diff --git a/imperative/python/megengine/functional/nn.py b/imperative/python/megengine/functional/nn.py index 898b52f6..f1b466e0 100644 --- a/imperative/python/megengine/functional/nn.py +++ b/imperative/python/megengine/functional/nn.py @@ -658,16 +658,16 @@ def max_pool2d( Refer to :class:`~.MaxPool2d` for more information. Args: - inp: input tensor of shape :math:`(N, C, H_{in}, W_{in})`. + inp: input tensor of shape :math:`(N, C, H_{\text{in}}, W_{\text{in}})`. kernel_size: size of the window used to calculate the max value. - stride: stride of the window. If not provided, its value is set to kernel_size. - Default: ``None`` - padding: implicit zero padding added on both sides. Default: :math:`0` + stride: stride of the window. Default value is ``kernel_size``. + padding: implicit zero padding added on both sides. Default: 0. Returns: - output tensor of shape `(N, C, H_{out}, W_{out})`. + output tensor of shape `(N, C, H_{\text{out}}, W_{\text{out}})`. Examples: + >>> import numpy as np >>> input = tensor(np.arange(1 * 1 * 3 * 4).astype(np.float32).reshape(1, 1, 3, 4)) >>> F.nn.max_pool2d(input, 2, 1, 0) Tensor([[[[ 5. 6. 7.] diff --git a/imperative/python/megengine/module/pooling.py b/imperative/python/megengine/module/pooling.py index 3d86ab66..e538924d 100644 --- a/imperative/python/megengine/module/pooling.py +++ b/imperative/python/megengine/module/pooling.py @@ -32,9 +32,9 @@ class _PoolNd(Module): class MaxPool2d(_PoolNd): r"""Applies a 2D max pooling over an input. - For instance, given an input of the size :math:`(N, C, H, W)` and + For instance, given an input of the size :`(N, C, H_{\text{in}}, W_{\text{in}})` and :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of - the size :math:`(N, C, H_{out}, W_{out})` through a process described as: + the size :math:`(N, C, H_{\text{out}}, W_{\text{out}})` through a process described as: .. math:: @@ -48,9 +48,9 @@ class MaxPool2d(_PoolNd): both sides for :attr:`padding` number of points. Args: - kernel_size: the size of the window to take a max over. - stride: the stride of the window. Default value is kernel_size. - padding: implicit zero padding to be added on both sides. + kernel_size: the size of the window. + stride: the stride of the window. Default value is ``kernel_size``. + padding: implicit zero padding to be added on both sides.Default: 0. Examples: >>> import numpy as np