def testPascalVocSegTestData(self):
dataset = data_generator.Dataset(
dataset_name='pascal_voc_seg',
split_name='val',
dataset_dir='research/deeplab/testing/pascal_voc_seg',
batch_size=1,
crop_size=[3, 3], # Use small size for testing.
min_resize_value=3,
max_resize_value=3,
resize_factor=None,
min_scale_factor=0.01,
max_scale_factor=2.0,
scale_factor_step_size=0.25,
is_training=False,
model_variant='mobilenet_v2')
self.assertAllEqual(dataset.num_of_classes, 21)
self.assertAllEqual(dataset.ignore_label, 255)
num_of_images = 3
with self.test_session() as sess:
iterator = dataset.get_one_shot_iterator()
for i in range(num_of_images):
batch = iterator.get_next()
batch, = sess.run([batch])
image_attributes = _get_attributes_of_image(i)
self.assertAllEqual(batch[common.IMAGE][0],
image_attributes.image)
self.assertAllEqual(batch[common.LABEL][0],
image_attributes.label)
self.assertEqual(batch[common.HEIGHT][0],
image_attributes.height)
self.assertEqual(batch[common.WIDTH][0],
image_attributes.width)
self.assertEqual(batch[common.IMAGE_NAME][0],
image_attributes.image_name)
# All data have been read.
with self.assertRaisesRegexp(tf.errors.OutOfRangeError, ''):
sess.run([iterator.get_next()])
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 train():
# From build_cityscapes_data.py: example = image_data, filename, height, width, seg_data
tf.logging.set_verbosity(tf.logging.INFO)
clone_batch_size = FLAGS.train_batch_size
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=None,
num_readers=10,
is_training=True,
should_shuffle=True,
should_repeat=True)
# reading batch: keys of samples ['height', 'width', 'image_name', 'label', 'image']
num_classes = dataset.num_of_classes
samples = dataset.get_one_shot_iterator().get_next()
in_imgs = samples['image'] / 255
labels = samples['label'] #channel=1
latents, skip = seg_encoder('Encoder', in_imgs, training=True)
if FLAGS.use_skip_1by1:
skip = tf.layers.conv2d(inputs=skip,
filters=32,
kernel_size=[1, 1],
strides=(1, 1),
use_bias=False,
padding="same")
if not FLAGS.use_skip:
skip = None
logits = seg_decoder('Decoder',
latents,
training=True,
num_classes=num_classes,
skip=skip)
#train_loss, _, _ = normal_loss(logits, labels, num_classes, dataset.ignore_label)
train_loss = softmax_cross_entropy_loss_mining(
logits,
labels,
num_classes,
dataset.ignore_label,
loss_weight=1.0,
upsample_logits=False,
hard_example_mining_step=FLAGS.hard_example_mining_step,
top_k_percent_pixels=FLAGS.top_k_percent_pixels,
scope='CI_Loss')
log_summaries(in_imgs, num_classes, logits, labels, train_loss)
step = tf.train.get_or_create_global_step()
main_optimizer = tf.train.AdamOptimizer(
learning_rate=1e-4) #1e-4/100ksteps/bs 2 ==> same but lr = 1e-5
main_step = main_optimizer.minimize(train_loss, global_step=step)
train_op = tf.group(main_step)
hooks = [
tf.train.StopAtStepHook(last_step=FLAGS.last_step),
tf.train.NanTensorHook(train_loss),
]
step_c = 0
with tf.train.MonitoredTrainingSession(hooks=hooks,
checkpoint_dir=FLAGS.checkpoint_dir,
save_checkpoint_secs=300,
save_summaries_secs=60) as sess:
while not sess.should_stop():
sess.run(train_op)
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.
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
if FLAGS.quantize_delay_step >= 0:
tf.contrib.quantize.create_eval_graph()
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)
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'
# Calculate iou for each class
metric_map = {}
iou_v, update_op = iou_each_class.iou(predictions,
labels,
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])
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)
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
if FLAGS.quantize_delay_step >= 0:
tf.contrib.quantize.create_eval_graph()
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)
######################################################################
############### Modified to evaluate all chekpoints in a folder ######
evaluation.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)
#####################################################################
###### read iou from tensorboard to csv file ########################
checkpoint_path_list = tf.train.get_checkpoint_state(
FLAGS.checkpoint_dir).all_model_checkpoint_paths
iterations = [
os.path.basename(a).split('-')[1] for a in checkpoint_path_list
]
iou_from_Tensorboard.to_csv(FLAGS.eval_logdir, dataset.num_of_classes,
iterations)
def main(unused_argv):
print("logging params")
print("Learning rate: ", FLAGS.base_learning_rate)
print("Momentum: ", FLAGS.momentum)
print("Weight decay: ", FLAGS.weight_decay)
print("training steps: ", FLAGS.training_number_of_steps)
print("Dataset name: ",FLAGS.dataset)
print("Using dataset for training: ",FLAGS.train_split)
print("Dataset directory: ",FLAGS.dataset_dir)
print("batch size: ", FLAGS.train_batch_size)
print("crop size: ", FLAGS.train_crop_size)
print("Model variant used: ",FLAGS.model_variant)
print("Train log directory: ", FLAGS.train_logdir)
train_list = []
val_list = []
count= 0
best_val_mean_iou = 0.718
dir_path='deeplab/best_ckpt/'
tf.logging.set_verbosity(tf.logging.INFO)
tf.gfile.MakeDirs(FLAGS.train_logdir)
tf.logging.info('Training on %s set', FLAGS.train_split)
graph = tf.Graph()
with graph.as_default():
with tf.device(tf.train.replica_device_setter(ps_tasks=FLAGS.num_ps_tasks)):
assert FLAGS.train_batch_size % FLAGS.num_clones == 0, (
'Training batch size not divisble by number of clones (GPUs).')
clone_batch_size = FLAGS.train_batch_size // FLAGS.num_clones # will be equivalent to train_batch_size
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=1, #check??
