IndexError: tuple index out of range

[lgc-china]

When I run code, I got the following error:

Traceback (most recent call last): File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 21, in main() File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 18, in main trainer.run() File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 73, in run train_loss = self.train(scaler,dice_loss) File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 26, in train edges = torch.from_numpy(get_gt_bnd(label_batch.numpy())).cuda() File "C:\pycharm_maset\CTO-main\CTOTrainer\util.py", line 16, in get_gt_bnd _gt_dil = dilation(_gt, disk(2)) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\misc.py", line 38, in func_out return func(image, footprint=footprint, *args, **kwargs) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\gray.py", line 321, in dilation ndi.grey_dilation(image, footprint=footprint, output=out) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\scipy\ndimage_morphology.py", line 1358, in grey_dilation sz = footprint.shape[ii] IndexError: tuple index out of range

I get the processed data by MedISeg\unet2d\NetworkTrainer\dataloaders\data_prepare.py Can you help me solve this bug

[xiaofang007]

Hi, may I double check what is the shape of sampled_batch['label'] in network_trainer? Thanks.

[675492062]

When I run code, I got the following error:

Traceback (most recent call last): File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 21, in main() File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 18, in main trainer.run() File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 73, in run train_loss = self.train(scaler,dice_loss) File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 26, in train edges = torch.from_numpy(get_gt_bnd(label_batch.numpy())).cuda() File "C:\pycharm_maset\CTO-main\CTOTrainer\util.py", line 16, in get_gt_bnd _gt_dil = dilation(_gt, disk(2)) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\misc.py", line 38, in func_out return func(image, footprint=footprint, *args, **kwargs) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\gray.py", line 321, in dilation ndi.grey_dilation(image, footprint=footprint, output=out) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\scipy\ndimage_morphology.py", line 1358, in grey_dilation sz = footprint.shape[ii] IndexError: tuple index out of range

I get the processed data by MedISeg\unet2d\NetworkTrainer\dataloaders\data_prepare.py Can you help me solve this bug

我也遇到了这种情况，应该是mask加载时用到了三个通道（data_prepare.py中）。但grey_dilation方法是针对单通道灰度图像的。

[xiaofang007]

When I run code, I got the following error: Traceback (most recent call last): File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 21, in main() File "C:\pycharm_maset\CTO-main\CTOTrainer\train.py", line 18, in main trainer.run() File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 73, in run train_loss = self.train(scaler,dice_loss) File "C:\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py", line 26, in train edges = torch.from_numpy(get_gt_bnd(label_batch.numpy())).cuda() File "C:\pycharm_maset\CTO-main\CTOTrainer\util.py", line 16, in get_gt_bnd _gt_dil = dilation(_gt, disk(2)) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\misc.py", line 38, in func_out return func(image, footprint=footprint, *args, **kwargs) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\gray.py", line 321, in dilation ndi.grey_dilation(image, footprint=footprint, output=out) File "C:\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\scipy\ndimage_morphology.py", line 1358, in grey_dilation sz = footprint.shape[ii] IndexError: tuple index out of range I get the processed data by MedISeg\unet2d\NetworkTrainer\dataloaders\data_prepare.py Can you help me solve this bug

我也遇到了这种情况，应该是mask加载时用到了三个通道（data_prepare.py中）。但grey_dilation方法是针对单通道灰度图像的。

Hi, yes, you are correct, that is why I am asking what the shape of the sampled_batch['label'] is. The sampled_batch['label'] needs to 3D (B,H,W) where there is no channel dimension, and the input image of dilation function should be 2D. Thanks.

[Taocunguo]

I have the same situation. Have you solved it?

[Taocunguo]

当我运行代码时，出现以下错误： 回溯（最近一次调用最后一次）：文件“C：\pycharm_maset\CTO-main\CTOTrainer\train.py”，第 21 行，在 main（） 文件“C：\pycharm_maset\CTO-main\CTOTrainer\train.py”，第 18 行，在 main trainer.run（） 文件“C：\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py”，第 73 行，运行 train_loss = self.train（scaler，dice_loss） 文件“C：\pycharm_maset\CTO-main\CTOTrainer\network_trainer.py”，第 26 行， 在火车边缘 = torch.from_numpy（get_gt_bnd（label_batch.numpy（）））.cuda（） 文件“C：\pycharm_maset\CTO-main\CTOTrainer\util.py”，第 16 行，get_gt_bnd _gt_dil = dilation（_gt， disk（2）） 文件“C：\Users\OO\anaconda\envs\Attention-gated-Networks-master\lib\site-packages\skimage\morphology\misc.py”，第 38 行，在 func_out return func（image， footprint=footprint， *args， **kwargs） 文件“C：\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\skimage\morphology\gray.py“，第 321 行，在扩张 ndi.grey_dilation（image， footprint=footprint， output=out） 文件”C：\Users\OO\anaconda\envs\Attention-Gated-Networks-master\lib\site-packages\scipy\ndimage_morphology.py“，第 1358 行，在 grey_dilation sz = footprint.shape[ii] IndexError：元组索引超出范围 我通过MedISeg \ unet2d \ NetworkTrainer \ dataloaders\data_prepare.py 获取处理后的数据 你能帮我解决这个错误吗

我也遇到了这种情况，应该是mask加载时用到了三个通道（data_prepare.py中）。但grey_dilation方法是针对单通道灰度图像的。 Can you tell me how to handle this? I'm new here

[xiaofang007]

I have the same situation. Have you solved it?

Hi, you need to make sure the label_batch (ground truth) is three dimensional (B,H,W) because the dilation function accepts 2D images as input.

[Taocunguo]

I have the same situation. Have you solved it?

Hi, you need to make sure the label_batch (ground truth) is three dimensional (B,H,W) because the dilation function accepts 2D images as input. Yes, I just read the mask into the grayscale and the resulting shape is torch.Size([32, 256, 256]), but I have a new problem, and it's described like this

Training starts save directory: /data2/tcg/CTO/Experiment/ISIC-2018/isic2018//baseline/CTO/fold_0 => Initial learning rate: 0.0001 => Batch size: 32 => Number of training iterations: 90 * 52 => Training epochs: 90 0%| | 0/90 [00:00<?, ?it/s]=> start training! /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [1216,0,0], thread: [0,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [1216,0,0], thread: [1,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [1329,0,0], thread: [30,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. 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/opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [55,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [56,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [57,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [58,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [59,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [60,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [61,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [62,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:276: operator(): block: [833,0,0], thread: [63,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. 0%| | 0/90 [00:03<?, ?it/s] Traceback (most recent call last): File "train.py", line 21, in main() File "train.py", line 18, in main trainer.run() File "/data2/tcg/CTO/CTOTrainer/network_trainer.py", line 74, in run train_loss = self.train(scaler,dice_loss) File "/data2/tcg/CTO/CTOTrainer/network_trainer.py", line 36, in train scaler.scale(loss).backward() File "/data2/tcg/anaconda3/envs/CTO/lib/python3.8/site-packages/torch/_tensor.py", line 307, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/data2/tcg/anaconda3/envs/CTO/lib/python3.8/site-packages/torch/autograd/init.py", line 154, in backward Variable._execution_engine.run_backward( RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

[ZMTangYan]

I have the same problem. How did you solve it?