def test_trainer_finetune(tmpdir):
model = DummyClassifier()
train_dl = torch.utils.data.DataLoader(DummyDataset())
val_dl = torch.utils.data.DataLoader(DummyDataset())
task = ClassificationTask(model, loss_fn=F.nll_loss)
trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir)
trainer.finetune(task, train_dl, val_dl, strategy=NoFreeze())
def test_finetuning(tmpdir: str, strategy):
train_dl = torch.utils.data.DataLoader(DummyDataset())
val_dl = torch.utils.data.DataLoader(DummyDataset())
task = ImageClassifier(10, backbone="resnet18")
trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir)
if strategy == "cls":
strategy = NoFreeze()
if strategy == 'chocolat' or strategy is None:
with pytest.raises(MisconfigurationException, match="strategy should be provided"):
trainer.finetune(task, train_dl, val_dl, strategy=strategy)
else:
trainer.finetune(task, train_dl, val_dl, strategy=strategy)
def configure_finetune_callback(self):
return [NoFreeze(), NoFreeze()]
def __len__(self) -> int:
return 100
class DummyClassifier(nn.Module):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
self.head = nn.LogSoftmax()
def forward(self, x):
return self.head(self.backbone(x))
@pytest.mark.parametrize("callbacks, should_warn", [([], False),
([NoFreeze()], True)])
def test_trainer_fit(tmpdir, callbacks, should_warn):
model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10),
nn.LogSoftmax())
train_dl = torch.utils.data.DataLoader(DummyDataset())
val_dl = torch.utils.data.DataLoader(DummyDataset())
task = ClassificationTask(model, loss_fn=F.nll_loss)
trainer = Trainer(fast_dev_run=True,
default_root_dir=tmpdir,
callbacks=callbacks)
if should_warn:
with pytest.warns(UserWarning,
match="trainer is using a fine-tuning callback"):
trainer.fit(task, train_dl, val_dl)
else:
predict_transform=make_transform(val_post_tensor_transform),
batch_size=8,
clip_sampler="uniform",
clip_duration=1,
video_sampler=RandomSampler,
decode_audio=False,
num_workers=8)
# 4. List the available models
print(VideoClassifier.available_backbones())
# out: ['efficient_x3d_s', 'efficient_x3d_xs', ... ,slowfast_r50', 'x3d_m', 'x3d_s', 'x3d_xs']
print(VideoClassifier.get_backbone_details("x3d_xs"))
# 5. Build the VideoClassifier with a PyTorchVideo backbone.
model = VideoClassifier(backbone="x3d_xs",
num_classes=datamodule.num_classes,
serializer=Labels(),
pretrained=False)
# 6. Finetune the model
trainer = flash.Trainer(fast_dev_run=True)
trainer.finetune(model, datamodule=datamodule, strategy=NoFreeze())
trainer.save_checkpoint("video_classification.pt")
# 7. Make a prediction
predictions = model.predict(
os.path.join(flash.PROJECT_ROOT, "data/kinetics/predict"))
print(predictions)
# ['marching', 'flying_kite', 'archery', 'high_jump', 'bowling']