is_training=True,
should_shuffle=True,
should_repeat=True)
train_tensor, summary_op = _train_deeplab_model(
dataset.get_one_shot_iterator(), dataset.num_of_classes,
dataset.ignore_label)
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
last_layers = model.get_extra_layer_scopes(
FLAGS.last_layers_contain_logits_only)
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)
scaffold = tf.train.Scaffold(
init_fn=init_fn,
summary_op=summary_op,
)
stop_hook = tf.train.StopAtStepHook(
last_step=FLAGS.training_number_of_steps)
profile_dir = FLAGS.profile_logdir
if profile_dir is not None:
tf.gfile.MakeDirs(profile_dir)
with tf.contrib.tfprof.ProfileContext(
enabled=profile_dir is not None, profile_dir=profile_dir):
with tf.train.MonitoredTrainingSession(
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
config=session_config,
scaffold=scaffold,
checkpoint_dir=FLAGS.train_logdir,
summary_dir=FLAGS.train_logdir,
log_step_count_steps=FLAGS.log_steps,
save_summaries_steps=FLAGS.save_summaries_secs,
save_checkpoint_secs=FLAGS.save_interval_secs,
hooks=[stop_hook]) as sess:
while not sess.should_stop():
count+=1
training_loss = sess.run([train_tensor])
if np.isnan(training_loss):
print("learning rate too high. exiting!")
exit()
try:
if count>5000 and count%200==0:
train_iou = subprocess.check_output([sys.executable, "deeplab/vistrain.py"])
val_iou = subprocess.check_output([sys.executable, "deeplab/vis.py"])
val_mean_iou = float(val_iou.decode("utf-8").split('\n')[-2])
val_list.append(val_mean_iou*100)
train_mean_iou=float(train_iou.decode("utf-8").split('\n')[-2])*100
train_list.append(train_mean_iou)
print("Mean IoU on training dataset: ", train_mean_iou)
print("Mean IoU on validation dataset: ", val_mean_iou)
sys.stdout.flush()
if val_mean_iou > best_val_mean_iou:
if os.path.isdir(dir_path): shutil.rmtree(dir_path)
print("Validation Mean IoU: ", val_mean_iou)
shutil.copytree(FLAGS.train_logdir, dir_path)
best_val_mean_iou = val_mean_iou
except:
print("Validation script returned non-zero status.")
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
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)
common.outputlogMessage('Training on %s set' % FLAGS.train_split)
common.outputlogMessage('Dataset: %s' % FLAGS.dataset)
common.outputlogMessage('train_crop_size: %s' % str(FLAGS.train_crop_size))
common.outputlogMessage(str(FLAGS.train_crop_size))
common.outputlogMessage('atrous_rates: %s' % str(FLAGS.atrous_rates))
common.outputlogMessage('number of classes: %s' % str(FLAGS.num_classes))
common.outputlogMessage('Ignore label value: %s' % str(FLAGS.ignore_label))
pid = os.getpid()
with open('train_py_pid.txt', 'w') as f_obj:
f_obj.writelines('%d' % pid)
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=True,
should_repeat=True,
num_classes=FLAGS.num_classes,
ignore_label=FLAGS.ignore_label)
# 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_fn = _build_deeplab
model_args = (dataset.get_one_shot_iterator(), {
common.OUTPUT_TYPE: dataset.num_of_classes
}, dataset.ignore_label)
clones = model_deploy.create_clones(config,
model_fn,
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))
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))
# 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)
# Set up deployment (i.e., multi-GPUs and/or multi-replicas).
# 设置多gpu训练的相关参数
config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones, # gpu数量
clone_on_cpu=FLAGS.clone_on_cpu, # 默认为False
replica_id=FLAGS.task, # taskId
num_replicas=FLAGS.num_replicas, # 默认为1
num_ps_tasks=FLAGS.num_ps_tasks) # 默认为0
# 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 # 各个gpu均分batch_size
tf.gfile.MakeDirs(FLAGS.train_logdir) # 创建存放训练日志的文件
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( # 定义数据集参数
dataset_name=FLAGS.dataset, # 数据集名称 cityscapes
split_name=FLAGS.train_split, # 指定带有train的tfrecorder数据集 默认为“train”
dataset_dir=FLAGS.dataset_dir, # 数据集目录 tfrecoder文件的数据集目录
batch_size=clone_batch_size, # 均分后各个gpu训练中指定batch_size 的大小
crop_size=[int(sz) for sz in FLAGS.train_crop_size], # 训练中裁剪的图像大小 513,513
min_resize_value=FLAGS.min_resize_value, # 默认为 None
max_resize_value=FLAGS.max_resize_value, # 默认为None
resize_factor=FLAGS.resize_factor, # 默认为None
min_scale_factor=FLAGS.min_scale_factor, # 训练中，图像变换尺度，用于数据增强 默认最小为0.5
max_scale_factor=FLAGS.max_scale_factor, # 训练中，图像变换尺度，用于数据增强 默认最大为２
scale_factor_step_size=FLAGS.scale_factor_step_size, # 训练中，图像变换尺度增加的步长,默认为0.25 从0.5到2
model_variant=FLAGS.model_variant, # 指定模型 xception_65
num_readers=4, # 读取数据个数 若多gpu可增大加快训练速度
is_training=True,
should_shuffle=True,
should_repeat=True)
# Create the global step on the device storing the variables.
with tf.device(config.variables_device()):
# 计数作用，每训练一个batch, global加１
global_step = tf.train.get_or_create_global_step()
# Define the model and create clones.
model_fn = _build_deeplab # 定义deeplab模型
model_args = (dataset.get_one_shot_iterator(), {
common.OUTPUT_TYPE: dataset.num_of_classes
}, dataset.ignore_label) #模型参数
clones = model_deploy.create_clones(config, model_fn, 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: # 默认为False
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))
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))
# 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( # 获取模型学习率
FLAGS.learning_policy, # poly学习策略
FLAGS.base_learning_rate, # 0.0001
FLAGS.learning_rate_decay_step, # 固定2000次进行一次学习率衰退
FLAGS.learning_rate_decay_factor, # 0.1
FLAGS.training_number_of_steps, # 训练次数 20000
FLAGS.learning_power, # poly power 0.9
FLAGS.slow_start_step, # 0
FLAGS.slow_start_learning_rate, # 1e-4 缓慢开始的学习率
decay_steps=FLAGS.decay_steps, # 0.0
end_learning_rate=FLAGS.end_learning_rate) # 0.0
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
# 模型训练优化器
if FLAGS.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
elif FLAGS.optimizer == 'adam': # adam优化器 寻找全局最优点的优化算法，引入了二次方梯度校正
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: # 默认为-1 忽略
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 # FLAGS.startup_delay_steps 默认为15
with tf.device(config.variables_device()):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer) # 计算total_loss
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)
# 获取梯度乘子
grad_mult = train_utils.get_model_gradient_multipliers(
last_layers, FLAGS.last_layer_gradient_multiplier)
# grad_mult : {'logits/semantic/biases': 2.0, 'logits/semantic/weights': 1.0}
if grad_mult:
grads_and_vars = slim.learning.multiply_gradients(
grads_and_vars, grad_mult)
