def prepare(tasks,
data_dir,
shards_num=1,
max_images=None,
ratio=1.0,
images_dir=None,
format=None): # Options for custom dataset
mkdir(data_dir)
for task in tasks:
# If task not in catalog, create custom task configuration
c = catalog.get(
task, {
"local": True,
"name": task,
"dir": images_dir,
"ratio": ratio,
"process": formats_catalog.get(format)
})
dirname = "{}/{}".format(data_dir, task)
mkdir(dirname)
# try:
print(misc.bold("Preparing the {} dataset...".format(c.name)))
fname = "{}/{}".format(dirname, c.filename)
if "local" not in c:
download = not ((os.path.exists(fname)
and verify_md5(fname, c.md5)))
path = get_path(c.url, dirname, path=c.filename)
if download:
print(
misc.bold("Downloading the data ({} GB)...".format(
c.size)))
download_file(c.url, path)
# print(misc.bold("Completed downloading {}".format(c.name)))
if path.endswith(".zip"):
if not is_unzipped(path, dirname):
print(misc.bold("Unzipping {}...".format(path)))
unzip(path, dirname)
# print(misc.bold("Completed unzipping {}".format(path)))
if "process" in c:
imgdir = images_dir if "local" in c else ("{}/{}".format(
dirname, c.dir))
shards_num = c.shards if max_images is None else shards_num
c.process(dirname,
imgdir,
ratio=c.ratio,
shards_num=shards_num,
max_imgs=max_images)
print(
misc.bcolored("Completed preparations for {}!".format(c.name),
"blue"))
def task(task, name, size, dir, redownload, download = None, prepare = lambda: None):
if task:
print(misc.bcolored("Preparing the {} dataset...".format(name), "blue"))
if download is not None and (redownload or not path.exists("{}/{}".format(dir, task))):
print(misc.bold("Downloading the data ({} GB)...".format(size), "blue"))
download()
print(misc("Completed downloading {}".format(name)))
prepare()
print(misc.bold("Completed preparations for {}!".format(name)))
except:
def load_nets(resume_pkl, lG, lD, lGs, recompile):
print(misc.bcolored("Loading networks from %s..." % resume_pkl, "white"))
rG, rD, rGs = misc.load_pkl(resume_pkl)[:3]
if recompile:
print(misc.bold("Copying nets..."));
lG.copy_vars_from(rG); lD.copy_vars_from(rD); lGs.copy_vars_from(rGs)
else:
lG, lD, lGs = rG, rD, rGs
return lG, lD, lGs
def verify_md5(filename, md5):
print(f"Verify MD5 for {filename}...")
with open(filename, "rb") as f:
new_md5 = hashlib.md5(f.read()).hexdigest()
result = md5 == new_md5
if result:
print(misc.bold("MD5 matches!"))
else:
print("MD5 doesn't match. Will redownload the file.")
return result
def prepare(tasks, data_dir, max_images = None,
ratio = 1.0, images_dir = None, format = None): # Options for custom dataset
os.makedirs(data_dir, exist_ok = True)
for task in tasks:
# If task not in catalog, create custom task configuration
c = catalog.get(task, EasyDict({
"local": True,
"name": task,
"dir": images_dir,
"ratio": ratio,
"process": formats_catalog.get(format)
}))
dirname = f"{data_dir}/{task}"
os.makedirs(dirname, exist_ok = True)
# try:
print(misc.bold(f"Preparing the {c.name} dataset..."))
if "local" not in c:
fname = f"{dirname}/{c.filename}"
download = not ((os.path.exists(fname) and verify_md5(fname, c.md5)))
path = get_path(c.url, dirname, path = c.filename)
if download:
print(misc.bold(f"Downloading the data ({c.size} GB)..."))
download_file(c.url, path)
if path.endswith(".zip"):
if not is_unzipped(path, dirname):
print(misc.bold(f"Unzipping {path}..."))
