def test_path_drop_weights(self):
# Tests the effect of path-drop on network's feature maps.
# It sets up a minimal-training process to check the
# feature before and after running the 'train_op' while
# path-drop is in effect.
train_val_test = 'train'
# overwrite the training iterations
self.train_config.max_iterations = 2
self.train_config.overwrite_checkpoints = True
# Overwrite path drop probabilities
model_config = config_builder.proto_to_obj(self.model_config)
model_config.path_drop_probabilities = [0.0, 0.8]
with tf.Graph().as_default():
# Set a graph-level seed
tf.set_random_seed(1245)
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=self.dataset)
prediction_dict = model.build()
losses_dict, total_loss = model.loss(prediction_dict)
global_summaries = set([])
# Optimizer
training_optimizer = optimizer_builder.build(
self.train_config.optimizer, global_summaries)
train_op = slim.learning.create_train_op(total_loss,
training_optimizer)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
for step in range(1, self.train_config.max_iterations):
feed_dict = model.create_feed_dict()
if step == 1:
current_feature_maps = sess.run(model.img_feature_maps,
feed_dict=feed_dict)
exp_feature_maps = current_feature_maps
train_op_loss = sess.run(train_op, feed_dict=feed_dict)
print('Step {}, Total Loss {:0.3f} '.format(
step, train_op_loss))
updated_feature_maps = sess.run(model.img_feature_maps,
feed_dict=feed_dict)
# The feature maps should have remained the same since
# the image path was dropped
np.testing.assert_array_almost_equal(updated_feature_maps,
exp_feature_maps,
decimal=4)
def test_rpn_loss(self):
# Use "val" so that the first sample is loaded each time
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
predictions = rpn_model.build()
loss, total_loss = rpn_model.loss(predictions)
feed_dict = rpn_model.create_feed_dict()
with self.test_session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
loss_dict_out = sess.run(loss, feed_dict=feed_dict)
print('Losses ', loss_dict_out)
class AvodModel(model.DetectionModel):
##############################
# Keys for Predictions
##############################
# Mini batch (mb) ground truth
PRED_MB_CLASSIFICATIONS_GT = 'avod_mb_classifications_gt'
PRED_MB_OFFSETS_GT = 'avod_mb_offsets_gt'
PRED_MB_ORIENTATIONS_GT = 'avod_mb_orientations_gt'
# Mini batch (mb) predictions
PRED_MB_CLASSIFICATION_LOGITS = 'avod_mb_classification_logits'
PRED_MB_CLASSIFICATION_SOFTMAX = 'avod_mb_classification_softmax'
PRED_MB_OFFSETS = 'avod_mb_offsets'
PRED_MB_ANGLE_VECTORS = 'avod_mb_angle_vectors'
# Top predictions after BEV NMS
PRED_TOP_CLASSIFICATION_LOGITS = 'avod_top_classification_logits'
PRED_TOP_CLASSIFICATION_SOFTMAX = 'avod_top_classification_softmax'
PRED_TOP_PREDICTION_ANCHORS = 'avod_top_prediction_anchors'
PRED_TOP_PREDICTION_BOXES_3D = 'avod_top_prediction_boxes_3d'
PRED_TOP_ORIENTATIONS = 'avod_top_orientations'
# Other box representations
PRED_TOP_BOXES_8C = 'avod_top_regressed_boxes_8c'
PRED_TOP_BOXES_4C = 'avod_top_prediction_boxes_4c'
# Mini batch (mb) predictions (for debugging)
PRED_MB_MASK = 'avod_mb_mask'
PRED_MB_POS_MASK = 'avod_mb_pos_mask'
PRED_MB_ANCHORS_GT = 'avod_mb_anchors_gt'
PRED_MB_CLASS_INDICES_GT = 'avod_mb_gt_classes'
