!45347 [MS][DOC]export some primitive doc
Merge pull request !45347 from mengyuanli/code_docs_export_primitive
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@ -132,6 +132,9 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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mindspore.ops.Adam
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mindspore.ops.AdamWeightDecay
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mindspore.ops.AdaptiveAvgPool2D
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mindspore.ops.AdaptiveAvgPool3D
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mindspore.ops.AdaptiveMaxPool2D
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mindspore.ops.AdaptiveMaxPool3D
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mindspore.ops.ApplyAdadelta
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mindspore.ops.ApplyAdagrad
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mindspore.ops.ApplyAdagradDA
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@ -185,6 +188,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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:nosignatures:
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:template: classtemplate.rst
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mindspore.ops.AdjustHue
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mindspore.ops.BoundingBoxDecode
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mindspore.ops.BoundingBoxEncode
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mindspore.ops.CheckValid
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@ -0,0 +1,8 @@
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mindspore.ops.AdaptiveAvgPool3D
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================================
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.. py:class:: mindspore.ops.AdaptiveAvgPool3D(output_size)
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三维自适应平均池化。
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更多参考详见 :func:`mindspore.ops.adaptive_avg_pool3d`。
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@ -0,0 +1,8 @@
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mindspore.ops.AdaptiveMaxPool2D
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===============================
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.. py:class:: mindspore.ops.AdaptiveMaxPool2D(output_size)
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二维自适应最大值池化。
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更多参考详见 :func:`mindspore.ops.adaptive_max_pool2d`。
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@ -0,0 +1,23 @@
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mindspore.ops.AdjustHue
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=======================
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.. py:class:: mindspore.ops.AdjustHue()
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调整 RGB 图像的色调。
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.. note::
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该运算是种将 RGB 图像转换为浮点表示的便捷方法。通过将图像转换为HSV色彩空间并移动色调通道中的强度来调整图像，然后转换回原始数据模式。
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当多个调整依次进行时尽量减少冗余转换的数量。
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输入：
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- **image** (Tensor) - 输入的Tensor。shape的最后一个维度的必须为3。dtype需要是float16或float32。Tensor的维度至少是3维。
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- **delta** (Tensor) - 色调通道的添加值。dtype需要是float32。Tensor必须是0维的。
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输出：
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Tensor，具有与 `image` 相同的shape和dtype。
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异常：
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- **TypeError** - 如果 `image` 或 `delta` 不是Tensor。
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- **TypeError** - 如果 `image` 的dtype不是：float32或float16。
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- **TypeError** - 如果 `delta` 的dtype不是：float32。
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- **ValueError** - 如果 `image` 的维度低于3维。
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@ -132,6 +132,9 @@ Optimizer
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mindspore.ops.Adam
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mindspore.ops.AdamWeightDecay
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mindspore.ops.AdaptiveAvgPool2D
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mindspore.ops.AdaptiveAvgPool3D
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mindspore.ops.AdaptiveMaxPool2D
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mindspore.ops.AdaptiveMaxPool3D
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mindspore.ops.ApplyAdadelta
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mindspore.ops.ApplyAdagrad
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mindspore.ops.ApplyAdagradDA
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@ -185,6 +188,7 @@ Image Processing
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:nosignatures:
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:template: classtemplate.rst
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mindspore.ops.AdjustHue
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mindspore.ops.BoundingBoxDecode
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mindspore.ops.BoundingBoxEncode
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mindspore.ops.CheckValid
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@ -131,7 +131,7 @@ class AdjustHue(Primitive):
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Inputs:
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- **image** (Tensor): RGB image or images. The size of the last dimension must be 3.
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the dtype is float16 or float32. At least 3-D.
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the dtype is float16 or float32. At least 3-D.
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- **delta** (Tensor): How much to add to the hue channel, the dtype is float32. Must be 0-D.
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Output:
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@ -139,9 +139,9 @@ class AdjustHue(Primitive):
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Raises:
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TypeError: If neither `image` nor `delta` is a tensor.
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TypeError: If the dtype of image not float16 or float32.
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TypeError: If the dtype of delta not float32.
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ValueError: If image have at less than 3 dimensions.
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TypeError: If the dtype of `image` is neither float16 nor float32.
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TypeError: If the dtype of `delta` not float32.
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ValueError: If the dimension of `image` is less than 3.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -375,55 +375,9 @@ class AdaptiveAvgPool2D(AdaptiveAvgPool2DV1):
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class AdaptiveMaxPool2D(Primitive):
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r"""
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AdaptiveMaxPool2D operation.
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Applies a 2D adaptive max pooling over an input signal composed of several input planes.
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This operator applies a 2D adaptive max pooling to an input signal composed of multiple input planes.
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That is, for any input size, the size of the specified output is H x W.
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The number of output features is equal to the number of input planes.
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The input and output data format can be "NCHW" and "CHW". N is the batch size, C is the number of channels,
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H is the feature height, and W is the feature width.
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For max adaptive pool2d:
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.. math::
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\begin{align}
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h_{start} &= floor(i * H_{in} / H_{out})\\
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h_{end} &= ceil((i + 1) * H_{in} / H_{out})\\
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w_{start} &= floor(j * W_{in} / W_{out})\\
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w_{end} &= ceil((j + 1) * W_{in} / W_{out})\\
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Output(i,j) &= {\max Input[h_{start}:h_{end}, w_{start}:w_{end}]}
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\end{align}
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Note:
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In Ascend, the second output `argmax` is invalid, please ignore it.
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Args:
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output_size (Union[int, tuple]): The target output size is H x W.
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ouput_size can be a tuple, or a single H for H x H, and H and W can be int or None
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which means the output size is the same as the input.
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return_indices (bool): If `return_indices` is True, the indices of max value would be output.
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Default: False.
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Inputs:
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- **input_x** (Tensor) - The input of AdaptiveMaxPool2D, which is a 3D or 4D tensor,
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with float16, float32 or float64 data type.
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Outputs:
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Tensor, with the same type as the `input_x`.
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Shape of the output is `input_x_shape[:len(input_x_shape) - len(out_shape)] + out_shape`.
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Raises:
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TypeError: If `output_size` is not int or tuple.
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TypeError: If `input_x` is not a tensor.
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TypeError: If `return_indices` is not a bool.
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TypeError: If dtype of `input_x` is not float16, float32 or float64.
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ValueError: If `output_size` is a tuple and the length of `output_size` is not 2.
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ValueError: If the dimension of `input_x` is not NCHW or CHW.
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ValueError: If `output_size` is less than -1.
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Refer to :func:`mindspore.ops.adaptive_max_pool2d` for more details.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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