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58 lines
2.1 KiB
58 lines
2.1 KiB
""" PyTorch selectable adaptive pooling
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Adaptive pooling with the ability to select the type of pooling from:
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* 'avg' - Average pooling
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* 'max' - Max pooling
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* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
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* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim
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Both a functional and a nn.Module version of the pooling is provided.
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Author: Ross Wightman (rwightman)
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def adaptive_avgmax_pool2d(x, pool_type='avg', output_size=1):
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"""Selectable global pooling function with dynamic input kernel size
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"""
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if pool_type == 'avgmax':
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x_avg = F.adaptive_avg_pool2d(x, output_size)
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x_max = F.adaptive_max_pool2d(x, output_size)
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x = 0.5 * (x_avg + x_max)
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elif pool_type == 'max':
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x = F.adaptive_max_pool2d(x, output_size)
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else:
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x = F.adaptive_avg_pool2d(x, output_size)
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return x
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class AdaptiveAvgMaxPool2d(torch.nn.Module):
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"""Selectable global pooling layer with dynamic input kernel size
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"""
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def __init__(self, output_size=1, pool_type='avg'):
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super(AdaptiveAvgMaxPool2d, self).__init__()
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self.output_size = output_size
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self.pool_type = pool_type
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if pool_type == 'avgmax':
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self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)])
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elif pool_type == 'max':
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self.pool = nn.AdaptiveMaxPool2d(output_size)
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else:
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if pool_type != 'avg':
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print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
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self.pool = nn.AdaptiveAvgPool2d(output_size)
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def forward(self, x):
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if self.pool_type == 'avgmax':
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x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0)
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else:
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x = self.pool(x)
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return x
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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+ 'output_size=' + str(self.output_size) \
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+ ', pool_type=' + self.pool_type + ')'
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