def project_image(proj, src_file, dst_dir, tmp_dir, video=False):
data_dir = '%s/dataset' % tmp_dir
if os.path.exists(data_dir):
shutil.rmtree(data_dir)
image_dir = '%s/images' % data_dir
tfrecord_dir = '%s/tfrecords' % data_dir
os.makedirs(image_dir, exist_ok=True)
shutil.copy(src_file, image_dir + '/')
dataset_tool.create_from_images_raw(tfrecord_dir, image_dir, shuffle=0)
dataset_obj = dataset.load_dataset(data_dir=data_dir,
tfrecord_dir='tfrecords',
max_label_size=0,
repeat=False,
shuffle_mb=0)
print('Projecting image "%s"...' % os.path.basename(src_file))
images, _labels = dataset_obj.get_minibatch_np(1)
images = misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
proj.start(images)
if video:
video_dir = '%s/video' % tmp_dir
os.makedirs(video_dir, exist_ok=True)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps),
end='',
flush=True)
proj.step()
if video:
filename = '%s/%08d.png' % (video_dir, proj.get_cur_step())
misc.save_image_grid(proj.get_images(), filename, drange=[-1, 1])
print('\r%-30s\r' % '', end='', flush=True)
os.makedirs(dst_dir, exist_ok=True)
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.png')
misc.save_image_grid(proj.get_images(), filename, drange=[-1, 1])
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.npy')
np.save(filename, proj.get_dlatents()[0])
def project_image(proj, targets, work_dir, resolution, num_snapshots):
filename = osp.join(work_dir, basename(work_dir))
video_out = cv2.VideoWriter(filename + '.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 24,
resolution)
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
misc.save_image_grid(targets, filename + '.jpg', drange=[-1, 1])
proj.start(targets)
pbar = ProgressBar(proj.num_steps)
while proj.get_cur_step() < proj.num_steps:
proj.step()
write_video_frame(proj, video_out)
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(proj.get_images(),
filename + '-%04d.jpg' % proj.get_cur_step(),
drange=[-1, 1])
pbar.upd()
dlats = proj.get_dlatents()
np.save(filename + '-%04d.npy' % proj.get_cur_step(), dlats)
video_out.release()
def project_image(proj, targets, labels, png_prefix, num_snapshots, save_npy,
npy_file_prefix):
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1, 1])
proj.start(targets)
if npy_file_prefix is not None and save_npy:
print("WILL SAVE npy_file to: " + npy_file_prefix + '_<LOSS>.npy')
while proj.get_cur_step() < proj.num_steps:
#print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps), end='', flush=True)
proj.step()
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(
proj.get_images(),
png_prefix + 'step%04d_%0.2f.png' %
(proj.get_cur_step(), proj._latest_loss_value),
drange=[-1, 1])
print('\r%-30s\r' % '', end='', flush=True)
if npy_file_prefix is not None and save_npy:
proj.save_npy(npy_file_prefix)
def project_image(proj,
targets,
png_prefix,
num_snapshots,
onlyfirstandlast=False):
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
if onlyfirstandlast:
snapshot_steps = {1, 300}
misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1, 1])
proj.start(targets)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps),
end='',
flush=True)
proj.step()
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(proj.get_images(),
png_prefix +
'step%04d.png' % proj.get_cur_step(),
drange=[-1, 1])
print('\r%-30s\r' % '', end='', flush=True)
def save_atts(atts, filename, grid_size, drange, grid_fakes, n_samples_per):
canvas = np.zeros(
[grid_fakes.shape[0], 1, grid_fakes.shape[2], grid_fakes.shape[3]])
# atts: [b, n_latents, 1, res, res]
for i in range(atts.shape[1]):
att_sp = atts[:, i] # [b, 1, x_h, x_w]
att_sp = (att_sp - att_sp.min()) / (att_sp.max() - att_sp.min())
grid_start_idx = i * n_samples_per
canvas[grid_start_idx:grid_start_idx +
n_samples_per] = att_sp[grid_start_idx:grid_start_idx +
n_samples_per]
# already_n_latents = 0
# for i, att in enumerate(atts):
# att_sp = att[-1] # [b, n_latents, 1, x_h, x_w]
# for j in range(att_sp.shape[1]):
# att_sp_sub = att_sp[:, j] # [b, 1, x_h, x_w]
# grid_start_idx = already_n_latents * n_samples_per
# canvas[grid_start_idx : grid_start_idx + n_samples_per] = att_sp_sub[grid_start_idx : grid_start_idx + n_samples_per]
# already_n_latents += 1
misc.save_image_grid(canvas, filename, drange=drange, grid_size=grid_size)
return
def project_image(proj, targets, png_prefix, num_snapshots):
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1, 1])
proj.start(targets)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps),
end='',
flush=True)
proj.step()
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(proj.get_images(),
png_prefix +
'step%04d.png' % proj.get_cur_step(),
drange=[-1, 1])
print('###step', proj.get_cur_step())
dlatents = proj.get_dlatents()
for dlatents1 in dlatents:
for dlatents2 in dlatents1:
str = ''
for e in dlatents2:
str = '{} {}'.format(str, e)
print('###', str)
print('\r%-30s\r' % '', end='', flush=True)
def project_image(proj, targets, png_prefix, num_snapshots):
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
print('target.shape', targets.shape)
misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1, 1])
proj.start(targets)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps),
end='',
flush=True)
proj.step()
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(
proj.get_images(),
png_prefix + 'corrupted-step%04d.png' % proj.get_cur_step(),
drange=[-1, 1])
cleanImgFile = png_prefix + 'clean-step%04d.png' % proj.get_cur_step(
)
print('Saving checkpoint %s' % cleanImgFile)
misc.save_image_grid(proj.get_clean_images(),
cleanImgFile,
drange=[-1, 1])
print('\r%-30s\r' % '', end='', flush=True)
e = err(x, x1)
# init = tf.global_variables_initializer
# sess = tf.Session()
# sess.run(init())
# sess.run(e)
# sess.run(abserr(x,x1))
tflib.run(e)
tflib.run(abserr(x, x1))
# img, img_re = sess.run([x, x1])
img, img_re = tflib.run([x, x1])
misc.save_image_grid(img,
dnnlib.make_run_dir_path('/gdata2/fengrl/img.png'),
drange=[-1, 1],
grid_size=[8, 1])
misc.save_image_grid(img_re,
dnnlib.make_run_dir_path('/gdata2/fengrl/img_re.png'),
drange=[-1, 1],
grid_size=[8, 1])
v = [v for v in tf.global_variables() if 'weight' in v.name]
w = v[4]
ww = v[4] * 5.0
f_shape = [16, 16]
d = tf.rsqrt(tf.reduce_sum(tf.square(w), axis=[0, 1, 2]) + 1e-8)
dd = tf.rsqrt(tf.reduce_sum(tf.square(ww), axis=[0, 1, 2]) + 1e-8)
w *= d[np.newaxis, np.newaxis, np.newaxis, :]
def training_loop_refinement(
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=True, # 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 = 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)
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)
del _rD, _rG
if resume_with_new_nets:
G.copy_vars_from(rGs)
Gs.copy_vars_from(rGs)
del rGs
else:
G = rG
Gs = rGs
# Set constant noise input for both G and Gs
if G_args.get("randomize_noise", None) == False:
noise_vars = [
var for name, var in G.components.synthesis.vars.items()
if name.startswith('noise')
]
rnd = np.random.RandomState(123)
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars}) # [height, width]
noise_vars = [
var for name, var in Gs.components.synthesis.vars.items()
if name.startswith('noise')
]
rnd = np.random.RandomState(123)
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars}) # [height, width]
# TESTS
# from PIL import Image
# reals, latents = training_set.get_minibatch_np(4)
# reals = np.transpose(reals, [0, 2, 3, 1])
# Image.fromarray(reals[0], 'RGB').save("test_reals.png")
# labels = training_set.get_random_labels_np(4)
# Gs_kwargs = dnnlib.EasyDict()
# Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
# fakes = Gs.run(latents, labels, minibatch_size=4, **Gs_kwargs)
# Image.fromarray(fakes[0], 'RGB').save("test_fakes_Gs_new.png")
# fakes = G.run(latents, labels, minibatch_size=4, **Gs_kwargs)
# Image.fromarray(fakes[0], 'RGB').save("test_fakes_G_new.png")
# Print layers and generate initial image snapshot.
G.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,
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)
for args, reg_interval in [(G_opt_args, G_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)
G_reg_opt = tflib.Optimizer(name='RegG', share=G_opt, **G_opt_args)
# Freeze layers
G_args.freeze_layers = list(G_args.get("freeze_layers", []))
def freeze_vars(gen, verbose=True):
assert len(G_args.freeze_layers) > 0
for name in list(gen.trainables.keys()):
if any(layer in name for layer in G_args.freeze_layers):
del gen.trainables[name]
if verbose: print(f"Freezed {name}")
# Build training graph for each GPU.
data_fetch_ops = []
loss_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')
if G_args.freeze_layers: freeze_vars(G_gpu, verbose=False)
# 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)]
with tf.control_dependencies(lod_assign_ops):
with tf.name_scope('G_loss'):
G_loss, G_reg = dnnlib.util.call_func_by_name(
G=G_gpu,
D=None,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
reals=reals_read,
latents=labels_read,
**G_loss_args)
loss_ops.append(G_loss)
# Register gradients.
if not lazy_regularization:
if G_reg is not None: G_loss += G_reg
else:
if G_reg is not None:
