guzaiwang / ce-net Goto Github PK

Thanks for your effort.

Could you please share your pretrained model?

Hi! Could you please provide Set_A.txt and Set_B.txt for experiment on ORIGA dataset? I searched online for how ORIGA dataset was divided into training set and test set, but found nothing. According to your data.py, Set_A.txt and Set_B.txt are used to divide ORIGA dataset, so I would be appreciated if you could provide them. Thx!

I run test_cenet.py, but i got the all black or all white mask. how can i solve this？
and i want to know where is your pretrained model? Thanks @Guzaiwang

Can I use cpu to run the program? The error is [WinError 10054] An existing connection was forcibly closed by the remote host

Hi, dear author, thank you for amazing segmentation network CENet and your open source code. I am wondering whether you can release the Retinal OCT layer Dataset for us to follow your work.

There is no test CE-Net code in main.py?

RuntimeError: The size of tensor a (38) must match the size of tensor b (37) at non-singleton dimension 2

在运行test_center中d4 = self.decoder4(e4) + e3时，pycharm报错，请问有人知道原因吗？非常感谢

First of all, I would like to thank you for disclosing your own code, which can give me a chance to learn. After practice your code, I have one question about
`

class MulticlassDiceLoss(nn.Module):
"""
requires one hot encoded target. Applies DiceLoss on each class iteratively.
requires input.shape[0:1] and target.shape[0:1] to be (N, C) where N is
batch size and C is number of classes
"""
def init(self):

    super(MulticlassDiceLoss, self).__init__()

def forward(self, input, target, weights=None):

    C = target.shape[1]
 
    totalLoss = 0

    for i in range(C):

        diceLoss = dice(input[:, i, :, :], target[:, i, :, :])

        if weights is not None:

            diceLoss *= weights[i]

        totalLoss += diceLoss

    return totalLoss

`,
the 'input' is ground truth，it has only one channel,how to transform the channel into channels which are equal to the predicted segmentation image?

就是你代码里并没有第一层输出网络和后面的连接，但是你论文中的图却有，不知道是不是我没有看懂你的代码。

已经将
ROOT= './dataset/CamVid'
BINARY_CLASS = 5
改为测试需要的值，并且写好读该数据的函数，测试用的loss为loss.py的MulticlassDiceLoss,因维度问题出现多出报错
N, H, W = target.size(0), target.size(2), target.size(3) IndexError: Dimension out of range (expected to be in range of [-3, 2], but got 3)
修正这个问题后又出现
diceLoss = dice(input[:, i, :, :], target[:, i,:, :]) IndexError: index 1 is out of bounds for dimension 1 with size 1

采用nn.CrossEntropyLoss()为loss,出现
if size_average and reduce: RuntimeError: bool value of Tensor with more than one value is ambiguous
能否上传一份测试多分类分割的示例代码呢

RuntimeError: Error(s) in loading state_dict for CE_Net_:
Missing key(s) in state_dict:

