def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info('Prepare to export model to: %s', FLAGS.export_path)
with tf.Graph().as_default():
image, image_size, resized_image_size = _create_input_tensors()
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
crop_size=FLAGS.crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
if tuple(FLAGS.inference_scales) == (1.0, ):
tf.logging.info('Exported model performs single-scale inference.')
predictions = model.predict_labels(
image,
model_options=model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Exported model performs multi-scale inference.')
predictions = model.predict_labels_multi_scale(
image,
model_options=model_options,
eval_scales=FLAGS.inference_scales,
add_flipped_images=FLAGS.add_flipped_images)
# Crop the valid regions from the predictions.
semantic_predictions = tf.slice(
predictions[common.OUTPUT_TYPE], [0, 0, 0],
[1, resized_image_size[0], resized_image_size[1]])
# Resize back the prediction to the original image size.
def _resize_label(label, label_size):
# Expand dimension of label to [1, height, width, 1] for resize operation.
label = tf.expand_dims(label, 3)
resized_label = tf.image.resize_images(
label,
label_size,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True)
return tf.squeeze(resized_label, 3)
semantic_predictions = _resize_label(semantic_predictions, image_size)
semantic_predictions = tf.identity(semantic_predictions,
name=_OUTPUT_NAME)
saver = tf.train.Saver(tf.model_variables())
tf.gfile.MakeDirs(os.path.dirname(FLAGS.export_path))
freeze_graph.freeze_graph_with_def_protos(
tf.get_default_graph().as_graph_def(add_shapes=True),
saver.as_saver_def(),
FLAGS.checkpoint_path,
_OUTPUT_NAME,
restore_op_name=None,
filename_tensor_name=None,
output_graph=FLAGS.export_path,
clear_devices=True,
initializer_nodes=None)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Get dataset-dependent information.
dataset = segmentation_dataset.get_dataset(FLAGS.dataset,
FLAGS.vis_split,
dataset_dir=FLAGS.dataset_dir)
train_id_to_eval_id = None
if dataset.name == segmentation_dataset.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)
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)
g = tf.Graph()
with g.as_default():
samples = input_generator.get(dataset,
FLAGS.vis_crop_size,
FLAGS.vis_batch_size,
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
dataset_split=FLAGS.vis_split,
is_training=False,
model_variant=FLAGS.model_variant)
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: dataset.num_classes},
crop_size=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.')
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 = predictions[common.OUTPUT_TYPE]
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 reisze the predictions back.
original_image = tf.squeeze(samples[common.ORIGINAL_IMAGE])
original_image_shape = tf.shape(original_image)
predictions = tf.slice(
predictions, [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 = tf.squeeze(
tf.image.resize_images(
tf.expand_dims(predictions, 3),
resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True), 3)
tf.train.get_or_create_global_step()
saver = tf.train.Saver(slim.get_variables_to_restore())
sv = tf.train.Supervisor(graph=g,
logdir=FLAGS.vis_logdir,
init_op=tf.global_variables_initializer(),
summary_op=None,
summary_writer=None,
global_step=None,
saver=saver)
num_batches = int(
math.ceil(dataset.num_samples / float(FLAGS.vis_batch_size)))
last_checkpoint = None
# Loop to visualize the results when new checkpoint is created.
num_iters = 0
while (FLAGS.max_number_of_iterations <= 0
or num_iters < FLAGS.max_number_of_iterations):
num_iters += 1
last_checkpoint = slim.evaluation.wait_for_new_checkpoint(
FLAGS.checkpoint_dir, last_checkpoint)
start = time.time()
tf.logging.info('Starting visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
tf.logging.info('Visualizing with model %s', last_checkpoint)
with sv.managed_session(FLAGS.master,
start_standard_services=False) as sess:
sv.start_queue_runners(sess)
sv.saver.restore(sess, last_checkpoint)
image_id_offset = 0
for batch in range(num_batches):
tf.logging.info('Visualizing batch %d / %d', batch + 1,
num_batches)
_process_batch(
sess=sess,
original_images=samples[common.ORIGINAL_IMAGE],
semantic_predictions=predictions,
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,
raw_save_dir=raw_save_dir,
train_id_to_eval_id=train_id_to_eval_id)
image_id_offset += FLAGS.vis_batch_size
tf.logging.info('Finished visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info('Prepare to export model to: %s', FLAGS.export_path)
with tf.Graph().as_default():
image, image_size, resized_image_size = _create_input_tensors()
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
crop_size=FLAGS.crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
if tuple(FLAGS.inference_scales) == (1.0, ):
tf.logging.info('Exported model performs single-scale inference.')
