[FEATURE]

Author: neyazbasheerCreated Feb 14, 2023Updated Sep 24, 2024

Labelsenhancement

Feature Request

Related to an existing Issue
A new implementation (Improvement, Extension)

Is your feature request related to a problem?

python

@deeplake.compute
def resize(sample_in, sample_out, new_size):
    ## First two arguments are always default arguments containing:
    #     1st argument is an element of the input iterable (list, dataset, array,...)
    #     2nd argument is a dataset sample
    ## Third argument is the required size for the output images
    
    # Append the label and image to the output sample
    sample_out.labels.append(sample_in.labels.numpy())
    sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))
    sample_out.info.append(sample_in.info.dict())
    
    return sample_out

In the above example when new samples are computed we need to make sure all tensors are passed to the sample_out even if it's unmodified. It will be really helpful if we can pipe them directly without explicitly having to get them as numpy or dict.

Description of the possible solution

python

@deeplake.compute
def resize(sample_in, sample_out, new_size):

    
    # Append the label and image to the output sample
    sample_out.labels.append(sample_in.labels)
    sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))
    sample_out.info.append(sample_in.info)
    return sample_out

An alternative solution to the problem can look like

python

@deeplake.compute
def resize(sample_in, sample_out, new_size):

    
    # Append the label and image to the output sample
    sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))
    sample_out.pipe_missing(sample_in)
    return sample_out

it could also be an additional decorator or an option in the existing decorator.

python

@deeplake.compute(pipe_missing=True)
def resize(sample_in, sample_out, new_size):

    
    # Append the label and image to the output sample
    sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))
    return sample_out

Source: activeloopai/deeplake

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