def get_tracker(model, dataset, wandb_log: bool, tensorboard_log: bool):
"""Factory method for the tracker
Arguments:
dataset {[type]}
wandb_log - Log using weight and biases
Returns:
[BaseTracker] -- tracker
"""
return ShapenetPartTracker(dataset, wandb_log=wandb_log, use_tensorboard=tensorboard_log)
def get_tracker(self, wandb_log: bool, tensorboard_log: bool):
"""Factory method for the tracker
Arguments:
wandb_log - Log using weight and biases
tensorboard_log - Log using tensorboard
Returns:
[BaseTracker] -- tracker
"""
return ShapenetPartTracker(self,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log)
def test_track(self):
tracker = ShapenetPartTracker(MockDataset())
model = MockModel()
tracker.track(model)
metrics = tracker.get_metrics(verbose=True)
for k in ["train_Cmiou", "train_Imiou"]:
self.assertAlmostEqual(metrics[k], 100, 5)
model.iter += 1
tracker.track(model)
metrics = tracker.get_metrics(verbose=True)
for k in ["train_Cmiou", "train_Imiou"]:
self.assertAlmostEqual(metrics[k], 100, 5)
model.iter += 1
tracker.track(model)
metrics = tracker.get_metrics(verbose=True)
self.assertAlmostEqual(metrics["train_Imiou"], 4 * 100 / 5)
self.assertAlmostEqual(metrics["train_Cmiou"], (100 + 200 / 3.0) / 2.0)