# All predictions (for debugging)
PRED_ALL_CLASSIFICATIONS = 'avod_classifications'
PRED_ALL_OFFSETS = 'avod_offsets'
PRED_ALL_ANGLE_VECTORS = 'avod_angle_vectors'
PRED_MAX_IOUS = 'avod_max_ious'
PRED_ALL_IOUS = 'avod_anchor_ious'
##############################
# Keys for Loss
##############################
LOSS_FINAL_CLASSIFICATION = 'avod_classification_loss'
LOSS_FINAL_REGRESSION = 'avod_regression_loss'
# (for debugging)
LOSS_FINAL_ORIENTATION = 'avod_orientation_loss'
LOSS_FINAL_LOCALIZATION = 'avod_localization_loss'
def __init__(self, model_config, train_val_test, dataset):
"""
Args:
model_config: configuration for the model
train_val_test: "train", "val", or "test"
dataset: the dataset that will provide samples and ground truth
"""
# Sets model configs (_config)
super(AvodModel, self).__init__(model_config)
self.dataset = dataset
# Dataset config
self._num_final_classes = self.dataset.num_classes + 1
# Input config
input_config = self._config.input_config
self._bev_pixel_size = np.asarray(
[input_config.bev_dims_h, input_config.bev_dims_w])
self._bev_depth = input_config.bev_depth
self._img_pixel_size = np.asarray(
[input_config.img_dims_h, input_config.img_dims_w])
self._img_depth = [input_config.img_depth]
# AVOD config
avod_config = self._config.avod_config
self._proposal_roi_crop_size = \
[avod_config.avod_proposal_roi_crop_size] * 2
self._positive_selection = avod_config.avod_positive_selection
self._nms_size = avod_config.avod_nms_size
self._nms_iou_threshold = avod_config.avod_nms_iou_thresh
self._path_drop_probabilities = self._config.path_drop_probabilities
self._box_rep = avod_config.avod_box_representation
if self._box_rep not in [
'box_3d', 'box_8c', 'box_8co', 'box_4c', 'box_4ca'
]:
raise ValueError('Invalid box representation', self._box_rep)
# Create the RpnModel
self._rpn_model = RpnModel(model_config, train_val_test, dataset)
if train_val_test not in ["train", "val", "test"]:
raise ValueError('Invalid train_val_test value,'
'should be one of ["train", "val", "test"]')
self._train_val_test = train_val_test
self._is_training = (self._train_val_test == 'train')
self.sample_info = {}
def build(self):
rpn_model = self._rpn_model
# Share the same prediction dict as RPN
prediction_dict = rpn_model.build()
top_anchors = prediction_dict[RpnModel.PRED_TOP_ANCHORS]
ground_plane = rpn_model.placeholders[RpnModel.PL_GROUND_PLANE]
class_labels = rpn_model.placeholders[RpnModel.PL_LABEL_CLASSES]
with tf.variable_scope('avod_projection'):
if self._config.expand_proposals_xz > 0.0:
expand_length = self._config.expand_proposals_xz
# Expand anchors along x and z
with tf.variable_scope('expand_xz'):
expanded_dim_x = top_anchors[:, 3] + expand_length
expanded_dim_z = top_anchors[:, 5] + expand_length
expanded_anchors = tf.stack([
top_anchors[:, 0], top_anchors[:, 1], top_anchors[:,
2],
expanded_dim_x, top_anchors[:, 4], expanded_dim_z
],
axis=1)
avod_projection_in = expanded_anchors
else:
avod_projection_in = top_anchors
with tf.variable_scope('bev'):
# Project top anchors into bev and image spaces
bev_proposal_boxes, bev_proposal_boxes_norm = \
anchor_projector.project_to_bev(
avod_projection_in,
self.dataset.kitti_utils.bev_extents)
# Reorder projected boxes into [y1, x1, y2, x2]
bev_proposal_boxes_tf_order = \
