AveragePool - 1 vs 10#
Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.
- AveragePool1 → AveragePool10 +1 -15
AveragePool1 → AveragePool10
RENAMED
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AveragePool consumes an input tensor X and applies average pooling across
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the tensor according to kernel sizes, stride sizes, and pad lengths.
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average pooling consisting of computing the average on all values of a
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subset of the input tensor according to the kernel size and downsampling the
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data into the output tensor Y for further processing. The output spatial shape will be following:
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::
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output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
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-
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or
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::
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output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
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-
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if ceil_mode is enabled
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* pad_shape[i] is sum of pads along axis i
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auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
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::
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VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
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SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
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And pad shape will be following if SAME_UPPER or SAME_LOWER:
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::
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pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
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The output of each pooling window is divided by the number of elements
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+
The output of each pooling window is divided by the number of elements exclude pad.
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**Attributes**
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* **auto_pad**:
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auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
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Where default value is NOTSET, which means explicit padding is used.
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SAME_UPPER or SAME_LOWER mean pad the input so that the output
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spatial size match the input.In case of odd number add the extra
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padding at the end for SAME_UPPER and at the beginning for
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SAME_LOWER. VALID mean no padding.
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* **ceil_mode**:
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Whether to use ceil or floor (default) to compute the output shape.
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* **count_include_pad**:
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Whether include pad pixels when calculating values for the edges.
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Default is 0, doesn't count include pad.
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* **kernel_shape** (required):
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The size of the kernel along each axis.
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* **pads**:
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Padding for the beginning and ending along each spatial axis, it can
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take any value greater than or equal to 0. The value represent the
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number of pixels added to the beginning and end part of the
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corresponding axis. pads format should be as follow [x1_begin,
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x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
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added at the beginning of axis i and xi_end, the number of pixels
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added at the end of axis i. This attribute cannot be used
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simultaneously with auto_pad attribute. If not present, the padding
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defaults to 0 along start and end of each spatial axis.
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* **strides**:
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Stride along each spatial axis.
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**Inputs**
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* **X** (heterogeneous) - **T**:
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Input data tensor from the previous operator; dimensions for image
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case are (N x C x H x W), where N is the batch size, C is the number
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of channels, and H and W are the height and the width of the data.
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For non image case, the dimensions are in the form of (N x C x D1 x
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D2 ... Dn), where N is the batch size. Optionally, if dimension
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denotation is in effect, the operation expects the input data tensor
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to arrive with the dimension denotation of [DATA_BATCH,
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DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
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**Outputs**
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* **Y** (heterogeneous) - **T**:
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Output data tensor from average or max pooling across the input
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tensor. Dimensions will vary based on various kernel, stride, and
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pad sizes. Floor value of the dimension is used
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**Type Constraints**
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* **T** in (
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tensor(double),
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tensor(float),
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tensor(float16)
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):
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Constrain input and output types to float tensors.
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