"""Pytorch Densenet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
fixed kwargs passthrough and addition of dynamic global avg/max pool.
"""
import re
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from torch.jit.annotations import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import BatchNormAct2d, create_norm_act, BlurPool2d, create_classifier
from .registry import register_model
__all__ = ['DenseNet']
def _cfg(url=''):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'features.conv0', 'classifier': 'classifier',
}
default_cfgs = {
'densenet121': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth'),
'densenet121d': _cfg(url=''),
'densenetblur121d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth'),
'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-b2777c0a.pth'),
'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-c1103571.pth'),
'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-8d451a50.pth'),
'densenet264': _cfg(url=''),
'densenet264d_iabn': _cfg(url=''),
'tv_densenet121': _cfg(url='https://download.pytorch.org/models/densenet121-a639ec97.pth'),
}
class DenseLayer(nn.Module):
def __init__(self, num_input_features, growth_rate, bn_size, norm_layer=BatchNormAct2d,
drop_rate=0., memory_efficient=False):
super(DenseLayer, self).__init__()
self.add_module('norm1', norm_layer(num_input_features)),
self.add_module('conv1', nn.Conv2d(
num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module('norm2', norm_layer(bn_size * growth_rate)),
self.add_module('conv2', nn.Conv2d(
bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
self.drop_rate = float(drop_rate)
self.memory_efficient = memory_efficient
def bottleneck_fn(self, xs):
# type: (List[torch.Tensor]) -> torch.Tensor
concated_features = torch.cat(xs, 1)
bottleneck_output = self.conv1(self.norm1(concated_features)) # noqa: T484
return bottleneck_output
# todo: rewrite when torchscript supports any
def any_requires_grad(self, x):
# type: (List[torch.Tensor]) -> bool
for tensor in x:
if tensor.requires_grad:
return True
return False
@torch.jit.unused # noqa: T484
def call_checkpoint_bottleneck(self, x):
# type: (List[torch.Tensor]) -> torch.Tensor
def closure(*xs):
return self.bottleneck_fn(xs)
return cp.checkpoint(closure, *x)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (List[torch.Tensor]) -> (torch.Tensor)
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> (torch.Tensor)
pass
# torchscript does not yet support *args, so we overload method
# allowing it to take either a List[Tensor] or single Tensor
def forward(self, x): # noqa: F811
if isinstance(x, torch.Tensor):
prev_features = [x]
else:
prev_features = x
if self.memory_efficient and self.any_requires_grad(prev_features):
if torch.jit.is_scripting():
raise Exception("Memory Efficient not supported in JIT")
bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
else:
bottleneck_output = self.bottleneck_fn(prev_features)
new_features = self.conv2(self.norm2(bottleneck_output))
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return new_features
class DenseBlock(nn.ModuleDict):
_version = 2
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, norm_layer=nn.ReLU,
drop_rate=0., memory_efficient=False):
super(DenseBlock, self).__init__()
for i in range(num_layers):
layer = DenseLayer(
num_input_features + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
norm_layer=norm_layer,
drop_rate=drop_rate,
memory_efficient=memory_efficient,
)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, init_features):
features = [init_features]
for name, layer in self.items():
new_features = layer(features)
features.append(new_features)
return torch.cat(features, 1)
class DenseTransition(nn.Sequential):
def __init__(self, num_input_features, num_output_features, norm_layer=nn.BatchNorm2d, aa_layer=None):
super(DenseTransition, self).__init__()
self.add_module('norm', norm_layer(num_input_features))
self.add_module('conv', nn.Conv2d(
num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
if aa_layer is not None:
self.add_module('pool', aa_layer(num_output_features, stride=2))
else:
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
class DenseNet(nn.Module):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), bn_size=4, stem_type='',
num_classes=1000, in_chans=3, global_pool='avg',
norm_layer=BatchNormAct2d, aa_layer=None, drop_rate=0, memory_efficient=False,
aa_stem_only=True):
self.num_classes = num_classes
self.drop_rate = drop_rate
super(DenseNet, self).__init__()
# Stem
deep_stem = 'deep' in stem_type # 3x3 deep stem
num_init_features = growth_rate * 2
if aa_layer is None:
stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
else:
stem_pool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=num_init_features, stride=2)])
if deep_stem:
stem_chs_1 = stem_chs_2 = growth_rate
if 'tiered' in stem_type:
stem_chs_1 = 3 * (growth_rate // 4)
stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (growth_rate // 4)
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False)),
('norm0', norm_layer(stem_chs_1)),
('conv1', nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False)),
('norm1', norm_layer(stem_chs_2)),
('conv2', nn.Conv2d(stem_chs_2, num_init_features, 3, stride=1, padding=1, bias=False)),
('norm2', norm_layer(num_init_features)),
('pool0', stem_pool),
]))
else:
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', norm_layer(num_init_features)),
('pool0', stem_pool),
]))
self.feature_info = [
dict(num_chs=num_init_features, reduction=2, module=f'features.norm{2 if deep_stem else 0}')]
current_stride = 4
# DenseBlocks
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = DenseBlock(
num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
norm_layer=norm_layer,
drop_rate=drop_rate,
memory_efficient=memory_efficient
)
module_name = f'denseblock{(i + 1)}'
self.features.add_module(module_name, block)
num_features = num_features + num_layers * growth_rate
transition_aa_layer = None if aa_stem_only else aa_layer
if i != len(block_config) - 1:
self.feature_info += [
dict(num_chs=num_features, reduction=current_stride, module='features.' + module_name)]
current_stride *= 2
trans = DenseTransition(
num_input_features=num_features, num_output_features=num_features // 2,
norm_layer=norm_layer, aa_layer=transition_aa_layer)
self.features.add_module(f'transition{i + 1}', trans)
num_features = num_features // 2
# Final batch norm
self.features.add_module('norm5', norm_layer(num_features))
self.feature_info += [dict(num_chs=num_features, reduction=current_stride, module='features.norm5')]
self.num_features = num_features
# Linear layer
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
return self.features(x)
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x)
# both classifier and block drop?
# if self.drop_rate > 0.:
# x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classifier(x)
return x
def _filter_torchvision_pretrained(state_dict):
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
return state_dict
def _create_densenet(variant, growth_rate, block_config, pretrained, **kwargs):
kwargs['growth_rate'] = growth_rate
kwargs['block_config'] = block_config
return build_model_with_cfg(
DenseNet, variant, pretrained,
default_cfg=default_cfgs[variant],
feature_cfg=dict(flatten_sequential=True), pretrained_filter_fn=_filter_torchvision_pretrained,
**kwargs)
@register_model
def densenet121(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
return model
@register_model
def densenetblur121d(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep',
aa_layer=BlurPool2d, **kwargs)
return model
@register_model
def densenet121d(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
pretrained=pretrained, **kwargs)
return model
@register_model
def densenet169(pretrained=False, **kwargs):
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet169', growth_rate=32, block_config=(6, 12, 32, 32), pretrained=pretrained, **kwargs)
return model
@register_model
def densenet201(pretrained=False, **kwargs):
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet201', growth_rate=32, block_config=(6, 12, 48, 32), pretrained=pretrained, **kwargs)
return model
@register_model
def densenet161(pretrained=False, **kwargs):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet161', growth_rate=48, block_config=(6, 12, 36, 24), pretrained=pretrained, **kwargs)
return model
@register_model
def densenet264(pretrained=False, **kwargs):
r"""Densenet-264 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'densenet264', growth_rate=48, block_config=(6, 12, 64, 48), pretrained=pretrained, **kwargs)
return model
@register_model
def densenet264d_iabn(pretrained=False, **kwargs):
r"""Densenet-264 model with deep stem and Inplace-ABN
"""
def norm_act_fn(num_features, **kwargs):
return create_norm_act('iabn', num_features, **kwargs)
model = _create_densenet(
'densenet264d_iabn', growth_rate=48, block_config=(6, 12, 64, 48), stem_type='deep',
norm_layer=norm_act_fn, pretrained=pretrained, **kwargs)
return model
@register_model
def tv_densenet121(pretrained=False, **kwargs):
r"""Densenet-121 model with original Torchvision weights, from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _create_densenet(
'tv_densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
return model