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