def _create_image_encoder(preprocess_fn, factory_fn, image_shape, batch_size=32,
session=None, checkpoint_path=None,
loss_mode="cosine"):
image_var = tf.placeholder(tf.uint8, (None, ) + image_shape)
preprocessed_image_var = tf.map_fn(
lambda x: preprocess_fn(x, is_training=False),
tf.cast(image_var, tf.float32))
l2_normalize = loss_mode == "cosine"
feature_var, _ = factory_fn(
preprocessed_image_var, l2_normalize=l2_normalize, reuse=None)
feature_dim = feature_var.get_shape().as_list()[-1]
if session is None:
session = tf.Session()
if checkpoint_path is not None:
slim.get_or_create_global_step()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, slim.get_variables_to_restore())
session.run(init_assign_op, feed_dict=init_feed_dict)
def encoder(data_x):
out = np.zeros((len(data_x), feature_dim), np.float32)
_run_in_batches(
lambda x: session.run(feature_var, feed_dict=x),
{image_var: data_x}, out, batch_size)
return out
return encoder
def load_ckpt(self, sess, ckpt='ckpts/vgg_16.ckpt'):
variables = slim.get_variables(scope='vgg_16',
suffix="weights") + slim.get_variables(
scope='vgg_16', suffix="biases")
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
ckpt, variables)
sess.run(init_assign_op, init_feed_dict)
def load_ckpt_path(sess, model_path, variables_to_restore=None):
if variables_to_restore is None:
variables_to_restore = slim.get_variables_to_restore()
restore_op, restore_fd = slim.assign_from_checkpoint(
model_path, variables_to_restore)
sess.run(restore_op, feed_dict=restore_fd)
print(f'{model_path} loaded')
def _create_image_encoder(preprocess_fn,
factory_fn,
image_shape,
batch_size=32,
session=None,
checkpoint_path=None,
loss_mode="cosine"):
image_var = tf.placeholder(tf.uint8, (None, ) + image_shape)
preprocessed_image_var = tf.map_fn(
lambda x: preprocess_fn(x, is_training=False),
tf.cast(image_var, tf.float32))
l2_normalize = loss_mode == "cosine"
feature_var, _ = factory_fn(preprocessed_image_var,
l2_normalize=l2_normalize,
reuse=None)
feature_dim = feature_var.get_shape().as_list()[-1]
if session is None:
session = tf.Session()
if checkpoint_path is not None:
#slim.get_or_create_global_step() ----------------------------------------------------------
tf.train.get_or_create_global_step()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, slim.get_variables_to_restore())
session.run(init_assign_op, feed_dict=init_feed_dict)
def encoder(data_x):
out = np.zeros((len(data_x), feature_dim), np.float32)
_run_in_batches(lambda x: session.run(feature_var, feed_dict=x),
{image_var: data_x}, out, batch_size)
return out
return encoder
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store {} weights from {}'.format(
len(variables), checkpoint))
if not variables:
logging.error('can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store %d weights from %s',
len(variables), checkpoint)
if not variables:
logging.error('Can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
def load_ckpt(sess, model_dir, variables_to_restore=None):
ckpt = tf.train.get_checkpoint_state(model_dir)
model_path = ckpt.model_checkpoint_path
if variables_to_restore is None:
variables_to_restore = slim.get_variables_to_restore()
restore_op, restore_fd = slim.assign_from_checkpoint(
model_path, variables_to_restore)
sess.run(restore_op, feed_dict=restore_fd)
print(f'{model_path} loaded')
def restore_discriminator(self, params):
d_vars = [var.name for var in self.dsc_vars]
variables = slim.get_variables_to_restore(include=d_vars)
print("variables_dsc: ", variables)
path = params.encoder_checkpoint_name if not self.isIdeRun else "../checkpoints/exp70/checkpoint/DCGAN.model-50"
print('restoring discriminator to [%s]...' % path)
init_restore_op, init_feed_dict = slim.assign_from_checkpoint(model_path=path, var_list=variables)
self.sess.run(init_restore_op, feed_dict=init_feed_dict)
print('discriminator restored.')
def restore_encoder(self, params):
enc_vars = [var.name for var in self.gen_vars if 'g_1' in var.name]
variables = slim.get_variables_to_restore(include=enc_vars)
# print("variables1: ", variables)
path = params.encoder_checkpoint_name if not self.isIdeRun else "../checkpoints/exp70/checkpoint/DCGAN.model-50"
print('restoring encoder to [%s]...' % path)
init_restore_op, init_feed_dict = slim.assign_from_checkpoint(model_path=path, var_list=variables)
self.sess.run(init_restore_op, feed_dict=init_feed_dict)
print('encoder restored.')
def restore_alexnet(self, chkp_name):
al_var_names = [var.name for var in self.an_vars]
variables = slim.get_variables_to_restore(include=al_var_names)
# print("variables1: ", variables)
path = chkp_name if not self.isIdeRun else "../checkpoints/exp70/checkpoint/DCGAN.model-50"
print('restoring alexnet from [%s]...' % path)
init_restore_op, init_feed_dict = slim.assign_from_checkpoint(model_path=path, var_list=variables)
self.sess.run(init_restore_op, feed_dict=init_feed_dict)
print('alexnet restored.')
def get_init_fn():
checkpoint_exclude_scopes=["InceptionV1/Logits", "InceptionV1/AuxLogits", "InceptionV2"]
exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]
print(exclusions)
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
checkpoint_exclude_scopes=["InceptionV2/Logits", "InceptionV2/AuxLogits", "InceptionV1"]
exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]
print(exclusions)
variables_to_restore_2 = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore_2.append(var)
report_init_assign_op, report_init_feed_dict = slim.assign_from_checkpoint(
os.path.join(report_inception_network_dir, 'inception_v1.ckpt'), variables_to_restore_2, ignore_missing_vars=True)
satelite_init_assign_op, satelite_init_feed_dict = slim.assign_from_checkpoint(
os.path.join(satelite_inception_network_dir, 'inception_v1.ckpt'), variables_to_restore, ignore_missing_vars=True)
def init_fn(sess):
sess.run(report_init_assign_op, report_init_feed_dict)
sess.run(satelite_init_assign_op, satelite_init_feed_dict)
return init_fn
def main(_):
tf.reset_default_graph()
env = environment.get_game_environment(FLAGS.maps,
multiproc=FLAGS.multiproc,
random_goal=FLAGS.random_goal,
random_spawn=FLAGS.random_spawn,
apple_prob=FLAGS.apple_prob)
exp = expert.Expert()
net = CMAP(num_iterations=FLAGS.vin_iterations,
estimate_scale=FLAGS.estimate_scale,
unified_fuser=FLAGS.unified_fuser,
unified_vin=FLAGS.unified_vin,
biased_fuser=FLAGS.biased_fuser,
biased_vin=FLAGS.biased_vin,
regularization=FLAGS.reg)
estimate_images = [estimate[0, -1, :, :, 0] for estimate in net.intermediate_tensors['estimate_map_list']]
goal_images = [goal[0, -1, :, :, 0] for goal in net.intermediate_tensors['goal_map_list']]
reward_images = [reward[0, -1, :, :, 0] for reward in net.intermediate_tensors['reward_map_list']]
value_images = [value[0, -1, :, :, 0] for value in net.intermediate_tensors['value_map_list']]
action_images = [action[0, -1, :, :, 0] for action in net.intermediate_tensors['action_map_list']]
step_history = tf.placeholder(tf.string, name='step_history')
step_history_op = tf.summary.text('game/step_history', step_history, collections=['game'])
global_step = slim.get_or_create_global_step()
update_global_step_op = tf.assign_add(global_step, 1)
init_op = tf.variables_initializer([global_step])
load_op, load_feed_dict = slim.assign_from_checkpoint(FLAGS.modeldir,
slim.get_variables_to_restore(exclude=[global_step.name]))
init_op = tf.group(init_op, load_op)
slim.learning.train(train_op=tf.no_op('train'),
logdir=FLAGS.logdir,
init_op=init_op,
init_feed_dict=load_feed_dict,
global_step=global_step,
train_step_fn=DAGGER_train_step,
train_step_kwargs=dict(env=env, exp=exp, net=net,
update_global_step_op=update_global_step_op,
step_history=step_history,
step_history_op=step_history_op,
estimate_maps=estimate_images,
goal_maps=goal_images,
reward_maps=reward_images,
value_maps=value_images,
action_maps=action_images),
number_of_steps=FLAGS.num_games,
save_interval_secs=300 if not FLAGS.debug else 60,
save_summaries_secs=300 if not FLAGS.debug else 60)
def initialize(self, checkpoint_path=None):
"""Overwrite default to make lazy"""
init_op = tf.variables_initializer(self.get_init_variables())
self.session.run(init_op)
if checkpoint_path is not None:
self.set_global_step(
RestoreTFModelHook.parse_global_step(checkpoint_path))
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path,
self.get_restore_variables(),
ignore_missing_vars=True)
self.session.run(init_assign_op, feed_dict=init_feed_dict)
self.logger.info("Lazily restored from {}".format(checkpoint_path))
def load(self, sess):
model_saver = self.get_saver()
ckpt = tf.train.latest_checkpoint(str(self.exp_dir),
latest_filename='%s_ckpt' %
self.scope)
if ckpt is None:
print('[ %s ] No ckpt found...' % self.scope)
return
print('Loading %s' % str(ckpt))
init_op, init_feed = slim.assign_from_checkpoint(
model_path=ckpt, var_list=self.vars(), ignore_missing_vars=True)
sess.run(init_op, init_feed)
# model_saver.restore(sess, ckpt)
return
def init_points(sess):
if Flags.net == 'D':
restore = slim.get_model_variables('train/densenet161')
restore = {change_checkpoint_name(var): var for var in restore}
init_points_dense_op, init_points_dense_feed_dict = slim.assign_from_checkpoint(
os.path.join(Flags.checkpoint_dir, 'tf-densenet161.ckpt'),
restore)
sess.run(init_points_dense_op, init_points_dense_feed_dict)
else:
restore = slim.get_model_variables('train/vgg_16')
restore = {
change_checkpoint_name3(var): var
for var in restore
}
init_points_vgg_op, init_points_vgg_feed_dict = slim.assign_from_checkpoint(
os.path.join(Flags.checkpoint_dir, 'vgg_16.ckpt'), restore)
sess.run(init_points_vgg_op, init_points_vgg_feed_dict)
def testTrainWithInitFromCheckpoint(self):
logdir1 = os.path.join(self.get_temp_dir(), 'tmp_logs1/')
logdir2 = os.path.join(self.get_temp_dir(), 'tmp_logs2/')
if tf.gfile.Exists(logdir1): # For running on jenkins.
