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cost_model.py
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cost_model.py
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import torch
import torch.nn as nn
import math
#from mypath import Path
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
class attentionblock(nn.Module):
def __init__(self,in_channel):
super(attentionblock, self).__init__()
#self.num_classes=num_classes
self.pool = nn.AdaptiveMaxPool3d(1)
self.in_channel = in_channel
self.conv = nn.Conv3d(self.in_channel, self.in_channel, kernel_size=(1, 1, 1))
self.linear = nn.Linear(self.in_channel * 3, self.in_channel * 3)
#print(self.linear)
def forward(self, input1, input2, input3):
#print(input1.shape,"inpu1")
#print(input2.shape,"input2")
input1 = self.pool(input1)
input2 = self.pool(input2)
input3 = self.pool(input3)
input1 = self.conv(input1)
input2 = self.conv(input2)
input3 = self.conv(input3)
input1 = input1.squeeze()
input2 = input2.squeeze()
input3 = input3.squeeze()
#print(input1.shape,"inpu1")
#print(input2.shape,"inpu2")
#print(input3.shape,"inpu3")
a = torch.cat((input1, input2, input3), 1)
#print(a.shape)
a = self.linear(a)
#print(a.shape,"attena")
a = F.softmax(a, dim = 0)
return a
class costblock(nn.Module):
def __init__(self, in_channel, channel, stride = 1):
super(costblock, self).__init__()
#self.size=size
self.stride = stride
#self.num_classes=num_classes
self.in_channel = in_channel
self.channel = channel
self.bn1 = nn.BatchNorm3d(self.channel)
self.bn2 = nn.BatchNorm3d(self.channel * 4)
#self.stride=stride
self.conv1 = nn.Conv3d(self.in_channel, self.channel, kernel_size = (1, 1, 1))
self.conv = nn.Conv2d(self.channel, self.channel, kernel_size = (3, 3),padding = (1, 1),stride = (self.stride, self.stride))
self.conv2 = nn.Conv3d(self.channel, self.channel * 4, kernel_size = (1, 1, 1))
self.attenblock = attentionblock(self.channel)
self.relu = nn.ReLU(inplace = True)
self.batchnorm = nn.BatchNorm2d(self.channel)
self.downsample = nn.Sequential()
if self.stride != 1 or self.in_channel != self.channel * 4:
self.downsample = nn.Sequential(
nn.Conv3d(self.in_channel, self.channel * 4, kernel_size=1, stride=(1, self.stride, self.stride), bias = False),
nn.BatchNorm3d(self.channel * 4)
)
def forward(self, input):
shortcut =self.downsample(input)
#print(shortcut.shape)
input = self.conv1(input)
input = self.bn1(input)
input = self.relu(input)
x1 = input.view(input.shape[0], input.shape[1], input.shape[2], input.shape[3] * input.shape[4])
x2 = input.transpose(2, 3)
x2 = x2.contiguous().view(x2.shape[0], x2.shape[1], x2.shape[2], x2.shape[3] * x2.shape[4])
x3 = input.transpose(2, 4)
x3 = x3.contiguous().view(x3.shape[0], x3.shape[1], x3.shape[2], x3.shape[3] * x3.shape[4])
#print(input.shape[3])
#out1=self.conv(x1).view(input.shape[0],input.shape[1],input.shape[2],int(input.shape[3]/self.stride),int(input.shape[4]/self.stride))
#out1=self.batchnorm(out1)
#out1=self.relu(out1)
#out2=self.conv(x2).view(input.shape[0],input.shape[1],input.shape[2],int(input.shape[3]/self.stride),int(input.shape[4]/self.stride))
#out3=self.conv(x3).view(input.shape[0],input.shape[1],input.shape[2],int(input.shape[3]/self.stride),int(input.shape[4]/self.stride))
out1 = self.conv(x1)
out1 = self.batchnorm(out1)
out1 = self.relu(out1)
out1 = out1.view(input.shape[0], input.shape[1], input.shape[2], int(input.shape[3]/self.stride), int(input.shape[4]/self.stride))
out2 = self.conv(x2)
out2 = self.batchnorm(out2)
out2 = self.relu(out2)
out2 = out2.view(input.shape[0], input.shape[1], input.shape[2], int(input.shape[3] / self.stride), int(input.shape[4] / self.stride))
out3 = self.conv(x3)
out3 = self.batchnorm(out3)
out3 = self.relu(out3)
out3 = out3.view(input.shape[0], input.shape[1], input.shape[2], int(input.shape[3] / self.stride), int(input.shape[4] / self.stride))
#out=torch.cat((out1,out2,out3),1)
a=self.attenblock(out1, out2, out3)
a1,a2,a3 = a.chunk(3, dim=1)
output1 = out1.permute(2, 3, 4, 0, 1) * a1 + out2.permute(2, 3, 4, 0, 1) * a2 + out3.permute(2, 3, 4, 0, 1) * a3
output1 = output1.permute(3, 4, 0, 1, 2)
output1 = self.conv2(output1)
output1 = self.bn2(output1)
#print(output1.shape)
output1 = output1 + shortcut
output1 = self.relu(output1)
return output1
class Cost(nn.Module):
"""
The Cost network.
"""
def __init__(self, num_classes,block, layers,pretrained=False):
super(Cost, self).__init__()
self.in_channels=64
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.max_pool = nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))
self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avg_pool = nn.AdaptiveAvgPool3d(1)
self.dropout = nn.Dropout(p=0.5)
#self.fc = nn.Linear(512 * 4, num_classes)
self.fc1 = nn.Linear(2048, 1024)
self.fc2 = nn.Linear(1024, 1024)
self.fc3 = nn.Linear(1024, num_classes)
self.__init_weight()
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.max_pool(out)
#print(out.shape)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
#out = self.fc(out)
out = self.relu(self.fc1(out))
out = self.dropout(out)
out = self.relu(self.fc2(out))
out = self.dropout(out)
out = self.fc3(out)
return out
def _make_layer(self, block, channels, n_blocks, stride=1):
assert n_blocks > 0, "number of blocks should be greater than zero"
layers = []
layers.append(block(self.in_channels, channels, stride))
self.in_channels = channels * 4
for i in range(1, n_blocks):
layers.append(block(self.in_channels, channels))
return nn.Sequential(*layers)
def __init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv3d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def get_1x_lr_params(model):
"""
This generator returns all the parameters for conv and two fc layers of the net.
"""
b = [model.conv1, model.layer1, model.layer2, model.layer3, model.layer4, model.fc1,
model.fc2]
for i in range(len(b)):
for k in b[i].parameters():
if k.requires_grad:
yield k
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the last fc layer of the net.
"""
b = [model.fc3]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def cost50(pretrained=Flase):
"""Constructs a Cost-Res50 model.
Args:
pretrained (bool): IF Ture,returns a model pre-trained on ImageNet
"""
model = Cost(costmodel, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict = Flase)
return model
def cost101(pretrained=Flase, **kwargs):
"""Constructs a Cost-Res101 model.
Args:
pretrained (bool):IF Ture,returns a model pre-trained on ImageNet
"""
model = Cost(costmodel, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']), strict = Flase)
return model
if __name__ == "__main__":
inputs = torch.rand(2, 3, 16, 224, 224)
net=Cost(101,costblock, [3,4,6,3])
#net = costblock(64,64,stride=2)
print(net.layer4[1].conv.weight.grad)
outputs = net(inputs)
print(outputs.size())