Comments (3)
Hi,
I don't know what's your problem you have met. In my understanding, the output needs to be seed into an activation function (softmax or sigmoid) and then calculate loss functions.
Best,
Xiangde.
from aceloss.
Hello,
I tried to use AC or ACE losses instead of CE loss for the binary segmentation. Though I have used a certain network for the CE loss many times, the network does not work for AC/ACE losses.
In the meantime, I used a label array whose shape is equal to the prediction. Also, its channels have zero or one values like the code below.
from aceloss import ACLossV2 criterion = ACLossV2(classes=2) outputs = model(inputs) masks2 = torch.zeros_like(outputs) masks2[:, 0, :, :] = (masks == 0).squeeze(1) # shape: [batch size, channel size, width, height] masks2[:, 1, :, :] = (masks == 1).squeeze(1) loss = criterion(outputs, masks2)
The loss value is larger than 1e4, and the output for the prediction looks meaningless. Did I miss something? I didn't change any code for the ACELoss class. Thank you.
I also encountered the same problem. UNET + MSELoss works normally, but ACLoss does not converge, even when I used the pre-trained model.
from aceloss.
I got same issue, the loss reduces slowly and IoU does not increase. I trained with BCELoss/CrossEntropyLoss before and my Network works well. Here is my code for Active Contour Loss
class ActiveContourLoss(torch.nn.Module):
def __init__(self, miu=1.0, numClasses=1):
super(ActiveContourLoss, self).__init__()
self.miu = miu
self.numClasses = numClasses
def forward(self, pred, mask):
'''
pred: prediction shape (B, numClasses, W, H)
mask: ground truth (B, W, H)
'''
if self.numClasses == 1:
pred = torch.sigmoid(pred)
else:
pred = torch.nn.functional.softmax(pred, dim=1)
min_pool_x = torch.nn.functional.max_pool2d(pred * -1, (3, 3), 1, 1) * -1
contour = torch.relu(torch.nn.functional.max_pool2d(
min_pool_x, (3, 3), 1, 1) - min_pool_x)
# length
length = torch.sum(torch.abs(contour))
# regions
label = torch.zeros_like(pred)
for k in range(self.numClasses):
value = k
if(self.numClasses == 1):
value = 1
label[:, k, :, :] = (mask == value)
label = label.float()
c_in = torch.ones_like(pred)
c_out = torch.zeros_like(pred)
region_in = torch.abs(torch.sum(pred * ((label - c_in) ** 2)))
region_out = torch.abs(torch.sum((1 - pred) * ((label - c_out) ** 2)))
region = self.miu * region_in + region_out
return (region + length)
from aceloss.
Related Issues (7)
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from aceloss.