# Create gradient update op.
grad_updates = optimizer.apply_gradients( # 将计算的梯度用于变量上，返回一个应用指定的梯度的操作 opration
grads_and_vars, global_step=global_step) # 对global_step进行自增
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 # 默认为None
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: # 获取预训练权重
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,
non_uniform_sampling=FLAGS.nus_preprocess,
output_target_sampling=FLAGS.eval_type == "nus")
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()
if FLAGS.eval_type == "nus":
sampling_location = _nus_locations(samples[common.IMAGE], False)
target_locations = samples[TARGET_SAMPLING]
mse, update_op = tf.metrics.mean_squared_error(sampling_location, target_locations)
# update_op = tf.Print(update_op, [mse])
# tf.summary.image("InputImages", samples[common.IMAGE])
# tf.summary.image("InputLabel", tf.to_float(samples[common.LABEL]) / 19)
# tf.summary.image("ResViz", viz(sampling_location))
tf.summary.scalar('mse', mse)
else:
crop_size = [int(sz) for sz in FLAGS.eval_crop_size]
if FLAGS.nus_preprocess:
crop_size = [FLAGS.nus_sampling_size] * 2
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: dataset.num_of_classes},
crop_size=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,
output_logits=FLAGS.nus_preprocess)
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)
if FLAGS.nus_preprocess:
with tf.name_scope("nus_interpolation"):
assert FLAGS.eval_batch_size == 1, "Only support eval_batch_size == 1"
sampling = samples[SAMPLING]
logits = predictions[common.OUTPUT_TYPE + "/logits"]
shape = tf.shape(samples[common.LABEL])[1:3]
predictions = tf.py_func(
nus.nus_interpolate,
[logits[0], sampling[0], shape],
logits.dtype,
)[None, ...]
predictions = tf.argmax(predictions, axis=3)
else:
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.
miou, update_op = tf.metrics.mean_iou(
predictions, labels, dataset.num_of_classes, weights=weights)
miou = tf.Print(miou, ["mIoU", miou])
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
if FLAGS.quantize_delay_step >= 0:
tf.contrib.quantize.create_eval_graph()
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)
dataset = data_generator.Dataset( # 获取验证集图片数据
dataset_name=FLAGS.dataset, # 数据集名称 cityscapes 默认为 pascal_voc_seg
split_name=FLAGS.eval_split, # 指定带有val的tfrecorder数据集 默认为“val”
dataset_dir=FLAGS.dataset_dir, # 数据集目录 tfrecoder文件的数据集目录
batch_size=FLAGS.eval_batch_size, # 每个batch包含的image数量 默认为1
crop_size=[int(sz)
for sz in FLAGS.eval_crop_size], # 评估时crop_size 默认为513,513
min_resize_value=FLAGS.min_resize_value, # 默认为None
max_resize_value=FLAGS.max_resize_value, # 默认为None
resize_factor=FLAGS.resize_factor, # 默认为None
model_variant=FLAGS.model_variant, # 模型的变体 本次训练为 xception_65
num_readers=2, # 并行读取图片的数量
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
tf.gfile.MakeDirs(FLAGS.eval_logdir) # 创建评估目录
tf.logging.info('Evaluating on %s set', FLAGS.eval_split)
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next() # 获取一次迭代的验证集数据
'''
samples:
{'image_name': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>,
'width': <tf.Tensor 'IteratorGetNext:5' shape=(?,) dtype=int64>,
'image': <tf.Tensor 'IteratorGetNext:1' shape=(?, 1024, 2048, 3) dtype=float32>,
'height': <tf.Tensor 'IteratorGetNext:0' shape=(?,) dtype=int64>,
'label': <tf.Tensor 'IteratorGetNext:3' shape=(?, 1024, 2048, 1) dtype=int32>,
'original_image': <tf.Tensor 'IteratorGetNext:4' shape=(?, ?, ?, 3) dtype=uint8>}
'''
model_options = common.ModelOptions( # 模型参数
outputs_to_num_classes={
common.OUTPUT_TYPE: dataset.num_of_classes
}, # {semantic: 19}
crop_size=[int(sz) for sz in FLAGS.eval_crop_size], # 1024,2048
atrous_rates=FLAGS.atrous_rates, # 6,12,18
output_stride=FLAGS.output_stride) # 16
# Set shape in order for tf.contrib.tfprof.model_analyzer to work properly.
samples[common.IMAGE].set_shape( # 设置形状
[
FLAGS.eval_batch_size, # 默认为1
int(FLAGS.eval_crop_size[0]),
int(FLAGS.eval_crop_size[1]),
3
])
if tuple(FLAGS.eval_scales) == (1.0, ): # 默认 评估尺度为1
tf.logging.info('Performing single-scale test.')
predictions = model.predict_labels(
samples[common.IMAGE],
model_options, # 进行每个像素点预测
image_pyramid=FLAGS.image_pyramid)
'''
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>}
'''
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]) # 预测标签
labels = tf.reshape(samples[common.LABEL], shape=[-1]) # 真实标签
weights = tf.to_float(tf.not_equal(labels,
dataset.ignore_label)) # 各标签权重
# 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' # MIoU
predictions_tag1 = 'accuracy_pixel' # 像素精度
for eval_scale in FLAGS.eval_scales: # 默认为单尺度[1.0]
predictions_tag += '_' + str(eval_scale)
predictions_tag1 += '_' + str(eval_scale)
if FLAGS.add_flipped_images: # 默认为False 不设置左右翻转来评估模型
predictions_tag += '_flipped'
predictions_tag1 += '_flipped'
# Define the evaluation metric.
metric_map = {}
num_classes = dataset.num_of_classes # 19
metric_map['eval/%s_overall' % predictions_tag] = tf.metrics.mean_iou(
labels=labels,
predictions=predictions,
num_classes=num_classes,
weights=weights)
'''
metric_map:
{'eval/miou_1.0_overall': (<tf.Tensor 'mean_iou/Select_1:0' shape=() dtype=float32>,
<tf.Tensor 'mean_iou/AssignAdd:0' shape=(19, 19) dtype=float64_ref>)}
'''
metric_map['eval/%s_overall_accuracy_' %
predictions_tag] = tf.metrics.accuracy(
labels=labels, predictions=predictions, weights=weights)
# IoU for each class.
'''
tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)
Returns a one-hot tensor.