tf.gfile.DeleteRecursively(logdir1)
if tf.gfile.Exists(logdir2): # For running on jenkins.
tf.gfile.DeleteRecursively(logdir2)
# First, train the model one step (make sure the error is high).
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
train_op = self.create_train_op()
loss = slim.learning.train(train_op, logdir1, number_of_steps=1)
self.assertGreater(loss, .5)
# Next, train the model to convergence.
g = tf.Graph()
with g.as_default():
tf.set_random_seed(1)
train_op = self.create_train_op()
loss = slim.learning.train(train_op, logdir1, number_of_steps=300)
self.assertLess(loss, .02)
# Finally, advance the model a single step and validate that the loss is
# still low.
g = tf.Graph()
with g.as_default():
tf.set_random_seed(2)
train_op = self.create_train_op()
model_variables = tf.all_variables()
model_path = os.path.join(logdir1, 'model.ckpt-300')
init_op = tf.initialize_all_variables()
op, init_feed_dict = slim.assign_from_checkpoint(
model_path, model_variables)
def InitAssignFn(sess):
sess.run(op, init_feed_dict)
loss = slim.learning.train(train_op,
logdir2,
number_of_steps=1,
init_op=init_op,
init_fn=InitAssignFn)
self.assertLess(loss, .02)
def make_init_fn(self, chpt_path):
# Handle model initialization from prior checkpoint
if chpt_path is None:
return None
var2restore = slim.get_variables_to_restore(exclude=self.exclude_scopes)
print('Variables to restore: {}'.format([v.op.name for v in var2restore]))
var2restore = remove_missing(var2restore, chpt_path)
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(chpt_path, var2restore)
sys.stdout.flush()
# Create an initial assignment function.
def init_fn(sess):
print('Restoring from: {}'.format(chpt_path))
sess.run(init_assign_op, init_feed_dict)
return init_fn
def main(unused_argv):
if not FLAGS.input_file_pattern:
raise ValueError("--input_file_pattern is required.")
if not FLAGS.train_dir:
raise ValueError("--train_dir is required.")
with open(FLAGS.model_config) as json_config_file:
model_config = json.load(json_config_file)
model_config = configuration.model_config(model_config, mode="train")
tf.logging.info("Building training graph.")
g = tf.Graph()
with g.as_default():
model = s2v_model.s2v(model_config, mode="train")
model.build()
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
train_tensor = tf.contrib.slim.learning.create_train_op(
total_loss=model.total_loss,
optimizer=optimizer,
clip_gradient_norm=FLAGS.clip_gradient_norm)
saver = tf.train.Saver(max_to_keep=FLAGS.max_ckpts)
checkpoint_path = model_config.checkpoint_path
variables_to_restore = slim.get_model_variables()
checkpoint_path = tf.train.latest_checkpoint(model_config.checkpoint_path)
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, variables_to_restore)
def InitAssignFn(sess):
sess.run(init_assign_op, init_feed_dict)
nsteps = int(FLAGS.nepochs * (FLAGS.num_train_inst / FLAGS.batch_size))
slim.learning.train(
train_op=train_tensor,
logdir=FLAGS.train_dir,
graph=g,
number_of_steps=nsteps,
save_summaries_secs=FLAGS.save_summaries_secs,
saver=saver,
save_interval_secs=FLAGS.save_model_secs,
init_fn=InitAssignFn
)
def _create_encoder(preprocess_fn,
network_factory,
image_shape,
batch_size=32,
session=None,
checkpoint_path=None,
read_from_file=False):
if read_from_file:
num_channels = image_shape[-1] if len(image_shape) == 3 else 1
input_var = tf.placeholder(tf.string, (None, ))
image_var = tf.map_fn(lambda x: tf.image.decode_jpeg(
tf.read_file(x), channels=num_channels),
input_var,
back_prop=False,
dtype=tf.uint8)
image_var = tf.image.resize_images(image_var, image_shape[:2])
else:
input_var = tf.placeholder(tf.uint8, (None, ) + image_shape)
image_var = input_var
preprocessed_image_var = tf.map_fn(
lambda x: preprocess_fn(x, is_training=False),
image_var,
back_prop=False,
dtype=tf.float32)
feature_var, _ = network_factory(preprocessed_image_var)
feature_dim = feature_var.get_shape().as_list()[-1]
if session is None:
session = tf.Session()
if checkpoint_path is not None:
tf.train.get_or_create_global_step()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, slim.get_model_variables())
session.run(init_assign_op, feed_dict=init_feed_dict)
def encoder(data_x):
out = np.zeros((len(data_x), feature_dim), np.float32)
queued_trainer.run_in_batches(
lambda x: session.run(feature_var, feed_dict=x),
{input_var: data_x}, out, batch_size)
return out
return encoder
def testTrainWithInitFromCheckpoint(self):
logdir1 = os.path.join(self.get_temp_dir(), "tmp_logs1/")
logdir2 = os.path.join(self.get_temp_dir(), "tmp_logs2/")
if tf.gfile.Exists(logdir1): # For running on jenkins.
tf.gfile.DeleteRecursively(logdir1)
if tf.gfile.Exists(logdir2): # For running on jenkins.
tf.gfile.DeleteRecursively(logdir2)
# First, train the model one step (make sure the error is high).
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
train_op = self.create_train_op()
loss = slim.learning.train(train_op, logdir1, number_of_steps=1)
self.assertGreater(loss, 0.5)
# Next, train the model to convergence.
g = tf.Graph()
with g.as_default():
tf.set_random_seed(1)
train_op = self.create_train_op()
loss = slim.learning.train(train_op, logdir1, number_of_steps=300)
self.assertLess(loss, 0.02)
# Finally, advance the model a single step and validate that the loss is
# still low.
g = tf.Graph()
with g.as_default():
tf.set_random_seed(2)
train_op = self.create_train_op()
model_variables = tf.all_variables()
model_path = os.path.join(logdir1, "model.ckpt-300")
init_op = tf.initialize_all_variables()
op, init_feed_dict = slim.assign_from_checkpoint(model_path, model_variables)
def InitAssignFn(sess):
sess.run(op, init_feed_dict)
loss = slim.learning.train(train_op, logdir2, number_of_steps=1, init_op=init_op, init_fn=InitAssignFn)
self.assertLess(loss, 0.02)
def val(config):
val_dataset = Dataset(os.path.join(config['input']['path']))
with tf.Graph().as_default():
with tf.name_scope('val') as scope:
val_loss, val_accuracy, val_summary = build_val_graph(config, val_dataset)
exclude = cnn_architectures.model_weight_excludes(config['model']['architecture'])
variables_to_restore = slim.get_variables_to_restore()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(config['model']['checkpoint'],
variables_to_restore)
# initialize
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(init_assign_op, init_feed_dict)
