def purge_checkpoints(log_dir_root, target_dir, verbose):
vprint = print if verbose else no_op.NoOp
ckpt_dir_glob = Saver.ckpt_dir_for_log_dir(path.join(log_dir_root, '*'))
ckpt_dir_matches = sorted(glob.glob(ckpt_dir_glob))
for ckpt_dir in ckpt_dir_matches:
log_dir = Saver.log_dir_from_ckpt_dir(ckpt_dir)
all_ckpts = Saver.all_ckpts_with_iterations(ckpt_dir)
if len(all_ckpts) <= 5:
vprint('Skipping {}'.format(log_dir))
continue
target_log_dir = path.join(target_dir, path.basename(log_dir))
target_ckpt_dir = Saver.ckpt_dir_for_log_dir(target_log_dir)
os.makedirs(target_ckpt_dir, exist_ok=True)
ckpts_to_keep = {
all_ckpts[2], all_ckpts[len(all_ckpts) // 2], all_ckpts[-1]
}
ckpts_to_move = set(all_ckpts) - ckpts_to_keep
vprint('Moving to {}:'.format(target_ckpt_dir))
for _, ckpt_to_move in ckpts_to_move:
# ckpt_to_move is /path/to/dir/ckpt-7000, add a * to match ckpt-7000.data, .meta, .index
for ckpt_file in glob.glob(ckpt_to_move + '*'):
vprint('- {}'.format(ckpt_file))
shutil.move(ckpt_file, target_ckpt_dir)
def iter_ckpt_dirs(log_dir_root, job_ids_str):
assert os.path.exists(log_dir_root), 'Invalid log dir: {}'.format(
log_dir_root)
job_ids = job_ids_str.strip().replace(';', ',').split(',')
assert len(job_ids) > 0, 'No job_ids!'
for job_id in job_ids:
# ckpt_dir_for_log_dir appends 'ckpts', which ensures that we only get training log dirs as matches,
# and not other or previous validation dir.
ckpt_dir_glob = Saver.ckpt_dir_for_log_dir(
path.join(log_dir_root, job_id + '*'))
ckpt_dir_matches = glob.glob(ckpt_dir_glob)
if len(ckpt_dir_matches) == 0:
print('*** ERR: No matches for {}'.format(ckpt_dir_glob))
continue
if len(ckpt_dir_matches) > 1:
print('*** ERR: Multiple matches for {}: {}'.format(
ckpt_dir_glob, '\n'.join(ckpt_dir_matches)))
continue
yield ckpt_dir_matches[0]
def train(autoencoder_config_path, probclass_config_path,
restore_manager: RestoreManager, log_dir_root, datasets: Datasets,
train_flags: TrainFlags, ckpt_interval_hours: float,
description: str):
ae_config, ae_config_rel_path = config_parser.parse(
autoencoder_config_path)
pc_config, pc_config_rel_path = config_parser.parse(probclass_config_path)
print_configs(('ae_config', ae_config), ('pc_config', pc_config))
continue_in_ckpt_dir = restore_manager and restore_manager.continue_in_ckpt_dir
if continue_in_ckpt_dir:
logdir = restore_manager.log_dir
else:
logdir = logdir_helpers.create_unique_log_dir(
[ae_config_rel_path, pc_config_rel_path],
log_dir_root,
restore_dir=restore_manager.ckpt_dir if restore_manager else None)
print(_LOG_DIR_FORMAT.format(logdir))
if description:
_write_to_sheets(logdir_helpers.log_date_from_log_dir(logdir),
ae_config_rel_path,
pc_config_rel_path,
description,
git_ref=_get_git_ref(),
log_dir_root=log_dir_root,
is_continue=continue_in_ckpt_dir)
ae_cls = autoencoder.get_network_cls(ae_config)
pc_cls = probclass.get_network_cls(pc_config)
# Instantiate autoencoder and probability classifier
ae = ae_cls(ae_config)
pc = pc_cls(pc_config, num_centers=ae_config.num_centers)
# train ---
ip_train = inputpipeline.InputPipeline(
inputpipeline.get_dataset(datasets.train),
ae_config.crop_size,
batch_size=ae_config.batch_size,
shuffle=False,
num_preprocess_threads=NUM_PREPROCESS_THREADS,
num_crops_per_img=NUM_CROPS_PER_IMG)
x_train = ip_train.get_batch()
enc_out_train = ae.encode(
x_train, is_training=True) # qbar is masked by the heatmap
x_out_train = ae.decode(enc_out_train.qbar, is_training=True)