G_reg_opt.register_gradients(
tf.reduce_mean(G_reg * G_reg_interval),
G_gpu.trainables)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
# Setup training ops.
data_fetch_op = tf.group(*data_fetch_ops)
loss_op = tf.reduce_mean(tf.concat(loss_ops, axis=0))
G_train_op = G_opt.apply_updates()
G_reg_op = G_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()
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
loss_per_batch_sum = 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()
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
}
tflib.run(data_fetch_op, feed_dict)
### TEST
# fakes = G.get_output_for(labels_read, training_set.get_random_labels_tf(minibatch_gpu_in), is_training=True) # this is without activation in ~[-1.5, 1.5]
# fakes = tf.clip_by_value(fakes, drange_net[0], drange_net[1])
# reals = reals_read
### TEST
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)
cur_nimg += sched.minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
if len(rounds) == 1:
loss, _ = tflib.run([loss_op, G_train_op], feed_dict)
# (loss, reals, fakes), _ = tflib.run([loss_op, G_train_op], feed_dict)
tflib.run([data_fetch_op], feed_dict)
# print(f"loss_tf {np.mean(loss)}")
# print(f"loss_np {np.mean(np.square(reals - fakes))}")
# print(f"loss_abs {np.mean(np.abs(reals - fakes))}")
loss_per_batch_sum += loss
#### TEST ####
# if cur_nimg == sched.minibatch_size or cur_nimg % 2048 == 0:
# from PIL import Image
# reals = np.transpose(reals, [0, 2, 3, 1])
# fakes = np.transpose(fakes, [0, 2, 3, 1])
# diff = np.abs(reals - fakes)
# print(diff.min(), diff.max())
# for idx, (fake, real) in enumerate(zip(fakes, reals)):
# fake -= fake.min()
# fake /= fake.max()
# fake *= 255
# fake = fake.astype(np.uint8)
# Image.fromarray(fake, 'RGB').save(f"fake_loss_{idx}.png")
# real -= real.min()
# real /= real.max()
# real *= 255
# real = real.astype(np.uint8)
# Image.fromarray(real, 'RGB').save(f"real_loss_{idx}.png")
####
if run_G_reg:
tflib.run(G_reg_op, feed_dict)
tflib.run([Gs_update_op], feed_dict)
# Slow path with gradient accumulation. FIXME: Probably wrong
else:
for _round in rounds:
loss, _, _ = tflib.run(
[loss_op, G_train_op, data_fetch_op], feed_dict)
loss_per_batch_sum += loss / len(rounds)
if run_G_reg:
tflib.run(G_reg_op, feed_dict)
tflib.run(Gs_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 * 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
tick_loss = loss_per_batch_sum * sched.minibatch_size / (
tick_kimg * 1000)
loss_per_batch_sum = 0
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d loss/px %-12.8f 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),
autosummary('Progress/loss_per_px', tick_loss),
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)
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))
misc.save_pkl((G, None, 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.
misc.save_pkl((G, None, Gs), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
def generate_images(network_pkl,
seeds,
truncation_psi,
data_dir=None,
dataset_name=None,
model=None):
G_args = EasyDict(func_name='training.' + model + '.G_main')
dataset_args = EasyDict(tfrecord_dir=dataset_name)
G_args.fmap_base = 8 << 10
tflib.init_tf()
training_set = dataset.load_dataset(data_dir=dnnlib.convert_path(data_dir),
verbose=True,
**dataset_args)
print('Constructing networks...')
Gs = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**G_args)
print('Loading networks from "%s"...' % network_pkl)
_, _, _Gs = pretrained_networks.load_networks(network_pkl)
Gs.copy_vars_from(_Gs)
noise_vars = [
var for name, var in Gs.components.synthesis.vars.items()
if name.startswith('noise')
]
Gs_kwargs = dnnlib.EasyDict()
# Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' %
(seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars}) # [height, width]
images, x_v, n_v, m_v = Gs.run(
z, None, **Gs_kwargs) # [minibatch, height, width, channel]
print(images.shape, n_v.shape, x_v.shape, m_v.shape)
misc.convert_to_pil_image(images[0], drange=[-1, 1]).save(
dnnlib.make_run_dir_path('seed%04d.png' % seed))
misc.save_image_grid(adjust_range(n_v),
dnnlib.make_run_dir_path('seed%04d-nv.png' %
seed),
drange=[-1, 1])
print(np.linalg.norm(x_v - m_v))
misc.save_image_grid(adjust_range(x_v).transpose([1, 0, 2, 3]),
dnnlib.make_run_dir_path('seed%04d-xv.png' %
seed),
drange=[-1, 1])
misc.save_image_grid(adjust_range(m_v).transpose([1, 0, 2, 3]),
dnnlib.make_run_dir_path('seed%04d-mv.png' %
seed),
drange=[-1, 1])
misc.save_image_grid(adjust_range(clip(x_v, 'cat')),
dnnlib.make_run_dir_path('seed%04d-xvs.png' %
seed),
drange=[-1, 1])
misc.save_image_grid(adjust_range(clip(m_v, 'ss')),
dnnlib.make_run_dir_path('seed%04d-mvs.png' %
seed),
drange=[-1, 1])
misc.save_image_grid(adjust_range(clip(m_v, 'ffhq')),
dnnlib.make_run_dir_path('seed%04d-fmvs.png' %
seed),
drange=[-1, 1])
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().
setname = None, # Model name
tf_config = {}, # Options for tflib.init_tf().
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?
mirror_augment_v = False, # Enable mirror augment vertically?
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 = 'latest', # 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.
restore_partial_fn = None, # Filename of network for partial restore
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 = dnnlib.submit_config.num_gpus
# Load training set.
training_set = dataset.load_dataset(verbose=True, **dataset_args)
# custom resolution - for saved model name below
resolution = training_set.resolution
if training_set.init_res != [4,4]:
init_res_str = '-%dx%d' % (training_set.init_res[0], training_set.init_res[1])
else:
init_res_str = ''
ext = 'png' if training_set.shape[0] == 4 else 'jpg'
print(' model base resolution', resolution)
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.%s'%ext), 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=resolution, label_size=training_set.label_size, **G_args)
D = tflib.Network('D', num_channels=training_set.shape[0], resolution=resolution, label_size=training_set.label_size, **D_args)
Gs = G.clone('Gs')
if resume_pkl is not None:
if resume_pkl == 'latest':
resume_pkl, resume_kimg = misc.locate_latest_pkl(dnnlib.submit_config.run_dir_root)
elif resume_pkl == 'restore_partial':
print(' Restore partially...')
# Initialize networks
G = tflib.Network('G', num_channels=training_set.shape[0], resolution=resolution, label_size=training_set.label_size, **G_args)
D = tflib.Network('D', num_channels=training_set.shape[0], resolution=resolution, label_size=training_set.label_size, **D_args)
Gs = G.clone('Gs')
# Load pre-trained networks
assert restore_partial_fn != None
G_partial, D_partial, Gs_partial = pickle.load(open(restore_partial_fn, 'rb'))
# Restore (subset of) pre-trained weights (only parameters that match both name and shape)
G.copy_compatible_trainables_from(G_partial)
D.copy_compatible_trainables_from(D_partial)
Gs.copy_compatible_trainables_from(Gs_partial)
else:
if resume_pkl is not None and resume_kimg == 0:
resume_pkl, resume_kimg = misc.locate_latest_pkl(resume_pkl)
print(' Loading networks from "%s", kimg %.3g' % (resume_pkl, resume_kimg))
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, D, Gs = rG, rD, rGs
# Print layers if needed 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, dnnlib.make_run_dir_path('fakes_init.%s'%ext), 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, mirror_augment_v, 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 = 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)
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(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()
print(' Training for %d kimg (%d left) \n' % (total_kimg, total_kimg-resume_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
cur_time = time.time()
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
if sched.lod == 0:
left_kimg = total_kimg - cur_nimg / 1000
left_sec = left_kimg * tick_time / tick_kimg
finaltime = time.asctime(time.localtime(cur_time + left_sec))
msg_final = '%ss left till %s ' % (shortime(left_sec), finaltime[11:16])
else:
msg_final = ''
# Report progress.
# print('tick %-4d kimg %-6.1f lod %-5.2f minibch %-3d:%d time %-8s min/tick %-6.3g %s sec/kimg %-7.3g gpumem %-4.1f %d lr %.2g ' % (
print('tick %-4d kimg %-6.1f time %-8s %s min/tick %-6.3g sec/kimg %-7.3g gpumem %-4.1f lr %.2g ' % (
autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
# autosummary('Progress/lod', sched.lod),
# autosummary('Progress/minibatch', sched.minibatch_size),
# autosummary('Progress/minibatch_gpu', sched.minibatch_gpu),
dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)),
msg_final,
autosummary('Timing/min_per_tick', tick_time / 60),
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),
sched.G_lrate))
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, dnnlib.make_run_dir_path('fake-%04d.%s' % (cur_nimg // 1000, ext)), 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('snapshot-%d-%s%s-%04d.pkl' % (resolution, setname[-1], init_res_str, cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
misc.save_pkl((Gs), dnnlib.make_run_dir_path('%s-%d-%s%s-%04d.pkl' % (setname[:-1], resolution, setname[-1], init_res_str, cur_nimg // 1000)))
# Update summaries and RunContext.
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((G, D, Gs), dnnlib.make_run_dir_path('snapshot-%d-%s%s-final.pkl' % (resolution, setname[-1], init_res_str)))
misc.save_pkl((Gs), dnnlib.make_run_dir_path('%s-%d-%s%s-final.pkl' % (setname[:-1], resolution, setname[-1], init_res_str)))
# All done.
summary_log.close()
training_set.close()
def generate_interpolation_between(submit_config,
network_pkl,
truncation_psi,
latents_file_start,
latents_file_end,
num_steps,
minibatch_size=4):
print('starting process of generating interpolation between ' +
latents_file_start + ' and ' + latents_file_end)
tflib.init_tf({'rnd.np_random_seed': 1000})
grid_size = (num_steps, 1)
grid_labels = []
f = open(latents_file_start, 'r')
start_original_latents = np.array(json.load(f))
f.close()
print('loaded start latents from ' + latents_file_start)
f = open(latents_file_end, 'r')
end_original_latents = np.array(json.load(f))
f.close()
print('loaded end latents from ' + latents_file_end)
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
w_avg = Gs.get_var('dlatent_avg') # [component]
Gs_syn_kwargs = dnnlib.EasyDict()
Gs_syn_kwargs.randomize_noise = False
Gs_syn_kwargs.minibatch_size = minibatch_size
all_latents = []
start_ltnts = start_original_latents[0]
end_ltnts = end_original_latents[0]
ltnt = np.array(start_ltnts)
each_step_increment = [((end_ltnts[i] - start_ltnts[i]) / num_steps)
for i in range(len(start_ltnts))
] #np.zeros(len(start_ltnts))
print(str(each_step_increment))
print('Generating W vectors...')