predictions = model.predict_labels(
image,
model_options=model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Exported model performs multi-scale inference.')
if FLAGS.quantize_delay_step >= 0:
raise ValueError(
'Quantize mode is not supported with multi-scale test.')
predictions = model.predict_labels_multi_scale(
image,
model_options=model_options,
eval_scales=FLAGS.inference_scales,
add_flipped_images=FLAGS.add_flipped_images)
raw_predictions = tf.identity(
tf.cast(predictions[common.OUTPUT_TYPE], tf.float32),
_RAW_OUTPUT_NAME)
raw_probabilities = tf.identity(
predictions[common.OUTPUT_TYPE + model.PROB_SUFFIX],
_RAW_OUTPUT_PROB_NAME)
# Crop the valid regions from the predictions.
semantic_predictions = raw_predictions[:, :resized_image_size[0], :
resized_image_size[1]]
semantic_probabilities = raw_probabilities[:, :resized_image_size[0], :
resized_image_size[1]]
# Resize back the prediction to the original image size.
def _resize_label(label, label_size):
# Expand dimension of label to [1, height, width, 1] for resize operation.
label = tf.expand_dims(label, 3)
resized_label = tf.image.resize_images(
label,
label_size,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True)
return tf.cast(tf.squeeze(resized_label, 3), tf.int32)
semantic_predictions = _resize_label(semantic_predictions, image_size)
semantic_predictions = tf.identity(semantic_predictions,
name=_OUTPUT_NAME)
semantic_probabilities = tf.image.resize_bilinear(
semantic_probabilities,
image_size,
align_corners=True,
name=_OUTPUT_PROB_NAME)
if FLAGS.quantize_delay_step >= 0:
contrib_quantize.create_eval_graph()
saver = tf.train.Saver(tf.all_variables())
dirname = os.path.dirname(FLAGS.export_path)
tf.gfile.MakeDirs(dirname)
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
freeze_graph.freeze_graph_with_def_protos(
graph_def,
saver.as_saver_def(),
FLAGS.checkpoint_path,
_OUTPUT_NAME + ',' + _OUTPUT_PROB_NAME,
restore_op_name=None,
filename_tensor_name=None,
output_graph=FLAGS.export_path,
clear_devices=True,
initializer_nodes=None)
if FLAGS.save_inference_graph:
tf.train.write_graph(graph_def, dirname, 'inference_graph.pbtxt')
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)
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},
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 = predictions[common.OUTPUT_TYPE]
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 = tf.slice(
predictions,
[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 = tf.squeeze(
tf.image.resize_images(tf.expand_dims(predictions, 3),
resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True), 3)
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,
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,
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
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)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options)
#session_config.gpu_options.allow_growth = True
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next()
#print(samples[common.IMAGE_NAME])
model_options = common.ModelOptions(
outputs_to_num_classes={
common.OUTPUT_TYPE: dataset.num_of_classes
},
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, logits = model.predict_labels(
samples[common.IMAGE],
model_options,
image_pyramid=FLAGS.image_pyramid,
skips=FLAGS.skips)
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,
skips=FLAGS.skips,
eval_scales=FLAGS.eval_scales,
add_flipped_images=FLAGS.add_flipped_images)
predictions = predictions[common.OUTPUT_TYPE]
predictions = tf.reshape(predictions, shape=[-1])
labels = tf.reshape(samples[common.LABEL], shape=[-1])
weights = tf.to_float(tf.not_equal(labels, dataset.ignore_label))