# stop_gradient is beneficial for training. it prevents multiple gradients flowing into the heatmap.
pc_in = tf.stop_gradient(enc_out_train.qbar)
bc_train = pc.bitcost(pc_in,
enc_out_train.symbols,
is_training=True,
pad_value=pc.auto_pad_value(ae))
bpp_train = bits.bitcost_to_bpp(bc_train, x_train)
d_train = Distortions(ae_config, x_train, x_out_train, is_training=True)
# summing over channel dimension gives 2D heatmap
heatmap2D = (tf.reduce_sum(enc_out_train.heatmap, 1)
if enc_out_train.heatmap is not None else None)
# loss ---
total_loss, H_real, pc_comps, ae_comps = get_loss(ae_config, ae, pc,
d_train.d_loss_scaled,
bc_train,
enc_out_train.heatmap)
train_op = get_train_op(ae_config, pc_config, ip_train, pc.variables(),
total_loss)
# test ---
with tf.name_scope('test'):
ip_test = inputpipeline.InputPipeline(
inputpipeline.get_dataset(datasets.test),
ae_config.crop_size,
batch_size=ae_config.batch_size,
num_preprocess_threads=NUM_PREPROCESS_THREADS,
num_crops_per_img=1,
big_queues=False,
shuffle=False)
x_test = ip_test.get_batch()
enc_out_test = ae.encode(x_test, is_training=False)
x_out_test = ae.decode(enc_out_test.qhard, is_training=False)
bc_test = pc.bitcost(enc_out_test.qhard,
enc_out_test.symbols,
is_training=False,
pad_value=pc.auto_pad_value(ae))
bpp_test = bits.bitcost_to_bpp(bc_test, x_test)
d_test = Distortions(ae_config, x_test, x_out_test, is_training=False)
try: # Try to get codec distnace for current dataset
codec_distance_ms_ssim = CodecDistance(datasets.codec_distance,
codec='bpg',
metric='ms-ssim')
get_distance = functools_ext.catcher(ValueError,
handler=functools_ext.const(
np.nan),
f=codec_distance_ms_ssim.distance)
get_distance = functools_ext.compose(np.float32,
get_distance) # cast to float32
d_BPG_test = tf.py_func(get_distance, [bpp_test, d_test.ms_ssim],
tf.float32,
stateful=False,
name='d_BPG')
d_BPG_test.set_shape(())
except CodecDistanceReadException as e:
print('Cannot compute CodecDistance: {}'.format(e))
d_BPG_test = tf.constant(np.nan, shape=(), name='ConstNaN')
# ---
train_logger = Logger()
test_logger = Logger()
distortion_name = ae_config.distortion_to_minimize
train_logger.add_summaries(d_train.summaries_with_prefix('train'))
# Visualize components of losses
train_logger.add_summaries([
tf.summary.scalar('train/PC_loss/{}'.format(name), comp)
for name, comp in pc_comps
])
train_logger.add_summaries([
tf.summary.scalar('train/AE_loss/{}'.format(name), comp)
for name, comp in ae_comps
])
train_logger.add_summaries([tf.summary.scalar('train/bpp', bpp_train)])
train_logger.add_console_tensor('loss={:.3f}', total_loss)
train_logger.add_console_tensor('ms_ssim={:.3f}', d_train.ms_ssim)
train_logger.add_console_tensor('bpp={:.3f}', bpp_train)
train_logger.add_console_tensor('H_real={:.3f}', H_real)
test_logger.add_summaries(d_test.summaries_with_prefix('test'))
test_logger.add_summaries([
tf.summary.scalar('test/bpp', bpp_test),
tf.summary.scalar('test/distance_BPG_MS-SSIM', d_BPG_test),
tf.summary.image('test/x_in',
prep_for_image_summary(x_test, n=3, name='x_in')),
tf.summary.image('test/x_out',
prep_for_image_summary(x_out_test, n=3, name='x_out'))
])
if heatmap2D is not None:
test_logger.add_summaries([
tf.summary.image(
'test/hm',
prep_for_grayscale_image_summary(heatmap2D,
n=3,
autoscale=True,
name='hm'))
])
test_logger.add_console_tensor('ms_ssim={:.3f}', d_test.ms_ssim)
test_logger.add_console_tensor('bpp={:.3f}', bpp_test)
test_logger.add_summaries([
tf.summary.histogram('centers', ae.get_centers_variable()),
tf.summary.histogram(
'test/qbar', enc_out_test.qbar[:ae_config.batch_size // 2, ...])
])
test_logger.add_console_tensor('d_BPG={:.6f}', d_BPG_test)
test_logger.add_console_tensor(Logger.Numpy1DFormatter('centers={}'),
ae.get_centers_variable())
print('Starting session and queues...')
with tf_helpers.start_queues_in_sess(
init_vars=restore_manager is None) as (sess, coord):
train_logger.finalize_with_sess(sess)
test_logger.finalize_with_sess(sess)
if restore_manager:
restore_manager.restore(sess)
saver = Saver(Saver.ckpt_dir_for_log_dir(logdir),
max_to_keep=1,
keep_checkpoint_every_n_hours=ckpt_interval_hours)
train_loop(ae_config,
sess,
coord,
train_op,
train_logger,
test_logger,
train_flags,
logdir,
saver,
is_restored=restore_manager is not None)
def _get_restore_ckpt_dir(restore_flag):
if Saver.is_ckpt_dir(restore_flag):
return restore_flag
if Saver.is_ckpt_dir(Saver.ckpt_dir_for_log_dir(restore_flag)):
return Saver.ckpt_dir_for_log_dir(restore_flag)
raise ValueError('Invalid ckpt dir: {}'.format(restore_flag))
