def retinanet_mask_3D(inputs,
num_classes,
retinanet_model=None,
anchor_params=None,
nms=True,
class_specific_filter=True,
crop_size=(14, 14),
mask_size=(28, 28, 28),
name='retinanet-mask-3D',
roi_submodels=None,
mask_dtype=K.floatx(),
**kwargs):
"""Construct a RetinaNet mask model on top of a retinanet bbox model.
Uses the retinanet bbox model and appends layers to compute masks.
Args:
inputs: List of tensorflow.keras.layers.Input.
The first input is the image, the second input the blob of masks.
num_classes: Integer, number of classes to classify.
retinanet_model: deepcell.model_zoo.retinanet.retinanet model,
returning regression and classification values.
anchor_params: Struct containing anchor parameters.
nms: Boolean, whether to use NMS.
class_specific_filter: Boolean, use class specific filtering.
roi_submodels: Submodels for processing ROIs.
mask_dtype: Data type of the masks, can be different from the main one.
name: Name of the model.
**kwargs: Additional kwargs to pass to the retinanet bbox model.
Returns:
Model with inputs as input and as output the output of each submodel
for each pyramid level and the detections. The order is as defined in
submodels.
```
[
regression, classification, other[0], other[1], ...,
boxes_masks, boxes, scores, labels, masks, other[0], other[1], ...
]
```
"""
if anchor_params is None:
anchor_params = AnchorParameters.default
if roi_submodels is None:
retinanet_dtype = K.floatx()
K.set_floatx(mask_dtype)
roi_submodels = default_roi_submodels(num_classes, crop_size,
mask_size, mask_dtype,
retinanet_dtype)
K.set_floatx(retinanet_dtype)
image = inputs
image_shape = Shape()(image)
if retinanet_model is None:
retinanet_model = retinanet(inputs=image,
num_classes=num_classes,
num_anchors=anchor_params.num_anchors(),
**kwargs)
# parse outputs
regression = retinanet_model.outputs[0]
classification = retinanet_model.outputs[1]
other = retinanet_model.outputs[2:]
features = [
retinanet_model.get_layer(name).output
for name in ['P3', 'P4', 'P5', 'P6', 'P7']
]
# build boxes
anchors = __build_anchors(anchor_params, features)
boxes = RegressBoxes(name='boxes')([anchors, regression])
boxes = ClipBoxes(name='clipped_boxes')([image, boxes])
# filter detections (apply NMS / score threshold / select top-k)
detections = FilterDetections(
nms=nms,
class_specific_filter=class_specific_filter,
max_detections=100,
name='filtered_detections')([boxes, classification] + other)
# split up in known outputs and "other"
boxes = detections[0]
scores = detections[1]
# get the region of interest features
roi_input = [image_shape, boxes, classification] + features
rois = RoiAlign(crop_size=crop_size)(roi_input)
# execute maskrcnn submodels
maskrcnn_outputs = [submodel(rois) for _, submodel in roi_submodels]
# concatenate boxes for loss computation
trainable_outputs = [
ConcatenateBoxes(name=name)([boxes, output])
for (name, _), output in zip(roi_submodels, maskrcnn_outputs)
]
# reconstruct the new output
outputs = [regression, classification] + other + trainable_outputs + \
detections + maskrcnn_outputs
return Model(inputs=inputs, outputs=outputs, name=name)
def retinanet_mask(inputs,
backbone_dict,
num_classes,
frames_per_batch=1,
backbone_levels=['C3', 'C4', 'C5'],
pyramid_levels=['P3', 'P4', 'P5', 'P6', 'P7'],
retinanet_model=None,
anchor_params=None,
nms=True,
panoptic=False,
class_specific_filter=True,
crop_size=(14, 14),
mask_size=(28, 28),
name='retinanet-mask',
roi_submodels=None,
max_detections=100,
score_threshold=0.05,
nms_threshold=0.5,
mask_dtype=K.floatx(),
**kwargs):
"""Construct a RetinaNet mask model on top of a retinanet bbox model.
Uses the retinanet bbox model and appends layers to compute masks.
Args:
inputs (tensor): List of tensorflow.keras.layers.Input.
The first input is the image, the second input the blob of masks.
backbone_dict (dict): A dictionary with the backbone layers.
num_classes (int): Integer, number of classes to classify.
frames_per_batch (int): Size of z axis in generated batches.
