The losses remain the same

[18972441546]

import torch

class CNNCRF(torch.nn.Module):
"""
Simple CNN-CRF model
"""
def init(self, cnn, crf):
super().init()
self.cnn = cnn
self.crf = crf

def forward(self, x):
    """
    x is a batch of input images
    """
    logits = self.cnn(x)
    logits = self.crf(x, logits)
    return logits

Create a CNN-CRF model from given cnn and crf

This is a PyTorch module that can be used in a usual way

model = CNNCRF(cnn, crf)

First I train unET and save the model, then I load the trained UNET model and train UNET and CRFS. I found the loss stuck at 0.693147. Do you have any suggestions?

[netw0rkf10w]

Could you tell me how you instantiated the CRF? Thanks.

[18972441546]

My code is shown below, but this is direct training, but loss is always constant.

import CRF
import torch
from unet_model import *
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
params = CRF.FrankWolfeParams(scheme='fixed', # constant stepsize
stepsize=1.0,
regularizer='l2',
lambda_=1.0, # regularization weight
lambda_learnable=False,
x0_weight=0.5, # useful for training, set to 0 if inference only
x0_weight_learnable=False)

crf = CRF.DenseGaussianCRF(classes=1,
alpha=160,
beta=0.05,
gamma=3.0,
spatial_weight=1.0,
bilateral_weight=1.0,
compatibility=1.0,
init='potts',
solver='fw',
iterations=5,
params=params)
class CNNCRF(torch.nn.Module):
"""
Simple CNN-CRF model
"""
def init(self, cnn, crf):
super().init()
self.cnn = cnn
self.crf = crf
def forward(self, x):
"""
x is a batch of input images
"""
logits = self.cnn(x)#get the tensor of cnn
logits = self.crf(x, logits)
return logits

Create a CNN-CRF model from given cnn and crf

This is a PyTorch module that can be used in a usual way

cnn=UNet()
UnetCrfs = CNNCRF(cnn, crf).to(device)

if name == 'main':
cnn=UNet(1,1)# 1 is symbol of the in_channel of cnn. 3 is symbol of the out_inchnnel of cnn
model = CNNCRF(cnn, crf).to(device)
data_path='./data'
isbi_dataset = ISBI_Loader(data_path)
train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,
batch_size=1,
shuffle=False)
for epoch in range(epochs):
net.train()
for image, label in train_loader:
optimizer.zero_grad()
image = image.to(device=device, dtype=torch.float32)
label = label.to(device=device, dtype=torch.float32)
pred = net(image)
loss = criterion(pred, label)
# print('{}/{}：Loss/train'.format(epoch + 1, epochs), loss.item())
if loss < best_loss:
best_loss = loss
torch.save(net.state_dict(), 'best_model.pth')
loss.backward()
optimizer.step()
pbar.update(1)

[netw0rkf10w]

Are you sure that training is successful without the CRF? Could you try replacing the line UnetCrfs = CNNCRF(cnn, crf).to(device) with UnetCrfs = cnn.to(device) and see what happens?

[18972441546]

thanks,it is ok.

[netw0rkf10w]

Great. Do not hesitate to let me know if you encounter any issues.