def build_codebook_from_name(experiment_name,
experiment_group='',
return_dataset=False,
return_decoder=False):
import os
import configparser
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path == None:
print 'Please define a workspace path:\n'
print 'export AE_WORKSPACE_PATH=/path/to/workspace\n'
exit(-1)
import utils as u
import tensorflow as tf
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
cfg_file_path = u.get_train_config_exp_file_path(log_dir, experiment_name)
dataset_path = u.get_dataset_path(workspace_path)
if os.path.exists(cfg_file_path):
args = configparser.ConfigParser()
args.read(cfg_file_path)
else:
print 'ERROR: Config File not found: ', cfg_file_path
exit()
with tf.variable_scope(experiment_name):
dataset = build_dataset(dataset_path, args)
x = tf.placeholder(tf.float32, [
None,
] + list(dataset.shape))
encoder = build_encoder(x, args)
codebook = build_codebook(encoder, dataset, args)
if return_decoder:
reconst_target = tf.placeholder(tf.float32, [
None,
] + list(dataset.shape))
decoder = build_decoder(reconst_target, encoder, args)
if return_dataset:
if return_decoder:
return codebook, dataset, decoder
else:
return codebook, dataset
else:
return codebook
def main():
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path == None:
print 'Please define a workspace path:\n'
print 'export AE_WORKSPACE_PATH=/path/to/workspace\n'
exit(-1)
gentle_stop = np.array((1, ), dtype=np.bool)
gentle_stop[0] = False
def on_ctrl_c(signal, frame):
gentle_stop[0] = True
signal.signal(signal.SIGINT, on_ctrl_c)
parser = argparse.ArgumentParser()
parser.add_argument("experiment_name")
parser.add_argument("-d", action='store_true', default=False)
parser.add_argument("-gen", action='store_true', default=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
debug_mode = arguments.d
generate_data = arguments.gen
cfg_file_path = u.get_config_file_path(workspace_path, experiment_name,
experiment_group)
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
ckpt_dir = u.get_checkpoint_dir(log_dir)
train_fig_dir = u.get_train_fig_dir(log_dir)
dataset_path = u.get_dataset_path(workspace_path)
if not os.path.exists(cfg_file_path):
print 'Could not find config file:\n'
print '{}\n'.format(cfg_file_path)
exit(-1)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
if not os.path.exists(train_fig_dir):
os.makedirs(train_fig_dir)
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
args = configparser.ConfigParser()
args.read(cfg_file_path)
shutil.copy2(cfg_file_path, log_dir)
with tf.variable_scope(experiment_name):
dataset = factory.build_dataset(dataset_path, args)
queue = factory.build_queue(dataset, args)
encoder = factory.build_encoder(queue.x, args, is_training=True)
decoder = factory.build_decoder(queue.y,
encoder,
args,
is_training=True)
ae = factory.build_ae(encoder, decoder, args)
codebook = factory.build_codebook(encoder, dataset, args)
train_op = factory.build_train_op(ae, args)
saver = tf.train.Saver(save_relative_paths=True)
num_iter = args.getint(
'Training', 'NUM_ITER') if not debug_mode else np.iinfo(np.int32).max
save_interval = args.getint('Training', 'SAVE_INTERVAL')
model_type = args.get('Dataset', 'MODEL')
if model_type == 'dsprites':
dataset.get_sprite_training_images(args)
else:
dataset.get_training_images(dataset_path, args)
dataset.load_bg_images(dataset_path)
if generate_data:
print 'finished generating synthetic training data for ' + experiment_name
print 'exiting...'
