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215 lines
9.0 KiB
215 lines
9.0 KiB
"""Pytorch Densenet implementation w/ tweaks
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This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
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fixed kwargs passthrough and addition of dynamic global avg/max pool.
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"""
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .registry import register_model
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from .helpers import load_pretrained
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from .adaptive_avgmax_pool import SelectAdaptivePool2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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import re
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__all__ = ['DenseNet']
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def _cfg(url=''):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'features.conv0', 'classifier': 'classifier',
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}
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default_cfgs = {
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'densenet121': _cfg(url='https://download.pytorch.org/models/densenet121-a639ec97.pth'),
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'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-b2777c0a.pth'),
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'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-c1103571.pth'),
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'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-8d451a50.pth'),
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}
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class _DenseLayer(nn.Sequential):
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def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
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super(_DenseLayer, self).__init__()
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self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
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self.add_module('relu1', nn.ReLU(inplace=True)),
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self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
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growth_rate, kernel_size=1, stride=1, bias=False)),
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self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
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self.add_module('relu2', nn.ReLU(inplace=True)),
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self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
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kernel_size=3, stride=1, padding=1, bias=False)),
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self.drop_rate = drop_rate
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def forward(self, x):
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new_features = super(_DenseLayer, self).forward(x)
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if self.drop_rate > 0:
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new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
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return torch.cat([x, new_features], 1)
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class _DenseBlock(nn.Sequential):
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def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
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self.add_module('denselayer%d' % (i + 1), layer)
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class _Transition(nn.Sequential):
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def __init__(self, num_input_features, num_output_features):
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super(_Transition, self).__init__()
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self.add_module('norm', nn.BatchNorm2d(num_input_features))
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self.add_module('relu', nn.ReLU(inplace=True))
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self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
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kernel_size=1, stride=1, bias=False))
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self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
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class DenseNet(nn.Module):
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r"""Densenet-BC model class, based on
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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growth_rate (int) - how many filters to add each layer (`k` in paper)
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block_config (list of 4 ints) - how many layers in each pooling block
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num_init_features (int) - the number of filters to learn in the first convolution layer
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bn_size (int) - multiplicative factor for number of bottle neck layers
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(i.e. bn_size * k features in the bottleneck layer)
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drop_rate (float) - dropout rate after each dense layer
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num_classes (int) - number of classification classes
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"""
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def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
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num_init_features=64, bn_size=4, drop_rate=0,
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num_classes=1000, in_chans=3, global_pool='avg'):
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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super(DenseNet, self).__init__()
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# First convolution
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self.features = nn.Sequential(OrderedDict([
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('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
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('norm0', nn.BatchNorm2d(num_init_features)),
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('relu0', nn.ReLU(inplace=True)),
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('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
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]))
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# Each denseblock
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num_features = num_init_features
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for i, num_layers in enumerate(block_config):
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block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
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bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
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self.features.add_module('denseblock%d' % (i + 1), block)
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num_features = num_features + num_layers * growth_rate
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if i != len(block_config) - 1:
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trans = _Transition(
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num_input_features=num_features, num_output_features=num_features // 2)
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self.features.add_module('transition%d' % (i + 1), trans)
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num_features = num_features // 2
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# Final batch norm
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self.features.add_module('norm5', nn.BatchNorm2d(num_features))
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# Linear layer
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self.num_features = num_features
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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def get_classifier(self):
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return self.classifier
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.num_classes = num_classes
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.classifier = nn.Linear(
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self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None
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def forward_features(self, x):
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x = self.features(x)
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x = F.relu(x, inplace=True)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.global_pool(x).flatten(1)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.classifier(x)
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return x
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def _filter_pretrained(state_dict):
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pattern = re.compile(
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r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
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for key in list(state_dict.keys()):
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res = pattern.match(key)
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if res:
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new_key = res.group(1) + res.group(2)
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state_dict[new_key] = state_dict[key]
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del state_dict[key]
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return state_dict
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@register_model
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def densenet121(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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r"""Densenet-121 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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"""
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default_cfg = default_cfgs['densenet121']
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
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num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=_filter_pretrained)
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return model
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@register_model
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def densenet169(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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r"""Densenet-169 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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"""
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default_cfg = default_cfgs['densenet169']
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
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num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=_filter_pretrained)
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return model
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@register_model
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def densenet201(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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r"""Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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"""
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default_cfg = default_cfgs['densenet201']
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
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num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=_filter_pretrained)
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return model
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@register_model
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def densenet161(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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r"""Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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"""
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default_cfg = default_cfgs['densenet161']
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model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
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num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=_filter_pretrained)
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return model
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