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AttributeError: type object 'FileHandler' has no attribute 'write_mask'

Got this issue while running the lizard_module.prepare_data()

I had the same issue with the person who had issued you with the title 'Data preparation issue' There was an error saying "AttributeError: type object 'FileHandler' has no attribute 'write_mask'". But after upgrading the module cellseg_models_pytorch I didn't get the same error. but after running the code in the first cell I get a blank file in train/images & train/labels.

misconfigurationexception: you passed accelerator='gpu', but you didn't pass gpus to trainer.

This is the code in cellseg_models_pytorch.datamodules.pannuke_datamodule.py in the python packages directory

This is the code in cellseg_models_pytorch.datamodules.pannuke_datamodule.py of your github code.

I installed your awesome package, cellseg_models_pytorch and am checking its running via 'pannuke_nuclei_segmentation_cellpose.ipynb'.

There was such a issue above. Could you check your examples with jupyter notebook do work?

Hello, this looks like a great tool! I'm trying to implement it and was hoping to start by running inferences on some MoNuSAC data using the HoVer-Net model. HoVer-Net has pretrained weights on their website, so I downloaded them (they strangely appear to be in a tar file) but they are not compatible with this setup. How can I get my hands on a compatible pretrained weights file for HoVer-Net MoNuSAC, or make the tar file compatible with PyTorch state dict loading?

Hello, I am a student in the College of Engineering and I am trying to learn, and now I am trying to run the code, but it does not work. Is it possible for me to contact you privately, please, to inquire about some things?
Thank you

Hello. I have been trying to use this library to test a few segmentation models on some internal data. I am working with large tiff images of size 30000x30000 tiff images. Due to the size of the image I decided to use the SlidingWindowInferer with HoverNet, however it was not able to run on 1 gpu and kept resulting in an out of memory issue. I then decided to break the image into individual patches and save the individual files. Due to some normalization the values in the matrix are float, if I convert this to integer all the values become 0 and so this is resulting in an empty patch. I am saving them using the tifffile package and then feeding the path to the ResizeInferer. I preserve the floats in the patch and load in the patches with the tifffile package since opencv cannot open them. While I am able to use Resize Inferer, the HoverNet segmentation is coming out completely empty, and the segmentations for Cellpose and Stardist are very similar although I do see more segmentations. The behavior is very odd, directly after segmentation from Stardist the individual patches are giving images such as the following:

Here are some of the parameters I use for Stardist (note some of these params are for other preprocessing or postprocessing steps):
params:
out_activations: '{"dist": None, "stardist": None}'
out_boundary_weights: '{"dist": False, "stardist": True}'
resize: '(256,256)'
overlap: 248
patch: 256
instance_postproc: 'stardist'
padding: '64'
batch_size: '1'
downsample_factor: 1
n_channels: 3
n_rays: '4'

Questions:

1. Is it possible to use the Sliding Window Inferer on 1 gpu, if so what are some key considerations to take when setting the params to allow for this? Any tips would be a great help!
2. I have checked the input images and they seem to be set up properly when inputted, and yet I still receive such odd results (shared screenshot). Do you have any recommendations on things to check for this?
3. Do all the models need to be trained beforehand, or do all the base model versions in the package have a pretrained version that are directly called and can be utilized without training beforehand?

Thank you for your generous sharing！ Could you give more training examples about Hovernet model and Omnipose model, i'm confused about their default setting in this code, e.g. the loss defined in hovernet seems different from your setting.

when i run [lizard_nuclei_segmentation_cellpose.ipynb].(https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/lizard_nuclei_segmentation_cellpose.ipynb)

Found all folds. Skip downloading.
Splitting the files into train, valid, and test sets.
Patch the data... This will take a while...
Extracting train patches to folders..: 100%|██████████| 70/70 [15:35<00:00, 13.36s/it, # of extracted tiles 2451]
Extracting valid patches to folders..: 100%|██████████| 70/70 [17:03<00:00, 14.62s/it, # of extracted tiles 2748]
Extracting test patches to folders..: 100%|██████████| 98/98 [13:35<00:00, 8.32s/it, # of extracted tiles 2627]

AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_7576\871233218.py in
10 im2 = csmp.utils.FileHandler.read_img(imgs[50])
11 im3 = csmp.utils.FileHandler.read_img(imgs[300])
---> 12 mask1 = csmp.utils.FileHandler.read_mask(masks[0], return_all=True)
13 mask2 = csmp.utils.FileHandler.read_mask(masks[50], return_all=True)
14 mask3 = csmp.utils.FileHandler.read_mask(masks[300], return_all=True)

AttributeError: type object 'FileHandler' has no attribute 'read_mask'

how do i fix this issue?

Hi, all

Thanks for your great works! Could you provide the pretrained weights and users could run inference directly without re-train the model again.

Best!