anchor_projector.reorder_projected_boxes(
bev_proposal_boxes)
bev_proposal_boxes_norm_tf_order = \
anchor_projector.reorder_projected_boxes(
bev_proposal_boxes_norm)
with tf.variable_scope('img'):
image_shape = tf.cast(
tf.shape(
rpn_model.placeholders[RpnModel.PL_IMG_INPUT])[0:2],
tf.float32)
img_proposal_boxes, img_proposal_boxes_norm = \
anchor_projector.tf_project_to_image_space(
avod_projection_in,
rpn_model.placeholders[RpnModel.PL_CALIB_P2],
image_shape)
# Only reorder the normalized img
img_proposal_boxes_norm_tf_order = \
anchor_projector.reorder_projected_boxes(
img_proposal_boxes_norm)
bev_feature_maps = rpn_model.bev_feature_maps
img_feature_maps = rpn_model.img_feature_maps
if not (self._path_drop_probabilities[0] ==
self._path_drop_probabilities[1] == 1.0):
with tf.variable_scope('avod_path_drop'):
img_mask = rpn_model.img_path_drop_mask
bev_mask = rpn_model.bev_path_drop_mask
img_feature_maps = tf.multiply(img_feature_maps, img_mask)
bev_feature_maps = tf.multiply(bev_feature_maps, bev_mask)
else:
bev_mask = tf.constant(1.0)
img_mask = tf.constant(1.0)
# ROI Pooling
with tf.variable_scope('avod_roi_pooling'):
def get_box_indices(boxes):
proposals_shape = boxes.get_shape().as_list()
if any(dim is None for dim in proposals_shape):
proposals_shape = tf.shape(boxes)
ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32)
multiplier = tf.expand_dims(
tf.range(start=0, limit=proposals_shape[0]), 1)
return tf.reshape(ones_mat * multiplier, [-1])
bev_boxes_norm_batches = tf.expand_dims(bev_proposal_boxes_norm,
axis=0)
# These should be all 0's since there is only 1 image
tf_box_indices = get_box_indices(bev_boxes_norm_batches)
# Do ROI Pooling on BEV
bev_rois = tf.image.crop_and_resize(
bev_feature_maps,
bev_proposal_boxes_norm_tf_order,
tf_box_indices,
self._proposal_roi_crop_size,
name='bev_rois')
# Do ROI Pooling on image
img_rois = tf.image.crop_and_resize(
img_feature_maps,
img_proposal_boxes_norm_tf_order,
tf_box_indices,
self._proposal_roi_crop_size,
name='img_rois')
# Fully connected layers (Box Predictor)
avod_layers_config = self.model_config.layers_config.avod_config
fc_output_layers = \
avod_fc_layers_builder.build(
layers_config=avod_layers_config,
input_rois=[bev_rois, img_rois],
input_weights=[bev_mask, img_mask],
num_final_classes=self._num_final_classes,
box_rep=self._box_rep,
top_anchors=top_anchors,
ground_plane=ground_plane,
is_training=self._is_training)
all_cls_logits = \
fc_output_layers[avod_fc_layers_builder.KEY_CLS_LOGITS]
all_offsets = fc_output_layers[avod_fc_layers_builder.KEY_OFFSETS]
# This may be None
all_angle_vectors = \
fc_output_layers.get(avod_fc_layers_builder.KEY_ANGLE_VECTORS)
with tf.variable_scope('softmax'):
all_cls_softmax = tf.nn.softmax(all_cls_logits)
######################################################
# Subsample mini_batch for the loss function
######################################################
# Get the ground truth tensors
anchors_gt = rpn_model.placeholders[RpnModel.PL_LABEL_ANCHORS]
if self._box_rep in ['box_3d', 'box_4ca']:
boxes_3d_gt = rpn_model.placeholders[RpnModel.PL_LABEL_BOXES_3D]
orientations_gt = boxes_3d_gt[:, 6]
elif self._box_rep in ['box_8c', 'box_8co', 'box_4c']:
boxes_3d_gt = rpn_model.placeholders[RpnModel.PL_LABEL_BOXES_3D]
