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import torch
import torch.nn as nn
import torch.nn.functional as F
from .gem import GeM
def adaptive_avgmax_pool1d(x, output_size=1):
x_avg = F.adaptive_avg_pool1d(x, output_size)
x_max = F.adaptive_max_pool1d(x, output_size)
return 0.5 * (x_avg + x_max)
def adaptive_catavgmax_pool1d(x, output_size=1):
x_avg = F.adaptive_avg_pool1d(x, output_size)
x_max = F.adaptive_max_pool1d(x, output_size)
return torch.cat((x_avg, x_max), 1)
def select_adaptive_pool1d(x, pool_type='avg', output_size=1):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avg':
x = F.adaptive_avg_pool1d(x, output_size)
elif pool_type == 'avgmax':
x = adaptive_avgmax_pool1d(x, output_size)
elif pool_type == 'catavgmax':
x = adaptive_catavgmax_pool1d(x, output_size)
elif pool_type == 'max':
x = F.adaptive_max_pool1d(x, output_size)
else:
assert False, 'Invalid pool type: %s' % pool_type
return x
class FastAdaptiveAvgPool1d(nn.Module):
def __init__(self, flatten=False):
super(FastAdaptiveAvgPool1d, self).__init__()
self.flatten = flatten
def forward(self, x):
return x.mean(2, keepdim=not self.flatten)
class AdaptiveAvgMaxPool1d(nn.Module):
def __init__(self, output_size=1):
super(AdaptiveAvgMaxPool1d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_avgmax_pool1d(x, self.output_size)
class AdaptiveCatAvgMaxPool1d(nn.Module):
def __init__(self, output_size=1):
super(AdaptiveCatAvgMaxPool1d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_catavgmax_pool1d(x, self.output_size)
class SelectAdaptivePool1d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(self, output_size=1, pool_type='fast', flatten=False):
super(SelectAdaptivePool1d, 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 = FastAdaptiveAvgPool1d(flatten)
self.flatten = nn.Identity()
elif pool_type == 'avg':
self.pool = nn.AdaptiveAvgPool1d(output_size)
elif pool_type == 'avgmax':
self.pool = AdaptiveAvgMaxPool1d(output_size)
elif pool_type == 'catavgmax':
self.pool = AdaptiveCatAvgMaxPool1d(output_size)
elif pool_type == 'max':
self.pool = nn.AdaptiveMaxPool1d(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 __repr__(self):
return self.__class__.__name__ + ' (' \
+ 'pool_type=' + self.pool_type \
+ ', flatten=' + str(self.flatten) + ')'
def create_pool1d_layer(name, **kwargs):
assert name in ["avg", "max", "fast", "avgmax", "catavgmax", "gem"]
if name != "gem":
pool1d_layer = SelectAdaptivePool1d(pool_type=name, flatten=True)
elif name == "gem":
pool1d_layer = GeM(dim=1, **kwargs)
return pool1d_layer |