def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Set up deployment (i.e., multi-GPUs and/or multi-replicas).
config = model_deploy.DeploymentConfig(num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.num_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Split the batch across GPUs.
assert FLAGS.train_batch_size % config.num_clones == 0, (
'Training batch size not divisble by number of clones (GPUs).')
clone_batch_size = FLAGS.train_batch_size // config.num_clones
tf.gfile.MakeDirs(FLAGS.train_logdir)
tf.logging.info('Training on %s set', FLAGS.train_split)
with tf.Graph().as_default() as graph:
with tf.device(config.inputs_device()):
dataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.train_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=clone_batch_size,
crop_size=[int(sz) for sz in FLAGS.train_crop_size],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
min_scale_factor=FLAGS.min_scale_factor,
max_scale_factor=FLAGS.max_scale_factor,
scale_factor_step_size=FLAGS.scale_factor_step_size,
model_variant=FLAGS.model_variant,
num_readers=4,
is_training=True,
should_shuffle=False,
should_repeat=True)
# Create the global step on the device storing the variables.
with tf.device(config.variables_device()):
global_step = tf.train.get_or_create_global_step()
# Define the model and create clones.
model_args = (dataset.get_one_shot_iterator(), {
common.OUTPUT_TYPE: dataset.num_of_classes,
common.INSTANCE: 1,
common.OFFSET: 2
}, dataset.ignore_label)
clones = model_deploy.create_clones(config,
_build_deeplab,
args=model_args)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
first_clone_scope = config.clone_scope(0)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
first_clone_scope)
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Add summaries for model variables.
for model_var in tf.model_variables():
summaries.add(tf.summary.histogram(model_var.op.name, model_var))
# Add summaries for images, labels, semantic predictions
if FLAGS.save_summaries_images:
summary_image = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.IMAGE)).strip('/'))
summaries.add(
tf.summary.image('samples/%s' % common.IMAGE, summary_image))
############ SEG LABEL ###############
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.LABEL)).strip('/'))
# Scale up summary image pixel values for better visualization.
pixel_scaling = max(1, 255 // dataset.num_of_classes)
summary_label = tf.cast(first_clone_label * pixel_scaling,
tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.LABEL, summary_label))
first_clone_output = graph.get_tensor_by_name(
('%s/%s:0' %
(first_clone_scope, common.OUTPUT_TYPE)).strip('/'))
predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1)
summary_predictions = tf.cast(predictions * pixel_scaling,
tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.OUTPUT_TYPE,
summary_predictions))
########### INSTANCE CENTER ###########
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' %
(first_clone_scope, common.LABEL_INSTANCE)).strip('/'))
# Scale up summary image pixel values for better visualization.
summary_label = tf.cast(first_clone_label, tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.LABEL_INSTANCE,
summary_label))
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.INSTANCE)).strip('/'))
# Scale up summary image pixel values for better visualization.
label_max_x = tf.reduce_max(first_clone_label)
first_clone_label = tf.multiply(
tf.divide(first_clone_label, label_max_x), 255.0)
#upscaling = tf.constant(255.0, dtype=tf.float32)
#summary_label = tf.cast(first_clone_label * upscaling, tf.uint8)
summary_label = tf.cast(first_clone_label, tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.INSTANCE,
summary_label))
########## INSTANCE OFFSET ###########
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' %
(first_clone_scope, common.LABEL_OFFSET)).strip('/'))
# Scale up (between 0-255) summary image pixel values for better visualization.
x_offset, y_offset, extra = tf.split(first_clone_label, 3, 3)
label_max_x = tf.reduce_max(x_offset)
x_offset = tf.multiply(tf.divide(x_offset, label_max_x), 255.0)
label_max_y = tf.reduce_max(y_offset)
y_offset = tf.multiply(tf.divide(y_offset, label_max_y), 255.0)
channel_padding = tf.zeros_like(x_offset)
first_clone_label = tf.concat(
[x_offset, y_offset, channel_padding], 3)
summary_label = tf.image.convert_image_dtype(first_clone_label,
dtype=tf.uint8,
saturate=True)
summaries.add(
tf.summary.image('samples/%s' % common.LABEL_OFFSET,
summary_label))
################ PREDICTIOS #######################
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.OFFSET)).strip('/'))