# Start the queue runners.
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess=sess, coord=coord)
print('graph built')
com_acc = 0.0
com_loss = 0.0
count = 0
for x in range(val_dataset.num_images() // config['parameters']['batch_size'] + 1):
acc_v, loss_v = sess.run([val_accuracy, val_loss])
com_acc += acc_v
com_loss += loss_v
count += 1
print('validation loss: {} validation_accuracy: {}'.format(com_loss / count, com_acc / count))
logging.info('accuracy = {}, loss = {}'.format(acc_v, loss_v))
def load_model(self, dirname):
self.init()
# Try to load the model from the given directory
latest_checkpoint = tf.train.latest_checkpoint(dirname)
# If no model available, append current model's scoped name
if latest_checkpoint is None:
dirname = os.path.join(dirname, self.scoped_name)
latest_checkpoint = tf.train.latest_checkpoint(dirname)
if latest_checkpoint is None:
raise RuntimeError(
'Model checkpoint not found at {}'.format(dirname))
with self.graph.as_default():
# Use the slim package to load the checkpoint - this gives a chance to ignore missing variables
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
latest_checkpoint, self.parameters, ignore_missing_vars=True)
self.sess.run(init_assign_op, feed_dict=init_feed_dict)
self.is_initialized = True
self.reset_performance_stats()
def train(args):
model = AppearanceNetwork(args)
save_directory = './save/'
log_file_path = './training.log'
log_file = open(log_file_path, 'w')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Graph().as_default():
global_step = tf.Variable(0, name='global_step', trainable=False)
image_patches_placeholder = tf.placeholder(
tf.float32, shape=[args.batch_size, 7, 128, 64, 3])
labels_placeholder = tf.placeholder(tf.float32,
shape=[args.batch_size])
lr = tf.Variable(args.base_learning_rate,
trainable=False,
name="learning_rate")
features, logits = model.inference(image_patches_placeholder)
loss = model.cross_entropy_loss(logits, labels_placeholder)
train_op = build_graph(args, global_step, lr, loss)
sess = tf.Session()
saver = tf.train.Saver(max_to_keep=100)
ckpt = tf.train.get_checkpoint_state('./save')
if ckpt is None:
init = tf.global_variables_initializer()
sess.run(init)
if args.pretrained_ckpt_path is not None:
# slim.get_or_create_global_step()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
args.pretrained_ckpt_path,
slim.get_variables_to_restore(exclude=[
"lstm", "fc_layer", "loss", "learning_rate", "softmax",
"global_step"
]))
sess.run(init_assign_op, feed_dict=init_feed_dict)
else:
print 'Loading Model from ' + ckpt.model_checkpoint_path
saver.restore(sess, ckpt.model_checkpoint_path)
best_epoch = -1
best_loss_epoch = 0.0
for curr_epoch in range(args.num_epoches):
training_loss_epoch = 0.0
valid_loss_epoch = 0.0
############################################# Training process ######################################
print 'Training epoch ' + str(curr_epoch +
1) + '........................'
training_data_loader = DataLoader(is_valid=False)
if curr_epoch % 10 == 0:
sess.run(
tf.assign(
lr,
args.base_learning_rate *
(args.decay_rate**curr_epoch / 10)))
training_data_loader.shuffle()
training_data_loader.reset_pointer()
for step in range(training_data_loader.num_batches):
start_time = time.time()
image_patches, labels = training_data_loader.next_batch()
_, loss_batch = sess.run(
[train_op, loss],
feed_dict={
image_patches_placeholder: image_patches,
labels_placeholder: labels
})
end_time = time.time()
training_loss_epoch += loss_batch
print(
"Training {}/{} (epoch {}), train_loss = {:.8f}, time/batch = {:.3f}"
.format(step + 1, training_data_loader.num_batches,
curr_epoch + 1, loss_batch, end_time - start_time))
print 'Epoch ' + str(curr_epoch +
1) + ' training is done! Saving model...'
checkpoint_path = os.path.join(save_directory, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=global_step)
############################################# Validating process ######################################
print 'Validating epoch ' + str(curr_epoch +
1) + '...........................'
valid_data_loader = DataLoader(is_valid=True)
valid_data_loader.shuffle()
valid_data_loader.reset_pointer()
for step in range(valid_data_loader.num_batches):
start_time = time.time()
image_patches, labels = valid_data_loader.next_batch()
loss_batch = sess.run(loss,
feed_dict={
image_patches_placeholder:
image_patches,
labels_placeholder: labels
})
end_time = time.time()
valid_loss_epoch += loss_batch
print(
"Validating {}/{} (epoch {}), valid_loss = {:.8f}, time/batch = {:.3f}"
.format(step + 1, valid_data_loader.num_batches,
curr_epoch + 1, loss_batch, end_time - start_time))
# Update best valid epoch
if best_epoch == -1 or best_loss_epoch > valid_loss_epoch:
best_epoch = curr_epoch + 1
best_loss_epoch = valid_loss_epoch
log_file.write('epoch ' + str(curr_epoch + 1) + '\n')
log_file.write(
str(curr_epoch + 1) + ',' + str(training_loss_epoch) + '\n')
log_file.write(
str(curr_epoch + 1) + ',' + str(valid_loss_epoch) + '\n')
log_file.write(str(best_epoch) + ',' + str(best_loss_epoch) + '\n')
log_file.close()
def main():
model = config.get('config', 'model')
logdir = utils.get_logdir(config)
if args.delete:
tf.logging.warn('delete logging directory: ' + logdir)
shutil.rmtree(logdir, ignore_errors=True)
cachedir = utils.get_cachedir(config)
with open(os.path.join(cachedir, 'names'), 'r') as f:
names = [line.strip() for line in f]
width = config.getint(model, 'width')
height = config.getint(model, 'height')
cell_width, cell_height = utils.calc_cell_width_height(config, width, height)
tf.logging.warn('(width, height)=(%d, %d), (cell_width, cell_height)=(%d, %d)' % (width, height, cell_width, cell_height))
yolo = importlib.import_module('model.' + model)
paths = [os.path.join(cachedir, profile + '.tfrecord') for profile in args.profile]
num_examples = sum(sum(1 for _ in tf.python_io.tf_record_iterator(path)) for path in paths)
tf.logging.warn('num_examples=%d' % num_examples)
with tf.name_scope('batch'):
image_rgb, labels = utils.data.load_image_labels(paths, len(names), width, height, cell_width, cell_height, config)
with tf.name_scope('per_image_standardization'):
image_std = tf.image.per_image_standardization(image_rgb)
batch = tf.train.shuffle_batch((image_std,) + labels, batch_size=args.batch_size,
capacity=config.getint('queue', 'capacity'), min_after_dequeue=config.getint('queue', 'min_after_dequeue'),
num_threads=multiprocessing.cpu_count()
)
global_step = tf.contrib.framework.get_or_create_global_step()
builder = yolo.Builder(args, config)
builder(batch[0], training=True)
with tf.name_scope('total_loss') as name:
builder.create_objectives(batch[1:])
total_loss = tf.losses.get_total_loss(name=name)
variables_to_restore = slim.get_variables_to_restore(exclude=args.exclude)
with tf.name_scope('optimizer'):
try:
decay_steps = config.getint('exponential_decay', 'decay_steps')
decay_rate = config.getfloat('exponential_decay', 'decay_rate')
staircase = config.getboolean('exponential_decay', 'staircase')
learning_rate = tf.train.exponential_decay(args.learning_rate, global_step, decay_steps, decay_rate, staircase=staircase)
tf.logging.warn('using a learning rate start from %f with exponential decay (decay_steps=%d, decay_rate=%f, staircase=%d)' % (args.learning_rate, decay_steps, decay_rate, staircase))
except (configparser.NoSectionError, configparser.NoOptionError):
learning_rate = args.learning_rate
tf.logging.warn('using a staionary learning rate %f' % args.learning_rate)
optimizer = get_optimizer(config, args.optimizer)(learning_rate)
tf.logging.warn('optimizer=' + args.optimizer)
train_op = slim.learning.create_train_op(total_loss, optimizer, global_step,
clip_gradient_norm=args.gradient_clip, summarize_gradients=config.getboolean('summary', 'gradients'),
)
if args.transfer:
path = os.path.expanduser(os.path.expandvars(args.transfer))
tf.logging.warn('transferring from ' + path)
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(path, variables_to_restore)
def init_fn(sess):
sess.run(init_assign_op, init_feed_dict)
tf.logging.warn('transferring from global_step=%d, learning_rate=%f' % sess.run((global_step, learning_rate)))
else:
init_fn = lambda sess: tf.logging.warn('global_step=%d, learning_rate=%f' % sess.run((global_step, learning_rate)))
summary(config)
tf.logging.warn('tensorboard --logdir ' + logdir)
slim.learning.train(train_op, logdir, master=args.master, is_chief=(args.task == 0),
global_step=global_step, number_of_steps=args.steps, init_fn=init_fn,
summary_writer=tf.summary.FileWriter(os.path.join(logdir, args.logname)),
save_summaries_secs=args.summary_secs, save_interval_secs=args.save_secs
)
def train():
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
pp = pprint.PrettyPrinter()
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(opt.checkpoint_dir):
os.makedirs(opt.checkpoint_dir)
with tf.Graph().as_default():
# Data Loader
loader = DataLoader(opt)
tgt_image, src_image_stack, intrinsics = loader.load_train_batch()
#print(tgt_image.shape, src_image_stack.shape)
