Hi, Thanks for your sharing. There is a error when I run the training code which is the sample.

TRAINING: Epoch: 1 / 360; LR: 1.0e-03; losses: [total: 3.1942 (3.1942); ] bench: [iou: 0.0106 (0.0106); ] : 0%| | 1/1000 [00:07<1:59:20, 7.17s/batches]

Traceback (most recent call last): File "/home/tt/zyj_ws/hyperseg/configs/train/cityscapes_efficientnet_b1_hyperseg-m.py", line 43, in main(exp_dir, train_dataset=train_dataset, val_dataset=val_dataset, train_img_transforms=train_img_transforms, File "/home/tt/zyj_ws/hyperseg/train.py", line 248, in main epoch_loss, epoch_iou = proces_epoch(train_loader, train=True) File "/home/tt/zyj_ws/hyperseg/train.py", line 104, in proces_epoch for i, (input, target) in enumerate(pbar): File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/tqdm/std.py", line 1185, in iter for obj in iterable: File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 517, in next data = self._next_data() File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1179, in _next_data return self._process_data(data) File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data data.reraise() File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/_utils.py", line 429, in reraise raise self.exc_type(msg) TypeError: Caught TypeError in DataLoader worker process 1. Original Traceback (most recent call last): File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop data = fetcher.fetch(index) File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/tt/zyj_ws/hyperseg/datasets/cityscapes.py", line 220, in getitem image, target = self.transforms(image, target) File "/home/tt/zyj_ws/hyperseg/datasets/seg_transforms.py", line 78, in call input = list(t(*input)) File "/home/tt/zyj_ws/hyperseg/datasets/seg_transforms.py", line 334, in call lbl = F.pad(lbl, (int(self.size[1] - lbl.size[0]), 0), self.lbl_fill, self.padding_mode).copy() File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torchvision/transforms/functional.py", line 426, in pad return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/torchvision/transforms/functional_pil.py", line 153, in pad image = ImageOps.expand(img, border=padding, **opts) File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/PIL/ImageOps.py", line 403, in expand draw.rectangle((0, 0, width - 1, height - 1), outline=color, width=border) File "/home/tt/anaconda3/envs/zyjenv/lib/python3.9/site-packages/PIL/ImageDraw.py", line 259, in rectangle self.draw.draw_rectangle(xy, ink, 0, width) TypeError: an integer is required (got type tuple)

According to the information from the Internet, this may be a problem in the transformation. Can you give me some suggestion on how to do it?

I have some problems when I train the model on my own data and I don't know if it is because that I make some mistakes when I load my data. Here are the details: Traceback (most recent call last): File "E:\zjy\hyperseg-main\hyperseg\train.py", line 254, in main epoch_loss, epoch_iou = proces_epoch(val_loader, train=False) File "E:\zjy\hyperseg-main\hyperseg\train.py", line 104, in proces_epoch for i, (input, target) in enumerate(pbar): File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\tqdm\std.py", line 1130, in iter for obj in iterable: File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next data = self._next_data() File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data return self._process_data(data) File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data data.reraise() File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch_utils.py", line 438, in reraise raise exception ValueError: Caught ValueError in DataLoader worker process 0. Original Traceback (most recent call last): File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data_utils\worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "E:\zjy\hyperseg-main\hyperseg\datasets\Massachusetts.py", line 104, in getitem img, target = self.transforms(img, target) File "E:\zjy\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 78, in call input = list(t(*input)) File "E:\zjy\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 199, in call img = larger_edge_resize(img, self.size, self.interpolation) File "E:\zjy\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 174, in larger_edge_resize return img.resize(size[::-1], interpolation) File "E:\anaconda3\envs\zhangjiaying\lib\site-packages\PIL\Image.py", line 1861, in resize raise ValueError( ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)

I encountered this error when I was training the model, and when I deleted all the "hyperseg" in the code, this error was not reported. Is this the right thing to do?

