def _restore_chkpt(self):
# Load the latest checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
# Select the last checkpoint
ckpt_idx = num_checkpoints - 1
return self._saver.last_checkpoints[ckpt_idx]
def run_latest_checkpoints(self):
"""Evaluation function for evaluating all the existing checkpoints.
This function just runs through all the existing checkpoints.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Load the latest checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config, self.model_name)
ckpt_indices = np.asarray(self.eval_config.ckpt_indices)
if ckpt_indices is not None:
if ckpt_indices[0] == -1:
# Restore the most recent checkpoint
ckpt_idx = num_checkpoints - 1
ckpt_indices = [ckpt_idx]
print(ckpt_idx, num_checkpoints, ckpt_indices)
for ckpt_idx in ckpt_indices:
checkpoint_to_restore = self._saver.last_checkpoints[ckpt_idx]
self.run_checkpoint_once(checkpoint_to_restore)
else:
last_checkpoint_id = -1
number_of_evaluations = 0
# go through all existing checkpoints
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
already_evaluated = ckpt_id in already_evaluated_ckpts
if already_evaluated or ckpt_id <= last_checkpoint_id:
number_of_evaluations = max(
(ckpt_idx + 1, number_of_evaluations))
continue
self.run_checkpoint_once(checkpoint_to_restore)
number_of_evaluations += 1
# Save the id of the latest evaluated checkpoint
last_checkpoint_id = ckpt_id
def train(model, train_config):
"""Training function for detection models.
Args:
model: The detection model object.
train_config: a train_*pb2 protobuf.
training i.e. loading RPN weights onto AVOD model.
"""
model = model
train_config = train_config
# Get model configurations
model_config = model.model_config
# Create a variable tensor to hold the global step
global_step_tensor = tf.Variable(
0, trainable=False, name='global_step')
#############################
# Get training configurations
#############################
max_iterations = train_config.max_iterations
summary_interval = train_config.summary_interval
checkpoint_interval = \
train_config.checkpoint_interval
max_checkpoints = train_config.max_checkpoints_to_keep
paths_config = model_config.paths_config
logdir = paths_config.logdir
if not os.path.exists(logdir):
os.makedirs(logdir)
checkpoint_dir = paths_config.checkpoint_dir
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_path = checkpoint_dir + '/' + \
model_config.checkpoint_name
global_summaries = set([])
# The model should return a dictionary of predictions
prediction_dict = model.build()
#WZN: for debug only
#img_input_debug = model._rpn_model._img_preprocessed
#bev_input_debug = model._rpn_model._bev_preprocessed
#bev_pooled_debug = model._rpn_model.bev_input_pooled
#img_pooled_debug = model._rpn_model.img_input_pooled
#import numpy as np
#import matplotlib.pyplot as plt
summary_histograms = train_config.summary_histograms
summary_img_images = train_config.summary_img_images
summary_bev_images = train_config.summary_bev_images
##############################
# Setup loss
##############################
losses_dict, total_loss = model.loss(prediction_dict)
# Optimizer
training_optimizer = optimizer_builder.build(
train_config.optimizer,
global_summaries,
global_step_tensor)
# Create the train op
with tf.variable_scope('train_op'):
train_op = slim.learning.create_train_op(
total_loss,
training_optimizer,
clip_gradient_norm=1.0,
global_step=global_step_tensor)
