plemeri/uacanet

Official PyTorch implementation of UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation (ACMMM 2021)

uacanet's Issues

result problem

I follow the settings of the readme, the code has not been modified in any way, but the effect is particularly poor, can you help me reproduce it?

Why is my eval result 0 except for Sm, meanEm, mae, maxEm?

I followed the steps in the readme to train and test UACANat-L, why in the end all datasets evaluated the result was 0

Some questions about Axial-attention

Hi~I have some questions about Axial-attention
Why there is no premute operation before view in mode h?

# for mode h
projected_query = self.query_conv(x).premute(0, 1, 3, 2).view(*view).permute(0, 2, 1)

I think premute is necessary. Although the shape of those values are correct to calculate，it has a very different meaning for mode h comparing to mode w. Without premute, the projected_query can't actually collect the columns to the dimension with size Hight
For example:

For mode W, the way of reshape is correct.

Without permute for mode H, it is obviously not what we want:

With permute for mode H,[0, 5, 10, 15] is the column of a.:

about "break" in train.py

train.py line:133,what the meaning about "break"??

About SFA implementation

Hello, Is it convenient to share the SFA code in this article? Suddenly found that the original author deleted this code

Multiple classes training

Hi, is it possible to use this model for multi-classes segmentation training? Thanks.

Results on ETIS-LaribPolypDB dataset

I tried to reproduce your results following your README instructions. I used the default UACANet-L config with a batch size of 8 instead of 32.

The mDICE I get on 3 runs on ETIS-LaribPolypDB is 0.52/0.56/0.53 while in the paper you get 0.76
Can you verify this? Why is the lower batch size affecting only this dataset?

Clarification on pretrained weights

When pretrained: True the pretrained weights in data/backbone_ckpt/res2net50_v1b_26w_4s-3cf99910.pth are used.
Where do these weights come from? Are they pretrained on colon images by you, or they are pretrained just on ImageNet?

Could you please tell me how to get the CVC-300，CVC-ColonDB，ETIS dataset

Could you please tell me how to get the CVC-300，CVC-ColonDB，ETIS dataset

about distributed training

A small question in Train.py

Is the syncbatchnorm useful to increase accuracy when using multiple GPUs?
Thank you

About checkpoint saved

In run/Train.py, Line122

if epoch % opt.Train.Checkpoint.checkpoint_epoch == 0: torch.save(model.module.state_dict() if args.device_num > 1 else model.state_dict( ), os.path.join(opt.Train.Checkpoint.checkpoint_dir, 'latest.pth'))

To my understanding, this code fragment save checkpoint by each 20 epochs , this can not ensure the checkpoint saved is the optimal during training.

And in Line 130,
if args.local_rank <= 0: torch.save(model.module.state_dict() if args.device_num > 1 else model.state_dict( ), os.path.join(opt.Train.Checkpoint.checkpoint_dir, 'latest.pth'))
this code just save the weights of last epoch, and it also can not ensure the checkpoint saved is the optimal.

Why don't you save the optimal checkpoint? Would you mind explaining it for me?
Many thanks to you！
Happy New Year!

Some questions about the ‘bce_iou_loss’ function

def bce_iou_loss(pred, mask):
    weight = 1 + 5 * torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)
  
    bce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
   
    pred = torch.sigmoid(pred)
    inter = pred * mask
    union = pred + mask
    iou = 1 - (inter + 1) / (union - inter + 1)

    weighted_bce = (weight * bce).sum(dim=(2, 3)) / weight.sum(dim=(2, 3))
    weighted_iou = (weight * iou).sum(dim=(2, 3)) / weight.sum(dim=(2, 3))

    return (weighted_bce + weighted_iou).mean()

I am looking forward to your reply！

About this Baseline model

What is the structure of this Baseline model? What kind of connection does it have with UACANet?

The evaluation result is not good.

I run the commad:

python Expr.py --config configs/UACANet-L.yaml

However, the result listed as follows:

The official result of UACANet-L mentioned at README is

dataset              meanDic    meanIoU    wFm     Sm    meanEm    mae    maxEm    maxDic    maxIoU    meanSen    maxSen    meanSpe    maxSpe
-----------------  ---------  ---------  -----  -----  --------  -----  -------  --------  --------  ---------  --------  ---------  --------
CVC-300                0.910      0.849  0.901  0.937     0.977  0.005    0.980     0.913     0.853      0.940     1.000      0.993     0.997
CVC-ClinicDB           0.926      0.880  0.928  0.943     0.974  0.006    0.976     0.929     0.883      0.943     1.000      0.992     0.996
Kvasir                 0.912      0.859  0.902  0.917     0.955  0.025    0.958     0.915     0.862      0.923     1.000      0.983     0.987
CVC-ColonDB            0.751      0.678  0.746  0.835     0.875  0.039    0.878     0.753     0.680      0.754     1.000      0.953     0.957
ETIS-LaribPolypDB      0.766      0.689  0.740  0.859     0.903  0.012    0.905     0.769     0.691      0.813     1.000      0.932     0.936

Why is the result I run so bad? I didn't change any configuration file.