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| """ 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. | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def adaptive_pool_feat_mult(pool_type='avg'): | |
| if pool_type == 'catavgmax': | |
| return 2 | |
| else: | |
| return 1 | |
| def adaptive_avgmax_pool2d(x, output_size=1): | |
| x_avg = F.adaptive_avg_pool2d(x, output_size) | |
| x_max = F.adaptive_max_pool2d(x, output_size) | |
| return 0.5 * (x_avg + x_max) | |
| def adaptive_catavgmax_pool2d(x, output_size=1): | |
| x_avg = F.adaptive_avg_pool2d(x, output_size) | |
| x_max = F.adaptive_max_pool2d(x, output_size) | |
| return torch.cat((x_avg, x_max), 1) | |
| def select_adaptive_pool2d(x, pool_type='avg', output_size=1): | |
| """Selectable global pooling function with dynamic input kernel size | |
| """ | |
| if pool_type == 'avg': | |
| x = F.adaptive_avg_pool2d(x, output_size) | |
| elif pool_type == 'avgmax': | |
| x = adaptive_avgmax_pool2d(x, output_size) | |
| elif pool_type == 'catavgmax': | |
| x = adaptive_catavgmax_pool2d(x, output_size) | |
| elif pool_type == 'max': | |
| x = F.adaptive_max_pool2d(x, output_size) | |
| else: | |
| assert False, 'Invalid pool type: %s' % pool_type | |
| return x | |
| class FastAdaptiveAvgPool2d(nn.Module): | |
| def __init__(self, flatten=False): | |
| super(FastAdaptiveAvgPool2d, self).__init__() | |
| self.flatten = flatten | |
| def forward(self, x): | |
| return x.mean((2, 3), keepdim=not self.flatten) | |
| class AdaptiveAvgMaxPool2d(nn.Module): | |
| def __init__(self, output_size=1): | |
| super(AdaptiveAvgMaxPool2d, self).__init__() | |
| self.output_size = output_size | |
| def forward(self, x): | |
| return adaptive_avgmax_pool2d(x, self.output_size) | |
| class AdaptiveCatAvgMaxPool2d(nn.Module): | |
| def __init__(self, output_size=1): | |
| super(AdaptiveCatAvgMaxPool2d, self).__init__() | |
| self.output_size = output_size | |
| def forward(self, x): | |
| return adaptive_catavgmax_pool2d(x, self.output_size) | |
| class SelectAdaptivePool2d(nn.Module): | |
| """Selectable global pooling layer with dynamic input kernel size | |
| """ | |
| def __init__(self, output_size=1, pool_type='fast', flatten=False): | |
| super(SelectAdaptivePool2d, self).__init__() | |
| self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing | |
| self.flatten = nn.Flatten(1) if flatten else nn.Identity() | |
| if pool_type == '': | |
| self.pool = nn.Identity() # pass through | |
| elif pool_type == 'fast': | |
| assert output_size == 1 | |
| self.pool = FastAdaptiveAvgPool2d(flatten) | |
| self.flatten = nn.Identity() | |
| elif pool_type == 'avg': | |
| self.pool = nn.AdaptiveAvgPool2d(output_size) | |
| elif pool_type == 'avgmax': | |
| self.pool = AdaptiveAvgMaxPool2d(output_size) | |
| elif pool_type == 'catavgmax': | |
| self.pool = AdaptiveCatAvgMaxPool2d(output_size) | |
| elif pool_type == 'max': | |
| self.pool = nn.AdaptiveMaxPool2d(output_size) | |
| else: | |
| assert False, 'Invalid pool type: %s' % pool_type | |
| def is_identity(self): | |
| return not self.pool_type | |
| def forward(self, x): | |
| x = self.pool(x) | |
| x = self.flatten(x) | |
| return x | |
| def feat_mult(self): | |
| return adaptive_pool_feat_mult(self.pool_type) | |
| def __repr__(self): | |
| return self.__class__.__name__ + ' (' \ | |
| + 'pool_type=' + self.pool_type \ | |
| + ', flatten=' + str(self.flatten) + ')' | |