def testExponentialWithoutLinearRamp(self):
p = self._testParams()
lr_util.SetExponentialLR(p.train,
p.input,
exp_start_epoch=0,
total_epoch=10)
schedule_layer = p.train.lr_schedule.Instantiate()
with self.session() as sess:
# Peak learning rate at 0.
self.assertEqual(sess.run(schedule_layer.Value(0)), 1.)
# Exponential ramp down within first epoch.
self.assertLess(sess.run(schedule_layer.Value(4)), 1.)
def testExponentialWithLinearRamp(self):
p = self._testParams()
lr_util.SetExponentialLR(p.train,
p.input,
warmup_epoch=1,
exp_start_epoch=2,
total_epoch=10,
warmup_init=0.)
schedule_layer = p.train.lr_schedule.Instantiate()
with self.session() as sess:
# Linear ramp up.
self.assertLess(sess.run(schedule_layer.Value(8)), 1.)
# Peak learning rate.
self.assertEqual(sess.run(schedule_layer.Value(16)), 1.)
# Still at peak learning rate.
self.assertEqual(sess.run(schedule_layer.Value(24)), 1.)
# Exponential ramp down.
self.assertLess(sess.run(schedule_layer.Value(48)), 1.)
def testExponentialWithLinearRamp(self):
p = self._testParams()
lr_util.SetExponentialLR(p.train,
p.input,
warmup_epoch=1,
exp_start_epoch=2,
total_epoch=10,
warmup_init=0.)
schedule_layer = p.train.lr_schedule.Instantiate()
with self.session():
# Linear ramp up.
with py_utils.GlobalStepContext(8):
self.assertLess(self.evaluate(schedule_layer.Value()), 1.)
# Peak learning rate.
with py_utils.GlobalStepContext(16):
self.assertEqual(self.evaluate(schedule_layer.Value()), 1.)
# Still at peak learning rate.
with py_utils.GlobalStepContext(24):
self.assertEqual(self.evaluate(schedule_layer.Value()), 1.)
# Exponential ramp down.
with py_utils.GlobalStepContext(48):
self.assertLess(self.evaluate(schedule_layer.Value()), 1.)
def Task(self):
num_classes = len(
kitti_input_generator.KITTILabelExtractor.KITTI_CLASS_NAMES)
p = starnet.ModelV2.Params(
num_classes,
num_anchor_bboxes_offsets=self.NUM_ANCHOR_BBOX_OFFSETS,
num_anchor_bboxes_rotations=self.NUM_ANCHOR_BBOX_ROTATIONS,
num_anchor_bboxes_dimensions=self.NUM_ANCHOR_BBOX_DIMENSIONS)
p.name = 'sparse_detector'
tp = p.train
tp.optimizer = optimizer.Adam.Params()
tp.clip_gradient_norm_to_value = 5
ep = p.eval
# Evaluate the whole dataset.
ep.samples_per_summary = 0
# To be tuned.
p.train.l2_regularizer_weight = 1e-4
# Adapted from V1 tuning.
tp.ema_decay = 0.99
# TODO(b/148537111): consider setting this to True.
tp.ema_decay_moving_vars = False
tp.learning_rate = 0.001
lr_util.SetExponentialLR(train_p=tp,
train_input_p=self.Train(),
exp_start_epoch=150,
total_epoch=650)
p.dimension_loss_weight = .3
p.location_loss_weight = 3.
p.loss_weight_classification = 1.
p.loss_weight_localization = 3.
p.rotation_loss_weight = 0.3
return p
def Task(self):
metadata = waymo_metadata.WaymoMetadata()
num_classes = len(metadata.ClassNames())
p = starnet.ModelV2.Params(
num_classes,
num_anchor_bboxes_offsets=self.NUM_ANCHOR_BBOX_OFFSETS,
num_anchor_bboxes_rotations=self.NUM_ANCHOR_BBOX_ROTATIONS,
num_anchor_bboxes_dimensions=self.NUM_ANCHOR_BBOX_DIMENSIONS,
num_laser_features=3)
# Update the Point Cloud Featurizer architecture
starnet_builder = starnet.Builder()
starnet_builder.linear_params_init = (
py_utils.WeightInit.KaimingUniformFanInRelu())
gin_layers = [[
self.GIN_HIDDEN_DIMS * 2, self.GIN_HIDDEN_DIMS * 4,
self.GIN_HIDDEN_DIMS
]] * self.NUM_GIN_LAYERS # pyformat: disable
p.cell_featurizer = starnet_builder.GINFeaturizerV2(
'feat',
num_laser_features=3,
fc_dims=self.GIN_HIDDEN_DIMS,
mlp_dims=gin_layers,
fc_use_bn=False)
p.cell_feature_dims = self.GIN_HIDDEN_DIMS * (self.NUM_GIN_LAYERS + 1)
p.output_decoder = waymo_decoder.WaymoOpenDatasetDecoder.Params()
p.max_nms_boxes = 512
p.use_oriented_per_class_nms = True
# Note: Sub-classes need to set nms_iou_threshold and nms_score_threshold
# appropriately.
p.nms_iou_threshold = [0.0] * num_classes
# TODO(jngiam): 1.1 for untrained classes is needed to avoid an issue
# with boxutils error.
p.nms_score_threshold = [1.1] * num_classes
p.name = 'starnet'
tp = p.train
tp.optimizer = optimizer.Adam.Params()
tp.clip_gradient_norm_to_value = 5
ep = p.eval
# Train set uses a smaller decoding set, so we can
# safely eval over the entire input.
ep.samples_per_summary = 0
# To be tuned.
p.train.l2_regularizer_weight = 1e-8
cluster = cluster_factory.Current()
train_cluster_p = cluster.params.Copy()
train_cluster_p.job = 'trainer_client'
train_cluster_p.mode = 'sync'
# When running a decoding only job, there are no trainer workers, so we set
# worker replicas to 1 as a dummy value.
if train_cluster_p.worker.replicas <= 0:
train_cluster_p.worker.replicas = 1
# Set learning rate and schedule.
with cluster_factory.Cluster(train_cluster_p):
train_input_p = self.Train()
# Adapted from V1 tuning.
tp.ema_decay = 0.99
# TODO(b/148537111): consider setting this to True.
tp.ema_decay_moving_vars = False
tp.learning_rate = 0.001
lr_util.SetExponentialLR(train_p=tp,
train_input_p=train_input_p,
exp_start_epoch=5,
total_epoch=75)
p.dimension_loss_weight = .3
p.location_loss_weight = 3.
p.loss_weight_classification = 1.
p.loss_weight_localization = 3.
p.rotation_loss_weight = 0.3
return p