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https://github.com/huggingface/pytorch-image-models.git
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372ad5fa0d
* All models updated with revised foward_features / forward_head interface * Vision transformer and MLP based models consistently output sequence from forward_features (pooling or token selection considered part of 'head') * WIP param grouping interface to allow consistent grouping of parameters for layer-wise decay across all model types * Add gradient checkpointing support to a significant % of models, especially popular architectures * Formatting and interface consistency improvements across models * layer-wise LR decay impl part of optimizer factory w/ scale support in scheduler * Poolformer and Volo architectures added
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
""" 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 torch import 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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def _create_pool(num_features, num_classes, pool_type='avg', use_conv=False):
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flatten_in_pool = not use_conv # flatten when we use a Linear layer after pooling
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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 # disable flattening if pooling is pass-through (no pooling)
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global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=flatten_in_pool)
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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() # pass-through (no classifier)
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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(num_features, num_classes, pool_type='avg', use_conv=False):
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global_pool, num_pooled_features = _create_pool(num_features, num_classes, pool_type, use_conv=use_conv)
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fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv)
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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__(self, in_chs, num_classes, pool_type='avg', drop_rate=0., use_conv=False):
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super(ClassifierHead, self).__init__()
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self.drop_rate = drop_rate
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self.global_pool, num_pooled_features = _create_pool(in_chs, num_classes, pool_type, use_conv=use_conv)
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self.fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv)
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self.flatten = nn.Flatten(1) if use_conv and pool_type 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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if self.drop_rate:
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x = F.dropout(x, p=float(self.drop_rate), training=self.training)
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if pre_logits:
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return x.flatten(1)
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else:
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x = self.fc(x)
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return self.flatten(x)
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