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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.init as init
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes,
expand1x1_planes, expand3x3_planes):
super(Fire, self).__init__()
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_activation = nn.ReLU(inplace=True)
self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
kernel_size=1)
self.expand1x1_activation = nn.ReLU(inplace=True)
self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
kernel_size=3, padding=1)
self.expand3x3_activation = nn.ReLU(inplace=True)
def forward(self, x):
x = self.squeeze_activation(self.squeeze(x))
return torch.cat([
self.expand1x1_activation(self.expand1x1(x)),
self.expand3x3_activation(self.expand3x3(x))], 1)
class Attention(nn.Module):
def __init__(self, inplanes, outplanes):
super(Attention, self).__init__()
self.weight = 1.0
self.inplanes = inplanes
self.attention = nn.Conv2d(inplanes, 1, kernel_size=1)
self.activation = nn.Sigmoid()
self.out = nn.Conv2d(2*inplanes, outplanes, kernel_size=1)
self.out_activation = nn.ReLU(inplace=True)
def forward(self, x):
mask = self.activation(self.weight*self.attention(x))
x = torch.cat([x, mask.expand(-1, self.inplanes, -1, -1)*x], 1)
return self.out_activation(self.out(x))
class SqueezeNet(nn.Module):
def __init__(self, version='1_1', num_classes=5):
super(SqueezeNet, self).__init__()
self.num_classes = num_classes
self.version = version
final_conv = None
if version == '1_0':
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=7, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(96, 16, 64, 64),
Fire(128, 16, 64, 64),
Fire(128, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 32, 128, 128),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(512, 64, 256, 256))
# Final convolution is initialized differently from the rest
final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
self.masks = nn.Sequential(
nn.Dropout(p=0.5),
final_conv)
self.attention = nn.Sigmoid()
self.head = nn.Sequential(
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
nn.LogSoftmax(dim=1))
elif version == '1_1':
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(64, 16, 64, 64),
Fire(128, 16, 64, 64),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(128, 32, 128, 128),
Fire(256, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
Fire(512, 64, 256, 256))
# Final convolution is initialized differently from the rest
final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
self.masks = nn.Sequential(
nn.Dropout(p=0.5),
final_conv)
self.attention = nn.Sigmoid()
self.head = nn.Sequential(
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
nn.LogSoftmax(dim=1))
elif version == 'FC':
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(64, 16, 64, 64),
Fire(128, 16, 64, 64),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(128, 32, 128, 128),
Fire(256, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
Fire(512, 64, 256, 256))
# Final convolution is initialized differently from the rest
final_fc = nn.Linear(512*13*13, self.num_classes)
self.head = nn.Sequential(
nn.Dropout(p=0.5),
final_fc,
nn.LogSoftmax(dim=1))
else:
raise ValueError("Unsupported SqueezeNet version {version}:"
"1_0/1_1/FC expected".format(version=version))
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m is final_conv:
init.normal_(m.weight, mean=0.0, std=0.01)
else:
init.kaiming_uniform_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, x):
if self.version == 'FC':
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.head(x)
return x, None
else:
x = self.features(x)
x = self.masks(x)
masks = self.attention(x)
x = self.head(x)
return x, masks
def squeezenet(version, snapshot=None, **kwargs):
model = SqueezeNet(version, **kwargs)
if snapshot is not None:
model.load_state_dict(torch.load(snapshot), strict=False)
return model