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)
#pdb.set_trace()
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 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 train(model_config, train_config, dataset_config):
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
train_val_test = 'train'
model_name = model_config.model_name
with tf.Graph().as_default():
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'bev_only_rpn_model':
model = BevOnlyRpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'bev_only_avod_model':
model = BevOnlyAvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
trainer.train(model, train_config)
def set_up_model(pipeline_config, data_split):
model_config, train_config, _, dataset_config = \
config_builder.get_configs_from_pipeline_file(
pipeline_config, is_training=False)
dataset_config = config_builder.proto_to_obj(dataset_config)
train_val_test = data_split
# Always run in test mode
dataset_config.data_split = 'test'
dataset_config.data_split_dir = 'testing'
dataset_config.has_labels = False
dataset_config.aug_list = []
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
model_name = model_config.model_name
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_ssd_model':
model = AvodSSDModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
return model
def train(model_config, train_config, dataset_config):
# 读取config文件里面的详细内容，包括：
# model_config：模型参数
# train_config：训练参数
# dataset_config：数据集参数
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
train_val_test = 'train'
# 包括avod_model 和 rpn_model
model_name = model_config.model_name
with tf.Graph().as_default():
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
trainer.train(model, train_config)
def train(model_config, train_config, dataset_config):
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
train_val_test = 'train'
model_name = model_config.model_name
with tf.Graph().as_default():
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'retinanet_model':
model = RetinanetModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
#import pdb
#pdb.set_trace()
trainer.train(model, train_config)
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)
def inference(model_config, eval_config, dataset_config, data_split,
ckpt_indices):
# Overwrite the defaults
dataset_config = config_builder.proto_to_obj(dataset_config)
dataset_config.data_split = data_split
dataset_config.data_split_dir = 'training'
if data_split == 'test':
dataset_config.data_split_dir = 'testing'
eval_config.eval_mode = 'test'
eval_config.evaluate_repeatedly = False
dataset_config.has_labels = False
# Enable this to see the actually memory being used
eval_config.allow_gpu_mem_growth = True
eval_config = config_builder.proto_to_obj(eval_config)
# Grab the checkpoint indices to evaluate
eval_config.ckpt_indices = ckpt_indices
# Remove augmentation during evaluation in test mode
dataset_config.aug_list = []
# Build the dataset object
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Setup the model
model_name = model_config.model_name
# Overwrite repeated field
model_config = config_builder.proto_to_obj(model_config)
# Switch path drop off during evaluation
model_config.path_drop_probabilities = [1.0, 1.0]
with tf.Graph().as_default():
if model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
elif model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
elif model_name == 'bev_only_rpn_model':
model = BevOnlyRpnModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
elif model_name == 'bev_only_avod_model':
model = BevOnlyAvodModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
else:
raise ValueError('Invalid model name {}'.format(model_name))
model_evaluator = Evaluator(model, dataset_config, eval_config)
model_evaluator.run_latest_checkpoints()
def test_disable_path_drop(self):
# Test path drop is disabled when the probabilities
# are set to 1.0.
train_val_test = 'train'
# Overwrite path drop probabilities
model_config = config_builder.proto_to_obj(self.model_config)
model_config.path_drop_probabilities = [1.0, 1.0]
with tf.Graph().as_default():
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=self.dataset)
model.build()
# These variables are set during path drop only
# in the case of no path-drop, they should be non-existence
self.assertFalse(hasattr(model, 'img_path_drop_mask'))
self.assertFalse(hasattr(model, 'bev_path_drop_mask'))
