39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import torch.nn as nn
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class MultiPooling(nn.Module):
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"""Pooling layers for features from multiple depth."""
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POOL_PARAMS = {
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'resnet50': [
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dict(kernel_size=10, stride=10, padding=4),
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dict(kernel_size=16, stride=8, padding=0),
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dict(kernel_size=13, stride=5, padding=0),
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dict(kernel_size=8, stride=3, padding=0),
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dict(kernel_size=6, stride=1, padding=0)
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]
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}
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POOL_SIZES = {'resnet50': [12, 6, 4, 3, 2]}
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POOL_DIMS = {'resnet50': [9216, 9216, 8192, 9216, 8192]}
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def __init__(self,
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pool_type='adaptive',
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in_indices=(0, ),
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backbone='resnet50'):
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super(MultiPooling, self).__init__()
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assert pool_type in ['adaptive', 'specified']
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if pool_type == 'adaptive':
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self.pools = nn.ModuleList([
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nn.AdaptiveAvgPool2d(self.POOL_SIZES[backbone][l])
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for l in in_indices
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])
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else:
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self.pools = nn.ModuleList([
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nn.AvgPool2d(**self.POOL_PARAMS[backbone][l])
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for l in in_indices
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])
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def forward(self, x):
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assert isinstance(x, (list, tuple))
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return [p(xx) for p, xx in zip(self.pools, x)]
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