# Save checkpoints regularly.
saver = tf.train.Saver(max_to_keep=max_checkpoints,
pad_step_number=True)
# Add the result of the train_op to the summary
tf.summary.scalar("training_loss", train_op)
# Add maximum memory usage summary op
# This op can only be run on device with gpu
# so it's skipped on travis
is_travis = 'TRAVIS' in os.environ
if not is_travis:
# tf.summary.scalar('bytes_in_use',
# tf.contrib.memory_stats.BytesInUse())
tf.summary.scalar('max_bytes',
tf.contrib.memory_stats.MaxBytesInUse())
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summary_merged = summary_utils.summaries_to_keep(
summaries,
global_summaries,
histograms=summary_histograms,
input_imgs=summary_img_images,
input_bevs=summary_bev_images
)
allow_gpu_mem_growth = train_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()
# Create unique folder name using datetime for summary writer
datetime_str = str(datetime.datetime.now())
logdir = logdir + '/train'
train_writer = tf.summary.FileWriter(logdir + '/' + datetime_str,
sess.graph)
# Create init op
init = tf.global_variables_initializer()
# Continue from last saved checkpoint
if not train_config.overwrite_checkpoints:
trainer_utils.load_checkpoints(checkpoint_dir,
saver)
if len(saver.last_checkpoints) > 0:
checkpoint_to_restore = saver.last_checkpoints[-1]
saver.restore(sess, checkpoint_to_restore)
else:
# Initialize the variables
sess.run(init)
else:
# Initialize the variables
sess.run(init)
# Read the global step if restored
global_step = tf.train.global_step(sess,
global_step_tensor)
print('Starting from step {} / {}'.format(
global_step, max_iterations))
# Main Training Loop
last_time = time.time()
for step in range(global_step, max_iterations + 1):
# Save checkpoint
if step % checkpoint_interval == 0:
global_step = tf.train.global_step(sess,
global_step_tensor)
saver.save(sess,
save_path=checkpoint_path,
global_step=global_step)
print('Step {} / {}, Checkpoint saved to {}-{:08d}'.format(
step, max_iterations,
checkpoint_path, global_step))
# Create feed_dict for inferencing
feed_dict = model.create_feed_dict()
#WZN: only run for debug
#bev_p,img_p = sess.run([bev_pooled_debug,img_pooled_debug],feed_dict)
#bev_p = np.squeeze(bev_p)
#img_p = np.squeeze(img_p)
#import pdb
#pdb.set_trace()
# Write summaries and train op
if step % summary_interval == 0:
current_time = time.time()
time_elapsed = current_time - last_time
last_time = current_time
train_op_loss, summary_out = sess.run(
[train_op, summary_merged], feed_dict=feed_dict)
print('Step {}, Total Loss {:0.3f}, Time Elapsed {:0.3f} s'.format(
step, train_op_loss, time_elapsed))
train_writer.add_summary(summary_out, step)
else:
# Run the train op only
sess.run(train_op, feed_dict)
# Close the summary writers
train_writer.close()
def repeated_checkpoint_run(self):
"""Periodically evaluates the checkpoints inside the `checkpoint_dir`.
This function evaluates all the existing checkpoints as they are being
generated. If there are none, it sleeps until new checkpoints become
available. Since there is no synchronization guarantee for the trainer
and evaluator, at each iteration it reloads all the checkpoints and
searches for the last checkpoint to continue from. This is meant to be
called in parallel to the trainer to evaluate the models regularly.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Copy kitti native eval code into the predictions folder
if self.do_kitti_native_eval:
evaluator_utils.copy_kitti_native_code(
self.model_config.checkpoint_name)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config, self.full_model)
tf.logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
last_checkpoint_id = -1
number_of_evaluations = 0
while True:
# Load current checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
start = time.time()
if number_of_evaluations >= num_checkpoints:
tf.logging.info(
'No new checkpoints found in %s.'
'Will try again in %d seconds', self.checkpoint_dir,
self.eval_wait_interval)
else:
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = \
self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
already_evaluated = ckpt_id in already_evaluated_ckpts
if already_evaluated or ckpt_id <= last_checkpoint_id:
number_of_evaluations = max(
(ckpt_idx + 1, number_of_evaluations))
continue
self.run_checkpoint_once(checkpoint_to_restore)
number_of_evaluations += 1
# Save the id of the latest evaluated checkpoint
last_checkpoint_id = ckpt_id
time_to_next_eval = start + self.eval_wait_interval - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
def run_profiler(pipeline_config_path, run_mode, data_split, ckpt_index):
avod_top_dir = avod.top_dir()
# Timeline results logfile
file_name = avod_top_dir + '/scripts/profilers/tf_profiler/' + \
'tf_timeline_output.json'
with tf.Session() as sess:
if run_mode == 'train':
# In train mode, data_split should not be 'test' as the test
# split does not have gt.
if data_split == 'test':
raise ValueError('Data split can only be train or val'
'in train mode.')
