def graph_fn(image_features, proposal_boxes):
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams_fn=self.
_build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4)
box_predictions = rfcn_box_predictor.predict(
[image_features],
num_predictions_per_location=[1],
scope='BoxPredictor',
proposal_boxes=proposal_boxes)
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
def build(argscope_fn,
box_predictor_config,
is_training,
num_classes,
add_background_class=True):
"""Builds box predictor based on the configuration.
Builds box predictor based on the configuration. See box_predictor.proto for
configurable options. Also, see box_predictor.py for more details.
Args:
argscope_fn: A function that takes the following inputs:
* hyperparams_pb2.Hyperparams proto
* a boolean indicating if the model is in training mode.
and returns a tf slim argscope for Conv and FC hyperparameters.
box_predictor_config: box_predictor_pb2.BoxPredictor proto containing
configuration.
is_training: Whether the models is in training mode.
num_classes: Number of classes to predict.
add_background_class: Whether to add an implicit background class.
Returns:
box_predictor: box_predictor.BoxPredictor object.
Raises:
ValueError: On unknown box predictor.
"""
if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor):
raise ValueError('box_predictor_config not of type '
'box_predictor_pb2.BoxPredictor.')
box_predictor_oneof = box_predictor_config.WhichOneof(
'box_predictor_oneof')
if box_predictor_oneof == 'convolutional_box_predictor':
config_box_predictor = box_predictor_config.convolutional_box_predictor
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
return build_convolutional_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
conv_hyperparams_fn=conv_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
box_code_size=config_box_predictor.box_code_size,
kernel_size=config_box_predictor.kernel_size,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
min_depth=config_box_predictor.min_depth,
max_depth=config_box_predictor.max_depth,
apply_sigmoid_to_scores=config_box_predictor.
apply_sigmoid_to_scores,
class_prediction_bias_init=(
config_box_predictor.class_prediction_bias_init),
use_depthwise=config_box_predictor.use_depthwise)
if box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
config_box_predictor = (
box_predictor_config.weight_shared_convolutional_box_predictor)
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
apply_batch_norm = config_box_predictor.conv_hyperparams.HasField(
'batch_norm')
# During training phase, logits are used to compute the loss. Only apply
# sigmoid at inference to make the inference graph TPU friendly.
score_converter_fn = build_score_converter(
config_box_predictor.score_converter, is_training)
# Optionally apply clipping to box encodings, when box_encodings_clip_range
# is set.
box_encodings_clip_range = (
BoxEncodingsClipRange(
min=config_box_predictor.box_encodings_clip_range.min,
max=config_box_predictor.box_encodings_clip_range.max)
if config_box_predictor.HasField('box_encodings_clip_range') else
None)
return build_weight_shared_convolutional_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
conv_hyperparams_fn=conv_hyperparams_fn,
depth=config_box_predictor.depth,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
box_code_size=config_box_predictor.box_code_size,
kernel_size=config_box_predictor.kernel_size,
class_prediction_bias_init=(
config_box_predictor.class_prediction_bias_init),
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
share_prediction_tower=config_box_predictor.share_prediction_tower,
apply_batch_norm=apply_batch_norm,
use_depthwise=config_box_predictor.use_depthwise,
score_converter_fn=score_converter_fn,
box_encodings_clip_range=box_encodings_clip_range)
if box_predictor_oneof == 'mask_rcnn_box_predictor':
config_box_predictor = box_predictor_config.mask_rcnn_box_predictor
fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams,
is_training)
conv_hyperparams_fn = None
if config_box_predictor.HasField('conv_hyperparams'):
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
return build_mask_rcnn_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
fc_hyperparams_fn=fc_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
box_code_size=config_box_predictor.box_code_size,
share_box_across_classes=(
config_box_predictor.share_box_across_classes),
predict_instance_masks=config_box_predictor.predict_instance_masks,
conv_hyperparams_fn=conv_hyperparams_fn,
mask_height=config_box_predictor.mask_height,
mask_width=config_box_predictor.mask_width,
mask_prediction_num_conv_layers=(
config_box_predictor.mask_prediction_num_conv_layers),
mask_prediction_conv_depth=(
config_box_predictor.mask_prediction_conv_depth),
masks_are_class_agnostic=(
config_box_predictor.masks_are_class_agnostic),
convolve_then_upsample_masks=(
config_box_predictor.convolve_then_upsample_masks))
if box_predictor_oneof == 'rfcn_box_predictor':
config_box_predictor = box_predictor_config.rfcn_box_predictor
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
crop_size=[
config_box_predictor.crop_height,
config_box_predictor.crop_width
],
num_spatial_bins=[
config_box_predictor.num_spatial_bins_height,
config_box_predictor.num_spatial_bins_width
],
depth=config_box_predictor.depth,
box_code_size=config_box_predictor.box_code_size)
return box_predictor_object
raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))
def build(argscope_fn, box_predictor_config, is_training, num_classes):
"""Builds box predictor based on the configuration.
