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308 | class ZambaVideoClassificationLightningModule(LightningModule):
def __init__(
self,
species: List[str],
lr: float = 1e-3,
scheduler: Optional[str] = None,
scheduler_params: Optional[dict] = None,
**kwargs,
):
super().__init__()
if (scheduler is None) and (scheduler_params is not None):
warnings.warn(
"scheduler_params provided without scheduler. scheduler_params will have no effect."
)
self.lr = lr
self.species = species
self.num_classes = len(species)
if scheduler is not None:
self.scheduler = torch.optim.lr_scheduler.__dict__[scheduler]
else:
self.scheduler = scheduler
self.scheduler_params = scheduler_params
self.model_class = type(self).__name__
self.save_hyperparameters("lr", "scheduler", "scheduler_params", "species")
self.hparams["model_class"] = self.model_class
self.training_step_outputs = []
self.validation_step_outputs = []
self.test_step_outputs = []
def forward(self, x):
return self.model(x)
def on_train_start(self):
metrics = {"val_macro_f1": {}}
if self.num_classes > 2:
metrics.update(
{f"val_top_{k}_accuracy": {} for k in DEFAULT_TOP_K if k < self.num_classes}
)
else:
metrics.update({"val_accuracy": {}})
# write hparams to hparams.yaml file, log metrics to tb hparams tab
self.logger.log_hyperparams(self.hparams, metrics)
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.binary_cross_entropy_with_logits(y_hat, y)
self.log("train_loss", loss.detach())
self.training_step_outputs.append(loss)
return loss
def _val_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.binary_cross_entropy_with_logits(y_hat, y)
self.log("val_loss", loss.detach())
y_proba = torch.sigmoid(y_hat.cpu()).numpy()
return {
"y_true": y.cpu().numpy().astype(int),
"y_pred": y_proba.round().astype(int),
"y_proba": y_proba,
}
def validation_step(self, batch, batch_idx):
output = self._val_step(batch, batch_idx)
self.validation_step_outputs.append(output)
return output
def test_step(self, batch, batch_idx):
output = self._val_step(batch, batch_idx)
self.test_step_outputs.append(output)
return output
@staticmethod
def aggregate_step_outputs(
outputs: Dict[str, np.ndarray]
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
y_true = np.vstack([output["y_true"] for output in outputs])
y_pred = np.vstack([output["y_pred"] for output in outputs])
y_proba = np.vstack([output["y_proba"] for output in outputs])
return y_true, y_pred, y_proba
def compute_and_log_metrics(
self, y_true: np.ndarray, y_pred: np.ndarray, y_proba: np.ndarray, subset: str
):
self.log(
f"{subset}_macro_f1",
f1_score(y_true, y_pred, average="macro", zero_division=0),
)
# if only two classes, skip top_k accuracy since not enough classes
if self.num_classes > 2:
for k in DEFAULT_TOP_K:
if k < self.num_classes:
self.log(
f"{subset}_top_{k}_accuracy",
top_k_accuracy_score(
y_true.argmax(
axis=1
), # top k accuracy only supports single label case
y_proba,
labels=np.arange(y_proba.shape[1]),
k=k,
),
)
else:
self.log(f"{subset}_accuracy", accuracy_score(y_true, y_pred))
for metric_name, label, metric in compute_species_specific_metrics(
y_true, y_pred, self.species
):
self.log(f"species/{subset}_{metric_name}/{label}", metric)
def on_validation_epoch_end(self):
"""Aggregates validation_step outputs to compute and log the validation macro F1 and top K
metrics.
Args:
outputs (List[dict]): list of output dictionaries from each validation step
containing y_pred and y_true.
"""
y_true, y_pred, y_proba = self.aggregate_step_outputs(self.validation_step_outputs)
self.compute_and_log_metrics(y_true, y_pred, y_proba, subset="val")
self.validation_step_outputs.clear() # free memory
def on_test_epoch_end(self):
y_true, y_pred, y_proba = self.aggregate_step_outputs(self.test_step_outputs)
self.compute_and_log_metrics(y_true, y_pred, y_proba, subset="test")
self.test_step_outputs.clear() # free memory
def predict_step(self, batch, batch_idx, dataloader_idx: Optional[int] = None):
x, y = batch
y_hat = self(x)
pred = torch.sigmoid(y_hat).cpu().numpy()
return pred
def _get_optimizer(self):
return torch.optim.Adam(filter(lambda p: p.requires_grad, self.parameters()), lr=self.lr)
def configure_optimizers(self):
"""
Setup the Adam optimizer. Note, that this function also can return a lr scheduler, which is
usually useful for training video models.
"""
optim = self._get_optimizer()
if self.scheduler is None:
return optim
else:
return {
"optimizer": optim,
"lr_scheduler": self.scheduler(
optim, **({} if self.scheduler_params is None else self.scheduler_params)
),
}
def to_disk(self, path: os.PathLike):
"""Save out model weights to a checkpoint file on disk.
Note: this does not include callbacks, optimizer_states, or lr_schedulers.
To include those, use `Trainer.save_checkpoint()` instead.
"""
checkpoint = {
"state_dict": self.state_dict(),
"hyper_parameters": self.hparams,
"global_step": self.global_step,
"pytorch-lightning_version": pl.__version__,
}
torch.save(checkpoint, path)
@classmethod
def from_disk(cls, path: os.PathLike, **kwargs):
# note: we always load models onto CPU; moving to GPU is handled by `devices` in pl.Trainer
return cls.load_from_checkpoint(path, map_location="cpu", **kwargs)
|