model, train_op = set_up_model_train_mode(pipeline_config_path,
data_split)
init = tf.global_variables_initializer()
sess.run(init)
elif run_mode == 'test':
model, model_config = set_up_model_test_mode(
pipeline_config_path, data_split)
paths_config = model_config.paths_config
checkpoint_dir = paths_config.checkpoint_dir
prediction_dict = model.build()
# Load the weights
saver = tf.train.Saver()
trainer_utils.load_checkpoints(checkpoint_dir, saver)
if not saver.last_checkpoints:
raise ValueError('Need existing checkpoints to run'
'in test_mode')
checkpoint_to_restore = saver.last_checkpoints[ckpt_index]
saver.restore(sess, checkpoint_to_restore)
else:
raise ValueError('Invalid run_mode {}'.format(run_mode))
feed_dict = model.create_feed_dict()
############################################
# Parameters and Shapes
############################################
graph = tf.get_default_graph()
# Print trainable variable parameter statistics to stdout.
ProfileOptionBuilder = tf.profiler.ProfileOptionBuilder
# Gives the total number of trainable parameters
param_stats = tf.profiler.profile(
graph,
options=ProfileOptionBuilder.trainable_variables_parameter())
# Gives the FLOPS for the ops
tf.profiler.profile(
graph, options=tf.profiler.ProfileOptionBuilder.float_operation())
run_metadata = tf.RunMetadata()
if run_mode == 'train':
sess.run(
[train_op],
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata,
feed_dict=feed_dict)
else:
# Run in test mode
sess.run(
prediction_dict,
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata,
feed_dict=feed_dict)
# The profiler gives us rounded FLOP counts
# So instead query it directly and count the total
op_missing_shape = 0
# op_missing_shape_names = []
total_flops = 0
for op in graph.get_operations():
try:
stats = ops.get_stats_for_node_def(graph, op.node_def, 'flops')
if stats.value:
total_flops += stats.value
except ValueError:
op_missing_shape += 1
# op_missing_shape_names.append(op.name)
print('=============================================================')
print('Number of ops with missing shape: ', op_missing_shape)
print('=============================================================')
############################################
# Log Time and Memory
############################################
# Log the analysis to file
# 'code' view organizes profile using Python call stack
opts = ProfileOptionBuilder(
ProfileOptionBuilder.time_and_memory()).with_timeline_output(
file_name).build()
tf.profiler.profile(graph,
run_meta=run_metadata,
cmd='code',
options=opts)
############################################
# Show Time and Memory on the console
############################################
tf.profiler.profile(
graph,
run_meta=run_metadata,
cmd='op',
options=tf.profiler.ProfileOptionBuilder.time_and_memory())
# print the total number of parameters
print('Total params: %d' % param_stats.total_parameters)
print('Total FLOPs: ', total_flops)
print('=============================================================')
def train(model, train_config):
"""Training function for detection models.
Args:
model: The detection model object.
train_config: a train_*pb2 protobuf.
training i.e. loading RPN weights onto AVOD model.
"""
model = model
train_config = train_config
# Get model configurations
model_config = model.model_config
# Create a variable tensor to hold the global step
global_step_tensor = tf.Variable(0, trainable=False, name='global_step')
#############################
# Get training configurations
#############################
max_iterations = train_config.max_iterations
summary_interval = train_config.summary_interval
checkpoint_interval = \
train_config.checkpoint_interval
max_checkpoints = train_config.max_checkpoints_to_keep
paths_config = model_config.paths_config
logdir = paths_config.logdir
if not os.path.exists(logdir):
os.makedirs(logdir)
checkpoint_dir = paths_config.checkpoint_dir
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_path = checkpoint_dir + '/' + \
model_config.checkpoint_name
global_summaries = set([])
# The model should return a dictionary of predictions
prediction_dict = model.build()
summary_histograms = train_config.summary_histograms
summary_img_images = train_config.summary_img_images
summary_bev_images = train_config.summary_bev_images
##############################
# Setup loss
##############################
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 = model.loss(prediction_dict)
# Optimizer
training_optimizer = optimizer_builder.build(train_config.optimizer,
global_summaries,
global_step_tensor)
# Create the train op
with tf.variable_scope('train_op'):
train_op = slim.learning.create_train_op(
total_loss,
training_optimizer,
clip_gradient_norm=1.0,
global_step=global_step_tensor)
