def generate_images(network_pkl,
n_imgs,
model_type,
n_discrete,
n_continuous,
n_samples_per=10):
print('Loading networks from "%s"...' % network_pkl)
tflib.init_tf()
if (model_type == 'info_gan') or (model_type == 'vc_gan_with_vc_head'):
_G, _D, I, Gs = misc.load_pkl(network_pkl)
else:
_G, _D, Gs = misc.load_pkl(network_pkl)
# _G, _D, Gs = pretrained_networks.load_networks(network_pkl)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8,
nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
for idx in range(n_imgs):
print('Generating image %d/%d ...' % (idx, n_imgs))
if n_discrete == 0:
grid_labels = np.zeros([n_continuous * n_samples_per, 0],
dtype=np.float32)
else:
grid_labels = np.zeros(
[n_discrete * n_continuous * n_samples_per, 0],
dtype=np.float32)
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete, n_continuous, n_samples_per, _G, grid_labels)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=4,
randomize_noise=False)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path('img_%04d.png' % idx),
drange=[-1, 1],
grid_size=grid_size)
def generate_images(network_pkl,
network_G_pkl,
n_imgs,
model_type,
n_discrete,
n_continuous,
use_std_in_m=None,
latent_type='uniform',
n_samples_per=10):
print('Loading networks from "%s"...' % network_pkl)
tflib.init_tf()
if model_type == 'hd_dis_model_with_cls':
# _G, _D, I, Gs = misc.load_pkl(network_pkl)
I, M, Is, I_info = misc.load_pkl(network_pkl)
else:
# _G, _D, Gs = misc.load_pkl(network_pkl)
I, M, Is = misc.load_pkl(network_pkl)
# Load pretrained GAN
_G, _D, Gs = misc.load_pkl(network_G_pkl)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8,
nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
for idx in range(n_imgs):
print('Generating image %d/%d ...' % (idx, n_imgs))
if n_discrete == 0:
grid_labels = np.zeros([n_continuous * n_samples_per, 0],
dtype=np.float32)
else:
grid_labels = np.zeros(
[n_discrete * n_continuous * n_samples_per, 0],
dtype=np.float32)
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete,
n_continuous,
n_samples_per,
_G,
grid_labels,
latent_type=latent_type)
prior_traj_latents = M.run(grid_latents,
is_validation=True,
minibatch_size=4)
if use_std_in_m is not None:
prior_traj_latents = prior_traj_latents[:, :prior_traj_latents.
shape[1] // 2]
grid_fakes = Gs.run(prior_traj_latents,
grid_labels,
is_validation=True,
minibatch_size=4,
randomize_noise=False)
print(grid_fakes.shape)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path('img_%04d.png' % idx),
drange=[-1, 1],
grid_size=grid_size)
frames = []
grid_fakes = np.reshape(grid_fakes, [
n_continuous, n_samples_per, grid_fakes.shape[1],
grid_fakes.shape[2], grid_fakes.shape[3]
])
for i in range(n_samples_per):
to_concat = [grid_fakes[j, i] for j in range(n_continuous)]
to_concat = tuple(to_concat)
grid_fake_pil = misc.convert_to_pil_image(
np.concatenate(to_concat, axis=2))
frames.append(grid_fake_pil)
frames[0].save(dnnlib.make_run_dir_path('latents_trav_%04d.gif' % idx),
format='GIF',
append_images=frames[1:],
save_all=True,
duration=100,
loop=0)
def training_loop_vc(
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
I_args={}, # Options for infogan-head/vcgan-head network.
I_info_args={}, # Options for infogan-head/vcgan-head 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().
use_info_gan=False, # Whether to use info-gan.
use_vc_head=False, # Whether to use vc-head.
use_vc_head_with_cls=False, # Whether to use classification in discriminator.
