diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py index 8a25869def..128659bd92 100644 --- a/mindspore/nn/layer/image.py +++ b/mindspore/nn/layer/image.py @@ -378,7 +378,7 @@ class PSNR(Cell): >>> img1 = Tensor(np.random.random((1,3,16,16))) >>> img2 = Tensor(np.random.random((1,3,16,16))) >>> psnr = net(img1, img2) - + [7.8297315] """ def __init__(self, max_val=1.0): super(PSNR, self).__init__() diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py index 7592fb6629..1bcfe76d0c 100644 --- a/mindspore/nn/loss/loss.py +++ b/mindspore/nn/loss/loss.py @@ -137,6 +137,7 @@ class MSELoss(_Loss): >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> loss(input_data, target_data) + 0.33333334 """ def construct(self, base, target): x = F.square(base - target) diff --git a/mindspore/ops/composite/random_ops.py b/mindspore/ops/composite/random_ops.py index 320309fe87..a165065d5b 100644 --- a/mindspore/ops/composite/random_ops.py +++ b/mindspore/ops/composite/random_ops.py @@ -79,10 +79,12 @@ def normal(shape, mean, stddev, seed=None): The dtype is float32. Examples: - >>> shape = (4, 16) + >>> shape = (2, 4) >>> mean = Tensor(1.0, mstype.float32) >>> stddev = Tensor(1.0, mstype.float32) >>> output = C.normal(shape, mean, stddev, seed=5) + [[1.0996436 0.44371283 0.11127508 -0.48055804] + [0.31989878 -1.0644426 1.5076542 1.2290289 ]] """ mean_dtype = F.dtype(mean) stddev_dtype = F.dtype(stddev) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index f1dcadffa4..8d5d6633e6 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -446,6 +446,7 @@ class Shape(Primitive): >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> shape = P.Shape() >>> output = shape(input_tensor) + (3, 2, 1) """ @prim_attr_register @@ -501,6 +502,9 @@ class Squeeze(PrimitiveWithInfer): >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> squeeze = P.Squeeze(2) >>> output = squeeze(input_tensor) + [[1. 1.] + [1. 1.] + [1. 1.]] """ @prim_attr_register @@ -748,6 +752,10 @@ class Split(PrimitiveWithInfer): >>> split = P.Split(1, 2) >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> output = split(x) + ([[1, 1], + [2, 2]], + [[1, 1], + [2, 2]]) """ @prim_attr_register @@ -828,7 +836,7 @@ class TruncatedNormal(PrimitiveWithInfer): - **shape** (tuple[int]) - The shape of the output tensor, is a tuple of positive integer. Outputs: - Tensor, the dat type of output tensor is the same as attribute `dtype`. + Tensor, the data type of output tensor is the same as attribute `dtype`. Examples: >>> shape = (1, 2, 3) @@ -2451,6 +2459,9 @@ class ScatterNd(PrimitiveWithInfer): >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32) >>> shape = (3, 3) >>> output = op(indices, update, shape) + [[0. 3.2 0.] + [0. 1.1 0.] + [0. 0. 0. ]] """ @prim_attr_register @@ -2676,6 +2687,8 @@ class ScatterNdUpdate(_ScatterNdOp): >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) >>> op = P.ScatterNdUpdate() >>> output = op(input_x, indices, update) + [[1. 0.3 3.6] + [0.4 2.2 -3.2]] """ @@ -3136,7 +3149,7 @@ class SpaceToBatch(PrimitiveWithInfer): Args: block_size (int): The block size of dividing blocks with value greater than 2. - paddings (list): The padding values for H and W dimension, containing 2 subtraction lists. + paddings (Union[tuple, list]): The padding values for H and W dimension, containing 2 subtraction lists. Each subtraction list contains 2 integer value. All values must be greater than 0. paddings[i] specifies the paddings for the spatial dimension i, which corresponds to the input dimension i+2. It is required that input_shape[i+2]+paddings[i][0]+paddings[i][1] @@ -3284,7 +3297,7 @@ class SpaceToBatchND(PrimitiveWithInfer): Args: block_shape (Union[list(int), tuple(int)]): The block shape of dividing block with all value greater than 1. The length of `block_shape` is M correspoding to the number of spatial dimensions. M must be 2. - paddings (list): The padding values for H and W dimension, containing 2 subtraction list. + paddings (Union[tuple, list]): The padding values for H and W dimension, containing 2 subtraction list. Each contains 2 integer value. All values must be greater than 0. `paddings[i]` specifies the paddings for the spatial dimension i, which corresponds to the input dimension i+2. diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 253057a3f8..645b94f005 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -413,6 +413,8 @@ class ReduceAll(_Reduce): >>> input_x = Tensor(np.array([[True, False], [True, True]])) >>> op = P.ReduceAll(keep_dims=True) >>> output = op(input_x, 1) + [[False] + [True ]] """ def __infer__(self, input_x, axis): @@ -1942,6 +1944,7 @@ class Mod(_MathBinaryOp): >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) >>> mod = P.Mod() >>> mod(input_x, input_y) + [-1. 