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""" Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .create_act import get_act_layer
from .create_norm import get_norm_layer
def _create_pool(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: Optional[str] = None,
):
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,
input_fmt=input_fmt,
)
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:
fc = nn.Linear(num_features, num_classes, bias=True)
return fc
def create_classifier(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: str = 'NCHW',
drop_rate: Optional[float] = None,
):
global_pool, num_pooled_features = _create_pool(
num_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=use_conv,
)
if drop_rate is not None:
dropout = nn.Dropout(drop_rate)
return global_pool, dropout, fc
return global_pool, fc
class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(
self,
in_features: int,
num_classes: int,
pool_type: str = 'avg',
drop_rate: float = 0.,
use_conv: bool = False,
input_fmt: str = 'NCHW',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
"""
super(ClassifierHead, self).__init__()
self.in_features = in_features
self.use_conv = use_conv
self.input_fmt = input_fmt
global_pool, fc = create_classifier(
in_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
self.global_pool = global_pool
self.drop = nn.Dropout(drop_rate)
self.fc = fc
self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
def reset(self, num_classes, pool_type=None):
if pool_type is not None and pool_type != self.global_pool.pool_type:
self.global_pool, self.fc = create_classifier(
self.in_features,
num_classes,
pool_type=pool_type,
use_conv=self.use_conv,
input_fmt=self.input_fmt,
)
self.flatten = nn.Flatten(1) if self.use_conv and pool_type else nn.Identity()
else:
num_pooled_features = self.in_features * self.global_pool.feat_mult()
self.fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=self.use_conv,
)
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
class NormMlpClassifierHead(nn.Module):
def __init__(
self,
in_features: int,
num_classes: int,
hidden_size: Optional[int] = None,
pool_type: str = 'avg',
drop_rate: float = 0.,
norm_layer: Union[str, Callable] = 'layernorm2d',
act_layer: Union[str, Callable] = 'tanh',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
hidden_size: The hidden size of the MLP (pre-logits FC layer) if not None.
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
norm_layer: Normalization layer type.
act_layer: MLP activation layer type (only used if hidden_size is not None).
"""
super().__init__()
self.in_features = in_features
self.hidden_size = hidden_size
self.num_features = in_features
self.use_conv = not pool_type
norm_layer = get_norm_layer(norm_layer)
act_layer = get_act_layer(act_layer)
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.norm = norm_layer(in_features)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
if hidden_size:
self.pre_logits = nn.Sequential(OrderedDict([
('fc', linear_layer(in_features, hidden_size)),
('act', act_layer()),
]))
self.num_features = hidden_size
else:
self.pre_logits = nn.Identity()
self.drop = nn.Dropout(drop_rate)
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def reset(self, num_classes, pool_type=None):
if pool_type is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
self.use_conv = self.global_pool.is_identity()
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
if self.hidden_size:
if ((isinstance(self.pre_logits.fc, nn.Conv2d) and not self.use_conv) or
(isinstance(self.pre_logits.fc, nn.Linear) and self.use_conv)):
with torch.no_grad():
new_fc = linear_layer(self.in_features, self.hidden_size)
new_fc.weight.copy_(self.pre_logits.fc.weight.reshape(new_fc.weight.shape))
new_fc.bias.copy_(self.pre_logits.fc.bias)
self.pre_logits.fc = new_fc
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.norm(x)
x = self.flatten(x)
x = self.pre_logits(x)
x = self.drop(x)
if pre_logits:
return x
x = self.fc(x)
return x
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