From 9b2265a66f14f83a9034f395e87aaa1bf412d7f3 Mon Sep 17 00:00:00 2001 From: lihongkang <[lihongkang1@huawei.com]> Date: Sat, 12 Dec 2020 17:26:47 +0800 Subject: [PATCH] fix bugs --- mindspore/nn/layer/activation.py | 2 +- mindspore/nn/layer/basic.py | 2 +- mindspore/nn/layer/embedding.py | 14 ++++---------- mindspore/nn/layer/image.py | 3 ++- mindspore/nn/layer/math.py | 3 ++- mindspore/ops/operations/math_ops.py | 16 +++++++++++++--- mindspore/ops/operations/nn_ops.py | 4 ---- 7 files changed, 23 insertions(+), 21 deletions(-) diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py index 02c920160e..75ac59c425 100644 --- a/mindspore/nn/layer/activation.py +++ b/mindspore/nn/layer/activation.py @@ -408,7 +408,7 @@ class Sigmoid(Cell): Applies sigmoid-type activation element-wise. Sigmoid function is defined as: - :math:`\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)}`, where :math:`x_i` is the element of the input. + :math:`\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)}`, where :math:`x_i` is the element of the input. Inputs: - **input_data** (Tensor) - The input of Tanh. diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index fb91f6a944..0cd630f766 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -98,7 +98,7 @@ class Dropout(Cell): Note: Each channel will be zeroed out independently on every construct call. - The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so + The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so that the output layer remains at a similar scale. During inference, this layer returns the same tensor as the input. diff --git a/mindspore/nn/layer/embedding.py b/mindspore/nn/layer/embedding.py index 3d57fc9777..d50cc912ab 100755 --- a/mindspore/nn/layer/embedding.py +++ b/mindspore/nn/layer/embedding.py @@ -176,11 +176,8 @@ class EmbeddingLookup(Cell): Examples: >>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32) >>> result = nn.EmbeddingLookup(4,2)(input_indices) - >>> print(result) - [[[ 0.00856617 0.01039034] - [ 0.00196276 -0.00094072]] - [[ 0.01279703 0.00078912] - [ 0.00084863 -0.00742412]]] + >>> print(result.shape) + (2, 2, 2) """ BATCH_SLICE = "batch_slice" FIELD_SLICE = "field_slice" @@ -350,11 +347,8 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup): >>> field_ids = Tensor([[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]], mindspore.int32) >>> net = nn.MultiFieldEmbeddingLookup(10, 2, field_size=2, operator='SUM') >>> out = net(input_indices, input_values, field_ids) - >>> print(out) - [[[-0.00478983 -0.00772568] - [-0.00968955 -0.00064902]] - [[-0.01251151 -0.01251151] - [-0.00196387 -0.00196387] + >>> print(out.shape) + (2, 2, 2) """ OPERATOR_SUM = 'SUM' OPERATOR_MEAN = 'MEAN' diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py index 50c63b26d5..4af3e6fcf3 100644 --- a/mindspore/nn/layer/image.py +++ b/mindspore/nn/layer/image.py @@ -219,11 +219,12 @@ class SSIM(Cell): Examples: >>> net = nn.SSIM() + >>> np.random.seed(0) >>> img1 = Tensor(np.random.random((1, 3, 16, 16)), mindspore.float32) >>> img2 = Tensor(np.random.random((1, 3, 16, 16)), mindspore.float32) >>> output = net(img1, img2) >>> print(output) - [0.12174469] + [-0.15189075] """ def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): super(SSIM, self).__init__() diff --git a/mindspore/nn/layer/math.py b/mindspore/nn/layer/math.py index 47075441a6..8b97b8a42f 100644 --- a/mindspore/nn/layer/math.py +++ b/mindspore/nn/layer/math.py @@ -315,6 +315,7 @@ class DiGamma(Cell): >>> input_x = Tensor(np.array([2, 3, 4]).astype(np.float32)) >>> op = nn.DiGamma() >>> output = op(input_x) + >>> print(output) [0.42278463 0.92278427 1.2561178] """ @@ -661,7 +662,7 @@ class LBeta(Cell): >>> input_y = Tensor(np.array([2.0, 3.0, 14.0, 15.0]).astype(np.float32)) >>> lbeta = nn.LBeta() >>> output = lbeta(input_y, input_x) - >>> print (output) + >>> print(output) [-1.7917596 -4.094345 -12.000229 -14.754799] """ diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index bd3b433af9..3b69b9e972 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -716,6 +716,8 @@ class MatMul(PrimitiveWithInfer): >>> input_x2 = Tensor(np.ones(shape=[3, 4]), mindspore.float32) >>> matmul = ops.MatMul() >>> output = matmul(input_x1, input_x2) + >>> print(output) + [[3. 3. 3. 3.]] """ @prim_attr_register @@ -2368,6 +2370,8 @@ class Acosh(PrimitiveWithInfer): >>> acosh = ops.Acosh() >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) >>> output = acosh(input_x) + >>> print(output) + [0. 0.9624236 1.7627472 5.298292] """ @prim_attr_register @@ -2522,7 +2526,7 @@ class Equal(_LogicBinaryOp): >>> equal = ops.Equal() >>> output = equal(input_x, 2.0) >>> print(output) - Tensor(shape=[3], dtype=Bool, value= [False, True, False]) + [False True False] >>> >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) @@ -3246,6 +3250,8 @@ class ACos(PrimitiveWithInfer): >>> acos = ops.ACos() >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) >>> output = acos(input_x) + >>> print(output) + [0.7377037 1.5307858 1.2661037 0.97641146] """ @prim_attr_register @@ -3360,12 +3366,15 @@ class NMSWithMask(PrimitiveWithInfer): ``Ascend`` ``GPU`` Examples: - >>> bbox = np.random.rand(128, 5) + >>> bbox = np.array([[0.4, 0.2, 0.4, 0.3, 0.1], [0.4, 0.3, 0.6, 0.8, 0.7]]) >>> bbox[:, 2] += bbox[:, 0] >>> bbox[:, 3] += bbox[:, 1] >>> inputs = Tensor(bbox, mindspore.float32) >>> nms = ops.NMSWithMask(0.5) >>> output_boxes, indices, mask = nms(inputs) + >>> print(output_boxes) + [[0.39990234 0.19995117 0.7998047 0.5 0.09997559] + [0.39990234 0.30004883 1. 1.0996094 0.7001953 ]] """ @prim_attr_register @@ -3520,6 +3529,7 @@ class Tan(PrimitiveWithInfer): >>> tan = ops.Tan() >>> input_x = Tensor(np.array([-1.0, 0.0, 1.0]), mindspore.float32) >>> output = tan(input_x) + >>> print(output) [-1.5574081 0. 1.5574081] """ @@ -3733,7 +3743,7 @@ class BitwiseOr(_BitwiseBinaryOp): >>> 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 = ops.BitwiseOr() - >>> boutput = itwise_or(input_x1, input_x2) + >>> output = bitwise_or(input_x1, input_x2) >>> print(output) [ 0 1 1 -1 -1 3 3] """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 86e316ed10..ef889eb8b3 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -3159,16 +3159,12 @@ class LSTM(PrimitiveWithInfer): >>> print(output) [[[0.9640267 0.9640267 ] [0.9640267 0.9640267 ]] - [[0.9950539 0.9950539 ] [0.9950539 0.9950539 ]] - [[0.99932843 0.99932843] [0.99932843 0.99932843]] - [[0.9999084 0.9999084 ] [0.9999084 0.9999084 ]] - [[0.9999869 0.9999869 ] [0.9999869 0.9999869 ]]] """