"firstconv.weight", "firstbn.weight", "firstbn.bias", "firstbn.running_mean", "firstbn.running_var", "encoder1.0.conv1.weight", "encoder1.0.bn1.weight", "encoder1.0.bn1.bias", "encoder1.0.bn1.running_mean", "encoder1.0.bn1.running_var", "encoder1.0.conv2.weight", "encoder1.0.bn2.weight", "encoder1.0.bn2.bias", "encoder1.0.bn2.running_mean", "encoder1.0.bn2.running_var", "encoder1.1.conv1.weight", "encoder1.1.bn1.weight", "encoder1.1.bn1.bias", "encoder1.1.bn1.running_mean", "encoder1.1.bn1.running_var", "encoder1.1.conv2.weight", "encoder1.1.bn2.weight", "encoder1.1.bn2.bias", "encoder1.1.bn2.running_mean", "encoder1.1.bn2.running_var", "encoder1.2.conv1.weight", "encoder1.2.bn1.weight", "encoder1.2.bn1.bias", "encoder1.2.bn1.running_mean", "encoder1.2.bn1.running_var", "encoder1.2.conv2.weight", "encoder1.2.bn2.weight", "encoder1.2.bn2.bias", "encoder1.2.bn2.running_mean", "encoder1.2.bn2.running_var", "encoder2.0.conv1.weight", "encoder2.0.bn1.weight", "encoder2.0.bn1.bias", "encoder2.0.bn1.running_mean", "encoder2.0.bn1.running_var", "encoder2.0.conv2.weight", "encoder2.0.bn2.weight", "encoder2.0.bn2.bias", "encoder2.0.bn2.running_mean", "encoder2.0.bn2.running_var", "encoder2.0.downsample.0.weight", "encoder2.0.downsample.1.weight", "encoder2.0.downsample.1.bias", "encoder2.0.downsample.1.running_mean", "encoder2.0.downsample.1.running_var", "encoder2.1.conv1.weight", "encoder2.1.bn1.weight", "encoder2.1.bn1.bias", "encoder2.1.bn1.running_mean", "encoder2.1.bn1.running_var", "encoder2.1.conv2.weight", "encoder2.1.bn2.weight", "encoder2.1.bn2.bias", "encoder2.1.bn2.running_mean", "encoder2.1.bn2.running_var", "encoder2.2.conv1.weight", "encoder2.2.bn1.weight", "encoder2.2.bn1.bias", "encoder2.2.bn1.running_mean", "encoder2.2.bn1.running_var", "encoder2.2.conv2.weight", "encoder2.2.bn2.weight", "encoder2.2.bn2.bias", "encoder2.2.bn2.running_mean", "encoder2.2.bn2.running_var", "encoder2.3.conv1.weight", "encoder2.3.bn1.weight", "encoder2.3.bn1.bias", "encoder2.3.bn1.running_mean", "encoder2.3.bn1.running_var", "encoder2.3.conv2.weight", "encoder2.3.bn2.weight", "encoder2.3.bn2.bias", "encoder2.3.bn2.running_mean", "encoder2.3.bn2.running_var", "encoder3.0.conv1.weight", "encoder3.0.bn1.weight", "encoder3.0.bn1.bias", "encoder3.0.bn1.running_mean", "encoder3.0.bn1.running_var", "encoder3.0.conv2.weight", "encoder3.0.bn2.weight", "encoder3.0.bn2.bias", "encoder3.0.bn2.running_mean", "encoder3.0.bn2.running_var", "encoder3.0.downsample.0.weight", "encoder3.0.downsample.1.weight", "encoder3.0.downsample.1.bias", "encoder3.0.downsample.1.running_mean", "encoder3.0.downsample.1.running_var", "encoder3.1.conv1.weight", "encoder3.1.bn1.weight", "encoder3.1.bn1.bias", "encoder3.1.bn1.running_mean", "encoder3.1.bn1.running_var", "encoder3.1.conv2.weight", "encoder3.1.bn2.weight", "encoder3.1.bn2.bias", "encoder3.1.bn2.running_mean", "encoder3.1.bn2.running_var", "encoder3.2.conv1.weight", "encoder3.2.bn1.weight", "encoder3.2.bn1.bias", "encoder3.2.bn1.running_mean", "encoder3.2.bn1.running_var", "encoder3.2.conv2.weight", "encoder3.2.bn2.weight", "encoder3.2.bn2.bias", "encoder3.2.bn2.running_mean", "encoder3.2.bn2.running_var", "encoder3.3.conv1.weight", "encoder3.3.bn1.weight", "encoder3.3.bn1.bias", "encoder3.3.bn1.running_mean", "encoder3.3.bn1.running_var", "encoder3.3.conv2.weight", "encoder3.3.bn2.weight", "encoder3.3.bn2.bias", "encoder3.3.bn2.running_mean", "encoder3.3.bn2.running_var", "encoder3.4.conv1.weight", "encoder3.4.bn1.weight", "encoder3.4.bn1.bias", "encoder3.4.bn1.running_mean", "encoder3.4.bn1.running_var", "encoder3.4.conv2.weight", "encoder3.4.bn2.weight", "encoder3.4.bn2.bias", "encoder3.4.bn2.running_mean", "encoder3.4.bn2.running_var", "encoder3.5.conv1.weight", "encoder3.5.bn1.weight", "encoder3.5.bn1.bias", "encoder3.5.bn1.running_mean", "encoder3.5.bn1.running_var", "encoder3.5.conv2.weight", "encoder3.5.bn2.weight", "encoder3.5