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?
traversal_grid=False, # Used for disentangled representation learning.
n_discrete=3, # Number of discrete latents in model.
n_continuous=4, # Number of continuous latents in model.
n_samples_per=10): # Number of samples for each line in traversal.
# Initialize dnnlib and TensorFlow.
tflib.init_tf(tf_config)
num_gpus = dnnlib.submit_config.num_gpus
# Load training set.
training_set = dataset.load_dataset(data_dir=dnnlib.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,
dnnlib.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)
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I = tflib.Network('I',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**I_args)
if use_vc_head_with_cls:
I_info = tflib.Network('I_info',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**I_info_args)
Gs = G.clone('Gs')
if resume_pkl is not None:
print('Loading networks from "%s"...' % resume_pkl)
if use_info_gan or use_vc_head:
rG, rD, rI, rGs = misc.load_pkl(resume_pkl)
elif use_vc_head_with_cls:
rG, rD, rI, rI_info, rGs = misc.load_pkl(resume_pkl)
else:
rG, rD, rGs = misc.load_pkl(resume_pkl)
if resume_with_new_nets:
G.copy_vars_from(rG)
D.copy_vars_from(rD)
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I.copy_vars_from(rI)
if use_vc_head_with_cls:
I_info.copy_vars_from(rI_info)
Gs.copy_vars_from(rGs)
else:
G = rG
D = rD
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I = rI
if use_vc_head_with_cls:
I_info = rI_info
Gs = rGs
# Print layers and generate initial image snapshot.
G.print_layers()
D.print_layers()
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I.print_layers()
if use_vc_head_with_cls:
I_info.print_layers()
# pdb.set_trace()
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
**sched_args)
if traversal_grid:
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete, n_continuous, n_samples_per, G, grid_labels)
else:
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
print('grid_latents.shape:', grid_latents.shape)
print('grid_labels.shape:', grid_labels.shape)
# pdb.set_trace()
grid_fakes, _ = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=False)
misc.save_image_grid(grid_fakes,
dnnlib.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')
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I_gpu = I if gpu == 0 else I.clone(I.name + '_shadow')
if use_vc_head_with_cls:
I_info_gpu = I_info if gpu == 0 else I_info.clone(
I_info.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'):
if use_info_gan or use_vc_head:
G_loss, G_reg, I_loss, _ = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
I=I_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
**G_loss_args)
elif use_vc_head_with_cls:
G_loss, G_reg, I_loss, I_info_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
I=I_gpu,
I_info=I_info_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
**G_loss_args)
else:
G_loss, G_reg = dnnlib.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 = dnnlib.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)
# print('G_gpu.trainables:', G_gpu.trainables)
# print('D_gpu.trainables:', D_gpu.trainables)
# print('I_gpu.trainables:', I_gpu.trainables)
if use_info_gan or use_vc_head:
GI_gpu_trainables = collections.OrderedDict(
list(G_gpu.trainables.items()) +
list(I_gpu.trainables.items()))
G_opt.register_gradients(tf.reduce_mean(G_loss + I_loss),
GI_gpu_trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss),
D_gpu.trainables)
# G_opt.register_gradients(tf.reduce_mean(I_loss),
# GI_gpu_trainables)
# D_opt.register_gradients(tf.reduce_mean(I_loss),
# D_gpu.trainables)
elif use_vc_head_with_cls:
GIIinfo_gpu_trainables = collections.OrderedDict(
list(G_gpu.trainables.items()) +
list(I_gpu.trainables.items()) +
list(I_info_gpu.trainables.items()))
G_opt.register_gradients(
tf.reduce_mean(G_loss + I_loss + I_info_loss),
GIIinfo_gpu_trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss),
D_gpu.trainables)
else:
G_opt.register_gradients(tf.reduce_mean(G_loss),
G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss),
D_gpu.trainables)
# if use_info_gan:
# # INFO-GAN-HEAD loss
# G_opt.register_gradients(tf.reduce_mean(I_loss),
# G_gpu.trainables)
# G_opt.register_gradients(tf.reduce_mean(I_loss),
# I_gpu.trainables)
# D_opt.register_gradients(tf.reduce_mean(I_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(dnnlib.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()
if use_info_gan or use_vc_head or use_vc_head_with_cls:
I.setup_weight_histograms()
if use_vc_head_with_cls:
I_info.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training for %d kimg...\n' % total_kimg)
dnnlib.RunContext.get().update('',
cur_epoch=resume_kimg,
max_epoch=total_kimg)
maintenance_time = dnnlib.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 dnnlib.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 = dnnlib.RunContext.get().get_time_since_last_update()
total_time = dnnlib.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),
dnnlib.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,
randomize_noise=False)
misc.save_image_grid(grid_fakes,
dnnlib.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 = dnnlib.make_run_dir_path('network-snapshot-%06d.pkl' %
(cur_nimg // 1000))
if use_info_gan or use_vc_head:
misc.save_pkl((G, D, I, Gs), pkl)
elif use_vc_head_with_cls:
misc.save_pkl((G, D, I, I_info, Gs), pkl)
else:
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=dnnlib.make_run_dir_path(),
data_dir=dnnlib.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)
dnnlib.RunContext.get().update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = dnnlib.RunContext.get(
).get_last_update_interval() - tick_time
# Save final snapshot.
if use_info_gan or use_vc_head:
misc.save_pkl((G, D, I, Gs),
dnnlib.make_run_dir_path('network-final.pkl'))
elif use_vc_head_with_cls:
misc.save_pkl((G, D, I, I_info, Gs),
dnnlib.make_run_dir_path('network-final.pkl'))
else:
misc.save_pkl((G, D, Gs),
dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
def training_loop_hd(
I_args={}, # Options for generator network.
M_args={}, # Options for discriminator network.
I_info_args={}, # Options for class network.
I_opt_args={}, # Options for generator optimizer.
I_loss_args={}, # Options for generator loss.
resume_G_pkl=None, # G network pickle to help training.
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.
I_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?
I_reg_interval=4, # How often the perform regularization for G? 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 I_args and M_args before resuming training?
traversal_grid=False, # Used for disentangled representation learning.
n_discrete=0, # Number of discrete latents in model.
n_continuous=10, # Number of continuous latents in model.
n_samples_per=4, # Number of samples for each line in traversal.
use_hd_with_cls=False, # If use info_loss.
resolution_manual=1024, # Resolution of generated images.
use_level_training=False, # If use level training (hierarchical optimization strategy).
level_I_kimg=1000, # Number of kimg of tick for I_level training.
use_std_in_m=False, # If output prior std in M net.
prior_latent_size=512, # Prior latent size.
use_hyperplane=False, # If use hyperplane model.
pretrained_type='with_stylegan2'): # Pretrained type for G.
# Initialize dnnlib and TensorFlow.
tflib.init_tf(tf_config)
num_gpus = dnnlib.submit_config.num_gpus
# Load training set.
training_set = dataset.load_dataset(data_dir=dnnlib.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,
dnnlib.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...')
I = tflib.Network('I',
num_channels=training_set.shape[0],
resolution=resolution_manual,
label_size=training_set.label_size,
**I_args)
M = tflib.Network('M',
num_channels=training_set.shape[0],
resolution=resolution_manual,
label_size=training_set.label_size,
**M_args)
Is = I.clone('Is')
if use_hd_with_cls:
I_info = tflib.Network('I_info',
num_channels=training_set.shape[0],
resolution=resolution_manual,
label_size=training_set.label_size,
**I_info_args)
if resume_pkl is not None:
print('Loading networks from "%s"...' % resume_pkl)
if use_hd_with_cls:
rI, rM, rIs, rI_info = misc.load_pkl(resume_pkl)
else:
rI, rM, rIs = misc.load_pkl(resume_pkl)
if resume_with_new_nets:
I.copy_vars_from(rI)
M.copy_vars_from(rM)
Is.copy_vars_from(rIs)
if use_hd_with_cls:
I_info.copy_vars_from(rI_info)
else:
I = rI
M = rM
Is = rIs
if use_hd_with_cls:
I_info = rI_info
print('Loading generator from "%s"...' % resume_G_pkl)
if pretrained_type == 'with_stylegan2':
rG, rD, rGs = misc.load_pkl(resume_G_pkl)
G = rG
D = rD
Gs = rGs
elif pretrained_type == 'with_cascadeVAE':
rG = misc.load_pkl(resume_G_pkl)