for i in range(grid_size[0] * grid_size[1]):
for inc_index in range(len(each_step_increment)):
ltnt[inc_index] += each_step_increment[inc_index]
all_latents.append(ltnt)
ltnt = np.array(ltnt)
all_z = np.stack(all_latents)
# all_z = np.stack([mutate_latents(original_latents[0], i) for i in range(grid_size[0]*grid_size[1])])
all_w = Gs.components.mapping.run(all_z,
None) # [minibatch, layer, component]
all_w = w_avg + (all_w -
w_avg) * truncation_psi # [minibatch, layer, component]
print('Generating images...')
all_images = Gs.components.synthesis.run(
all_w, **Gs_syn_kwargs) # [minibatch, height, width, channel]
misc.save_image_grid(all_images,
dnnlib.make_run_dir_path('interpolation.png'),
drange=[-1, 1],
grid_size=grid_size)
def training_loop(
submit_config,
G_args = {}, # 生成网络的设置。
D_args = {}, # 判别网络的设置。
G_opt_args = {}, # 生成网络优化器设置。
D_opt_args = {}, # 判别网络优化器设置。
G_loss_args = {}, # 生成损失设置。
D_loss_args = {}, # 判别损失设置。
dataset_args = {}, # 数据集设置。
sched_args = {}, # 训练计划设置。
grid_args = {}, # setup_snapshot_image_grid()相关设置。
metric_arg_list = [], # 指标方法设置。
tf_config = {}, # tflib.init_tf()相关设置。
G_smoothing_kimg = 10.0, # 生成器权重的运行平均值的半衰期。
D_repeats = 1, # G每迭代一次训练判别器多少次。
minibatch_repeats = 4, # 调整训练参数前要运行的minibatch的数量。
reset_opt_for_new_lod = True, # 引入新层时是否重置优化器内部状态（例如Adam时刻）？
total_kimg = 15000, # 训练的总长度，以成千上万个真实图像为统计。
mirror_augment = False, # 启用镜像增强？
drange_net = [-1,1], # 将图像数据馈送到网络时使用的动态范围。
image_snapshot_ticks = 1, # 多久导出一次图像快照？
network_snapshot_ticks = 10, # 多久导出一次网络模型存储？
save_tf_graph = False, # 在tfevents文件中包含完整的TensorFlow计算图吗？
save_weight_histograms = False, # 在tfevents文件中包括权重直方图？
resume_run_id = None, # 运行已有ID或载入已有网络pkl以从中恢复训练，None = 从头开始。
resume_snapshot = None, # 要从哪恢复训练的快照的索引，None = 自动检测。
resume_kimg = 0.0, # 在训练开始时给定当前训练进度。影响报告和训练计划。
resume_time = 0.0): # 在训练开始时给定统计时间。影响报告。
# 初始化dnnlib和TensorFlow。
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# 载入训练集。
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
# 构建网络。
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
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')
G.print_layers(); D.print_layers()
# 构建计算图与优化器
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
# tf.placeholder:可以理解为形参，用于定于过程，具体执行时再赋具体的值。
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5 ** tf.div(tf.cast(minibatch_in, tf.float32), G_smoothing_kimg * 1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tflib.Optimizer(name='TrainG', learning_rate=lrate_in, **G_opt_args)
D_opt = tflib.Optimizer(name='TrainD', learning_rate=lrate_in, **D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [tf.assign(G_gpu.find_var('lod'), lod_in), tf.assign(D_gpu.find_var('lod'), lod_in)]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment, training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_split, **G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=D_opt, training_set=training_set, minibatch_size=minibatch_split, reals=reals, labels=labels, **D_loss_args)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
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)
# 设置快照图像网格
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
# 建立运行目录
print('Setting up run dir...')
misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
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)
# 训练
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# 选择训练参数并配置训练操作。
sched = training_schedule(cur_nimg=cur_nimg, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus, 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
# 进行训练。
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
cur_nimg += sched.minibatch
tflib.run([G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
# 每个tick执行一次维护任务。
done = (cur_nimg >= total_kimg * 1000)
if 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 = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# 报告进度。
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 %-4.1f' % (
autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
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))
# 保存快照。
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(submit_config.run_dir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl, run_dir=submit_config.run_dir, num_gpus=submit_config.num_gpus, tf_config=tf_config)
# 更新摘要和RunContext。
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod, cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# 保存最终结果。
misc.save_pkl((G, D, Gs), os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def recon_real_one_img(network_pkl, img, mask_dir, num_snapshots, recontype,
superres_factor, num_steps):
if not runSerial:
sys.stdout = open('logs/' + str(os.getpid()) + ".out", "w")
#sys.stderr = open(str(os.getpid()) + ".err", "w")
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
imgSize, nrChannelsNet, fakeImg = getImgSize(Gs)
imgShort = img.split('/')[-1] # without full path
forward, forwardTrue = constructForwardModel(recontype, imgSize,
nrChannelsNet, mask_dir,
imgShort, superres_factor)
print('Loading image "%s"...' % img)
image = cv2.imread(
img, cv2.IMREAD_UNCHANGED
) # without this flat, cv2 automatically converts to 3-channels
ndim = len(image.shape)
print('Input image has shape:', image.shape)
if ndim == 2:
nrChannels = 1
image = image.reshape(1, image.shape[0], image.shape[1], 1)
else:
nrChannels = image.shape[-1]
if nrChannels == 3:
image = cv2.cvtColor(image,
cv2.COLOR_BGR2RGB) # convert from BGR to RGB
image = image.reshape(1, image.shape[0], image.shape[1],
image.shape[2])
#fakeImg = forward(fakeImg) # sample of f(G(z)) for checking dimensions and channels
checkDimensions(fakeImg,
nrChannels,
inputWidth=image.shape[1],
inputHeight=image.shape[2])
image = np.transpose(image, (0, 3, 1, 2)) # convert from NHWC to NCWH
print('reshaped shape:', image.shape)
image = misc.adjust_dynamic_range(image, [0, 255], [-1, 1])
# save true image
png_prefix = dnnlib.make_run_dir_path(imgShort[:-4] + '-')
misc.save_image_grid(image, png_prefix + 'true.png', drange=[-1, 1])
imgTrue = image # this is the true image
# generate corrupted image, with true forward corruption model
imgCorrupted = forwardTrue(tf.convert_to_tensor(image))
imgCorrupted = imgCorrupted.eval()
proj = projector.Projector(forward, num_steps)
proj.set_network(Gs)
project_image(proj,
targets=imgCorrupted,
png_prefix=png_prefix,
num_snapshots=num_snapshots)
imgRecon = proj.get_clean_images()
if recontype == 'inpaint':
# for inpainting, also merge the reconstruction with target image
imgMerged = tf.where(
forward.mask, imgRecon,
imgCorrupted).eval() # if true, then recon, else imgCorrupted
misc.save_image_grid(imgMerged,
png_prefix + 'merged.png',
drange=[-1, 1])
else:
imgMerged = None
return imgCorrupted, imgRecon, imgMerged, imgTrue
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()
def training_loop(
submit_config,
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().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # 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=10, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
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_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
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.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
# ajay - move init to after graph creation?
tflib.init_tf(tf_config)
# Load training set.
print('ajay - config data dir', config.data_dir)
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
num_hosts=hvd.size(),
index=hvd.rank(),
**dataset_args)
# Construct networks.
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')
# with tf.device('/gpu:0'):
# if resume_run_id is not None:
# network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
# print('Loading networks from "%s"...' % network_pkl)
# G, D, Gs = misc.load_pkl(network_pkl)
# else:
# 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')
G.print_layers()
D.print_layers()
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_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tf.train.AdamOptimizer(learning_rate=lrate_in,
beta1=0.0,
beta2=0.99,
epsilon=1e-8)
G_opt = hvd.DistributedOptimizer(G_opt)
D_opt = tf.train.AdamOptimizer(learning_rate=lrate_in,
beta1=0.0,
beta2=0.99,
epsilon=1e-8)
D_opt = hvd.DistributedOptimizer(D_opt)
G_gpu = G
D_gpu = D
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
# ajay - check if unique minibatch is guaranteed i.e sharding is done right!
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
**D_loss_args)
G_grads = G_opt.compute_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_grads = D_opt.compute_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_gradients(G_grads)
D_train_op = D_opt.apply_gradients(D_grads)
# Horovod
init_op = tf.initialize_all_variables()
bcast_op = hvd.broadcast_global_variables(0)
# ajay
tf.get_default_session().run([init_op])
tflib.run([bcast_op])
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
# todo: ajay - note num_gpus need to change to hvd size when going multi-node
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
if hvd.rank() == 0:
print('Setting up run dir...')
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
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...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < (total_kimg * 1000):
if ctx.should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
sched.lod)
# todo: ajay - find a way to manually resetoptimizer
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
tflib.assert_tf_initialized()
G_opt_reset_op = [var.initializer for var in G_opt.variables()]
D_opt_reset_op = [var.initializer for var in D_opt.variables()]
tflib.run(G_opt_reset_op)
tflib.run(D_opt_reset_op)
# G_opt.reset_optimizer_state(); D_opt.reset_optimizer_state()
prev_lod = sched.lod
# grp_train_op = tf.group(D_train_op, [Gs_update_op])
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch #// submit_config.num_gpus
tflib.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
# Perform maintenance tasks once per tick.
done = (cur_nimg >= (total_kimg * 1000))
if 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 = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
# ajay
#ajay mod
if hvd.rank() == 0:
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f '
%
(autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
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('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
# 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 %-4.1f' % (
# autosummary('Progress/tick', cur_tick),
# autosummary('Progress/kimg', cur_nimg / 1000.0),
# autosummary('Progress/lod', sched.lod),
# autosummary('Progress/minibatch', sched.minibatch),
# 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 cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
submit_config.run_dir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
# ajay - note modifying to 1 for eval
metrics.run(pkl,
run_dir=submit_config.run_dir,
num_gpus=1,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
if hvd.rank() == 0:
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
if hvd.rank() == 0:
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def training_auto_loop(
Enc_args={}, # Options for encoder network.
