Example on how load and save from Hugging Face Hub 🔗

from huggingface_hub import notebook_login

/opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm

notebook_login()

ModuleNotFoundError Traceback (most recent call last)

File /opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/huggingface_hub/_login.py:340, in notebook_login(new_session, write_permission) 339 try: */} 340 import ipywidgets.widgets as widgets # type: ignore 341 from IPython.display import display # type: ignore

ModuleNotFoundError: No module named 'ipywidgets'

During handling of the above exception, another exception occurred:

ImportError Traceback (most recent call last)

Cell In[4], line 1 -- */} 1 notebook_login()

File /opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/huggingface_hub/utils/_deprecation.py:101, in _deprecate_arguments.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs) 99 message += "\n\n" + custom_message 100 warnings.warn(message, FutureWarning) */} 101 return f(*args, **kwargs)

File /opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/huggingface_hub/utils/_deprecation.py:31, in _deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs) 29 extra_args = len(args) - len(all_args) 30 if extra_args <= 0:

*/} 31 return f(*args, **kwargs) 32 # extra_args > 0 33 args_msg = [ 34 f"{name}='{arg}'" if isinstance(arg, str) else f"{name}={arg}" 35 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:]) 36 ]

File /opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/huggingface_hub/_login.py:343, in notebook_login(new_session, write_permission) 341 from IPython.display import display # type: ignore 342 except ImportError: */} 343 raise ImportError( 344 "The notebook_login function can only be used in a notebook (Jupyter or" 345 " Colab) and you need the ipywidgets module: pip install ipywidgets." 346 ) 347 if not new_session and get_token() is not None: 348 logger.info("User is already logged in.")

ImportError: The notebook_login function can only be used in a notebook (Jupyter or Colab) and you need the ipywidgets module: pip install ipywidgets.

import albumentations as A
 
transform = A.Compose(
    [
        A.RandomCrop(256, 256),
        A.HorizontalFlip(),
        A.RandomBrightnessContrast(),
        A.RGBShift(),
        A.Normalize(),
    ],
    seed=137,
    strict=True,
)
 
evaluation_transform = A.Compose(
    [
        A.PadIfNeeded(256, 256),
        A.Normalize(),
    ],
    seed=137,
    strict=True,
)
 
transform.save_pretrained("qubvel-hf/albu", key="train")
# ^ this will save the transform to a directory "qubvel-hf/albu" with filename "albumentations_config_train.json"
 
transform.save_pretrained("qubvel-hf/albu", key="train", push_to_hub=True)
# ^ this will save the transform to a directory "qubvel-hf/albu" with filename "albumentations_config_train.json"
# + push the transform to the Hub to the repository "qubvel-hf/albu"
 
transform.push_to_hub("qubvel-hf/albu", key="train")
# ^ this will push the transform to the Hub to the repository "qubvel-hf/albu" (without saving it locally)
 
loaded_transform = A.Compose.from_pretrained("qubvel-hf/albu", key="train")
# ^ this will load the transform from local folder if exist or from the Hub repository "qubvel-hf/albu"
 
evaluation_transform.save_pretrained("qubvel-hf/albu", key="eval", push_to_hub=True)
# ^ this will save the transform to a directory "qubvel-hf/albu" with filename "albumentations_config_eval.json"
 
loaded_evaluation_transform = A.Compose.from_pretrained("qubvel-hf/albu", key="eval")
# ^ this will load the transform from the Hub repository "qubvel-hf/albu"

# check
import numpy as np
 
image = np.random.randint(0, 255, (100, 200, 3), dtype=np.uint8)
 
preprocessed_image_1 = evaluation_transform(image=image)["image"]
preprocessed_image_2 = loaded_evaluation_transform(image=image)["image"]
 
assert np.allclose(preprocessed_image_1, preprocessed_image_2)

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Example on how load and save from Hugging Face Hub 🔗