G = rG
Gs = rG
# Print layers and generate initial image snapshot.
I.print_layers()
M.print_layers()
# pdb.set_trace()
training_set_resolution_log2 = int(np.log2(resolution_manual))
sched = training_schedule(
cur_nimg=total_kimg * 1000,
training_set_resolution_log2=training_set_resolution_log2,
**sched_args)
if not use_hyperplane:
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete, n_continuous, n_samples_per, G, grid_labels)
print('grid_size:', grid_size)
print('grid_latents.shape:', grid_latents.shape)
print('grid_labels.shape:', grid_labels.shape)
if resolution_manual >= 256:
grid_size = (grid_size[0], grid_size[1] // 5)
grid_latents = grid_latents[:grid_latents.shape[0] // 5]
grid_labels = grid_labels[:grid_labels.shape[0] // 5]
prior_traj_latents = M.run(grid_latents,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
if use_std_in_m:
prior_traj_latents = prior_traj_latents[:, :prior_latent_size]
else:
grid_size = (n_samples_per, n_continuous)
grid_labels = np.tile(grid_labels[:1],
(n_continuous * n_samples_per, 1))
latent_dirs = get_latent_dirs(n_continuous)
prior_traj_latents = get_prior_traj_by_dirs(latent_dirs, M,
n_samples_per,
prior_latent_size,
grid_labels, sched)
if resolution_manual >= 256:
grid_size = (grid_size[0], grid_size[1] // 5)
prior_traj_latents = prior_traj_latents[:prior_traj_latents.
shape[0] // 5]
grid_labels = grid_labels[:grid_labels.shape[0] // 5]
print('prior_traj_latents.shape:', prior_traj_latents.shape)
# pdb.set_trace()
prior_traj_latents_show = np.reshape(
prior_traj_latents, [-1, n_samples_per, prior_latent_size])
print_traj(prior_traj_latents_show)
grid_fakes = Gs.run(prior_traj_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
normalize_latents=False)
grid_fakes = add_outline(grid_fakes, width=1)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path('fakes_init.png'),
drange=drange_net,
grid_size=grid_size)
if (n_continuous == 2) and (n_discrete == 0):
n_per_line = 20
ex_latent_value = 3
prior_grid_latents, prior_grid_labels = get_2d_grid_latents(
low=-ex_latent_value,
high=ex_latent_value,
n_per_line=n_per_line,
grid_labels=grid_labels)
grid_showing_fakes = Gs.run(prior_grid_latents,
prior_grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
normalize_latents=False)
grid_showing_fakes = add_outline(grid_showing_fakes, width=1)
misc.save_image_grid(
grid_showing_fakes,
dnnlib.make_run_dir_path('fakes_init_2d_prior_grid.png'),
drange=drange_net,
grid_size=[n_per_line, n_per_line])
img_to_draw = Image.open(
dnnlib.make_run_dir_path('fakes_init_2d_prior_grid.png'))
img_to_draw = img_to_draw.convert('RGB')
img_to_draw = draw_traj_on_prior_grid(img_to_draw,
prior_traj_latents_show,
ex_latent_value, n_per_line)
img_to_draw.save(
dnnlib.make_run_dir_path('fakes_init_2d_prior_grid_drawn.png'))
if use_level_training:
ending_level = training_set_resolution_log2 - 1
else:
ending_level = 1
# 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)
Is_beta = 0.5**tf.div(tf.cast(minibatch_size_in,
tf.float32), I_smoothing_kimg *
1000.0) if I_smoothing_kimg > 0.0 else 0.0
# Setup optimizers.
I_opt_args = dict(I_opt_args)
for args, reg_interval in [(I_opt_args, I_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
I_opts = []
I_reg_opts = []
for n_level in range(ending_level):
I_opts.append(tflib.Optimizer(name='TrainI_%d' % n_level,
**I_opt_args))
I_reg_opts.append(
tflib.Optimizer(name='RegI_%d' % n_level,
share=I_opts[-1],
**I_opt_args))
# Build training graph for each GPU.
for gpu in range(num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
# Create GPU-specific shadow copies of I and M.
I_gpu = I if gpu == 0 else I.clone(I.name + '_shadow')
M_gpu = M if gpu == 0 else M.clone(M.name + '_shadow')
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
if use_hd_with_cls:
I_info_gpu = I_info if gpu == 0 else I_info.clone(I_info.name +
'_shadow')
# Evaluate loss functions.
lod_assign_ops = []
I_losses = []
I_regs = []
if 'lod' in I_gpu.vars:
lod_assign_ops += [tf.assign(I_gpu.vars['lod'], lod_in)]
if 'lod' in M_gpu.vars:
lod_assign_ops += [tf.assign(M_gpu.vars['lod'], lod_in)]
for n_level in range(ending_level):
with tf.control_dependencies(lod_assign_ops):
with tf.name_scope('I_loss_%d' % n_level):
if use_hd_with_cls:
I_loss, I_reg = dnnlib.util.call_func_by_name(
I=I_gpu,
M=M_gpu,
G=G_gpu,
I_info=I_info_gpu,
opt=I_opts[n_level],
training_set=training_set,
minibatch_size=minibatch_gpu_in,
**I_loss_args)
else:
I_loss, I_reg = dnnlib.util.call_func_by_name(
I=I_gpu,
M=M_gpu,
G=G_gpu,
opt=I_opts[n_level],
n_levels=(n_level +
1) if use_level_training else None,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
**I_loss_args)
I_losses.append(I_loss)
I_regs.append(I_reg)
# Register gradients.
if not lazy_regularization:
if I_regs[n_level] is not None:
I_losses[n_level] += I_regs[n_level]
else:
if I_regs[n_level] is not None:
I_reg_opts[n_level].register_gradients(
tf.reduce_mean(I_regs[n_level] * I_reg_interval),
I_gpu.trainables)
if use_hd_with_cls:
MIIinfo_gpu_trainables = collections.OrderedDict(
list(M_gpu.trainables.items()) +
list(I_gpu.trainables.items()) +
list(I_info_gpu.trainables.items()))
I_opts[n_level].register_gradients(
tf.reduce_mean(I_losses[n_level]),
MIIinfo_gpu_trainables)
else:
MI_gpu_trainables = collections.OrderedDict(
list(M_gpu.trainables.items()) +
list(I_gpu.trainables.items()))
I_opts[n_level].register_gradients(
tf.reduce_mean(I_losses[n_level]), MI_gpu_trainables)
# Setup training ops.
I_train_ops = []
I_reg_ops = []
for n_level in range(ending_level):
I_train_ops.append(I_opts[n_level].apply_updates())
I_reg_ops.append(I_reg_opts[n_level].apply_updates(allow_no_op=True))
Is_update_op = Is.setup_as_moving_average_of(I, beta=Is_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(dnnlib.make_run_dir_path())
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
I.setup_weight_histograms()
M.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training for %d kimg...\n' % total_kimg)
dnnlib.RunContext.get().update('',
cur_epoch=resume_kimg,
max_epoch=total_kimg)
maintenance_time = dnnlib.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 dnnlib.RunContext.get().should_stop(): break
n_level = 0 if not use_level_training else min(
cur_nimg // (level_I_kimg * 1000), training_set_resolution_log2 -
2)