Because the training failed, I tried to download cityscapes_efficientnet_b1_hyperseg-m.pth to hyperseg/weights, and renamed it to model_best.pth, I try python test.py 'weights'
-td "hyper_seg.datasets.cityscapes.CityscapesDataset('data/cityscapes',split='val',mode='fine')"\ -it "seg_transforms.LargerEdgeResize([512,1024])"
--gpus 0 Looking forward to your reply！

(liuhaomag) [email protected]:~/data/LH/hyperseg$ python configs/train/cityscapes_efficientnet_b1_hyperseg-m.py
Traceback (most recent call last):
  File "configs/train/cityscapes_efficientnet_b1_hyperseg-m.py", line 46, in <module>
    scheduler=scheduler, pretrained=pretrained, model=model, criterion=criterion, batch_scheduler=batch_scheduler)
  File "/home/cv428/data/LH/hyperseg/train.py", line 187, in main
    train_dataset = obj_factory(train_dataset, transforms=train_transforms)
  File "/home/cv428/data/LH/hyperseg/utils/obj_factory.py", line 57, in obj_factory
    return obj_exp(*args, **kwargs)
  File "/home/cv428/data/LH/hyperseg/datasets/cityscapes.py", line 156, in __init__
    raise RuntimeError('Dataset not found or incomplete. Please make sure all required folders for the'
RuntimeError: Dataset not found or incomplete. Please make sure all required folders for the specified "split" and "mode" are inside the "root" directory
 

First, great work !

I was trying to train in Python and save it for C++ inference. The classic approach doesn't work:

annotation_script_module = torch.jit.script(model)
annotation_script_module.save("my_path")

Do you have any suggestion on how to do it?

Thank you for your sharing. I have tried to retrain the model on Pascal VOC dataset, but the miou is only about 0.7 after 50 epoches and it hardly increased, may I know is it correct? or should I continue the training for 160 epoches?

While I use the test script of vocdataset, it will occur a bug----[Errno 2] No such file or directory: 'data/vocsbd/Vocdevkit/V0C2012/3PEGImages/2007_000033.jp9. How to deal with it?

Hello, thank you so much for releasing your code! I am currently training the model on a custom dataset, and I was wondering if it is possible to freeze the gradient updates for a select number of classes? I am currently zeroing out the gradients from the decoder level 4 BatchNorm3 layer because that is the only layer that has the size of the number of classes within my custom dataset, but when I attempt this, it appears that the weights of the classes that I try to freeze are still changing.

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

Hello! @YuvalNirkin,

I would like to know what is the difference between the torchvision.transforms.Resize function and your designed hyperseg.datasets.seg_transforms.LargerEdgeResize function?

The results of global accuray and mIoU are slightly difference when I use these two transfromation separably in test phrase.

Wish your reply.

While I solve the bug of camvid dataset, I find another bug of the script of train camvid. When running the script of train camvid, it will occur "AttributeError: 'RandomRotation' object has no attribute 'resample' ". Tips: the script of test camvid can run. I don't know how to deal with it.

Sorry to bother you, I have encontered a probelm: Assertion t >= 0 && t < n_classes failed when I try to use your model to my datasets, I have changed the num_class in my datasets, but I still face the problem, can u help me? here is my change: self.classes = list(range(2)) self.image_classes = calc_classes_per_image(self.masks, 2, cache_file)

The variable signal_index is not getting updated inside the init_signal2weights function in hyperseg_v1_0.py. Hence, it is assigning singal_index = 0 for every meta block in the decoder.

Because of this inside the apply_signal2weights function in each meta block, we will not be able to use all the channels of the input signal channels. Essentially we are using [0:signal_channels] of the whole signal where signal_channels of each block is always less than input signal channels.

        # Inside apply_signal2weights function
        w = self.signal2weights(s[:, self.signal_index:self.signal_index + self.signal_channels])[:, :self.hyper_params]

So, is the signal_index supposed to be zero for every meta block?