# Build Model
model = GeoNetModel(opt, tgt_image, src_image_stack, intrinsics)
loss = model.total_loss
#mask = {}
#mask["mask"] = model.tgt_image_tile_pyramid[0][0:,:,3:4]
#mask["target"] = model.tgt_image
# Train Op
summary_mask = tf.summary.image('v1_mask',
model.dispnet_inputs_mask[:1, :, :, :],
1)
#summary_mask_swell = tf.summary.image('v1_mask_swell', model.dispnet_inputs_mask_swell[:1,:,:,:], 1)
summary_fwd_tgt_ignore = tf.summary.image(
'v1_fwd_tgt_ignore', model.fwd_tgt_ignore[0][:1, :, :, :], 1)
summary_bwd_src_ignore = tf.summary.image(
'v1_bwd_src_ignore', model.bwd_src_ignore[0][:1, :, :, :], 1)
summary_fwd_tgt_ignore_full = tf.summary.image(
'v1_fwd_tgt_ignore_full',
model.fwd_tgt_ignore_full[0][:1, :, :, :], 1)
summary_bwd_src_ignore_full = tf.summary.image(
'v1_bwd_src_ignore_full',
model.bwd_src_ignore_full[0][:1, :, :, :], 1)
summary_img = tf.summary.image('v1_warp_img',
model.tgt_image[:1, :, :, :3], 3)
summary_depth = tf.summary.image('v1_depth', model.pred_depth[0][:1],
1)
summary_rigid_fwd = tf.summary.image(
'v1_rigid_fwd',
tf.concat([
model.fwd_rigid_flow_origin_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_rigid_bwd = tf.summary.image(
'v1_rigid_bwd',
tf.concat([
model.bwd_rigid_flow_origin_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_flow_fwd = tf.summary.image(
'v1_rflow_fwd',
tf.concat([
model.fwd_rigid_flow_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_flow_bwd = tf.summary.image(
'v1_rflow_bwd',
tf.concat([
model.bwd_rigid_flow_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_res_fwd = tf.summary.image(
'v1_res_fwd',
tf.concat([
model.fwd_res_flow_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_res_bwd = tf.summary.image(
'v1_res_bwd',
tf.concat([
model.bwd_res_flow_pyramid[0][4:5],
tf.zeros((1, 128, 416, 1))
], 3), 3)
summary_warp_fwd = tf.summary.image(
'v1_warp_fwd', model.fwd_rigid_warp_pyramid[0][4:5, :, :, :3], 3)
summary_warp_bwd = tf.summary.image(
'v1_warp_bwd', model.fwd_rigid_warp_pyramid[0][:1, :, :, :3], 3)
summary_rigid_warp_loss = tf.summary.scalar('v1_rigid_warp_loss',
model.rigid_warp_loss)
summary_disp_smooth_loss = tf.summary.scalar('v1_disp_smooth_loss',
model.disp_smooth_loss)
summary_flow_warp_loss = tf.summary.scalar('v1_flow_warp_loss',
model.flow_warp_loss)
summary_flow_smooth_loss = tf.summary.scalar('v1_flow_smooth_loss',
model.flow_smooth_loss)
summary_rigid_smooth_loss = tf.summary.scalar('v1_rigid_smooth_loss',
model.rigid_smooth_loss)
#summary_depth_constraint_loss = tf.summary.scalar('v1_depth_constraint_loss', model.depth_constraint_loss)
summary_flow_consistency_loss = tf.summary.scalar(
'v1_flow_consistency_loss', model.flow_consistency_loss)
summary_rigid_consistency_loss = tf.summary.scalar(
'v1_rigid_consistency_loss', model.rigid_consistency_loss)
merged_summary = tf.summary.merge_all()
if opt.mode == 'train_flow' and opt.flownet_type == "residual":
# we pretrain DepthNet & PoseNet, then finetune ResFlowNetS
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"flow_net")
vars_to_restore = slim.get_variables_to_restore(
include=["depth_net", "pose_net"])
else:
train_vars = [var for var in tf.trainable_variables()]
vars_to_restore = slim.get_model_variables()
if opt.init_ckpt_file != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
opt.init_ckpt_file, vars_to_restore)
optim = tf.train.AdamOptimizer(opt.learning_rate, 0.9)
train_op = slim.learning.create_train_op(loss,
optim,
variables_to_train=train_vars)
# Global Step
global_step = tf.Variable(0, name='global_step', trainable=False)
incr_global_step = tf.assign(global_step, global_step + 1)
# Parameter Count
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) \
for v in train_vars])
# Saver
saver = tf.train.Saver([var for var in tf.model_variables()] + \
[global_step],
max_to_keep=opt.max_to_keep)
# Session
sv = tf.train.Supervisor(logdir=opt.checkpoint_dir,
save_summaries_secs=0,
saver=None)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with sv.managed_session(config=config) as sess:
writer = tf.summary.FileWriter('../logs_v1', sess.graph)
print('Trainable variables: ')
for var in train_vars:
print(var.name)
print(var.shape)
print("parameter_count =", sess.run(parameter_count))
if opt.init_ckpt_file != None:
sess.run(init_assign_op, init_feed_dict)
start_time = time.time()
for step in range(1, opt.max_steps):
fetches = {
"train": train_op,
"global_step": global_step,
"incr_global_step": incr_global_step,
#"input": model.tgt_image
}
if step % 10 == 0:
mysum = sess.run(merged_summary)
writer.add_summary(mysum, step)
writer.flush()
if step % 100 == 0:
fetches["loss"] = loss
results = sess.run(fetches)
#aaaa = np.array(sess.run(mask)["target"])
#print(results["input"][0,:,:,3:4])
#print aaaa[0,100:,:50,3]
#print aaaa[0,100:,:50,3].max()
#print aaaa[0,100:,:50,3].min()
#print aaaa[0,100:,:50,3].shape
if step % 100 == 0:
time_per_iter = (time.time() - start_time) / 100
start_time = time.time()
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f' \
% (step, time_per_iter, results["loss"]))
if step % opt.save_ckpt_freq == 0:
saver.save(sess,
os.path.join(opt.checkpoint_dir, 'model'),
global_step=step)
def train():
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
if not os.path.exists(opt.checkpoint_dir):
os.makedirs(opt.checkpoint_dir)
with tf.Graph().as_default():
global_step = tf.Variable(0, name='global_step', trainable=False)
incr_global_step = tf.assign(global_step, global_step + 1)
optim = tf.train.AdamOptimizer(opt.learning_rate, 0.9)
loader = DataLoader(opt)
losses = []
img_losses = []
rigid_warp_losses = []
disp_smooth_losses = []
sem_losses = []
sem_warp_losses = []
sem_mask_losses = []
sem_edge_losses = []
sem_seg_losses = []
ins0_seg_losses = []
ins1_edge_seg_losses = []
ins_losses = []
with tf.variable_scope(tf.get_variable_scope()):
for i in range(opt.num_gpus):
with tf.device('/gpu:{:d}'.format(i)):
with tf.name_scope('gpu{:d}'.format(i)):
# Get images batch from data loader
tgt_image, src_image_stack, intrinsics, tgt_sem_tuple, src_sem_stack_tuple, tgt_ins_tuple, src_ins_stack_tuple = loader.load_train_batch(
)
# Build Model
model = SIGNetModel(opt, tgt_image, src_image_stack,
intrinsics, tgt_sem_tuple,
src_sem_stack_tuple, tgt_ins_tuple,
src_ins_stack_tuple)
# Handle losses
losses.append(model.total_loss)
tf.get_variable_scope().reuse_variables()
img_losses.append(model.img_loss)
rigid_warp_losses.append(model.rigid_warp_loss)
disp_smooth_losses.append(model.disp_smooth_loss)
if opt.sem_as_loss:
sem_losses.append(model.sem_loss)
if opt.sem_warp_explore:
sem_warp_losses.append(model.sem_warp_loss)
if opt.sem_mask_explore:
sem_mask_losses.append(model.sem_mask_loss)
if opt.sem_edge_explore:
sem_edge_losses.append(model.sem_edge_loss)
if opt.ins_as_loss:
ins_losses.append(model.ins_loss)
if opt.sem_assist and opt.add_segnet:
sem_seg_losses.append(model.sem_seg_loss)
ins0_seg_losses.append(model.ins0_seg_loss)
ins1_edge_seg_losses.append(
model.ins1_edge_seg_loss)
#TODO tensorboard
tf.summary.image('tgt_image_g%02d' % (i),
tgt_image,
max_outputs=opt.max_outputs)
tf.summary.image('src_image_prev_g%02d' % (i),
src_image_stack[:, :, :, :3],
max_outputs=opt.max_outputs)
tf.summary.image('src_image_next_g%02d' % (i),
src_image_stack[:, :, :, 3:],
max_outputs=opt.max_outputs)
tf.summary.scalar('loss_g%02d' % (i), model.total_loss)
tf.summary.scalar('img_loss_g%02d' % (i),
model.img_loss)
tf.summary.scalar('rigid_warp_loss_g%02d' % (i),
model.rigid_warp_loss)
tf.summary.scalar('disp_smooth_loss_g%02d' % (i),
model.disp_smooth_loss)
if opt.sem_as_loss:
tf.summary.scalar('sem_loss_g%02d' % (i),
model.sem_loss)
if opt.sem_warp_explore:
tf.summary.scalar('sem_warp_loss_g%02d' % (i),
model.sem_warp_loss)
if opt.ins_as_loss:
tf.summary.scalar('ins_loss_g%02d' % (i),
model.ins_loss)
if opt.sem_assist and opt.add_segnet:
tf.summary.scalar('sem_seg_loss_g%02d' % (i),
model.sem_seg_loss)
tf.summary.scalar('ins0_seg_loss_g%02d' % (i),
model.ins0_seg_loss)
tf.summary.scalar('ins1_edge_seg_loss_g%02d' % (i),
model.ins1_edge_seg_loss)
#TODO Add bookkeeping ops
if i == 0:
# Train Op
if opt.mode == 'train_flow' and opt.flownet_type == "residual":
train_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
"flow_net")
else:
#TODO try to enable a solution to fix posenet weight in first stage
if opt.mode == 'train_rigid' and opt.fixed_posenet:
if opt.new_sem_dispnet:
train_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
"depth_sem_net")
else:
train_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
"depth_net")
else:
train_vars = [
var
for var in tf.trainable_variables()
]
loading_net = ["depth_net", "pose_net"]
if opt.new_sem_dispnet:
loading_net.append("depth_sem_net")
if opt.new_sem_posenet:
loading_net.append("pose_sem_net")
vars_to_restore = slim.get_variables_to_restore(
include=loading_net)
if opt.init_ckpt_file != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
opt.init_ckpt_file, vars_to_restore)
#TODO Cal mean losses among gpus, and track the loss in TF Summary.