else:
raise NotImplementedError('Ground truth tensors not implemented')
# Project anchor_gts to 2D bev
with tf.variable_scope('avod_gt_projection'):
bev_anchor_boxes_gt, _ = anchor_projector.project_to_bev(
anchors_gt, self.dataset.kitti_utils.bev_extents)
bev_anchor_boxes_gt_tf_order = \
anchor_projector.reorder_projected_boxes(bev_anchor_boxes_gt)
with tf.variable_scope('avod_box_list'):
# Convert to box_list format
anchor_box_list_gt = box_list.BoxList(bev_anchor_boxes_gt_tf_order)
anchor_box_list = box_list.BoxList(bev_proposal_boxes_tf_order)
mb_mask, mb_class_label_indices, mb_gt_indices = \
self.sample_mini_batch(
anchor_box_list_gt=anchor_box_list_gt,
anchor_box_list=anchor_box_list,
class_labels=class_labels)
# Create classification one_hot vector
with tf.variable_scope('avod_one_hot_classes'):
mb_classification_gt = tf.one_hot(
mb_class_label_indices,
depth=self._num_final_classes,
on_value=1.0 - self._config.label_smoothing_epsilon,
off_value=(self._config.label_smoothing_epsilon /
self.dataset.num_classes))
# TODO: Don't create a mini batch in test mode
# Mask predictions
with tf.variable_scope('avod_apply_mb_mask'):
# Classification
mb_classifications_logits = tf.boolean_mask(
all_cls_logits, mb_mask)
mb_classifications_softmax = tf.boolean_mask(
all_cls_softmax, mb_mask)
# Offsets
mb_offsets = tf.boolean_mask(all_offsets, mb_mask)
# Angle Vectors
if all_angle_vectors is not None:
mb_angle_vectors = tf.boolean_mask(all_angle_vectors, mb_mask)
else:
mb_angle_vectors = None
# Encode anchor offsets
with tf.variable_scope('avod_encode_mb_anchors'):
mb_anchors = tf.boolean_mask(top_anchors, mb_mask)
if self._box_rep == 'box_3d':
# Gather corresponding ground truth anchors for each mb sample
mb_anchors_gt = tf.gather(anchors_gt, mb_gt_indices)
mb_offsets_gt = anchor_encoder.tf_anchor_to_offset(
mb_anchors, mb_anchors_gt)
# Gather corresponding ground truth orientation for each
# mb sample
mb_orientations_gt = tf.gather(orientations_gt, mb_gt_indices)
elif self._box_rep in ['box_8c', 'box_8co']:
# Get boxes_3d ground truth mini-batch and convert to box_8c
mb_boxes_3d_gt = tf.gather(boxes_3d_gt, mb_gt_indices)
if self._box_rep == 'box_8c':
mb_boxes_8c_gt = \
box_8c_encoder.tf_box_3d_to_box_8c(mb_boxes_3d_gt)
elif self._box_rep == 'box_8co':
mb_boxes_8c_gt = \
box_8c_encoder.tf_box_3d_to_box_8co(mb_boxes_3d_gt)
# Convert proposals: anchors -> box_3d -> box8c
proposal_boxes_3d = \
box_3d_encoder.anchors_to_box_3d(top_anchors, fix_lw=True)
proposal_boxes_8c = \
box_8c_encoder.tf_box_3d_to_box_8c(proposal_boxes_3d)
# Get mini batch offsets
mb_boxes_8c = tf.boolean_mask(proposal_boxes_8c, mb_mask)
mb_offsets_gt = box_8c_encoder.tf_box_8c_to_offsets(
mb_boxes_8c, mb_boxes_8c_gt)
# Flatten the offsets to a (N x 24) vector
mb_offsets_gt = tf.reshape(mb_offsets_gt, [-1, 24])
elif self._box_rep in ['box_4c', 'box_4ca']:
# Get ground plane for box_4c conversion
ground_plane = self._rpn_model.placeholders[
self._rpn_model.PL_GROUND_PLANE]
# Convert gt boxes_3d -> box_4c
mb_boxes_3d_gt = tf.gather(boxes_3d_gt, mb_gt_indices)
mb_boxes_4c_gt = box_4c_encoder.tf_box_3d_to_box_4c(
mb_boxes_3d_gt, ground_plane)
# Convert proposals: anchors -> box_3d -> box_4c
proposal_boxes_3d = \
