how to run functions

[Palashio]

so i've got a set of 3D images (128, 128, 128) (both images and their appropriate masks). I want to run this implementation of UNet on it. How would I do this? They're .nii files so i've coded up my on dataset loader for it. I'm a bit confused on where to get started as there aren't enough reference resources.

[Palashio]

I have it working so that this is what my training script looks like so far:

    for epoch in range(epochs):
        print(' - training - ')
        for i, (images, masks) in enumerate(train_loader):
            images = images.to(device)
            masks = masks.to(device)

where images, masks are both sizes (1, 1, 128, 128, 128) and train_loader looks like this:

train_loader = DataLoader(dataset=Dataset(partition['orig'], partition['segment']), 
                          batch_size = batch_size, shuffle = True)

where partition['orig] is the original image and partition['segment'] is the segmented image. the batch size is also 1.

[zyl200846]

i will update info this weekend so that you can run the code successfully. Its been busy since i joined the job. sorry for causing inconvenience.

best regards，

Jielong

Thanks & regards, Jielong a Hong

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I have it working so that this is what my training script looks like so far:

for epoch in range(epochs):
    print(' - training - ')
    for i, (images, masks) in enumerate(train_loader):
        images = images.to(device)
        masks = masks.to(device)

where images, masks are both sizes (1, 1, 128, 128, 128) and train_loader looks like this:

train_loader = DataLoader(dataset=Dataset(partition['orig'], partition['segment']), batch_size = batch_size, shuffle = True)

where partition['orig] is the original image and partition['segment'] is the segmented image. the batch size is also 1.

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[NadiyaC249]

why i got a vary poor performance on the model ？（use the brats dataset to segment brain tumor）