Builds box predictor based on the configuration. See box_predictor.proto for
configurable options. Also, see box_predictor.py for more details.
Args:
argscope_fn: A function that takes the following inputs:
* hyperparams_pb2.Hyperparams proto
* a boolean indicating if the model is in training mode.
and returns a tf slim argscope for Conv and FC hyperparameters.
box_predictor_config: box_predictor_pb2.BoxPredictor proto containing
configuration.
is_training: Whether the models is in training mode.
num_classes: Number of classes to predict.
Returns:
box_predictor: box_predictor.BoxPredictor object.
Raises:
ValueError: On unknown box predictor.
"""
if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor):
raise ValueError('box_predictor_config not of type '
'box_predictor_pb2.BoxPredictor.')
box_predictor_oneof = box_predictor_config.WhichOneof(
'box_predictor_oneof')
if box_predictor_oneof == 'convolutional_box_predictor':
config_box_predictor = box_predictor_config.convolutional_box_predictor
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
box_predictor_object = (
convolutional_box_predictor.ConvolutionalBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
min_depth=config_box_predictor.min_depth,
max_depth=config_box_predictor.max_depth,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.
dropout_keep_probability,
kernel_size=config_box_predictor.kernel_size,
box_code_size=config_box_predictor.box_code_size,
apply_sigmoid_to_scores=config_box_predictor.
apply_sigmoid_to_scores,
class_prediction_bias_init=(
config_box_predictor.class_prediction_bias_init),
use_depthwise=config_box_predictor.use_depthwise))
return box_predictor_object
if box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
config_box_predictor = (
box_predictor_config.weight_shared_convolutional_box_predictor)
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
apply_batch_norm = config_box_predictor.conv_hyperparams.HasField(
'batch_norm')
box_predictor_object = (
convolutional_box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
depth=config_box_predictor.depth,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
kernel_size=config_box_predictor.kernel_size,
box_code_size=config_box_predictor.box_code_size,
class_prediction_bias_init=config_box_predictor.
class_prediction_bias_init,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.
dropout_keep_probability,
share_prediction_tower=config_box_predictor.
share_prediction_tower,
apply_batch_norm=apply_batch_norm))
return box_predictor_object
if box_predictor_oneof == 'mask_rcnn_box_predictor':
config_box_predictor = box_predictor_config.mask_rcnn_box_predictor
fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams,
is_training)
conv_hyperparams_fn = None
if config_box_predictor.HasField('conv_hyperparams'):
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
box_prediction_head = box_head.BoxHead(
is_training=is_training,
num_classes=num_classes,
fc_hyperparams_fn=fc_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
box_code_size=config_box_predictor.box_code_size,
share_box_across_classes=(
config_box_predictor.share_box_across_classes))
class_prediction_head = class_head.ClassHead(
is_training=is_training,
num_classes=num_classes,
fc_hyperparams_fn=fc_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability)
third_stage_heads = {}
if config_box_predictor.predict_instance_masks:
third_stage_heads[
mask_rcnn_box_predictor.MASK_PREDICTIONS] = mask_head.MaskHead(
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
mask_height=config_box_predictor.mask_height,
mask_width=config_box_predictor.mask_width,
mask_prediction_num_conv_layers=(
config_box_predictor.mask_prediction_num_conv_layers),
mask_prediction_conv_depth=(
config_box_predictor.mask_prediction_conv_depth),
masks_are_class_agnostic=(
config_box_predictor.masks_are_class_agnostic))
box_predictor_object = mask_rcnn_box_predictor.MaskRCNNBoxPredictor(
is_training=is_training,
num_classes=num_classes,
box_prediction_head=box_prediction_head,
class_prediction_head=class_prediction_head,
third_stage_heads=third_stage_heads)
return box_predictor_object
if box_predictor_oneof == 'rfcn_box_predictor':
config_box_predictor = box_predictor_config.rfcn_box_predictor
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
crop_size=[
config_box_predictor.crop_height,
config_box_predictor.crop_width
],
num_spatial_bins=[
config_box_predictor.num_spatial_bins_height,
config_box_predictor.num_spatial_bins_width
],
depth=config_box_predictor.depth,
box_code_size=config_box_predictor.box_code_size)
return box_predictor_object
raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))
def build(argscope_fn, box_predictor_config, is_training, num_classes,
add_background_class=True):
if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor):
raise ValueError('box_predictor_config not of type '
'box_predictor_pb2.BoxPredictor.')