loss = tf.stack(axis=0, values=losses)
loss = tf.reduce_mean(loss, 0)
tf.summary.scalar('loss', loss)
rigid_warp_loss = tf.stack(axis=0, values=rigid_warp_losses)
rigid_warp_loss = tf.reduce_mean(rigid_warp_loss, 0)
tf.summary.scalar('rigid_warp_loss', rigid_warp_loss)
tf.summary.scalar(
'unit_rigid_warp_loss',
rigid_warp_loss / (opt.rigid_warp_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
disp_smooth_loss = tf.stack(axis=0, values=disp_smooth_losses)
disp_smooth_loss = tf.reduce_mean(disp_smooth_loss, 0)
tf.summary.scalar('disp_smooth_loss', disp_smooth_loss)
tf.summary.scalar(
'unit_disp_smooth_loss',
disp_smooth_loss / (opt.disp_smooth_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
img_loss = tf.stack(axis=0, values=img_losses)
img_loss = tf.reduce_mean(img_loss, 0)
tf.summary.scalar('img_loss', img_loss)
if opt.sem_as_loss:
sem_loss = tf.stack(axis=0, values=sem_losses)
sem_loss = tf.reduce_mean(sem_loss, 0)
tf.summary.scalar('sem_loss', sem_loss)
if opt.sem_warp_explore:
sem_warp_loss = tf.stack(axis=0, values=sem_warp_losses)
sem_warp_loss = tf.reduce_mean(sem_warp_loss, 0)
tf.summary.scalar('sem_warp_loss', model.sem_warp_loss)
tf.summary.scalar(
'unit_sem_warp_loss', model.sem_warp_loss /
(opt.sem_warp_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
if opt.sem_mask_explore:
sem_mask_loss = tf.stack(axis=0, values=sem_mask_losses)
sem_mask_loss = tf.reduce_mean(sem_mask_loss, 0)
tf.summary.scalar('sem_mask_loss', model.sem_mask_loss)
tf.summary.scalar(
'unit_sem_mask_loss', model.sem_mask_loss /
(opt.sem_mask_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
if opt.sem_edge_explore:
sem_edge_loss = tf.stack(axis=0, values=sem_edge_losses)
sem_edge_loss = tf.reduce_mean(sem_edge_loss, 0)
tf.summary.scalar('sem_edge_loss', model.sem_edge_loss)
tf.summary.scalar(
'unit_sem_edge_loss', model.sem_edge_loss /
(opt.sem_edge_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
if opt.sem_assist and opt.add_segnet:
sem_seg_loss = tf.stack(axis=0, values=sem_seg_losses)
sem_seg_loss = tf.reduce_mean(sem_seg_loss, 0)
tf.summary.scalar('sem_seg_loss', sem_seg_loss)
tf.summary.scalar(
'unit_sem_seg_loss', model.sem_seg_loss /
(opt.sem_seg_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
ins0_seg_loss = tf.stack(axis=0, values=ins0_seg_losses)
ins0_seg_loss = tf.reduce_mean(ins0_seg_loss, 0)
tf.summary.scalar('ins0_seg_loss', ins0_seg_loss)
tf.summary.scalar(
'unit_ins0_seg_loss', model.ins0_seg_loss /
(opt.ins0_seg_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
ins1_edge_seg_loss = tf.stack(axis=0, values=ins1_edge_seg_losses)
ins1_edge_seg_loss = tf.reduce_mean(ins1_edge_seg_loss, 0)
tf.summary.scalar('ins1_edge_seg_loss', ins1_edge_seg_loss)
tf.summary.scalar(
'unit_ins1_edge_seg_loss', model.ins1_edge_seg_loss /
(opt.ins1_edge_seg_weight +
tf.convert_to_tensor(1e-8, dtype=tf.float32)))
if opt.ins_as_loss:
ins_loss = tf.stack(axis=0, values=ins_losses)
ins_loss = tf.reduce_mean(ins_loss, 0)
tf.summary.scalar('ins_loss', ins_loss)
train_op = slim.learning.create_train_op(
loss,
optim,
variables_to_train=train_vars,
colocate_gradients_with_ops=True)
# Saver
saver = tf.train.Saver([var for var in tf.model_variables()] + \
[global_step],
max_to_keep=opt.max_to_keep)
merged_summary = tf.summary.merge_all()
# Session
sv = tf.train.Supervisor(logdir=opt.checkpoint_dir,
save_summaries_secs=0,
saver=None)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with sv.managed_session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.summary_dir, sess.graph)
if opt.init_ckpt_file != None:
sess.run(init_assign_op, init_feed_dict)
start_time = time.time()
for step in range(1, opt.max_steps):
fetches = {
"train": train_op,
"global_step": global_step,
"incr_global_step": incr_global_step
}
if step % opt.print_interval == 0:
fetches["loss"] = loss
fetches["img_loss"] = img_loss
if opt.sem_as_loss:
fetches["sem_loss"] = sem_loss
if opt.ins_as_loss:
fetches["ins_loss"] = ins_loss
if opt.add_segnet:
fetches["sem_seg_loss"] = sem_seg_loss
fetches["ins0_seg_loss"] = ins0_seg_loss
fetches["ins1_edge_seg_loss"] = ins1_edge_seg_loss
results = sess.run(fetches)
#TODO Write TF Summary to file.
if step % opt.save_summ_freq == 0:
step_summary = sess.run(merged_summary)
train_writer.add_summary(step_summary, step)
if step % opt.print_interval == 0:
time_per_iter = (time.time() -
start_time) / opt.print_interval
start_time = time.time()
if opt.sem_as_loss:
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f ImgLoss: %.3f SemLoss: %.3f' \
% (step, time_per_iter, results["loss"], results["img_loss"], results["sem_loss"]))
elif opt.ins_as_loss:
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f ImgLoss: %.3f InsLoss: %.3f' \
% (step, time_per_iter, results["loss"], results["img_loss"], results["ins_loss"]))
else:
print('Iteration: [%7d] | Time: %4.4fs/iter | ImgLoss: %.3f' \
% (step, time_per_iter, results["loss"]))
if step % opt.save_ckpt_freq == 0:
saver.save(sess,
os.path.join(opt.checkpoint_dir, 'model'),
global_step=step)
def train():
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
pp = pprint.PrettyPrinter()
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(opt.checkpoint_dir):
os.makedirs(opt.checkpoint_dir)
with tf.Graph().as_default():
# Data Loader
loader = DataLoader(opt)
tgt_image, src_image_stack, intrinsics = loader.load_train_batch()
# Build Model
model = GeoNetModel(opt, tgt_image, src_image_stack, intrinsics)
loss = model.total_loss
# Train Op
if opt.mode == 'train_flow' and opt.flownet_type == "residual":
# we pretrain DepthNet & PoseNet, then finetune ResFlowNetS
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "flow_net")
vars_to_restore = slim.get_variables_to_restore(include=["depth_net", "pose_net"])
else:
train_vars = [var for var in tf.trainable_variables()]
vars_to_restore = slim.get_model_variables()
if opt.init_ckpt_file != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
opt.init_ckpt_file, vars_to_restore)
optim = tf.train.AdamOptimizer(opt.learning_rate, 0.9)
train_op = slim.learning.create_train_op(loss, optim,
variables_to_train=train_vars)
# Global Step
global_step = tf.Variable(0,
name='global_step',
trainable=False)
incr_global_step = tf.assign(global_step,
global_step+1)
# Parameter Count
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) \
for v in train_vars])
# Saver
saver = tf.train.Saver([var for var in tf.model_variables()] + \
[global_step],
max_to_keep=opt.max_to_keep)
# Session
sv = tf.train.Supervisor(logdir=opt.checkpoint_dir,
save_summaries_secs=0,
saver=None)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with sv.managed_session(config=config) as sess:
print('Trainable variables: ')
for var in train_vars:
print(var.name)
print("parameter_count =", sess.run(parameter_count))
if opt.init_ckpt_file != None:
sess.run(init_assign_op, init_feed_dict)
start_time = time.time()
for step in range(1, opt.max_steps):
fetches = {
"train": train_op,
"global_step": global_step,
"incr_global_step": incr_global_step
}
if step % 100 == 0:
fetches["loss"] = loss
results = sess.run(fetches)
if step % 100 == 0:
time_per_iter = (time.time() - start_time) / 100
start_time = time.time()
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f' \
% (step, time_per_iter, results["loss"]))
if step % opt.save_ckpt_freq == 0:
saver.save(sess, os.path.join(opt.checkpoint_dir, 'model'), global_step=step)
def run(self,
feed_generator,
train_op,
log_dir="/tmp/slim_trainer/",
restore_path=None,
variables_to_restore=None,
run_id=None,
max_checkpoints_to_keep=0,
**kwargs):
""" Run training.