def inferPerspective(model_config, eval_config, dataset_config,
additional_cls):
model_name = model_config.model_name
entity_perspect_dir = dataset_config.dataset_dir + dataset_config.data_split_dir + '/'
logging.debug("Inferring perspective: %s\n %s\n %s",
dataset_config.data_split, entity_perspect_dir,
dataset_config.dataset_dir)
files_in_range = create_split.create_split(dataset_config.dataset_dir,
entity_perspect_dir,
dataset_config.data_split)
# If there are no files within the range cfg.MIN_IDX, cfg.MAX_IDX
# then skip this perspective
if not files_in_range:
logging.debug(
"No files within the range cfg.MIN_IDX, cfg.MAX_IDX, skipping perspective"
)
return
if not additional_cls:
estimate_ground_planes.estimate_ground_planes(entity_perspect_dir,
dataset_config, 0)
# Build the dataset object
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
#Switch inference output directory
model_config.paths_config.pred_dir = entity_perspect_dir + '/{}/'.format(
cfg.AVOD_OUTPUT_DIR)
logging.debug("Prediction directory: %s",
model_config.paths_config.pred_dir)
with tf.Graph().as_default():
if model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
elif model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
else:
raise ValueError('Invalid model name {}'.format(model_name))
model_evaluator = Evaluator(model, dataset_config, eval_config)
model_evaluator.run_latest_checkpoints()
save_kitti_predictions.convertPredictionsToKitti(
dataset, model_config.paths_config.pred_dir, additional_cls)
def train(model_config, train_config, dataset_config):
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
train_val_test = 'train'
model_name = model_config.model_name
with tf.Graph().as_default():
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
trainer.train(model, train_config)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
trainer.train(model, train_config)
elif model_name == 'avod_moe_model':
model = AvodMoeModel(model_config,
train_val_test=train_val_test,
dataset=dataset)
trainer_moe.train(model, train_config)
elif model_name == 'epbrm':
model = epBRM(model_config, dataset=dataset)
epbrm_trainer.train(model, train_config)
elif model_name == 'avod_model_new_bev':
model = AvodModelBEV(model_config,
train_val_test=train_val_test,
dataset=dataset)
elif model_name == 'avod_model_double_fusion_new_bev':
model = AvodModelDoubleFusionBEV(model_config,
train_val_test=train_val_test,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
if model_name == 'avod_model_new_bev' or model_name == 'avod_model_double_fusion_new_bev':
trainer_new_bev.train(model, train_config)
else:
trainer.train(model, train_config)
def set_up_model_test_mode(pipeline_config_path, data_split):
"""Returns the model and its config in test mode."""
model_config, _, _, dataset_config = \
config_builder.get_configs_from_pipeline_file(
pipeline_config_path, is_training=False)
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Overwrite the defaults
dataset_config = config_builder.proto_to_obj(dataset_config)
# Use the validation set
dataset_config.data_split = data_split
dataset_config.data_split_dir = 'training'
if data_split == 'test':
dataset_config.data_split_dir = 'testing'
# Remove augmentation when in test mode
dataset_config.aug_list = []
# Build the dataset object
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
model_name = model_config.model_name
if model_name == 'rpn_model':
model = RpnModel(model_config, train_val_test='test', dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config, train_val_test='test', dataset=dataset)
elif model_name == 'avod_ssd_model':
model = AvodSSDModel(model_config,
train_val_test='test',
dataset=dataset)
else:
raise ValueError('Invalid model_name')
return model, model_config
def set_up_model_train_mode(pipeline_config_path, data_split):
"""Returns the model and its train_op."""
model_config, train_config, _, dataset_config = \
config_builder.get_configs_from_pipeline_file(
pipeline_config_path, is_training=True)
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
model_name = model_config.model_name
if model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=data_split,
dataset=dataset)
elif model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=data_split,
dataset=dataset)
elif model_name == 'avod_ssd_model':
model = AvodSSDModel(model_config,
train_val_test=data_split,
dataset=dataset)
