You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
263 lines
10 KiB
263 lines
10 KiB
"""VGG
|
|
|
|
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
|
|
timm functionality.
|
|
|
|
Copyright 2021 Ross Wightman
|
|
"""
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from typing import Union, List, Dict, Any, cast
|
|
|
|
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
|
from .helpers import build_model_with_cfg
|
|
from .fx_features import register_notrace_module
|
|
from .layers import ClassifierHead
|
|
from .registry import register_model
|
|
|
|
__all__ = [
|
|
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
|
|
'vgg19_bn', 'vgg19',
|
|
]
|
|
|
|
|
|
def _cfg(url='', **kwargs):
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
|
|
'crop_pct': 0.875, 'interpolation': 'bilinear',
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'first_conv': 'features.0', 'classifier': 'head.fc',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = {
|
|
'vgg11': _cfg(url='https://download.pytorch.org/models/vgg11-bbd30ac9.pth'),
|
|
'vgg13': _cfg(url='https://download.pytorch.org/models/vgg13-c768596a.pth'),
|
|
'vgg16': _cfg(url='https://download.pytorch.org/models/vgg16-397923af.pth'),
|
|
'vgg19': _cfg(url='https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'),
|
|
'vgg11_bn': _cfg(url='https://download.pytorch.org/models/vgg11_bn-6002323d.pth'),
|
|
'vgg13_bn': _cfg(url='https://download.pytorch.org/models/vgg13_bn-abd245e5.pth'),
|
|
'vgg16_bn': _cfg(url='https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'),
|
|
'vgg19_bn': _cfg(url='https://download.pytorch.org/models/vgg19_bn-c79401a0.pth'),
|
|
}
|
|
|
|
|
|
cfgs: Dict[str, List[Union[str, int]]] = {
|
|
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
|
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
|
'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
|
|
'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
|
|
}
|
|
|
|
|
|
@register_notrace_module # reason: FX can't symbolically trace control flow in forward method
|
|
class ConvMlp(nn.Module):
|
|
|
|
def __init__(self, in_features=512, out_features=4096, kernel_size=7, mlp_ratio=1.0,
|
|
drop_rate: float = 0.2, act_layer: nn.Module = None, conv_layer: nn.Module = None):
|
|
super(ConvMlp, self).__init__()
|
|
self.input_kernel_size = kernel_size
|
|
mid_features = int(out_features * mlp_ratio)
|
|
self.fc1 = conv_layer(in_features, mid_features, kernel_size, bias=True)
|
|
self.act1 = act_layer(True)
|
|
self.drop = nn.Dropout(drop_rate)
|
|
self.fc2 = conv_layer(mid_features, out_features, 1, bias=True)
|
|
self.act2 = act_layer(True)
|
|
|
|
def forward(self, x):
|
|
if x.shape[-2] < self.input_kernel_size or x.shape[-1] < self.input_kernel_size:
|
|
# keep the input size >= 7x7
|
|
output_size = (max(self.input_kernel_size, x.shape[-2]), max(self.input_kernel_size, x.shape[-1]))
|
|
x = F.adaptive_avg_pool2d(x, output_size)
|
|
x = self.fc1(x)
|
|
x = self.act1(x)
|
|
x = self.drop(x)
|
|
x = self.fc2(x)
|
|
x = self.act2(x)
|
|
return x
|
|
|
|
|
|
class VGG(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
cfg: List[Any],
|
|
num_classes: int = 1000,
|
|
in_chans: int = 3,
|
|
output_stride: int = 32,
|
|
mlp_ratio: float = 1.0,
|
|
act_layer: nn.Module = nn.ReLU,
|
|
conv_layer: nn.Module = nn.Conv2d,
|
|
norm_layer: nn.Module = None,
|
|
global_pool: str = 'avg',
|
|
drop_rate: float = 0.,
|
|
) -> None:
|
|
super(VGG, self).__init__()
|
|
assert output_stride == 32
|
|
self.num_classes = num_classes
|
|
self.num_features = 4096
|
|
self.drop_rate = drop_rate
|
|
self.feature_info = []
|
|
prev_chs = in_chans
|
|
net_stride = 1
|
|
pool_layer = nn.MaxPool2d
|
|
layers: List[nn.Module] = []
|
|
for v in cfg:
|
|
last_idx = len(layers) - 1
|
|
if v == 'M':
|
|
self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{last_idx}'))
|
|
layers += [pool_layer(kernel_size=2, stride=2)]
|
|
net_stride *= 2
|
|
else:
|
|
v = cast(int, v)
|
|
conv2d = conv_layer(prev_chs, v, kernel_size=3, padding=1)
|
|
if norm_layer is not None:
|
|
layers += [conv2d, norm_layer(v), act_layer(inplace=True)]
|
|
else:
|
|
layers += [conv2d, act_layer(inplace=True)]
|
