🐛 Bug
There seems to be an issue with ImageClassificationData.from_dataset method. It fails to create the expected format, where the labels can be accessed via datamodul.labels.
To Reproduce
The error occured with the following code adapted from the example
...
datamodule=ImageClassificationData.from_datasets(
train_dataset=train_dataset,
val_dataset=valid_dataset,
batch_size = 32
)
# 2. Build the task
model = ImageClassifier(backbone="efficientnet_b0", labels=datamodule.labels)
...The datasets are created via
...
train_val_dataset = datasets.ImageFolder(train_val_folder)
....
train_dataset, valid_dataset = random_split(dataset=train_val_dataset, lengths=[no_train_images ,no_valid_images], generator=torch.Generator().manual_seed(42))
Expected behavior
I'd expect the from_dataset method to create a valid datamodule to use for training.
Environment
- OS (e.g., Linux): Colab instance
- Python version: 3.8 I guess
- PyTorch/Lightning/Flash Version (e.g., 1.10/1.5/0.7): 1.13.0+cu116/1.8.6/0.8.1.post0
🐛 Bug
There seems to be an issue with
ImageClassificationData.from_datasetmethod. It fails to create the expected format, where the labels can be accessed viadatamodul.labels.To Reproduce
The error occured with the following code adapted from the example
The datasets are created via
Expected behavior
I'd expect the
from_datasetmethod to create a valid datamodule to use for training.Environment