else:
raise ValueError('Invalid model_name')
prediction_dict = model.build()
losses_dict, total_loss = model.loss(prediction_dict)
# These parameters are required to set up the optimizer
global_summaries = set([])
global_step_tensor = tf.Variable(0, trainable=False)
training_optimizer = optimizer_builder.build(train_config.optimizer,
global_summaries,
global_step_tensor)
# Set up the train op
train_op = slim.learning.create_train_op(total_loss, training_optimizer)
return model, train_op
class AvodModel(model.DetectionModel):
TRAIN_REFINE = True
##############################
# 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'
PRED_MB_POS_GT = 'avod_mb_pos_gt'
PRED_MB_NEG_GT = 'avod_mb_neg_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)
#pdb.set_trace()
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 _norm(self, x):
norm = tf.norm(x, axis=[1, 2], keep_dims=True)
x = tf.divide(x, norm + 1e-7)
return x
def _sub_mean(self, x):
mean = tf.reduce_mean(x, axis=[1, 2], keep_dims=True)
x = x - mean
return x
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)
with tf.variable_scope('img_r'):
image_r_shape = tf.cast(
tf.shape(
rpn_model.placeholders[RpnModel.PL_IMG_R_INPUT])[0:2],
tf.float32)
img_r_proposal_boxes, img_r_proposal_boxes_norm = \
anchor_projector.tf_project_to_image_space(
avod_projection_in,
rpn_model.placeholders[RpnModel.PL_CALIB_P3],
image_r_shape)
# Only reorder the normalized img
img_r_proposal_boxes_norm_tf_order = \
anchor_projector.reorder_projected_boxes(
img_r_proposal_boxes_norm)
#bev_feature_maps = rpn_model.bev_feature_maps
img_feature_maps = rpn_model.img_feature_maps
img_r_feature_maps = rpn_model.img_r_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_r_mask = rpn_model.img_r_path_drop_mask
img_feature_maps = tf.multiply(img_feature_maps,
img_mask)
#bev_feature_maps = tf.multiply(bev_feature_maps,
# bev_mask)
img_r_feature_maps = tf.multiply(img_r_feature_maps,
img_r_mask)
else:
#bev_mask = tf.constant(1.0)
img_mask = tf.constant(1.0)
img_r_mask = tf.constant(1.0)
"""
img_mask = tf.constant(1.0)
img_r_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')
"""
img_boxes_norm_batches = tf.expand_dims(img_proposal_boxes_norm,
axis=0)
# These should be all 0's since there is only 1 image
tf_box_indices = get_box_indices(img_boxes_norm_batches)
# Do ROI Pooling on image
img_rois = tf.image.crop_and_resize(
img_feature_maps,
img_proposal_boxes_norm_tf_order,
tf_box_indices, (32, 32),
name='img_rois')
img_r_rois = tf.image.crop_and_resize(
img_r_feature_maps,
img_r_proposal_boxes_norm_tf_order,
tf_box_indices, (32, 32),
name='img_r_rois')
img_rois = self._sub_mean(img_rois)
img_r_rois = self._sub_mean(img_r_rois)
cos_simi = tf.reduce_sum(img_rois * img_r_rois, \
axis=[1, 2], keep_dims=True)
cos_simi = cos_simi / (tf.norm(img_rois + 1e-5, axis=[1, 2], keep_dims=True) * \
tf.norm(img_r_rois + 1e-5, axis=[1, 2], keep_dims=True))
cos_simi = tf.nn.relu(cos_simi)
img_rois = tf.image.resize_bilinear(
img_rois, self._proposal_roi_crop_size) * cos_simi
img_r_rois = tf.image.resize_bilinear(
img_r_rois, self._proposal_roi_crop_size) * cos_simi
# 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=[img_rois, img_r_rois],
input_weights=[img_mask, img_r_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)
with tf.variable_scope('img_r_avod_rois'):
# ROIs on image input
mb_img_r_anchors_norm = tf.boolean_mask(
img_r_proposal_boxes_norm_tf_order, mb_mask)
mb_img_r_box_indices = tf.zeros_like(mb_gt_indices, dtype=tf.int32)
# Do test ROI pooling on mini batch
img_r_input_rois = tf.image.crop_and_resize(
self._rpn_model._img_r_preprocessed, mb_img_r_anchors_norm,
mb_img_r_box_indices, (32, 32))
tf.summary.image('img_r_avod_rois',
img_r_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
prediction_dict['img_r_mask'] = img_r_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, rpn_acc_all, rpn_acc_pos, \
rpn_acc_score_neg, rpn_acc_score_pos = self._rpn_model.loss(prediction_dict)
losses_output, avod_acc_all, avod_acc_pos = 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'):
if self.TRAIN_REFINE:
total_loss = rpn_loss + avod_loss
else:
total_loss = rpn_loss
rpn_score_2d_loss = loss_dict[self._rpn_model.LOSS_RPN_SCORE_2D]
rpn_class_loss = loss_dict[self._rpn_model.LOSS_RPN_OBJECTNESS]
rpn_reg_loss = loss_dict[self._rpn_model.LOSS_RPN_REGRESSION]
refine_class_loss = classification_loss
