Need help in dealing with out of bounds

Beginners
2

Solved by putting these limiters on the code:

from torchvision.transforms import ColorJitter
from transformers import SegformerFeatureExtractor


jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)

def train_transforms(example_batch):
    # images = [jitter(x) for x in example_batch['pixel_values']]
    # labels = [x.convert("L") for x in example_batch['label']]
    # inputs = feature_extractor(images, labels)
    # return inputs
    images = [jitter(x) for x in example_batch['pixel_values']]
    labels = [x.convert("L") for x in example_batch['label']]  # Convert label images to L mode (greyscale)
    labels = [np.array(label) for label in labels]  # Convert labels to numpy arrays for clamping
    labels = [np.clip(label, 0, num_labels - 1) for label in labels]  # Clamp labels to the valid range
    inputs = feature_extractor(images, labels, return_tensors="pt")
    return inputs

def val_transforms(example_batch):
    # images=[x for x in example_batch['pixel_values']]
    # labels = [x.convert("L") for x in example_batch['label']]
    # inputs = feature_extractor(images, labels)

    # return inputs
    images = [x for x in example_batch['pixel_values']]
    labels = [x.convert("L") for x in example_batch['label']]
    labels = [np.array(label) for label in labels]
    labels = [np.clip(label, 0, num_labels - 1) for label in labels]
    inputs = feature_extractor(images, labels, return_tensors="pt")
    return inputs

# Set transforms
train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)

And correcting this, I didn’t realize I redefining it again with empty feature extractor ()


from transformers import SegformerFeatureExtractor

feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")

I will update as to how it works after reading the docs, but so far I need to handle the training memories… what is a good epoch for a model and how do you decide that?

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