.bn2.bias", "encoder3.5.bn2.running_mean", "encoder3.5.bn2.running_var", "encoder4.0.conv1.weight", "encoder4.0.bn1.weight", "encoder4.0.bn1.bias", "encoder4.0.bn1.running_mean", "encoder4.0.bn1.running_var", "encoder4.0.conv2.weight", "encoder4.0.bn2.weight", "encoder4.0.bn2.bias", "encoder4.0.bn2.running_mean", "encoder4.0.bn2.running_var", "encoder4.0.downsample.0.weight", "encoder4.0.downsample.1.weight", "encoder4.0.downsample.1.bias", "encoder4.0.downsample.1.running_mean", "encoder4.0.downsample.1.running_var", "encoder4.1.conv1.weight", "encoder4.1.bn1.weight", "encoder4.1.bn1.bias", "encoder4.1.bn1.running_mean", "encoder4.1.bn1.running_var", "encoder4.1.conv2.weight", "encoder4.1.bn2.weight", "encoder4.1.bn2.bias", "encoder4.1.bn2.running_mean", "encoder4.1.bn2.running_var", "encoder4.2.conv1.weight", "encoder4.2.bn1.weight", "encoder4.2.bn1.bias", "encoder4.2.bn1.running_mean", "encoder4.2.bn1.running_var", "encoder4.2.conv2.weight", "encoder4.2.bn2.weight", "encoder4.2.bn2.bias", "encoder4.2.bn2.running_mean", "encoder4.2.bn2.running_var", "dblock.dilate1.weight", "dblock.dilate1.bias", "dblock.dilate2.weight", "dblock.dilate2.bias", "dblock.dilate3.weight", "dblock.dilate3.bias", "dblock.conv1x1.weight", "dblock.conv1x1.bias", "spp.conv.weight", "spp.conv.bias", "decoder4.conv1.weight", "decoder4.conv1.bias", "decoder4.norm1.weight", "decoder4.norm1.bias", "decoder4.norm1.running_mean", "decoder4.norm1.running_var", "decoder4.deconv2.weight", "decoder4.deconv2.bias", "decoder4.norm2.weight", "decoder4.norm2.bias", "decoder4.norm2.running_mean", "decoder4.norm2.running_var", "decoder4.conv3.weight", "decoder4.conv3.bias", "decoder4.norm3.weight", "decoder4.norm3.bias", "decoder4.norm3.running_mean", "decoder4.norm3.running_var", "decoder3.conv1.weight", "decoder3.conv1.bias", "decoder3.norm1.weight", "decoder3.norm1.bias", "decoder3.norm1.running_mean", "decoder3.norm1.running_var", "decoder3.deconv2.weight", "decoder3.deconv2.bias", "decoder3.norm2.weight", "decoder3.norm2.bias", "decoder3.norm2.running_mean", "decoder3.norm2.running_var", "decoder3.conv3.weight", "decoder3.conv3.bias", "decoder3.norm3.weight", "decoder3.norm3.bias", "decoder3.norm3.running_mean", "decoder3.norm3.running_var", "decoder2.conv1.weight", "decoder2.conv1.bias", "decoder2.norm1.weight", "decoder2.norm1.bias", "decoder2.norm1.running_mean", "decoder2.norm1.running_var", "decoder2.deconv2.weight", "decoder2.deconv2.bias", "decoder2.norm2.weight", "decoder2.norm2.bias", "decoder2.norm2.running_mean", "decoder2.norm2.running_var", "decoder2.conv3.weight", "decoder2.conv3.bias", "decoder2.norm3.weight", "decoder2.norm3.bias", "decoder2.norm3.running_mean", "decoder2.norm3.running_var", "decoder1.conv1.weight", "decoder1.conv1.bias", "decoder1.norm1.weight", "decoder1.norm1.bias", "decoder1.norm1.running_mean", "decoder1.norm1.running_var", "decoder1.deconv2.weight", "decoder1.deconv2.bias", "decoder1.norm2.weight", "decoder1.norm2.bias", "decoder1.norm2.running_mean", "decoder1.norm2.running_var", "decoder1.conv3.weight", "decoder1.conv3.bias", "decoder1.norm3.weight", "decoder1.norm3.bias", "decoder1.norm3.running_mean", "decoder1.norm3.running_var", "finaldeconv1.weight", "finaldeconv1.bias", "finalconv2.weight", "finalconv2.bias", "finalconv3.weight", "finalconv3.bias".
Unexpected key(s) in state_dict: "module.firstconv.weight", "module.firstbn.weight", "module.firstbn.bias", "module.firstbn.running_mean", "module.firstbn.running_var", "module.firstbn.num_batches_tracked", "module.encoder1.0.conv1.weight", "module.encoder1.0.bn1.weight", "module.encoder1.0.bn1.bias", "module.encoder1.0.bn1.running_mean", "module.encoder1.0.bn1.running_var", "module.encoder1.0.bn1.num_batches_tracked", "module.encoder1.0.conv2.weight", "module.encoder1.0.bn2.weight", "module.encoder1.0.bn2.bias", "module.encoder1.0.bn2.running_mean", "module.encoder1.0.bn2.running_var", "module.encoder1.0.bn2.num_batches_tracked", "module.encoder1.1.conv1.weight", "module.encoder1.1.bn1.weight", "module.encoder1.1.bn1.bias", "module.encoder1.1.bn1.running_mean", "module.encoder1.1.bn1.running_var", "module.encoder1.1.bn1.num_batches_tracked", "module.encoder1.1.conv2.weight", "module.encoder1.1.bn2.weight", "module.encoder1.1.bn2.bias", "module.encoder1.1.bn2.running_mean", "module.encoder1.1.bn2.running_var", "module.encoder1.1.bn2.num_batches_tracked", "module.encoder1.2.conv1.weight", "module.encoder1.2.bn1.weight", "module.encoder1.2.bn1.bias", "module.encoder1.2.bn1.running_mean", "module.encoder1.2.bn1.running_var", "module.encoder1.2.bn1.num_batches_tracked", "module.encoder1.2.conv2.weight", "module.encoder1.2.bn2.weight", "module.encoder1.2.bn2.bias", "module.encoder1.2.bn2.running_mean", "module.encoder1.2.bn2.running_var", "module.encoder1.2.bn2.num_batches_tracked", "module.encoder2.0.conv1.weight", "module.encoder2.0.bn1.weight", "module.encoder2.0.bn1.bias", "module.encoder2.0.bn1.running_mean", "module.encoder2.0.bn1.running_var", "module.encoder2.0.bn1.num_batches_tracked", "module.encoder2.0.conv2.weight", "module.encoder2.0.bn2.weight", "module.encoder2.0.bn2.bias", "module.encoder2.0.bn2.running_mean", "module.encoder2.0.bn2.running_var", "module.encoder2.0.bn2.num_batches_tracked", "module.encoder2.0.downsample.0.weight", 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"module.decoder4.norm3.running_mean", "module.decoder4.norm3.running_var", "module.decoder4.norm3.num_batches_tracked", "module.decoder3.conv1.weight", "module.decoder3.conv1.bias", "module.decoder3.norm1.weight", "module.decoder3.norm1.bias", "module.decoder3.norm1.running_mean", "module.decoder3.norm1.running_var", "module.decoder3.norm1.num_batches_tracked", "module.decoder3.deconv2.weight", "module.decoder3.deconv2.bias", "module.decoder3.norm2.weight", "module.decoder3.norm2.bias", "module.decoder3.norm2.running_mean", "module.decoder3.norm2.running_var", "module.decoder3.norm2.num_batches_tracked", "module.decoder3.conv3.weight", "module.decoder3.conv3.bias", "module.decoder3.norm3.weight", "module.decoder3.norm3.bias", "module.decoder3.norm3.running_mean", "module.decoder3.norm3.running_var", "module.decoder3.norm3.num_batches_tracked", "module.decoder2.conv1.weight", "module.decoder2.conv1.bias", "module.decoder2.norm1.weight", "module.decoder2.norm1.bias", "module.decoder2.norm1.running_mean", "module.decoder2.norm1.running_var", "module.decoder2.norm1.num_batches_tracked", "module.decoder2.deconv2.weight", "module.decoder2.deconv2.bias", "module.decoder2.norm2.weight", "module.decoder2.norm2.bias", "module.decoder2.norm2.running_mean", "module.decoder2.norm2.running_var", "module.decoder2.norm2.num_batches_tracked", "module.decoder2.conv3.weight", "module.decoder2.conv3.bias", "module.decoder2.norm3.weight", "module.decoder2.norm3.bias", "module.decoder2.norm3.running_mean", "module.decoder2.norm3.running_var", "module.decoder2.norm3.num_batches_tracked", "module.decoder1.conv1.weight", "module.decoder1.conv1.bias", "module.decoder1.norm1.weight", "module.decoder1.norm1.bias", "module.decoder1.norm1.running_mean", "module.decoder1.norm1.running_var", "module.decoder1.norm1.num_batches_tracked", "module.decoder1.deconv2.weight", "module.decoder1.deconv2.bias", "module.decoder1.norm2.weight", "module.decoder1.norm2.bias", "module.decoder1.norm2.running_mean", "module.decoder1.norm2.running_var", "module.decoder1.norm2.num_batches_tracked", "module.decoder1.conv3.weight", "module.decoder1.conv3.bias", "module.decoder1.norm3.weight", "module.decoder1.norm3.bias", "module.decoder1.norm3.running_mean", "module.decoder1.norm3.running_var", "module.decoder1.norm3.num_batches_tracked", "module.finaldeconv1.weight", "module.finaldeconv1.bias", "module.finalconv2.weight", "module.finalconv2.bias", "module.finalconv3.weight", "module.finalconv3.bias".

