# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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import torch.nn.functional as F
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from torch import nn
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class Flatten(nn.Module):
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def forward(self, input):
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return input.view(input.size(0), -1)
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class GeneralizedMeanPooling(nn.Module):
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r"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
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The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
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- At p = infinity, one gets Max Pooling
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- At p = 1, one gets Average Pooling
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The output is of size H x W, for any input size.
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The number of output features is equal to the number of input planes.
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Args:
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output_size: the target output size of the image of the form H x W.
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Can be a tuple (H, W) or a single H for a square image H x H
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H and W can be either a ``int``, or ``None`` which means the size will
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be the same as that of the input.
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"""
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def __init__(self, norm, output_size=1, eps=1e-6):
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super(GeneralizedMeanPooling, self).__init__()
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assert norm > 0
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self.p = float(norm)
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self.output_size = output_size
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self.eps = eps
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def forward(self, x):
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x = x.clamp(min=self.eps).pow(self.p)
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return torch.nn.functional.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.p) + ', ' \
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+ 'output_size=' + str(self.output_size) + ')'
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class GeneralizedMeanPoolingP(GeneralizedMeanPooling):
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""" Same, but norm is trainable
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"""
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def __init__(self, norm=3, output_size=1, eps=1e-6):
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super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps)
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self.p = nn.Parameter(torch.ones(1) * norm)
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class AdaptiveAvgMaxPool2d(nn.Module):
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def __init__(self):
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super(AdaptiveAvgMaxPool2d, self).__init__()
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self.avgpool = FastGlobalAvgPool2d()
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def forward(self, x):
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x_avg = self.avgpool(x, self.output_size)
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x_max = F.adaptive_max_pool2d(x, 1)
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x = x_max + x_avg
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return x
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class FastGlobalAvgPool2d(nn.Module):
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def __init__(self, flatten=False):
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super(FastGlobalAvgPool2d, self).__init__()
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self.flatten = flatten
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def forward(self, x):
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if self.flatten:
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in_size = x.size()
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return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
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else:
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return x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
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