Tracker
Custom trackers to log data and files during experiments.
Base class for trackers.
- class ecgan.evaluation.tracker.base_tracker.BaseTracker(entity, project, run_name, save_pdf=False)[source]
Bases:
abc.ABCBase class for trackers.
- static collate_metrics(metrics)[source]
Transform a list with dictionaries to a single dictionary.
All values are collated in their respective keys and are then averaged.
- Parameters
metrics (
List[Dict]) -- A list of metrics.- Return type
Dict- Returns
Dictionary with the collated metrics.
- abstract watch(model)[source]
Pass a module that should be watched during training.
- Return type
None
- abstract log_metrics(metrics)[source]
Take a dictionary with metrics and log content.
- Parameters
metrics (
Dict) -- Dict in shape {metric: val, ...}- Return type
None
- abstract log_checkpoint(module_checkpoint, fold)[source]
Save a module checkpoint.
The checkpoint is a dictionary containing its state dict and optimizer parameters.
- Parameters
module_checkpoint (
Dict) -- Dictionary with model weights.fold (
int) -- Current fold. Should be extracted before beginning to upload to avoid concurrency.
- Return type
None
Tracker storing data locally.
- class ecgan.evaluation.tracker.local_tracker.LocalTracker(entity, project, run_name, save_pdf=True, base_root='results')[source]
Bases:
ecgan.evaluation.tracker.base_tracker.BaseTrackerClass to manage calculating metrics and logging them on the local file system.
- watch(model)[source]
Watch models during training - not supported in LocalTracker.
- Return type
None
- log_metrics(metrics)[source]
Take a dictionary with metrics and log content.
- Parameters
metrics (
Dict) -- Dict in shape {metric: val, ...}.- Return type
None
- log_checkpoint(module_checkpoint, fold)[source]
Save a module checkpoint.
The checkpoint is a dictionary containing its state dict and optimizer parameters.
- Parameters
module_checkpoint (
Dict) -- Dictionary with model weights.fold (
int) -- Current fold. Should be extracted before beginning to upload to avoid concurrency.
- Return type
None
Tracker storing data using weights and biases.
- class ecgan.evaluation.tracker.wb_tracker.WBTracker(entity, project, run_name, save_locally=False, save_pdf=False, save_checkpoint_s3=False)[source]
Bases:
ecgan.evaluation.tracker.base_tracker.BaseTrackerClass to manage calculating metrics and logging them with Weights & Biases (W&B).
- Parameters
entity (
str) -- Weights and Biases entity name (given by W&B).project (
str) -- Weights and Biases project name (chosen by user).run_name (
str) -- Weights and Biases run name (chosen by user).save_pdf (
bool) -- Flag to save data as pdf ADDITIONALLY to normal plots.save_locally (
bool) -- Flag to retain local storage of data.
- close()[source]
Close training run.
The uploading might require some time. The run is interrupted if the upload did not complete after 90 seconds. You can manually upload the data afterwards with a local sync.
- log_metrics(metrics)[source]
Take a dictionary with metrics and log content.
- Parameters
metrics (
Dict) -- Dict in shape {metric: val, ...}.- Return type
None
- log_checkpoint(module_checkpoint, fold)[source]
Save a module checkpoint.
The checkpoint is a dictionary containing its state dict and optimizer parameters.
- Parameters
module_checkpoint (
Dict) -- Dictionary with model weights.fold (
int) -- Current fold. Should be extracted before beginning to upload to avoid concurrency.
- Return type
None
Tracker factory to retrieve the instance of a tracker defined in the configuration file.