ndices表示输入的多个数值，通常是矩阵形式；depth表示输出的尺寸。
'''
one_hot_predictions = tf.one_hot(predictions, num_classes)
one_hot_predictions = tf.reshape(one_hot_predictions,
[-1, num_classes]) # 预测输出的one_hot
one_hot_labels = tf.one_hot(labels, num_classes)
one_hot_labels = tf.reshape(one_hot_labels,
[-1, num_classes]) # 真实label的one_hot
for c in range(num_classes):
predictions_tag_c = '%s_class_%d' % (predictions_tag, c
) # miou_1.0_class_c
predictions_tag_c1 = '%s_class_%d' % (predictions_tag1, 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)
tn, tn_op = tf.metrics.true_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))
ap = tf.where(tf.greater(tp + fn, 0.0),
(tp + tn) / (tp + tn + fn + fp), tf.constant(np.NaN))
metric_map['eval/%s' % predictions_tag_c] = (iou, tp_fp_fn_op)
metric_map['eval/%s' % predictions_tag_c1] = (ap, tp_fp_fn_op)
(metrics_to_values,
metrics_to_updates) = contrib_metrics.aggregate_metric_map(metric_map)
'''
(metrics_to_values, metrics_to_updates):
({'eval/miou_1.0_class_5': <tf.Tensor 'Select_6:0' shape=() dtype=float32>,
'eval/miou_1.0_class_18': <tf.Tensor 'Select_19:0' shape=() dtype=float32>,
'eval/miou_1.0_class_13': <tf.Tensor 'Select_14:0' shape=() dtype=float32>,
'eval/miou_1.0_class_1': <tf.Tensor 'Select_2:0' shape=() dtype=float32>,
'eval/miou_1.0_overall': <tf.Tensor 'mean_iou/Select_1:0' shape=() dtype=float32>,
'eval/miou_1.0_class_17': <tf.Tensor 'Select_18:0' shape=() dtype=float32>,
'eval/miou_1.0_class_8': <tf.Tensor 'Select_9:0' shape=() dtype=float32>,
'eval/miou_1.0_class_2': <tf.Tensor 'Select_3:0' shape=() dtype=float32>,
'eval/miou_1.0_class_0': <tf.Tensor 'Select_1:0' shape=() dtype=float32>,
'eval/miou_1.0_class_3': <tf.Tensor 'Select_4:0' shape=() dtype=float32>,
'eval/miou_1.0_class_14': <tf.Tensor 'Select_15:0' shape=() dtype=float32>,
'eval/miou_1.0_class_11': <tf.Tensor 'Select_12:0' shape=() dtype=float32>,
'eval/miou_1.0_class_6': <tf.Tensor 'Select_7:0' shape=() dtype=float32>,
'eval/miou_1.0_class_15': <tf.Tensor 'Select_16:0' shape=() dtype=float32>,
'eval/miou_1.0_class_4': <tf.Tensor 'Select_5:0' shape=() dtype=float32>,
'eval/miou_1.0_class_9': <tf.Tensor 'Select_10:0' shape=() dtype=float32>,
'eval/miou_1.0_class_16': <tf.Tensor 'Select_17:0' shape=() dtype=float32>,
'eval/miou_1.0_class_7': <tf.Tensor 'Select_8:0' shape=() dtype=float32>,
'eval/miou_1.0_class_10': <tf.Tensor 'Select_11:0' shape=() dtype=float32>,
'eval/miou_1.0_class_12': <tf.Tensor 'Select_13:0' shape=() dtype=float32>},
{'eval/miou_1.0_class_5': <tf.Operation 'group_deps_5' type=NoOp>,
'eval/miou_1.0_class_18': <tf.Operation 'group_deps_18' type=NoOp>,
'eval/miou_1.0_class_13': <tf.Operation 'group_deps_13' type=NoOp>,
'eval/miou_1.0_class_1': <tf.Operation 'group_deps_1' type=NoOp>,
'eval/miou_1.0_overall': <tf.Tensor 'mean_iou/AssignAdd:0' shape=(19, 19) dtype=float64_ref>,
'eval/miou_1.0_class_17': <tf.Operation 'group_deps_17' type=NoOp>,
'eval/miou_1.0_class_8': <tf.Operation 'group_deps_8' type=NoOp>,
'eval/miou_1.0_class_2': <tf.Operation 'group_deps_2' type=NoOp>,
'eval/miou_1.0_class_0': <tf.Operation 'group_deps' type=NoOp>,
'eval/miou_1.0_class_3': <tf.Operation 'group_deps_3' type=NoOp>,
'eval/miou_1.0_class_14': <tf.Operation 'group_deps_14' type=NoOp>,
'eval/miou_1.0_class_11': <tf.Operation 'group_deps_11' type=NoOp>,
'eval/miou_1.0_class_6': <tf.Operation 'group_deps_6' type=NoOp>,
'eval/miou_1.0_class_15': <tf.Operation 'group_deps_15' type=NoOp>,
'eval/miou_1.0_class_4': <tf.Operation 'group_deps_4' type=NoOp>,
'eval/miou_1.0_class_9': <tf.Operation 'group_deps_9' type=NoOp>,
'eval/miou_1.0_class_16': <tf.Operation 'group_deps_16' type=NoOp>,
'eval/miou_1.0_class_7': <tf.Operation 'group_deps_7' type=NoOp>,
'eval/miou_1.0_class_10': <tf.Operation 'group_deps_10' type=NoOp>,
'eval/miou_1.0_class_12': <tf.Operation 'group_deps_12' type=NoOp>})
'''
'''
tf.Print(input, data, message=None, first_n=None, summarize=None, name=None)
最低要求两个输入，input和data，input是需要打印的变量的名字，data要求是一个list，里面包含要打印的内容。
'''
summary_ops = []
for metric_name, metric_value in six.iteritems(metrics_to_values):
op = tf.summary.scalar(metric_name, metric_value) # 显示标量信息
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: # 为0 暂不考虑
num_eval_iters = FLAGS.max_number_of_evaluations
if FLAGS.quantize_delay_step >= 0: # -1 暂不考虑
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)
def save_output_samples(checkpoint_dir,
best_chekpnt,
eval_preprocess_threads=8,
eval_crop_size=[1024, 2048]):
eval_batch_size = 1
compressed_reconstructed_dir = os.path.join(
checkpoint_dir, 'compressed_reconstructed_images')
if not os.path.exists(compressed_reconstructed_dir):
os.makedirs(compressed_reconstructed_dir)
logger.info('Creating directory ' + compressed_reconstructed_dir +
'/')
eval_split = 'val'
num_sample_output = 20
dataset = data_generator.Dataset(
dataset_name='cityscapes',
split_name=eval_split,
dataset_dir=
'/datatmp/Experiments/belbarashy/datasets/Cityscapes/tfrecord/',
batch_size=eval_batch_size,
crop_size=[int(sz) for sz in eval_crop_size],
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
model_variant=None,
num_readers=eval_preprocess_threads,
