gtr_runner
dreem.models.gtr_runner
¶
Module containing training, validation and inference logic.
GTRRunner
¶
Bases: LightningModule
A lightning wrapper around GTR model.
Used for training, validation and inference.
Source code in dreem/models/gtr_runner.py
class GTRRunner(LightningModule):
"""A lightning wrapper around GTR model.
Used for training, validation and inference.
"""
DEFAULT_METRICS = {
"train": [],
"val": ["num_switches"],
"test": ["num_switches"],
}
DEFAULT_TRACKING = {
"train": False,
"val": True,
"test": True,
}
def __init__(
self,
model_cfg: dict = None,
tracker_cfg: dict = None,
loss_cfg: dict = None,
optimizer_cfg: dict = None,
scheduler_cfg: dict = None,
metrics: dict[str, list[str]] = None,
persistent_tracking: dict[str, bool] = None,
):
"""Initialize a lightning module for GTR.
Args:
model_cfg: hyperparameters for GlobalTrackingTransformer
tracker_cfg: The parameters used for the tracker post-processing
loss_cfg: hyperparameters for AssoLoss
optimizer_cfg: hyper parameters used for optimizer.
Only used to overwrite `configure_optimizer`
scheduler_cfg: hyperparameters for lr_scheduler used to overwrite `configure_optimizer
metrics: a dict containing the metrics to be computed during train, val, and test.
persistent_tracking: a dict containing whether to use persistent tracking during train, val and test inference.
"""
super().__init__()
self.save_hyperparameters()
model_cfg = model_cfg if model_cfg else {}
loss_cfg = loss_cfg if loss_cfg else {}
tracker_cfg = tracker_cfg if tracker_cfg else {}
_ = model_cfg.pop("ckpt_path", None)
self.model = GlobalTrackingTransformer(**model_cfg)
self.loss = AssoLoss(**loss_cfg)
self.tracker = Tracker(**tracker_cfg)
self.optimizer_cfg = optimizer_cfg
self.scheduler_cfg = scheduler_cfg
self.metrics = metrics if metrics is not None else self.DEFAULT_METRICS
self.persistent_tracking = (
persistent_tracking
if persistent_tracking is not None
else self.DEFAULT_TRACKING
)
def forward(
self,
ref_instances: list["dreem.io.Instance"],
query_instances: list["dreem.io.Instance"] = None,
) -> torch.Tensor:
"""Execute forward pass of the lightning module.
Args:
ref_instances: a list of `Instance` objects containing crops and other data needed for transformer model
query_instances: a list of `Instance` objects used as queries in the decoder. Mostly used for inference.
Returns:
An association matrix between objects
"""
asso_preds = self.model(ref_instances, query_instances)
return asso_preds
def training_step(
self, train_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single training step for model.
Args:
train_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the train loss plus any other metrics specified
"""
result = self._shared_eval_step(train_batch[0], mode="train")
self.log_metrics(result, len(train_batch[0]), "train")
return result
def validation_step(
self, val_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single val step for model.
Args:
val_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the val loss plus any other metrics specified
"""
result = self._shared_eval_step(val_batch[0], mode="val")
self.log_metrics(result, len(val_batch[0]), "val")
return result
def test_step(
self, test_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single test step for model.
Args:
test_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the val loss plus any other metrics specified
"""
result = self._shared_eval_step(test_batch[0], mode="test")
self.log_metrics(result, len(test_batch[0]), "test")
return result
def predict_step(
self, batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> list["dreem.io.Frame"]:
"""Run inference for model.
Computes association + assignment.
Args:
batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A list of dicts where each dict is a frame containing the predicted track ids
"""
frames_pred = self.tracker(self.model, batch[0])
return frames_pred
def _shared_eval_step(
self, frames: list["dreem.io.Frame"], mode: str
) -> dict[str, float]:
"""Run evaluation used by train, test, and val steps.
Args:
frames: A list of dicts where each dict is a frame containing gt data
mode: which metrics to compute and whether to use persistent tracking or not
Returns:
a dict containing the loss and any other metrics specified by `eval_metrics`
"""
try:
instances = [instance for frame in frames for instance in frame.instances]
if len(instances) == 0:
return None
eval_metrics = self.metrics[mode]
persistent_tracking = self.persistent_tracking[mode]
logits = self(instances)
logits = [asso.matrix for asso in logits]
loss = self.loss(logits, frames)
return_metrics = {"loss": loss}
if eval_metrics is not None and len(eval_metrics) > 0:
self.tracker.persistent_tracking = persistent_tracking
frames_pred = self.tracker(self.model, frames)
frames_mm = metrics.to_track_eval(frames_pred)
clearmot = metrics.get_pymotmetrics(frames_mm, eval_metrics)
return_metrics.update(clearmot.to_dict())
return_metrics["batch_size"] = len(frames)
except Exception as e:
print(f"Failed on frame {frames[0].frame_id} of video {frames[0].video_id}")
raise (e)
return return_metrics
def configure_optimizers(self) -> dict:
"""Get optimizers and schedulers for training.
