adaptive_avgmax_pool.py

""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
    * 'avg' - Average pooling
    * 'max' - Max pooling
    * 'avgmax' - Sum of average and max pooling re-scaled by 0.5
    * 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim

Both a functional and a nn.Module version of the pooling is provided.

Author: Ross Wightman (rwightman)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F


def pooling_factor(pool_type='avg'):
    return 2 if pool_type == 'avgmaxc' else 1


def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
    """Selectable global pooling function with dynamic input kernel size
    """
    if pool_type == 'avgmaxc':
        x = torch.cat([
            F.avg_pool2d(
                x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad),
            F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
        ], dim=1)
    elif pool_type == 'avgmax':
        x_avg = F.avg_pool2d(
                x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
        x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
        x = 0.5 * (x_avg + x_max)
    elif pool_type == 'max':
        x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
    else:
        if pool_type != 'avg':
            print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
        x = F.avg_pool2d(
            x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
    return x


class AdaptiveAvgMaxPool2d(torch.nn.Module):
    """Selectable global pooling layer with dynamic input kernel size
    """
    def __init__(self, output_size=1, pool_type='avg'):
        super(AdaptiveAvgMaxPool2d, self).__init__()
        self.output_size = output_size
        self.pool_type = pool_type
        if pool_type == 'avgmaxc' or pool_type == 'avgmax':
            self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)])
        elif pool_type == 'max':
            self.pool = nn.AdaptiveMaxPool2d(output_size)
        else:
            if pool_type != 'avg':
                print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
            self.pool = nn.AdaptiveAvgPool2d(output_size)

    def forward(self, x):
        if self.pool_type == 'avgmaxc':
            x = torch.cat([p(x) for p in self.pool], dim=1)
        elif self.pool_type == 'avgmax':
            x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0)
        else:
            x = self.pool(x)
        return x

    def factor(self):
        return pooling_factor(self.pool_type)

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + 'output_size=' + str(self.output_size) \
               + ', pool_type=' + self.pool_type + ')'