# Scale up (between 0-255) summary image pixel values for better visualization.
#first_clone_label = tf.squeeze(first_clone_label, 0)
x_offset, y_offset = tf.split(first_clone_label, 2, 3)
label_max_x = tf.reduce_max(x_offset)
x_offset = tf.multiply(tf.divide(x_offset, label_max_x), 255.0)
label_max_y = tf.reduce_max(y_offset)
y_offset = tf.multiply(tf.divide(y_offset, label_max_y), 255.0)
channel_padding = tf.zeros_like(x_offset)
first_clone_label = tf.concat(
[x_offset, y_offset, channel_padding], 3)
#first_clone_label = tf.expand_dims(first_clone_label, 0)
summary_label = tf.cast(first_clone_label, tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.OFFSET, summary_label))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Build the optimizer based on the device specification.
with tf.device(config.optimizer_device()):
learning_rate = train_utils.get_model_learning_rate(
FLAGS.learning_policy,
FLAGS.base_learning_rate,
FLAGS.learning_rate_decay_step,
FLAGS.learning_rate_decay_factor,
FLAGS.training_number_of_steps,
FLAGS.learning_power,
FLAGS.slow_start_step,
FLAGS.slow_start_learning_rate,
decay_steps=FLAGS.decay_steps,
end_learning_rate=FLAGS.end_learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate,
FLAGS.momentum)
elif FLAGS.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate=FLAGS.adam_learning_rate,
epsilon=FLAGS.adam_epsilon)
else:
raise ValueError('Unknown optimizer')
if FLAGS.quantize_delay_step >= 0:
if FLAGS.num_clones > 1:
raise ValueError(
'Quantization doesn\'t support multi-clone yet.')
contrib_quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay_step)
startup_delay_steps = FLAGS.task * FLAGS.startup_delay_steps
with tf.device(config.variables_device()):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer)
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Modify the gradients for biases and last layer variables.
last_layers = model.get_extra_layer_scopes(
FLAGS.last_layers_contain_logits_only)
grad_mult = train_utils.get_model_gradient_multipliers(
last_layers, FLAGS.last_layer_gradient_multiplier)
if grad_mult:
grads_and_vars = slim.learning.multiply_gradients(
grads_and_vars, grad_mult)
# Create gradient update op.
grad_updates = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(
tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries))
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
# Start the training.
profile_dir = FLAGS.profile_logdir
if profile_dir is not None:
tf.gfile.MakeDirs(profile_dir)
with contrib_tfprof.ProfileContext(enabled=profile_dir is not None,
profile_dir=profile_dir):
init_fn = None
if FLAGS.tf_initial_checkpoint:
init_fn = train_utils.get_model_init_fn(
FLAGS.train_logdir,
FLAGS.tf_initial_checkpoint,
FLAGS.initialize_last_layer,
last_layers,
ignore_missing_vars=True)
slim.learning.train(train_tensor,
logdir=FLAGS.train_logdir,
log_every_n_steps=FLAGS.log_steps,
master=FLAGS.master,
number_of_steps=FLAGS.training_number_of_steps,
is_chief=(FLAGS.task == 0),
session_config=session_config,
startup_delay_steps=startup_delay_steps,
init_fn=init_fn,
summary_op=summary_op,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
dataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.eval_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=FLAGS.eval_batch_size,
crop_size=[int(sz) for sz in FLAGS.eval_crop_size],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
model_variant=FLAGS.model_variant,
num_readers=2,
is_training=False,
should_shuffle=False,
should_repeat=False)
tf.gfile.MakeDirs(FLAGS.eval_logdir)
tf.logging.info('Evaluating on %s set', FLAGS.eval_split)
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next()
