performance issue

[soans1994]

hello author,

i have implemented similar fcn with vgg layers. But my fcn32 works better than fcn16 and 8. why is this?. Can you please take a look on 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