forked from mindspore-Ecosystem/mindspore
fix api bugs
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32899f3333
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757667d90e
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@ -7,11 +7,14 @@ mindspore.ops.full
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参数:
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- **size** (Union(tuple[int], list[int])) - 指定输出Tensor的shape。
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- **fill_value** (number.Number) - 用来填充输出Tensor的值。
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- **dtype** (mindspore.dtype) - 指定输出Tensor的数据类型。数据类型只支持 `bool_ <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 和 `number <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 。
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- **fill_value** (number.Number) - 用来填充输出Tensor的值。当前不支持复数类型。
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关键字参数:
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- **dtype** (mindspore.dtype) - 指定输出Tensor的数据类型。数据类型只支持 `bool_` 和 `number` ,更多细节详见 :class:`mindspore.dtype` 。默认值:None。
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返回:
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Tensor。
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异常:
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- **TypeError** - `size` 不是元组。
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- **TypeError** - `size` 中包含小于0的成员。
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- **ValueError** - `size` 中包含小于0的成员。
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@ -7,7 +7,7 @@ mindspore.ops.full_like
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参数:
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- **x** (Tensor) - `x` 的shape决定输出Tensor的shape。
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- **fill_value** (number.Number) - 用来填充输出Tensor的值。
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- **fill_value** (number.Number) - 用来填充输出Tensor的值。当前不支持复数类型。
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关键字参数:
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- **dtype** (mindspore.dtype, 可选) - 指定输出Tensor的数据类型。数据类型只支持 `bool_` 和 `number` ,更多细节详见 :class:`mindspore.dtype` 。默认值:None。
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@ -373,7 +373,7 @@ def hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype
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if window_length <= 1:
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return Tensor(np.ones(window_length))
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if dtype is not None and dtype not in mstype.float_type:
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raise TypeError(f"For array function 'hamming_window', 'dtype' must be floating pont dtypes, but got {dtype}.")
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raise TypeError(f"For array function 'hamming_window', 'dtype' must be floating point dtypes, but got {dtype}.")
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if periodic:
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window_length += 1
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@ -638,7 +638,7 @@ def one_hot(indices, depth, on_value, off_value, axis=-1):
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return onehot(indices, depth, on_value, off_value)
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def fill(type, shape, value):
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def fill(type, shape, value): # pylint: disable=redefined-outer-name
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"""
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Create a Tensor of the specified shape and fill it with the specified value.
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@ -724,17 +724,18 @@ def full(size, fill_value, *, dtype=None): # pylint: disable=redefined-outer-nam
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Args:
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size (Union(tuple[int], list[int])): The specified shape of output tensor.
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fill_value (number.Number): Value to fill the returned tensor.
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dtype (mindspore.dtype): The specified type of output tensor. The data type only supports
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`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ and
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`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ .
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fill_value (number.Number): Value to fill the returned tensor. Complex numbers are not supported for now.
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Keyword Args:
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dtype (mindspore.dtype): The specified type of output tensor. `bool_` and `number` are supported, for details,
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please refer to :class:`mindspore.dtype` . Default: None.
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Returns:
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Tensor.
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Raises:
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TypeError: If `size` is not a tuple or list.
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TypeError: The element in `size` is less than 0.
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ValueError: The element in `size` is less than 0.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -768,11 +769,11 @@ def full_like(x, fill_value, *, dtype=None):
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Args:
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x (Tensor): The shape of `x` will determine shape of the output Tensor.
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fill_value (number.Number): Value to fill the returned Tensor.
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fill_value (number.Number): Value to fill the returned Tensor. Complex numbers are not supported for now.
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Keyword Args:
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dtype (mindspore.dtype, optional): The specified type of output tensor. The data type only supports
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`bool_` and `number` , for details, please refer to :class:`mindspore.dtype` . Default: None.
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dtype (mindspore.dtype, optional): The specified type of output tensor. `bool_` and `number` are supported,
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for details, please refer to :class:`mindspore.dtype` . Default: None.
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Returns:
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Tensor.
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@ -860,8 +861,9 @@ def chunk(x, chunks, axis=0):
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size1 = _tuple_setitem(arr_shape, axis, length1)
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start2 = _tuple_setitem(start1, axis, length1)
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size2 = _tuple_setitem(arr_shape, axis, length2)
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res = P.Split(axis, true_chunks)(tensor_slice(x, start1, size1)) + \
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P.Split(axis, 1)(tensor_slice(x, start2, size2))
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res = P.Split(axis, true_chunks)(tensor_slice(x, start1, size1))
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if length2:
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res += P.Split(axis, 1)(tensor_slice(x, start2, size2))
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return res
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@ -3599,8 +3599,6 @@ def cosine_embedding_loss(input1, input2, target, margin=0.0, reduction="mean"):
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if margin_f > 1.0 or margin_f < -1.0:
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raise ValueError(f"For ops.cosine_embedding_loss, the value of 'margin' should be in [-1, 1],"
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f"but got {margin_f}.")
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# if target > 0, 1-cosine(input1, input2)
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# else, max(0, cosine(input1, input2)-margin)
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prod_sum = _get_cache_prim(P.ReduceSum)()(input1 * input2, (1,))
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square1 = _get_cache_prim(P.ReduceSum)()(ops.square(input1), (1,))
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square2 = _get_cache_prim(P.ReduceSum)()(ops.square(input2), (1,))
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