- paddle.nn.functional. max_pool2d ( x, kernel_size, stride=None, padding=0, return_mask=False, ceil_mode=False, data_format='NCHW', name=None ) [source]
This API implements max pooling 2d operation. See more details in api_nn_pooling_MaxPool2d .
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 Name. Usually name is no need to set and None by default.
The output tensor of pooling result. The data type is same as input tensor.
- Return type
import paddle import paddle.nn.functional as F # max pool2d x = paddle.uniform([1, 3, 32, 32], paddle.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],