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scnet.py
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scnet.py
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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Jiang-Jiang Liu
## Email: j04.liu@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""SCNet variants"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['SCNet', 'scnet50', 'scnet101', 'scnet50_v1d', 'scnet101_v1d']
model_urls = {
'scnet50': 'https://backseason.oss-cn-beijing.aliyuncs.com/scnet/scnet50-dc6a7e87.pth',
'scnet50_v1d': 'https://backseason.oss-cn-beijing.aliyuncs.com/scnet/scnet50_v1d-4109d1e1.pth',
'scnet101': 'https://backseason.oss-cn-beijing.aliyuncs.com/scnet/scnet101-44c5b751.pth',
# 'scnet101_v1d': coming soon...
}
class SCConv(nn.Module):
def __init__(self, inplanes, planes, stride, padding, dilation, groups, pooling_r, norm_layer):
super(SCConv, self).__init__()
self.k2 = nn.Sequential(
nn.AvgPool2d(kernel_size=pooling_r, stride=pooling_r),
nn.Conv2d(inplanes, planes, kernel_size=3, stride=1,
padding=padding, dilation=dilation,
groups=groups, bias=False),
norm_layer(planes),
)
self.k3 = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=3, stride=1,
padding=padding, dilation=dilation,
groups=groups, bias=False),
norm_layer(planes),
)
self.k4 = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=padding, dilation=dilation,
groups=groups, bias=False),
norm_layer(planes),
)
def forward(self, x):
identity = x
out = torch.sigmoid(torch.add(identity, F.interpolate(self.k2(x), identity.size()[2:]))) # sigmoid(identity + k2)
out = torch.mul(self.k3(x), out) # k3 * sigmoid(identity + k2)
out = self.k4(out) # k4
return out
class SCBottleneck(nn.Module):
"""SCNet SCBottleneck
"""
expansion = 4
pooling_r = 4 # down-sampling rate of the avg pooling layer in the K3 path of SC-Conv.
def __init__(self, inplanes, planes, stride=1, downsample=None,
cardinality=1, bottleneck_width=32,
avd=False, dilation=1, is_first=False,
norm_layer=None):
super(SCBottleneck, self).__init__()
group_width = int(planes * (bottleneck_width / 64.)) * cardinality
self.conv1_a = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
self.bn1_a = norm_layer(group_width)
self.conv1_b = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
self.bn1_b = norm_layer(group_width)
self.avd = avd and (stride > 1 or is_first)
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
stride = 1
self.k1 = nn.Sequential(
nn.Conv2d(
group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False),
norm_layer(group_width),
)
self.scconv = SCConv(
group_width, group_width, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, pooling_r=self.pooling_r, norm_layer=norm_layer)
self.conv3 = nn.Conv2d(
group_width * 2, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes*4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out_a= self.conv1_a(x)
out_a = self.bn1_a(out_a)
out_b = self.conv1_b(x)
out_b = self.bn1_b(out_b)
out_a = self.relu(out_a)
out_b = self.relu(out_b)
out_a = self.k1(out_a)
out_b = self.scconv(out_b)
out_a = self.relu(out_a)
out_b = self.relu(out_b)
if self.avd:
out_a = self.avd_layer(out_a)
out_b = self.avd_layer(out_b)
out = self.conv3(torch.cat([out_a, out_b], dim=1))
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SCNet(nn.Module):
""" SCNet Variants Definations
Parameters
----------
block : Block
Class for the residual block.
layers : list of int
Numbers of layers in each block.
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained SCNet yielding a stride-8 model.
deep_stem : bool, default False
Replace 7x7 conv in input stem with 3 3x3 conv.
avg_down : bool, default False
Use AvgPool instead of stride conv when
downsampling in the bottleneck.
norm_layer : object
Normalization layer used (default: :class:`torch.nn.BatchNorm2d`).
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
def __init__(self, block, layers, groups=1, bottleneck_width=32,
num_classes=1000, dilated=False, dilation=1,
deep_stem=False, stem_width=64, avg_down=False,
avd=False, norm_layer=nn.BatchNorm2d):
self.cardinality = groups
self.bottleneck_width = bottleneck_width
# ResNet-D params
self.inplanes = stem_width*2 if deep_stem else 64
self.avg_down = avg_down
self.avd = avd
super(SCNet, self).__init__()
conv_layer = nn.Conv2d
if deep_stem:
self.conv1 = nn.Sequential(
conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width*2, kernel_size=3, stride=1, padding=1, bias=False),
)
else:
self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
if dilated or dilation == 4:
self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
dilation=2, norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=4, norm_layer=norm_layer)
elif dilation==2:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilation=1, norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=2, norm_layer=norm_layer)
else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
norm_layer=norm_layer)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, norm_layer):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,
is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
ceil_mode=True, count_include_pad=False))
else:
down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
ceil_mode=True, count_include_pad=False))
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=1, bias=False))
else:
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
layers = []
if dilation == 1 or dilation == 2:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, dilation=1, is_first=is_first,
norm_layer=norm_layer))
elif dilation == 4:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, dilation=2, is_first=is_first,
norm_layer=norm_layer))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, dilation=dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def scnet50(pretrained=False, **kwargs):
"""Constructs a SCNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = SCNet(SCBottleneck, [3, 4, 6, 3],
deep_stem=False, stem_width=32, avg_down=False,
avd=False, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['scnet50']))
return model
def scnet50_v1d(pretrained=False, **kwargs):
"""Constructs a SCNet-50_v1d model described in
`Bag of Tricks <https://arxiv.org/pdf/1812.01187.pdf>`_.
`ResNeSt: Split-Attention Networks <https://arxiv.org/pdf/2004.08955.pdf>`_.
Compared with default SCNet(SCNetv1b), SCNetv1d replaces the 7x7 conv
in the input stem with three 3x3 convs. And in the downsampling block,
a 3x3 avg_pool with stride 2 is added before conv, whose stride is
changed to 1.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = SCNet(SCBottleneck, [3, 4, 6, 3],
deep_stem=True, stem_width=32, avg_down=True,
avd=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['scnet50_v1d']))
return model
def scnet101(pretrained=False, **kwargs):
"""Constructs a SCNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = SCNet(SCBottleneck, [3, 4, 23, 3],
deep_stem=False, stem_width=64, avg_down=False,
avd=False, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['scnet101']))
return model
def scnet101_v1d(pretrained=False, **kwargs):
"""Constructs a SCNet-101_v1d model described in
`Bag of Tricks <https://arxiv.org/pdf/1812.01187.pdf>`_.
`ResNeSt: Split-Attention Networks <https://arxiv.org/pdf/2004.08955.pdf>`_.
Compared with default SCNet(SCNetv1b), SCNetv1d replaces the 7x7 conv
in the input stem with three 3x3 convs. And in the downsampling block,
a 3x3 avg_pool with stride 2 is added before conv, whose stride is
changed to 1.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = SCNet(SCBottleneck, [3, 4, 23, 3],
deep_stem=True, stem_width=64, avg_down=True,
avd=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['scnet101_v1d']))
return model
if __name__ == '__main__':
images = torch.rand(1, 3, 224, 224).cuda(0)
model = scnet101(pretrained=True)
model = model.cuda(0)
print(model(images).size())