box_3d_encoder.anchors_to_box_3d(top_anchors, fix_lw=True)
proposal_boxes_4c = \
box_4c_encoder.tf_box_3d_to_box_4c(proposal_boxes_3d,
ground_plane)
# Get mini batch
mb_boxes_4c = tf.boolean_mask(proposal_boxes_4c, mb_mask)
mb_offsets_gt = box_4c_encoder.tf_box_4c_to_offsets(
mb_boxes_4c, mb_boxes_4c_gt)
if self._box_rep == 'box_4ca':
# Gather corresponding ground truth orientation for each
# mb sample
mb_orientations_gt = tf.gather(orientations_gt,
mb_gt_indices)
else:
raise NotImplementedError(
'Anchor encoding not implemented for', self._box_rep)
######################################################
# ROI summary images
######################################################
avod_mini_batch_size = \
self.dataset.kitti_utils.mini_batch_utils.avod_mini_batch_size
with tf.variable_scope('bev_avod_rois'):
mb_bev_anchors_norm = tf.boolean_mask(
bev_proposal_boxes_norm_tf_order, mb_mask)
mb_bev_box_indices = tf.zeros_like(mb_gt_indices, dtype=tf.int32)
# Show the ROIs of the BEV input density map
# for the mini batch anchors
bev_input_rois = tf.image.crop_and_resize(
self._rpn_model._bev_preprocessed, mb_bev_anchors_norm,
mb_bev_box_indices, (32, 32))
bev_input_roi_summary_images = tf.split(bev_input_rois,
self._bev_depth,
axis=3)
tf.summary.image('bev_avod_rois',
bev_input_roi_summary_images[-1],
max_outputs=avod_mini_batch_size)
with tf.variable_scope('img_avod_rois'):
# ROIs on image input
mb_img_anchors_norm = tf.boolean_mask(
img_proposal_boxes_norm_tf_order, mb_mask)
mb_img_box_indices = tf.zeros_like(mb_gt_indices, dtype=tf.int32)
# Do test ROI pooling on mini batch
img_input_rois = tf.image.crop_and_resize(
self._rpn_model._img_preprocessed, mb_img_anchors_norm,
mb_img_box_indices, (32, 32))
tf.summary.image('img_avod_rois',
img_input_rois,
max_outputs=avod_mini_batch_size)
######################################################
# Final Predictions
######################################################
# Get orientations from angle vectors
if all_angle_vectors is not None:
with tf.variable_scope('avod_orientation'):
all_orientations = \
orientation_encoder.tf_angle_vector_to_orientation(
all_angle_vectors)
# Apply offsets to regress proposals
with tf.variable_scope('avod_regression'):
if self._box_rep == 'box_3d':
prediction_anchors = \
anchor_encoder.offset_to_anchor(top_anchors,
all_offsets)
elif self._box_rep in ['box_8c', 'box_8co']:
# Reshape the 24-dim regressed offsets to (N x 3 x 8)
reshaped_offsets = tf.reshape(all_offsets, [-1, 3, 8])
# Given the offsets, get the boxes_8c
prediction_boxes_8c = \
box_8c_encoder.tf_offsets_to_box_8c(proposal_boxes_8c,
reshaped_offsets)
# Convert corners back to box3D
prediction_boxes_3d = \
box_8c_encoder.box_8c_to_box_3d(prediction_boxes_8c)
# Convert the box_3d to anchor format for nms
prediction_anchors = \
box_3d_encoder.tf_box_3d_to_anchor(prediction_boxes_3d)
elif self._box_rep in ['box_4c', 'box_4ca']:
# Convert predictions box_4c -> box_3d
prediction_boxes_4c = \
box_4c_encoder.tf_offsets_to_box_4c(proposal_boxes_4c,
all_offsets)
prediction_boxes_3d = \
box_4c_encoder.tf_box_4c_to_box_3d(prediction_boxes_4c,
ground_plane)
# Convert to anchor format for nms
prediction_anchors = \
box_3d_encoder.tf_box_3d_to_anchor(prediction_boxes_3d)
else:
raise NotImplementedError('Regression not implemented for',
self._box_rep)
# Apply Non-oriented NMS in BEV
with tf.variable_scope('avod_nms'):
bev_extents = self.dataset.kitti_utils.bev_extents
with tf.variable_scope('bev_projection'):
# Project predictions into BEV
avod_bev_boxes, _ = anchor_projector.project_to_bev(
prediction_anchors, bev_extents)
avod_bev_boxes_tf_order = \
anchor_projector.reorder_projected_boxes(
avod_bev_boxes)
# Get top score from second column onward
all_top_scores = tf.reduce_max(all_cls_logits[:, 1:], axis=1)
# Apply NMS in BEV
nms_indices = tf.image.non_max_suppression(
avod_bev_boxes_tf_order,
all_top_scores,
max_output_size=self._nms_size,
iou_threshold=self._nms_iou_threshold)
# Gather predictions from NMS indices
top_classification_logits = tf.gather(all_cls_logits, nms_indices)
top_classification_softmax = tf.gather(all_cls_softmax,
nms_indices)
top_prediction_anchors = tf.gather(prediction_anchors, nms_indices)
if self._box_rep == 'box_3d':
top_orientations = tf.gather(all_orientations, nms_indices)
elif self._box_rep in ['box_8c', 'box_8co']:
top_prediction_boxes_3d = tf.gather(prediction_boxes_3d,
nms_indices)
top_prediction_boxes_8c = tf.gather(prediction_boxes_8c,
nms_indices)
elif self._box_rep == 'box_4c':
top_prediction_boxes_3d = tf.gather(prediction_boxes_3d,
nms_indices)
top_prediction_boxes_4c = tf.gather(prediction_boxes_4c,
nms_indices)
elif self._box_rep == 'box_4ca':
top_prediction_boxes_3d = tf.gather(prediction_boxes_3d,
nms_indices)
top_prediction_boxes_4c = tf.gather(prediction_boxes_4c,
nms_indices)
top_orientations = tf.gather(all_orientations, nms_indices)
else:
raise NotImplementedError('NMS gather not implemented for',
self._box_rep)
if self._train_val_test in ['train', 'val']:
# Additional entries are added to the shared prediction_dict
# Mini batch predictions
prediction_dict[self.PRED_MB_CLASSIFICATION_LOGITS] = \
mb_classifications_logits
prediction_dict[self.PRED_MB_CLASSIFICATION_SOFTMAX] = \
mb_classifications_softmax
prediction_dict[self.PRED_MB_OFFSETS] = mb_offsets
# Mini batch ground truth
prediction_dict[self.PRED_MB_CLASSIFICATIONS_GT] = \
mb_classification_gt
prediction_dict[self.PRED_MB_OFFSETS_GT] = mb_offsets_gt
# Top NMS predictions
prediction_dict[self.PRED_TOP_CLASSIFICATION_LOGITS] = \
top_classification_logits
prediction_dict[self.PRED_TOP_CLASSIFICATION_SOFTMAX] = \
top_classification_softmax
prediction_dict[self.PRED_TOP_PREDICTION_ANCHORS] = \
top_prediction_anchors
# Mini batch predictions (for debugging)
prediction_dict[self.PRED_MB_MASK] = mb_mask
# prediction_dict[self.PRED_MB_POS_MASK] = mb_pos_mask
prediction_dict[self.PRED_MB_CLASS_INDICES_GT] = \
mb_class_label_indices
# All predictions (for debugging)
prediction_dict[self.PRED_ALL_CLASSIFICATIONS] = \
all_cls_logits
prediction_dict[self.PRED_ALL_OFFSETS] = all_offsets
# Path drop masks (for debugging)
prediction_dict['bev_mask'] = bev_mask
prediction_dict['img_mask'] = img_mask
else:
# self._train_val_test == 'test'
prediction_dict[self.PRED_TOP_CLASSIFICATION_SOFTMAX] = \
top_classification_softmax
prediction_dict[self.PRED_TOP_PREDICTION_ANCHORS] = \
top_prediction_anchors
if self._box_rep == 'box_3d':
prediction_dict[self.PRED_MB_ANCHORS_GT] = mb_anchors_gt
