def extract(tfrecord_dir, output_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tflib.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir,
max_label_size=0,
repeat=False,
shuffle_mb=0)
tflib.init_uninitialized_vars()
print('Extracting images to "%s"' % output_dir)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
idx = 0
while True:
if idx % 10 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images, _labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if images.shape[1] == 1:
img = PIL.Image.fromarray(images[0][0], 'L')
else:
img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
img.save(os.path.join(output_dir, 'img%08d.png' % idx))
idx += 1
print('Extracted %d images.' % idx)
def display(tfrecord_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tflib.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir,
max_label_size='full',
repeat=False,
shuffle_mb=0)
tflib.init_uninitialized_vars()
import cv2 # pip install opencv-python
idx = 0
while True:
try:
images, labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if idx == 0:
print('Displaying images')
cv2.namedWindow('dataset_tool')
print('Press SPACE or ENTER to advance, ESC to exit')
print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
cv2.imshow('dataset_tool', images[0].transpose(
1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
idx += 1
if cv2.waitKey() == 27:
break
print('\nDisplayed %d images.' % idx)
def run(network_pkl, metrics, dataset, data_dir, mirror_augment):
print('Evaluating metrics "%s" for "%s"...' %
(','.join(metrics), network_pkl))
tflib.init_tf()
network_pkl = pretrained_networks.get_path_or_url(network_pkl)
dataset_args = EasyDict(tfrecord_dir=dataset, shuffle_mb=0)
num_gpus = submit_config.num_gpus
metric_group = metric_base.MetricGroup(
[metric_defaults[metric] for metric in metrics])
metric_group.run(network_pkl,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment,
num_gpus=num_gpus)
def load_networks(path_or_gdrive_path):
path_or_url = get_path_or_url(path_or_gdrive_path)
if path_or_url in _cached_networks:
return _cached_networks[path_or_url]
if util.is_url(path_or_url):
stream = util.open_url(path_or_url, cache_dir='.stylegan2-cache')
else:
stream = open(path_or_url, 'rb')
tflib.init_tf()
with stream:
G, D, Gs = pickle.load(stream, encoding='latin1')
_cached_networks[path_or_url] = G, D, Gs
return G, D, Gs
def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
max_label_size = 0 if ignore_labels else 'full'
print('Loading dataset "%s"' % tfrecord_dir_a)
tflib.init_tf({'gpu_options.allow_growth': True})
dset_a = dataset.TFRecordDataset(tfrecord_dir_a,
max_label_size=max_label_size,
repeat=False,
shuffle_mb=0)
print('Loading dataset "%s"' % tfrecord_dir_b)
dset_b = dataset.TFRecordDataset(tfrecord_dir_b,
max_label_size=max_label_size,
repeat=False,
shuffle_mb=0)
tflib.init_uninitialized_vars()
print('Comparing datasets')
idx = 0
identical_images = 0
identical_labels = 0
while True:
if idx % 100 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images_a, labels_a = dset_a.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_a, labels_a = None, None
try:
images_b, labels_b = dset_b.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_b, labels_b = None, None
if images_a is None or images_b is None:
if images_a is not None or images_b is not None:
print('Datasets contain different number of images')
break
if images_a.shape == images_b.shape and np.all(images_a == images_b):
identical_images += 1
else:
print('Image %d is different' % idx)
if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
identical_labels += 1
else:
print('Label %d is different' % idx)
idx += 1
print('Identical images: %d / %d' % (identical_images, idx))
if not ignore_labels:
print('Identical labels: %d / %d' % (identical_labels, idx))
def training_loop(
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
data_dir=None, # Directory to load datasets from.
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
lazy_regularization=True, # Perform regularization as a separate training step?
G_reg_interval=4, # How often the perform regularization for G? Ignored if lazy_regularization=False.
D_reg_interval=16, # How often the perform regularization for D? Ignored if lazy_regularization=False.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=25000, # Total length of the training, measured in thousands of real images.
mirror_augment=False, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=50, # How often to save image snapshots? None = only save 'reals.png' and 'fakes-init.png'.
network_snapshot_ticks=50, # How often to save network snapshots? None = only save 'networks-final.pkl'.
