Pytorchlightning examples not working

[jmandivarapu1]

I am getting the following errors while I tried to run pytorchlighting based examples. Same for all the algorithms also

   from learn2learn.utils.lightning import EpisodicBatcher
ModuleNotFoundError: No module named 'learn2learn.utils.lightning'; 'learn2learn.utils' is not a package

[jmandivarapu1]

I created subfiles from the github source available

#!/usr/bin/env python3

"""
Example for running few-shot algorithms with the PyTorch Lightning wrappers.
"""

import learn2learn as l2l
import pytorch_lightning as pl
from argparse import ArgumentParser

from utlis import EpisodicBatcher
from utlis import TrackTestAccuracyCallback,NoLeaveProgressBar
# from learn2learn.algorithms import (
#     LightningPrototypicalNetworks,
#     LightningMetaOptNet,
#     LightningMAML,
#     LightningANIL,
# )

from lighting_algo import (
    LightningPrototypicalNetworks,
    # LightningMetaOptNet,
    # LightningMAML,
    # LightningANIL,
)
def main():
    parser = ArgumentParser(conflict_handler="resolve", add_help=True)
    # add model and trainer specific args
    parser = LightningPrototypicalNetworks.add_model_specific_args(parser)
    # parser = LightningMetaOptNet.add_model_specific_args(parser)
    # parser = LightningMAML.add_model_specific_args(parser)
    # parser = LightningANIL.add_model_specific_args(parser)
    parser = pl.Trainer.add_argparse_args(parser)

    # add script-specific args
    parser.add_argument("--algorithm", type=str, default="protonet")
    parser.add_argument("--dataset", type=str, default="mini-imagenet")
    parser.add_argument("--root", type=str, default="~/data")
    parser.add_argument("--meta_batch_size", type=int, default=16)
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()
    dict_args = vars(args)

    pl.seed_everything(args.seed)

    # Create tasksets using the benchmark interface
    if False and args.dataset in ["mini-imagenet", "tiered-imagenet"]:
        data_augmentation = "lee2019"
    else:
        data_augmentation = "normalize"
    tasksets = l2l.vision.benchmarks.get_tasksets(
        name=args.dataset,
        train_samples=args.train_queries + args.train_shots,
        train_ways=args.train_ways,
        test_samples=args.test_queries + args.test_shots,
        test_ways=args.test_ways,
        root=args.root,
        data_augmentation=data_augmentation,
    )
    episodic_data = EpisodicBatcher(
        tasksets.train,
        tasksets.validation,
        tasksets.test,
        epoch_length=args.meta_batch_size * 10,
    )

    # init model
    if args.dataset in ["mini-imagenet", "tiered-imagenet"]:
        model = l2l.vision.models.ResNet12(output_size=args.train_ways)
    else:  # CIFAR-FS, FC100
        model = l2l.vision.models.CNN4(
            output_size=args.train_ways,
            hidden_size=64,
            embedding_size=64*4,
        )
    features = model.features
    classifier = model.classifier

    # init algorithm
    if args.algorithm == "protonet":
        algorithm = LightningPrototypicalNetworks(features=features, **dict_args)
    elif args.algorithm == "maml":
        algorithm = LightningMAML(model, **dict_args)
    elif args.algorithm == "anil":
        algorithm = LightningANIL(features, classifier, **dict_args)
    elif args.algorithm == "metaoptnet":
        algorithm = LightningMetaOptNet(features, **dict_args)

    trainer = pl.Trainer.from_argparse_args(
        args,
        gpus=1,
        accumulate_grad_batches=args.meta_batch_size,
        callbacks=[
           TrackTestAccuracyCallback(),
           NoLeaveProgressBar(),
        ],
    )
    trainer.fit(model=algorithm, datamodule=episodic_data)
    trainer.test(ckpt_path="best")

if __name__ == "__main__":
    main()

But now I started getting below error

  File "/home/ubuntu/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/supporters.py", line 507, in _nested_calc_num_data
    raise TypeError(f'Expected data to be int, Sequence or Mapping, but got {type(data).__name__}')
TypeError: Expected data to be int, Sequence or Mapping, but got Epochifier

[seba-1511]

Hello @jmandivarapu1 ,

Which version of lightning and learn2learn are you using? The import should work in recent versions, and your second error might happen because of newer versions of lightning.

[jmandivarapu1]

Hello @jmandivarapu1 ,

Which version of lightning and learn2learn are you using? The import should work in recent versions, and your second error might happen because of newer versions of lightning.

Hi Just unistalled and reinstalled the package but still the same (run the code from https://github.com/learnables/learn2learn/blob/master/examples/vision/lightning/main.py) and pytorch-lightning==1.3.5

[jmandivarapu1]

Hello @jmandivarapu1 ,

Which version of lightning and learn2learn are you using? The import should work in recent versions, and your second error might happen because of newer versions of lightning.

Can you also let me know what version of pytorch-lighting did authors use while building those examples. I can install that version

[seba-1511]

As far as I know our lightning implementations are only compatible with version 1.0.2 (there were breaking changes after that). Can you try that one and let me know if you still see the errors above?

[jmandivarapu1]

1.0.2

Yeah it works now which is okay for me as of now. But it only works for my code which created as shown in the second comment from the top but doesn't work with the lear2learn latest code as you can see that lighting examples are under learn2learn/learn2learn/algorithms/lightning/ but the in the code the import is from from learn2learn.algorithms import