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""" 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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from typing import Optional, Tuple, Union |
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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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from .format import get_spatial_dim, get_channel_dim |
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_int_tuple_2_t = Union[int, Tuple[int, int]] |
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def adaptive_pool_feat_mult(pool_type='avg'): |
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if pool_type.endswith('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: _int_tuple_2_t = 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: _int_tuple_2_t = 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: _int_tuple_2_t = 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 FastAdaptiveAvgPool(nn.Module): |
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def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'): |
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super(FastAdaptiveAvgPool, self).__init__() |
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self.flatten = flatten |
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self.dim = get_spatial_dim(input_fmt) |
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def forward(self, x): |
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return x.mean(self.dim, keepdim=not self.flatten) |
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class FastAdaptiveMaxPool(nn.Module): |
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def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'): |
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super(FastAdaptiveMaxPool, self).__init__() |
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self.flatten = flatten |
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self.dim = get_spatial_dim(input_fmt) |
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def forward(self, x): |
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return x.amax(self.dim, keepdim=not self.flatten) |
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class FastAdaptiveAvgMaxPool(nn.Module): |
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def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'): |
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super(FastAdaptiveAvgMaxPool, self).__init__() |
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self.flatten = flatten |
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self.dim = get_spatial_dim(input_fmt) |
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def forward(self, x): |
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x_avg = x.mean(self.dim, keepdim=not self.flatten) |
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x_max = x.amax(self.dim, keepdim=not self.flatten) |
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return 0.5 * x_avg + 0.5 * x_max |
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class FastAdaptiveCatAvgMaxPool(nn.Module): |
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def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'): |
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super(FastAdaptiveCatAvgMaxPool, self).__init__() |
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self.flatten = flatten |
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self.dim_reduce = get_spatial_dim(input_fmt) |
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if flatten: |
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self.dim_cat = 1 |
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else: |
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self.dim_cat = get_channel_dim(input_fmt) |
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def forward(self, x): |
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x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten) |
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x_max = x.amax(self.dim_reduce, keepdim=not self.flatten) |
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return torch.cat((x_avg, x_max), self.dim_cat) |
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class AdaptiveAvgMaxPool2d(nn.Module): |
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def __init__(self, output_size: _int_tuple_2_t = 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: _int_tuple_2_t = 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__( |
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self, |
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output_size: _int_tuple_2_t = 1, |
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pool_type: str = 'fast', |
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flatten: bool = False, |
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input_fmt: str = 'NCHW', |
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): |
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super(SelectAdaptivePool2d, self).__init__() |
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assert input_fmt in ('NCHW', 'NHWC') |
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self.pool_type = pool_type or '' |
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pool_type = pool_type.lower() |
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if not pool_type: |
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self.pool = nn.Identity() |
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self.flatten = nn.Flatten(1) if flatten else nn.Identity() |
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elif pool_type.startswith('fast') or input_fmt != 'NCHW': |
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assert output_size == 1, 'Fast pooling and non NCHW input formats require output_size == 1.' |
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if pool_type.endswith('catavgmax'): |
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self.pool = FastAdaptiveCatAvgMaxPool(flatten, input_fmt=input_fmt) |
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elif pool_type.endswith('avgmax'): |
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self.pool = FastAdaptiveAvgMaxPool(flatten, input_fmt=input_fmt) |
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elif pool_type.endswith('max'): |
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self.pool = FastAdaptiveMaxPool(flatten, input_fmt=input_fmt) |
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elif pool_type == 'fast' or pool_type.endswith('avg'): |
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self.pool = FastAdaptiveAvgPool(flatten, input_fmt=input_fmt) |
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else: |
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assert False, 'Invalid pool type: %s' % pool_type |
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self.flatten = nn.Identity() |
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else: |
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assert input_fmt == 'NCHW' |
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if 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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elif pool_type == 'avg': |
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self.pool = nn.AdaptiveAvgPool2d(output_size) |
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else: |
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assert False, 'Invalid pool type: %s' % pool_type |
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self.flatten = nn.Flatten(1) if flatten else nn.Identity() |
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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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