplus() + >>> softplus = ops.Softplus() >>> output = softplus(input_x) >>> print(output) [1.3132615 2.126928 3.0485873 4.01815 5.0067153] @@ -272,7 +272,7 @@ class Softsign(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([0, -1, 2, 30, -30]), mindspore.float32) - >>> softsign = P.Softsign() + >>> softsign = ops.Softsign() >>> output = softsign(input_x) >>> print(output) [ 0. -0.5 0.6666667 0.9677419 -0.9677419] @@ -308,7 +308,7 @@ class ReLU(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) - >>> relu = P.ReLU() + >>> relu = ops.ReLU() >>> output = relu(input_x) >>> print(output) [[0. 4. 0.] @@ -345,7 +345,7 @@ class ReLU6(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) - >>> relu6 = P.ReLU6() + >>> relu6 = ops.ReLU6() >>> result = relu6(input_x) >>> print(result) [[0. 4. 0.] @@ -383,7 +383,7 @@ class ReLUV2(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[[[1, -2], [-3, 4]], [[-5, 6], [7, -8]]]]), mindspore.float32) - >>> relu_v2 = P.ReLUV2() + >>> relu_v2 = ops.ReLUV2() >>> output, mask= relu_v2(input_x) >>> print(output) [[[[1. 0.] @@ -464,7 +464,7 @@ class Elu(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) - >>> elu = P.Elu() + >>> elu = ops.Elu() >>> output = elu(input_x) >>> print(output) [[-0.63212055 4. -0.99966455] @@ -508,7 +508,7 @@ class HSwish(PrimitiveWithInfer): ``GPU`` Examples: - >>> hswish = P.HSwish() + >>> hswish = ops.HSwish() >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> result = hswish(input_x) >>> print(result) @@ -549,7 +549,7 @@ class Sigmoid(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) - >>> sigmoid = P.Sigmoid() + >>> sigmoid = ops.Sigmoid() >>> output = sigmoid(input_x) >>> print(output) [0.7310586 0.880797 0.95257413 0.98201376 0.9933072 ] @@ -590,7 +590,7 @@ class HSigmoid(PrimitiveWithInfer): ``GPU`` Examples: - >>> hsigmoid = P.HSigmoid() + >>> hsigmoid = ops.HSigmoid() >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> result = hsigmoid(input_x) >>> print(result) @@ -631,7 +631,7 @@ class Tanh(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) - >>> tanh = P.Tanh() + >>> tanh = ops.Tanh() >>> output = tanh(input_x) >>> print(output) [0.7615941 0.9640276 0.9950547 0.9993293 0.9999092] @@ -697,11 +697,11 @@ class FusedBatchNorm(Primitive): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class FusedBatchNormNet(nn.Cell): >>> def __init__(self): >>> super(FusedBatchNormNet, self).__init__() - >>> self.fused_batch_norm = P.FusedBatchNorm() + >>> self.fused_batch_norm = ops.FusedBatchNorm() >>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale") >>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias") >>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean") @@ -794,11 +794,11 @@ class FusedBatchNormEx(PrimitiveWithInfer): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class FusedBatchNormExNet(nn.Cell): >>> def __init__(self): >>> super(FusedBatchNormExNet, self).__init__() - >>> self.fused_batch_norm_ex = P.FusedBatchNormEx() + >>> self.fused_batch_norm_ex = ops.FusedBatchNormEx() >>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale") >>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias") >>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean") @@ -874,7 +874,7 @@ class BNTrainingReduce(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones([128, 3, 32, 3]), mindspore.float32) - >>> bn_training_reduce = P.BNTrainingReduce() + >>> bn_training_reduce = ops.BNTrainingReduce() >>> output = bn_training_reduce(input_x) >>> print(output) ([1.22880000e+04, 1.22880000e+04, 1.22880000e+04], @@ -940,7 +940,7 @@ class BNTrainingUpdate(PrimitiveWithInfer): >>> offset = Tensor(np.ones([2]), mindspore.float32) >>> mean = Tensor(np.ones([2]), mindspore.float32) >>> variance = Tensor(np.ones([2]), mindspore.float32) - >>> bn_training_update = P.BNTrainingUpdate() + >>> bn_training_update = ops.BNTrainingUpdate() >>> output = bn_training_update(input_x, sum, square_sum, scale, offset, mean, variance) >>> print(output) ([[[[2.73200464e+00, 2.73200464e+00], @@ -1041,7 +1041,7 @@ class BatchNorm(PrimitiveWithInfer): >>> bias = Tensor(np.ones([2]), mindspore.float32) >>> mean = Tensor(np.ones([2]), mindspore.float32) >>> variance = Tensor(np.ones([2]), mindspore.float32) - >>> batch_norm = P.BatchNorm() + >>> batch_norm = ops.BatchNorm() >>> output = batch_norm(input_x, scale, bias, mean, variance) >>> print(output) ([[1.0, 1.0], @@ -1147,7 +1147,7 @@ class Conv2D(PrimitiveWithInfer): Examples: >>> input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32) >>> weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32) - >>> conv2d = P.Conv2D(out_channel=32, kernel_size=3) + >>> conv2d = ops.Conv2D(out_channel=32, kernel_size=3) >>> output = conv2d(input, weight) >>> print(output.shape) (10, 32, 30, 30) @@ -1295,7 +1295,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer): Examples: >>> input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32) >>> weight = Tensor(np.ones([1, 32, 3, 3]), mindspore.float32) - >>> depthwise_conv2d = P.DepthwiseConv2dNative(channel_multiplier = 3, kernel_size = (3, 3)) + >>> depthwise_conv2d = ops.DepthwiseConv2dNative(channel_multiplier = 3, kernel_size = (3, 3)) >>> output = depthwise_conv2d(input, weight) >>> print(output.shape) (10, 96, 30, 30) @@ -1508,7 +1508,7 @@ class MaxPool(_Pool): Examples: >>> input_tensor = Tensor(np.arange(1 * 3 * 3 * 4).reshape((1, 3, 3, 4)), mindspore.float32) - >>> maxpool_op = P.MaxPool(padding="VALID", ksize=2, strides=1) + >>> maxpool_op = ops.MaxPool(padding="VALID", ksize=2, strides=1) >>> output_tensor = maxpool_op(input_tensor) """ @@ -1563,7 +1563,7 @@ class MaxPoolWithArgmax(_Pool): Examples: >>> input_tensor = Tensor(np.arange(1 * 3 * 3 * 4).reshape((1, 3, 3, 4)), mindspore.float32) - >>> maxpool_arg_op = P.MaxPoolWithArgmax(padding="VALID", ksize=2, strides=1) + >>> maxpool_arg_op = ops.MaxPoolWithArgmax(padding="VALID", ksize=2, strides=1) >>> output_tensor, argmax = maxpool_arg_op(input_tensor) """ @@ -1649,11 +1649,11 @@ class AvgPool(_Pool): >>> import mindspore.nn as nn >>> import numpy as np >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.avgpool_op = P.AvgPool(padding="VALID", ksize=2, strides=1) + ... self.avgpool_op = ops.AvgPool(padding="VALID", ksize=2, strides=1) ... ... def construct(self, x): ... result = self.avgpool_op(x) @@ -1720,7 +1720,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer): >>> dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32) >>> weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32) >>> x = Tensor(np.ones([10, 32, 32, 32])) - >>> conv2d_backprop_input = P.Conv2DBackpropInput(out_channel=32, kernel_size=3) + >>> conv2d_backprop_input = ops.Conv2DBackpropInput(out_channel=32, kernel_size=3) >>> output = conv2d_backprop_input(dout, weight, F.shape(x)) >>> print(output.shape) (10, 32, 32, 32) @@ -1840,7 +1840,7 @@ class BiasAdd(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.arange(6).reshape((2, 3)), mindspore.float32) >>> bias = Tensor(np.random.random(3).reshape((3,)), mindspore.float32) - >>> bias_add = P.BiasAdd() + >>> bias_add = ops.BiasAdd() >>> output = bias_add(input_x, bias) >>> print(output) [[0.4662124 1.2493685 2.3611782] @@ -1890,7 +1890,7 @@ class TopK(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> topk = P.TopK(sorted=True) + >>> topk = ops.TopK(sorted=True) >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16) >>> k = 3 >>> values, indices = topk(input_x, k) @@ -1944,7 +1944,7 @@ class SoftmaxCrossEntropyWithLogits(PrimitiveWithInfer): Examples: >>> logits = Tensor([[2, 4, 1, 4, 5], [2, 1, 2, 4, 3]], mindspore.float32) >>> labels = Tensor([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]], mindspore.float32) - >>> softmax_cross = P.SoftmaxCrossEntropyWithLogits() + >>> softmax_cross = ops.SoftmaxCrossEntropyWithLogits() >>> loss, dlogits = softmax_cross(logits, labels) >>> print(loss) [0.5899297 0.52374405] @@ -2125,7 +2125,7 @@ class SmoothL1Loss(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> loss = P.SmoothL1Loss() + >>> loss = ops.SmoothL1Loss() >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> output = loss(input_data, target_data) @@ -2172,7 +2172,7 @@ class L2Loss(PrimitiveWithInfer): Examples >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float16) - >>> l2_loss = P.L2Loss() + >>> l2_loss = ops.L2Loss() >>> output = l2_loss(input_x) >>> print(output) 7.0 @@ -2212,7 +2212,7 @@ class DataFormatDimMap(PrimitiveWithInfer): Examples: >>> x = Tensor([0, 1, 2, 3], mindspore.int32) - >>> dfdm = P.DataFormatDimMap() + >>> dfdm = ops.DataFormatDimMap() >>> output = dfdm(x) >>> print(output) [0 3 1 2] @@ -2261,7 +2261,7 @@ class RNNTLoss(PrimitiveWithInfer): >>> labels = np.array([[1, 2]]).astype(np.int32) >>> input_length = np.array([T] * B).astype(np.int32) >>> label_length = np.array([len(l) for l in labels]).astype(np.int32) - >>> rnnt_loss = P.RNNTLoss(blank_label=blank) + >>> rnnt_loss = ops.RNNTLoss(blank_label=blank) >>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length)) """ @@ -2324,7 +2324,7 @@ class SGD(PrimitiveWithCheck): ``Ascend`` ``GPU`` Examples: - >>> sgd = P.SGD() + >>> sgd = ops.SGD() >>> parameters = Tensor(np.array([2, -0.5, 1.7, 4]), mindspore.float32) >>> gradient = Tensor(np.array([1, -1, 0.5, 2]), mindspore.float32) >>> learning_rate = Tensor(0.01, mindspore.float32) @@ -2406,7 +2406,7 @@ class ApplyRMSProp(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> apply_rms = P.ApplyRMSProp() + >>> apply_rms = ops.ApplyRMSProp() >>> input_x = Tensor(1., mindspore.float32) >>> mean_square = Tensor(2., mindspore.float32) >>> moment = Tensor(1., mindspore.float32) @@ -2508,7 +2508,7 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> centered_rms_prop = P.ApplyCenteredRMSProp() + >>> centered_rms_prop = ops.ApplyCenteredRMSProp() >>> input_x = Tensor(np.arange(-2, 2).astype(np.float32).reshape(2, 2), mindspore.float32) >>> mean_grad = Tensor(np.arange(4).astype(np.float32).reshape(2, 2), mindspore.float32) >>> mean_square = Tensor(np.arange(-3, 1).astype(np.float32).reshape(2, 2), mindspore.float32) @@ -2605,7 +2605,7 @@ class LayerNorm(Primitive): >>> input_x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]), mindspore.float32) >>> gamma = Tensor(np.ones([3]), mindspore.float32) >>> beta = Tensor(np.ones([3]), mindspore.float32) - >>> layer_norm = P.LayerNorm() + >>> layer_norm = ops.LayerNorm() >>> output, mean, variance = layer_norm(input_x, gamma, beta) >>> print(output) [[-0.2247448 1. 2.2247448] @@ -2650,7 +2650,7 @@ class L2Normalize(PrimitiveWithInfer): ``Ascend`` Examples: - >>> l2_normalize = P.L2Normalize() + >>> l2_normalize = ops.L2Normalize() >>> input_x = Tensor(np.random.randint(-256, 256, (2, 3, 4)), mindspore.float32) >>> output = l2_normalize(input_x) >>> print(output) @@ -2697,7 +2697,7 @@ class DropoutGenMask(Primitive): ``Ascend`` Examples: - >>> dropout_gen_mask = P.DropoutGenMask() + >>> dropout_gen_mask = ops.DropoutGenMask() >>> shape = (2, 4, 5) >>> keep_prob = Tensor(0.5, mindspore.float32) >>> output = dropout_gen_mask(shape, keep_prob) @@ -2738,8 +2738,8 @@ class DropoutDoMask(PrimitiveWithInfer): >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> shape = (2, 2, 3) >>> keep_prob = Tensor(0.5, mindspore.float32) - >>> dropout_gen_mask = P.DropoutGenMask() - >>> dropout_do_mask = P.DropoutDoMask() + >>> dropout_gen_mask = ops.DropoutGenMask() + >>> dropout_do_mask = ops.DropoutDoMask() >>> mask = dropout_gen_mask(shape, keep_prob) >>> output = dropout_do_mask(x, mask, keep_prob) >>> print(output) @@ -2810,7 +2810,7 @@ class ResizeBilinear(PrimitiveWithInfer): Examples: >>> tensor = Tensor([[[[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]]], mindspore.float32) - >>> resize_bilinear = P.ResizeBilinear((5, 5)) + >>> resize_bilinear = ops.ResizeBilinear((5, 5)) >>> output = resize_bilinear(tensor) >>> print(output) [[[[1. 2. 3. 4. 5.] @@ -2870,7 +2870,7 @@ class OneHot(PrimitiveWithInfer): Examples: >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32) >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32) - >>> onehot = P.OneHot() + >>> onehot = ops.OneHot() >>> output = onehot(indices, depth, on_value, off_value) >>> print(output) [[1. 0. 0.] @@ -2929,7 +2929,7 @@ class Gelu(PrimitiveWithInfer): Examples: >>> tensor = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) - >>> gelu = P.Gelu() + >>> gelu = ops.Gelu() >>> result = gelu(tensor) >>> print(result) [0.841192 1.9545976 2.9963627] @@ -2974,7 +2974,7 @@ class GetNext(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 2, 'shared_name') + >>> get_next = ops.