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View Code? Open in Web Editor NEWThis repository containts all code for my master thesis where the task is to perform semantic segmentation of colorectal polyps using Fully Convolutional Networks.
This repository containts all code for my master thesis where the task is to perform semantic segmentation of colorectal polyps using Fully Convolutional Networks.
hello author,
i have implemented similar fcn vgg like yours. But my fcn32 gives better results than fcn 16 and 8. why is this. Can you please check my code. thank you
import torch
import torch.nn as nn
import torchvision.models as models
from pytorch_model_summary import summary
vgg16 = models.vgg16(pretrained=True)
for param in vgg16.features.parameters():
param.requires_grad = False
#False Total params: 185,771,904 Trainable params: 171,057,216 Non-trainable params: 14,714,688
#true Total params: 185,771,904 Trainable params: 185,771,904 Non-trainable params: 0
class fcn(nn.Module):
def init(self):
super(fcn, self).init()
self.features = vgg16.features
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
#nn.Dropout2d(),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
#nn.Dropout2d(),
nn.Conv2d(4096, 32, 1),
nn.ConvTranspose2d(32, 32, 224, stride=32)
)
def forward(self, x):
x = self.features(x)#/32
x = self.classifier(x)
#print(x.shape)
return x
class fcn16(nn.Module):
def init(self):
super(fcn16, self).init()
self.features = vgg16.features
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
nn.Conv2d(4096, 32, 1)
)
self.score_pool4 = nn.Conv2d(512, 32, 1)
self.upscore2 = nn.ConvTranspose2d(32, 32, 14, stride=2, bias=False)
self.upscore16 = nn.ConvTranspose2d(32, 32, 16, stride=16, bias=False)
def forward(self, x):
pool4 = self.features:-7#512 features /16
pool5 = self.features-7:#512 features /16/2=/32
pool5_upscored = self.upscore2(self.classifier(pool5))#32 class features stride2 /32*2=/16
pool4_scored = self.score_pool4(pool4)#32 features /16
combined = pool4_scored + pool5_upscored
#combined = torch.cat([pool4_scored, pool5_upscored])
res = self.upscore16(combined)# /1
return res
class fcn8(nn.Module):
def init(self):
super(fcn8, self).init()
self.features = vgg16.features
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
nn.Conv2d(4096, 32, 1)
)
self.score_pool4 = nn.Conv2d(512, 32, 1)
self.score_pool3 = nn.Conv2d(256, 32, 1)
self.upscore2 = nn.ConvTranspose2d(32, 32, 14, stride=2, bias=False)
self.upscore3 = nn.ConvTranspose2d(32, 32, 2, stride=2, bias=False)
#self.upscore16 = nn.ConvTranspose2d(32, 32, 16, stride=16, bias=False)
self.upscore8 = nn.ConvTranspose2d(32, 32, 8, stride=8, bias=False)
def forward(self, x):
pool3 = self.features:-14#256 features /8
pool4 = self.features-14:-7#512 features /8/2=16
pool5 = self.features-7:#512 features /16/2=/32
pool5_upscored = self.upscore2(self.classifier(pool5))#32 class features stride2 /322=/16
pool4_scored = self.score_pool4(pool4)#32 class features /16
pool3_scored = self.score_pool3(pool3)#32 class features /8
combined = pool4_scored + pool5_upscored #/16
#print(combined.shape)
combined_upscored = self.upscore3(combined)#32 class features stride2 /162=/8
#print(combined_upscored.shape)
combined2 = pool3_scored + combined_upscored
#print(combined2.shape)
#res = self.upscore16(combined)#/1
res = self.upscore8(combined2)#/1
#print(res.shape)
return res
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