# Save checkpoints regularly.
saver = tf.train.Saver(max_to_keep=max_checkpoints, pad_step_number=True)
# Add the result of the train_op to the summary
tf.summary.scalar("training_loss", train_op)
# Add maximum memory usage summary op
# This op can only be run on device with gpu
# so it's skipped on travis
is_travis = 'TRAVIS' in os.environ
if not is_travis:
# tf.summary.scalar('bytes_in_use',
# tf.contrib.memory_stats.BytesInUse())
tf.summary.scalar('max_bytes', tf.contrib.memory_stats.MaxBytesInUse())
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summary_merged = summary_utils.summaries_to_keep(
summaries,
global_summaries,
histograms=summary_histograms,
input_imgs=summary_img_images,
input_bevs=summary_bev_images)
allow_gpu_mem_growth = train_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()
# Create unique folder name using datetime for summary writer
datetime_str = str(datetime.datetime.now())
logdir = logdir + '/train'
train_writer = tf.summary.FileWriter(logdir + '/' + datetime_str,
sess.graph)
# Create init op
init = tf.global_variables_initializer()
# Continue from last saved checkpoint
if not train_config.overwrite_checkpoints:
trainer_utils.load_checkpoints(checkpoint_dir, saver)
all_variables = tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES)
sess.run(tf.variables_initializer(all_variables))
var_list = [
var for var in all_variables if "beta" not in var.name
and 'Adam' not in var.name and 'Average' not in var.name
]
saver_part = tf.train.Saver(var_list=var_list)
if train_config.use_pretrained:
saver_part.restore(sess, train_config.pretrained)
print('Model loaded from: {}'.format(train_config.pretrained))
step_base = tf.train.global_step(sess, global_step_tensor)
elif len(saver.last_checkpoints) > 0:
checkpoint_to_restore = saver.last_checkpoints[-1]
saver_part.restore(sess, checkpoint_to_restore)
step_base = 0
else:
# Initialize the variables
sess.run(init)
else:
# Initialize the variables
sess.run(init)
global_step = tf.train.global_step(sess, global_step_tensor) - step_base
print('Starting from step {} / {}'.format(global_step, max_iterations))
# Main Training Loop
last_time = time.time()
for step in range(global_step, max_iterations + 1):
# Save checkpoint
if step % checkpoint_interval == 0:
global_step = tf.train.global_step(sess,
global_step_tensor) - step_base
saver.save(sess,
save_path=checkpoint_path,
global_step=global_step)
print('Step {} / {}, Checkpoint saved to {}-{:08d}'.format(
step, max_iterations, checkpoint_path, global_step))
# Create feed_dict for inferencing
feed_dict = model.create_feed_dict()
# Write summaries and train op
if step % summary_interval == 0:
current_time = time.time()
time_elapsed = current_time - last_time
last_time = current_time
#train_op_loss, summary_out = sess.run(
# [train_op, summary_merged], feed_dict=feed_dict)
train_op_loss, summary_out, \
rpn_score_2d_loss_np, \
rpn_acc_score_neg_np, \
rpn_acc_score_pos_np, \
rpn_class_loss_np, rpn_reg_loss_np, \
rpn_acc_all_np, rpn_acc_pos_np, \
refine_class_loss_np, refine_reg_loss_np, \
avod_acc_all_np, avod_acc_pos_np = sess.run(
[train_op, summary_merged,
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], feed_dict=feed_dict)
print('Step {}, Total Loss {:0.3f} | Score {:0.3f}, Acc {:0.2f} {:0.2f} | RPN Class {:0.3f}, Reg {:0.3f}, Acc {:0.2f} {:0.2f} | Final Class {:0.3f}, Reg {:0.3f}, Acc {:0.2f} {:0.2f}'.format(\
step, train_op_loss, rpn_score_2d_loss_np, rpn_acc_score_neg_np * 100, \
rpn_acc_score_pos_np * 100, rpn_class_loss_np, rpn_reg_loss_np, rpn_acc_all_np * 100, \
rpn_acc_pos_np * 100, refine_class_loss_np, refine_reg_loss_np, avod_acc_all_np * 100, avod_acc_pos_np * 100))
train_writer.add_summary(summary_out, step)
else:
# Run the train op only
sess.run(train_op, feed_dict)
# Close the summary writers
train_writer.close()
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 repeated_checkpoint_run(self):
"""Periodically evaluates the checkpoints inside the `checkpoint_dir`.