refine_reg_loss = final_reg_loss
return loss_dict, \
total_loss, \
rpn_score_2d_loss, \
rpn_acc_score_neg, \
rpn_acc_score_pos, \
rpn_class_loss, \
rpn_reg_loss, \
rpn_acc_all, \
rpn_acc_pos, \
refine_class_loss, \
refine_reg_loss, \
avod_acc_all, \
avod_acc_pos
class AvodModelDoubleFusionBEV(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(AvodModelDoubleFusionBEV, 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 = {}
####################################################################################
# TODO PROJECT: scale the features to features with larger maximum values
def scale_bev(self, bev_rois, img_rois):
val_to_mul = tf.divide(self.max_img_feature_val,
self.max_bev_feature_val)
return tf.multiply(bev_rois, val_to_mul)
def scale_img(self, bev_rois, img_rois):
val_to_mul = tf.divide(self.max_bev_feature_val,
self.max_img_feature_val)
return tf.multiply(bev_rois, val_to_mul)
####################################################################################
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 are boxes' x and z coordinate relative to bev_extents
# bev_proposal_boxes_norm are normalized boxes in bev_extents' range
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
# tf_box_indices contains 1D tensor with size [num_boxes], each element specifies
# batch index to whom this box belongs. Because the batch size here is 1, so it
# doesn't matter
# bev_rois is a 4-D tensor of shape [num_boxes, crop_height, crop_width, depth]
####################################################################################
# TODO PROJECT: set bev_feature_maps or img_feature_maps to zeros for testing
# bev_feature_maps = tf.zeros_like(bev_feature_maps)
# self.bev_feature_maps = tf.zeros_like(bev_feature_maps)
# bev_feature_maps = self.bev_feature_maps
####################################################################################
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')
####################################################################################
# TODO PROJECT: create member variables for accessing
# bev_rois4moe = tf.image.crop_and_resize(
# bev_feature_maps,
# bev_proposal_boxes_norm_tf_order,
# tf_box_indices,
# [28,28],
# name='bev_rois4moe')
# # Do ROI Pooling on image
# img_rois4moe = tf.image.crop_and_resize(
# img_feature_maps,
# img_proposal_boxes_norm_tf_order,
# tf_box_indices,
# [28,28],
# name='img_rois4moe')
####################################################################################
####################################################################################
# TODO PROJECT: create member variables for accessing
# self.bev_rois = bev_rois
# self.img_rois = img_rois
self.bev_boxes = bev_proposal_boxes_tf_order
self.bev_boxes_norm = bev_proposal_boxes_norm
self.img_boxes = img_proposal_boxes
self.img_boxes_norm = img_proposal_boxes_norm
# self.bev_mask = rpn_model.bev_path_drop_mask
# self.img_mask = rpn_model.img_path_drop_mask
####################################################################################
####################################################################################
# TODO PROJECT: scale the features to features with larger maximum values
# self.max_img_feature_val = tf.reduce_max(img_rois, axis=None)
# self.max_bev_feature_val = tf.reduce_max(bev_rois, axis=None)
#
# bev_rois_moe = tf.cond(tf.greater(self.max_img_feature_val, self.max_bev_feature_val),
# lambda: self.scale_bev(bev_rois, img_rois),
# lambda: bev_rois)
# img_rois_moe = tf.cond(tf.greater(self.max_bev_feature_val, self.max_img_feature_val),
# lambda: self.scale_img(bev_rois, img_rois),
# lambda: img_rois)
####################################################################################
####################################################################################
# TODO PROJECT: insert code here to add mixture of experts
# self._moe_model = MoeModel(img_rois, bev_rois, img_proposal_boxes, bev_proposal_boxes)
# self._moe_model = MoeModel(img_feature_maps, bev_feature_maps, img_proposal_boxes, bev_proposal_boxes)
# self._moe_model._set_up_input_pls()
# self.moe_prediction = self._moe_model.build()
####################################################################################
####################################################################################