Is overridden by config but defaults to Adam + ReduceLROnPlateau.
Returns:
an optimizer config dict containing the optimizer, scheduler, and scheduler params
"""
# todo: init from config
if self.optimizer_cfg is None:
optimizer = torch.optim.Adam(self.parameters(), lr=1e-4, betas=(0.9, 0.999))
else:
optimizer = init_optimizer(self.parameters(), self.optimizer_cfg)
if self.scheduler_cfg is None:
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, "min", 0.5, 10
)
else:
scheduler = init_scheduler(optimizer, self.scheduler_cfg)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
"interval": "epoch",
"frequency": 10,
},
}
def log_metrics(self, result: dict, batch_size: int, mode: str) -> None:
"""Log metrics computed during evaluation.
Args:
result: A dict containing metrics to be logged.
batch_size: the size of the batch used to compute the metrics
mode: One of {'train', 'test' or 'val'}. Used as prefix while logging.
"""
if result:
batch_size = result.pop("batch_size")
for metric, val in result.items():
if isinstance(val, torch.Tensor):
val = val.item()
self.log(f"{mode}_{metric}", val, batch_size=batch_size)
def on_validation_epoch_end(self):
"""Execute hook for validation end.
Currently, we simply clear the gpu cache and do garbage collection.
"""
gc.collect()
torch.cuda.empty_cache()
__init__(model_cfg=None, tracker_cfg=None, loss_cfg=None, optimizer_cfg=None, scheduler_cfg=None, metrics=None, persistent_tracking=None)
¶
Initialize a lightning module for GTR.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model_cfg |
dict
|
hyperparameters for GlobalTrackingTransformer |
None
|
tracker_cfg |
dict
|
The parameters used for the tracker post-processing |
None
|
loss_cfg |
dict
|
hyperparameters for AssoLoss |
None
|
optimizer_cfg |
dict
|
hyper parameters used for optimizer.
Only used to overwrite |
None
|
scheduler_cfg |
dict
|
hyperparameters for lr_scheduler used to overwrite `configure_optimizer |
None
|
metrics |
dict[str, list[str]]
|
a dict containing the metrics to be computed during train, val, and test. |
None
|
persistent_tracking |
dict[str, bool]
|
a dict containing whether to use persistent tracking during train, val and test inference. |
None
|
Source code in dreem/models/gtr_runner.py
def __init__(
self,
model_cfg: dict = None,
tracker_cfg: dict = None,
loss_cfg: dict = None,
optimizer_cfg: dict = None,
scheduler_cfg: dict = None,
metrics: dict[str, list[str]] = None,
persistent_tracking: dict[str, bool] = None,
):
"""Initialize a lightning module for GTR.
Args:
model_cfg: hyperparameters for GlobalTrackingTransformer
tracker_cfg: The parameters used for the tracker post-processing
loss_cfg: hyperparameters for AssoLoss
optimizer_cfg: hyper parameters used for optimizer.
Only used to overwrite `configure_optimizer`
scheduler_cfg: hyperparameters for lr_scheduler used to overwrite `configure_optimizer
metrics: a dict containing the metrics to be computed during train, val, and test.
persistent_tracking: a dict containing whether to use persistent tracking during train, val and test inference.
"""
super().__init__()
self.save_hyperparameters()
model_cfg = model_cfg if model_cfg else {}
loss_cfg = loss_cfg if loss_cfg else {}
tracker_cfg = tracker_cfg if tracker_cfg else {}
_ = model_cfg.pop("ckpt_path", None)
self.model = GlobalTrackingTransformer(**model_cfg)
self.loss = AssoLoss(**loss_cfg)
self.tracker = Tracker(**tracker_cfg)
self.optimizer_cfg = optimizer_cfg
self.scheduler_cfg = scheduler_cfg
self.metrics = metrics if metrics is not None else self.DEFAULT_METRICS
self.persistent_tracking = (
persistent_tracking
if persistent_tracking is not None
else self.DEFAULT_TRACKING
)
configure_optimizers()
¶
Get optimizers and schedulers for training.
Is overridden by config but defaults to Adam + ReduceLROnPlateau.
Returns:
| Type | Description |
|---|---|
dict
|
an optimizer config dict containing the optimizer, scheduler, and scheduler params |
Source code in dreem/models/gtr_runner.py
def configure_optimizers(self) -> dict:
"""Get optimizers and schedulers for training.
Is overridden by config but defaults to Adam + ReduceLROnPlateau.
