def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False):
"""Builds the DeepMAC meta architecture."""
feature_extractor = DummyFeatureExtractor(channel_means=(1.0, 2.0, 3.0),
channel_stds=(10., 20., 30.),
bgr_ordering=False,
num_feature_outputs=2,
stride=4)
image_resizer_fn = functools.partial(preprocessor.resize_to_range,
min_dimension=128,
max_dimension=128,
pad_to_max_dimesnion=True)
object_center_params = center_net_meta_arch.ObjectCenterParams(
classification_loss=losses.WeightedSigmoidClassificationLoss(),
object_center_loss_weight=1.0,
min_box_overlap_iou=1.0,
max_box_predictions=5,
use_labeled_classes=False)
if use_dice_loss:
classification_loss = losses.WeightedDiceClassificationLoss(False)
else:
classification_loss = losses.WeightedSigmoidClassificationLoss()
deepmac_params = deepmac_meta_arch.DeepMACParams(
classification_loss=classification_loss,
dim=8,
task_loss_weight=1.0,
pixel_embedding_dim=2,
allowed_masked_classes_ids=[],
mask_size=16,
mask_num_subsamples=-1,
use_xy=True,
network_type='hourglass10',
use_instance_embedding=True,
num_init_channels=8,
predict_full_resolution_masks=predict_full_resolution_masks,
postprocess_crop_size=128,
max_roi_jitter_ratio=0.0,
roi_jitter_mode='random')
object_detection_params = center_net_meta_arch.ObjectDetectionParams(
localization_loss=losses.L1LocalizationLoss(),
offset_loss_weight=1.0,
scale_loss_weight=0.1)
return deepmac_meta_arch.DeepMACMetaArch(
is_training=True,
add_summaries=False,
num_classes=6,
feature_extractor=feature_extractor,
object_center_params=object_center_params,
deepmac_params=deepmac_params,
object_detection_params=object_detection_params,
image_resizer_fn=image_resizer_fn)
def object_center_proto_to_params(oc_config):
"""Converts CenterNet.ObjectCenter proto to parameter namedtuple."""
loss = losses_pb2.Loss()
# Add dummy localization loss to avoid the loss_builder throwing error.
# TODO(yuhuic): update the loss builder to take the localization loss
# directly.
loss.localization_loss.weighted_l2.CopyFrom(
losses_pb2.WeightedL2LocalizationLoss())
loss.classification_loss.CopyFrom(oc_config.classification_loss)
classification_loss, _, _, _, _, _, _ = (losses_builder.build(loss))
return center_net_meta_arch.ObjectCenterParams(
classification_loss=classification_loss,
object_center_loss_weight=oc_config.object_center_loss_weight,
heatmap_bias_init=oc_config.heatmap_bias_init,
min_box_overlap_iou=oc_config.min_box_overlap_iou,
max_box_predictions=oc_config.max_box_predictions,
use_labeled_classes=oc_config.use_labeled_classes)
def build_meta_arch(**override_params):
"""Builds the DeepMAC meta architecture."""
params = dict(predict_full_resolution_masks=False,
use_instance_embedding=True,
mask_num_subsamples=-1,
network_type='hourglass10',
use_xy=True,
pixel_embedding_dim=2,
dice_loss_prediction_probability=False,
color_consistency_threshold=0.5,
use_dice_loss=False,
box_consistency_loss_normalize='normalize_auto',
box_consistency_tightness=False,
task_loss_weight=1.0,
color_consistency_loss_weight=1.0,
box_consistency_loss_weight=1.0,
num_init_channels=8,
dim=8,
allowed_masked_classes_ids=[],
mask_size=16,
postprocess_crop_size=128,
max_roi_jitter_ratio=0.0,
roi_jitter_mode='random',
color_consistency_dilation=2,
color_consistency_warmup_steps=0,
color_consistency_warmup_start=0)
params.update(override_params)
feature_extractor = DummyFeatureExtractor(channel_means=(1.0, 2.0, 3.0),
channel_stds=(10., 20., 30.),
bgr_ordering=False,
num_feature_outputs=2,
stride=4)
image_resizer_fn = functools.partial(preprocessor.resize_to_range,
min_dimension=128,
max_dimension=128,
pad_to_max_dimesnion=True)
object_center_params = center_net_meta_arch.ObjectCenterParams(
classification_loss=losses.WeightedSigmoidClassificationLoss(),
object_center_loss_weight=1.0,
min_box_overlap_iou=1.0,
max_box_predictions=5,
use_labeled_classes=False)
use_dice_loss = params.pop('use_dice_loss')
dice_loss_prediction_prob = params.pop('dice_loss_prediction_probability')
if use_dice_loss:
classification_loss = losses.WeightedDiceClassificationLoss(
squared_normalization=False,
is_prediction_probability=dice_loss_prediction_prob)
else:
classification_loss = losses.WeightedSigmoidClassificationLoss()
deepmac_params = deepmac_meta_arch.DeepMACParams(
classification_loss=classification_loss, **params)
object_detection_params = center_net_meta_arch.ObjectDetectionParams(
localization_loss=losses.L1LocalizationLoss(),
offset_loss_weight=1.0,
scale_loss_weight=0.1)
return deepmac_meta_arch.DeepMACMetaArch(
is_training=True,
add_summaries=False,
num_classes=6,
feature_extractor=feature_extractor,
object_center_params=object_center_params,
deepmac_params=deepmac_params,
object_detection_params=object_detection_params,
image_resizer_fn=image_resizer_fn)