This function evaluates all the existing checkpoints as they are being
generated. If there are none, it sleeps until new checkpoints become
available. Since there is no synchronization guarantee for the trainer
and evaluator, at each iteration it reloads all the checkpoints and
searches for the last checkpoint to continue from. This is meant to be
called in parallel to the trainer to evaluate the models regularly.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Copy kitti native eval code into the predictions folder
if self.do_kitti_native_eval:
evaluator_utils.copy_kitti_native_code(
self.model_config.checkpoint_name)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config)
else:
already_evaluated_ckpts = []
tf.logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
last_checkpoint_id = -1
number_of_evaluations = 0
#Dont have to add summary(for model inference at each sample) at repeated evaluation..
#only care avg loss at each ckpt step.
#self.summary_merged = None
evaluated_ckpts = [ckpt for ckpt in already_evaluated_ckpts]
while True:
# Load current checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
no_newckpts = True
evaluated_ckpts.sort()
start = time.time()
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = \
self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
if ckpt_id == 0 or ckpt_id in evaluated_ckpts:
continue
else:
no_newckpts = False
print('evaluated ckpts: ', evaluated_ckpts)
print('processing ckpt id: ', ckpt_id)
self.run_checkpoint_once(checkpoint_to_restore)
evaluated_ckpts.append(ckpt_id)
time_to_next_eval = start + self.eval_wait_interval - time.time()
if no_newckpts:
tf.logging.info(
'No new checkpoints found in %s.'
'Will try again in %d seconds', self.checkpoint_dir,
self.eval_wait_interval)
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
def train(model, train_config):
"""Training function for detection models.
Args:
model: The detection model object.
train_config: a train_*pb2 protobuf.
training i.e. loading RPN weights onto AVOD model.
"""
model = model
train_config = train_config
# Get model configurations
model_config = model.model_config
# Create a variable tensor to hold the global step
global_step_tensor = tf.Variable(0, trainable=False, name='global_step')
#############################
# Get training configurations
#############################
max_iterations = train_config.max_iterations
summary_interval = train_config.summary_interval
checkpoint_interval = \
train_config.checkpoint_interval
max_checkpoints = train_config.max_checkpoints_to_keep
paths_config = model_config.paths_config
logdir = paths_config.logdir
if not os.path.exists(logdir):
os.makedirs(logdir)
checkpoint_dir = paths_config.checkpoint_dir
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_path = checkpoint_dir + '/' + \
model_config.checkpoint_name
global_summaries = set([])
# The model should return a dictionary of predictions
prediction_dict = model.build()
summary_histograms = train_config.summary_histograms
summary_img_images = train_config.summary_img_images
summary_bev_images = train_config.summary_bev_images
##############################
# Setup loss
##############################
losses_dict, total_loss = model.loss(prediction_dict)
##############################################################################################
# Select trainable variables of the original AVOD model to set gradient to 0(var0)
var_moe = [
var for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='mix_of_experts')
]
var0 = [var for var in tf.trainable_variables()]
var_all_but_var_moe = [
var for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
]
for var in var_moe:
var0.remove(var)
var_all_but_var_moe.remove(var)
##############################################################################################
# Create optimizer with 0 gradient for the AVOD model and an optimizer for the MoE
training_optimizer0 = tf.train.GradientDescentOptimizer(0.0)
training_optimizer1 = optimizer_builder.build(train_config.optimizer,
global_summaries,
global_step_tensor)
##############################################################################################
# Create the train op. train_op1 is for MoE and train_op0 is for AVOD
with tf.variable_scope('train_op'):
#Create training operations
train_op1 = slim.learning.create_train_op(
total_loss,
training_optimizer1,
variables_to_train=var_moe,
clip_gradient_norm=1.0,
global_step=global_step_tensor)
train_op0 = slim.learning.create_train_op(
total_loss,
training_optimizer0,
variables_to_train=var0,
clip_gradient_norm=1.0,
global_step=global_step_tensor)
train_op = tf.group(train_op1, train_op0)
##############################################################################################