If equal to 1, assumes 2D data.
backbone_levels (list): The backbone levels to be used.
to create the feature pyramid. Defaults to ['C3', 'C4', 'C5'].
pyramid_levels (list): The pyramid levels to attach regression and
classification heads to. Defaults to ['P3', 'P4', 'P5', 'P6', 'P7'].
retinanet_model (tensorflow.keras.Model): RetinaNet model that predicts
regression and classification values.
anchor_params (AnchorParameters): Struct containing anchor parameters.
nms (bool): Whether to use non-maximum suppression
for the filtering step.
panoptic (bool): Flag for adding the semantic head for panoptic
segmentation tasks. Defaults to false.
class_specific_filter (bool): Use class specific filtering.
crop_size (tuple): 2-length tuple for the x-y size of the crops.
Used to create default roi_submodels.
mask_size (tuple): 2-length tuple for the x-y size of the masks.
Used to create default roi_submodels.
name (str): Name of the model.
roi_submodels (list): Submodels for processing ROIs.
max_detections (int): The maximum number of detections allowed.
score_threshold (float): Minimum score for the FilterDetections layer.
nms_threshold (float): Minimimum NMS for the FilterDetections layer.
mask_dtype (str): Dtype to use for mask tensors.
kwargs (dict): Additional kwargs to pass to the retinanet bbox model.
Returns:
tensorflow.keras.Model: Model with inputs as input and as output
the output of each submodel for each pyramid level and the
detections. The order is as defined in submodels.
```
[
regression, classification, other[0], ...,
boxes_masks, boxes, scores, labels, masks, other[0], ...
]
```
"""
if anchor_params is None:
anchor_params = AnchorParameters.default
if roi_submodels is None:
retinanet_dtype = K.floatx()
K.set_floatx(mask_dtype)
roi_submodels = default_roi_submodels(num_classes, crop_size,
mask_size, frames_per_batch,
mask_dtype, retinanet_dtype)
K.set_floatx(retinanet_dtype)
image = inputs
image_shape = Shape()(image)
if retinanet_model is None:
retinanet_model = retinanet(inputs=image,
backbone_dict=backbone_dict,
num_classes=num_classes,
backbone_levels=backbone_levels,
pyramid_levels=pyramid_levels,
panoptic=panoptic,
num_anchors=anchor_params.num_anchors(),
frames_per_batch=frames_per_batch,
**kwargs)
# parse outputs
regression = retinanet_model.outputs[0]
classification = retinanet_model.outputs[1]
if panoptic:
# Determine the number of semantic heads
n_semantic_heads = len([
1 for layer in retinanet_model.layers if 'semantic' in layer.name
])
# The panoptic output should not be sent to filter detections
other = retinanet_model.outputs[2:-n_semantic_heads]
semantic = retinanet_model.outputs[-n_semantic_heads:]
else:
other = retinanet_model.outputs[2:]
features = [
retinanet_model.get_layer(name).output for name in pyramid_levels
]
# build boxes
anchors = __build_anchors(anchor_params,
features,
frames_per_batch=frames_per_batch)
boxes = RegressBoxes(name='boxes')([anchors, regression])
boxes = ClipBoxes(name='clipped_boxes')([image, boxes])
# filter detections (apply NMS / score threshold / select top-k)
detections = FilterDetections(
nms=nms,
nms_threshold=nms_threshold,
score_threshold=score_threshold,
class_specific_filter=class_specific_filter,
max_detections=max_detections,
name='filtered_detections')([boxes, classification] + other)
# split up in known outputs and "other"
boxes = detections[0]
scores = detections[1]
# get the region of interest features
#
# roi_input = [image_shape, boxes, classification] + features
# rois = _RoiAlign(crop_size=crop_size)(roi_input)
fpn = features[0]
fpn = UpsampleLike()([fpn, image])
rois = RoiAlign(crop_size=crop_size)([boxes, fpn])
# execute maskrcnn submodels
maskrcnn_outputs = [submodel(rois) for _, submodel in roi_submodels]
# concatenate boxes for loss computation
trainable_outputs = [
ConcatenateBoxes(name=name)([boxes, output])
for (name, _), output in zip(roi_submodels, maskrcnn_outputs)
]
# reconstruct the new output
outputs = [regression, classification] + other + trainable_outputs + \
detections + maskrcnn_outputs
if panoptic:
outputs += list(semantic)
model = Model(inputs=inputs, outputs=outputs, name=name)
model.backbone_levels = backbone_levels
model.pyramid_levels = pyramid_levels
return model