Hi,
I’m finetuning the Google’s Vision Transformer over my custom dataset. I am formatting my dataset to a HuggingFace’s Dataset format in the following manner:
This is the initial csv:
Each example from the dataset should have 3 features:
- image: A PIL Image
- image_file_path: The
strpath to the image file that was loaded as image - labels: A
datasets.ClassLabelfeature, which is an integer representation of the label.
I am casting it to an Image Dataset as instructed in the official HuggingFace Docs
paths_dict = {'image': df['Image_Path'].tolist()}
with output:
{'image': ['/kaggle/working/PendantImages/Pendant_UG00198-1Y0000_1_lar.jpg',
'/kaggle/working/EarringsImages/Earring_BISJ0029S17_YAA18DIG6XXXXXXXX_ABCD00-PICS-00004-1024-5542.png',
'/kaggle/working/BangleImages/Bangle_513220VIZ2A00_1.jpg',
'/kaggle/working/EarringsImages/Earring_BISP0419S13_YAA18DIG6XXXXXXXX_ABCD00-PICS-00004-1024-37602.png',
'/kaggle/working/NecklaceImages/Necklace_BIDK0846N11_YAA18DIG6XXXXXXXX_ABCD00-PICS-00003-1024-65037.png',
'/kaggle/working/EarringsImages/Earring_BICM0379S07_YAA18DIG6XXXXXXXX_ABCD00-PICS-00004-1024-53001.png', ...}
Here’s the cast to image format operation:
from datasets import Dataset, Image
hub_dataset = Dataset.from_dict(paths_dict, split = "train").cast_column("image", Image())
with output:
Dataset({
features: ['image'],
num_rows: 6278
})
And here’s the rest columns cast operations with final results:
hub_dataset = hub_dataset.add_column('image_file_path', df['Image_Path'])
hub_dataset = hub_dataset.add_column('labels', df['Class'])
hub_dataset = hub_dataset.cast_column('labels', datasets.features.features.ClassLabel(names=[1,2,3,4]))
hub_dataset.features
with output:
{'image': Image(decode=True, id=None),
'image_file_path': Value(dtype='string', id=None),
'labels': ClassLabel(num_classes=4, names=[1, 2, 3, 4], id=None)}
Here’s the processor function and the collate function for preprocessing it for ViT
from transformers import ViTImageProcessor
model_name = 'google/vit-base-patch16-224-in21k'
processor = ViTImageProcessor.from_pretrained(model_name)
def transform(example_batch):
# Take a list of PIL images and turn them to pixel values
inputs = processor([x for x in example_batch['image']], return_tensors='pt')
inputs['labels'] = example_batch['labels']
return inputs
prepared_df = hub_dataset.with_transform(transform)
def collate_fn(batch):
return {
'pixel_values': torch.stack([x['pixel_values'] for x in batch]),
'labels': torch.tensor([x['labels'] for x in batch])
}
Here’s the model that I created:
labels = hub_dataset.features['labels'].names #[1,2,3,4]
model = ViTForImageClassification.from_pretrained(
model_name,
num_labels=len(labels),
id2label={str(i): c for i, c in enumerate(labels)},
label2id={c: str(i) for i, c in enumerate(labels)}
)
The training args:
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir = "/kaggle/working/vit-base-jwlry",
# per_device_train_batch_size = 16,
evaluation_strategy = "steps",
# num_train_epochs = 4,
fp16 = True,
# use_cpu = True,
# save_steps = 100,
# eval_steps = 100,
# logging_steps = 10,
learning_rate = 2e-4,
save_total_limit = 2,
remove_unused_columns = False,
push_to_hub = False,
report_to = 'tensorboard',
load_best_model_at_end = True
)
trainer = Trainer(
model = model,
args = training_args,
data_collator = collate_fn,
compute_metrics = compute_metrics,
train_dataset = prepared_df[0:100],
eval_dataset = prepared_df[0:100],
tokenizer = processor
)
I am encountering the error in the train() step:
train_results = trainer. Train()
This is the exact error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[38], line 1
----> 1 train_results = trainer.train()
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1537, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1535 hf_hub_utils.enable_progress_bars()
1536 else:
-> 1537 return inner_training_loop(
1538 args=args,
1539 resume_from_checkpoint=resume_from_checkpoint,
1540 trial=trial,
1541 ignore_keys_for_eval=ignore_keys_for_eval,
1542 )
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1821, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
1818 rng_to_sync = True
1820 step = -1
-> 1821 for step, inputs in enumerate(epoch_iterator):
1822 total_batched_samples += 1
1824 if self.args.include_num_input_tokens_seen:
File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:448, in DataLoaderShard.__iter__(self)
446 # We iterate one batch ahead to check when we are at the end
447 try:
--> 448 current_batch = next(dataloader_iter)
449 except StopIteration:
450 yield
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)
631 if self._sampler_iter is None:
632 # TODO(https://github.com/pytorch/pytorch/issues/76750)
633 self._reset() # type: ignore[call-arg]
--> 634 data = self._next_data()
635 self._num_yielded += 1
636 if self._dataset_kind == _DatasetKind.Iterable and \
637 self._IterableDataset_len_called is not None and \
638 self._num_yielded > self._IterableDataset_len_called:
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)
676 def _next_data(self):
677 index = self._next_index() # may raise StopIteration
--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
679 if self._pin_memory:
680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/transformers/feature_extraction_utils.py:88, in BatchFeature.__getitem__(self, item)
86 return self.data[item]
87 else:
---> 88 raise KeyError("Indexing with integers is not available when using Python based feature extractors")
KeyError: 'Indexing with integers is not available when using Python based feature extractors'
Can anyone help me identify the issue, and suggest some workarounds for it? I am running it on a kaggle notebook with following specs:
Disk: 73 GB
CPU and RAM: 29GB
GPU: T4 x2 of 15GB each
Here’s the guide i am following for finetuning ViT for my dataset: Fine-Tune ViT for Image Classification with 
Transformers (huggingface.co)