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 2, 'shared_name') >>> feature, label = get_next() """ @@ -3026,11 +3026,11 @@ class PReLU(PrimitiveWithInfer): >>> import mindspore.nn as nn >>> import numpy as np >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.prelu = P.PReLU() + >>> self.prelu = ops.PReLU() >>> def construct(self, input_x, weight): >>> result = self.prelu(input_x, weight) >>> return result @@ -3214,7 +3214,7 @@ class SigmoidCrossEntropyWithLogits(PrimitiveWithInfer): Examples: >>> logits = Tensor(np.array([[-0.8, 1.2, 0.7], [-0.1, -0.4, 0.7]]).astype(np.float32)) >>> labels = Tensor(np.array([[0.3, 0.8, 1.2], [-0.6, 0.1, 2.2]]).astype(np.float32)) - >>> sigmoid = P.SigmoidCrossEntropyWithLogits() + >>> sigmoid = ops.SigmoidCrossEntropyWithLogits() >>> output = sigmoid(logits, labels) >>> print(output) [[0.6111007 0.5032824 0.26318604] @@ -3257,7 +3257,7 @@ class Pad(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) - >>> pad_op = P.Pad(((1, 2), (2, 1))) + >>> pad_op = ops.Pad(((1, 2), (2, 1))) >>> output = pad_op(input_tensor) >>> print(output) [[ 0. 0. 0. 0. 0. 0. ], @@ -3325,13 +3325,13 @@ class MirrorPad(PrimitiveWithInfer): Examples: >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.nn as nn >>> import numpy as np >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.pad = P.MirrorPad(mode="REFLECT") + ... self.pad = ops.MirrorPad(mode="REFLECT") ... def construct(self, x, paddings): ... return self.pad(x, paddings) ... @@ -3411,7 +3411,7 @@ class ROIAlign(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32) >>> rois = Tensor(np.array([[0, 0.2, 0.3, 0.2, 0.3]]), mindspore.float32) - >>> roi_align = P.ROIAlign(2, 2, 0.5, 2) + >>> roi_align = ops.ROIAlign(2, 2, 0.5, 2) >>> output = roi_align(input_tensor, rois) >>> print(output) [[[[1.775 2.025] @@ -3501,11 +3501,11 @@ class Adam(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_adam = P.Adam() + ... self.apply_adam = ops.Adam() ... self.var = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="m") ... self.v = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="v") @@ -3604,12 +3604,12 @@ class AdamNoUpdateParam(PrimitiveWithInfer): >>> import mindspore as ms >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.adam = P.AdamNoUpdateParam() + >>> self.adam = ops.AdamNoUpdateParam() >>> self.m = Parameter(Tensor(np.array([[0.1, 0.1, 0.1], [0.2, 0.2, 0.2]]).astype(np.float32)), >>> name="m") >>> self.v = Parameter(Tensor(np.array([[0.1, 0.1, 0.1], [0.2, 0.2, 0.2]]).astype(np.float32)), @@ -3717,12 +3717,12 @@ class FusedSparseAdam(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_adam = P.FusedSparseAdam() + ... self.sparse_apply_adam = ops.FusedSparseAdam() ... self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="m") ... self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="v") @@ -3855,12 +3855,12 @@ class FusedSparseLazyAdam(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_lazyadam = P.FusedSparseLazyAdam() + ... self.sparse_apply_lazyadam = ops.FusedSparseLazyAdam() ... self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="m") ... self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="v") @@ -3969,11 +3969,11 @@ class FusedSparseFtrl(PrimitiveWithInfer): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class SparseApplyFtrlNet(nn.Cell): ... def __init__(self): ... super(SparseApplyFtrlNet, self).__init__() - ... self.sparse_apply_ftrl = P.FusedSparseFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5) + ... self.sparse_apply_ftrl = ops.FusedSparseFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5) ... self.var = Parameter(Tensor(np.random.rand(3, 1, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(3, 1, 2).astype(np.float32)), name="accum") ... self.linear = Parameter(Tensor(np.random.rand(3, 1, 2).astype(np.float32)), name="linear") @@ -4068,11 +4068,11 @@ class FusedSparseProximalAdagrad(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_proximal_adagrad = P.FusedSparseProximalAdagrad() + ... self.sparse_apply_proximal_adagrad = ops.FusedSparseProximalAdagrad() ... self.var = Parameter(Tensor(np.random.rand(3, 1, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(3, 1, 2).astype(np.float32)), name="accum") ... self.lr = Tensor(0.01, mstype.float32) @@ -4162,11 +4162,11 @@ class KLDivLoss(PrimitiveWithInfer): >>> import mindspore.nn as nn >>> import numpy as np >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.kldiv_loss = P.KLDivLoss() + ... self.kldiv_loss = ops.KLDivLoss() ... def construct(self, x, y): ... result = self.kldiv_loss(x, y) ... return result @@ -4241,11 +4241,11 @@ class BinaryCrossEntropy(PrimitiveWithInfer): >>> import mindspore.nn as nn >>> import numpy as np >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.binary_cross_entropy = P.BinaryCrossEntropy() + ... self.binary_cross_entropy = ops.BinaryCrossEntropy() ... def construct(self, x, y, weight): ... result = self.binary_cross_entropy(x, y, weight) ... return result @@ -4341,12 +4341,12 @@ class ApplyAdaMax(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_ada_max = P.ApplyAdaMax() + ... self.apply_ada_max = ops.ApplyAdaMax() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="m") ... self.v = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="v") @@ -4473,12 +4473,12 @@ class ApplyAdadelta(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_adadelta = P.ApplyAdadelta() + ... self.apply_adadelta = ops.ApplyAdadelta() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="accum") ... self.accum_update = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="accum_update") @@ -4585,12 +4585,12 @@ class ApplyAdagrad(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_adagrad = P.ApplyAdagrad() + ... self.apply_adagrad = ops.ApplyAdagrad() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="accum") ... def construct(self, lr, grad): @@ -4678,12 +4678,12 @@ class ApplyAdagradV2(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_adagrad_v2 = P.ApplyAdagradV2(epsilon=1e-6) + ... self.apply_adagrad_v2 = ops.ApplyAdagradV2(epsilon=1e-6) ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="accum") ... def construct(self, lr, grad): @@ -4772,12 +4772,12 @@ class SparseApplyAdagrad(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_adagrad = P.SparseApplyAdagrad(lr=1e-8) + ... self.sparse_apply_adagrad = ops.SparseApplyAdagrad(lr=1e-8) ... self.var = Parameter(Tensor(np.ones([1, 1, 1]).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.ones([1, 1, 1]).astype(np.float32)), name="accum") ... def construct(self, grad, indices): @@ -4867,12 +4867,12 @@ class SparseApplyAdagradV2(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.common.dtype as mstype >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_adagrad_v2 = P.SparseApplyAdagradV2(lr=1e-8, epsilon=1e-6) + ... self.sparse_apply_adagrad_v2 = ops.SparseApplyAdagradV2(lr=1e-8, epsilon=1e-6) ... self.var = Parameter(Tensor(np.ones([1, 1, 1]).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.ones([1, 1, 1]).astype(np.float32)), name="accum") ... @@ -4962,11 +4962,11 @@ class ApplyProximalAdagrad(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_proximal_adagrad = P.ApplyProximalAdagrad() + ... self.apply_proximal_adagrad = ops.ApplyProximalAdagrad() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="accum") ... self.lr = 0.01 @@ -5076,11 +5076,11 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad() + ... self.sparse_apply_proximal_adagrad = ops.SparseApplyProximalAdagrad() ... self.var = Parameter(Tensor(np.random.rand(1, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(1, 2).astype(np.float32)), name="accum") ... self.lr = 0.01 @@ -5175,11 +5175,11 @@ class ApplyAddSign(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_add_sign = P.ApplyAddSign() + ... self.apply_add_sign = ops.ApplyAddSign() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="m") ... self.lr = 0.001 @@ -5293,11 +5293,11 @@ class ApplyPowerSign(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_power_sign = P.ApplyPowerSign() + ... self.apply_power_sign = ops.ApplyPowerSign() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.m = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="m") ... self.lr = 0.001 @@ -5393,11 +5393,11 @@ class ApplyGradientDescent(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_gradient_descent = P.ApplyGradientDescent() + ... self.apply_gradient_descent = ops.ApplyGradientDescent() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.alpha = 0.001 ... def construct(self, delta): @@ -5471,11 +5471,11 @@ class ApplyProximalGradientDescent(PrimitiveWithInfer): >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor, Parameter - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.apply_proximal_gradient_descent = P.ApplyProximalGradientDescent() + ... self.apply_proximal_gradient_descent = ops.ApplyProximalGradientDescent() ... self.var = Parameter(Tensor(np.random.rand(2, 2).astype(np.float32)), name="var") ... self.alpha = 0.001 ... self.l1 = 0.0 @@ -5555,18 +5555,17 @@ class LARSUpdate(PrimitiveWithInfer): Examples: >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P - >>> from mindspore.ops import functional as F + >>> from mindspore.ops import operations as ops >>> import mindspore.nn as nn >>> import numpy as np >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.lars = P.LARSUpdate() - ... self.reduce = P.ReduceSum() + ... self.lars = ops.LARSUpdate() + ... self.reduce = ops.ReduceSum() ... def construct(self, weight, gradient): - ... w_square_sum = self.reduce(F.square(weight)) - ... grad_square_sum = self.reduce(F.square(gradient)) + ... w_square_sum = self.reduce(ops.square(weight)) + ... grad_square_sum = self.reduce(ops.square(gradient)) ... grad_t = self.lars(weight, gradient, w_square_sum, grad_square_sum, 0.0, 1.0) ... return grad_t ... @@ -5649,11 +5648,11 @@ class ApplyFtrl(PrimitiveWithInfer): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class ApplyFtrlNet(nn.Cell): ... def __init__(self): ... super(ApplyFtrlNet, self).__init__() - ... self.apply_ftrl = P.ApplyFtrl() + ... self.apply_ftrl = ops.ApplyFtrl() ... self.lr = 0.001 ... self.l1 = 0.0 ... self.l2 = 0.0 @@ -5748,11 +5747,11 @@ class SparseApplyFtrl(PrimitiveWithCheck): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class SparseApplyFtrlNet(nn.Cell): ... def __init__(self): ... super(SparseApplyFtrlNet, self).__init__() - ... self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5) + ... self.sparse_apply_ftrl = ops.SparseApplyFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5) ... self.var = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="accum") ... self.linear = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="linear") @@ -5853,11 +5852,11 @@ class SparseApplyFtrlV2(PrimitiveWithInfer): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class SparseApplyFtrlV2Net(nn.Cell): ... def __init__(self): ... super(SparseApplyFtrlV2Net, self).__init__() - ... self.sparse_apply_ftrl_v2 = P.SparseApplyFtrlV2(lr=0.01, l1=0.0, l2=0.0, + ... self.sparse_apply_ftrl_v2 = ops.SparseApplyFtrlV2(lr=0.01, l1=0.0, l2=0.0, ... l2_shrinkage=0.0, lr_power=-0.5) ... self.var = Parameter(Tensor(np.random.rand(1, 2).astype(np.float32)), name="var") ... self.accum = Parameter(Tensor(np.random.rand(1, 2).astype(np.float32)), name="accum") @@ -5932,7 +5931,7 @@ class Dropout(PrimitiveWithInfer): - **mask** (Tensor) - with the same shape as the input tensor. Examples: - >>> dropout = P.Dropout(keep_prob=0.5) + >>> dropout = ops.Dropout(keep_prob=0.5) >>> x = Tensor((20, 16, 50, 50), mindspore.float32) >>> output, mask = dropout(x) >>> print(output) @@ -5994,7 +5993,7 @@ class CTCLoss(PrimitiveWithInfer): >>> labels_indices = Tensor(np.array([[0, 0], [1, 0]]), mindspore.int64) >>> labels_values = Tensor(np.array([2, 2]), mindspore.int32) >>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32) - >>> ctc_loss = P.CTCLoss() + >>> ctc_loss = ops.CTCLoss() >>> loss, gradient = ctc_loss(inputs, labels_indices, labels_values, sequence_length) >>> print(loss) [0.69121575 0.5381993 ] @@ -6072,7 +6071,7 @@ class CTCGreedyDecoder(PrimitiveWithInfer): ... def __init__(self): ... super(CTCGreedyDecoderNet, self).__init__() ... self.ctc_greedy_decoder = P.CTCGreedyDecoder() - ... self.assert_op = P.Assert(300) + ... self.assert_op = ops.Assert(300) ... ... def construct(self, inputs, sequence_length): ... out = self.ctc_greedy_decoder(inputs,sequence_length) @@ -6175,7 +6174,7 @@ class BasicLSTMCell(PrimitiveWithInfer): >>> c = Tensor(np.random.rand(1, 2).astype(np.float16)) >>> w = Tensor(np.random.rand(34, 8).astype(np.float16)) >>> b = Tensor(np.random.rand(8, ).astype(np.float16)) - >>> lstm = P.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh') + >>> lstm = ops.