is_training=False,
should_shuffle=False,
should_repeat=False)
samples = dataset.get_one_shot_iterator().get_next()
in_imgs = samples['image'] / 255
depth = samples['depth'] / 255
labels = samples['label']
num_classes = dataset.num_of_classes
# =================================== arch
_, _, _, _, _, _, _, _, logits, _ = \
build_model(in_imgs, depth, None, num_classes, mode='testing')
# ===================================
predictions = tf.argmax(logits, 3) # batch*H*W*1
with tf.Session() as sess:
if best_chekpnt is None:
latest = tf.train.latest_checkpoint(checkpoint_dir=checkpoint_dir)
best_chekpnt = latest
tf.train.Saver().restore(sess, save_path=best_chekpnt)
for i in range(num_sample_output):
test_file_name = str(i)
depth_path = os.path.join(compressed_reconstructed_dir,
test_file_name + '_depth' + '.png')
orig_path = os.path.join(compressed_reconstructed_dir,
test_file_name + '_orig' + '.png')
map_gt_path = os.path.join(compressed_reconstructed_dir,
test_file_name + '_map_gt' + '.png')
map_pred_path = os.path.join(compressed_reconstructed_dir,
test_file_name + '_map_pred' + '.png')
p, l, input_img, dep = sess.run(
[predictions, labels, in_imgs, depth])
l = np.squeeze(l)
p = np.squeeze(p)
input_img = np.squeeze(input_img)
dep = np.squeeze(dep)
p[l == 255] = 255
colored_label = get_dataset_colormap.label_to_color_image(
l, 'cityscapes')
colored_pred = get_dataset_colormap.label_to_color_image(
p, 'cityscapes')
dep_jet = cv2.applyColorMap(np.uint8(dep * (255 * 2)),
cv2.COLORMAP_JET)
cv2.imwrite(depth_path, dep_jet)
colored_pred = np.uint8(colored_pred[:, :, ::-1])
cv2.imwrite(map_pred_path, colored_pred)
colored_label = np.uint8(colored_label[:, :, ::-1])
cv2.imwrite(map_gt_path, colored_label)
input_img = np.uint8(input_img[:, :, ::-1] * 255)
cv2.imwrite(orig_path, input_img)
def eval_seg(checkpoint_dir,
eval_preprocess_threads=8,
eval_crop_size=[1024, 2048],
eval_logdir='tmp_eval_log/',
eval_batch_size=1,
eval_repeatedly=False):
eval_split = 'val'
dataset = data_generator.Dataset(
dataset_name='cityscapes',
split_name=eval_split,
dataset_dir=
'/datatmp/Experiments/belbarashy/datasets/Cityscapes/tfrecord/',
batch_size=eval_batch_size,
crop_size=[int(sz) for sz in eval_crop_size],
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
model_variant=None,
num_readers=eval_preprocess_threads,
is_training=False,
should_shuffle=False,
should_repeat=False)
tf.gfile.MakeDirs(eval_logdir)
logger.info('Evaluating on ' + eval_split + ' set')
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next()
# Set shape in order for tf.contrib.tfprof.model_analyzer to work properly.
samples['image'].set_shape([
eval_batch_size,
int(eval_crop_size[0]),
int(eval_crop_size[1]), 3
])
num_classes = dataset.num_of_classes
in_imgs = samples['image'] / 255
depth = samples['depth'] / 255
labels = samples['label']
# =================================== arch
_, _, _, _, _, _, _, _, logits, _ = \
build_model(in_imgs, depth, None, num_classes, mode='testing')
if logits is None:
highest_val_miou = 0
best_chekpnt = None
return highest_val_miou, best_chekpnt
# ===================================
predictions = tf.argmax(logits, 3)
predictions = tf.reshape(predictions, shape=[-1])
labels = tf.reshape(labels, shape=[-1])
weights = tf.to_float(tf.not_equal(labels, dataset.ignore_label))
labels = tf.where(tf.equal(labels, dataset.ignore_label),
tf.zeros_like(labels), labels)
predictions_tag = 'miou'
eval_scales = [1.0]
for eval_scale in eval_scales:
predictions_tag += '_' + str(eval_scale)
# Define the evaluation metric ==> mIOU over class
miou, update_op = tf.metrics.mean_iou(predictions,
labels,
num_classes,
weights=weights)
tf.summary.scalar(predictions_tag, miou)
summary_op = tf.summary.merge_all()
summary_hook = tf.contrib.training.SummaryAtEndHook(
log_dir=eval_logdir, summary_op=summary_op)
hooks = [summary_hook]
num_eval_iters = 100000
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)
latest = tf.train.latest_checkpoint(checkpoint_dir=checkpoint_dir)
if eval_repeatedly:
logger.info('start evaluation repeatedly')
tf.contrib.training.evaluate_repeatedly(
master='',
checkpoint_dir=checkpoint_dir,
eval_ops=[update_op],
max_number_of_evaluations=num_eval_iters,
hooks=hooks,
eval_interval_secs=eval_interval_secs)
else:
logger.info('start evaluating last 5 checkpoints')
checkpnts_paths = tf.train.get_checkpoint_state(
checkpoint_dir=checkpoint_dir).all_model_checkpoint_paths
best_chekpnt = latest
highest_val_miou = 0
for chekpnt_path in checkpnts_paths:
final_m = tf.contrib.training.evaluate_once(
checkpoint_path=chekpnt_path,
master='',
eval_ops=[update_op],
final_ops=miou,
hooks=hooks)
if final_m > highest_val_miou:
highest_val_miou = final_m
best_chekpnt = chekpnt_path
logger.info(chekpnt_path + ' ==> mIOU ' + str(final_m))
logger.info('==============================================')
logger.info('highest_val_miou = ' + str(highest_val_miou))
logger.info('best_chekpnt = ' + str(best_chekpnt))
logger.info('==============================================')
if not (eval_repeatedly):
tf.reset_default_graph()
save_output_samples(checkpoint_dir, best_chekpnt,
eval_preprocess_threads, eval_crop_size)