exit()
bar = progressbar.ProgressBar(maxval=num_iter,
widgets=[
' [',
progressbar.Timer(), ' | ',
progressbar.Counter('%0{}d / {}'.format(
len(str(num_iter)), num_iter)),
' ] ',
progressbar.Bar(), ' (',
progressbar.ETA(), ') '
])
gpu_options = tf.GPUOptions(allow_growth=True,
per_process_gpu_memory_fraction=0.9)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
chkpt = tf.train.get_checkpoint_state(ckpt_dir)
if chkpt and chkpt.model_checkpoint_path:
saver.restore(sess, chkpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
merged_loss_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(ckpt_dir, sess.graph)
if not debug_mode:
print 'Training with %s model' % args.get(
'Dataset', 'MODEL'), os.path.basename(
args.get('Paths', 'MODEL_PATH'))
bar.start()
queue.start(sess)
for i in xrange(ae.global_step.eval(), num_iter):
if not debug_mode:
sess.run(train_op)
if i % 10 == 0:
loss = sess.run(merged_loss_summary)
summary_writer.add_summary(loss, i)
bar.update(i)
if (i + 1) % save_interval == 0:
saver.save(sess,
checkpoint_file,
global_step=ae.global_step)
this_x, this_y = sess.run([queue.x, queue.y])
reconstr_train = sess.run(decoder.x,
feed_dict={queue.x: this_x})
train_imgs = np.hstack(
(u.tiles(this_x, 4,
4), u.tiles(reconstr_train, 4,
4), u.tiles(this_y, 4, 4)))
cv2.imwrite(
os.path.join(train_fig_dir,
'training_images_%s.png' % i),
train_imgs * 255)
else:
this_x, this_y = sess.run([queue.x, queue.y])
reconstr_train = sess.run(decoder.x,
feed_dict={queue.x: this_x})
cv2.imshow(
'sample batch',
np.hstack((u.tiles(this_x, 3,
3), u.tiles(reconstr_train, 3,
3), u.tiles(this_y, 3, 3))))
k = cv2.waitKey(0)
if k == 27:
break
if gentle_stop[0]:
break
queue.stop(sess)
if not debug_mode:
bar.finish()
if not gentle_stop[0] and not debug_mode:
print 'To create the embedding run:\n'
print 'ae_embed {}\n'.format(full_name)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
obj_id = arguments.obj_id
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
cfg_file_path = u.get_config_file_path(path_workspace, experiment_name,
experiment_group)
list_models = [int(obj_id)]
log_dir = u.get_log_dir(path_workspace, experiment_name, experiment_group)
ckpt_dir = os.path.join(log_dir,
'checkpoints_lambda{:d}'.format(int(lambda_reconst)))
checkpoint_file = u.get_checkpoint_basefilename(ckpt_dir)
train_fig_dir = os.path.join(
log_dir, 'train_figures_lambda{:d}'.format(int(lambda_reconst)))
dataset_path = u.get_dataset_path(path_workspace)
print('dataset_path', dataset_path)
if not os.path.exists(cfg_file_path):
print('Could not find config file:\n')
print('{}\n'.format(cfg_file_path))
exit(-1)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
if not os.path.exists(train_fig_dir):
os.makedirs(train_fig_dir)
if not os.path.exists(dataset_path):
def main():
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path == None:
print 'Please define a workspace path:\n'
print 'export AE_WORKSPACE_PATH=/path/to/workspace\n'
exit(-1)
parser = argparse.ArgumentParser()
parser.add_argument("experiment_name")
parser.add_argument('--at_step', default=None, required=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
at_step = arguments.at_step
cfg_file_path = u.get_config_file_path(workspace_path, experiment_name,
experiment_group)
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
ckpt_dir = u.get_checkpoint_dir(log_dir)
dataset_path = u.get_dataset_path(workspace_path)
print checkpoint_file
print ckpt_dir
print '#' * 20
if not os.path.exists(cfg_file_path):
print 'Could not find config file:\n'
print '{}\n'.format(cfg_file_path)
exit(-1)
args = configparser.ConfigParser()
args.read(cfg_file_path)
with tf.variable_scope(experiment_name):
dataset = factory.build_dataset(dataset_path, args)
queue = factory.build_queue(dataset, args)
encoder = factory.build_encoder(queue.x, args)
decoder = factory.build_decoder(queue.y, encoder, args)
ae = factory.build_ae(encoder, decoder, args)
codebook = factory.build_codebook(encoder, dataset, args)
saver = tf.train.Saver(save_relative_paths=True)
batch_size = args.getint('Training', 'BATCH_SIZE')
model = args.get('Dataset', 'MODEL')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
print ckpt_dir
print '#' * 20
factory.restore_checkpoint(sess, saver, ckpt_dir, at_step=at_step)
# chkpt = tf.train.get_checkpoint_state(ckpt_dir)
# if chkpt and chkpt.model_checkpoint_path:
# print chkpt.model_checkpoint_path
# saver.restore(sess, chkpt.model_checkpoint_path)
# else:
# print 'No checkpoint found. Expected one in:\n'
# print '{}\n'.format(ckpt_dir)
# exit(-1)
if model == 'dsprites':
codebook.update_embedding_dsprites(sess, args)
else:
codebook.update_embedding(sess, batch_size)
print 'Saving new checkoint ..',
saver.save(sess, checkpoint_file, global_step=ae.global_step)
print 'done',