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh') >>> output = lstm(x, h, c, w, b) >>> print(output) (Tensor(shape=[1, 2], dtype=Float16, value= @@ -6288,13 +6287,13 @@ class DynamicRNN(PrimitiveWithInfer): >>> import numpy as np >>> from mindspore import Parameter >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> import mindspore.context as context >>> context.set_context(mode=context.GRAPH_MODE) >>> class DynamicRNNNet(nn.Cell): >>> def __init__(self): >>> super(DynamicRNNNet, self).__init__() - >>> self.dynamic_rnn = P.DynamicRNN() + >>> self.dynamic_rnn = ops.DynamicRNN() >>> >>> def construct(self, x, w, b, init_h, init_c): >>> out = self.dynamic_rnn(x, w, b, None, init_h, init_c) @@ -6395,7 +6394,7 @@ class InTopK(PrimitiveWithInfer): Examples: >>> x1 = Tensor(np.array([[1, 8, 5, 2, 7], [4, 9, 1, 3, 5]]), mindspore.float32) >>> x2 = Tensor(np.array([1, 3]), mindspore.int32) - >>> in_top_k = P.InTopK(3) + >>> in_top_k = ops.InTopK(3) >>> output = in_top_k(x1, x2) >>> print(output) [ True False] @@ -6442,7 +6441,7 @@ class LRN(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1, 2, 2, 2), mindspore.float32) - >>> lrn = P.LRN() + >>> lrn = ops.LRN() >>> output = lrn(x) >>> print(output) [[[[0.18990143 0.59475636] diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py index f0a03f0829..88dff291bb 100644 --- a/mindspore/ops/operations/other_ops.py +++ b/mindspore/ops/operations/other_ops.py @@ -47,7 +47,7 @@ class Assign(PrimitiveWithCheck): ... self.y = mindspore.Parameter(Tensor([1.0], mindspore.float32), name="y") ... ... def construct(self, x): - ... P.Assign()(self.y, x) + ... ops.Assign()(self.y, x) ... return self.y ... >>> x = Tensor([2.0], mindspore.float32) @@ -85,7 +85,7 @@ class InplaceAssign(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.inplace_assign = P.InplaceAssign() + ... self.inplace_assign = ops.InplaceAssign() ... ... def construct(self, x): ... val = x - 1.0 @@ -129,7 +129,7 @@ class BoundingBoxEncode(PrimitiveWithInfer): Examples: >>> anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) >>> groundtruth_box = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32) - >>> boundingbox_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)) + >>> boundingbox_encode = ops.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)) >>> output = boundingbox_encode(anchor_box, groundtruth_box) >>> print(output) [[ 5.0000000e-01 5.0000000e-01 -6.5504000e+04 6.9335938e-01] @@ -185,7 +185,7 @@ class BoundingBoxDecode(PrimitiveWithInfer): Examples: >>> 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), + >>> boundingbox_decode = ops.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) >>> output = boundingbox_decode(anchor_box, deltas) >>> print(output) @@ -245,11 +245,11 @@ class CheckValid(PrimitiveWithInfer): >>> import mindspore.nn as nn >>> import numpy as np >>> from mindspore import Tensor - >>> from mindspore.ops import operations as P + >>> from mindspore.ops import operations as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self.check_valid = P.CheckValid() + ... self.check_valid = ops.CheckValid() ... def construct(self, x, y): ... valid_result = self.check_valid(x, y) ... return valid_result @@ -313,7 +313,7 @@ class IOU(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> iou = P.IOU() + >>> iou = ops.IOU() >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) >>> output = iou(anchor_boxes, gt_boxes) @@ -363,16 +363,16 @@ class MakeRefKey(Primitive): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> from mindspore.ops import functional as F + >>> from mindspore.ops import functional as ops >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.y = mindspore.Parameter(Tensor(np.ones([6, 8, 10]), mindspore.int32), name="y") - ... self.make_ref_key = P.MakeRefKey("y") + ... self.make_ref_key = ops.MakeRefKey("y") ... ... def construct(self, x): ... key = self.make_ref_key() - ... ref = F.make_ref(key, x, self.y) + ... ref = ops.make_ref(key, x, self.y) ... return ref * x ... >>> x = Tensor(np.ones([3, 4, 5]), mindspore.int32) @@ -451,7 +451,7 @@ class CheckBprop(PrimitiveWithInfer): Examples: >>> input_x = (Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32),) >>> input_y = (Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32),) - >>> out = P.CheckBprop()(input_x, input_y) + >>> out = ops.CheckBprop()(input_x, input_y) """ @prim_attr_register @@ -519,7 +519,7 @@ class ConfusionMatrix(PrimitiveWithInfer): Tensor, the confusion matrix, with shape (`num_classes`, `num_classes`). Examples: - >>> confusion_matrix = P.ConfusionMatrix(4) + >>> confusion_matrix = ops.ConfusionMatrix(4) >>> labels = Tensor([0, 1, 1, 3], mindspore.int32) >>> predictions = Tensor([1, 2, 1, 3], mindspore.int32) >>> output = confusion_matrix(labels, predictions) @@ -567,7 +567,7 @@ class PopulationCount(PrimitiveWithInfer): ``Ascend`` Examples: - >>> population_count = P.PopulationCount() + >>> population_count = ops.PopulationCount() >>> x_input = Tensor([0, 1, 3], mindspore.int16) >>> output = population_count(x_input) >>> print(output) diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index 04d4ff977e..618c8e1ebd 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -39,7 +39,7 @@ class StandardNormal(PrimitiveWithInfer): Examples: >>> shape = (4, 16) - >>> stdnormal = P.StandardNormal(seed=2) + >>> stdnormal = ops.StandardNormal(seed=2) >>> output = stdnormal(shape) >>> result = output.shape >>> print(result) @@ -90,7 +90,7 @@ class StandardLaplace(PrimitiveWithInfer): Examples: >>> shape = (4, 16) - >>> stdlaplace = P.StandardLaplace(seed=2) + >>> stdlaplace = ops.StandardLaplace(seed=2) >>> output = stdlaplace(shape) >>> result = output.shape >>> print(result) @@ -148,7 +148,7 @@ class Gamma(PrimitiveWithInfer): >>> shape = (2, 2) >>> alpha = Tensor(1.0, mstype.float32) >>> beta = Tensor(1.0, mstype.float32) - >>> gamma = P.Gamma(seed=3) + >>> gamma = ops.Gamma(seed=3) >>> output = gamma(shape, alpha, beta) >>> print(output) [[0.21962446 0.33740655] @@ -206,7 +206,7 @@ class Poisson(PrimitiveWithInfer): Examples: >>> shape = (4, 16) >>> mean = Tensor(5.0, mstype.float32) - >>> poisson = P.Poisson(seed=5) + >>> poisson = ops.Poisson(seed=5) >>> output = poisson(shape, mean) """ @@ -265,7 +265,7 @@ class UniformInt(PrimitiveWithInfer): >>> shape = (2, 4) >>> minval = Tensor(1, mstype.int32) >>> maxval = Tensor(5, mstype.int32) - >>> uniform_int = P.UniformInt(seed=10) + >>> uniform_int = ops.UniformInt(seed=10) >>> output = uniform_int(shape, minval, maxval) >>> print(output) [[4 2 1 3] @@ -318,7 +318,7 @@ class UniformReal(PrimitiveWithInfer): Examples: >>> shape = (2, 2) - >>> uniformreal = P.UniformReal(seed=2) + >>> uniformreal = ops.UniformReal(seed=2) >>> output = uniformreal(shape) >>> print(output) [[0.4359949 0.18508208] @@ -374,7 +374,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> rnd_choice_mask = P.RandomChoiceWithMask() + >>> rnd_choice_mask = ops.RandomChoiceWithMask() >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool)) >>> output_y, output_mask = rnd_choice_mask(input_x) >>> result = output_y.shape @@ -426,7 +426,7 @@ class RandomCategorical(PrimitiveWithInfer): >>> class Net(nn.Cell): ... def __init__(self, num_sample): ... super(Net, self).__init__() - ... self.random_categorical = P.RandomCategorical(mindspore.int64) + ... self.random_categorical = ops.RandomCategorical(mindspore.int64) ... self.num_sample = num_sample ... def construct(self, logits, seed=0): ... return self.random_categorical(logits, self.num_sample, seed) @@ -502,7 +502,7 @@ class Multinomial(PrimitiveWithInfer): Examples: >>> input = Tensor([0., 9., 4., 0.], mstype.float32) - >>> multinomial = P.Multinomial(seed=10) + >>> multinomial = ops.Multinomial(seed=10) >>> output = multinomial(input, 2) """ @@ -561,7 +561,7 @@ class UniformCandidateSampler(PrimitiveWithInfer): each of sampled_candidates. Shape: (num_sampled, ). Examples: - >>> sampler = P.UniformCandidateSampler(1, 3, False, 4) + >>> sampler = ops.UniformCandidateSampler(1, 3, False, 4) >>> output1, output2, output3 = sampler(Tensor(np.array([[1],[3],[4],[6],[3]], dtype=np.int32))) >>> print(output1, output2, output3) [1, 1, 3], [[0.75], [0.75], [0.75], [0.75], [0.75]], [0.75, 0.75, 0.75] diff --git a/mindspore/ops/operations/sparse_ops.py b/mindspore/ops/operations/sparse_ops.py index e9977c5e62..b1b4bd4e7e 100644 --- a/mindspore/ops/operations/sparse_ops.py +++ b/mindspore/ops/operations/sparse_ops.py @@ -38,7 +38,7 @@ class SparseToDense(PrimitiveWithInfer): >>> indices = Tensor([[0, 1], [1, 2]]) >>> values = Tensor([1, 2], dtype=ms.float32) >>> dense_shape = (3, 4) - >>> out = P.SparseToDense()(indices, values, dense_shape) + >>> out = ops.SparseToDense()(indices, values, dense_shape) """ @prim_attr_register