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pytorch-image-models/timm/models/layers/classifier.py

57 lines
2.3 KiB

""" Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .linear import Linear
def _create_pool(num_features, num_classes, pool_type='avg', use_conv=False):
flatten_in_pool = not use_conv # flatten when we use a Linear layer after pooling
if not pool_type:
assert num_classes == 0 or use_conv,\
'Pooling can only be disabled if classifier is also removed or conv classifier is used'
flatten_in_pool = False # disable flattening if pooling is pass-through (no pooling)
global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=flatten_in_pool)
num_pooled_features = num_features * global_pool.feat_mult()
return global_pool, num_pooled_features
def _create_fc(num_features, num_classes, use_conv=False):
if num_classes <= 0:
fc = nn.Identity() # pass-through (no classifier)
elif use_conv:
fc = nn.Conv2d(num_features, num_classes, 1, bias=True)
else:
# NOTE: using my Linear wrapper that fixes AMP + torchscript casting issue
fc = Linear(num_features, num_classes, bias=True)
return fc
def create_classifier(num_features, num_classes, pool_type='avg', use_conv=False):
global_pool, num_pooled_features = _create_pool(num_features, num_classes, pool_type, use_conv=use_conv)
fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv)
return global_pool, fc
class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0., use_conv=False):
super(ClassifierHead, self).__init__()
self.drop_rate = drop_rate
self.global_pool, num_pooled_features = _create_pool(in_chs, num_classes, pool_type, use_conv=use_conv)
self.fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv)
self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
def forward(self, x):
x = self.global_pool(x)
if self.drop_rate:
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
x = self.fc(x)
x = self.flatten(x)
return x