return highest_val_miou, best_chekpnt
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
# Prepare for visualization.
tf.gfile.MakeDirs(FLAGS.vis_logdir)
save_dir = os.path.join(FLAGS.vis_logdir, _SEMANTIC_PREDICTION_SAVE_FOLDER)
save_dir1 = os.path.join(FLAGS.vis_logdir, _SEMANTIC_PREDICTION_SAVE_FOLDER1)
tf.gfile.MakeDirs(save_dir)
raw_save_dir = os.path.join(
FLAGS.vis_logdir, _RAW_SEMANTIC_PREDICTION_SAVE_FOLDER)
raw_save_dir_label = os.path.join(
FLAGS.vis_logdir, _LABEL_SEMANTIC_PREDICTION_SAVE_FOLDER)
tf.gfile.MakeDirs(raw_save_dir)
tf.gfile.MakeDirs(raw_save_dir_label)
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]
predict = 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)
miou_tf, update_op_tf = tf.metrics.mean_iou(
predict, labels, dataset.num_of_classes, weights=weights)
from deeplab import my_metrics
iou_v, update_op = my_metrics.iou(predict, labels, dataset.num_of_classes, weights=weights)
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:
tf.contrib.quantize.create_eval_graph()
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:
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,
gt_labels=samples[common.LABEL],
image_names=samples[common.IMAGE_NAME],
image_heights=samples[common.HEIGHT],
image_widths=samples[common.WIDTH],
image_id_offset=image_id_offset,
update_op=update_op,
iou=iou_v,
save_dir=save_dir,
save_dir1=save_dir1,
raw_save_dir=raw_save_dir,
raw_save_dir_label=raw_save_dir_label,
mtf = miou_tf,
utf = update_op_tf,
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
mean_eval = np.array(mean_iou_eval)
print("Mean IoU on validation dataset: ", np.mean(mean_eval))
print("Mean IoU for background: ", np.nanmean(miou_class1_back))
print("Mean IoU for stem: ", np.nanmean(miou_class2_stem))
print("Mean IoU for callus: ", np.nanmean(miou_class3_cal))
print("Mean IoU for shoot: ", np.nanmean(miou_class4_shoot))
print("Renaming files.")
segmentation_res_path = os.path.join('./', FLAGS.vis_logdir, _LABEL_SEMANTIC_PREDICTION_SAVE_FOLDER)
for file in os.listdir(segmentation_res_path):
src = file
dst = os.path.join(segmentation_res_path, src[2:-5] + '.png')
os.rename(os.path.join(segmentation_res_path, src), dst)
print(np.mean(mean_eval))
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
tf.gfile.MakeDirs(FLAGS.train_logdir)
tf.logging.info('Training on %s set', FLAGS.train_split)
graph = tf.Graph()
with graph.as_default():
with tf.device(
tf.train.replica_device_setter(ps_tasks=FLAGS.num_ps_tasks)):
assert FLAGS.train_batch_size % FLAGS.num_clones == 0, (
'Training batch size not divisble by number of clones (GPUs).')
clone_batch_size = FLAGS.train_batch_size // FLAGS.num_clones
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=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=2,
is_training=True,
should_shuffle=True,
should_repeat=True)
vdataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.trainval_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=FLAGS.trainval_batch_size,
crop_size=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=2,
is_training=True,
should_shuffle=False,
should_repeat=False)
viterator = vdataset.get_initializable_iterator()
next_element = viterator.get_next()
val_image = tf.placeholder(tf.float32,
shape=(None, FLAGS.train_crop_size[0],
FLAGS.train_crop_size[1], 3))
val_label = tf.placeholder(tf.int32,
shape=(None, FLAGS.train_crop_size[0],
FLAGS.train_crop_size[1], 1))
train_tensor, summary_op = _train_deeplab_model(
dataset.get_one_shot_iterator(), dataset.num_of_classes,
dataset.ignore_label)
val_tensor = _val_loss(dataset=vdataset,
image=val_image,
label=val_label,
num_of_classes=vdataset.num_of_classes,
ignore_label=vdataset.ignore_label)
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
last_layers = model.get_extra_layer_scopes(
FLAGS.last_layers_contain_logits_only)
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)
scaffold = tf.train.Scaffold(
init_fn=init_fn,
summary_op=summary_op,
)
stop_hook = tf.train.StopAtStepHook(FLAGS.training_number_of_steps)
# Validation set variables
epoch = 0
val_loss_per_epoch = []
steps_per_epoch = int(dataset.num_samples / FLAGS.train_batch_size)
saver = tf.train.Saver(max_to_keep=1)
profile_dir = FLAGS.profile_logdir
if profile_dir is not None:
tf.gfile.MakeDirs(profile_dir)
with tf.contrib.tfprof.ProfileContext(enabled=profile_dir
is not None,
profile_dir=profile_dir):
with tf.train.MonitoredTrainingSession(
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
config=session_config,
scaffold=scaffold,
checkpoint_dir=FLAGS.train_logdir,
log_step_count_steps=FLAGS.log_steps,
save_summaries_steps=FLAGS.save_summaries_secs,
save_checkpoint_secs=FLAGS.save_interval_secs,
hooks=[]) as sess:
while not sess.should_stop():
step = sess.run(tf.train.get_global_step())
sess.run([train_tensor])
if step % steps_per_epoch == 0:
count_validation = 0
stop_training = False
val_losses = []
sess.run(viterator.initializer)
while True:
try:
val_element = sess.run(next_element)
val_loss, val_summary = sess.run(
val_tensor,
feed_dict={
val_image:
val_element[common.IMAGE],
val_label:
val_element[common.LABEL]
})
val_losses.append(val_loss)
count_validation += 1
#print(' {} [validation] {} {}'.format(count_validation, val_loss, val_element[common.IMAGE_NAME]))
except tf.errors.OutOfRangeError:
total_val_loss = sum(val_losses) / len(
val_losses)
val_loss_per_epoch.append(total_val_loss)
print(' {} [validation loss] {}'.format(
count_validation *
FLAGS.train_batch_size,
total_val_loss))
print(' {} [current epoch] {}'.format(
step, epoch))
break
if epoch > 0:
min_delta = 0.01
patience = 8
stop_training = early_stopping(
epoch, val_loss_per_epoch, min_delta,
patience, sess, saver, total_val_loss)
# Stops training if current model val loss is worse than previous model val loss
if stop_training:
break
epoch += 1
def train(l_args):
"""Trains the model."""