Dec_args={}, # Options for decoder network.
opt_args={}, # Options for encoder optimizer.
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().
tf_config={}, # Options for tflib.init_tf().
data_dir=None, # Directory to load datasets from.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
total_kimg=25000, # Total length of the training, measured in thousands of real images.
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?
# 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, _ = 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'):
print('Constructing networks...')
Enc = tflib.Network('Encoder',
resolution=training_set.shape[1],
out_channels=16,
**Enc_args)
Dec = tflib.Network('Decoder',
resolution=training_set.shape[1] // 4,
in_channels=16,
**Dec_args)
# Print layers and generate initial image snapshot.
Enc.print_layers()
Dec.print_layers()
sched = sched_args
sched.tick_kimg = 4
grid_codes = Enc.run((grid_reals / 127.5) - 1.0,
minibatch_size=sched.minibatch_gpu)
grid_fakes = Dec.run(grid_codes, minibatch_size=sched.minibatch_gpu)
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'):
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)
# Setup optimizers.
opt_args = dict(opt_args)
opt_args['minibatch_multiplier'] = minibatch_multiplier
opt_args['learning_rate'] = lrate_in
opt = tflib.Optimizer(name='TrainAuto', **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 Enc and Dec.
Enc_gpu = Enc if gpu == 0 else Enc.clone(Enc.name + '_shadow')
Dec_gpu = Dec if gpu == 0 else Dec.clone(Dec.name + '_shadow')
# Fetch training data via temporary variables.
with tf.name_scope('DataFetch'):
reals_var = tf.Variable(
name='reals',
trainable=False,
initial_value=tf.zeros([sched.minibatch_gpu] +
training_set.shape))
reals_write, labels_write = training_set.get_minibatch_tf()
reals_write, _ = process_reals(reals_write, labels_write, 0.0,
False,
training_set.dynamic_range,
drange_net)
reals_write = tf.concat(
[reals_write, reals_var[minibatch_gpu_in:]], axis=0)
data_fetch_ops += [tf.assign(reals_var, reals_write)]
reals_read = reals_var[:minibatch_gpu_in]
# Evaluate loss functions.
with tf.name_scope('loss'):
loss = dnnlib.util.call_func_by_name(Enc=Enc_gpu,
Dec=Dec_gpu,
opt=opt,
reals=reals_read,
**loss_args)
# Register gradients.
opt.register_gradients(
tf.reduce_mean(loss),
list(Enc_gpu.trainables.values()) +
list(Dec_gpu.trainables.values()))
# Setup training ops.
data_fetch_op = tf.group(*data_fetch_ops)
train_op = opt.apply_updates()
# 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:
Enc.setup_weight_histograms()
Dec.setup_weight_histograms()
print('Training for %d kimg...\n' % total_kimg)
dnnlib.RunContext.get().update('', max_epoch=total_kimg)
maintenance_time = dnnlib.RunContext.get().get_last_update_interval()
cur_nimg = 0
cur_tick = -1
tick_start_nimg = cur_nimg
while cur_nimg < total_kimg * 1000:
if dnnlib.RunContext.get().should_stop(): break
# Choose training parameters and configure training ops.
assert sched.minibatch_size % (sched.minibatch_gpu * num_gpus) == 0
training_set.configure(sched.minibatch_gpu)
# Run training ops.
feed_dict = {
lrate_in: sched.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)
cur_nimg += sched.minibatch_size
# Fast path without gradient accumulation.
if len(rounds) == 1:
tflib.run(data_fetch_op, feed_dict)
tflib.run(train_op, feed_dict)
# Slow path with gradient accumulation.
else:
for _round in rounds:
tflib.run(data_fetch_op, feed_dict)
tflib.run(train_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()
# Report progress.
print(
'tick %-5d kimg %-8.1f 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/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_codes = Enc.run((grid_reals / 127.5) - 1.0,
minibatch_size=sched.minibatch_gpu)
grid_fakes = Dec.run(grid_codes,
minibatch_size=sched.minibatch_gpu)
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))
misc.save_pkl((Enc, Dec), pkl)
# Update summaries and RunContext.
tflib.autosummary.save_summaries(summary_log, cur_nimg)
dnnlib.RunContext.get().update('%.2f' % 0.0,
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((Enc, Dec), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
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)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
print('Setting up run dir...')
misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
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...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
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.
AE_opt_args=None, # Options for autoencoder optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
AE_loss_args=None, # Options for autoencoder loss.
dataset_args={}, # Options for dataset.load_dataset().
dataset_args_eval={}, # 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().
train_data_dir=None, # Directory to load datasets from.
eval_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=True, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=True, # 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,
resume_with_own_vars=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 = dnnlib.submit_config.num_gpus
# Load training set.
print("Loading train set from %s..." % dataset_args.tfrecord_dir)
training_set = dataset.load_dataset(
data_dir=dnnlib.convert_path(train_data_dir),
verbose=True,
**dataset_args)
print("Loading eval set from %s..." % dataset_args_eval.tfrecord_dir)
eval_set = dataset.load_dataset(
data_dir=dnnlib.convert_path(eval_data_dir),
verbose=True,
**dataset_args_eval)
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)
# Freeze Discriminator
if D_args['freeze']:
num_layers = np.log2(training_set.resolution) - 1
layers = int(np.round(num_layers * 3. / 8.))
scope = ['Output', 'scores_out']
for layer in range(layers):
scope += ['.*%d' % 2**layer]
if 'train_scope' in D_args:
scope[-1] += '.*%d' % D_args['train_scope']
D_args['train_scope'] = scope
# Construct or load networks.
with tf.device('/gpu:0'):
if resume_pkl is '' or resume_with_new_nets or resume_with_own_vars:
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 '':
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
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
# SVD stuff
if 'syn_svd' in G_args or 'map_svd' in G_args:
# Run graph to calculate SVD
grid_latents_smol = grid_latents[:1]
rho = np.array([1])
grid_fakes = G.run(grid_latents_smol,
grid_labels,
rho,
is_validation=True)
grid_fakes = Gs.run(grid_latents_smol,
grid_labels,
rho,
is_validation=True)
load_d_fake = D.run(grid_reals[:1], rho, is_validation=True)
with tf.device('/gpu:0'):
# Create SVD-decomposed graph
rG, rD, rGs = G, D, Gs
G_lambda_mask = {
var: np.ones(G.vars[var].shape[-1])
for var in G.vars if 'SVD/s' in var
}
D_lambda_mask = {
'D/' + var: np.ones(D.vars[var].shape[-1])
for var in D.vars if 'SVD/s' in var
}
G_reduce_dims = {
var: (0, int(Gs.vars[var].shape[-1]))
for var in Gs.vars if 'SVD/s' in var
}
G_args['lambda_mask'] = G_lambda_mask
G_args['reduce_dims'] = G_reduce_dims
D_args['lambda_mask'] = D_lambda_mask
# Create graph with no SVD operations
G = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=rG.input_shapes[1][1],
factorized=True,
**G_args)
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=rD.input_shapes[1][1],
factorized=True,
**D_args)
Gs = G.clone('Gs')
grid_fakes = G.run(grid_latents_smol,
grid_labels,
rho,
is_validation=True,
minibatch_size=1)
grid_fakes = Gs.run(grid_latents_smol,
grid_labels,
rho,
is_validation=True,
minibatch_size=1)
G.copy_vars_from(rG)
D.copy_vars_from(rD)
Gs.copy_vars_from(rGs)
# Reduce per-gpu minibatch size to fit in 16GB GPU memory
if grid_reals.shape[2] >= 1024:
sched_args.minibatch_gpu_base = 2
print('Batch size', sched_args.minibatch_gpu_base)
# 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:])
rho = np.array([1])
grid_fakes = Gs.run(grid_latents,
grid_labels,
rho,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path('fakes_init.png'),
drange=drange_net,
grid_size=grid_size)
if resume_pkl is not '':
load_d_real = rD.run(grid_reals[:1], rho, is_validation=True)
load_d_fake = rD.run(grid_fakes[:1], rho, is_validation=True)
d_fake = D.run(grid_fakes[:1], rho, is_validation=True)
d_real = D.run(grid_reals[:1], rho, is_validation=True)
print('Factorized fake', d_fake, 'loaded fake', load_d_fake,
'factorized real', d_real, 'loaded real', load_d_real)
print('(should match)')
# 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)
if AE_opt_args is not None:
AE_opt_args = dict(AE_opt_args)
AE_opt_args['minibatch_multiplier'] = minibatch_multiplier
AE_opt_args['learning_rate'] = lrate_in
AE_opt = tflib.Optimizer(name='TrainAE', **AE_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'):
if G_loss_args['func_name'] == 'training.loss.G_l1':
G_loss_args['reals'] = reals_read
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)
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(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()
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)
ae_iter_mul = 10
ae_rounds = range(0, sched.minibatch_size,
sched.minibatch_gpu * num_gpus * ae_iter_mul)
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:
_g_loss, _ = tflib.run([G_loss, 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):
print('g loss', _g_loss)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu)
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))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=dnnlib.make_run_dir_path(),
data_dir=dnnlib.convert_path(eval_data_dir),
num_gpus=num_gpus,
tf_config=tf_config,
rho=rho)
# 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((G, D, Gs), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
eval_set.close()
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_vae(
G_args={}, # Options for generator network.
E_args={}, # Options for encoder 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.
minibatch_repeats=1, # Number of minibatches to run before adjusting training parameters.
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=0, # Number of discrete latents in model.
n_continuous=4, # Number of continuous latents in model.
topk_dims_to_show=20, # Number of top disentant dimensions to show in a snapshot.
subgroup_sizes_ls=None,
subspace_sizes_ls=None,
forward_eg=False,
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
# If use Discriminator.
use_D = D_args is not None
# 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)
grid_fakes = add_outline(grid_reals, width=1)
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...')