# Set ignore_label regions to label 0, because metrics.mean_iou requires
# range of labels = [0, dataset.num_classes). Note the ignore_label regions
# are not evaluated since the corresponding regions contain weights = 0.
labels = tf.where(tf.equal(labels, dataset.ignore_label),
tf.zeros_like(labels), labels)
predictions_tag = 'miou'
for eval_scale in FLAGS.eval_scales:
predictions_tag += '_' + str(eval_scale)
if FLAGS.add_flipped_images:
predictions_tag += '_flipped'
# Define the evaluation metric.
metric_map = {}
# to remove "predictions out of bound error"
indices = tf.squeeze(
tf.where(tf.less_equal(labels, dataset.num_of_classes - 1)), 1)
labels_ind = tf.cast(tf.gather(labels, indices), tf.int32)
predictions_ind = tf.gather(predictions, indices)
# end of insert
miou, update_miou = tf.metrics.mean_iou(labels_ind,
predictions_ind,
dataset.num_of_classes,
weights=weights,
name="mean_iou")
tf.summary.scalar(predictions_tag, miou)
# Define the evaluation metric IOU for individual classes
iou_v, update_op = my_metrics.iou(labels_ind,
predictions_ind,
dataset.num_of_classes,
weights=weights)
for index in range(0, dataset.num_of_classes):
metric_map['class_' + str(index) + '_iou'] = (iou_v[index],
update_op[index])
tf.summary.scalar('class_' + str(index) + '_iou', iou_v[index])
# Confusion matrix save hook. It updates the confusion matrix on tensorboard at the end of eval loop.
confusionMatrixSaveHook = confusion_matrix.SaverHook(
labels=['BG', 'water', 'ice', 'snow', 'clutter'],
confusion_matrix_tensor_name='mean_iou/total_confusion_matrix',
summary_writer=tf.summary.FileWriterCache.get(
str(FLAGS.eval_logdir)))
summary_op = tf.summary.merge_all()
summary_hook = tf.contrib.training.SummaryAtEndHook(
log_dir=FLAGS.eval_logdir, summary_op=summary_op)
hooks = [summary_hook, confusionMatrixSaveHook]
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:
tf.contrib.quantize.create_eval_graph()
tf.contrib.training.evaluate_repeatedly(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
eval_ops=[update_miou, update_op],
max_number_of_evaluations=num_eval_iters,
hooks=hooks,
eval_interval_secs=FLAGS.eval_interval_secs)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Get dataset-dependent information.
dataset = segmentation_dataset.get_dataset(FLAGS.dataset,
FLAGS.eval_split,
dataset_dir=FLAGS.dataset_dir)
tf.gfile.MakeDirs(FLAGS.eval_logdir)
tf.logging.info('Evaluating on %s set', FLAGS.eval_split)
with tf.Graph().as_default():
samples = input_generator.get(dataset,
FLAGS.eval_crop_size,
FLAGS.eval_batch_size,
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
dataset_split=FLAGS.eval_split,
is_training=False,
model_variant=FLAGS.model_variant)
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: dataset.num_classes},
crop_size=FLAGS.eval_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,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Performing 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 = predictions[common.OUTPUT_TYPE]
predictions = tf.reshape(predictions, shape=[-1])
labels = tf.reshape(samples[common.LABEL], shape=[-1])
weights = tf.to_float(tf.not_equal(labels, dataset.ignore_label))
# Set ignore_label regions to label 0, because metrics.mean_iou requires
# range of labels = [0, dataset.num_classes). Note the ignore_label regions
# are not evaluated since the corresponding regions contain weights = 0.
labels = tf.where(tf.equal(labels, dataset.ignore_label),
tf.zeros_like(labels), labels)
predictions_tag = 'miou'
for eval_scale in FLAGS.eval_scales:
predictions_tag += '_' + str(eval_scale)
if FLAGS.add_flipped_images:
predictions_tag += '_flipped'