prediction_dict[self.PRED_MB_ORIENTATIONS_GT] = mb_orientations_gt
prediction_dict[self.PRED_MB_ANGLE_VECTORS] = mb_angle_vectors
prediction_dict[self.PRED_TOP_ORIENTATIONS] = top_orientations
# For debugging
prediction_dict[self.PRED_ALL_ANGLE_VECTORS] = all_angle_vectors
elif self._box_rep in ['box_8c', 'box_8co']:
prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
top_prediction_boxes_3d
# Store the corners before converting for visualization purposes
prediction_dict[self.PRED_TOP_BOXES_8C] = top_prediction_boxes_8c
elif self._box_rep == 'box_4c':
prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
top_prediction_boxes_3d
prediction_dict[self.PRED_TOP_BOXES_4C] = top_prediction_boxes_4c
elif self._box_rep == 'box_4ca':
if self._train_val_test in ['train', 'val']:
prediction_dict[self.PRED_MB_ORIENTATIONS_GT] = \
mb_orientations_gt
prediction_dict[self.PRED_MB_ANGLE_VECTORS] = mb_angle_vectors
prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
top_prediction_boxes_3d
prediction_dict[self.PRED_TOP_BOXES_4C] = top_prediction_boxes_4c
prediction_dict[self.PRED_TOP_ORIENTATIONS] = top_orientations
else:
raise NotImplementedError('Prediction dict not implemented for',
self._box_rep)
# prediction_dict[self.PRED_MAX_IOUS] = max_ious
# prediction_dict[self.PRED_ALL_IOUS] = all_ious
return prediction_dict
def sample_mini_batch(self, anchor_box_list_gt, anchor_box_list,
class_labels):
with tf.variable_scope('avod_create_mb_mask'):
# Get IoU for every anchor
all_ious = box_list_ops.iou(anchor_box_list_gt, anchor_box_list)
max_ious = tf.reduce_max(all_ious, axis=0)
max_iou_indices = tf.argmax(all_ious, axis=0)
# Sample a pos/neg mini-batch from anchors with highest IoU match
mini_batch_utils = self.dataset.kitti_utils.mini_batch_utils
mb_mask, mb_pos_mask = mini_batch_utils.sample_avod_mini_batch(
max_ious)
mb_class_label_indices = mini_batch_utils.mask_class_label_indices(
mb_pos_mask, mb_mask, max_iou_indices, class_labels)
mb_gt_indices = tf.boolean_mask(max_iou_indices, mb_mask)
return mb_mask, mb_class_label_indices, mb_gt_indices
def create_feed_dict(self):
feed_dict = self._rpn_model.create_feed_dict()
self.sample_info = self._rpn_model.sample_info
return feed_dict
def loss(self, prediction_dict):
# Note: The loss should be using mini-batch values only
loss_dict, rpn_loss = self._rpn_model.loss(prediction_dict)
losses_output = avod_loss_builder.build(self, prediction_dict)
classification_loss = \
losses_output[avod_loss_builder.KEY_CLASSIFICATION_LOSS]
final_reg_loss = losses_output[avod_loss_builder.KEY_REGRESSION_LOSS]
avod_loss = losses_output[avod_loss_builder.KEY_AVOD_LOSS]
offset_loss_norm = \
losses_output[avod_loss_builder.KEY_OFFSET_LOSS_NORM]
loss_dict.update({self.LOSS_FINAL_CLASSIFICATION: classification_loss})
loss_dict.update({self.LOSS_FINAL_REGRESSION: final_reg_loss})
# Add localization and orientation losses to loss dict for plotting
loss_dict.update({self.LOSS_FINAL_LOCALIZATION: offset_loss_norm})
ang_loss_loss_norm = losses_output.get(
avod_loss_builder.KEY_ANG_LOSS_NORM)
if ang_loss_loss_norm is not None:
loss_dict.update({self.LOSS_FINAL_ORIENTATION: ang_loss_loss_norm})
with tf.variable_scope('model_total_loss'):
total_loss = rpn_loss + avod_loss
return loss_dict, total_loss