model_options = common.ModelOptions(
outputs_to_num_classes={
common.OUTPUT_TYPE: dataset.num_of_classes,
common.INSTANCE: 1,
common.OFFSET: 2
},
crop_size=[int(sz) for sz in FLAGS.eval_crop_size],
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
# Set shape in order for tf.contrib.tfprof.model_analyzer to work properly.
samples[common.IMAGE].set_shape([
FLAGS.eval_batch_size,
int(FLAGS.eval_crop_size[0]),
int(FLAGS.eval_crop_size[1]), 3
])
if tuple(FLAGS.eval_scales) == (1.0, ):
tf.logging.info('Performing single-scale test.')
predictions = model.predict_labels(
samples[common.IMAGE],
model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Performing multi-scale test.')
if FLAGS.quantize_delay_step >= 0:
raise ValueError(
'Quantize mode is not supported with multi-scale test.')
predictions_semantic = predictions[common.OUTPUT_TYPE]
predictions_center_points = predictions[common.INSTANCE]
predictions_offset_vectors = predictions[common.OFFSET]
# tf Non-maxima Suppression
# Pooling based NMS for Pooling Instance Centers
# Filtering predictions that are less than 0.1
instance_prediction = generate_instance_segmentation(
predictions_semantic, predictions_center_points,
predictions_offset_vectors)
category_prediction = tf.squeeze(predictions_semantic)
category_label = tf.squeeze(samples[common.LABEL][0])
not_ignore_mask = tf.not_equal(category_label, 255)
category_label = tf.cast(
category_label * tf.cast(not_ignore_mask, tf.int32), tf.int32)
instance_label = tf.squeeze(samples[common.LABEL_INSTANCE_IDS][0])
category_prediction = category_prediction * tf.cast(
not_ignore_mask, tf.int64)
instance_prediction = instance_prediction * tf.cast(
not_ignore_mask, tf.int64)
# Define the evaluation metric.
metric_map = {}
metric_map[
'panoptic_quality'] = streaming_metrics.streaming_panoptic_quality(
category_label,
instance_label,
category_prediction,
instance_prediction,
num_classes=19,
max_instances_per_category=256,
ignored_label=255,
offset=256 * 256)
metric_map[
'parsing_covering'] = streaming_metrics.streaming_parsing_covering(
category_label,
instance_label,
category_prediction,
instance_prediction,
num_classes=19,
max_instances_per_category=256,
ignored_label=255,
offset=256 * 256,
normalize_by_image_size=True)
metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map(
metric_map)
summary_ops = []
for metric_name, metric_value in metrics_to_values.iteritems():
if metric_name == 'panoptic_quality':
[pq, sq, rq, total_tp, total_fn,
total_fp] = tf.unstack(metric_value, 6, axis=0)
panoptic_metrics = {
# Panoptic quality.
'pq': pq,
# Segmentation quality.
'sq': sq,
# Recognition quality.
'rq': rq,
# Total true positives.
'total_tp': total_tp,
# Total false negatives.
'total_fn': total_fn,
# Total false positives.
'total_fp': total_fp,
}
# Find the valid classes that will be used for evaluation. We will
# ignore the `ignore_label` class and other classes which have (tp + fn
# + fp) equal to 0.
valid_classes = tf.logical_and(
tf.not_equal(tf.range(0, dataset.num_of_classes),
dataset.ignore_label),
tf.not_equal(total_tp + total_fn + total_fp, 0))
for target_metric, target_value in panoptic_metrics.iteritems(
):
output_metric_name = '{}_{}'.format(
metric_name, target_metric)
op = tf.summary.scalar(
output_metric_name,
tf.reduce_mean(
tf.boolean_mask(target_value, valid_classes)))
op = tf.Print(op, [target_value],
output_metric_name + '_classwise: ',
summarize=dataset.num_of_classes)
op = tf.Print(op, [
tf.reduce_mean(
tf.boolean_mask(target_value, valid_classes))
],
output_metric_name + '_mean: ',
summarize=1)
summary_ops.append(op)
elif metric_name == 'parsing_covering':
[
per_class_covering, total_per_class_weighted_ious,
total_per_class_gt_areas
] = tf.unstack(metric_value, 3, axis=0)
