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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.functional as F
import torchvision
class VGG16Unet(nn.Module):
def __init__(self, features = None):
super(VGG16Unet, self).__init__()
# Encoder pre-trained features
self.features = features
# upsamplig block
self.up = nn.UpsamplingBilinear2d(scale_factor = 2.0)
# Rest blocks
# Encoder
self.enc_block0 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True),
nn.Conv2d(64, 64, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True)
)
self.enc_block1 = nn.Sequential(
nn.MaxPool2d(kernel_size = 2, stride = 2),
nn.Conv2d(64, 128, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True),
nn.Conv2d(128, 128, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True)
)
self.enc_block2 = nn.Sequential(
nn.MaxPool2d(kernel_size = 2, stride = 2),
nn.Conv2d(128, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True),
nn.Conv2d(256, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True),
nn.Conv2d(256, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True)
)
self.enc_block3 = nn.Sequential(
nn.MaxPool2d(kernel_size = 2, stride = 2),
nn.Conv2d(256, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True)
)
self.enc_block4 = nn.Sequential(
nn.MaxPool2d(kernel_size = 2, stride = 2),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True)
)
self.bottle = nn.Sequential(
nn.MaxPool2d(kernel_size = 2, stride = 2),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True)
)
# Decoder
# unet connection with enc_block5 (input will be 1024)
self.dec_block4 = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True)
)
# unet connection with enc_block5 (input will be 512)
self.dec_block3 = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(512),
nn.ReLU(inplace = True),
nn.Conv2d(512, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True),
nn.Conv2d(256, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True)
)
self.dec_block2 = nn.Sequential(
nn.Conv2d(512, 256, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(256),
nn.ReLU(inplace = True),
nn.Conv2d(256, 128, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True),
nn.Conv2d(128, 128, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True)
)
self.dec_block1 = nn.Sequential(
nn.Conv2d(256, 128, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True),
nn.Conv2d(128, 64, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True)
)
self.dec_block0 = nn.Sequential(
nn.Conv2d(128, 64, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True),
nn.Conv2d(64, 64, kernel_size = 3, padding = 1, stride = 1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True)
)
self.prediction = nn.Conv2d(64, 3, kernel_size = 3, padding = 1, stride = 1)
def init_weights(self, init = 'xavier'):
if init == 'xavier':
init_func = torch.nn.init.xavier_normal_
else:
init_func = torch.nn.init.normal_
if self.features is not None:
# pretrained weights initialization
self.enc_block0._modules['0'].weight = self.features._modules['0'].weight
self.enc_block0._modules['3'].weight = self.features._modules['2'].weight
self.enc_block1._modules['1'].weight = self.features._modules['5'].weight
self.enc_block1._modules['4'].weight = self.features._modules['7'].weight
self.enc_block2._modules['1'].weight = self.features._modules['10'].weight
self.enc_block2._modules['4'].weight = self.features._modules['12'].weight
self.enc_block2._modules['7'].weight = self.features._modules['14'].weight
self.enc_block3._modules['1'].weight = self.features._modules['17'].weight
self.enc_block3._modules['4'].weight = self.features._modules['19'].weight
self.enc_block3._modules['7'].weight = self.features._modules['21'].weight
self.enc_block4._modules['1'].weight = self.features._modules['24'].weight
self.enc_block4._modules['4'].weight = self.features._modules['26'].weight
self.enc_block4._modules['7'].weight = self.features._modules['28'].weight
else:
init_func(self.enc_block0._modules['0'].weight)
init_func(self.enc_block0._modules['3'].weight)
init_func(self.enc_block1._modules['1'].weight)
init_func(self.enc_block1._modules['4'].weight)
init_func(self.enc_block2._modules['1'].weight)
init_func(self.enc_block2._modules['4'].weight)
init_func(self.enc_block2._modules['7'].weight)
init_func(self.enc_block3._modules['1'].weight)
init_func(self.enc_block3._modules['4'].weight)
init_func(self.enc_block3._modules['7'].weight)
init_func(self.enc_block4._modules['1'].weight)
init_func(self.enc_block4._modules['4'].weight)
init_func(self.enc_block4._modules['7'].weight)
#### Decoder Initialization
init_func(self.bottle._modules['1'].weight)
init_func(self.dec_block4._modules['0'].weight)
init_func(self.dec_block4._modules['3'].weight)
init_func(self.dec_block4._modules['6'].weight)
init_func(self.dec_block3._modules['0'].weight)
init_func(self.dec_block3._modules['3'].weight)
init_func(self.dec_block3._modules['6'].weight)
init_func(self.dec_block2._modules['0'].weight)
init_func(self.dec_block2._modules['3'].weight)
init_func(self.dec_block2._modules['6'].weight)
init_func(self.dec_block1._modules['0'].weight)
init_func(self.dec_block1._modules['3'].weight)
init_func(self.dec_block0._modules['0'].weight)
init_func(self.dec_block0._modules['3'].weight)
init_func(self.prediction.weight)
def forward(self, x):
x0 = self.enc_block0(x)
x1 = self.enc_block1(x0)
x2 = self.enc_block2(x1)
x3 = self.enc_block3(x2)
x4 = self.enc_block4(x3)
b = self.bottle(x4)
y4 = self.up(b)
y4 = torch.cat((x4, y4), dim = 1)
y4 = self.dec_block4(y4)
y3 = self.up(y4)
y3 = torch.cat((x3, y3), dim = 1)
y3 = self.dec_block3(y3)
y2 = self.up(y3)
y2 = torch.cat((x2, y2), dim = 1)
y2 = self.dec_block2(y2)
y1 = self.up(y2)
y1 = torch.cat((x1, y1), dim = 1)
y1 = self.dec_block1(y1)
y0 = self.up(y1)
y0 = torch.cat((x0, y0), dim = 1)
y0 = self.dec_block0(y0)
pred = self.prediction(y0)
return pred