# Save checkpoints regularly.
saver = tf.train.Saver(max_to_keep=max_checkpoints, pad_step_number=True)
# Add the result of the train_op to the summary
tf.summary.scalar("training_loss", train_op1)
# Add maximum memory usage summary op
# This op can only be run on device with gpu
# so it's skipped on travis
is_travis = 'TRAVIS' in os.environ
if not is_travis:
# tf.summary.scalar('bytes_in_use',
# tf.contrib.memory_stats.BytesInUse())
tf.summary.scalar('max_bytes', tf.contrib.memory_stats.MaxBytesInUse())
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summary_merged = summary_utils.summaries_to_keep(
summaries,
global_summaries,
histograms=summary_histograms,
input_imgs=summary_img_images,
input_bevs=summary_bev_images)
allow_gpu_mem_growth = train_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()
# Create unique folder name using datetime for summary writer
datetime_str = str(datetime.datetime.now())
logdir = logdir + '/train'
train_writer = tf.summary.FileWriter(logdir + '/' + datetime_str,
sess.graph)
# Create init op
init = tf.global_variables_initializer()
# Continue from last saved checkpoint
if not train_config.overwrite_checkpoints:
trainer_utils.load_checkpoints(checkpoint_dir, saver)
if len(saver.last_checkpoints) > 0:
checkpoint_to_restore = saver.last_checkpoints[-1]
saver.restore(sess, checkpoint_to_restore)
else:
# Initialize the variables
# Restore checkpoints from original avod model. Give the correct path to restore
checkpoint_path_start = train_config.moe_config.initial_avod_checkpoint_path
variables_to_restore = dict()
for var in var_all_but_var_moe:
variables_to_restore[var.op.name] = slim.get_unique_variable(
var.op.name)
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path_start, variables_to_restore)
sess.run(init)
sess.run(init_assign_op, init_feed_dict)
##############################################################################################
else:
# Initialize the variables
sess.run(init)
# Read the global step if restored
global_step = tf.train.global_step(sess, global_step_tensor)
print('Starting from step {} / {}'.format(global_step, max_iterations))
# Main Training Loop
last_time = time.time()
for step in range(global_step, max_iterations + 1):
# Save checkpoint
if step % checkpoint_interval == 0:
global_step = tf.train.global_step(sess, global_step_tensor)
saver.save(sess,
save_path=checkpoint_path,
global_step=global_step)
print('Step {} / {}, Checkpoint saved to {}-{:08d}'.format(
step, max_iterations, checkpoint_path, global_step))
# Create feed_dict for inferencing
feed_dict = model.create_feed_dict()
# Write summaries and train op
if step % summary_interval == 0:
current_time = time.time()
time_elapsed = current_time - last_time
last_time = current_time
train_op_loss, summary_out = sess.run([train_op1, summary_merged],
feed_dict=feed_dict)
print(train_op_loss)
print('Step {}, Total Loss {:0.3f}, Time Elapsed {:0.3f} s'.format(
step, train_op_loss, time_elapsed))
train_writer.add_summary(summary_out, step)
else:
# Run the train op only
sess.run(train_op1, feed_dict)
# Close the summary writers
train_writer.close()
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])
