wickstrom / thesis Goto Github PK

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