def generate_grids(network,
seeds,
latent_pair,
n_samples_per=10,
bound=2,
rot=0,
load_gan=False):
tflib.init_tf()
print('Loading networks from "%s"...' % network)
if load_gan:
_G, _D, I, G = misc.load_pkl(network)
else:
E, G = get_return_v(misc.load_pkl(network), 2)
G_kwargs = dnnlib.EasyDict()
G_kwargs.is_validation = True
G_kwargs.randomize_noise = True
G_kwargs.minibatch_size = 8
distance_measure = misc.load_pkl(
'http://d36zk2xti64re0.cloudfront.net/stylegan1/networks/metrics/vgg16_zhang_perceptual.pkl'
)
distance_ls = []
for seed_idx, seed in enumerate(seeds):
print('Generating images for seed %d (%d/%d) ...' %
(seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = sample_grid_z(rnd, G, latent_pair, n_samples_per, bound, rot)
images = get_return_v(
G.run(z, None, **G_kwargs),
1) # [n_samples_per*n_samples_per, channel, height, width]
distance_ls.append(
measure_distance(images, n_samples_per, distance_measure))
images = add_outline(images, width=1)
n_samples_square, c, h, w = np.shape(images)
assert n_samples_square == n_samples_per * n_samples_per
images = np.reshape(images, (n_samples_per, n_samples_per, c, h, w))
images = np.transpose(images, [0, 3, 1, 4, 2])
images = np.reshape(images, (n_samples_per * h, n_samples_per * w, c))
images = misc.adjust_dynamic_range(images, [0, 1], [0, 255])
images = np.rint(images).clip(0, 255).astype(np.uint8)
PIL.Image.fromarray(images, 'RGB').save(
dnnlib.make_run_dir_path('seed%04d.png' % seed))
print('mean_distance:', np.mean(np.array(distance_ls)))
def training_loop_ps_sc(
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
I_args={}, # Options for infogan-head/ps-sc-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_ps_head=False, # Whether to use ps-head.
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=0, # Number of discrete latents in model.
n_continuous=4, # Number of continuous latents in model.
return_atts=False, # If return attention maps.
return_I_atts=False, # If return I_attention maps of vpex.
avg_mv_for_I=False, # If use average moving for I.
topk_dims_to_show=20, # Number of top disentant dimensions to show in a snapshot.
cascade_alt_freq_k=1, # Frequency in k for cascade_dim altering.
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 include I
include_I = use_info_gan or use_ps_head
# 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...')
print('G_args:', G_args)
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 include_I:
I = tflib.Network('I',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**I_args)
if avg_mv_for_I:
Is = I.clone('Is')
Gs = G.clone('Gs')
if resume_pkl is not None:
print('Loading networks from "%s"...' % resume_pkl)
if include_I:
if avg_mv_for_I:
rG, rD, rI, rGs, rIs = misc.load_pkl(resume_pkl)
else:
rG, rD, rI, 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 include_I:
I.copy_vars_from(rI)
if avg_mv_for_I:
Is.copy_vars_from(rIs)
Gs.copy_vars_from(rGs)
else:
G = rG
D = rD
if include_I:
I = rI
if avg_mv_for_I:
Is = rIs
Gs = rGs
# Print layers and generate initial image snapshot.
G.print_layers()
D.print_layers()
if include_I:
I.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)
print('topk_dims_to_show:', topk_dims_to_show)
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)
if return_atts:
grid_fakes, atts = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
return_atts=True,
resolution=training_set.shape[1])
# atts: [b, n_latents, 1, res, res]
atts = atts[:, topk_dims]
save_atts(atts,
filename=dnnlib.make_run_dir_path('fakes_atts_init.png'),
grid_size=grid_size,
drange=[0, 1],
grid_fakes=grid_fakes,
n_samples_per=n_samples_per)
else:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True)
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 include_I and return_I_atts:
I_tmp = Is if avg_mv_for_I else I
_, atts = I_tmp.run(grid_fakes,
grid_fakes,
grid_latents,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
return_atts=True,
resolution=training_set.shape[1])
save_atts(atts,
filename=dnnlib.make_run_dir_path('fakes_I_atts_init.png'),
grid_size=grid_size,
drange=[0, 1],
grid_fakes=grid_fakes,
n_samples_per=n_samples_per)
# 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
cascade_dim = tf.placeholder(tf.int32, name='cascade_dim', shape=[])
# 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 include_I:
I_gpu = I if gpu == 0 else I.clone(I.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 include_I:
G_loss, G_reg = 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,
cascade_dim=cascade_dim,
**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)
if include_I:
GI_gpu_trainables = collections.OrderedDict(
list(G_gpu.trainables.items()) +
list(I_gpu.trainables.items()))
G_opt.register_gradients(tf.reduce_mean(G_loss),
GI_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)
# 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)
if avg_mv_for_I:
Is_update_op = Is.setup_as_moving_average_of(I, 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 include_I:
I.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
# Calculate which cascade_dim is to use.
cur_nimg_k = cur_nimg // int(cascade_alt_freq_k * 1000)
sched_cascade_dim = cur_nimg_k % n_continuous
# 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,
cascade_dim: sched_cascade_dim
}
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)
if avg_mv_for_I:
tflib.run([D_train_op, Gs_update_op, Is_update_op],
feed_dict)
else:
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)
if avg_mv_for_I:
tflib.run([Gs_update_op, Is_update_op], feed_dict)
else:
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 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 include_I:
if avg_mv_for_I:
misc.save_pkl((G, D, I, Gs, Is), pkl)
else:
misc.save_pkl((G, D, I, Gs), pkl)
else:
misc.save_pkl((G, D, Gs), 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,
include_I=include_I,
avg_mv_for_I=avg_mv_for_I,
Gs_kwargs=dict(is_validation=True,
return_atts=False),
mapping_nodup=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:])
if return_atts:
grid_fakes, atts = Gs.run(
grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True,
return_atts=True,
resolution=training_set.shape[1])
# atts: [b, n_latents, 1, res, res]
atts = atts[:, topk_dims]
save_atts(atts,
filename=dnnlib.make_run_dir_path(
'fakes_atts%06d.png' % (cur_nimg // 1000)),
grid_size=grid_size,
drange=[0, 1],
grid_fakes=grid_fakes,
n_samples_per=n_samples_per)
else:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
randomize_noise=True)
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 include_I and return_I_atts:
if avg_mv_for_I:
I_tmp = Is
else:
I_tmp = I
_, atts = I_tmp.run(grid_fakes,
grid_fakes,
grid_latents,
is_validation=True,
minibatch_size=sched.minibatch_gpu,
return_atts=True,
resolution=training_set.shape[1])
atts = atts[:, topk_dims]
save_atts(atts,
filename=dnnlib.make_run_dir_path(
'fakes_I_atts%06d.png' % (cur_nimg // 1000)),
grid_size=grid_size,
drange=[0, 1],
grid_fakes=grid_fakes,
n_samples_per=n_samples_per)
# 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 include_I:
if avg_mv_for_I:
misc.save_pkl((G, D, I, Gs, Is),
dnnlib.make_run_dir_path('network-final.pkl'))
else:
misc.save_pkl((G, D, I, 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 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 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 plot_rot_fn(network,
seeds,
latent_pair,
n_samples_per,
bound,
rot_start,
rot_end,
rot_interval,
coord_adj,
load_gan=False):
tflib.init_tf()
print('Loading networks from "%s"...' % network)
if load_gan:
_G, _D, I, G = misc.load_pkl(network)
else:
E, G = get_return_v(misc.load_pkl(network), 2)
G_kwargs = dnnlib.EasyDict()
G_kwargs.is_validation = True
G_kwargs.randomize_noise = True
G_kwargs.minibatch_size = 8
distance_measure = misc.load_pkl(
'http://d36zk2xti64re0.cloudfront.net/stylegan1/networks/metrics/vgg16_zhang_perceptual.pkl'
)
distance_rot_ls = []
rot_ls = list(range(int(rot_start), int(rot_end) + 1, int(rot_interval)))
mark_idxs = []
for rot_idx, rot in enumerate(rot_ls):
print('Generating images for rotation degree %d (%d/%d) ...' %
(rot, rot_idx, len(rot_ls)))
if rot in [-180, -90, 0, 90, 180]:
mark_idxs.append(rot_idx)
distance_ls = []
for seed_idx, seed in enumerate(seeds):
rnd = np.random.RandomState(seed)
z = sample_grid_z(rnd, G, latent_pair, n_samples_per, bound, rot)
images = get_return_v(
G.run(z, None, **G_kwargs),
1) # [n_samples_per*n_samples_per, channel, height, width]
distance_ls.append(
measure_distance(images, n_samples_per, distance_measure))
# save grids
if seed_idx < 10 and rot == 0:
images_2 = add_outline(images, width=1)
n_samples_square, c, h, w = np.shape(images_2)
assert n_samples_square == n_samples_per * n_samples_per
images_2 = np.reshape(images_2,
(n_samples_per, n_samples_per, c, h, w))
images_2 = np.transpose(images_2, [0, 3, 1, 4, 2])
images_2 = np.reshape(
images_2, (n_samples_per * h, n_samples_per * w, c))
images_2 = misc.adjust_dynamic_range(images_2, [0, 1],
[0, 255])
images_2 = np.rint(images_2).clip(0, 255).astype(np.uint8)
PIL.Image.fromarray(images_2, 'RGB').save(
dnnlib.make_run_dir_path('seed%04d.png' % seed))
distance_rot_ls.append(np.mean(np.array(distance_ls)))
plot_fn(rot_ls,
distance_rot_ls,
rot_start,
rot_end,
mark_idxs,
coord_adj=coord_adj)