Easiest way of fine-tuning HuggingFace video classification models.
π Features
video-transformers uses:
-
π€ accelerate for distributed training, -
π€ evaluate for evaluation, -
pytorchvideo for dataloading
and supports:
-
creating and fine-tunining video models using transformers and timm vision models
-
experiment tracking with neptune, tensorboard and other trackers
-
exporting fine-tuned models in ONNX format
-
pushing fine-tuned models into HuggingFace Hub
-
loading pretrained models from HuggingFace Hub
-
Automated Gradio app, and space creation
π Installation
- Install
Pytorch:
conda install pytorch=1.11.0 torchvision=0.12.0 cudatoolkit=11.3 -c pytorch- Install pytorchvideo and transformers from main branch:
pip install git+https://github.com/facebookresearch/pytorchvideo.git
pip install git+https://github.com/huggingface/transformers.git- Install
video-transformers:
pip install video-transformers
π₯ Usage
- Prepare video classification dataset in such folder structure (.avi and .mp4 extensions are supported):
train_root
label_1
video_1
video_2
...
label_2
video_1
video_2
...
...
val_root
label_1
video_1
video_2
...
label_2
video_1
video_2
...
...- Fine-tune Timesformer (from HuggingFace) video classifier:
from torch.optim import AdamW
from video_transformers import VideoModel
from video_transformers.backbones.transformers import TransformersBackbone
from video_transformers.data import VideoDataModule
from video_transformers.heads import LinearHead
from video_transformers.trainer import trainer_factory
from video_transformers.utils.file import download_ucf6
backbone = TransformersBackbone("facebook/timesformer-base-finetuned-k400", num_unfrozen_stages=1)
download_ucf6("./")
datamodule = VideoDataModule(
train_root="ucf6/train",
val_root="ucf6/val",
batch_size=4,
num_workers=4,
num_timesteps=8,
preprocess_input_size=224,
preprocess_clip_duration=1,
preprocess_means=backbone.mean,
preprocess_stds=backbone.std,
preprocess_min_short_side=256,
preprocess_max_short_side=320,
preprocess_horizontal_flip_p=0.5,
)
head = LinearHead(hidden_size=backbone.num_features, num_classes=datamodule.num_classes)
model = VideoModel(backbone, head)
optimizer = AdamW(model.parameters(), lr=1e-4)
Trainer = trainer_factory("single_label_classification")
trainer = Trainer(datamodule, model, optimizer=optimizer, max_epochs=8)
trainer.fit()- Fine-tune ConvNeXT (from HuggingFace) + Transformer based video classifier:
from torch.optim import AdamW
from video_transformers import TimeDistributed, VideoModel
from video_transformers.backbones.transformers import TransformersBackbone
from video_transformers.data import VideoDataModule
from video_transformers.heads import LinearHead
from video_transformers.necks import TransformerNeck
from video_transformers.trainer import trainer_factory
from video_transformers.utils.file import download_ucf6
backbone = TimeDistributed(TransformersBackbone("facebook/convnext-small-224", num_unfrozen_stages=1))
neck = TransformerNeck(
num_features=backbone.num_features,
num_timesteps=8,
transformer_enc_num_heads=4,
transformer_enc_num_layers=2,
dropout_p=0.1,
)
download_ucf6("./")
datamodule = VideoDataModule(
train_root="ucf6/train",
val_root="ucf6/val",
batch_size=4,
num_workers=4,
num_timesteps=8,
preprocess_input_size=224,
preprocess_clip_duration=1,
preprocess_means=backbone.mean,
preprocess_stds=backbone.std,
preprocess_min_short_side=256,
preprocess_max_short_side=320,
preprocess_horizontal_flip_p=0.5,
)
head = LinearHead(hidden_size=neck.num_features, num_classes=datamodule.num_classes)
model = VideoModel(backbone, head, neck)
optimizer = AdamW(model.parameters(), lr=1e-4)
Trainer = trainer_factory("single_label_classification")
trainer = Trainer(
datamodule,
model,
optimizer=optimizer,
max_epochs=8
)
trainer.fit()- Fine-tune Resnet18 (from HuggingFace) + GRU based video classifier:
from video_transformers import TimeDistributed, VideoModel
from video_transformers.backbones.transformers import TransformersBackbone
from video_transformers.data import VideoDataModule
from video_transformers.heads import LinearHead
from video_transformers.necks import GRUNeck
from video_transformers.trainer import trainer_factory
from video_transformers.utils.file import download_ucf6
backbone = TimeDistributed(TransformersBackbone("microsoft/resnet-18", num_unfrozen_stages=1))
neck = GRUNeck(num_features=backbone.num_features, hidden_size=128, num_layers=2, return_last=True)
download_ucf6("./")
datamodule = VideoDataModule(
train_root="ucf6/train",
val_root="ucf6/val",
batch_size=4,
num_workers=4,
num_timesteps=8,
preprocess_input_size=224,
preprocess_clip_duration=1,
preprocess_means=backbone.mean,
preprocess_stds=backbone.std,
preprocess_min_short_side=256,
preprocess_max_short_side=320,
preprocess_horizontal_flip_p=0.5,
)
head = LinearHead(hidden_size=neck.hidden_size, num_classes=datamodule.num_classes)
model = VideoModel(backbone, head, neck)
Trainer = trainer_factory("single_label_classification")
trainer = Trainer(
datamodule,
model,
max_epochs=8
)
trainer.fit()- Perform prediction for a single file or folder of videos:
from video_transformers import VideoModel
model = VideoModel.from_pretrained(model_name_or_path)
model.predict(video_or_folder_path="video.mp4")
>> [{'filename': "video.mp4", 'predictions': {'class1': 0.98, 'class2': 0.02}}]
π€ Full HuggingFace Integration
- Push your fine-tuned model to the hub:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.push_to_hub('model_name')- Load any pretrained video-transformer model from the hub:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.from_pretrained('account_name/model_name')- Push your model to HuggingFace hub with auto-generated model-cards:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.push_to_hub('account_name/app_name')- (Incoming feature) Push your model as a Gradio app to HuggingFace Space:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.push_to_space('account_name/app_name')
π Multiple tracker support
-
Tensorboard tracker is enabled by default.
-
To add Neptune/Layer ... tracking:
from video_transformers.tracking import NeptuneTracker
from accelerate.tracking import WandBTracker
trackers = [
NeptuneTracker(EXPERIMENT_NAME, api_token=NEPTUNE_API_TOKEN, project=NEPTUNE_PROJECT),
WandBTracker(project_name=WANDB_PROJECT)
]
trainer = Trainer(
datamodule,
model,
trackers=trackers
)
πΈοΈ ONNX support
- Convert your trained models into ONNX format for deployment:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.to_onnx(quantize=False, opset_version=12, export_dir="runs/exports/", export_filename="model.onnx")
π€ Gradio support
- Convert your trained models into Gradio App for deployment:
from video_transformers import VideoModel
model = VideoModel.from_pretrained("runs/exp/checkpoint")
model.to_gradio(examples=['video.mp4'], export_dir="runs/exports/", export_filename="app.py")Contributing
Before opening a PR:
- Install required development packages:
pip install -e ."[dev]"- Reformat with black and isort:
python -m tests.run_code_style format