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_pkl=None, # Network pickle to resume training from, None = train from scratch.
resume_kimg=0.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0, # Assumed wallclock time at the beginning. Affects reporting.
resume_with_new_nets=False
): # Construct new networks according to G_args and D_args before resuming training?
# Initialize dnnlib and TensorFlow.
tflib.init_tf(tf_config)
num_gpus = submit_config.num_gpus
# Load training set.
training_set = dataset.load_dataset(data_dir=convert_path(data_dir),
verbose=True,
**dataset_args)
grid_size, grid_reals, grid_labels = misc.setup_snapshot_image_grid(
training_set, **grid_args)
misc.save_image_grid(grid_reals,
make_run_dir_path('reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
# Construct or load networks.
with tf.device('/gpu:0'):
if resume_pkl is None or resume_with_new_nets:
print('Constructing networks...')
G = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**G_args)
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**D_args)
Gs = G.clone('Gs')
if resume_pkl is not None:
print('Loading networks from "%s"...' % resume_pkl)
rG, rD, rGs = misc.load_pkl(resume_pkl)
if resume_with_new_nets:
G.copy_vars_from(rG)
D.copy_vars_from(rD)
Gs.copy_vars_from(rGs)
else:
G = rG
D = rD
Gs = rGs
# Print layers and generate initial image snapshot.
G.print_layers()
D.print_layers()
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
**sched_args)
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
misc.save_image_grid(grid_fakes,
make_run_dir_path('fakes_init.png'),
drange=drange_net,
grid_size=grid_size)
# Setup training inputs.
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_size_in = tf.placeholder(tf.int32,
name='minibatch_size_in',
shape=[])
minibatch_gpu_in = tf.placeholder(tf.int32,
name='minibatch_gpu_in',
shape=[])
minibatch_multiplier = minibatch_size_in // (minibatch_gpu_in *
num_gpus)
Gs_beta = 0.5**tf.div(tf.cast(minibatch_size_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
# Setup optimizers.
G_opt_args = dict(G_opt_args)
D_opt_args = dict(D_opt_args)
for args, reg_interval in [(G_opt_args, G_reg_interval),
(D_opt_args, D_reg_interval)]:
args['minibatch_multiplier'] = minibatch_multiplier
args['learning_rate'] = lrate_in
if lazy_regularization:
mb_ratio = reg_interval / (reg_interval + 1)
args['learning_rate'] *= mb_ratio
if 'beta1' in args: args['beta1'] **= mb_ratio
if 'beta2' in args: args['beta2'] **= mb_ratio
G_opt = tflib.Optimizer(name='TrainG', **G_opt_args)
D_opt = tflib.Optimizer(name='TrainD', **D_opt_args)
G_reg_opt = tflib.Optimizer(name='RegG', share=G_opt, **G_opt_args)
D_reg_opt = tflib.Optimizer(name='RegD', share=D_opt, **D_opt_args)
# Build training graph for each GPU.
data_fetch_ops = []
for gpu in range(num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
# Create GPU-specific shadow copies of G and D.
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
# Fetch training data via temporary variables.
with tf.name_scope('DataFetch'):
sched = training_schedule(cur_nimg=int(resume_kimg * 1000),
training_set=training_set,
**sched_args)
reals_var = tf.Variable(
name='reals',
trainable=False,
initial_value=tf.zeros([sched.minibatch_gpu] +
training_set.shape))
labels_var = tf.Variable(name='labels',
trainable=False,
initial_value=tf.zeros([
sched.minibatch_gpu,
training_set.label_size
]))
reals_write, labels_write = training_set.get_minibatch_tf()
reals_write, labels_write = process_reals(
reals_write, labels_write, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
reals_write = tf.concat(
[reals_write, reals_var[minibatch_gpu_in:]], axis=0)
labels_write = tf.concat(
[labels_write, labels_var[minibatch_gpu_in:]], axis=0)
data_fetch_ops += [tf.assign(reals_var, reals_write)]
data_fetch_ops += [tf.assign(labels_var, labels_write)]
reals_read = reals_var[:minibatch_gpu_in]
labels_read = labels_var[:minibatch_gpu_in]