# Choose training parameters and configure training ops.
sched = training_schedule(
cur_nimg=cur_nimg,
training_set_resolution_log2=training_set_resolution_log2,
**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):
I_opts[n_level].reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
feed_dict = {
lod_in: sched.lod,
lrate_in: sched.I_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_I_reg = (lazy_regularization
and running_mb_counter % I_reg_interval == 0)
cur_nimg += sched.minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
if len(rounds) == 1:
tflib.run(I_train_ops[n_level], feed_dict)
if run_I_reg:
tflib.run(I_reg_ops[n_level], feed_dict)
tflib.run([Is_update_op], feed_dict)
# Slow path with gradient accumulation.
else:
for _round in rounds:
tflib.run(I_train_ops[n_level], feed_dict)
if run_I_reg:
for _round in rounds:
tflib.run(I_reg_ops[n_level], feed_dict)
tflib.run(Is_update_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 * 100 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = dnnlib.RunContext.get().get_time_since_last_update()
total_time = dnnlib.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),
dnnlib.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):
if not use_hyperplane:
prior_traj_latents = M.run(
grid_latents,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
if use_std_in_m:
prior_traj_latents = prior_traj_latents[:, :
prior_latent_size]
else:
prior_traj_latents = get_prior_traj_by_dirs(
latent_dirs, M, n_samples_per, prior_latent_size,
grid_labels, sched)
if resolution_manual >= 256:
prior_traj_latents = prior_traj_latents[:
prior_traj_latents
.shape[0] // 5]
prior_traj_latents_show = np.reshape(
prior_traj_latents, [-1, n_samples_per, prior_latent_size])
print_traj(prior_traj_latents_show)
grid_fakes = Gs.run(prior_traj_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
normalize_latents=False)
grid_fakes = add_outline(grid_fakes, width=1)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path(
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
if (n_continuous == 2) and (n_discrete == 0):
n_per_line = 20
ex_latent_value = 3
prior_grid_latents, prior_grid_labels = get_2d_grid_latents(
low=-ex_latent_value,
high=ex_latent_value,
n_per_line=n_per_line,
grid_labels=grid_labels)
grid_showing_fakes = Gs.run(
prior_grid_latents,
prior_grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
normalize_latents=False)
grid_showing_fakes = add_outline(grid_showing_fakes,
width=1)
misc.save_image_grid(grid_showing_fakes,
dnnlib.make_run_dir_path(
'fakes_2d_prior_grid%06d.png' %
(cur_nimg // 1000)),
drange=drange_net,
grid_size=[n_per_line, n_per_line])
img_to_draw = Image.open(
dnnlib.make_run_dir_path(
'fakes_2d_prior_grid%06d.png' %
(cur_nimg // 1000)))
img_to_draw = img_to_draw.convert('RGB')
img_to_draw = draw_traj_on_prior_grid(
img_to_draw, prior_traj_latents_show, ex_latent_value,
n_per_line)
img_to_draw.save(
dnnlib.make_run_dir_path(
'fakes_2d_prior_grid_drawn%06d.png' %
(cur_nimg // 1000)))
if network_snapshot_ticks is not None and (
cur_tick % network_snapshot_ticks == 0 or done):
pkl = dnnlib.make_run_dir_path('network-snapshot-%06d.pkl' %
(cur_nimg // 1000))
misc.save_pkl((I, M, Is), pkl)
metrics.run(pkl,
run_dir=dnnlib.make_run_dir_path(),
data_dir=dnnlib.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)
dnnlib.RunContext.get().update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = dnnlib.RunContext.get(
).get_last_update_interval() - tick_time
# Save final snapshot.
misc.save_pkl((I, M, Is), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()