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from __future__ import division, absolute_import
import math
from collections import OrderedDict
import torch.nn as nn
from torch.utils import model_zoo
__all__ = [
'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
]
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
pretrained_settings = {
'senet154': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
'se_resnet50': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
'se_resnet101': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
'se_resnet152': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
'se_resnext50_32x4d': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
'se_resnext101_32x4d': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'num_classes': 1000
}
},
}
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(
channels, channels // reduction, kernel_size=1, padding=0
)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(
channels // reduction, channels, kernel_size=1, padding=0
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
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):
residual = 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:
residual = self.downsample(x)
out = self.se_module(out) + residual
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 = int(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 SENet(nn.Module):
"""Squeeze-and-excitation network.
Reference:
Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.
Public keys:
- ``senet154``: SENet154.
- ``se_resnet50``: ResNet50 + SE.
- ``se_resnet101``: ResNet101 + SE.
- ``se_resnet152``: ResNet152 + SE.
- ``se_resnext50_32x4d``: ResNeXt50 (groups=32, width=4) + SE.
- ``se_resnext101_32x4d``: ResNeXt101 (groups=32, width=4) + SE.
- ``se_resnet50_fc512``: (ResNet50 + SE) + FC.
"""
def __init__(
self,
num_classes,
loss,
block,
layers,
groups,
reduction,
dropout_p=0.2,
inplanes=128,
input_3x3=True,
downsample_kernel_size=3,
downsample_padding=1,
last_stride=2,
fc_dims=None,
**kwargs
):
"""
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 `classifier` layer.
"""
super(SENet, self).__init__()
self.inplanes = inplanes
self.loss = loss
if input_3x3:
layer0_modules = [
(
'conv1',
nn.Conv2d(3, 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(
3,
inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False
)
),
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
# To preserve compatibility with Caffe weights `ceil_mode=True`
# is used instead of `padding=1`.
layer0_modules.append(
('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
)
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
self.layer1 = self._make_layer(
block,
planes=64,
blocks=layers[0],
groups=groups,
reduction=reduction,
downsample_kernel_size=1,
downsample_padding=0
)
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.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.layer4 = self._make_layer(
block,
planes=512,
blocks=layers[3],
stride=last_stride,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = self._construct_fc_layer(
fc_dims, 512 * block.expansion, dropout_p
)
self.classifier = nn.Linear(self.feature_dim, num_classes)
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 = []
layers.append(
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)
def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
"""
Construct fully connected layer
- fc_dims (list or tuple): dimensions of fc layers, if None,
no fc layers are constructed
- input_dim (int): input dimension
- dropout_p (float): dropout probability, if None, dropout is unused
"""
if fc_dims is None:
self.feature_dim = input_dim
return None
assert isinstance(
fc_dims, (list, tuple)
), 'fc_dims must be either list or tuple, but got {}'.format(
type(fc_dims)
)
layers = []
for dim in fc_dims:
layers.append(nn.Linear(input_dim, dim))
layers.append(nn.BatchNorm1d(dim))
layers.append(nn.ReLU(inplace=True))
if dropout_p is not None:
layers.append(nn.Dropout(p=dropout_p))
input_dim = dim
self.feature_dim = fc_dims[-1]
return nn.Sequential(*layers)
def featuremaps(self, x):
x = self.layer0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def forward(self, x):
f = self.featuremaps(x)
v = self.global_avgpool(f)
v = v.view(v.size(0), -1)
if self.fc is not None:
v = self.fc(v)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError("Unsupported loss: {}".format(self.loss))
def init_pretrained_weights(model, model_url):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
def senet154(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEBottleneck,
layers=[3, 8, 36, 3],
groups=64,
reduction=16,
dropout_p=0.2,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['senet154']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model
def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNetBottleneck,
layers=[3, 4, 6, 3],
groups=1,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnet50']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model
def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNetBottleneck,
layers=[3, 4, 6, 3],
groups=1,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=1,
fc_dims=[512],
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnet50']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model
def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNetBottleneck,
layers=[3, 4, 23, 3],
groups=1,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnet101']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model
def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNetBottleneck,
layers=[3, 8, 36, 3],
groups=1,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnet152']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model
def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNeXtBottleneck,
layers=[3, 4, 6, 3],
groups=32,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnext50_32x4d']['imagenet']['url'
]
init_pretrained_weights(model, model_url)
return model
def se_resnext101_32x4d(
num_classes, loss='softmax', pretrained=True, **kwargs
):
model = SENet(
num_classes=num_classes,
loss=loss,
block=SEResNeXtBottleneck,
layers=[3, 4, 23, 3],
groups=32,
reduction=16,
dropout_p=None,
inplanes=64,
input_3x3=False,
downsample_kernel_size=1,
downsample_padding=0,
last_stride=2,
fc_dims=None,
**kwargs
)
if pretrained:
model_url = pretrained_settings['se_resnext101_32x4d']['imagenet'][
'url']
init_pretrained_weights(model, model_url)
return model
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