E = tflib.Network('E',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
input_shape=[None] + training_set.shape,
**E_args)
G = tflib.Network(
'G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
input_shape=[None, n_discrete +
G_args.latent_size] if not forward_eg else [
None, n_discrete + G_args.latent_size +
sum(subgroup_sizes_ls)
],
**G_args)
if use_D:
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
input_shape=[None, D_args.latent_size],
**D_args)
if resume_pkl is not None:
print('Loading networks from "%s"...' % resume_pkl)
if use_D:
rE, rG, rD = misc.load_pkl(resume_pkl)
else:
rE, rG = misc.load_pkl(resume_pkl)
if resume_with_new_nets:
E.copy_vars_from(rE)
G.copy_vars_from(rG)
if use_D:
D.copy_vars_from(rD)
else:
E = rE
G = rG
if use_D:
D = rD
# Print layers and generate initial image snapshot.
E.print_layers()
G.print_layers()
if use_D:
D.print_layers()
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
**sched_args)
if traversal_grid:
if topk_dims_to_show > 0:
topk_dims = np.arange(min(topk_dims_to_show, n_continuous))
else:
topk_dims = np.arange(n_continuous)
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete, n_continuous, n_samples_per, G, grid_labels, topk_dims)
else:
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
print('grid_size:', grid_size)
print('grid_latents.shape:', grid_latents.shape)
print('grid_labels.shape:', grid_labels.shape)
grid_fakes, _, _, _, _, _, _, lie_vars = get_return_v(
G.run(append_gfeats(grid_latents, G) if forward_eg else grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True), 8)
print('Lie_vars:', lie_vars[0])
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)
# Setup training inputs.
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
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)
# Setup optimizers.
G_opt_args = dict(G_opt_args)
G_opt_args['minibatch_multiplier'] = minibatch_multiplier
G_opt_args['learning_rate'] = lrate_in
G_opt = tflib.Optimizer(name='TrainG', **G_opt_args)
if use_D:
D_opt_args = dict(D_opt_args)
D_opt_args['minibatch_multiplier'] = minibatch_multiplier
D_opt_args['learning_rate'] = lrate_in
D_opt = tflib.Optimizer(name='TrainD', **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.
E_gpu = E if gpu == 0 else E.clone(E.name + '_shadow')
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
if use_D:
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, 0., 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.
if use_D:
with tf.name_scope('G_loss'):
G_loss = dnnlib.util.call_func_by_name(
E=E_gpu,
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
reals=reals_read,
labels=labels_read,
**G_loss_args)
with tf.name_scope('D_loss'):
D_loss = dnnlib.util.call_func_by_name(
E=E_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)
else:
with tf.name_scope('G_loss'):
G_loss = dnnlib.util.call_func_by_name(
E=E_gpu,
G=G_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
reals=reals_read,
labels=labels_read,
**G_loss_args)
# Register gradients.
EG_gpu_trainables = collections.OrderedDict(
list(E_gpu.trainables.items()) +
list(G_gpu.trainables.items()))
G_opt.register_gradients(tf.reduce_mean(G_loss), EG_gpu_trainables)
# G_opt.register_gradients(G_loss,
# EG_gpu_trainables)
if use_D:
D_opt.register_gradients(tf.reduce_mean(D_loss),
D_gpu.trainables)
# D_opt.register_gradients(D_loss,
# D_gpu.trainables)
# Setup training ops.
data_fetch_op = tf.group(*data_fetch_ops)
G_train_op = G_opt.apply_updates()
if use_D:
D_train_op = D_opt.apply_updates()
# 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()
if use_D:
D.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, 0)
# Run training ops.
feed_dict = {
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)
cur_nimg += sched.minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
if len(rounds) == 1:
tflib.run([G_train_op], feed_dict)
tflib.run([data_fetch_op], feed_dict)
if use_D:
tflib.run([D_train_op], feed_dict)
# Slow path with gradient accumulation.
else:
for _round in rounds:
tflib.run(G_train_op, feed_dict)
for _round in rounds:
tflib.run(data_fetch_op, feed_dict)
if use_D:
tflib.run(D_train_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 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/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 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_D:
misc.save_pkl((E, G, D), pkl)
else:
misc.save_pkl((E, G), pkl)
met_outs = 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,
is_vae=True,
use_D=use_D,
Gs_kwargs=dict(is_validation=True))
if topk_dims_to_show > 0:
if 'tpl_per_dim' in met_outs:
avg_distance_per_dim = met_outs[
'tpl_per_dim'] # shape: (n_continuous)
topk_dims = np.argsort(
avg_distance_per_dim
)[::-1][:topk_dims_to_show] # shape: (20)
else:
topk_dims = np.arange(
min(topk_dims_to_show, n_continuous))
else:
topk_dims = np.arange(n_continuous)
if image_snapshot_ticks is not None and (
cur_tick % image_snapshot_ticks == 0 or done):
if traversal_grid:
grid_size, grid_latents, grid_labels = get_grid_latents(
n_discrete, n_continuous, n_samples_per, G,
grid_labels, topk_dims)
else:
grid_latents = np.random.randn(np.prod(grid_size),
*G.input_shape[1:])
grid_fakes, _, _, _, _, _, _, lie_vars = get_return_v(
G.run(append_gfeats(grid_latents, G)
if forward_eg else grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True), 8)
print('Lie_vars:', lie_vars[0])
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)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
dnnlib.RunContext.get().update('%.2f' % 0,
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_D:
misc.save_pkl((E, G, D), dnnlib.make_run_dir_path('network-final.pkl'))
else:
misc.save_pkl((E, G), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
def embed(batch_size,
resolution,
imgs,
network,
iteration,
result_dir,
seed=6600):
tf.reset_default_graph()
print('Loading networks from "%s"...' % network)
tflib.init_tf()
_, _, G = pretrained_networks.load_networks(network)
img_in = tf.placeholder(tf.float32)
opt = tf.train.AdamOptimizer(learning_rate=0.01,
beta1=0.9,
beta2=0.999,
epsilon=1e-8)
noise_vars = [
var for name, var in G.components.synthesis.vars.items()
if name.startswith('noise')
]
alpha_vars = [
var for name, var in G.components.synthesis.vars.items()
if name.endswith('alpha')
]
alpha_eval = [alpha.eval() for alpha in alpha_vars]
G_kwargs = dnnlib.EasyDict()
G_kwargs.randomize_noise = False
G_syn = G.components.synthesis
rnd = np.random.RandomState(seed)
dlatent_avg = [
var for name, var in G.vars.items() if name.startswith('dlatent_avg')
][0].eval()
dlatent_avg = np.expand_dims(np.expand_dims(dlatent_avg, 0), 1)
dlatent_avg = dlatent_avg.repeat(12, 1)
dlatent = tf.get_variable('dlatent',
dtype=tf.float32,
initializer=tf.constant(dlatent_avg),
trainable=True)
synth_img = G_syn.get_output_for(dlatent, is_training=False, **G_kwargs)
# synth_img = (synth_img + 1.0) / 2.0
with tf.variable_scope('mse_loss'):
mse_loss = tf.reduce_mean(tf.square(img_in - synth_img))
with tf.variable_scope('perceptual_loss'):
vgg_in = tf.concat([img_in, synth_img], 0)
tf.keras.backend.set_image_data_format('channels_first')
vgg = tf.keras.applications.VGG16(
include_top=False,
input_tensor=vgg_in,
input_shape=(3, 128, 128),
weights='/gdata2/fengrl/metrics/vgg.h5',
pooling=None)
h1 = vgg.get_layer('block1_conv1').output
h2 = vgg.get_layer('block1_conv2').output
h3 = vgg.get_layer('block3_conv2').output
h4 = vgg.get_layer('block4_conv2').output
pcep_loss = tf.reduce_mean(tf.square(h1[0] - h1[1])) + tf.reduce_mean(tf.square(h2[0] - h2[1])) + \
tf.reduce_mean(tf.square(h3[0] - h3[1])) + tf.reduce_mean(tf.square(h4[0] - h4[1]))
loss = 0.5 * mse_loss + 0.5 * pcep_loss
with tf.control_dependencies([loss]):
train_op = opt.minimize(loss, var_list=[dlatent])
reset_opt = tf.variables_initializer(opt.variables())
reset_dl = tf.variables_initializer([dlatent])
tflib.init_uninitialized_vars()
# rnd = np.random.RandomState(seed)
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars}) # [height, width]
idx = 0
metrics_l = []
metrics_p = []
metrics_m = []
metrics_d = []
metrics_args = [metric_defaults[x] for x in ['fid50k', 'ppl_wend']]
metrics_fun = metric_base.MetricGroup(metrics_args)
for temperature in [0.2, 0.5, 1.0, 1.5, 2.0, 10.0]:
tflib.set_vars({
alpha: scale_alpha(alpha_np, temperature)
for alpha, alpha_np in zip(alpha_vars, alpha_eval)
})
# misc.save_pkl((G, G, G), os.path.join(result_dir, 'temp%f.pkl' % temperature))
# metrics_fun.run(os.path.join(result_dir, 'temp%f.pkl' % temperature), run_dir=result_dir,
# data_dir='/gdata/fengrl/noise_test_dset/tfrecords',
# dataset_args=dnnlib.EasyDict(tfrecord_dir='ffhq-128', shuffle_mb=0),
# mirror_augment=True, num_gpus=1)
for img in imgs:
img = np.expand_dims(img, 0)
loss_list = []
p_loss_list = []
m_loss_list = []
dl_list = []
si_list = []
tflib.run([reset_opt, reset_dl])
for i in range(iteration):
loss_, p_loss_, m_loss_, dl_, si_, _ = tflib.run(
[loss, pcep_loss, mse_loss, dlatent, synth_img, train_op],
{img_in: img})
loss_list.append(loss_)
p_loss_list.append(p_loss_)
m_loss_list.append(m_loss_)
dl_loss_ = np.sum(np.square(dl_ - dlatent_avg))
dl_list.append(dl_loss_)
if i % 500 == 0:
si_list.append(si_)
if i % 100 == 0:
print(
'Temperature %f, idx %d, Loss %f, mse %f, ppl %f, dl %f, step %d'
% (temperature, idx, loss_, m_loss_, p_loss_, dl_loss_,
i))
print('Temperature %f, idx %d, loss: %f, ppl: %f, mse: %f, d: %f' %
(temperature, idx, loss_list[-1], p_loss_list[-1],
m_loss_list[-1], dl_list[-1]))
metrics_l.append(loss_list[-1])
metrics_p.append(p_loss_list[-1])
metrics_m.append(m_loss_list[-1])
metrics_d.append(dl_list[-1])
misc.save_image_grid(np.concatenate(si_list, 0),
os.path.join(
result_dir,
'temp%fsi%d.png' % (temperature, idx)),
drange=[-1, 1])
misc.save_image_grid(
si_list[-1],
os.path.join(result_dir,
'temp%fsifinal%d.png' % (temperature, idx)),
drange=[-1, 1])
with open(
os.path.join(result_dir,
'temp%fmetric_l%d.txt' % (temperature, idx)),
'w') as f:
for l_ in loss_list:
f.write(str(l_) + '\n')
with open(
os.path.join(result_dir,
'temp%fmetric_p%d.txt' % (temperature, idx)),
'w') as f:
for l_ in p_loss_list:
f.write(str(l_) + '\n')
with open(
os.path.join(result_dir,
'temp%fmetric_m%d.txt' % (temperature, idx)),
'w') as f:
for l_ in m_loss_list:
f.write(str(l_) + '\n')
with open(
os.path.join(result_dir,
'temp%fmetric_d%d.txt' % (temperature, idx)),
'w') as f:
for l_ in dl_list:
f.write(str(l_) + '\n')
idx += 1
l_mean = np.mean(metrics_l)
p_mean = np.mean(metrics_p)
m_mean = np.mean(metrics_m)
d_mean = np.mean(metrics_d)
with open(
os.path.join(result_dir,
'Temp%fmetric_lmpd.txt' % temperature), 'w') as f:
for i in range(len(metrics_l)):
f.write(
str(metrics_l[i]) + ' ' + str(metrics_m[i]) + ' ' +
str(metrics_p[i]) + ' ' + str(metrics_d[i]) + '\n')
print(
'Overall metrics: temp %f, loss_mean %f, ppl_mean %f, mse_mean %f, d_mean %f'
% (temperature, l_mean, p_mean, m_mean, d_mean))
with open(os.path.join(result_dir, 'mean_metrics'), 'a') as f:
f.write('Temperature %f\n' % temperature)
f.write('loss %f\n' % l_mean)
f.write('mse %f\n' % m_mean)
f.write('ppl %f\n' % p_mean)
f.write('dl %f\n' % d_mean)
def embed(batch_size, resolution, imgs, network, iteration, result_dir, seed=6600):
tf.reset_default_graph()
G_args = dnnlib.EasyDict(func_name='training.networks_stylegan2_alpha.G_main')
G_args.fmap_base = 8 << 10
print('Loading networks from "%s"...' % network)
tflib.init_tf()
G = tflib.Network('G', num_channels=3, resolution=128, **G_args)
_, _, Gs = pretrained_networks.load_networks(network)
G.copy_vars_from(Gs)
img_in = tf.placeholder(tf.float32)
opt = tf.train.AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999, epsilon=1e-8)
opt_T = tf.train.AdamOptimizer(learning_rate=0.002, beta1=0.9, beta2=0.999, epsilon=1e-8)
noise_vars = [var for name, var in G.components.synthesis.vars.items() if name.startswith('noise')]
alpha_vars = [var for name, var in G.components.synthesis.vars.items() if name.endswith('alpha')]
alpha_evals = [alpha.eval() for alpha in alpha_vars]
G_kwargs = dnnlib.EasyDict()
G_kwargs.randomize_noise = False
G_syn = G.components.synthesis
rnd = np.random.RandomState(seed)
dlatent_avg = [var for name, var in G.vars.items() if name.startswith('dlatent_avg')][0].eval()
dlatent_avg = np.expand_dims(np.expand_dims(dlatent_avg, 0), 1)
dlatent_avg = dlatent_avg.repeat(12, 1)
dlatent = tf.get_variable('dlatent', dtype=tf.float32, initializer=tf.constant(dlatent_avg),
trainable=True)
T = tf.get_variable('T', dtype=tf.float32, initializer=tf.constant(0.95))
alpha_pre = [scale_alpha_exp(alpha_eval, T) for alpha_eval in alpha_evals]
synth_img = G_syn.get_output_for(dlatent, is_training=False, alpha_pre=alpha_pre, **G_kwargs)
# synth_img = (synth_img + 1.0) / 2.0
with tf.variable_scope('mse_loss'):
mse_loss = tf.reduce_mean(tf.square(img_in - synth_img))
with tf.variable_scope('perceptual_loss'):
vgg_in = tf.concat([img_in, synth_img], 0)
tf.keras.backend.set_image_data_format('channels_first')
vgg = tf.keras.applications.VGG16(include_top=False, input_tensor=vgg_in, input_shape=(3, 128, 128),
weights='/gdata2/fengrl/metrics/vgg.h5',
pooling=None)
h1 = vgg.get_layer('block1_conv1').output
h2 = vgg.get_layer('block1_conv2').output
h3 = vgg.get_layer('block3_conv2').output
h4 = vgg.get_layer('block4_conv2').output
pcep_loss = tf.reduce_mean(tf.square(h1[0] - h1[1])) + tf.reduce_mean(tf.square(h2[0] - h2[1])) + \
tf.reduce_mean(tf.square(h3[0] - h3[1])) + tf.reduce_mean(tf.square(h4[0] - h4[1]))
loss = 0.5 * mse_loss + 0.5 * pcep_loss
with tf.control_dependencies([loss]):
grads = tf.gradients(mse_loss, [dlatent, T])
train_op1 = opt.apply_gradients(zip([grads[0]], [dlatent]))
train_op2 = opt_T.apply_gradients(zip([grads[1]], [T]))
train_op = tf.group(train_op1, train_op2)
reset_opt = tf.variables_initializer(opt.variables()+opt_T.variables())
reset_dl = tf.variables_initializer([dlatent, T])
tflib.init_uninitialized_vars()
# rnd = np.random.RandomState(seed)
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
idx = 0
metrics_l = []
metrics_p = []
metrics_m = []
metrics_d = []
T_list = []
for img in imgs:
img = np.expand_dims(img, 0)
loss_list = []
p_loss_list = []
m_loss_list = []
dl_list = []
si_list = []
# tflib.set_vars({alpha: alpha_np for alpha, alpha_np in zip(alpha_vars, alpha_evals)})
tflib.run([reset_opt, reset_dl])
for i in range(iteration):
loss_, p_loss_, m_loss_, dl_, si_, t_, _ = tflib.run([loss, pcep_loss, mse_loss, dlatent, synth_img, T, train_op],
{img_in: img})
loss_list.append(loss_)
p_loss_list.append(p_loss_)
m_loss_list.append(m_loss_)
dl_loss_ = np.sum(np.square(dl_-dlatent_avg))
dl_list.append(dl_loss_)
if i % 500 == 0:
si_list.append(si_)
if i % 100 == 0:
print('idx %d, Loss %f, mse %f, ppl %f, dl %f, t %f, step %d' % (idx, loss_, m_loss_, p_loss_, dl_loss_, t_, i))
print('T: %f, loss: %f, ppl: %f, mse: %f, d: %f' % (t_,
loss_list[-1],
p_loss_list[-1],
m_loss_list[-1],
dl_list[-1]))
metrics_l.append(loss_list[-1])
metrics_p.append(p_loss_list[-1])
metrics_m.append(m_loss_list[-1])
metrics_d.append(dl_list[-1])
T_list.append(t_)
misc.save_image_grid(np.concatenate(si_list, 0), os.path.join(result_dir, 'si%d.png' % idx), drange=[-1, 1])
misc.save_image_grid(si_list[-1], os.path.join(result_dir, 'sifinal%d.png' % idx),
drange=[-1, 1])
with open(os.path.join(result_dir, 'metric_l%d.txt' % idx), 'w') as f:
for l_ in loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_p%d.txt' % idx), 'w') as f:
for l_ in p_loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_m%d.txt' % idx), 'w') as f:
for l_ in m_loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_d%d.txt' % idx), 'w') as f:
for l_ in dl_list:
f.write(str(l_) + '\n')
idx += 1
l_mean = np.mean(metrics_l)
p_mean = np.mean(metrics_p)
m_mean = np.mean(metrics_m)
d_mean = np.mean(metrics_d)
with open(os.path.join(result_dir, 'metric_lmpd.txt'), 'w') as f:
f.write(str(alpha_evals)+'\n')
for i in range(len(metrics_l)):
f.write(str(T_list[i])+' '+str(metrics_l[i])+' '+str(metrics_m[i])+' '+str(metrics_p[i])+' '+str(metrics_d[i])+'\n')
print('Overall metrics: loss_mean %f, ppl_mean %f, mse_mean %f, d_mean %f' % (l_mean, p_mean, m_mean, d_mean))
with open(os.path.join(result_dir, 'mean_metrics.txt'), 'w') as f:
f.write('loss %f\n' % l_mean)
f.write('mse %f\n' % m_mean)
f.write('ppl %f\n' % p_mean)
f.write('dl %f\n' % d_mean)
def generate_traversals(network_pkl,
seeds,
tpl_metric,
n_samples_per,
topk_dims_to_show,
return_atts=False,
bound=2):
tflib.init_tf()
print('Loading networks from "%s"...' % network_pkl)
# _G, _D, Gs = pretrained_networks.load_networks(network_pkl)
_G, _D, I, Gs = get_return_v(misc.load_pkl(network_pkl), 4)
# noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
# Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = True
if return_atts:
Gs_kwargs.return_atts = True,
n_continuous = Gs.input_shape[1]
grid_labels = np.zeros([1, 0], dtype=np.float32)
# Eval tpl
if topk_dims_to_show > 0:
metric_args = metric_defaults[tpl_metric]
metric = dnnlib.util.call_func_by_name(**metric_args)
met_outs = metric._evaluate(Gs, {}, 1)
if 'tpl_per_dim' in met_outs:
avg_distance_per_dim = met_outs[
'tpl_per_dim'] # shape: (n_continuous)
topk_dims = np.argsort(
avg_distance_per_dim)[::-1][:topk_dims_to_show] # shape: (20)
else:
topk_dims = np.arange(min(topk_dims_to_show, n_continuous))
else:
topk_dims = np.arange(n_continuous)
for seed_idx, seed in enumerate(seeds):
grid_size, grid_latents, grid_labels = get_grid_latents(
0, n_continuous, n_samples_per, Gs, grid_labels, topk_dims)
# images, _ = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
grid_fakes, atts = get_return_v(
Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=2,
randomize_noise=True), 2)
if return_atts:
atts = atts[:, topk_dims]
save_atts(atts,
filename=dnnlib.make_run_dir_path('atts_seed%04d.png' %
seed),
grid_size=grid_size,
drange=[0, 1],
grid_fakes=grid_fakes,
n_samples_per=n_samples_per)
grid_fakes = add_outline(grid_fakes, width=1)
misc.save_image_grid(grid_fakes,
dnnlib.make_run_dir_path('travs_seed%04d.png' %
seed),
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 embed(batch_size,
resolution,
imgs,
network,
iteration,
result_dir,
seed=6600):
tf.reset_default_graph()
print('Loading networks from "%s"...' % network)
tflib.init_tf()
_, _, G = pretrained_networks.load_networks(network)
img_in = tf.placeholder(tf.float32)
step = tf.get_variable('step',
dtype=tf.float32,
initializer=tf.constant(0.0))
lr = tf.train.exponential_decay(0.01,
global_step=step,
decay_steps=3500,
decay_rate=0.5)
opt = tf.train.AdamOptimizer(learning_rate=0.01,
beta1=0.9,
beta2=0.999,
epsilon=1e-8)
noise_vars = [
var for name, var in G.components.synthesis.vars.items()
if name.startswith('noise')
]
G_kwargs = dnnlib.EasyDict()
G_kwargs.randomize_noise = False
G_syn = G.components.synthesis
rnd = np.random.RandomState(seed)
dlatent_avg = [
var for name, var in G.vars.items() if name.startswith('dlatent_avg')
][0].eval()
dlatent_avg = np.expand_dims(np.expand_dims(dlatent_avg, 0), 1)
dlatent_avg = dlatent_avg.repeat(12, 1)
dlatent = tf.get_variable('dlatent',
dtype=tf.float32,
initializer=tf.constant(dlatent_avg),
trainable=True)
synth_img = G_syn.get_output_for(dlatent, is_training=False, **G_kwargs)
# synth_img = (synth_img + 1.0) / 2.0
with tf.variable_scope('mse_loss'):
mse_loss = tf.reduce_mean(tf.square(img_in - synth_img))
with tf.variable_scope('perceptual_loss'):
vgg_in = tf.concat([img_in, synth_img], 0)
tf.keras.backend.set_image_data_format('channels_first')
vgg = tf.keras.applications.VGG16(
include_top=False,
input_tensor=vgg_in,
input_shape=(3, 128, 128),
weights='/gdata2/fengrl/metrics/vgg.h5',
pooling=None)
h1 = vgg.get_layer('block1_conv1').output
h2 = vgg.get_layer('block1_conv2').output
h3 = vgg.get_layer('block3_conv2').output
h4 = vgg.get_layer('block4_conv2').output
pcep_loss = tf.reduce_mean(tf.square(h1[0] - h1[1])) + tf.reduce_mean(tf.square(h2[0] - h2[1])) + \
tf.reduce_mean(tf.square(h3[0] - h3[1])) + tf.reduce_mean(tf.square(h4[0] - h4[1]))
loss = 0.5 * mse_loss + 0.5 * pcep_loss
with tf.control_dependencies([loss, tf.assign_add(step, 1.0)]):
train_op = opt.minimize(mse_loss, var_list=[dlatent])
reset_opt = tf.variables_initializer(opt.variables())
reset_dl = tf.variables_initializer([dlatent, step])
tflib.init_uninitialized_vars()
# rnd = np.random.RandomState(seed)
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars}) # [height, width]
idx = 0
metrics_l = []
metrics_p = []
metrics_m = []
metrics_d = []
for img in imgs:
img = np.expand_dims(img, 0)
loss_list = []
p_loss_list = []
m_loss_list = []
dl_list = []
si_list = []
tflib.run([reset_opt, reset_dl])
print(lr.eval())
for i in range(iteration):
loss_, p_loss_, m_loss_, dl_, si_, _ = tflib.run(
[loss, pcep_loss, mse_loss, dlatent, synth_img, train_op],
{img_in: img})
loss_list.append(loss_)
p_loss_list.append(p_loss_)
m_loss_list.append(m_loss_)
dl_loss_ = np.sum(np.square(dl_ - dlatent_avg))
dl_list.append(dl_loss_)
if i % 500 == 0:
si_list.append(si_)
if i % 100 == 0:
print('idx %d, Loss %f, mse %f, ppl %f, dl %f, step %d' %
(idx, loss_, m_loss_, p_loss_, dl_loss_, i))
print('loss: %f, ppl: %f, mse: %f, d: %f' %
(loss_list[-1], p_loss_list[-1], m_loss_list[-1], dl_list[-1]))
metrics_l.append(loss_list[-1])
metrics_p.append(p_loss_list[-1])
metrics_m.append(m_loss_list[-1])
metrics_d.append(dl_list[-1])
misc.save_image_grid(np.concatenate(si_list, 0),
os.path.join(result_dir, 'si%d.png' % idx),
drange=[-1, 1])
misc.save_image_grid(si_list[-1],
os.path.join(result_dir, 'sifinal%d.png' % idx),
drange=[-1, 1])
with open(os.path.join(result_dir, 'metric_l%d.txt' % idx), 'w') as f:
for l_ in loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_p%d.txt' % idx), 'w') as f:
for l_ in p_loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_m%d.txt' % idx), 'w') as f:
for l_ in m_loss_list:
f.write(str(l_) + '\n')
with open(os.path.join(result_dir, 'metric_d%d.txt' % idx), 'w') as f:
for l_ in dl_list:
f.write(str(l_) + '\n')
idx += 1
l_mean = np.mean(metrics_l)
p_mean = np.mean(metrics_p)
m_mean = np.mean(metrics_m)
d_mean = np.mean(metrics_d)
with open(os.path.join(result_dir, 'metric_lmpd.txt'), 'w') as f:
for i in range(len(metrics_l)):
f.write(
str(metrics_l[i]) + ' ' + str(metrics_m[i]) + ' ' +
str(metrics_p[i]) + ' ' + str(metrics_d[i]) + '\n')
print(
'Overall metrics: loss_mean %f, ppl_mean %f, mse_mean %f, d_mean %f' %
(l_mean, p_mean, m_mean, d_mean))
with open(os.path.join(result_dir, 'mean_metrics.txt'), 'w') as f:
f.write('loss %f\n' % l_mean)
f.write('mse %f\n' % m_mean)
f.write('ppl %f\n' % p_mean)
f.write('dl %f\n' % d_mean)
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.
loss_args={}, # Options for 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 metrics.
metric_args={}, # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
ema_start_kimg=None, # Start of the exponential moving average. Default to the half-life period.
G_ema_kimg=10, # Half-life of the exponential moving average of generator weights.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
lazy_regularization=False, # 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=4, # 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=2, # How often to save image snapshots? None = only save 'reals.png' and 'fakes-init.png'.
network_snapshot_ticks=1, # 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?
if ema_start_kimg is None:
ema_start_kimg = G_ema_kimg
# Initialize dnnlib and TensorFlow.
tflib.init_tf(tf_config)
num_gpus = dnnlib.submit_config.num_gpus
# Load training set.
training_set = dataset.load_dataset(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)
Gs = G.clone('Gs')
if resume_pkl is not None:
resume_networks = misc.load_pkl(resume_pkl)
rG, rD, rGs = resume_networks
if resume_with_new_nets:
G.copy_vars_from(rG)
D.copy_vars_from(rD)
Gs.copy_vars_from(rGs)
else:
G, D, Gs = rG, rD, 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,
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=[])
G_lrate_in = tf.placeholder(tf.float32, name='G_lrate_in', shape=[])
D_lrate_in = tf.placeholder(tf.float32, name='D_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=[])
run_D_reg_in = tf.placeholder(tf.bool, name='run_D_reg', shape=[])
minibatch_multiplier = minibatch_size_in // (minibatch_gpu_in *
num_gpus)
Gs_beta_mul_in = tf.placeholder(tf.float32,
name='Gs_beta_in',
shape=[])
Gs_beta = 0.5**tf.div(tf.cast(minibatch_size_in, tf.float32),
G_ema_kimg * 1000.0) if G_ema_kimg > 0.0 else 0.0
# Setup optimizers.
G_opt_args = dict(G_opt_args)
D_opt_args = dict(D_opt_args)
G_opt_args['learning_rate'] = G_lrate_in
D_opt_args['learning_rate'] = D_lrate_in
for args in [G_opt_args, D_opt_args]:
args['minibatch_multiplier'] = minibatch_multiplier
G_opt = tflib.Optimizer(name='TrainG', **G_opt_args)
D_opt = tflib.Optimizer(name='TrainD', **D_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):
with tf.name_scope('DataFetch'):
reals_read, labels_read = training_set.get_minibatch_tf()
reals_read = process_reals(reals_read, lod_in, mirror_augment,
training_set.dynamic_range,
drange_net)
# 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')
# 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('loss'):
G_loss, D_loss, D_reg = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
training_set=training_set,
minibatch_size=minibatch_gpu_in,
reals=reals_read,
real_labels=labels_read,
**loss_args)
# Register gradients.
if not lazy_regularization:
if D_reg is not None:
D_loss += D_reg
else:
if D_reg is not None:
D_loss = tf.cond(run_D_reg_in,
lambda: D_loss + D_reg * D_reg_interval,
lambda: D_loss)
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.
