def _process_batch(tensor_dict,
sess,
batch_index,
counters,
losses_dict=None):
"""Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
losses_dict: Optional dictonary of scalar loss tensors.
Returns:
result_dict: a dictionary of numpy arrays
result_losses_dict: a dictionary of scalar losses. This is empty if input
losses_dict is None.
"""
try:
if not losses_dict:
losses_dict = {}
result_dict, result_losses_dict = sess.run(
[tensor_dict, losses_dict])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}, {}
global_step = tf.train.global_step(sess, tf.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualize_groundtruth_boxes,
groundtruth_box_visualization_color=eval_config.
groundtruth_box_visualization_color,
min_score_thresh=eval_config.min_score_threshold,
max_num_predictions=eval_config.max_num_boxes_to_visualize,
skip_scores=eval_config.skip_scores,
skip_labels=eval_config.skip_labels,
keep_image_id_for_visualization_export=eval_config.
keep_image_id_for_visualization_export)
return result_dict, result_losses_dict
def _process_batch(tensor_dict, sess, batch_index, counters, update_op):
"""Evaluates tensors in tensor_dict, visualizing the first K examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
update_op: An update op that has to be run along with output tensors. For
example this could be an op to compute statistics for slim metrics.
Returns:
result_dict: a dictionary of numpy arrays
"""
if batch_index >= eval_config.num_visualizations:
if 'original_image' in tensor_dict:
tensor_dict = {
k: v
for (k, v) in tensor_dict.items() if k != 'original_image'
}
try:
(result_dict, _) = sess.run([tensor_dict, update_op])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}
global_step = tf.train.global_step(sess, slim.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualization_export_dir)
return result_dict
def _process_batch(tensor_dict,
sess,
batch_index,
counters,
losses_dict=None):
"""Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
losses_dict: Optional dictonary of scalar loss tensors.
Returns:
result_dict: a dictionary of numpy arrays
result_losses_dict: a dictionary of scalar losses. This is empty if input
losses_dict is None.
"""
try:
if not losses_dict:
losses_dict = {}
trace = False
if batch_index == 0 and trace:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
else:
run_options = None
run_metadata = None
start_time = time.time()
result_dict, result_losses_dict = sess.run(
[tensor_dict, losses_dict],
options=run_options,
run_metadata=run_metadata)
if (batch_index % 100 == 0):
logging.info('Step %d: %.3f sec', batch_index,
time.time() - start_time)
if batch_index == 0 and trace:
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
dir = 'logs'
if not os.path.exists(dir):
os.makedirs(dir)
with open(
dir + '/rfcn-timeline-' +
time.strftime("%Y%m%d-%H%M%S") + '.json', 'w') as file:
file.write(
trace.generate_chrome_trace_format(show_memory=False))
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}, {}
global_step = tf.train.global_step(sess, tf.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualize_groundtruth_boxes,
groundtruth_box_visualization_color=eval_config.
groundtruth_box_visualization_color,
min_score_thresh=eval_config.min_score_threshold,
max_num_predictions=eval_config.max_num_boxes_to_visualize,
skip_scores=eval_config.skip_scores,
skip_labels=eval_config.skip_labels,
keep_image_id_for_visualization_export=eval_config.
keep_image_id_for_visualization_export)
return result_dict, result_losses_dict