# Define the evaluation metric.
metric_map = {}
metric_map[predictions_tag] = tf.metrics.mean_iou(predictions,
labels,
dataset.num_classes,
weights=weights)
metrics_to_values, metrics_to_updates = (
tf.contrib.metrics.aggregate_metric_map(metric_map))
for metric_name, metric_value in six.iteritems(metrics_to_values):
slim.summaries.add_scalar_summary(metric_value,
metric_name,
print_summary=True)
num_batches = int(
math.ceil(dataset.num_samples / float(FLAGS.eval_batch_size)))
tf.logging.info('Eval num images %d', dataset.num_samples)
tf.logging.info('Eval batch size %d and num batch %d',
FLAGS.eval_batch_size, num_batches)
num_eval_iters = None
if FLAGS.max_number_of_evaluations > 0:
num_eval_iters = FLAGS.max_number_of_evaluations
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_logdir,
num_evals=num_batches,
eval_op=list(metrics_to_updates.values()),
max_number_of_evaluations=num_eval_iters,
eval_interval_secs=FLAGS.eval_interval_secs)
def main(unused_argv):
FLAGS.comb_dropout_keep_prob = 1.0
FLAGS.image_keep_prob = 1.0
FLAGS.elements_keep_prob = 1.0
# Get dataset-dependent information.
tf.gfile.MakeDirs(FLAGS.eval_logdir)
tf.logging.info('Evaluating on %s set', FLAGS.split)
with tf.Graph().as_default():
samples = model_input.get_input_fn(FLAGS)()
# Get model segmentation predictions.
num_classes = model_input.dataset_descriptors[
FLAGS.dataset].num_classes
output_to_num_classes = model.get_output_to_num_classes(FLAGS)
if tuple(FLAGS.eval_scales) == (1.0, ):
tf.logging.info('Performing single-scale test.')
predictions, probs = model.predict_labels(
samples['image'],
samples,
FLAGS,
outputs_to_num_classes=output_to_num_classes,
image_pyramid=FLAGS.image_pyramid,
merge_method=FLAGS.merge_method,
atrous_rates=FLAGS.atrous_rates,
add_image_level_feature=FLAGS.add_image_level_feature,
aspp_with_batch_norm=FLAGS.aspp_with_batch_norm,
aspp_with_separable_conv=FLAGS.aspp_with_separable_conv,
multi_grid=FLAGS.multi_grid,
depth_multiplier=FLAGS.depth_multiplier,
output_stride=FLAGS.output_stride,
decoder_output_stride=FLAGS.decoder_output_stride,
decoder_use_separable_conv=FLAGS.decoder_use_separable_conv,
crop_size=[FLAGS.image_size, FLAGS.image_size],
logits_kernel_size=FLAGS.logits_kernel_size,
model_variant=FLAGS.model_variant)
else:
tf.logging.info('Performing multi-scale test.')
predictions, probs = model.predict_labels_multi_scale(
samples['image'],
samples,
FLAGS,
outputs_to_num_classes=output_to_num_classes,
eval_scales=FLAGS.eval_scales,
add_flipped_images=FLAGS.add_flipped_images,
merge_method=FLAGS.merge_method,
atrous_rates=FLAGS.atrous_rates,
add_image_level_feature=FLAGS.add_image_level_feature,
aspp_with_batch_norm=FLAGS.aspp_with_batch_norm,
aspp_with_separable_conv=FLAGS.aspp_with_separable_conv,
multi_grid=FLAGS.multi_grid,
depth_multiplier=FLAGS.depth_multiplier,
output_stride=FLAGS.output_stride,
decoder_output_stride=FLAGS.decoder_output_stride,
decoder_use_separable_conv=FLAGS.decoder_use_separable_conv,
crop_size=[FLAGS.image_size, FLAGS.image_size],
logits_kernel_size=FLAGS.logits_kernel_size,
model_variant=FLAGS.model_variant)
metric_map = {}
for output in output_to_num_classes:
output_predictions = predictions[output]
output_probs = probs[output]
if output == 'segment':
output_predictions = tf.expand_dims(output_predictions, 3)
if num_classes == 2:
labels = samples['label']
iou, weights = model.foreground_iou(
labels, output_predictions, FLAGS)
soft_iou, _ = model.foreground_iou(
labels, output_probs[:, :, :, 1:2], FLAGS)
metric_map['mIOU'] = tf.metrics.mean(iou)
metric_map['soft_mIOU'] = tf.metrics.mean(soft_iou)
high_prob_overlaps = calc_high_prob_overlaps(
labels, output_probs, weights)
metric_map['highestOverlaps'] = tf.metrics.mean(
high_prob_overlaps)
output_probs *= weights
else:
output_predictions = tf.reshape(output_predictions,
shape=[-1])
labels = tf.reshape(samples['label'], shape=[-1])
weights = tf.to_float(
tf.not_equal(
labels, model_input.dataset_descriptors[
FLAGS.dataset].ignore_label))
