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models-Depthwise-Inception3.py
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models-Depthwise-Inception3.py
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import torch.nn as nn
import torch
import torch.nn.functional as F
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv2d") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
elif classname.find("Linear") != -1:
torch.nn.init.constant_(m.weight.data, 0.0)
if m.bias is not None:
torch.nn.init.constant_(m.bias.data, 0.0)
class InceptionA(nn.Module):
def __init__(self, in_channels, pool_features, conv_block=None):
super(InceptionA, self).__init__()
if conv_block is None:
conv_block = nn.Conv2d
self.branch1x1 = conv_block(in_channels, 16, kernel_size=1)
self.branch5x5_1 = conv_block(in_channels, 24, kernel_size=1)
self.branch5x5_2 = conv_block(24, 32, kernel_size=5, padding=2)
self.branch3x3dbl_1 = conv_block(in_channels, 32, kernel_size=1)
self.branch3x3dbl_2 = conv_block(32, 32, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(32, 32, kernel_size=3, padding=1)
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class SegmentNet(nn.Module):
def __init__(self, in_channels=3, init_weights=True):
super(SegmentNet, self).__init__()
# self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
# self.bn1 = nn.BatchNorm2d(32)
self.layer1 = nn.Sequential(
nn.Conv2d(in_channels, 32, 5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, 5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(2)
)
self.Mixed_1b = InceptionA(32, pool_features=2)
# self.Mixed_1c = InceptionA(82, pool_features=2)
self.layer3 = nn.Sequential(
nn.Conv2d(82, 64, 5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2)
)
self.layer4 = nn.Sequential(
nn.Conv2d(64, 1024, 15, stride=1, padding=7), # 64,1024
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True)
)
self.layer5 = nn.Sequential(
nn.Conv2d(1024, 1, 1),
nn.ReLU(inplace=True)
)
if init_weights == True:
pass
def forward(self, x):
x = self.layer1(x)
# x = F.max_pool2d(x, kernel_size=3, stride=2)
x1 = self.Mixed_1b(x)
x1 = F.max_pool2d(x1, kernel_size=3, stride=2, padding=1)
# print('x1:', x1.shape)
# x2 = self.Mixed_1c(x1)
# x2 = F.max_pool2d(x2, kernel_size=5, stride=2, padding=2)
# print('x2:', x2.shape)
# x3 = self.Mixed_1d(x2)
# x3 = F.max_pool2d(x3, kernel_size=5, stride=2, padding=2)
# x3 = self.layers(x) # torch.Size([4, 64, 88, 32]), torch.Size([4, 256, 88, 32])
# print('x3:', x3.shape)
x3 = self.layer3(x1)
x4 = self.layer4(x3) # torch.Size([4, 1024, 88, 32])
x5 = self.layer5(x4) # torch.Size([4, 1, 88, 32])
# print('x:', x4.shape, x5.shape)
return {"f":x4, "seg":x5}
class DecisionNet(nn.Module):
def __init__(self, init_weights=True):
super(DecisionNet, self).__init__()
self.layer1 = nn.Sequential(
# nn.MaxPool2d(2),
# nn.Conv2d(1025, 8, 5, stride=1, padding=2),
# nn.BatchNorm2d(8),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(2),
# Depthwise+Pointwise
nn.Conv2d(1025, 1025, kernel_size=5, stride=1, padding=2, groups=1025, bias=False),
nn.BatchNorm2d(1025),
nn.Conv2d(1025, 8, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(8),
nn.MaxPool2d(2),
# nn.Conv2d(8, 16, 5, stride=1, padding=2),
# nn.BatchNorm2d(16),
# nn.ReLU(inplace=True),
# # nn.MaxPool2d(2),
# Depthwise+Pointwise
nn.Conv2d(8, 8, kernel_size=5, stride=1, padding=2, groups=8, bias=False),
nn.BatchNorm2d(8),
nn.Conv2d(8, 16, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(16),
nn.MaxPool2d(2),
nn.Conv2d(16, 32, 5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
nn.Linear(66, 1, bias=False),
nn.Sigmoid()
)
if init_weights == True:
pass
def forward(self, f, s):
xx = torch.cat((f, s), 1)
x1 = self.layer1(xx)
x2 = x1.view(x1.size(0), x1.size(1), -1)
s2 = s.view(s.size(0), s.size(1), -1)
x_max, x_max_idx = torch.max(x2, dim=2)
x_avg = torch.mean(x2, dim=2)
s_max, s_max_idx = torch.max(s2, dim=2)
s_avg = torch.mean(s2, dim=2)
y = torch.cat((x_max, x_avg, s_avg, s_max), 1)
y = y.view(y.size(0), -1)
return self.fc(y)
if __name__=='__main__':
snet = SegmentNet()
dnet = DecisionNet()
img = torch.randn(4, 3, 704, 256)
snet.eval()
snet = snet.cuda()
dnet = dnet.cuda()
img = img.cuda()
ret = snet(img)
f = ret["f"]
s = ret["seg"]
c = dnet(f, s)
print(c)
pass