From aecffe264a2a3cddc8978f823066a78fe0388f49 Mon Sep 17 00:00:00 2001 From: Wei Shengyu Date: Tue, 23 Aug 2022 11:22:23 +0800 Subject: [PATCH] add doc back (#45316) --- python/paddle/nn/functional/pooling.py | 58 ++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py index f44f9515dd7..21e2aafe916 100755 --- a/python/paddle/nn/functional/pooling.py +++ b/python/paddle/nn/functional/pooling.py @@ -1057,6 +1057,64 @@ def max_pool2d(x, ceil_mode=False, data_format="NCHW", name=None): + """ + This API implements max pooling 2d operation. + See more details in :ref:`api_nn_pooling_MaxPool2d` . + Args: + x (Tensor): The input tensor of pooling operator which is a 4-D tensor with + shape [N, C, H, W]. The format of input tensor is `"NCHW"` or + `"NHWC"`, where `N` is batch size, `C` is the number of channels, + `H` is the height of the feature, and `W` is the width of the + feature. The data type if float32 or float64. + kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, + it must contain two integers, (kernel_size_Height, kernel_size_Width). + Otherwise, the pool kernel size will be a square of an int. + stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list, + it must contain two integers, (stride_Height, stride_Width). + Otherwise, the pool stride size will be a square of an int. + padding (string|int|list|tuple): The padding size. Padding could be in one of the following forms. + 1. A string in ['valid', 'same']. + 2. An int, which means the feature map is zero padded by size of `padding` on every sides. + 3. A list[int] or tuple(int) whose length is 2, [pad_height, pad_weight] whose value means the padding size of each dimension. + 4. A list[int] or tuple(int) whose length is 4. [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side. + 5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0). + The default value is 0. + ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape + return_mask (bool): Whether to return the max indices along with the outputs. Default False, only support `"NCHW"` data format + data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. + The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: + `[batch_size, input_channels, input_height, input_width]`. + name(str, optional): For detailed information, please refer + to :ref:`api_guide_Name`. Usually name is no need to set and + None by default. + Returns: + Tensor: The output tensor of pooling result. The data type is same as input tensor. + + Raises: + ValueError: If `padding` is a string, but not "SAME" or "VALID". + ValueError: If `padding` is "VALID", but `ceil_mode` is True. + ShapeError: If the output's shape calculated is not greater than 0. + + Examples: + .. code-block:: python + import paddle + import paddle.nn.functional as F + import numpy as np + + # max pool2d + x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32)) + out = F.max_pool2d(x, + kernel_size=2, + stride=2, padding=0) + # output.shape [1, 3, 16, 16] + # for return_mask=True + out, max_indices = F.max_pool2d(x, + kernel_size=2, + stride=2, + padding=0, + return_mask=True) + # out.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16], + """ kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size') if stride is None: stride = kernel_size -- GitLab