How to use hub - 10 common examples

def download():
    vol = hub.load(name='imagenet/image:val')[400:600]
    a = (vol.mean(axis=(1,2,3)) == 0).sum()
    print(vol.mean(axis=(1,2,3)) == 0)

def test_public_access_no_creds():
    x = hub.load('imagenet')
    assert x[0].mean() == 1

import hub
import numpy as np

def download():
    vol = hub.load(name='imagenet/image:val')[400:600]
    a = (vol.mean(axis=(1,2,3)) == 0).sum()
    print(vol.mean(axis=(1,2,3)) == 0)

mnist = hub.array((50000, 28, 28, 1), name="jason/mnist:v2", dtype='float32')
mnist[0, :] = np.random.random((1, 28, 28, 1)).astype('float32')

print(mnist[0,0,0,0])
# TODO load
mnist = hub.load(name='jason/mnist:v1')

print(mnist[0].shape)
print(mnist[0,0,0,0])

def test_aws_wo_hub_creds():
    os.system('mv ~/.hub ~/.hub_arxiv')
    import hub
    x = hub.array((100, 100, 100), 'image/test:smth', dtype='uint8')
    print(x.shape)
    os.system('mv ~/.hub_arxiv ~/.hub')

def test_wo_aws_or_hub_creds():
    os.system('mv ~/.aws ~/.aws_arxiv')
    os.system('mv ~/.hub ~/.hub_arxiv')
    try:
        import hub
        x = hub.array((100, 100, 100), 'image/test:smth', dtype='uint8')
        print(x.shape)
    except Exception as err:
        print('pass', err)
        pass
    os.system('mv ~/.hub_arxiv ~/.hub')
    os.system('mv ~/.aws_arxiv ~/.aws')

import hub
import numpy as np
x = hub.array((10,10,10,10), name="davit/example:1", dtype='uint8')
#[0] = np.zeros((1,10,10,10), dtype='uint8') # need to assign
x[1,0,0,0] = 1

import hub
import numpy as np

shape = (10, 10, 10)
x = hub.array(shape, name="test/example:1", dtype='uint8')
x[10]

def test_pytorch():
    # Create arrays
    datahub = hub.fs("./data/cache").connect()
    images = datahub.array(
        name="test/dataloaders/images3",
        shape=(100, 100, 100),
        chunk=(1, 100, 100),
        dtype="uint8",
    )
    labels = datahub.array(
        name="test/dataloaders/labels3", shape=(100, 1), chunk=(100, 1), dtype="uint8"
    )
    # Create dataset
    ds = datahub.dataset(
        name="test/loaders/dataset2", components={"images": images, "labels": labels}
    )
    # Transform to Pytorch
    train_dataset = ds.to_pytorch()
    # Create data loader

def test_broadcasting():
    print("- Broadcasting")
    datahub = hub.fs("./data/cache").connect()
    shape = (100, 100, 100)
    chunk = (50, 50, 50)
    x = datahub.array(name="test/example:3", shape=shape, chunk=chunk, dtype="uint8")
    x[0, 0, 0] = 11
    assert x[0, 0, 0] == 11
    x[0] = 10
    assert x[0].mean() == 10
    x[1] = np.ones((100, 100), dtype="uint8")
    assert x[1].mean() == 1
    x[3, 90, :] = np.ones((1, 1, 100), dtype="uint8")
    assert x[3, 90].mean() == 1
    print("passed")

def test_dataset():
    datahub = hub.fs("./data/cache").connect()
    x = datahub.array(name='test/example:input', shape = (100, 25, 25), chunk=(20, 5, 5), dtype='uint8')
    y = datahub.array(name='test/example:label', shape = (100, 4), chunk = (20, 2), dtype='uint8')

    ds = datahub.dataset(components={
        'input': x,
        'label': y
    }, name='test/dataset:train3')
    assert ds[0]['input'].shape == (25, 25)
    assert ds['input'].shape[0] == 100   # return single array
    assert ds['label', 0].mean() == 0  # equivalent ds['train'][0]