it seems Unet in your code can outperform CE_Net in my training. I directly used your code and retrained the Unet, I got a accuracy of [acc: 0.956 | sen: 0.844 | auc:0.98]. This should better than the results of CE_Net. could you please share you weights?

Hello, thanks for sharing the code, I am trying to test the network on different medical images with different numbers of input channels, after reading the code, I think the network does not accept images that do not have 3 channels, even though I may be wrong , but I would like to hear from you if it really is.

Hi, I am wondering how to reproduce the retinal vessel segmentation results in the paper with DRIVE dataset, such as the details of training and the inference. Thank you!

Hello, I am trying to run the script however I am getting the same error on two different machines

(torch-kernel) mb01761@heron158:/research/CE-Net/src$ python main.py
/conda/.conda/envs/torch-kernel/lib/python3.10/site-packages/scipy/init.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.4
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
GPUS device is [0]
training chunk_sizes: [24]
The output will be saved to /data/UBT_Seg/binSeg/ORIGA_OD_cenet_dice_bce_loss
Traceback (most recent call last):
File "/vol/research/Neurocomp/mb01761/research/CE-Net/src/main.py", line 126, in
main(opt)
File "/vol/research/Neurocomp/mb01761/research/CE-Net/src/main.py", line 40, in main
logger = Logger(opt)
File "/vol/research/Neurocomp/mb01761/research/CE-Net/src/lib/logger.py", line 25, in init
os.makedirs(opt.save_dir)
File "/vol/research/TopDownVideo/mb01761/conda/.conda/envs/torch-kernel/lib/python3.10/os.py", line 215, in makedirs
makedirs(head, exist_ok=exist_ok)
File "/vol/research/TopDownVideo/mb01761/conda/.conda/envs/torch-kernel/lib/python3.10/os.py", line 215, in makedirs
makedirs(head, exist_ok=exist_ok)
File "/vol/research/TopDownVideo/mb01761/conda/.conda/envs/torch-kernel/lib/python3.10/os.py", line 215, in makedirs
makedirs(head, exist_ok=exist_ok)
File "/vol/research/TopDownVideo/mb01761/conda/.conda/envs/torch-kernel/lib/python3.10/os.py", line 225, in makedirs
mkdir(name, mode)
PermissionError: [Errno 13] Permission denied: '/data'

Could you release TTA(test time augument) code?

I can see the data augmentation in Readme.
But I can‘t find the data.py in your fold.

Does anyone have the same situation as me? In the process of training the network, the learning rate update directly becomes 0

Can you configure the environment for the next code? For example, torch version? Thank you

感觉作者的引用不太全呀，还有代码最好写上based on哪个开源代码

i run the main.py, parameter is default,then run the test_cenet.py,but the prediction is not good,could you give me some suggestion?thank you very much!

I am so proud of you.

I run the python -m visdom.server command in the anaconda3 environment and connect to the web page, but then I can’t continue to operate. What is going on?