1. 0.] """ def infer_value(self, x, y): @@ -3269,6 +3272,7 @@ class SquareSumAll(PrimitiveWithInfer): >>> input_x2 = Tensor(np.random.randint([3, 2, 5, 7]), mindspore.float32) >>> square_sum_all = P.SquareSumAll() >>> square_sum_all(input_x1, input_x2) + (27, 26) """ @prim_attr_register diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 84c025c6af..dd4c934198 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2955,6 +2955,10 @@ class MirrorPad(PrimitiveWithInfer): >>> paddings = Tensor([[1,1],[2,2]]) >>> pad = Net() >>> ms_output = pad(Tensor(x), paddings) + [[0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144] + [0.31417271 0.96308136 0.934709 0.96308136 0.31417271 0.96308136 0.934709 ] + [0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144] + [0.31417271 0.96308136 0.934709 0.96308136 0.31417271 0.96308136 0.934709 ]] """ @prim_attr_register @@ -4196,15 +4200,16 @@ class SparseApplyAdagrad(PrimitiveWithInfer): >>> def __init__(self): >>> super(Net, self).__init__() >>> self.sparse_apply_adagrad = P.SparseApplyAdagrad(lr=1e-8) - >>> self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="var") - >>> self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="accum") + >>> 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): >>> out = self.sparse_apply_adagrad(self.var, self.accum, grad, indices) >>> return out >>> net = Net() - >>> grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32)) - >>> indices = Tensor([0, 1, 2], mstype.int32) + >>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32)) + >>> indices = Tensor([0], mstype.int32) >>> result = net(grad, indices) + ([[[1.0]]], [[[1.0]]]) """ __mindspore_signature__ = ( @@ -4283,16 +4288,17 @@ class SparseApplyAdagradV2(PrimitiveWithInfer): >>> def __init__(self): >>> super(Net, self).__init__() >>> self.sparse_apply_adagrad_v2 = P.SparseApplyAdagradV2(lr=1e-8, epsilon=1e-6) - >>> self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="var") - >>> self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="accum") + >>> 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): >>> out = self.sparse_apply_adagrad_v2(self.var, self.accum, grad, indices) >>> return out >>> net = Net() - >>> grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32)) - >>> indices = Tensor([0, 1, 2], mstype.int32) + >>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32)) + >>> indices = Tensor([0], mstype.int32) >>> result = net(grad, indices) + ([[[1.0]]], [[[1.67194188]]]) """ __mindspore_signature__ = ( @@ -5089,18 +5095,19 @@ class SparseApplyFtrl(PrimitiveWithCheck): >>> 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.var = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="var") - >>> self.accum = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="accum") - >>> self.linear = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="linear") + >>> 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") >>> >>> def construct(self, grad, indices): >>> out = self.sparse_apply_ftrl(self.var, self.accum, self.linear, grad, indices) >>> return out >>> >>> net = SparseApplyFtrlNet() - >>> grad = Tensor(np.random.rand(3, 3).astype(np.float32)) - >>> indices = Tensor(np.ones([3]), mindspore.int32) + >>> grad = Tensor(np.random.rand(1, 1).astype(np.float32)) + >>> indices = Tensor(np.ones([1]), mindspore.int32) >>> output = net(grad, indices) + ([[1.02914639e-01]], [[7.60280550e-01]], [[7.64630079e-01]]) """ __mindspore_signature__ = ( diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index 307ad4ff5e..4a5e03accb 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -341,6 +341,10 @@ class RandomChoiceWithMask(PrimitiveWithInfer): >>> rnd_choice_mask = P.RandomChoiceWithMask() >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool)) >>> output_y, output_mask = rnd_choice_mask(input_x) + >>> output_y.shape + (256, 2) + >>> output_mask.shape + (256,) """ @prim_attr_register