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
"""
if self.is_patch:
return PatchRegistrationTracker(self,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log)
else:
if self.is_end2end:
raise NotImplementedError("implement end2end tracker")
else:
return FragmentRegistrationTracker(
num_points=self.num_points,
tau_1=self.tau_1,
tau_2=self.tau_2,
rot_thresh=self.rot_thresh,
trans_thresh=self.trans_thresh,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log)
def test_track_all(self):
tracker = FragmentRegistrationTracker(MockDataset(), stage="test", tau_2=0.83, num_points=100)
model = MockModel()
tracker.reset("test")
model.iter = 0
for i in range(4):
tracker.track(model)
model.iter += 1
metrics = tracker.get_metrics()
self.assertAlmostEqual(metrics["test_hit_ratio"], (4 * 1.0 + 4 * 0.9 + 4 * 0.8 + 0.9 + 0.84 + 0.8 + 0.7) / 16)
self.assertAlmostEqual(metrics["test_feat_match_ratio"], (4 * 1 + 4 * 1 + 4 * 0 + 2 * 1 + 2 * 0) / 16)
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
"""
if self.is_patch:
return PatchRegistrationTracker(self, wandb_log=wandb_log, use_tensorboard=tensorboard_log)
else:
return FragmentRegistrationTracker(self, wandb_log=wandb_log, use_tensorboard=tensorboard_log)
def test_track_batch(self):
tracker = FragmentRegistrationTracker(MockDataset(), stage="test", tau_2=0.83, num_points=100)
model = MockModel()
list_hit_ratio = [1.0, 0.9, 0.8, (0.9 + 0.84 + 0.8 + 0.7) / 4]
list_feat_match_ratio = [1.0, 1.0, 0.0, 0.5]
for i in range(4):
tracker.track(model)
metrics = tracker.get_metrics()
# the most important metrics in registration
self.assertAlmostEqual(metrics["test_hit_ratio"], list_hit_ratio[i])
self.assertAlmostEqual(metrics["test_feat_match_ratio"], list_feat_match_ratio[i])
tracker.reset("test")
model.iter += 1
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 FragmentRegistrationTracker(self,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log,
tau_1=self.tau_1,
rot_thresh=self.rot_thresh,
trans_thresh=self.trans_thresh)
def get_tracker(model, dataset, wandb_log: bool, tensorboard_log: bool):
"""
Factory method for the tracker
Arguments:
task {str} -- task description
dataset {[type]}
wandb_log - Log using weight and biases
Returns:
[BaseTracker] -- tracker
"""
if (dataset.is_patch):
return PatchRegistrationTracker(dataset,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log)
else:
return FragmentRegistrationTracker(dataset,
wandb_log=wandb_log,
use_tensorboard=tensorboard_log)