Parameters
----------
feed_generator : Iterator[ndarray, ...]
An iterator or generator that returns batches of training data; must
return a one-to-one correspondence with the `enqueue_vars` passed
to the constructor of this class.
train_op : tf.Tensor
The training operation created with `slim.learning.create_train_op`.
log_dir : Optional[str]
Path to TensorFlow log directory. This value is used in conjunction
with `run_id` to generate the checkpoint and summary directory;
defaults to '/tmp/slim_trainer'.
restore_path : Optional[str]
An optional checkpoint path. If not None, resumes training from the
given checkpoint.
variables_to_restore : Optional[List[str]]
An optional list of variable scopes. If not None, only restores
variables under the given scope. This value is ignored if
`restore_path` is None.
run_id : Optional[str]
A string that identifies this training run. The checkpoints and
TensorFlow summaries are stored in `log_dir/run_id`. If None, a
random ID will be generated. Point tensorboard to this directory to
monitor training progress.
max_checkpoints_to_keep : int
Keep only the `max_checkpoints_to_keep` newest checkpoints. If 0,
keep all checkpoints.
kwargs:
Additional named arguments passed on to tf.slim.learning.train,
e.g., `number_of_steps=100` to run 100 iterations of training.
"""
if restore_path is not None:
if variables_to_restore is None:
variables_to_restore = slim.get_variables_to_restore()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
restore_path, variables_to_restore)
self._init_fns.append(
lambda sess: sess.run(init_assign_op, init_feed_dict))
self._feed_generator = ThreadSafeIterator(feed_generator)
self._coordinator = tf.train.Coordinator()
if run_id is None:
run_id = _generate_run_id(6)
log_dir = os.path.join(log_dir, run_id)
print("---------------------------------------")
print("Run ID: ", run_id)
print("Log directory: ", log_dir)
print("---------------------------------------")
saver = tf.compat.v1.train.Saver(max_to_keep=max_checkpoints_to_keep)
try:
slim.learning.train(train_op,
log_dir,
self._train_step_fn,
saver=saver,
**kwargs)
except UnboundLocalError:
# NOTE(nwojke): Due to a bug in slim, a local variable 'total_loss'
# is referenced when an exception is raised during training. We
# catch the exception here because it occurs whenever we close the
# queue with self._stop_all_threads().
pass
self._wait_for_threads()
def __init__(self, FLAGS, session, prefix='model'):
'''initialize model
'''
self.sess = session
self.action_dim = FLAGS.action_dim
# self.output_size = FLAGS.output_size
self.prefix = prefix
self.device = FLAGS.device
self.FLAGS = FLAGS
self.lr = self.FLAGS.learning_rate
self.global_step = tf.Variable(0, name='global_step', trainable=False)
#define the input size of the network input
if self.FLAGS.network == 'mobile':
self.input_size = [
mobile_net.default_image_size[FLAGS.depth_multiplier],
mobile_net.default_image_size[FLAGS.depth_multiplier], 3
]
elif self.FLAGS.network == 'mobile_nfc':
self.input_size = [
mobile_nfc_net.default_image_size[FLAGS.depth_multiplier],
mobile_nfc_net.default_image_size[FLAGS.depth_multiplier],
3 * self.FLAGS.n_frames
]
elif sum([
self.FLAGS.network.startswith(name)
for name in ['alex', 'squeeze', 'tiny']
]):
versions = {
'alex': alex_net,
'alex_v1': alex_net_v1,
'alex_v2': alex_net_v2,
'alex_v3': alex_net_v3,
'alex_v4': alex_net_v4,
'squeeze': squeeze_net,
'squeeze_v1': squeeze_net_v1,
'squeeze_v2': squeeze_net_v2,
'squeeze_v3': squeeze_net_v3,
'tiny': tiny_net,
'tiny_v1': tiny_net_v1,
'tiny_v2': tiny_net_v2,
'tiny_v2r': tiny_net_v2_r,
'tiny_v3': tiny_net_v3,
'tiny_v4': tiny_net_v4,
'tiny_CAM': tiny_CAM_net
}
self.input_size = versions[self.FLAGS.network].default_image_size
else:
raise NotImplementedError('Network is unknown: ',
self.FLAGS.network)
self.input_size = [None] + self.input_size
self.output_size = int(
self.action_dim if not self.FLAGS.discrete else self.action_dim *
self.FLAGS.action_quantity)
# define a network for training and for evaluation
self.inputs = tf.placeholder(tf.float32,
shape=self.input_size,
name='Inputs')
self.endpoints = {}
for mode in ['train', 'eval']:
self.define_network(mode)
params = sum([
reduce(lambda x, y: x * y,
v.get_shape().as_list())
for v in tf.trainable_variables()
])
print("total number of parameters: {0}".format(params))
if self.FLAGS.discrete:
self.define_discrete_bins(FLAGS.action_bound,
FLAGS.action_quantity)
self.add_discrete_control_layers(self.endpoints['train'])
self.add_discrete_control_layers(self.endpoints['eval'])
# Only feature extracting part is initialized from pretrained model
if not self.FLAGS.continue_training:
# make sure you exclude the prediction layers of the model
list_to_exclude = ["global_step"]
list_to_exclude.append("MobilenetV1/control")
list_to_exclude.append("MobilenetV1/aux_depth")
list_to_exclude.append("H_fc_control")
list_to_exclude.append("outputs")
list_to_exclude.append("MobilenetV1/q_depth")
list_to_exclude.append("Omega")
print("[model.py]: only load feature extracting part in network.")
else: #If continue training
print("[model.py]: continue training of total network.")
# list_to_exclude = ["Omega"]
list_to_exclude = []
# In case of lifelonglearning and continue learning:
# add variables for importance weights of previous domain and keep optimal variables for previous domain
if self.FLAGS.lifelonglearning or self.FLAGS.update_importance_weights:
self.define_importance_weights(self.endpoints['train'])
variables_to_restore = slim.get_variables_to_restore(
exclude=list_to_exclude)
# get latest folder out of training directory if there is no checkpoint file
if self.FLAGS.checkpoint_path[0] != '/':
self.FLAGS.checkpoint_path = self.FLAGS.summary_dir + self.FLAGS.checkpoint_path
if not os.path.isfile(self.FLAGS.checkpoint_path + '/checkpoint'):
try:
self.FLAGS.checkpoint_path = self.FLAGS.checkpoint_path + '/' + [
mpath
for mpath in sorted(os.listdir(self.FLAGS.checkpoint_path))
if os.path.isdir(self.FLAGS.checkpoint_path + '/' +
mpath) and not mpath[-3:] == 'val'
and os.path.isfile(self.FLAGS.checkpoint_path + '/' +
mpath + '/checkpoint')
][-1]
except:
pass
if not self.FLAGS.scratch:
print('checkpoint: {}'.format(self.FLAGS.checkpoint_path))
try:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
tf.train.latest_checkpoint(self.FLAGS.checkpoint_path),
variables_to_restore)
except Exception as e:
print(
"Failed to initialize network {0} with checkpoint {1} so training from scratch: {2}"
.format(FLAGS.network, FLAGS.checkpoint_path, e.message))
FLAGS.scratch = True
# create saver for checkpoints
self.saver = tf.train.Saver(max_to_keep=10,
keep_checkpoint_every_n_hours=1)
# Add the loss and metric functions to the graph for both endpoints of train and eval.
self.targets = tf.placeholder(
tf.int32,
[None, self.action_dim]) if FLAGS.discrete else tf.placeholder(
tf.float32, [None, self.action_dim])
self.depth_targets = tf.placeholder(tf.float32, [None, 55, 74])
self.define_metrics(self.endpoints['eval'])
if self.FLAGS.continue_training and self.FLAGS.lifelonglearning:
self.define_star_variables(self.endpoints['train'])
# Define the training op based on the total loss
self.define_loss(self.endpoints['train'])
self.define_train()
# Define summaries
self.build_summaries()
init_all = tf_variables.global_variables_initializer()
self.sess.run([init_all])
if not self.FLAGS.scratch:
self.sess.run([init_assign_op], init_feed_dict)
print('Successfully loaded model from:{}'.format(
self.FLAGS.checkpoint_path))
else:
print('Training model from scratch so no initialization.')
