def main(argv=()):
del argv # Unused.
eval_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train_bkp')
log_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name,
'eval_%s' % FLAGS.eval_set)
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
g = tf.Graph()
with g.as_default():
eval_params = FLAGS
eval_params.batch_size = 1
eval_params.step_size = FLAGS.num_views
###########
## model ##
###########
model = model_ptn.model_PTN(eval_params)
##########
## data ##
##########
eval_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
eval_params.eval_set,
eval_params.batch_size,
eval_params.image_size,
eval_params.vox_size,
is_training=False)
inputs = model.preprocess_with_all_views(eval_data)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(is_training=False, run_projection=False)
outputs = model_fn(inputs)
#############
## metrics ##
#############
names_to_values, names_to_updates = model.get_metrics(inputs, outputs)
del names_to_values
################
## evaluation ##
################
num_batches = eval_data['num_samples']
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=eval_dir,
logdir=log_dir,
num_evals=num_batches,
eval_op=names_to_updates.values(),
eval_interval_secs=FLAGS.eval_interval_secs)
def main(argv=()):
del argv # Unused.
eval_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
log_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name,
'eval_%s' % FLAGS.eval_set)
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
g = tf.Graph()
with g.as_default():
eval_params = FLAGS
eval_params.batch_size = 1
eval_params.step_size = FLAGS.num_views
###########
## model ##
###########
model = model_ptn.model_PTN(eval_params)
##########
## data ##
##########
eval_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
eval_params.eval_set,
eval_params.batch_size,
eval_params.image_size,
eval_params.vox_size,
is_training=False)
inputs = model.preprocess_with_all_views(eval_data)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(is_training=False, run_projection=False)
outputs = model_fn(inputs)
#############
## metrics ##
#############
names_to_values, names_to_updates = model.get_metrics(inputs, outputs)
del names_to_values
################
## evaluation ##
################
num_batches = eval_data['num_samples']
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=eval_dir,
logdir=log_dir,
num_evals=num_batches,
eval_op=names_to_updates.values(),
eval_interval_secs=FLAGS.eval_interval_secs)
def main(_):
train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name,
'train_bkp')
save_image_dir = os.path.join(train_dir, 'images')
if not os.path.exists(train_dir):
os.makedirs(train_dir)
if not os.path.exists(save_image_dir):
os.makedirs(save_image_dir)
g = tf.Graph()
with g.as_default():
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
global_step = slim.get_or_create_global_step()
###########
## model ##
###########
model = model_ptn.model_PTN(FLAGS)
##########
## data ##
##########
train_data = model.get_inputs(FLAGS.inp_dir,
FLAGS.dataset_name,
'train_bkp',
FLAGS.batch_size,
FLAGS.image_size,
FLAGS.vox_size,
is_training=True)
inputs = model.preprocess(train_data, FLAGS.step_size)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(is_training=True,
reuse=False,
run_projection=True)
outputs = model_fn(inputs)
##################
## train_scopes ##
##################
if FLAGS.init_model:
train_scopes = ['decoder']
init_scopes = ['encoder']
else:
train_scopes = ['encoder', 'decoder']
##########
## loss ##
##########
task_loss = model.get_loss(inputs, outputs)
regularization_loss = model.get_regularization_loss(train_scopes)
loss = task_loss + regularization_loss
###############
## optimizer ##
###############
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
if FLAGS.sync_replicas:
optimizer = tf.train.SyncReplicasOptimizer(
optimizer,
replicas_to_aggregate=FLAGS.workers_replicas -
FLAGS.backup_workers,
total_num_replicas=FLAGS.worker_replicas)