# TODO PROJECT: weight the feature before average img and bev
# img_weights = tf.reshape(self.moe_prediction['img_weight'],[-1,1,1,1])
# bev_weights = tf.reshape(self.moe_prediction['bev_weight'],[-1,1,1,1])
# img_weights = 0.5 * tf.ones([1024,1,1,1], tf.float32)
# bev_weights = 0.5 * tf.ones([1024,1,1,1], tf.float32)
# weighted_img_rois = tf.multiply(img_weights,img_rois)
# weighted_bev_rois = tf.multiply(bev_weights,bev_rois)
####################################################################################
####################################################################################
# TODO PROJECT: create fused bev
_, bev_mar_boxes_norm = cf.add_margin_to_regions(
bev_proposal_boxes, self.dataset.kitti_utils.bev_extents)
bev_pixels_loc = cf.bev_pixel_eq_1_loc(
self._rpn_model._bev_preprocessed)
max_height = self.dataset.config.kitti_utils_config.bev_generator.slices.height_hi
min_height = self.dataset.config.kitti_utils_config.bev_generator.slices.height_lo
num_slices = self.dataset.config.kitti_utils_config.bev_generator.slices.num_slices
height_list = [
min_height + (2 * x + 1) * (max_height - min_height) /
(2.0 * num_slices) for x in range(num_slices)
]
print("bev_preprocess shape: ",
(self._rpn_model._bev_preprocessed).shape)
velo_pc = cf.bev_pixel_loc_to_3d_velo(
bev_pixels_loc,
tf.shape(self._rpn_model._bev_preprocessed)[1:3], height_list,
self.dataset.kitti_utils.bev_extents)
print("PL_CALIB_P2 shape: ",
self._rpn_model.placeholders[RpnModel.PL_CALIB_P2].shape)
p_2d = anchor_projector.project_to_image_tensor(
tf.transpose(tf.cast(velo_pc, tf.float32)),
self._rpn_model.placeholders[RpnModel.PL_CALIB_P2])
print("image feature maps [0] shape: ", img_feature_maps[0].shape)
features_at_p_2d = tf.gather_nd(
img_feature_maps[0], tf.cast(tf.round(tf.transpose(p_2d)),
tf.int32))
print("features_at_p_2d shape: ", features_at_p_2d.shape)
new_bev = cf.create_fused_bev(
tf.shape(self._rpn_model._bev_preprocessed), bev_pixels_loc,
features_at_p_2d)
# raise Exception("finish fused_bev generation!")
self._new_bev_feature_extractor = feature_extractor_builder.get_extractor(
self.model_config.layers_config.bev_feature_extractor)
self.new_bev_feature_maps, self.new_bev_end_points = \
self._new_bev_feature_extractor.build(
new_bev,
self._bev_pixel_size,
self._is_training,
scope='new_bev_vgg'
)
new_bev_rois = tf.image.crop_and_resize(
self.new_bev_feature_maps,
bev_proposal_boxes_norm_tf_order,
tf_box_indices,
self._proposal_roi_crop_size,
name='new_bev_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)
####################################################################################
# TODO PROJECT: average img and bev features first and then concat with new bev
rois_sum = tf.reduce_sum([bev_rois, img_rois], axis=0)
rois_mean = tf.divide(rois_sum, tf.reduce_sum([bev_mask, img_mask]))
fc_output_layers = \
avod_fc_layers_builder.build(
layers_config=avod_layers_config,
input_rois=[rois_mean, new_bev_rois],
input_weights=[1, 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)
####################################################################################
####################################################################################
# TODO PROJECT: input weighted bev_rois and img_rois to output layer
# fc_output_layers = \
# avod_fc_layers_builder.build(
# layers_config=avod_layers_config,
# input_rois=[weighted_bev_rois, weighted_img_rois],
# input_weights=[bev_mask * bev_weights, img_mask * img_weights],
# 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
# 8c means 8 corners
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
# 4c means 4 corners
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
################################################################################
# TODO PROJECT: plot weight
################################################################################
return loss_dict, total_loss
def test(model_config, eval_config,
dataset_config, data_split,
ckpt_indices):
# Overwrite the defaults
dataset_config = config_builder.proto_to_obj(dataset_config)
dataset_config.data_split = data_split
dataset_config.data_split_dir = 'training'
if data_split == 'test':
dataset_config.data_split_dir = 'testing'
eval_config.eval_mode = 'test'
eval_config.evaluate_repeatedly = False
dataset_config.has_labels = False
# Enable this to see the actually memory being used
eval_config.allow_gpu_mem_growth = True
eval_config = config_builder.proto_to_obj(eval_config)