Returns:
an optimizer config dict containing the optimizer, scheduler, and scheduler params
"""
# todo: init from config
if self.optimizer_cfg is None:
optimizer = torch.optim.Adam(self.parameters(), lr=1e-4, betas=(0.9, 0.999))
else:
optimizer = init_optimizer(self.parameters(), self.optimizer_cfg)
if self.scheduler_cfg is None:
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, "min", 0.5, 10
)
else:
scheduler = init_scheduler(optimizer, self.scheduler_cfg)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
"interval": "epoch",
"frequency": 10,
},
}
forward(ref_instances, query_instances=None)
¶
Execute forward pass of the lightning module.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ref_instances |
list[Instance]
|
a list of |
required |
query_instances |
list[Instance]
|
a list of |
None
|
Returns:
| Type | Description |
|---|---|
Tensor
|
An association matrix between objects |
Source code in dreem/models/gtr_runner.py
def forward(
self,
ref_instances: list["dreem.io.Instance"],
query_instances: list["dreem.io.Instance"] = None,
) -> torch.Tensor:
"""Execute forward pass of the lightning module.
Args:
ref_instances: a list of `Instance` objects containing crops and other data needed for transformer model
query_instances: a list of `Instance` objects used as queries in the decoder. Mostly used for inference.
Returns:
An association matrix between objects
"""
asso_preds = self.model(ref_instances, query_instances)
return asso_preds
log_metrics(result, batch_size, mode)
¶
Log metrics computed during evaluation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
result |
dict
|
A dict containing metrics to be logged. |
required |
batch_size |
int
|
the size of the batch used to compute the metrics |
required |
mode |
str
|
One of {'train', 'test' or 'val'}. Used as prefix while logging. |
required |
Source code in dreem/models/gtr_runner.py
def log_metrics(self, result: dict, batch_size: int, mode: str) -> None:
"""Log metrics computed during evaluation.
Args:
result: A dict containing metrics to be logged.
batch_size: the size of the batch used to compute the metrics
mode: One of {'train', 'test' or 'val'}. Used as prefix while logging.
"""
if result:
batch_size = result.pop("batch_size")
for metric, val in result.items():
if isinstance(val, torch.Tensor):
val = val.item()
self.log(f"{mode}_{metric}", val, batch_size=batch_size)
on_validation_epoch_end()
¶
Execute hook for validation end.
Currently, we simply clear the gpu cache and do garbage collection.
predict_step(batch, batch_idx)
¶
Run inference for model.
Computes association + assignment.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
batch |
list[list[Frame]]
|
A single batch from the dataset which is a list of |
required |
batch_idx |
int
|
the batch number used by lightning |
required |
Returns:
| Type | Description |
|---|---|
list[Frame]
|
A list of dicts where each dict is a frame containing the predicted track ids |
Source code in dreem/models/gtr_runner.py
def predict_step(
self, batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> list["dreem.io.Frame"]:
"""Run inference for model.
Computes association + assignment.
Args:
batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A list of dicts where each dict is a frame containing the predicted track ids
"""
frames_pred = self.tracker(self.model, batch[0])
return frames_pred
test_step(test_batch, batch_idx)
¶
Execute single test step for model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
test_batch |
list[list[Frame]]
|
A single batch from the dataset which is a list of |
required |
batch_idx |
int
|
the batch number used by lightning |
required |
Returns:
| Type | Description |
|---|---|
dict[str, float]
|
A dict containing the val loss plus any other metrics specified |
Source code in dreem/models/gtr_runner.py
def test_step(
self, test_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single test step for model.
Args:
test_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the val loss plus any other metrics specified
"""
result = self._shared_eval_step(test_batch[0], mode="test")
self.log_metrics(result, len(test_batch[0]), "test")
return result
training_step(train_batch, batch_idx)
¶
Execute single training step for model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
train_batch |
list[list[Frame]]
|
A single batch from the dataset which is a list of |
required |
batch_idx |
int
|
the batch number used by lightning |
required |
Returns:
| Type | Description |
|---|---|
dict[str, float]
|
A dict containing the train loss plus any other metrics specified |
Source code in dreem/models/gtr_runner.py
def training_step(
self, train_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single training step for model.
Args:
train_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the train loss plus any other metrics specified
"""
result = self._shared_eval_step(train_batch[0], mode="train")
self.log_metrics(result, len(train_batch[0]), "train")
return result
validation_step(val_batch, batch_idx)
¶
Execute single val step for model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
val_batch |
list[list[Frame]]
|
A single batch from the dataset which is a list of |
required |
batch_idx |
int
|
the batch number used by lightning |
required |
Returns:
| Type | Description |
|---|---|
dict[str, float]
|
A dict containing the val loss plus any other metrics specified |
Source code in dreem/models/gtr_runner.py
def validation_step(
self, val_batch: list[list["dreem.io.Frame"]], batch_idx: int
) -> dict[str, float]:
"""Execute single val step for model.
Args:
val_batch: A single batch from the dataset which is a list of `Frame` objects
with length `clip_length` containing Instances and other metadata.
batch_idx: the batch number used by lightning
Returns:
A dict containing the val loss plus any other metrics specified
"""
result = self._shared_eval_step(val_batch[0], mode="val")
self.log_metrics(result, len(val_batch[0]), "val")
return result