# Set ignore_label regions to label 0, because metrics.mean_iou
# requires range of labels=[0, dataset.num_classes).
# Note the ignore_label regions are not evaluated since
# the corresponding regions contain weights=0.
labels = tf.where(
tf.equal(
labels, model_input.dataset_descriptors[
FLAGS.dataset].ignore_label),
tf.zeros_like(labels), labels)
predictions_tag = 'mIOU'
for eval_scale in FLAGS.eval_scales:
predictions_tag += '_' + str(eval_scale)
if FLAGS.add_flipped_images:
predictions_tag += '_flipped'
# Define the evaluation metric.
metric_map[predictions_tag] = slim.metrics.mean_iou(
output_predictions,
labels,
num_classes,
weights=weights)
def label_summary(labels, weights, name):
tf.summary.image(
name,
tf.reshape(
tf.cast(
tf.to_float(labels * 255) /
tf.to_float(num_classes), tf.uint8) *
tf.cast(weights, tf.uint8),
[-1, FLAGS.image_size, FLAGS.image_size, 1]), 8)
label_summary(labels, weights, 'label')
label_summary(output_predictions, weights,
'output_predictions')
tf.summary.image('logits',
tf.expand_dims(output_probs[:, :, :, 1], 3))
elif output == 'regression':
labels = samples['label']
ignore_mask = model.get_ignore_mask(labels, FLAGS)
accurate = calc_accuracy_in_box(labels, output_probs,
ignore_mask)
metric_map['inBoxAccuracy'] = tf.metrics.mean(accurate)
tf.summary.image('image', samples['image'], 8)
metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map(
metric_map)
for metric_name, metric_value in metrics_to_values.iteritems():
metric_value = tf.Print(metric_value, [metric_value], metric_name)
tf.summary.scalar(metric_name, metric_value)
num_batches = int(
math.ceil(FLAGS.num_samples / float(FLAGS.batch_size)))
tf.logging.info('Eval num images %d', FLAGS.num_samples)
tf.logging.info('Eval batch size %d and num batch %d',
FLAGS.batch_size, num_batches)
slim.evaluation.evaluation_loop(
master='',
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_logdir,
num_evals=num_batches,
eval_op=metrics_to_updates.values(),
summary_op=tf.summary.merge_all(),
max_number_of_evaluations=None,
eval_interval_secs=FLAGS.eval_interval_secs)
def main(unused_argv):
# Get dataset-dependent information.
# 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)
raw_save_dir = os.path.join(FLAGS.vis_logdir,
_RAW_SEMANTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(raw_save_dir)
num_vis_examples = FLAGS.num_vis_examples
print('Visualizing on set', FLAGS.split)
g = tf.Graph()
with g.as_default():
samples = model_input.get_input_fn(FLAGS)()
outputs_to_num_classes = model.get_output_to_num_classes(FLAGS)
# Get model segmentation predictions.
if tuple(FLAGS.eval_scales) == (1.0, ):
tf.logging.info('Performing single-scale test.')