# Find the valid classes that will be used for evaluation. We will
# ignore the `void_label` class and other classes which have
# total_per_class_weighted_ious + total_per_class_gt_areas equal to 0.
valid_classes = tf.logical_and(
tf.not_equal(tf.range(0, dataset.num_of_classes),
dataset.ignore_label),
tf.not_equal(
total_per_class_weighted_ious +
total_per_class_gt_areas, 0))
op = tf.summary.scalar(
metric_name,
tf.reduce_mean(
tf.boolean_mask(per_class_covering, valid_classes)))
op = tf.Print(op, [per_class_covering],
metric_name + '_classwise: ',
summarize=dataset.num_of_classes)
op = tf.Print(op, [
tf.reduce_mean(
tf.boolean_mask(per_class_covering, valid_classes))
],
metric_name + '_mean: ',
summarize=1)
summary_ops.append(op)
else:
raise ValueError('The metric_name "%s" is not supported.' %
metric_name)
num_eval_iters = None
if FLAGS.max_number_of_evaluations > 0:
num_eval_iters = FLAGS.max_number_of_evaluations
if FLAGS.quantize_delay_step >= 0:
contrib_quantize.create_eval_graph()
contrib_tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=contrib_tfprof.model_analyzer.
TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
contrib_tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
metric_values = slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_logdir,
num_evals=20,
eval_op=metrics_to_updates.values(),
final_op=metrics_to_values.values(),
summary_op=tf.summary.merge(summary_ops),
max_number_of_evaluations=FLAGS.max_number_of_evaluations,
eval_interval_secs=FLAGS.eval_interval_secs)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Get dataset-dependent information.
dataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.vis_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=FLAGS.vis_batch_size,
crop_size=[int(sz) for sz in FLAGS.vis_crop_size],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
model_variant=FLAGS.model_variant,
is_training=False,
should_shuffle=False,
should_repeat=False)
train_id_to_eval_id = None
if dataset.dataset_name == data_generator.get_cityscapes_dataset_name():
tf.logging.info('Cityscapes requires converting train_id to eval_id.')
train_id_to_eval_id = _CITYSCAPES_TRAIN_ID_TO_EVAL_ID
# Prepare for visualization.
tf.gfile.MakeDirs(FLAGS.vis_logdir)
save_dir = os.path.join(FLAGS.vis_logdir, _SEMANTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(save_dir)
instance_save_dir = os.path.join(FLAGS.vis_logdir,
_INSTANCE_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(instance_save_dir)
regression_save_dir = os.path.join(FLAGS.vis_logdir,
_OFFSET_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(regression_save_dir)
panoptic_save_dir = os.path.join(FLAGS.vis_logdir,
_PANOPTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(panoptic_save_dir)
raw_save_dir = os.path.join(FLAGS.vis_logdir,
_RAW_SEMANTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(raw_save_dir)
tf.logging.info('Visualizing on %s set', FLAGS.vis_split)
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next()
model_options = common.ModelOptions(
outputs_to_num_classes={
common.OUTPUT_TYPE: dataset.num_of_classes,
common.INSTANCE: 1,
common.OFFSET: 2
},
crop_size=[int(sz) for sz in FLAGS.vis_crop_size],
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
if tuple(FLAGS.eval_scales) == (1.0, ):
tf.logging.info('Performing single-scale test.')
predictions = model.predict_labels(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Performing multi-scale test.')
if FLAGS.quantize_delay_step >= 0:
raise ValueError(
'Quantize mode is not supported with multi-scale test.')
