From bce8108587597b0097c359493f272475d07444ef Mon Sep 17 00:00:00 2001 From: lilinjie Date: Fri, 21 Oct 2022 17:29:26 +0800 Subject: [PATCH] fix docs --- .../ops/mindspore.ops.func_broadcast_to.rst | 10 ++++---- .../mindspore/ops/function/array_func.py | 25 +++++++++++-------- .../mindspore/ops/operations/random_ops.py | 2 +- 3 files changed, 20 insertions(+), 17 deletions(-) diff --git a/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst b/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst index f356bc9fef0..9033e05bfba 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst @@ -11,17 +11,17 @@ mindspore.ops.broadcast_to - 如果不相等，分以下三种情况： - - 情况一：如果目标shape该维的值为-1， 则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ； + - 情况一：如果目标shape该维的值为-1，则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ； - - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:` (1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ； + - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:`(1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ； - - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。 + - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。 至此输出shape后面m维就确定好了，现在看一下前面 :math:`*` 维，有以下两种情况： - - 如果额外的 :math:`*` 维中不含有-1，则输入shape从低维度补充维度使之与目标shape维度一致，比如说目标shape为 :math:` (3, 1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，则输入shape增维变成 :math:`(1, 1, 1, 1, 5, 9)`，根据上面提到的情况二可以得出输出shape为 :math:` (3, 1, 4, 1, 5, 9)`； + - 如果额外的 :math:`*` 维中不含有-1，则输入shape从低维度补充维度使之与目标shape维度一致，比如说目标shape为 :math:`(3, 1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，则输入shape增维变成 :math:`(1, 1, 1, 1, 5, 9)`，根据上面提到的情况二可以得出输出shape为 :math:`(3, 1, 4, 1, 5, 9)`； - - 如果额外的 :math:`*` 维中含有-1，说明此时该-1对应一个不存在的维度，不支持广播。比如说目标shape为 :math:` (3, -1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，此时不进行增维处理，而是直接报错。 + - 如果额外的 :math:`*` 维中含有-1，说明此时该-1对应一个不存在的维度，不支持广播。比如说目标shape为 :math:`(3, -1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，此时不进行增维处理，而是直接报错。 参数： - **x** (Tensor) - 第一个输入，任意维度的Tensor，数据类型为float16、float32、int32、int8、uint8、bool。 diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index e275c5cd169..1717b335284 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -3251,37 +3251,40 @@ def affine_grid(theta, output_size, align_corners=False): def broadcast_to(x, shape): """ Broadcasts input tensor to a given shape. The dim of input shape must be smaller - than or equal to that of target shape, suppose input shape :math:`(x1, x2, ..., xm)`, - target shape :math:`(*, y_1, y_2, ..., y_m)`. The broadcast rules are as follows: + than or equal to that of target shape. Suppose input shape is :math:`(x1, x2, ..., xm)`, + target shape is :math:`(*, y_1, y_2, ..., y_m)`, where :math:`*` means any additional dimension. + The broadcast rules are as follows: Compare the value of `x_m` and `y_m`, `x_{m-1}` and `y_{m-1}`, ..., `x_1` and `y_1` consecutively and decide whether these shapes are broadcastable and what the broadcast result is. If the value pairs at a specific dim are equal, then that value goes right into that dim of output shape. - With an input shape :math:`(2, 3)`, target shape :math:`(2, 3)` , the inferred outpyt shape is :math:`(2, 3)`. + With an input shape :math:`(2, 3)`, target shape :math:`(2, 3)` , the inferred output shape is :math:`(2, 3)`. If the value pairs are unequal, there are three cases: - Case 1: Value of target shape is -1, then the value of the output shape is that of the input shape's. - With an input shape :math:`(3, 3)`, target shape :math:`(-1, 3)`, the output shape is :math:`(3, 3)`. + Case 1: If the value of the target shape in the dimension is -1, the value of the + output shape in the dimension is the value of the corresponding input shape in the dimension. - Case 2: Value of target shape is not -1 but the value ot the input shape is 1, then the value of the output shape - is that of the target shape's. With an input shape :math:`(1, 3)`, target + Case 2: If the value of target shape in the dimension is not -1, but the corresponding + value in the input shape is 1, then the corresponding value of the output shape + is that of the target shape. With an input shape :math:`(1, 3)`, target shape :math:`(8, 3)`, the output shape is :math:`(8, 3)`. - Case 3: All other cases mean that the two shapes are not broadcastable. + Case 3: If the corresponding values of the two shapes do not satisfy the above cases, + it means that broadcasting from the input shape to the target shape is not supported. So far we got the last m dims of the outshape, now focus on the first :math:`*` dims, there are two cases: If the first :math:`*` dims of output shape does not have -1 in it, then fill the input shape with ones until their length are the same, and then refer to - Case 2 mentioned above to calculate the output shape. With target shape :math:` (3, 1, 4, 1, 5, 9)`, + Case 2 mentioned above to calculate the output shape. With target shape :math:`(3, 1, 4, 1, 5, 9)`, input shape :math:`(1, 5, 9)`, the filled input shape will be :math:`(1, 1, 1, 1, 5, 9)` and thus the - output shape is :math:` (3, 1, 4, 1, 5, 9)`. + output shape is :math:`(3, 1, 4, 1, 5, 9)`. If the first :math:`*` dims of output shape have -1 in it, it implies this -1 is conrresponding to - a non-existing dim so they're not broadcastable. With target shape :math:` (3, -1, 4, 1, 5, 9)`, + a non-existing dim so they're not broadcastable. With target shape :math:`(3, -1, 4, 1, 5, 9)`, input shape :math:`(1, 5, 9)`, instead of operating the dim-filling process first, it raises errors directly. Args: diff --git a/mindspore/python/mindspore/ops/operations/random_ops.py b/mindspore/python/mindspore/ops/operations/random_ops.py index a4a651ef6ce..3875f584459 100755 --- a/mindspore/python/mindspore/ops/operations/random_ops.py +++ b/mindspore/python/mindspore/ops/operations/random_ops.py @@ -807,7 +807,7 @@ class UniformCandidateSampler(PrimitiveWithInfer): Examples: >>> sampler = ops.UniformCandidateSampler(1, 3, False, 4, 1) - >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int32))) + >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int64))) >>> print(output1.shape) (3,) >>> print(output2.shape)