# Evaluate loss functions.
lod_assign_ops = []
if 'lod' in G_gpu.vars:
lod_assign_ops += [tf.assign(G_gpu.vars['lod'], lod_in)]
if 'lod' in D_gpu.vars:
lod_assign_ops += [tf.assign(D_gpu.vars['lod'], lod_in)]
with tf.control_dependencies(lod_assign_ops):
with tf.name_scope('G_loss'):
G_loss, G_reg = util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
**G_loss_args)
with tf.name_scope('D_loss'):
D_loss, D_reg = util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
reals=reals_read,
labels=labels_read,
**D_loss_args)
# Register gradients.
if not lazy_regularization:
if G_reg is not None: G_loss += G_reg
if D_reg is not None: D_loss += D_reg
else:
if G_reg is not None:
G_reg_opt.register_gradients(
tf.reduce_mean(G_reg * G_reg_interval),
G_gpu.trainables)
if D_reg is not None:
D_reg_opt.register_gradients(
tf.reduce_mean(D_reg * D_reg_interval),
D_gpu.trainables)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
# Setup training ops.
data_fetch_op = tf.group(*data_fetch_ops)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
G_reg_op = G_reg_opt.apply_updates(allow_no_op=True)
D_reg_op = D_reg_opt.apply_updates(allow_no_op=True)
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
# Finalize graph.
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
tflib.init_uninitialized_vars()
print('Initializing logs...')
summary_log = tf.summary.FileWriter(make_run_dir_path())
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training for %d kimg...\n' % total_kimg)
RunContext.get().update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = RunContext.get().get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = -1
tick_start_nimg = cur_nimg
prev_lod = -1.0
running_mb_counter = 0
while cur_nimg < total_kimg * 1000:
if RunContext.get().should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
**sched_args)
assert sched.minibatch_size % (sched.minibatch_gpu * num_gpus) == 0
training_set.configure(sched.minibatch_gpu, sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
G_opt.reset_optimizer_state()
D_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
feed_dict = {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_size_in: sched.minibatch_size,
minibatch_gpu_in: sched.minibatch_gpu
}
for _repeat in range(minibatch_repeats):
rounds = range(0, sched.minibatch_size,
sched.minibatch_gpu * num_gpus)
run_G_reg = (lazy_regularization
and running_mb_counter % G_reg_interval == 0)
run_D_reg = (lazy_regularization
and running_mb_counter % D_reg_interval == 0)
cur_nimg += sched.minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
if len(rounds) == 1:
tflib.run([G_train_op, data_fetch_op], feed_dict)
if run_G_reg:
tflib.run(G_reg_op, feed_dict)
tflib.run([D_train_op, Gs_update_op], feed_dict)
if run_D_reg:
tflib.run(D_reg_op, feed_dict)
# Slow path with gradient accumulation.
else:
for _round in rounds:
tflib.run(G_train_op, feed_dict)
if run_G_reg:
for _round in rounds:
tflib.run(G_reg_op, feed_dict)
tflib.run(Gs_update_op, feed_dict)
for _round in rounds:
tflib.run(data_fetch_op, feed_dict)
tflib.run(D_train_op, feed_dict)
if run_D_reg:
for _round in rounds:
tflib.run(D_reg_op, feed_dict)
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if cur_tick < 0 or cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = RunContext.get().get_time_since_last_update()
total_time = RunContext.get().get_time_since_start() + resume_time
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %.1f'
%
(autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch_size),
util.format_time(autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb',
peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# Save snapshots.
if image_snapshot_ticks is not None and (
cur_tick % image_snapshot_ticks == 0 or done):
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
misc.save_image_grid(grid_fakes,
make_run_dir_path('fakes%06d.png' %
(cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
if network_snapshot_ticks is not None and (
cur_tick % network_snapshot_ticks == 0 or done):
pkl = make_run_dir_path('network-snapshot-%06d.pkl' %
(cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=make_run_dir_path(),
data_dir=convert_path(data_dir),
num_gpus=num_gpus,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
RunContext.get().update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = RunContext.get().get_last_update_interval(
) - tick_time
# Save final snapshot.
misc.save_pkl((G, D, Gs), make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()