predictions = model.predict_labels_multi_scale(
samples[common.IMAGE],
model_options=model_options,
eval_scales=FLAGS.eval_scales,
add_flipped_images=FLAGS.add_flipped_images)
predictions_semantic = predictions[common.OUTPUT_TYPE]
predictions_instance = predictions[common.INSTANCE]
predictions_regression = predictions[common.OFFSET]
if FLAGS.min_resize_value and FLAGS.max_resize_value:
# Only support batch_size = 1, since we assume the dimensions of original
# image after tf.squeeze is [height, width, 3].
assert FLAGS.vis_batch_size == 1
# Reverse the resizing and padding operations performed in preprocessing.
# First, we slice the valid regions (i.e., remove padded region) and then
# we resize the predictions back.
original_image = tf.squeeze(samples[common.ORIGINAL_IMAGE])
original_image_shape = tf.shape(original_image)
predictions_semantic = tf.slice(
predictions_semantic, [0, 0, 0],
[1, original_image_shape[0], original_image_shape[1]])
resized_shape = tf.to_int32([
tf.squeeze(samples[common.HEIGHT]),
tf.squeeze(samples[common.WIDTH])
])
predictions_semantic = tf.squeeze(
tf.image.resize_images(
tf.expand_dims(predictions_semantic, 3),
resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True), 3)
############################### POST PROCESSING LOGITS FROM INSTANCE CENTER #####################
predictions_instance = tf.slice(
predictions_instance, [0, 0, 0],
[1, original_image_shape[0], original_image_shape[1]])
resized_shape = tf.to_int32([
tf.squeeze(samples[common.HEIGHT]),
tf.squeeze(samples[common.WIDTH])
])
predictions_instance = tf.squeeze(
tf.image.resize_images(
tf.expand_dims(predictions_instance, 3),
resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True), 3)
############################### POST PROCESSING LOGITS FROM INSTANCE REGRESSION #####################
predictions_regression = tf.slice(
predictions_regression, [0, 0, 0, 0],
[1, original_image_shape[0], original_image_shape[1], 1])
resized_shape = tf.to_int32([
tf.squeeze(samples[common.HEIGHT]),
tf.squeeze(samples[common.WIDTH]), 2
])
predictions_regression = tf.image.resize_images(
predictions_regression,
resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True)
tf.train.get_or_create_global_step()
if FLAGS.quantize_delay_step >= 0:
contrib_quantize.create_eval_graph()
num_iteration = 0
max_num_iteration = FLAGS.max_number_of_iterations
checkpoints_iterator = contrib_training.checkpoints_iterator(
FLAGS.checkpoint_dir, min_interval_secs=FLAGS.eval_interval_secs)
for checkpoint_path in checkpoints_iterator:
num_iteration += 1
tf.logging.info('Starting visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
tf.logging.info('Visualizing with model %s', checkpoint_path)
scaffold = tf.train.Scaffold(
init_op=tf.global_variables_initializer())
session_creator = tf.train.ChiefSessionCreator(
scaffold=scaffold,
master=FLAGS.master,
checkpoint_filename_with_path=checkpoint_path)
with tf.train.MonitoredSession(session_creator=session_creator,
hooks=None) as sess:
batch = 0
image_id_offset = 0
while not sess.should_stop():
tf.logging.info('Visualizing batch %d', batch + 1)
_process_batch(
sess=sess,
original_images=samples[common.ORIGINAL_IMAGE],
semantic_predictions=predictions_semantic,
instance_predictions=predictions_instance,
regression_predictions=predictions_regression,
image_names=samples[common.IMAGE_NAME],
image_heights=samples[common.HEIGHT],
image_widths=samples[common.WIDTH],
image_id_offset=image_id_offset,
save_dir=save_dir,
instance_save_dir=instance_save_dir,
regression_save_dir=regression_save_dir,
panoptic_save_dir=panoptic_save_dir,
raw_save_dir=raw_save_dir,
train_id_to_eval_id=train_id_to_eval_id)
image_id_offset += FLAGS.vis_batch_size
batch += 1
tf.logging.info('Finished visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
if max_num_iteration > 0 and num_iteration >= max_num_iteration:
break