# FOR DEBUGGING:
# for v in tf.trainable_variables():
# # # assign importance weights 1 to everyting ==> keep everything as close as possible
# # # self.sess.run([tf.assign(self.importance_weights[v.name], np.ones((v.get_shape().as_list()))) for i,v in enumerate(tf.trainable_variables())])
# # assign importance weights 1 to everything > 10**-5, else 0 ==> binair importance over the weights ==> freeze some part and leave other
# old_weight = self.sess.run(self.importance_weights[v.name])
# new_weight = old_weight > 10**-2
# new_weight = new_weight.astype(np.float32)
# self.sess.run(tf.assign(self.importance_weights[v.name], new_weight))
if self.FLAGS.continue_training and self.FLAGS.lifelonglearning:
# print info on loaded importance weights
for v in tf.trainable_variables():
weights = self.sess.run(self.importance_weights[v.name])
weights = weights.flatten()
# print("{0}: {1} ({2}) min: {3} max: {4}".format(v.name, np.mean(weights), np.var(weights), np.amin(weights), np.amax(weights)))
print("| {0} | {1} | {2} | {3} | ".format(
v.name, np.percentile(weights, 1),
np.percentile(weights, 50), np.percentile(weights, 100)))
# assign star_variables after initialization
self.sess.run([
tf.assign(self.star_variables[v.name], v)
for v in tf.trainable_variables()
])
def unpickle(file):
fo = open(file, 'rb')
dict = cPickle.load(fo)
fo.close()
data = dict['data']
imgs = np.transpose(np.reshape(data,(-1,32,32,3), order='F'),axes=(0,2,1,3)) #order batch,x,y,color
y = np.asarray(dict['labels'], dtype='uint8')
return y, imgs
y, imgs = unpickle('/Users/oli/Dropbox/data/CIFAR-10/cifar-10-batches-py/test_batch')
y.shape, imgs.shape
tf.reset_default_graph()
images = tf.placeholder(tf.float32, [None, None, None, 3])
imgs_scaled = tf.image.resize_images(images, (224,224))
slim.nets.vgg.vgg_16(imgs_scaled, is_training=False)
variables_to_restore = slim.get_variables_to_restore()
print('Number of variables to restore {}'.format(len(variables_to_restore)))
init_assign_op, init_feed_dict = slim.assign_from_checkpoint('/Users/oli/Dropbox/server_sync/tf_slim_models/vgg_16.ckpt', variables_to_restore)
sess = tf.Session()
sess.run(init_assign_op, init_feed_dict)
g = tf.get_default_graph()
feed = g.get_tensor_by_name('Placeholder:0')
fetch = g.get_tensor_by_name('vgg_16/fc6/BiasAdd:0')
# Feeding 3 images through the net just for testing
feed_vals = imgs[0:3]
res = sess.run(fetch, feed_dict={feed:feed_vals})
np.shape(feed_vals), res.shape
def create_image_trainer(image_shape,
num_classes,
epochs,
batch_size=32,
learning_rate_base=0.01,
learning_rate_decay_interval=7500,
learning_rate_decay=0.99,
checkpoint_path=None,
model_save_path='model',
max_to_keep=100,
log_file_path='log/train.log'):
image_var = tf.placeholder(tf.uint8, (None, ) + image_shape)
labels_ = tf.placeholder(tf.float32, (None, num_classes))
preprocessed_image_var = tf.map_fn(
lambda x: _preprocess(x, is_training=True),
tf.cast(image_var, tf.float32))
l2_normalize = True
factory_fn = _network_factory(num_classes=num_classes,
is_training=True,
weight_decay=1e-8)
feature_var, logits_var = factory_fn(preprocessed_image_var,
l2_normalize=l2_normalize,
reuse=None)
feature_dim = feature_var.get_shape().as_list()[-1]
classification_loss = slim.losses.softmax_cross_entropy(
logits_var, labels_)
total_loss = slim.losses.get_total_loss(add_regularization_losses=True)
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(learning_rate_base, global_step,
learning_rate_decay_interval,
learning_rate_decay)
optimizer = tf.train.AdamOptimizer(learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = optimizer.minimize(total_loss, global_step=global_step)
session = tf.Session()
#from tensorflow.python import debug as tf_debug
#session = tf_debug.LocalCLIDebugWrapperSession(session)
if checkpoint_path is not None:
saver = tf.train.Saver(slim.get_variables_to_restore())
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, slim.get_variables_to_restore())
session.run(init_assign_op, feed_dict=init_feed_dict)
else:
session.run(tf.global_variables_initializer())
def trainer(data_x_paths, data_y):
data_y = np.asarray(data_y)
saver = tf.train.Saver(max_to_keep=max_to_keep)
logging.basicConfig(filename=log_file_path,
filemode="w",
level=logging.DEBUG)
data_len = len(data_x_paths)
num_batches = int(data_len / batch_size)
for epoch in range(epochs):
indexs = np.arange(data_len)
np.random.shuffle(indexs)
s, e = 0, 0
for i in range(num_batches):
s, e = i * batch_size, (i + 1) * batch_size
data_x_batch = np.asarray([
cv2.resize(
cv2.imread(data_x_paths[index], cv2.IMREAD_COLOR),
(image_shape[1], image_shape[0]))
for index in indexs[s:e]
])
data_y_batch = data_y[indexs[s:e]]
batch_data_dict = {
image_var: data_x_batch,
labels_: data_y_batch
}
_, loss_value, global_step_value = session.run(
[train_step, total_loss, global_step],
feed_dict=batch_data_dict)
print(global_step_value, loss_value)
logging.info("%d: %g" % (global_step_value, loss_value))
if (i + 1) % 1000 == 0:
print("save model checkpoint for %d..." %
global_step_value)
saver.save(session,
os.path.join(model_save_path, 'model.ckpt'),
global_step=global_step)
if e < data_len:
data_x_batch = np.asarray([
cv2.resize(
cv2.imread(data_x_paths[index], cv2.IMREAD_COLOR),
(image_shape[1], image_shape[0]))
for index in indexs[e:]
])
data_y_batch = data_y[indexs[s:e]]
batch_data_dict = {
image_var: data_x_batch,
labels_: data_y_batch
}
_, loss_value, global_step_value = session.run(
[train_step, total_loss, global_step],
feed_dict=batch_data_dict)
print(global_step_value, loss_value)
logging.info("%d: %g" % (global_step_value, loss_value))
print("save model checkpoint for %d..." % global_step_value)
saver.save(session,
os.path.join(model_save_path, 'model.ckpt'),
global_step=global_step)
return trainer
name='Anchor_Placeholder')
positive_inputs = tf.placeholder(tf.float32, [None, 224, 224, 3],
name='Positive_Placeholder')
with slim.arg_scope(vgg_arg_scope()):
positive, net1 = vgg_16(positive_inputs)
with slim.arg_scope(vgg_arg_scope(reuse=True)):
anchor, net2 = vgg_16(anchor_inputs)
#print net2
#for key,value in net2.items():
# print '{}\t : {}'.format(key,value.shape)
variables_to_restore = slim.get_variables_to_restore(
exclude=['vgg_16/fc8'])
#variables_to_restore = slim.get_variables_to_restore()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
'./vgg_16.ckpt', variables_to_restore)
print 'LOSS.........................................................'