# Grab the checkpoint indices to evaluate
eval_config.ckpt_indices = ckpt_indices
# Remove augmentation during evaluation in test mode
dataset_config.aug_list = []
# Build the dataset object
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Setup the model
model_name = model_config.model_name
# Overwrite repeated field
model_config = config_builder.proto_to_obj(model_config)
# Switch path drop off during evaluation
model_config.path_drop_probabilities = [1.0, 1.0]
with tf.Graph().as_default():
if model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
elif model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=eval_config.eval_mode,
dataset=dataset)
else:
raise ValueError('Invalid model name {}'.format(model_name))
#model_evaluator = Evaluator(model, dataset_config, eval_config)
#model_evaluator.run_latest_checkpoints()
# Create a variable tensor to hold the global step
global_step_tensor = tf.Variable(0, trainable=False, name='global_step')
allow_gpu_mem_growth = eval_config.allow_gpu_mem_growth
if allow_gpu_mem_growth:
# GPU memory config
config = tf.ConfigProto()
config.gpu_options.allow_growth = allow_gpu_mem_growth
_sess = tf.Session(config=config)
else:
_sess = tf.Session()
_prediction_dict = model.build()
_saver = tf.train.Saver()
trainer_utils.load_checkpoints(model_config.paths_config.checkpoint_dir,
_saver)
num_checkpoints = len(_saver.last_checkpoints)
print("test:",num_checkpoints)
checkpoint_to_restore = _saver.last_checkpoints[num_checkpoints-1]
_saver.restore(_sess, checkpoint_to_restore)
num_samples = model.dataset.num_samples
num_valid_samples = 0
current_epoch = model.dataset.epochs_completed
while current_epoch == model.dataset.epochs_completed:
# Keep track of feed_dict speed
start_time = time.time()
feed_dict = model.create_feed_dict()
feed_dict_time = time.time() - start_time
# Get sample name from model
sample_name = model.sample_info['sample_name']
num_valid_samples += 1
print("Step: {} / {}, Inference on sample {}".format(
num_valid_samples, num_samples,
sample_name))
print("test mode")
inference_start_time = time.time()
# Don't calculate loss or run summaries for test
predictions = _sess.run(_prediction_dict,
feed_dict=feed_dict)
inference_time = time.time() - inference_start_time
print("inference time:", inference_time)
predictions_and_scores = get_avod_predicted_boxes_3d_and_scores(predictions)
#print(predictions_and_scores)
#im_path = os.path.join(dataset_dir, 'training/image_2/{:06d}.png'.format(img_idx))
#im = cv2.imread(im_path)
#cv2.imshow('result',im)
#cv2.waitKey(30)
prediction_boxes_3d = predictions_and_scores[:, 0:7]
prediction_scores = predictions_and_scores[:, 7]
prediction_class_indices = predictions_and_scores[:, 8]
gt_classes = ['Car']
fig_size = (10, 6.1)
avod_score_threshold = 0.1
if len(prediction_boxes_3d) > 0:
# Apply score mask
avod_score_mask = prediction_scores >= avod_score_threshold
prediction_boxes_3d = prediction_boxes_3d[avod_score_mask]
prediction_scores = prediction_scores[avod_score_mask]
prediction_class_indices = \
prediction_class_indices[avod_score_mask]
if len(prediction_boxes_3d) > 0:
dataset_dir = model.dataset.dataset_dir
sample_name = (model.dataset.sample_names[model.dataset._index_in_epoch - 1])
img_idx = int(sample_name)
print("frame_index",img_idx)
image_path = model.dataset.get_rgb_image_path(sample_name)
image = Image.open(image_path)
image_size = image.size
if model.dataset.has_labels:
gt_objects = obj_utils.read_labels(dataset.label_dir, img_idx)
else:
gt_objects = []
filtered_gt_objs = model.dataset.kitti_utils.filter_labels(
gt_objects, classes=gt_classes)
stereo_calib = calib_utils.read_calibration(dataset.calib_dir,
img_idx)
calib_p2 = stereo_calib.p2
# Project the 3D box predictions to image space
image_filter = []
final_boxes_2d = []
for i in range(len(prediction_boxes_3d)):
box_3d = prediction_boxes_3d[i, 0:7]
img_box = box_3d_projector.project_to_image_space(
box_3d, calib_p2,
truncate=True, image_size=image_size,
discard_before_truncation=False)
if img_box is not None:
image_filter.append(True)
final_boxes_2d.append(img_box)
else:
image_filter.append(False)
final_boxes_2d = np.asarray(final_boxes_2d)
final_prediction_boxes_3d = prediction_boxes_3d[image_filter]
final_scores = prediction_scores[image_filter]
final_class_indices = prediction_class_indices[image_filter]
num_of_predictions = final_boxes_2d.shape[0]
