def __init__(self, params):
super(MaskrcnnModel, self).__init__(params)
# For eval metrics.
self._params = params
self._keras_model = None
self._include_mask = params.architecture.include_mask
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._rpn_head_fn = factory.rpn_head_generator(params)
self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal)
self._sample_rois_fn = target_ops.ROISampler(params.roi_sampling)
self._sample_masks_fn = target_ops.MaskSampler(
params.architecture.mask_target_size,
params.mask_sampling.num_mask_samples_per_image)
self._frcnn_head_fn = factory.fast_rcnn_head_generator(params)
if self._include_mask:
self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params)
# Loss function.
self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss)
self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss)
self._frcnn_class_loss_fn = losses.FastrcnnClassLoss()
self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss)
if self._include_mask:
self._mask_loss_fn = losses.MaskrcnnLoss()
self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator(
params.postprocess)
self._transpose_input = params.train.transpose_input
assert not self._transpose_input, 'Transpose input is not supportted.'
def __init__(self, params):
super(OlnMaskModel, self).__init__(params)
self._params = params
# Different heads and layers.
self._include_rpn_class = params.architecture.include_rpn_class
self._include_mask = params.architecture.include_mask
self._include_frcnn_class = params.architecture.include_frcnn_class
self._include_frcnn_box = params.architecture.include_frcnn_box
self._include_centerness = params.rpn_head.has_centerness
self._include_box_score = (params.frcnn_head.has_scoring
and params.architecture.include_frcnn_box)
self._include_mask_score = (params.mrcnn_head.has_scoring
and params.architecture.include_mask)
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._rpn_head_fn = factory.rpn_head_generator(params)
if self._include_centerness:
self._rpn_head_fn = factory.oln_rpn_head_generator(params)
else:
self._rpn_head_fn = factory.rpn_head_generator(params)
self._generate_rois_fn = roi_ops.OlnROIGenerator(params.roi_proposal)
self._sample_rois_fn = target_ops.ROIScoreSampler(params.roi_sampling)
self._sample_masks_fn = target_ops.MaskSampler(
params.architecture.mask_target_size,
params.mask_sampling.num_mask_samples_per_image)
if self._include_box_score:
self._frcnn_head_fn = factory.oln_box_score_head_generator(params)
else:
self._frcnn_head_fn = factory.fast_rcnn_head_generator(params)
if self._include_mask:
if self._include_mask_score:
self._mrcnn_head_fn = factory.oln_mask_score_head_generator(
params)
else:
self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params)
# Loss function.
self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss)
self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss)
if self._include_centerness:
self._rpn_iou_loss_fn = losses.OlnRpnIoULoss()
self._rpn_center_loss_fn = losses.OlnRpnCenterLoss()
self._frcnn_class_loss_fn = losses.FastrcnnClassLoss()
self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss)
if self._include_box_score:
self._frcnn_box_score_loss_fn = losses.OlnBoxScoreLoss(
params.frcnn_box_score_loss)
if self._include_mask:
self._mask_loss_fn = losses.MaskrcnnLoss()
self._generate_detections_fn = postprocess_ops.OlnDetectionGenerator(
params.postprocess)
self._transpose_input = params.train.transpose_input
assert not self._transpose_input, 'Transpose input is not supportted.'
