Step 4: Accessing and Updating Data
Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
Deep Lake Datasets can be loaded from a variety of storage locations using:
import deeplake
# Local Filepath
ds = deeplake.load('./my_dataset_path') # Similar functionality to deeplake.dataset(path)
# S3
ds = deeplake.load('s3://my_dataset_bucket', creds={...})
# Public Dataset hosted by Activeloop
## Activeloop Storage - See Step 6
ds = deeplake.load('hub://activeloop/public_dataset_name')
# Dataset in another organization on Activeloop Platform
ds = deeplake.load('hub://org_name/dataset_name')
Since
ds = deeplake.dataset(path)can be used to both create and load datasets, you may accidentally create a new dataset if there is a typo in the path you provided while intending to load a dataset. If that occurs, simply use ds.delete() to remove the unintended dataset permanently.Deep Lake allows you to reference specific tensors using keys or via the "." notation outlined below.
Note: data is still not loaded by these commands.
### NO HIERARCHY ###
ds.images # is equivalent to
ds['images']
ds.labels # is equivalent to
ds['labels']
### WITH HIERARCHY ###
ds.localization.boxes # is equivalent to
ds['localization/boxes']
ds.localization.labels # is equivalent to
ds['localization/labels']
Data within the tensors is loaded and accessed using the
.numpy() , .data() , and .tobytes() commands. When the underlying data can be converted to a numpy array, .data() and .numpy() return equivalent objects.# Indexing
img = ds.images[0].numpy() # Fetch the 1st image and return a NumPy array
label = ds.labels[0].numpy(aslist=True) # Fetch the 1st label and store it as a
# as a list
# frame = ds.videos[0][4].numpy() # Fetch the 5th frame in the 1st video
# and return a NumPy array
text_labels = ds.labels[0].data()['value'] # Fetch the first labels and return them as text
# Slicing
imgs = ds.images[0:100].numpy() # Fetch 100 images and return a NumPy array
# The method above produces an exception if
# the images are not all the same size
labels = ds.labels[0:100].numpy(aslist=True) # Fetch 100 labels and store
# them as a list of NumPy arrays
The
.numpy()method produces an exception if all samples in the requested tensor do not have a uniform shape. If that's the case, running .numpy(aslist=True)returns a list of NumPy arrays, where the indices of the list correspond to different samples. Existing data in a Deep Lake dataset can be updated using:
ds.images[1] = deeplake.read('https://i.postimg.cc/Yq2SNz9J/photo-1534567110243-8875d64ca8ff.jpg') # If the URI is not public, credentials should be specified using deeplake.read(URI, creds = {...})
ds.labels[1] = 'giraffe' # Tensors of htype = 'class_label' can be updated with either numeric values or text
Image.fromarray(ds.images[1].numpy())
Last modified 26d ago