with tf.name_scope('contrastive-loss'):
loss = tf.contrib.losses.metric_learning.contrastive_loss(
labels=label,
embeddings_anchor=anchor,
embeddings_positive=positive,
margin=margin)
print 'contrastive-loss::', loss.op.name
with tf.name_scope('Prediction'):
pred = tf.norm(positive - anchor, ord='euclidean')
#pred = tf.sqrt(tf.reduce_mean(tf.square(positive-anchor),1))
with tf.Session(config=tf.ConfigProto(
def _create_image_encoder(preprocess_fn,
factory_fn,
image_shape,
batch_size=32,
session=None,
checkpoint_path=None,
loss_mode="cosine"):
image_var = tf.placeholder(tf.uint8, (None, ) + image_shape)
image_var = tf.Print(image_var, [image_var], message="placeholder")
preprocessed_image_var = tf.map_fn(
lambda x: preprocess_fn(x, is_training=False),
tf.cast(image_var, tf.float32))
l2_normalize = loss_mode == "cosine"
feature_var, _ = factory_fn(preprocessed_image_var,
l2_normalize=l2_normalize,
reuse=None)
feature_dim = feature_var.get_shape().as_list()[-1]
if session is None:
session = tf.Session()
if checkpoint_path is not None:
slim.get_or_create_global_step()
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path, slim.get_variables_to_restore())
session.run(init_assign_op, feed_dict=init_feed_dict)
def get_print_val(sess, name, feed_dict=None, doprint=True):
if feed_dict:
output = sess.run(name, feed_dict=feed_dict)
if doprint:
print name, ' transposed ', output.transpose(0, 3, 1, 2).shape
print output.transpose(0, 3, 1, 2)[0, 0, 0, 0:20]
print output.transpose(0, 3, 1, 2)[0, 0, 1, 0:20]
else:
output = sess.run(name)
if doprint:
print name, output.shape
print output
return output
def encoder(data_x):
out = np.zeros((len(data_x), feature_dim), np.float32)
print 'datax:', data_x.shape
print(data_x[0, 0, 0:100, 0])
print(data_x[0, 0, 0:100, 1])
print(data_x[0, 0, 0:100, 2])
#print(data_x[1,0,0:100,0])
#print(data_x[1,0,0:100,1])
#print(data_x[1,0,0:100,2])
#_run_in_batches(
# lambda x: session.run(feature_var, feed_dict=x),
# #lambda x: session.run('conv1_1/Elu:0', feed_dict=x),
# {image_var: data_x}, out, batch_size)
if 1:
elu_1_data = get_print_val(session,
'Elu_1:0',
feed_dict={image_var: data_x})
get_print_val(session,
'conv3_1/1/Elu:0',
feed_dict={image_var: data_x})
#else:
conv3_1_weights = get_print_val(session,
'conv3_1/1/weights:0',
doprint=False)
mean = get_print_val(session,
'conv3_1/1/conv3_1/1/bn/moving_mean:0')
var = get_print_val(session,
'conv3_1/1/conv3_1/1/bn/moving_variance:0')
offset = get_print_val(session, 'conv3_1/1/conv3_1/1/bn/beta:0')
print 'offset ', offset.shape, type(offset[0])
session.close()
scale = np.ones(offset.shape, dtype=np.float32)
print 'scale ', scale.shape, type(scale[0])
####### new graph
elu_1 = tf.placeholder(tf.float32, (None, ) + (64, 32, 32))
print 'elu_1 shape', elu_1.get_shape()
dd = tf.nn.conv2d(elu_1,
conv3_1_weights, [1, 2, 2, 1],
'SAME',
name='conv3_1')
dd = tf.nn.batch_normalization(dd, mean, var, offset, scale, 1e-3)
dd = tf.nn.elu(dd, name='conv3_1/elu')
#cn = elu_1.get_shape().as_list()[-1]*2
#scope = 'conv3_1'
#conv_weight_init = tf.truncated_normal_initializer(stddev=1e-3)
#conv_bias_init = tf.zeros_initializer()
#conv_regularizer = slim.l2_regularizer(1e-8)
#elu_1_conv = slim.conv2d(
# elu_1, cn, [3, 3], stride=2, activation_fn=tf.nn.elu, padding="SAME",
# normalizer_fn=_batch_norm_fn, weights_initializer=conv_weight_init,
# biases_initializer=conv_bias_init, weights_regularizer=conv_regularizer,
# scope=scope + "/1")
sess = tf.Session()
get_print_val(sess, 'conv3_1:0', feed_dict={elu_1: elu_1_data})
############
return out
return encoder
def train():
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
pp = pprint.PrettyPrinter()
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(opt.checkpoint_dir):
os.makedirs(opt.checkpoint_dir)
with tf.Graph().as_default():
# Data Loader
loader = DataLoader(opt)
tgt_image, src_image_stack, intrinsics = loader.load_train_batch()
# Build Model
model = GeoNetModel(opt, tgt_image, src_image_stack, intrinsics)
loss = model.total_loss
# Train Op
if opt.mode == 'train_flow' and opt.flownet_type == "residual":
# we pretrain DepthNet & PoseNet, then finetune ResFlowNetS
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "flow_net")
vars_to_restore = slim.get_variables_to_restore(include=["depth_net", "pose_net"])
else:
train_vars = [var for var in tf.trainable_variables()]
vars_to_restore = slim.get_model_variables()
if opt.init_ckpt_file != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
opt.init_ckpt_file, vars_to_restore)
optim = tf.train.AdamOptimizer(opt.learning_rate, 0.9)
train_op = slim.learning.create_train_op(loss, optim,
variables_to_train=train_vars)
# Global Step
global_step = tf.Variable(0,
name='global_step',
trainable=False)
incr_global_step = tf.assign(global_step,
global_step+1)
# Parameter Count
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) \
for v in train_vars])
# Saver
saver = tf.train.Saver([var for var in tf.model_variables()] + \
[global_step],
max_to_keep=opt.max_to_keep)
# Session
sv = tf.train.Supervisor(logdir=opt.checkpoint_dir,
save_summaries_secs=0,
saver=None)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with sv.managed_session(config=config) as sess:
print('Trainable variables: ')
for var in train_vars:
print(var.name)
print("parameter_count =", sess.run(parameter_count))
if opt.init_ckpt_file != None:
sess.run(init_assign_op, init_feed_dict)
start_time = time.time()
for step in range(1, opt.max_steps):
fetches = {
"train": train_op,
"global_step": global_step,
"incr_global_step": incr_global_step
}
if step % 100 == 0:
fetches["loss"] = loss
results = sess.run(fetches)
if step % 100 == 0:
time_per_iter = (time.time() - start_time) / 100
start_time = time.time()
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f' \
% (step, time_per_iter, results["loss"]))
if step % opt.save_ckpt_freq == 0:
saver.save(sess, os.path.join(opt.checkpoint_dir, 'model'), global_step=step)
def train(finetune):
is_training = True
# data pipeline
imgs, true_boxes = gen_data_batch(cfg.data_path, cfg.batch_size*cfg.train.num_gpus)
imgs_split = tf.split(imgs, cfg.train.num_gpus)
true_boxes_split = tf.split(true_boxes, cfg.train.num_gpus)
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0.), trainable=False)
#lr = tf.train.piecewise_constant(global_step, cfg.train.lr_steps, cfg.train.learning_rate)
#optimizer = tf.train.AdamOptimizer(learning_rate=lr)
learn_rate_decay_step = int(cfg.train.num_samples / cfg.batch_size / cfg.train.num_gpus * cfg.train.learn_rate_decay_epoch)
learning_rate = tf.train.exponential_decay(cfg.train.learn_rate, global_step, learn_rate_decay_step, cfg.train.learn_rate_decay, staircase=True)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# Calculate the gradients for each model tower.
tower_grads = []
with tf.variable_scope(tf.get_variable_scope()):
for i in range(cfg.train.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (cfg.train.tower, i)) as scope:
model = SenseClsNet(imgs_split[i], true_boxes_split[i], is_training)
loss = model.compute_loss()
tf.get_variable_scope().reuse_variables()
grads = optimizer.compute_gradients(loss)
tower_grads.append(grads)
if i == 0:
current_loss = loss
update_op = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# print(tf.GraphKeys.UPDATE_OPS)
# print(update_op)
# print(grads)
# vars_det = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="BioRecNet")
grads = average_gradients(tower_grads)
with tf.control_dependencies(update_op):
apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step)
train_op = tf.group(apply_gradient_op,*update_op)
# GPU config
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Create a saver
saver = tf.train.Saver(max_to_keep=1000)
ckpt_dir = cfg.ckpt_path
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
# init
sess.run(tf.global_variables_initializer())
if finetune:
checkpoint = './pre_train.ckpt'
# variables_to_restore = slim.get_variables_to_restore()
# init_assign_op, init_feed_dict = slim.assign_from_checkpoint(checkpoint, variables_to_restore, ignore_missing_vars=True)
# sess.run(init_assign_op, init_feed_dict)
variables_to_restore = get_variables_to_restore(exclude_global_pool=True)
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(checkpoint, variables_to_restore, ignore_missing_vars=True)
sess.run(init_assign_op, init_feed_dict)
# running
cnt_epoch = 0
for i in range(1, cfg.train.max_batches):
_, loss_, lr_ = sess.run([train_op, current_loss, learning_rate])
if(i % 5 == 0):
print(i,': ', loss_, ' lr: ', lr_)
if int(i) % int(cfg.train.num_samples / cfg.train.num_gpus / cfg.batch_size) == 0:
cnt_epoch += 1
saver.save(sess, ckpt_dir+'senceCls', global_step=cnt_epoch, write_meta_graph=True)