predictions, probs = model.predict_labels(
samples['image'],
samples,
FLAGS,
outputs_to_num_classes=outputs_to_num_classes,
image_pyramid=FLAGS.image_pyramid,
merge_method=FLAGS.merge_method,
atrous_rates=FLAGS.atrous_rates,
add_image_level_feature=FLAGS.add_image_level_feature,
aspp_with_batch_norm=FLAGS.aspp_with_batch_norm,
aspp_with_separable_conv=FLAGS.aspp_with_separable_conv,
multi_grid=FLAGS.multi_grid,
depth_multiplier=FLAGS.depth_multiplier,
output_stride=FLAGS.output_stride,
decoder_output_stride=FLAGS.decoder_output_stride,
decoder_use_separable_conv=FLAGS.decoder_use_separable_conv,
crop_size=[FLAGS.image_size, FLAGS.image_size],
logits_kernel_size=FLAGS.logits_kernel_size,
model_variant=FLAGS.model_variant)
else:
tf.logging.info('Performing multi-scale test.')
predictions, probs = model.predict_labels_multi_scale(
samples['image'],
samples,
FLAGS,
outputs_to_num_classes=outputs_to_num_classes,
eval_scales=FLAGS.eval_scales,
add_flipped_images=FLAGS.add_flipped_images,
merge_method=FLAGS.merge_method,
atrous_rates=FLAGS.atrous_rates,
add_image_level_feature=FLAGS.add_image_level_feature,
aspp_with_batch_norm=FLAGS.aspp_with_batch_norm,
aspp_with_separable_conv=FLAGS.aspp_with_separable_conv,
multi_grid=FLAGS.multi_grid,
depth_multiplier=FLAGS.depth_multiplier,
output_stride=FLAGS.output_stride,
decoder_output_stride=FLAGS.decoder_output_stride,
decoder_use_separable_conv=FLAGS.decoder_use_separable_conv,
crop_size=[FLAGS.image_size, FLAGS.image_size],
logits_kernel_size=FLAGS.logits_kernel_size,
model_variant=FLAGS.model_variant)
if FLAGS.output_mode == 'segment':
predictions = tf.squeeze(
tf.cast(predictions[FLAGS.output_mode], tf.int32))
probs = probs[FLAGS.output_mode]
labels = tf.squeeze(tf.cast(samples['label'], tf.int32))
weights = tf.cast(
tf.not_equal(
labels, model_input.dataset_descriptors[
FLAGS.dataset].ignore_label), tf.int32)
labels *= weights
predictions *= weights
tf.train.get_or_create_global_step()
saver = tf.train.Saver(contrib_slim.get_variables_to_restore())
sv = tf.train.Supervisor(graph=g,
logdir=FLAGS.vis_logdir,
init_op=tf.global_variables_initializer(),
summary_op=None,
summary_writer=None,
global_step=None,
saver=saver)
num_batches = int(
math.ceil(num_vis_examples / float(FLAGS.batch_size)))