if l_args.verbose:
tf.logging.set_verbosity(tf.logging.INFO)
# Create input data pipeline.
dataset = data_generator.Dataset(
dataset_name='cityscapes',
split_name='train',
dataset_dir=
'/datatmp/Experiments/belbarashy/datasets/Cityscapes/tfrecord/',
batch_size=l_args.batchsize,
crop_size=[int(sz) for sz in [l_args.patchsize, l_args.patchsize]],
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=0.5,
max_scale_factor=2.,
scale_factor_step_size=0.25,
model_variant=None,
num_readers=l_args.preprocess_threads,
is_training=True,
should_shuffle=True,
should_repeat=True)
# reading batch: keys of samples ['height', 'width', 'image_name', 'label', 'image']
num_classes = dataset.num_of_classes
samples = dataset.get_one_shot_iterator().get_next()
#num_pixels = l_args.batchsize * l_args.patchsize ** 2
x = samples['image'] / 255
depth = samples['depth'] / 255
labels = samples['label']
# Build autoencoder.
train_loss, train_bpp, train_mse, x_tilde, _, _, _, entropy_bottleneck, seg_logits, seg_loss = \
build_model(x, depth, l_args.lmbda, num_classes, mode = 'training', seg_labels = labels,
ignore_label = dataset.ignore_label)
# Minimize loss and auxiliary loss, and execute update op.
step = tf.train.get_or_create_global_step()
main_optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
main_step = main_optimizer.minimize(train_loss, global_step=step)
if entropy_bottleneck is not None:
aux_optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)
aux_step = aux_optimizer.minimize(entropy_bottleneck.losses[0])
entropy_bottleneck.visualize(
) ## Creates summary for the probability mass function (PMF) estimated in the bottleneck
train_op = tf.group(main_step, aux_step, entropy_bottleneck.updates[0])
else:
train_op = tf.group(main_step)
log_all_summaries(x, x_tilde, seg_logits, labels, train_loss, train_bpp,
train_mse, seg_loss)
hooks = [
tf.train.StopAtStepHook(last_step=l_args.last_step),
tf.train.NanTensorHook(train_loss),
]
with tf.train.MonitoredTrainingSession(
hooks=hooks,
checkpoint_dir=l_args.checkpoint_dir,
save_checkpoint_secs=300,
save_summaries_secs=60) as sess:
while not sess.should_stop():
sess.run(train_op)
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(
model_name=FLAGS.model_name,
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)
def eval(l_args, expDir, lmbda, best_chekpnt, val_miou):
train_dir = l_args.checkpoint_dir
metrics_path = os.path.join(train_dir, 'metrics_args.pkl')
l_args.lmbda = lmbda
compressed_reconstructed_dir = os.path.join(
train_dir, 'compressed_reconstructed_images')
if not os.path.exists(compressed_reconstructed_dir):
os.makedirs(compressed_reconstructed_dir)
val_split_size = 500
dataset = data_generator.Dataset(
dataset_name='cityscapes',
split_name='val',
dataset_dir=
'/datatmp/Experiments/belbarashy/datasets/Cityscapes/tfrecord/',
batch_size=1, #l_args.batchsize
crop_size=[int(sz) for sz in [1024, 2048]],
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
model_variant=None,
num_readers=l_args.preprocess_threads,
is_training=False,
should_shuffle=False,
should_repeat=False)
# reading batch: keys of samples ['height', 'width', 'image_name', 'label', 'image']
num_classes = dataset.num_of_classes
samples = dataset.get_one_shot_iterator().get_next()
x = samples['image'] / 255
depth = samples['depth'] / 255
labels = samples['label']
num_pixels = tf.to_float(tf.reduce_prod(tf.shape(x)[:-1]))
# ======================== Input image dim should be multiple of 16
x_shape = tf.shape(x)
x_shape = tf.ceil(x_shape / 16) * 16
x = tf.image.resize_images(x, (x_shape[1], x_shape[2]))
# ========================
""" build model """
_, eval_bpp, _, x_hat, y_hat, y, string, _, seg_logits, seg_loss = \
build_model(x, depth, l_args.lmbda, num_classes, mode = 'testing')
# Bring both images back to 0..255 range.
x *= 255
img_file_name = tf.placeholder(tf.string)
noReconstuction = False
if x_hat is None:
noReconstuction = True
save_reconstructed_op = None
else:
x_hat_to_save = tf.identity(x_hat[0, :, :, :])
x_hat = tf.clip_by_value(x_hat, 0, 1)
x_hat = tf.round(x_hat * 255)
mse = tf.reduce_mean(tf.squared_difference(x, x_hat))
psnr = tf.squeeze(tf.image.psnr(x_hat, x, 255))
msssim = tf.squeeze(tf.image.ssim_multiscale(x_hat, x, 255))
# Write reconstructed image out as a PNG file.
save_reconstructed_op = save_image(img_file_name, x_hat_to_save)
logger.info('Testing the model on ' + str(val_split_size) +
' images and save the reconstructed images')
msel, psnrl, msssiml, msssim_dbl, eval_bppl, bppl = [], [], [], [], [], []
with tf.Session() as sess:
# Load the latest model checkpoint, get the compressed string and the tensor
# shapes.
if best_chekpnt is None:
latest = tf.train.latest_checkpoint(
checkpoint_dir=l_args.checkpoint_dir)
best_chekpnt = latest
tf.train.Saver().restore(sess, save_path=best_chekpnt)
for i in range(val_split_size):
test_file_name = str(i)
compressed_im_path = os.path.join(
compressed_reconstructed_dir,
test_file_name + '_compressed' + '.bin')
reconstucted_im_path = os.path.join(
compressed_reconstructed_dir,
test_file_name + '_reconstructed' + '.png')
im_metrics_path = os.path.join(
compressed_reconstructed_dir,
test_file_name + '_metrics' + '.pkl')
l_args.output = reconstucted_im_path
if (i < 50) and not (noReconstuction):
eval_bpp_, mse_, psnr_, msssim_, num_pixels_, string_, x_shape, y_shape, _ = \
sess.run( [eval_bpp, mse, psnr, msssim, num_pixels, string,
tf.shape(x), tf.shape(y), save_reconstructed_op],
feed_dict={img_file_name:reconstucted_im_path})
else:
if eval_bpp is not None:
if noReconstuction:
eval_bpp_, num_pixels_, string_, x_shape, y_shape = \
sess.run( [eval_bpp, num_pixels, string, tf.shape(x), tf.shape(y)],
feed_dict={img_file_name:reconstucted_im_path})
mse_ = 0
psnr_ = 0
msssim_ = 0
else:
eval_bpp_, mse_, psnr_, msssim_, num_pixels_, string_, x_shape, y_shape = \
sess.run( [eval_bpp, mse, psnr, msssim, num_pixels, string,tf.shape(x), tf.shape(y)],
feed_dict={img_file_name:reconstucted_im_path})
else:
mse_ = 0
psnr_ = 0
msssim_ = 0
eval_bpp_ = 0
num_pixels_ = None
string_ = None
x_shape = None
y_shape = None
if i < 50 and (string_
is not None): # save only the first 50 test samples
# Write a binary file with the shape information and the compressed string.
with open(compressed_im_path, "wb") as f:
f.write(np.array(x_shape[1:-1], dtype=np.uint16).tobytes())
f.write(np.array(y_shape[1:-1], dtype=np.uint16).tobytes())
f.write(string_)
if string_ is not None:
# The actual bits per pixel including overhead.
bpp_ = (8 + len(string_)) * 8 / num_pixels_
else:
bpp_ = 0
print("Mean squared error: {:0.4f}".format(mse_))
print("PSNR (dB): {:0.2f}".format(psnr_))
print("Multiscale SSIM: {:0.4f}".format(msssim_))
print("Multiscale SSIM (dB): {:0.2f}".format(
-10 * np.log10(1 - msssim_)))
print("Information content in bpp: {:0.4f}".format(eval_bpp_))
print("Actual bits per pixel: {:0.4f}".format(bpp_))
msssim_db_ = (-10 * np.log10(1 - msssim_))
im_metrics = {
'mse': mse_,
'psnr': psnr_,
'msssim': msssim_,
'msssim_db': msssim_db_,
'eval_bpp': eval_bpp_,
'bpp': bpp_
}
with open(im_metrics_path, "wb") as fp:
pickle.dump(im_metrics, fp)
msel.append(mse_)
psnrl.append(psnr_)
msssiml.append(msssim_)
msssim_dbl.append(msssim_db_)
eval_bppl.append(eval_bpp_)
bppl.append(bpp_)
logger.info(
'Averaging metrics and save them with the exp_args in pickle file metrics_args.pkl'
)
mse_ = np.mean(msel)
psnr_ = np.mean(psnrl)
msssim_ = np.mean(msssiml)
eval_bpp_ = np.mean(eval_bppl)
bpp_ = np.mean(bppl)
msssim_db_ = np.mean(msssim_dbl)
logger.info('MSE = ' + str(mse_))
logger.info('PSNR = ' + str(psnr_))
logger.info('MS-SSIM = ' + str(msssim_))
logger.info('MS-SSIM db = ' + str(msssim_db_))
logger.info('Eval_bpp = ' + str(eval_bpp_))
logger.info('bpp = ' + str(bpp_))
logger.info('mIOU = ' + str(val_miou))
exp_avg_metrics = {
'mse': mse_,
'psnr': psnr_,
'msssim': msssim_,
'msssim_db': msssim_db_,
'eval_bpp': eval_bpp_,
'bpp': bpp_,
'mIOU': val_miou,
'chk_pnt': best_chekpnt
}
with open(metrics_path, "wb") as fp:
pickle.dump({
'exp_avg_metrics': exp_avg_metrics,
'exp_args': l_args
}, fp)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
tf.gfile.MakeDirs(FLAGS.train_logdir)
tf.logging.info('Training on %s set', FLAGS.train_split)
graph = tf.Graph()
with graph.as_default():
with tf.device(
tf.train.replica_device_setter(ps_tasks=FLAGS.num_ps_tasks)):
assert FLAGS.train_batch_size % FLAGS.num_clones == 0, (
'Training batch size not divisble by number of clones (GPUs).')
clone_batch_size = FLAGS.train_batch_size // FLAGS.num_clones
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=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=2,
is_training=True,
should_shuffle=True,
should_repeat=True)
train_tensor, summary_op = _train_deeplab_model(
dataset.get_one_shot_iterator(), dataset.num_of_classes,
dataset.ignore_label)
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
last_layers = model.get_extra_layer_scopes(
FLAGS.last_layers_contain_logits_only)
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)
scaffold = tf.train.Scaffold(
init_fn=init_fn,
summary_op=summary_op,
)
stop_hook = tf.train.StopAtStepHook(FLAGS.training_number_of_steps)
profile_dir = FLAGS.profile_logdir
if profile_dir is not None:
tf.gfile.MakeDirs(profile_dir)
with tf.contrib.tfprof.ProfileContext(enabled=profile_dir
is not None,
profile_dir=profile_dir):
with tf.train.MonitoredTrainingSession(
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
config=session_config,
scaffold=scaffold,
checkpoint_dir=FLAGS.train_logdir,
log_step_count_steps=FLAGS.log_steps,
save_summaries_steps=FLAGS.save_summaries_secs,
save_checkpoint_secs=FLAGS.save_interval_secs,
hooks=[stop_hook]) as sess:
while not sess.should_stop():
sess.run([train_tensor])
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.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)
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_of_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 = {}
# ============ Added by B.A.D. =====================
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)
# ==============================================
metric_map[predictions_tag] = tf.metrics.mean_iou(
predictions, labels, dataset.num_of_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)