Gs_update_op = Gs.setup_as_moving_average_of(G,
beta=Gs_beta * Gs_beta_mul_in)
with tf.control_dependencies([Gs_update_op]):
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
# 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()
metrics = metric_base.MetricGroup(metric_arg_list, **metric_args)
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)
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,
G_lrate_in: sched.G_lrate,
D_lrate_in: sched.D_lrate,
minibatch_size_in: sched.minibatch_size,
minibatch_gpu_in: sched.minibatch_gpu,
Gs_beta_mul_in: 1 if cur_nimg >= ema_start_kimg * 1000 else 0,
}
for _repeat in range(minibatch_repeats):
rounds = range(0, sched.minibatch_size,
sched.minibatch_gpu * num_gpus)
run_D_reg = (lazy_regularization
and running_mb_counter % D_reg_interval == 0)
feed_dict[run_D_reg_in] = run_D_reg
cur_nimg += sched.minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
for _ in rounds:
tflib.run(G_train_op, feed_dict)
tflib.run(D_train_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)
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))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=dnnlib.make_run_dir_path(),
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((G, D, Gs), dnnlib.make_run_dir_path('network-final.pkl'))
# All done.
summary_log.close()
training_set.close()
def joint_train(
submit_config,
opt,
metric_arg_list,
sched_args = {}, # 训练计划设置。
grid_args = {}, # setup_snapshot_image_grid()相关设置。
dataset_args = {}, # 数据集设置。
total_kimg = 15000, # 训练的总长度，以成千上万个真实图像为统计。
drange_net = [-1,1], # 将图像数据馈送到网络时使用的动态范围。
image_snapshot_ticks = 1, # 多久导出一次图像快照？
network_snapshot_ticks = 10, # 多久导出一次网络模型存储？
D_repeats = 1, # G每迭代一次训练判别器多少次。
minibatch_repeats = 4, # 调整训练参数前要运行的minibatch的数量。
mirror_augment = False, # 启用镜像增强？
reset_opt_for_new_lod = True, # 引入新层时是否重置优化器内部状态（例如Adam时刻）？
save_tf_graph = False, # 在tfevents文件中包含完整的TensorFlow计算图吗？
save_weight_histograms = False, # 在tfevents文件中包括权重直方图？
resume_run_id = None, # 运行已有ID或载入已有网络pkl以从中恢复训练，None = 从头开始。
resume_snapshot = None, # 要从哪恢复训练的快照的索引，None = 自动检测。
resume_kimg = 0.0, # 在训练开始时给定当前训练进度。影响报告和训练计划。
resume_time = 0.0, # 在训练开始时给定统计时间。影响报告。
*args,
**kwargs
):
output_dir = opt.output_dir
graph_kwargs = util.set_graph_kwargs(opt)
graph_util = importlib.import_module('graphs.' + opt.model + '.graph_util')
constants = importlib.import_module('graphs.' + opt.model + '.constants')
model = graphs.find_model_using_name(opt.model, opt.transform)
g = model(submit_config=submit_config, dataset_args=dataset_args, **graph_kwargs, **kwargs)
g.initialize_graph()
# create training samples
#num_samples = opt.num_samples
# if opt.model == 'biggan' and opt.biggan.category is not None:
# graph_inputs = graph_util.graph_input(g, num_samples, seed=0, category=opt.biggan.category)
# else:
# graph_inputs = graph_util.graph_input(g, num_samples, seed=0)
w_snapshot_ticks = opt.model_save_freq
ctx = dnnlib.RunContext(submit_config, train)
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
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)
# 设置快照图像网格
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(g.G, training_set, **grid_args)
sched = training_loop.training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
grid_fakes = g.Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
# 建立运行目录
print('Setting up run dir...')
misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
g.G.setup_weight_histograms(); g.D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
# 训练
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
loss_values = []
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# 选择训练参数并配置训练操作。
sched = training_loop.training_schedule(cur_nimg=cur_nimg, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus, 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.G_opt.reset_optimizer_state(); # D_opt.reset_optimizer_state()
prev_lod = sched.lod
# 进行训练。
for _mb_repeat in range(minibatch_repeats):
alpha_for_graph, alpha_for_target = g.get_train_alpha(constants.BATCH_SIZE)
if not isinstance(alpha_for_graph, list):
alpha_for_graph = [alpha_for_graph]
alpha_for_target = [alpha_for_target]
for ag, at in zip(alpha_for_graph, alpha_for_target):
feed_dict_out = graph_util.graph_input(g, constants.BATCH_SIZE, seed=0)
out_zs = g.sess.run(g.outputs_orig, feed_dict_out)
target_fn, mask_out = g.get_target_np(out_zs, at)
feed_dict = feed_dict_out
feed_dict[g.alpha] = ag
feed_dict[g.target] = target_fn
feed_dict[g.mask] = mask_out
feed_dict[g.lod_in] = sched.lod
feed_dict[g.lrate_in] = sched.D_lrate
feed_dict[g.minibatch_in] = sched.minibatch
curr_loss, _, Gs_op, G_op = g.sess.run([g.joint_loss, g.train_step, g.Gs_update_op, g.G_train_op], feed_dict=feed_dict)
loss_values.append(curr_loss)
cur_nimg += sched.minibatch
#tflib.run([g.Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
#tflib.run([g.G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
# 每个tick执行一次维护任务。
done = (cur_nimg >= total_kimg * 1000)
if 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 = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# 报告进度。
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 %-4.1f' % (
autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
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))
# 保存快照。
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = g.Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(submit_config.run_dir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((g.G, g.D, g.Gs), pkl)
metrics.run(pkl, run_dir=submit_config.run_dir, num_gpus=submit_config.num_gpus, tf_config=tf_config)
if cur_tick % w_snapshot_ticks == 0 or done:
g.saver.save(g.sess, './{}/model_{}.ckpt'.format(
output_dir, (cur_nimg // 1000)),
write_meta_graph=False, write_state=False)
# 更新摘要和RunContext。
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod, cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# 保存最终结果。
misc.save_pkl((g.G, g.D, g.Gs), os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
loss_values = np.array(loss_values)
np.save('./{}/loss_values.npy'.format(output_dir), loss_values)
f, ax = plt.subplots(figsize=(10, 4))
ax.plot(loss_values)
f.savefig('./{}/loss_values.png'.format(output_dir))
def training_loop(
submit_config,
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().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # 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=1, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
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_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
resume_kimg=10000.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
#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')
url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ'
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
G, D, Gs = pickle.load(f)
print('Loading pretrained FFHQ network')
G.print_layers()
D.print_layers()
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_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tflib.Optimizer(name='TrainG',
learning_rate=lrate_in,
**G_opt_args)
D_opt = tflib.Optimizer(name='TrainD',
learning_rate=lrate_in,
**D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
**D_loss_args)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
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)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
print('Setting up run dir...')
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
cmd = "gsutil cp " + os.path.join(submit_config.run_dir, 'fakes%06d.png' %
resume_kimg) + " gs://stylegan_out"
response = subprocess.run(cmd, shell=True)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
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...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
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.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
tflib.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if 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 = ctx.get_time_since_last_update()
total_time = ctx.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 %-4.1f'
% (autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
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 cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
submit_config.run_dir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
cmd = "gsutil cp " + os.path.join(
submit_config.run_dir, 'fakes%06d.png' %
(cur_nimg // 1000)) + " gs://stylegan_out"
response = subprocess.run(cmd, shell=True)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=submit_config.run_dir,
num_gpus=submit_config.num_gpus,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def main():
# Initialize TensorFlow.
tflib.init_tf()
# Load pre-trained network.
#url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
#with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
_G, _D, Gs = misc.load_pkl('SNI.pkl') #pickle.load(f)
# _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
# _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
# Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
# Print network details.
Gs.print_layers()
number_unique_faces = 2 #They appear on rows of the generated figure
variations_per_face = 5 #They appear on columns of the generated figure
# Pick latent vector.
#rnd = np.random.RandomState(2)
#latents = rnd.randn(number_unique_faces, Gs.input_shape[1])
latents = np.random.randn(number_unique_faces, Gs.input_shape[1])
grid_latents = misc.randomize_global_codes(latents, variations_per_face)
grid_fakes = Gs.run(grid_latents, None, truncation_psi=0.7)
misc.save_image_grid(grid_fakes,
'example_fakes_global.png',
drange=[-1, 1],
grid_size=(variations_per_face, number_unique_faces))
grid_latents = misc.randomize_global_shared_codes(latents,
variations_per_face)
grid_fakes = Gs.run(grid_latents, None, truncation_psi=0.7)
misc.save_image_grid(grid_fakes,
'example_fakes_shared.png',
drange=[-1, 1],
grid_size=(variations_per_face, number_unique_faces))
grid_latents = misc.randomize_all_local_codes(latents, variations_per_face)
grid_fakes = Gs.run(grid_latents, None, truncation_psi=0.7)
misc.save_image_grid(grid_fakes,
'example_fakes_alllocal.png',
drange=[-1, 1],
grid_size=(variations_per_face, number_unique_faces))
mask = np.zeros(shape=(8, 8, 1))
mask[4:8, 2:6] = 1
grid_latents = misc.randomize_specific_local_codes(latents, mask,
variations_per_face)
grid_fakes = Gs.run(grid_latents, None, truncation_psi=0.7)
misc.save_image_grid(grid_fakes,
'example_fakes_mouth.png',
drange=[-1, 1],
grid_size=(variations_per_face, number_unique_faces))
mask = np.zeros(shape=(8, 8, 1))
mask[0:3, 1:7] = 1
grid_latents = misc.randomize_specific_local_codes(latents, mask,
variations_per_face)
grid_fakes = Gs.run(grid_latents, None, truncation_psi=0.7)
misc.save_image_grid(grid_fakes,
'example_fakes_hair.png',
drange=[-1, 1],
grid_size=(variations_per_face, number_unique_faces))