# Convert to objs
final_prediction_objs = \
[box_3d_encoder.box_3d_to_object_label(
prediction, obj_type='Prediction')
for prediction in final_prediction_boxes_3d]
for (obj, score) in zip(final_prediction_objs, final_scores):
obj.score = score
pred_fig, pred_2d_axes, pred_3d_axes = \
vis_utils.visualization(dataset.rgb_image_dir,
img_idx,
display=False,
fig_size=fig_size)
draw_predictions(filtered_gt_objs,
calib_p2,
num_of_predictions,
final_prediction_objs,
final_class_indices,
final_boxes_2d,
pred_2d_axes,
pred_3d_axes,
True,
True,
gt_classes,
False)
#cv2.imshow('result',pred_fig)
print(type(pred_fig))
pred_fig.canvas.draw()
img = np.fromstring(pred_fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
img = img.reshape(pred_fig.canvas.get_width_height()[::-1] + (3,))
cv2.imshow('result',img)
#draw bird view
kitti_utils = model.dataset.kitti_utils
print(img.shape[0:2])
point_cloud = kitti_utils.get_point_cloud(
'lidar', img_idx, (370, 1242))
ground_plane = kitti_utils.get_ground_plane(sample_name)
bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)
density_map = np.array(bev_images.get("density_map"))
_, box_points_norm = box_3d_projector.project_to_bev(
final_prediction_boxes_3d, [[-40, 40], [0, 70]])
density_map = draw_boxes(density_map, box_points_norm)
cv2.imshow('lidar',density_map)
cv2.waitKey(-1)
def test_create_path_drop_masks(self):
# Tests creating path drop choices
# based on the given probabilities
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
rpn_model.build()
##################################
# Test-Case 1 : Keep img, Keep bev
##################################
p_img = tf.constant(0.6)
p_bev = tf.constant(0.85)
# Set the random numbers for testing purposes
rand_choice = [0.53, 0.83, 0.05]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 1.0)
##################################
# Test-Case 2 : Kill img, Keep bev
##################################
p_img = tf.constant(0.2)
p_bev = tf.constant(0.85)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 0.0)
np.testing.assert_array_equal(bev_mask_out, 1.0)
##################################
# Test-Case 3 : Keep img, Kill bev
##################################
p_img = tf.constant(0.9)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 0.0)
##############################################
# Test-Case 4 : Kill img, Kill bev, third flip
##############################################
p_img = tf.constant(0.0)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 0.0)
# Because of the third condition, we expect to be keeping bev
np.testing.assert_array_equal(bev_mask_out, 1.0)
##############################################
# Test-Case 5 : Kill img, Kill bev, third flip
##############################################
# Let's flip the third chance and keep img instead
rand_choice = [0.53, 0.83, 0.61]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
p_img = tf.constant(0.0)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
# Because of the third condition, we expect to be keeping img
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 0.0)
def test_load_model_weights(self):
# Tests loading weights
train_val_test = 'train'
# Overwrite the training iterations
self.train_config.max_iterations = 1
self.train_config.overwrite_checkpoints = True
with tf.Graph().as_default():
model = RpnModel(self.model_config,
train_val_test=train_val_test,
dataset=self.dataset)
trainer.train(model, self.train_config)
paths_config = self.model_config.paths_config
rpn_checkpoint_dir = paths_config.checkpoint_dir
# load the weights back in
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
trainer_utils.load_checkpoints(rpn_checkpoint_dir, saver)
checkpoint_to_restore = saver.last_checkpoints[-1]
trainer_utils.load_model_weights(sess, checkpoint_to_restore)
rpn_vars = slim.get_model_variables()
rpn_weights = sess.run(rpn_vars)
self.assertGreater(len(rpn_weights), 0,
msg='Loaded RPN weights are empty')
with tf.Graph().as_default():
model = AvodModel(self.model_config,
train_val_test=train_val_test,
dataset=self.dataset)
model.build()
# load the weights back in
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
trainer_utils.load_checkpoints(rpn_checkpoint_dir, saver)
checkpoint_to_restore = saver.last_checkpoints[-1]
trainer_utils.load_model_weights(sess, checkpoint_to_restore)
avod_vars = slim.get_model_variables()
avod_weights = sess.run(avod_vars)
