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=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=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 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)
num_iteration = 0
max_num_iteration = FLAGS.max_number_of_iterations
checkpoints_iterator = tf.contrib.training.checkpoints_iterator(
FLAGS.checkpoint_dir, min_interval_secs=FLAGS.eval_interval_secs)
for checkpoint_path in checkpoints_iterator:
if max_num_iteration > 0 and num_iteration > max_num_iteration:
break
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)
tf.train.get_or_create_global_step()
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()))
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)
# Get dataset-dependent information.
dataset = data_generator.Dataset( # 获取相应数据集
dataset_name=FLAGS.dataset, # 数据集名称
split_name=FLAGS.vis_split, # 用于语义分割的数据集的tfrecorder文件 默认带有val
dataset_dir=FLAGS.dataset_dir, # 数据集目录
batch_size=FLAGS.vis_batch_size, # 一次性处理的image_batch_size 默认为1
crop_size=[int(sz)
for sz in FLAGS.vis_crop_size], #crop_size 默认为513,513
min_resize_value=FLAGS.min_resize_value, # None
max_resize_value=FLAGS.max_resize_value, # None
resize_factor=FLAGS.resize_factor, # None
model_variant=FLAGS.
model_variant, # 模型的变体 默认为mobilenet_v2 本次训练为 xception_65
is_training=False, # 不训练
should_shuffle=False, # 不将输入的数据随机打乱
should_repeat=False) # 不一直重复
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) # 可视化图片放置的文件夹
save_dir = os.path.join(FLAGS.vis_logdir,
_SEMANTIC_PREDICTION_SAVE_FOLDER) # 创建存放文件夹
tf.gfile.MakeDirs(save_dir) # 创建segmentation_results文件夹
raw_save_dir = os.path.join(FLAGS.vis_logdir,
_RAW_SEMANTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(raw_save_dir) # 创建 raw_segmentation_results文件夹
tf.logging.info('Visualizing on %s set', FLAGS.vis_split)
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next() # 获取数据
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], # 1024,2048
atrous_rates=FLAGS.atrous_rates, # 6,12,18
output_stride=FLAGS.output_stride) # 4
if tuple(FLAGS.eval_scales) == (1.0, ): # 不缩放进行评估
tf.logging.info('Performing single-scale test.')
predictions = model.predict_labels( # 标签预测 跟eval一样
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid)
else: # 多尺度评估
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': <tf.Tensor 'ArgMax:0' shape=(1, 1024, 2048) dtype=int64>,
'semantic_prob': <tf.Tensor 'Softmax:0' shape=(1, 1024, 2048, 19) dtype=float32>}
'''
predictions = predictions[common.OUTPUT_TYPE]
if FLAGS.min_resize_value and FLAGS.max_resize_value: # None 暂不考虑
# 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)
# 计数作用，每进行一个batch, global加１
tf.train.get_or_create_global_step()
if FLAGS.quantize_delay_step >= 0: # 默认为-1
contrib_quantize.create_eval_graph()
num_iteration = 0
max_num_iteration = FLAGS.max_number_of_iterations # 0
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], # <tf.Tensor 'IteratorGetNext:4' shape=(?, ?, ?, 3) dtype=uint8>
semantic_predictions=
predictions, # <tf.Tensor 'ArgMax:0' shape=(1, 1024, 2048) dtype=int64>
image_names=samples[
common.
IMAGE_NAME], # <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>
image_heights=samples[
common.
HEIGHT], # <tf.Tensor 'IteratorGetNext:0' shape=(?,) dtype=int64>
image_widths=samples[
common.
WIDTH], # <tf.Tensor 'IteratorGetNext:5' shape=(?,) dtype=int64>
image_id_offset=image_id_offset, # 0
save_dir=save_dir, # 语义分割结果放置的路径
raw_save_dir=raw_save_dir,
train_id_to_eval_id=train_id_to_eval_id
) # 只有cityscape中不为None
image_id_offset += FLAGS.vis_batch_size # 可视化的imageId
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