##############
## train_op ##
##############
train_op = model.get_train_op_for_scope(loss, optimizer,
train_scopes)
###########
## saver ##
###########
saver = tf.train.Saver(
max_to_keep=np.minimum(5, FLAGS.worker_replicas + 1))
if FLAGS.task == 0:
params = FLAGS
params.batch_size = params.num_views
params.step_size = 1
model.set_params(params)
val_data = model.get_inputs(params.inp_dir,
params.dataset_name,
'val',
params.batch_size,
params.image_size,
params.vox_size,
is_training=False)
val_inputs = model.preprocess(val_data, params.step_size)
# Note: don't compute loss here
reused_model_fn = model.get_model_fn(is_training=False,
reuse=True)
val_outputs = reused_model_fn(val_inputs)
with tf.device(tf.DeviceSpec(device_type='CPU')):
vis_input_images = val_inputs['images_1'] * 255.0
vis_gt_projs = (val_outputs['masks_1'] * (-1) + 1) * 255.0
vis_pred_projs = (val_outputs['projs_1'] *
(-1) + 1) * 255.0
vis_gt_projs = tf.concat([vis_gt_projs] * 3, axis=3)
vis_pred_projs = tf.concat([vis_pred_projs] * 3, axis=3)
# rescale
new_size = [FLAGS.image_size] * 2
vis_gt_projs = tf.image.resize_nearest_neighbor(
vis_gt_projs, new_size)
vis_pred_projs = tf.image.resize_nearest_neighbor(
vis_pred_projs, new_size)
# flip
# vis_gt_projs = utils.image_flipud(vis_gt_projs)
# vis_pred_projs = utils.image_flipud(vis_pred_projs)
# vis_gt_projs is of shape [batch, height, width, channels]
write_disk_op = model.write_disk_grid(
global_step=global_step,
log_dir=save_image_dir,
input_images=vis_input_images,
gt_projs=vis_gt_projs,
pred_projs=vis_pred_projs,
input_voxels=val_inputs['voxels'],
output_voxels=val_outputs['voxels_1'])
with tf.control_dependencies([write_disk_op]):
train_op = tf.identity(train_op)
#############
## init_fn ##
#############
if FLAGS.init_model:
init_fn = model.get_init_fn(init_scopes)
else:
init_fn = None
##############
## training ##
##############
slim.learning.train(train_op=train_op,
logdir=train_dir,
init_fn=init_fn,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
number_of_steps=FLAGS.max_number_of_steps,
saver=saver,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def main(_):
train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
save_image_dir = os.path.join(train_dir, 'images')
if not os.path.exists(train_dir):
os.makedirs(train_dir)
if not os.path.exists(save_image_dir):
os.makedirs(save_image_dir)
g = tf.Graph()
with g.as_default():
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
global_step = slim.get_or_create_global_step()
###########
## model ##
###########
model = model_ptn.model_PTN(FLAGS)
##########
## data ##
##########
train_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
'train',
FLAGS.batch_size,
FLAGS.image_size,
FLAGS.vox_size,
is_training=True)
inputs = model.preprocess(train_data, FLAGS.step_size)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(
is_training=True, reuse=False, run_projection=True)
outputs = model_fn(inputs)
##################
## train_scopes ##
##################
if FLAGS.init_model:
train_scopes = ['decoder']
init_scopes = ['encoder']
else:
train_scopes = ['encoder', 'decoder']
##########
## loss ##
##########
task_loss = model.get_loss(inputs, outputs)
regularization_loss = model.get_regularization_loss(train_scopes)
loss = task_loss + regularization_loss
###############
## optimizer ##
###############
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
if FLAGS.sync_replicas:
optimizer = tf.train.SyncReplicasOptimizer(
optimizer,
replicas_to_aggregate=FLAGS.workers_replicas - FLAGS.backup_workers,
total_num_replicas=FLAGS.worker_replicas)
##############
## train_op ##
##############
train_op = model.get_train_op_for_scope(loss, optimizer, train_scopes)
###########
## saver ##
###########
saver = tf.train.Saver(max_to_keep=np.minimum(5,
FLAGS.worker_replicas + 1))
if FLAGS.task == 0:
params = FLAGS
params.batch_size = params.num_views
params.step_size = 1
model.set_params(params)
