Note

You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.models.utils.multi_pooling

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule


class MultiPooling(BaseModule):
    """Pooling layers for features from multiple depth.

    Args:
        pool_type (str): Pooling type for the feature map. Options are
            'adaptive' and 'specified'. Defaults to 'adaptive'.
        in_indices (Sequence[int]): Output from which backbone stages.
            Defaults to (0, ).
        backbone (str): The selected backbone. Defaults to 'resnet50'.
    """

    POOL_PARAMS = {
        'resnet50': [
            dict(kernel_size=10, stride=10, padding=4),
            dict(kernel_size=16, stride=8, padding=0),
            dict(kernel_size=13, stride=5, padding=0),
            dict(kernel_size=8, stride=3, padding=0),
            dict(kernel_size=6, stride=1, padding=0)
        ]
    }
    POOL_SIZES = {'resnet50': [12, 6, 4, 3, 2]}
    POOL_DIMS = {'resnet50': [9216, 9216, 8192, 9216, 8192]}

    def __init__(self,
                 pool_type='adaptive',
                 in_indices=(0, ),
                 backbone='resnet50'):
        super(MultiPooling, self).__init__()
        assert pool_type in ['adaptive', 'specified']
        assert backbone == 'resnet50', 'Now only support resnet50.'
        if pool_type == 'adaptive':
            self.pools = nn.ModuleList([
                nn.AdaptiveAvgPool2d(self.POOL_SIZES[backbone][i])
                for i in in_indices
            ])
        else:
            self.pools = nn.ModuleList([
                nn.AvgPool2d(**self.POOL_PARAMS[backbone][i])
                for i in in_indices
            ])

    def forward(self, x):
        assert isinstance(x, (list, tuple))
        return [p(xx) for p, xx in zip(self.pools, x)]