box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof')
if box_predictor_oneof == 'convolutional_box_predictor':
config_box_predictor = box_predictor_config.convolutional_box_predictor
conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
is_training)
# Optionally apply clipping to box encodings, when box_encodings_clip_range
# is set.
box_encodings_clip_range = None
if config_box_predictor.HasField('box_encodings_clip_range'):
box_encodings_clip_range = BoxEncodingsClipRange(
min=config_box_predictor.box_encodings_clip_range.min,
max=config_box_predictor.box_encodings_clip_range.max)
return build_convolutional_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
conv_hyperparams_fn=conv_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
box_code_size=config_box_predictor.box_code_size,
kernel_size=config_box_predictor.kernel_size,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
min_depth=config_box_predictor.min_depth,
max_depth=config_box_predictor.max_depth,
apply_sigmoid_to_scores=config_box_predictor.apply_sigmoid_to_scores,
class_prediction_bias_init=(
config_box_predictor.class_prediction_bias_init),
use_depthwise=config_box_predictor.use_depthwise,
box_encodings_clip_range=box_encodings_clip_range)
if box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
config_box_predictor = (
box_predictor_config.weight_shared_convolutional_box_predictor)
conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
is_training)
apply_batch_norm = config_box_predictor.conv_hyperparams.HasField(
'batch_norm')
# During training phase, logits are used to compute the loss. Only apply
# sigmoid at inference to make the inference graph TPU friendly.
score_converter_fn = build_score_converter(
config_box_predictor.score_converter, is_training)
# Optionally apply clipping to box encodings, when box_encodings_clip_range
# is set.
box_encodings_clip_range = None
if config_box_predictor.HasField('box_encodings_clip_range'):
box_encodings_clip_range = BoxEncodingsClipRange(
min=config_box_predictor.box_encodings_clip_range.min,
max=config_box_predictor.box_encodings_clip_range.max)
return build_weight_shared_convolutional_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
conv_hyperparams_fn=conv_hyperparams_fn,
depth=config_box_predictor.depth,
num_layers_before_predictor=(
config_box_predictor.num_layers_before_predictor),
box_code_size=config_box_predictor.box_code_size,
kernel_size=config_box_predictor.kernel_size,
class_prediction_bias_init=(
config_box_predictor.class_prediction_bias_init),
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
share_prediction_tower=config_box_predictor.share_prediction_tower,
apply_batch_norm=apply_batch_norm,
use_depthwise=config_box_predictor.use_depthwise,
score_converter_fn=score_converter_fn,
box_encodings_clip_range=box_encodings_clip_range)
if box_predictor_oneof == 'mask_rcnn_box_predictor':
config_box_predictor = box_predictor_config.mask_rcnn_box_predictor
fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams,
is_training)
conv_hyperparams_fn = None
if config_box_predictor.HasField('conv_hyperparams'):
conv_hyperparams_fn = argscope_fn(
config_box_predictor.conv_hyperparams, is_training)
return build_mask_rcnn_box_predictor(
is_training=is_training,
num_classes=num_classes,
add_background_class=add_background_class,
fc_hyperparams_fn=fc_hyperparams_fn,
use_dropout=config_box_predictor.use_dropout,
dropout_keep_prob=config_box_predictor.dropout_keep_probability,
box_code_size=config_box_predictor.box_code_size,
share_box_across_classes=(
config_box_predictor.share_box_across_classes),
predict_instance_masks=config_box_predictor.predict_instance_masks,
conv_hyperparams_fn=conv_hyperparams_fn,
mask_height=config_box_predictor.mask_height,
mask_width=config_box_predictor.mask_width,
mask_prediction_num_conv_layers=(
config_box_predictor.mask_prediction_num_conv_layers),
mask_prediction_conv_depth=(
config_box_predictor.mask_prediction_conv_depth),
masks_are_class_agnostic=(
config_box_predictor.masks_are_class_agnostic),
convolve_then_upsample_masks=(
config_box_predictor.convolve_then_upsample_masks))
if box_predictor_oneof == 'rfcn_box_predictor':
config_box_predictor = box_predictor_config.rfcn_box_predictor
conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
is_training)
box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
crop_size=[config_box_predictor.crop_height,
config_box_predictor.crop_width],
num_spatial_bins=[config_box_predictor.num_spatial_bins_height,
config_box_predictor.num_spatial_bins_width],
depth=config_box_predictor.depth,
box_code_size=config_box_predictor.box_code_size)
return box_predictor_object
raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))
