def init_img_grid(dataset, input_shape, device, run_dir, log):
if not log:
return None, None, None
grid_size, images, labels = misc.setup_snapshot_img_grid(dataset)
misc.save_img_grid(images, os.path.join(run_dir, "reals.png"), drange = [0, 255], grid_size = grid_size)
grid_z = torch.randn([labels.shape[0], *input_shape[1:]], device = device)
grid_c = torch.from_numpy(labels).to(device)
return grid_size, grid_z, grid_c
def training_loop(
# Configurations
cG={},
cD={}, # Generator and Discriminator command-line arguments
dataset_args={}, # dataset.load_dataset() options
sched_args={}, # train.TrainingSchedule options
vis_args={}, # vis.eval options
grid_args={}, # train.setup_snapshot_img_grid() options
metric_arg_list=[], # MetricGroup Options
tf_config={}, # tflib.init_tf() options
eval=False, # Evaluation mode
train=False, # Training mode
# Data
data_dir=None, # Directory to load datasets from
total_kimg=25000, # Total length of the training, measured in thousands of real images
mirror_augment=False, # Enable mirror augmentation?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks
ratio=1.0, # Image height/width ratio in the dataset
# Optimization
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters
lazy_regularization=True, # Perform regularization as a separate training step?
smoothing_kimg=10.0, # Half-life of the running average of generator weights
clip=None, # Clip gradients threshold
# Resumption
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
recompile=False, # Recompile network from source code (otherwise loads from snapshot)
# Logging
summarize=True, # Create TensorBoard summaries
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
img_snapshot_ticks=3, # How often to save image snapshots? None = disable
network_snapshot_ticks=3, # How often to save network snapshots? None = only save networks-final.pkl
last_snapshots=10, # Maximal number of prior snapshots to save
eval_images_num=50000, # Sample size for the metrics
printname="", # Experiment name for logging
# Architecture
merge=False): # Generate several images and then merge them
# Initialize dnnlib and TensorFlow
tflib.init_tf(tf_config)
num_gpus = dnnlib.submit_config.num_gpus
cG.name, cD.name = "g", "d"
# Load dataset, configure training scheduler and metrics object
dataset = data.load_dataset(data_dir=dnnlib.convert_path(data_dir),
verbose=True,
**dataset_args)
sched = training_schedule(sched_args,
cur_nimg=total_kimg * 1000,
dataset=dataset)
metrics = metric_base.MetricGroup(metric_arg_list)
# Construct or load networks
with tf.device("/gpu:0"):
no_op = tf.no_op()
G, D, Gs = None, None, None
if resume_pkl is None or recompile:
misc.log("Constructing networks...", "white")
G = tflib.Network("G",
num_channels=dataset.shape[0],
resolution=dataset.shape[1],
label_size=dataset.label_size,
**cG.args)
D = tflib.Network("D",
num_channels=dataset.shape[0],
resolution=dataset.shape[1],
label_size=dataset.label_size,
**cD.args)
Gs = G.clone("Gs")
if resume_pkl is not None:
G, D, Gs = load_nets(resume_pkl, G, D, Gs, recompile)
G.print_layers()
D.print_layers()
# Train/Evaluate/Visualize
# Labels are optional but not essential
grid_size, grid_reals, grid_labels = misc.setup_snapshot_img_grid(
dataset, **grid_args)
misc.save_img_grid(grid_reals,
dnnlib.make_run_dir_path("reals.png"),
drange=dataset.dynamic_range,
grid_size=grid_size)
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
if eval:
# Save a snapshot of the current network to evaluate
pkl = dnnlib.make_run_dir_path("network-eval-snapshot-%06d.pkl" %
resume_kimg)
misc.save_pkl((G, D, Gs), pkl, remove=False)
# Quantitative evaluation
metric = metrics.run(pkl,
num_imgs=eval_images_num,
run_dir=dnnlib.make_run_dir_path(),
data_dir=dnnlib.convert_path(data_dir),
num_gpus=num_gpus,
ratio=ratio,
tf_config=tf_config,
mirror_augment=mirror_augment)
# Qualitative evaluation
visualize.eval(G,
dataset,
batch_size=sched.minibatch_gpu,
drange_net=drange_net,
ratio=ratio,
**vis_args)
if not train:
dataset.close()
exit()
# Setup training inputs
misc.log("Building TensorFlow graph...", "white")
with tf.name_scope("Inputs"), tf.device("/cpu:0"):
lrate_in_g = tf.placeholder(tf.float32, name="lrate_in_g", shape=[])
lrate_in_d = tf.placeholder(tf.float32, name="lrate_in_d", shape=[])
step = tf.placeholder(tf.int32, name="step", 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)
beta = 0.5**tf.div(tf.cast(minibatch_size_in,
tf.float32), smoothing_kimg *
1000.0) if smoothing_kimg > 0.0 else 0.0
# Set optimizers
for cN, lr in [(cG, lrate_in_g), (cD, lrate_in_d)]:
set_optimizer(cN, lr, minibatch_multiplier, lazy_regularization, clip)
# 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
for cN, N in [(cG, G), (cD, D)]:
cN.gpu = N if gpu == 0 else N.clone(N.name + "_shadow")
Gs_gpu = Gs if gpu == 0 else Gs.clone(Gs.name + "_shadow")
# Fetch training data via temporary variables
with tf.name_scope("DataFetch"):
reals, labels = dataset.get_minibatch_tf()
reals = process_reals(reals, dataset.dynamic_range, drange_net,
mirror_augment)
reals, reals_fetch = read_data(
reals, "reals", [sched.minibatch_gpu] + dataset.shape,
minibatch_gpu_in)
labels, labels_fetch = read_data(
labels, "labels",
[sched.minibatch_gpu, dataset.label_size],
minibatch_gpu_in)
data_fetch_ops += [reals_fetch, labels_fetch]
# Evaluate loss functions
with tf.name_scope("G_loss"):
cG.loss, cG.reg = dnnlib.util.call_func_by_name(
G=cG.gpu,
D=cD.gpu,
dataset=dataset,
reals=reals,
minibatch_size=minibatch_gpu_in,
**cG.loss_args)
with tf.name_scope("D_loss"):
cD.loss, cD.reg = dnnlib.util.call_func_by_name(
G=cG.gpu,
D=cD.gpu,
dataset=dataset,
reals=reals,
labels=labels,
minibatch_size=minibatch_gpu_in,
**cD.loss_args)
for cN in [cG, cD]:
set_optimizer_ops(cN, lazy_regularization, no_op)
# Setup training ops
data_fetch_op = tf.group(*data_fetch_ops)
for cN in [cG, cD]:
cN.train_op = cN.opt.apply_updates()
cN.reg_op = cN.reg_opt.apply_updates(allow_no_op=True)
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=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()
# Tensorboard summaries
if summarize:
misc.log("Initializing logs...", "white")
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()
# Initialize training
misc.log("Training for %d kimg..." % total_kimg, "white")
dnnlib.RunContext.get().update("",
cur_epoch=resume_kimg,
max_epoch=total_kimg)
maintenance_time = dnnlib.RunContext.get().get_last_update_interval()
cur_tick, running_mb_counter = -1, 0
cur_nimg = int(resume_kimg * 1000)
tick_start_nimg = cur_nimg
for cN in [cG, cD]:
cN.lossvals_agg = {
k: None
for k in ["loss", "reg", "norm", "reg_norm"]
}
cN.opt.reset_optimizer_state()
# Training loop
while cur_nimg < total_kimg * 1000:
if dnnlib.RunContext.get().should_stop():
break
# Choose training parameters and configure training ops
sched = training_schedule(sched_args,
cur_nimg=cur_nimg,
dataset=dataset)