|
prev_chs = v
|
|
self.features = nn.Sequential(*layers)
|
|
self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{len(layers) - 1}'))
|
|
self.pre_logits = ConvMlp(
|
|
prev_chs, self.num_features, 7, mlp_ratio=mlp_ratio,
|
|
drop_rate=drop_rate, act_layer=act_layer, conv_layer=conv_layer)
|
|
self.head = ClassifierHead(
|
|
self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
|
|
|
|
self._initialize_weights()
|
|
|
|
def get_classifier(self):
|
|
return self.head.fc
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
self.num_classes = num_classes
|
|
self.head = ClassifierHead(
|
|
self.num_features, self.num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.features(x)
|
|
x = self.pre_logits(x)
|
|
return x
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x
|
|
|
|
def _initialize_weights(self) -> None:
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
nn.init.constant_(m.weight, 1)
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.Linear):
|
|
nn.init.normal_(m.weight, 0, 0.01)
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
|
|
def _filter_fn(state_dict):
|
|
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
|
out_dict = {}
|
|
for k, v in state_dict.items():
|
|
k_r = k
|
|
k_r = k_r.replace('classifier.0', 'pre_logits.fc1')
|
|
k_r = k_r.replace('classifier.3', 'pre_logits.fc2')
|
|
k_r = k_r.replace('classifier.6', 'head.fc')
|
|
if 'classifier.0.weight' in k:
|
|
v = v.reshape(-1, 512, 7, 7)
|
|
if 'classifier.3.weight' in k:
|
|
v = v.reshape(-1, 4096, 1, 1)
|
|
out_dict[k_r] = v
|
|
return out_dict
|
|
|
|
|
|
def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
|
|
cfg = variant.split('_')[0]
|
|
# NOTE: VGG is one of few models with stride==1 features w/ 6 out_indices [0..5]
|
|
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5))
|
|
model = build_model_with_cfg(
|
|
VGG, variant, pretrained,
|
|
default_cfg=default_cfgs[variant],
|
|
model_cfg=cfgs[cfg],
|
|
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
|
|
pretrained_filter_fn=_filter_fn,
|
|
**kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vgg11(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 11-layer model (configuration "A") from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(**kwargs)
|
|
return _create_vgg('vgg11', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg11_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 11-layer model (configuration "A") with batch normalization
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
|
|
return _create_vgg('vgg11_bn', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg13(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 13-layer model (configuration "B")
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(**kwargs)
|
|
return _create_vgg('vgg13', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg13_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 13-layer model (configuration "B") with batch normalization
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
|
|
return _create_vgg('vgg13_bn', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg16(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 16-layer model (configuration "D")
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(**kwargs)
|
|
return _create_vgg('vgg16', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg16_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 16-layer model (configuration "D") with batch normalization
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
|
|
return _create_vgg('vgg16_bn', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg19(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 19-layer model (configuration "E")
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(**kwargs)
|
|
return _create_vgg('vgg19', pretrained=pretrained, **model_args)
|
|
|
|
|
|
@register_model
|
|
def vgg19_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
|
|
r"""VGG 19-layer model (configuration 'E') with batch normalization
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
|
"""
|
|
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
|
|
return _create_vgg('vgg19_bn', pretrained=pretrained, **model_args) |