def test_image_classifier_finetune(tmpdir):
with mock_encoded_video_dataset_file() as (
mock_csv,
label_videos,
total_duration,
):
half_duration = total_duration / 2 - 1e-9
datamodule = VideoClassificationData.from_paths(
train_data_path=mock_csv,
clip_sampler="uniform",
clip_duration=half_duration,
video_sampler=SequentialSampler,
decode_audio=False,
)
for sample in datamodule.train_dataset.dataset:
expected_t_shape = 5
assert sample["video"].shape[1] == expected_t_shape
assert len(VideoClassifier.available_models()) > 5
train_transform = {
"post_tensor_transform": Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(8),
RandomShortSideScale(min_size=256, max_size=320),
RandomCrop(244),
RandomHorizontalFlip(p=0.5),
]),
),
]),
"per_batch_transform_on_device": Compose([
ApplyTransformToKey(
key="video",
transform=K.VideoSequential(
K.Normalize(torch.tensor([0.45, 0.45, 0.45]), torch.tensor([0.225, 0.225, 0.225])),
K.augmentation.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
data_format="BCTHW",
same_on_frame=False
)
),
]),
}
datamodule = VideoClassificationData.from_paths(
train_data_path=mock_csv,
clip_sampler="uniform",
clip_duration=half_duration,
video_sampler=SequentialSampler,
decode_audio=False,
train_transform=train_transform
)
model = VideoClassifier(num_classes=datamodule.num_classes, pretrained=False)
trainer = flash.Trainer(fast_dev_run=True)
trainer.finetune(model, datamodule=datamodule)
ApplyTransformToKey(key="video",
transform=K.VideoSequential(
*per_batch_transform_on_device,
data_format="BCTHW",
same_on_frame=False)),
]),
}
# 3. Load the data from directories.
datamodule = VideoClassificationData.from_paths(
train_data_path=os.path.join(_PATH_ROOT, "data/kinetics/train"),
val_data_path=os.path.join(_PATH_ROOT, "data/kinetics/val"),
predict_data_path=os.path.join(_PATH_ROOT, "data/kinetics/predict"),
clip_sampler="uniform",
clip_duration=2,
video_sampler=RandomSampler,
decode_audio=False,
train_transform=make_transform(train_post_tensor_transform),
val_transform=make_transform(val_post_tensor_transform),
predict_transform=make_transform(val_post_tensor_transform),
num_workers=8,
batch_size=8,
)
# 4. List the available models
print(VideoClassifier.available_models())
# out: ['efficient_x3d_s', 'efficient_x3d_xs', ... ,slowfast_r50', 'x3d_m', 'x3d_s', 'x3d_xs']
print(VideoClassifier.get_model_details("x3d_xs"))
# 5. Build the model - `x3d_xs` comes with `nn.Softmax` by default for their `head_activation`.
model = VideoClassifier(model="x3d_xs", num_classes=datamodule.num_classes)
model.serializer = Labels()