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pytorch-image-models/timm/models/senet.py

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"""
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
FIXME I'm deprecating this model and moving them to ResNet as I don't want to maintain duplicate
support for extras like dilation, switchable BN/activations, feature extraction, etc that don't exist here.
"""
import math
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model
__all__ = ['SENet']
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
**kwargs
}
default_cfgs = {
'legacy_senet154': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_senet154-e9eb9fe6.pth'),
'legacy_seresnet18': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
interpolation='bicubic'),
'legacy_seresnet34': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'),
'legacy_seresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
'legacy_seresnet101': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'),
'legacy_seresnet152': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'),
'legacy_seresnext26_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
interpolation='bicubic'),
'legacy_seresnext50_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext50_32x4d-f3651bad.pth'),
'legacy_seresnext101_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext101_32x4d-37725eac.pth'),
}
def _weight_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
x = x.mean((2, 3), keepdim=True)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class Bottleneck(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + shortcut
out = self.relu(out)
return out
class SEBottleneck(Bottleneck):
"""
Bottleneck for SENet154.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes * 2)
self.conv2 = nn.Conv2d(
planes * 2, planes * 4, kernel_size=3, stride=stride,
padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes * 4)
self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBottleneck(Bottleneck):
"""
ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
implementation and uses `stride=stride` in `conv1` and not in `conv2`
(the latter is used in the torchvision implementation of ResNet).
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNeXtBottleneck(Bottleneck):
"""
ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4):
super(SEResNeXtBottleneck, self).__init__()
width = math.floor(planes * (base_width / 64)) * groups
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes, reduction=reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + shortcut
out = self.relu(out)
return out
class SENet(nn.Module):
def __init__(
self, block, layers, groups, reduction, drop_rate=0.2,
in_chans=3, inplanes=64, input_3x3=False, downsample_kernel_size=1,
downsample_padding=0, num_classes=1000, global_pool='avg'):
"""
Parameters
----------
block (nn.Module): Bottleneck class.
- For SENet154: SEBottleneck
- For SE-ResNet models: SEResNetBottleneck
- For SE-ResNeXt models: SEResNeXtBottleneck
layers (list of ints): Number of residual blocks for 4 layers of the
network (layer1...layer4).
groups (int): Number of groups for the 3x3 convolution in each
bottleneck block.
- For SENet154: 64
- For SE-ResNet models: 1
- For SE-ResNeXt models: 32
reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
- For all models: 16
dropout_p (float or None): Drop probability for the Dropout layer.
If `None` the Dropout layer is not used.
- For SENet154: 0.2
- For SE-ResNet models: None
- For SE-ResNeXt models: None
inplanes (int): Number of input channels for layer1.
- For SENet154: 128
- For SE-ResNet models: 64
- For SE-ResNeXt models: 64
input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
a single 7x7 convolution in layer0.
- For SENet154: True
- For SE-ResNet models: False
- For SE-ResNeXt models: False
downsample_kernel_size (int): Kernel size for downsampling convolutions
in layer2, layer3 and layer4.
- For SENet154: 3
- For SE-ResNet models: 1
- For SE-ResNeXt models: 1
downsample_padding (int): Padding for downsampling convolutions in
layer2, layer3 and layer4.
- For SENet154: 1
- For SE-ResNet models: 0
- For SE-ResNeXt models: 0
num_classes (int): Number of outputs in `last_linear` layer.
- For all models: 1000
"""
super(SENet, self).__init__()
self.inplanes = inplanes
self.num_classes = num_classes
self.drop_rate = drop_rate
if input_3x3:
layer0_modules = [
('conv1', nn.Conv2d(in_chans, 64, 3, stride=2, padding=1, bias=False)),
('bn1', nn.BatchNorm2d(64)),
('relu1', nn.ReLU(inplace=True)),
('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)),
('bn2', nn.BatchNorm2d(64)),
('relu2', nn.ReLU(inplace=True)),
('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1, bias=False)),
('bn3', nn.BatchNorm2d(inplanes)),
('relu3', nn.ReLU(inplace=True)),
]
else:
layer0_modules = [
('conv1', nn.Conv2d(
in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
# To preserve compatibility with Caffe weights `ceil_mode=True` is used instead of `padding=1`.
self.pool0 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='layer0')]
self.layer1 = self._make_layer(
block,
planes=64,
blocks=layers[0],
groups=groups,
reduction=reduction,
downsample_kernel_size=1,
downsample_padding=0
)
self.feature_info += [dict(num_chs=64 * block.expansion, reduction=4, module='layer1')]
self.layer2 = self._make_layer(
block,
planes=128,
blocks=layers[1],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=128 * block.expansion, reduction=8, module='layer2')]
self.layer3 = self._make_layer(
block,
planes=256,
blocks=layers[2],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=256 * block.expansion, reduction=16, module='layer3')]
self.layer4 = self._make_layer(
block,
planes=512,
blocks=layers[3],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=512 * block.expansion, reduction=32, module='layer4')]
self.num_features = 512 * block.expansion
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
for m in self.modules():
_weight_init(m)
def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
downsample_kernel_size=1, downsample_padding=0):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes, planes * block.expansion, kernel_size=downsample_kernel_size,
stride=stride, padding=downsample_padding, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups, reduction))
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^layer0', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.layer0(x)
x = self.pool0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return x if pre_logits else self.last_linear(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_senet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(SENet, variant, pretrained, **kwargs)
@register_model
def legacy_seresnet18(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet18', pretrained, **model_args)
@register_model
def legacy_seresnet34(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet34', pretrained, **model_args)
@register_model
def legacy_seresnet50(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet50', pretrained, **model_args)
@register_model
def legacy_seresnet101(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet101', pretrained, **model_args)
@register_model
def legacy_seresnet152(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet152', pretrained, **model_args)
@register_model
def legacy_senet154(pretrained=False, **kwargs):
model_args = dict(
block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16,
downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs)
return _create_senet('legacy_senet154', pretrained, **model_args)
@register_model
def legacy_seresnext26_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext26_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext50_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 6, 3], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext50_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext101_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 23, 3], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext101_32x4d', pretrained, **model_args)
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