last_checkpoint = None
# Infinite loop to visualize the results when new checkpoint is created.
while True:
last_checkpoint = contrib_slim.evaluation.wait_for_new_checkpoint(
FLAGS.checkpoint_dir, last_checkpoint)
start = time.time()
print('Starting visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
print('Visualizing with model %s', last_checkpoint)
print('Visualizing with model ', last_checkpoint)
with sv.managed_session(FLAGS.master,
start_standard_services=False) as sess:
# sv.start_queue_runners(sess)
sv.saver.restore(sess, last_checkpoint)
image_id_offset = 0
refs = []
for batch in range(num_batches):
print('Visualizing batch', batch + 1, num_batches)
refs.extend(
_process_batch(sess=sess,
samples=samples,
semantic_predictions=predictions,
labels=labels,
image_id_offset=image_id_offset,
save_dir=save_dir))
image_id_offset += FLAGS.batch_size
print('Finished visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
FLAGS.dense_prediction_cell_json = './core/dense_prediction_cell_branch5_top1_cityscapes.json' if USE_DPC else ''
chkpt_path = CHECKPOINT_PATH
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=input_size[1:3],
atrous_rates=None,
output_stride=OUTPUT_STRIDE)
g = tf.Graph()
with g.as_default():
with tf.Session(graph=g) as sess:
inputs = tf.placeholder(
tf.float32, input_size, name=input_tensor_name)
outputs_to_scales_to_logits = model.predict_labels(
inputs,
model_options=model_options)
predictions = tf.cast(
outputs_to_scales_to_logits[common.OUTPUT_TYPE], tf.int32)
output_tensor_name = predictions.name.split(':')[0]
sess.run(tf.global_variables_initializer())
if chkpt_path:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(chkpt_path))
constant_graph = tf.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
# The graph_def is used to retrieve the nodes
tf.get_default_graph().as_graph_def(),
# The output node names are used to select the usefull nodes
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
},
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 = 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 = predictions[common.OUTPUT_TYPE]
predictions = tf.reshape(predictions, shape=[-1])
labels = tf.reshape(samples[common.LABEL], shape=[-1])
weights = tf.to_float(tf.not_equal(labels, dataset.ignore_label))
# Set ignore_label regions to label 0, because metrics.mean_iou requires
# range of labels = [0, dataset.num_classes). Note the ignore_label regions
# are not evaluated since the corresponding regions contain weights = 0.
labels = tf.where(tf.equal(labels, dataset.ignore_label),
tf.zeros_like(labels), labels)
predictions_tag = 'miou'
for eval_scale in FLAGS.eval_scales:
predictions_tag += '_' + str(eval_scale)
if FLAGS.add_flipped_images:
predictions_tag += '_flipped'
# Define the evaluation metric.
metric_map = {}
num_classes = dataset.num_of_classes
metric_map['eval/%s_overall' % predictions_tag] = tf.metrics.mean_iou(
labels=labels,
predictions=predictions,
num_classes=num_classes,
weights=weights)
# IoU for each class.
one_hot_predictions = tf.one_hot(predictions, num_classes)
one_hot_predictions = tf.reshape(one_hot_predictions,
[-1, num_classes])
one_hot_labels = tf.one_hot(labels, num_classes)
one_hot_labels = tf.reshape(one_hot_labels, [-1, num_classes])
for c in range(num_classes):
predictions_tag_c = '%s_class_%d' % (predictions_tag, c)
tp, tp_op = tf.metrics.true_positives(
labels=one_hot_labels[:, c],
predictions=one_hot_predictions[:, c],
weights=weights)
fp, fp_op = tf.metrics.false_positives(
labels=one_hot_labels[:, c],
predictions=one_hot_predictions[:, c],
weights=weights)
fn, fn_op = tf.metrics.false_negatives(
labels=one_hot_labels[:, c],
predictions=one_hot_predictions[:, c],
weights=weights)
tp_fp_fn_op = tf.group(tp_op, fp_op, fn_op)
iou = tf.where(tf.greater(tp + fn, 0.0), tp / (tp + fn + fp),
tf.constant(np.NaN))
metric_map['eval/%s' % predictions_tag_c] = (iou, tp_fp_fn_op)
(metrics_to_values,
metrics_to_updates) = contrib_metrics.aggregate_metric_map(metric_map)
summary_ops = []
for metric_name, metric_value in six.iteritems(metrics_to_values):
op = tf.summary.scalar(metric_name, metric_value)
op = tf.Print(op, [metric_value], metric_name)
summary_ops.append(op)
summary_op = tf.summary.merge(summary_ops)
summary_hook = contrib_training.SummaryAtEndHook(
log_dir=FLAGS.eval_logdir, summary_op=summary_op)
hooks = [summary_hook]
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)
contrib_training.evaluate_repeatedly(
checkpoint_dir=FLAGS.checkpoint_dir,
master=FLAGS.master,
eval_ops=list(metrics_to_updates.values()),
max_number_of_evaluations=num_eval_iters,
hooks=hooks,
eval_interval_secs=FLAGS.eval_interval_secs)
