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 testWrongDeepLabVariant(self):
model_options = common.ModelOptions(
[])._replace(model_variant='no_such_variant')
with self.assertRaises(ValueError):
model._get_logits(images=[], model_options=model_options)
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="mobilenet_v2")
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)
mask = tf.squeeze(samples[common.LABEL])
original_images = tf.squeeze(samples[common.ORIGINAL_IMAGE])
images = tf.squeeze(samples[common.IMAGE])
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
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)))
# 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
start = time.time()
tf.logging.info('Starting visualization at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
with sv.managed_session(FLAGS.master,
start_standard_services=False) as sess:
sv.start_queue_runners(sess)
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=original_images,
resize_images=images,
image_names=samples[common.IMAGE_NAME],
mask=mask,
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.gfile.MakeDirs(FLAGS.inference_dir)
g = tf.Graph()
with g.as_default():
image_name = FLAGS.image_path.split('/')[-1]
image_name, image_extension = image_name.split('.')
supported_extensions = ['png', 'jpeg', 'jpg']
if not any(image_extension == extension
for extension in supported_extensions):
raise ValueError('Image extension "{}" not supported...'.format(
image_extension))
reader = build_data.ImageReader(image_extension)
image = reader.decode_image(
tf.gfile.FastGFile(FLAGS.image_path, 'r').read())
image = tf.identity(image)
original_image_dimensions = image.get_shape().as_list()[0:2]
original_image_dimensions = reversed(original_image_dimensions)
image = tf.image.resize_images(image, [480, 640],
method=tf.image.ResizeMethod.BILINEAR,
align_corners=True)
image.set_shape([None, None, 3])
image = tf.expand_dims(image, 0)
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
crop_size=FLAGS.inference_crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
predictions = model.predict_labels(image,
model_options=model_options,
image_pyramid=None)
predictions = predictions[common.OUTPUT_TYPE]
# predictions = tf.image.resize_images(
# predictions, original_image_dimensions,
# method=tf.image.ResizeMethod.BILINEAR,
# align_corners=True)
param_stats = tf.profiler.profile(
tf.get_default_graph(),
options=tf.profiler.ProfileOptionBuilder.
trainable_variables_parameter())
print('Total parameters: ', param_stats.total_parameters)
total_parameters = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
print('Total parameters: ', total_parameters)
tf.train.get_or_create_global_step()
saver = tf.train.Saver(slim.get_variables_to_restore())
sv = tf.train.Supervisor(graph=g,
logdir=FLAGS.inference_dir,
init_op=tf.global_variables_initializer(),
summary_op=None,
summary_writer=None,
global_step=None,
saver=saver)
with sv.managed_session(start_standard_services=False) as sess:
sv.start_queue_runners(sess)
sv.saver.restore(sess, FLAGS.checkpoint_path)
semantic_predictions = sess.run(predictions)
result = np.array(semantic_predictions, dtype=np.uint8)
result = np.squeeze(result)
result = cv2.resize(result, tuple(original_image_dimensions))
# save raw result...
save_annotation.save_annotation(result,
FLAGS.inference_dir,
_RAW_FORMAT % image_name,
add_colormap=False)
# save result as color image...
save_annotation.save_annotation(result,
FLAGS.inference_dir,
_PREDICTION_FORMAT % image_name,
add_colormap=True,
colormap_type=FLAGS.dataset)
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=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=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, FLAGS.eval_crop_size[0],
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.')
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.
# insert by trobr
indices = tf.squeeze(
tf.where(tf.less_equal(labels, dataset.num_of_classes - 1)), 1)
labels = tf.cast(tf.gather(labels, indices), tf.int32)
predictions = tf.gather(predictions, indices)
# end of insert
miou, update_op = tf.metrics.mean_iou(predictions,
labels,
dataset.num_of_classes,
weights=weights)
tf.summary.scalar(predictions_tag, miou)
summary_op = tf.summary.merge_all()
summary_hook = tf.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
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.
TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
tf.contrib.training.evaluate_repeatedly(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
eval_ops=[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)
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)
predictions = tf.cast(predictions[common.OUTPUT_TYPE], tf.float32)
# Crop the valid regions from the predictions.
raw_predictions = tf.identity(predictions, _RAW_OUTPUT_NAME)
semantic_predictions = tf.slice(
raw_predictions, [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.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)
saver = tf.train.Saver(tf.model_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,
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')