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119 lines
3.8 KiB
119 lines
3.8 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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Hacked together by / Copyright 2020 Ross Wightman
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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_pool_feat_mult(pool_type='avg'):
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if pool_type == 'catavgmax':
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return 2
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else:
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return 1
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def adaptive_avgmax_pool2d(x, output_size=1):
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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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return 0.5 * (x_avg + x_max)
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def adaptive_catavgmax_pool2d(x, output_size=1):
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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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return torch.cat((x_avg, x_max), 1)
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def select_adaptive_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 == 'avg':
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x = F.adaptive_avg_pool2d(x, output_size)
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elif pool_type == 'avgmax':
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x = adaptive_avgmax_pool2d(x, output_size)
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elif pool_type == 'catavgmax':
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x = adaptive_catavgmax_pool2d(x, output_size)
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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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assert False, 'Invalid pool type: %s' % pool_type
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return x
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class FastAdaptiveAvgPool2d(nn.Module):
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def __init__(self, flatten=False):
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super(FastAdaptiveAvgPool2d, self).__init__()
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self.flatten = flatten
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def forward(self, x):
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return x.mean((2, 3), keepdim=not self.flatten)
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class AdaptiveAvgMaxPool2d(nn.Module):
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def __init__(self, output_size=1):
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super(AdaptiveAvgMaxPool2d, self).__init__()
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self.output_size = output_size
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def forward(self, x):
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return adaptive_avgmax_pool2d(x, self.output_size)
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class AdaptiveCatAvgMaxPool2d(nn.Module):
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def __init__(self, output_size=1):
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super(AdaptiveCatAvgMaxPool2d, self).__init__()
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self.output_size = output_size
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def forward(self, x):
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return adaptive_catavgmax_pool2d(x, self.output_size)
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class SelectAdaptivePool2d(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='fast', flatten=False):
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super(SelectAdaptivePool2d, self).__init__()
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self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing
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self.flatten = nn.Flatten(1) if flatten else nn.Identity()
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if pool_type == '':
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self.pool = nn.Identity() # pass through
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elif pool_type == 'fast':
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assert output_size == 1
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self.pool = FastAdaptiveAvgPool2d(flatten)
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self.flatten = nn.Identity()
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elif pool_type == 'avg':
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self.pool = nn.AdaptiveAvgPool2d(output_size)
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elif pool_type == 'avgmax':
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self.pool = AdaptiveAvgMaxPool2d(output_size)
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elif pool_type == 'catavgmax':
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self.pool = AdaptiveCatAvgMaxPool2d(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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assert False, 'Invalid pool type: %s' % pool_type
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def is_identity(self):
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return not self.pool_type
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def forward(self, x):
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x = self.pool(x)
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x = self.flatten(x)
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return x
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def feat_mult(self):
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return adaptive_pool_feat_mult(self.pool_type)
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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+ 'pool_type=' + self.pool_type \
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+ ', flatten=' + str(self.flatten) + ')'
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