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"""
pool2d-family modules.
"""
from aitemplate.compiler.ops import avg_pool2d, max_pool2d
from aitemplate.frontend.nn.module import Module
[docs]class MaxPool2d(Module):
r"""Applies a 2D max pooling over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size :math:`(N, H, W, C)`,
output :math:`(N, H_{out}, W_{out}, C)` and :attr:`kernel_size` :math:`(kH, kW)`
can be precisely described as:
.. math::
\begin{aligned}
out(N_i, h, w, C_j) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
& \text{input}(N_i, \text{stride[0]} \times h + m,
\text{stride[1]} \times w + n, C_j)
\end{aligned}
If :attr:`padding` is non-zero, then the input is implicitly padded with negative infinity on 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
padding: implicit zero padding to be added on both sides
"""
def __init__(self, kernel_size, stride, padding=0):
super().__init__()
self.op = max_pool2d(kernel_size, stride, padding)
[docs] def forward(self, *args):
r"""Applies MaxPool2d on the input."""
assert len(args) == 1
x = args[0]
return self.op(x)
[docs]class AvgPool2d(Module):
r"""Applies a 2D average pooling over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size :math:`(N, H, W, C)`,
output :math:`(N, H_{out}, W_{out}, C)` and :attr:`kernel_size` :math:`(kH, kW)`
can be precisely described as:
.. math::
out(N_i, h, w, C_j) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
input(N_i, stride[0] \times h + m, stride[1] \times w + n, C_j)
If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides
for :attr:`padding` number of points.
Note:
When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding
or the input. Sliding windows that would start in the right padded region are ignored.
Args:
kernel_size: the size of the window to take an avg over
stride: the stride of the window
padding: implicit zero padding to be added on both sides
"""
def __init__(self, kernel_size, stride, padding):
super().__init__()
self.op = avg_pool2d(kernel_size, stride, padding)
[docs] def forward(self, *args):
r"""Applies AvgPool2d on the input."""
assert len(args) == 1
x = args[0]
return self.op(x)