# AVOD weights should include both RPN + AVOD weights
self.assertGreater(len(avod_weights),
len(rpn_weights),
msg='Expected more weights for AVOD')
# grab weights corresponding to RPN by index
# since the model variables are ordered
rpn_len = len(rpn_weights)
loaded_rpn_vars = avod_vars[0:rpn_len]
rpn_weights_reload = sess.run(loaded_rpn_vars)
# Make sure the reloaded weights match the originally
# loaded weights
for i in range(rpn_len):
np.testing.assert_array_equal(rpn_weights_reload[i],
rpn_weights[i])
def evaluate(model_config, eval_config, dataset_config):
# Parse eval config
eval_mode = eval_config.eval_mode
if eval_mode not in ['val', 'test']:
raise ValueError('Evaluation mode can only be set to `val` or `test`')
evaluate_repeatedly = eval_config.evaluate_repeatedly
# Parse dataset config
data_split = dataset_config.data_split
if data_split == 'train':
dataset_config.data_split_dir = 'training'
dataset_config.has_labels = True
elif data_split.startswith('val'):
dataset_config.data_split_dir = 'training'
# Don't load labels for val split when running in test mode
if eval_mode == 'val':
dataset_config.has_labels = True
elif eval_mode == 'test':
dataset_config.has_labels = False
elif data_split == 'test':
dataset_config.data_split_dir = 'testing'
dataset_config.has_labels = False
else:
raise ValueError('Invalid data split', data_split)
# Convert to object to overwrite repeated fields
dataset_config = config_builder.proto_to_obj(dataset_config)
# Remove augmentation during evaluation
dataset_config.aug_list = []
# Build the dataset object
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Setup the model
model_name = model_config.model_name
# Convert to object to overwrite repeated fields
model_config = config_builder.proto_to_obj(model_config)
# Switch path drop off during evaluation
model_config.path_drop_probabilities = [1.0, 1.0]
with tf.Graph().as_default():
if model_name == 'avod_model':
model = AvodModel(model_config,
train_val_test=eval_mode,
dataset=dataset)
elif model_name == 'rpn_model':
model = RpnModel(model_config,
train_val_test=eval_mode,
dataset=dataset)
else:
raise ValueError('Invalid model name {}'.format(model_name))
model_evaluator = Evaluator(model, dataset_config, eval_config)
if evaluate_repeatedly:
model_evaluator.repeated_checkpoint_run()
else:
model_evaluator.run_latest_checkpoints()
def test_path_drop_input_multiplication(self):
# Tests the result of final image/bev inputs
# based on the path drop decisions
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
rpn_model.build()
# Shape of input feature map
dummy_img_feature_shape = [1, 30, 50, 2]
random_values = np.random.randint(low=1.0,
high=256.0,
size=2).astype(np.float32)
dummy_img_feature_map = tf.fill(dummy_img_feature_shape,
random_values[0])
# Assume both features map are the same size, this is not
# the case inside the network
dummy_bev_feature_map = tf.fill(dummy_img_feature_shape,
random_values[1])
##################################
# Test-Case 1 : Keep img, Kill bev
##################################
exp_img_input = np.full(dummy_img_feature_shape, random_values[0])
exp_bev_input = np.full(dummy_img_feature_shape, 0.0)
p_img = tf.constant(0.6)
p_bev = tf.constant(0.4)
# Set the random numbers for testing purposes
rand_choice = [0.53, 0.83, 0.05]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
final_img_input = tf.multiply(dummy_img_feature_map,
img_mask)
final_bev_input = tf.multiply(dummy_bev_feature_map,
bev_mask)
with self.test_session():
final_img_input_out = final_img_input.eval()
final_bev_input_out = final_bev_input.eval()
np.testing.assert_array_equal(final_img_input_out,
exp_img_input)
np.testing.assert_array_equal(final_bev_input_out,
exp_bev_input)
##################################
# Test-Case 2 : Kill img, Keep bev
##################################
exp_img_input = np.full(dummy_img_feature_shape, 0)
exp_bev_input = np.full(dummy_img_feature_shape, random_values[1])
p_img = tf.constant(0.4)
p_bev = tf.constant(0.9)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
final_img_input = tf.multiply(dummy_img_feature_map,
img_mask)
final_bev_input = tf.multiply(dummy_bev_feature_map,
bev_mask)
with self.test_session():
final_img_input_out = final_img_input.eval()
final_bev_input_out = final_bev_input.eval()
np.testing.assert_array_equal(final_img_input_out,
exp_img_input)
np.testing.assert_array_equal(final_bev_input_out,
exp_bev_input)
