performance issue

[soans1994]

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.

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 /32*2=/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 /16*2=/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