val_data = model.get_inputs(
params.inp_dir,
params.dataset_name,
'val',
params.batch_size,
params.image_size,
params.vox_size,
is_training=False)
val_inputs = model.preprocess(val_data, params.step_size)
# Note: don't compute loss here
reused_model_fn = model.get_model_fn(is_training=False, reuse=True)
val_outputs = reused_model_fn(val_inputs)
with tf.device(tf.DeviceSpec(device_type='CPU')):
vis_input_images = val_inputs['images_1'] * 255.0
vis_gt_projs = (val_outputs['masks_1'] * (-1) + 1) * 255.0
vis_pred_projs = (val_outputs['projs_1'] * (-1) + 1) * 255.0
vis_gt_projs = tf.concat([vis_gt_projs] * 3, axis=3)
vis_pred_projs = tf.concat([vis_pred_projs] * 3, axis=3)
# rescale
new_size = [FLAGS.image_size] * 2
vis_gt_projs = tf.image.resize_nearest_neighbor(
vis_gt_projs, new_size)
vis_pred_projs = tf.image.resize_nearest_neighbor(
vis_pred_projs, new_size)
# flip
# vis_gt_projs = utils.image_flipud(vis_gt_projs)
# vis_pred_projs = utils.image_flipud(vis_pred_projs)
# vis_gt_projs is of shape [batch, height, width, channels]
write_disk_op = model.write_disk_grid(
global_step=global_step,
log_dir=save_image_dir,
input_images=vis_input_images,
gt_projs=vis_gt_projs,
pred_projs=vis_pred_projs,
input_voxels=val_inputs['voxels'],
output_voxels=val_outputs['voxels_1'])
with tf.control_dependencies([write_disk_op]):
train_op = tf.identity(train_op)
#############
## init_fn ##
#############
if FLAGS.init_model:
init_fn = model.get_init_fn(init_scopes)
else:
init_fn = None
##############
## training ##
##############
slim.learning.train(
train_op=train_op,
logdir=train_dir,
init_fn=init_fn,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
number_of_steps=FLAGS.max_number_of_steps,
saver=saver,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def main(argv=()):
del argv # Unused.
#eval_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
#log_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name,
# 'eval_%s' % FLAGS.eval_set)
#if not os.path.exists(eval_dir):
# os.makedirs(eval_dir)
#if not os.path.exists(log_dir):
# os.makedirs(log_dir)
g = tf.Graph()
with g.as_default():
eval_params = FLAGS
eval_params.batch_size = 1
eval_params.step_size = FLAGS.num_views
###########
## model ##
###########
model = model_ptn.model_PTN(eval_params)
##########
## data ##
##########
eval_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
eval_params.eval_set,
eval_params.batch_size,
eval_params.image_size,
eval_params.vox_size,
is_training=False)
inputs = model.preprocess_with_all_views(eval_data)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(is_training=False, run_projection=False)
outputs = model_fn(inputs)
#############
## metrics ##
#############
names_to_values, names_to_updates = model.get_metrics(inputs, outputs)
del names_to_values
################
## evaluation ##
################
num_batches = eval_data['num_samples']
sess = tf.Session()
tf.train.start_queue_runners(sess=sess)
saver = tf.train.Saver()
def restore_from_checkpoint(sess, saver):
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not ckpt or not ckpt.model_checkpoint_path:
return False
saver.restore(sess, ckpt.model_checkpoint_path)
return True
if not restore_from_checkpoint(sess, saver):
raise NotImplementedError
init = tf.global_variables_initializer()
sess.run(init)
init = tf.local_variables_initializer()
sess.run(init)
for i in range(num_batches):
print('Running {} batch out of {} batches.'.format(i, num_batches))
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(list(names_to_updates.values()), options=options,
run_metadata=run_metadata)
cg = CompGraph('ptn', run_metadata, tf.get_default_graph())
cg_tensor_dict = cg.get_tensors()
cg_sorted_keys = sorted(cg_tensor_dict.keys())
cg_sorted_items = []
for cg_key in cg_sorted_keys:
cg_sorted_items.append(tf.shape(cg_tensor_dict[cg_key]))
cg_sorted_shape = sess.run(cg_sorted_items)
cg.op_analysis(dict(zip(cg_sorted_keys, cg_sorted_shape)),
'ptn.pickle')
exit(0)