assert sched.minibatch_size % (sched.minibatch_gpu * num_gpus) == 0
dataset.configure(sched.minibatch_gpu)
# Run training ops
feed_dict = {
lrate_in_g: sched.G_lrate,
lrate_in_d: sched.D_lrate,
minibatch_size_in: sched.minibatch_size,
minibatch_gpu_in: sched.minibatch_gpu,
step: sched.kimg
}
# Several iterations before updating training parameters
for _repeat in range(minibatch_repeats):
rounds = range(0, sched.minibatch_size,
sched.minibatch_gpu * num_gpus)
for cN in [cG, cD]:
cN.run_reg = lazy_regularization and (running_mb_counter %
cN.reg_interval == 0)
cur_nimg += sched.minibatch_size
running_mb_counter += 1
for cN in [cG, cD]:
cN.lossvals = {
k: None
for k in ["loss", "reg", "norm", "reg_norm"]
}
# Gradient accumulation
for _round in rounds:
cG.lossvals.update(
tflib.run([cG.train_op, cG.ops], feed_dict)[1])
if cG.run_reg:
_, cG.lossvals["reg_norm"] = tflib.run(
[cG.reg_op, cG.reg_norm], feed_dict)
tflib.run(data_fetch_op, feed_dict)
cD.lossvals.update(
tflib.run([cD.train_op, cD.ops], feed_dict)[1])
if cD.run_reg:
_, cD.lossvals["reg_norm"] = tflib.run(
[cD.reg_op, cD.reg_norm], feed_dict)
tflib.run([Gs_update_op], feed_dict)
# Track loss statistics
for cN in [cG, cD]:
for k in cN.lossvals_agg:
cN.lossvals_agg[k] = emaAvg(cN.lossvals_agg[k],
cN.lossvals[k])
# 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 %s kimg %s loss/reg: G (%s %s) D (%s %s) grad norms: G (%s %s) D (%s %s) "
+ "time %s sec/kimg %s maxGPU %sGB %s") %
(misc.bold("%-5d" % autosummary("Progress/tick", cur_tick)),
misc.bcolored(
"{:>8.1f}".format(
autosummary("Progress/kimg", cur_nimg / 1000.0)),
"red"),
misc.bcolored("{:>6.3f}".format(cG.lossvals_agg["loss"] or 0),
"blue"),
misc.bold("{:>6.3f}".format(cG.lossvals_agg["reg"] or 0)),
misc.bcolored("{:>6.3f}".format(cD.lossvals_agg["loss"] or 0),
"blue"),
misc.bold("{:>6.3f}".format(cD.lossvals_agg["reg"] or 0)),
misc.cond_bcolored(cG.lossvals_agg["norm"], 20.0, "red"),
misc.cond_bcolored(cG.lossvals_agg["reg_norm"], 20.0, "red"),
misc.cond_bcolored(cD.lossvals_agg["norm"], 20.0, "red"),
misc.cond_bcolored(cD.lossvals_agg["reg_norm"], 20.0, "red"),
misc.bold("%-10s" % dnnlib.util.format_time(
autosummary("Timing/total_sec", total_time))),
"{:>7.2f}".format(
autosummary("Timing/sec_per_kimg",
tick_time / tick_kimg)),
"{:>4.1f}".format(
autosummary("Resources/peak_gpu_mem_gb",
peak_gpu_mem_op.eval() / 2**30)), printname))
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 img_snapshot_ticks is not None and (
cur_tick % img_snapshot_ticks == 0 or done):
visualize.eval(G,
dataset,
batch_size=sched.minibatch_gpu,
training=True,
step=cur_nimg // 1000,
grid_size=grid_size,
latents=grid_latents,
labels=grid_labels,
drange_net=drange_net,
ratio=ratio,
**vis_args)
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, remove=False)
if cur_tick % network_snapshot_ticks == 0 or done:
metric = metrics.run(
pkl,
num_imgs=eval_images_num,
run_dir=dnnlib.make_run_dir_path(),
data_dir=dnnlib.convert_path(data_dir),
num_gpus=num_gpus,
ratio=ratio,
tf_config=tf_config,
mirror_augment=mirror_augment)
if last_snapshots > 0:
misc.rm(
sorted(
glob.glob(dnnlib.make_run_dir_path(
"network*.pkl")))[:-last_snapshots])
# Update summaries and RunContext
if summarize:
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
dnnlib.RunContext.get().update(None,
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"),
remove=False)
# All done
if summarize:
summary_log.close()
dataset.close()