unzip(path, dirname)
if "process" in c:
imgdir = images_dir if "local" in c else (f"{dirname}/{c.dir}")
c.process(dirname, imgdir, ratio = c.ratio, max_imgs = max_images)
print(misc.bcolored(f"Completed preparations for {c.name}!", "blue"))
def run(**args):
args = EasyDict(args)
train = EasyDict(run_func_name="training.training_loop.training_loop"
) # training loop options
sched = EasyDict() # TrainingSchedule options
vis = EasyDict() # visualize.eval() options
grid = EasyDict(size="1080p",
layout="random") # setup_snapshot_img_grid() options
sc = dnnlib.SubmitConfig() # dnnlib.submit_run() options
# Environment configuration
tf_config = {
"rnd.np_random_seed": 1000,
"allow_soft_placement": True,
"gpu_options.per_process_gpu_memory_fraction": 1.0
}
if args.gpus != "":
num_gpus = len(args.gpus.split(","))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
# Networks configuration
cG = set_net("G", reg_interval=4)
cD = set_net("D", reg_interval=16)
# Dataset configuration
ratios = {
"clevr": 0.75,
"lsun-bedrooms": 0.72,
"cityscapes": 0.5,
"ffhq": 1.0
}
args.ratio = ratios.get(args.dataset, args.ratio)
dataset_args = EasyDict(tfrecord_dir=args.dataset,
max_imgs=args.train_images_num,
ratio=args.ratio,
num_threads=args.num_threads)
for arg in ["data_dir", "mirror_augment", "total_kimg"]:
cset(train, arg, args[arg])
# Training and Optimizations configuration
for arg in ["eval", "train", "recompile", "last_snapshots"]:
cset(train, arg, args[arg])
# Round to the closest multiply of minibatch size for validity
args.batch_size -= args.batch_size % args.minibatch_size
args.minibatch_std_size -= args.minibatch_std_size % args.minibatch_size
args.latent_size -= args.latent_size % args.components_num
if args.latent_size == 0:
print(
misc.bcolored(
"Error: latent-size is too small. Must best a multiply of components-num.",
"red"))
exit()
sched_args = {
"G_lrate": "g_lr",
"D_lrate": "d_lr",
"minibatch_size": "batch_size",
"minibatch_gpu": "minibatch_size"
}
for arg, cmd_arg in sched_args.items():
cset(sched, arg, args[cmd_arg])
cset(train, "clip", args.clip)
# Logging and metrics configuration
metrics = [metric_defaults[x] for x in args.metrics]
cset(cG.args, "truncation_psi", args.truncation_psi)
for arg in ["summarize", "keep_samples", "eval_images_num"]:
cset(train, arg, args[arg])
# Visualization
args.vis_imgs = args.vis_images
args.vis_ltnts = args.vis_latents
vis_types = [
"imgs", "ltnts", "maps", "layer_maps", "interpolations", "noise_var",
"style_mix"
]
# Set of all the set visualization types option
vis.vis_types = {arg for arg in vis_types if args["vis_{}".format(arg)]}
vis_args = {
"attention": "transformer",
"grid": "vis_grid",
"num": "vis_num",
"rich_num": "vis_rich_num",
"section_size": "vis_section_size",
"intrp_density": "intrpolation_density",
"intrp_per_component": "intrpolation_per_component",
"alpha": "blending_alpha"
}
for arg, cmd_arg in vis_args.items():
cset(vis, arg, args[cmd_arg])
# Networks architecture
cset(cG.args, "architecture", args.g_arch)
cset(cD.args, "architecture", args.d_arch)
cset(cG.args, "tanh", args.tanh)
# Latent sizes
if args.components_num > 1:
if not (args.transformer or args.kgan):
print(
misc.bcolored(
"Error: components-num > 1 but the model is not using components.",
"red"))
print(
misc.bcolored(
"Either add --transformer for GANsformer or --kgan for k-GAN).",
"red"))
exit()
args.latent_size = int(args.latent_size / args.components_num)
cD.args.latent_size = cG.args.latent_size = cG.args.dlatent_size = args.latent_size
cset([cG.args, cD.args, vis], "components_num", args.components_num)
# mapping-dim default value is the latent-size
if args.mapping_dim is None:
args.mapping_dim = args.latent_size
# Mapping network
for arg in ["layersnum", "lrmul", "dim", "resnet", "shared_dim"]:
cset(cG.args, arg, args["mapping_{}".format(arg)])
# StyleGAN settings
for arg in ["style", "latent_stem", "fused_modconv", "local_noise"]:
cset(cG.args, arg, args[arg])
cD.args.mbstd_group_size = args.minibatch_std_size
# GANsformer
cset(cG.args, "transformer", args.transformer)
cset(cD.args, "transformer", args.d_transformer)
args.norm = args.normalize
for arg in [
"norm", "integration", "ltnt_gate", "img_gate", "kmeans",
"kmeans_iters", "mapping_ltnt2ltnt"
]:
cset(cG.args, arg, args[arg])
for arg in ["use_pos", "num_heads"]:
cset([cG.args, cD.args], arg, args[arg])
# Positional encoding
for arg in ["dim", "init", "directions_num"]:
field = "pos_{}".format(arg)
cset([cG.args, cD.args], field, args[field])
# k-GAN
for arg in ["layer", "type", "same"]:
field = "merge_{}".format(arg)
cset(cG.args, field, args[field])
cset([cG.args, train], "merge", args.kgan)
# Attention
for arg in [
"start_res", "end_res", "ltnt2ltnt", "img2img", "local_attention"
]:
cset(cG.args, arg, args["g_{}".format(arg)])
cset(cD.args, arg, args["d_{}".format(arg)])
cset(cG.args, "img2ltnt", args.g_img2ltnt)
cset(cD.args, "ltnt2img", args.d_ltnt2img)
# Mixing and dropout
for arg in [
"style_mixing", "component_mixing", "component_dropout",
"attention_dropout"
]:
cset(cG.args, arg, args[arg])
# Loss and regularization
gloss_args = {
"loss_type": "g_loss",
"reg_weight": "g_reg_weight",
"pathreg": "pathreg",
}
dloss_args = {"loss_type": "d_loss", "reg_type": "d_reg", "gamma": "gamma"}
for arg, cmd_arg in gloss_args.items():
cset(cG.loss_args, arg, args[cmd_arg])
for arg, cmd_arg in dloss_args.items():
cset(cD.loss_args, arg, args[cmd_arg])
##### Experiments management:
# Whenever we start a new experiment we store its result in a directory named 'args.expname:000'.
# When we rerun a training or evaluation command it restores the model from that directory by default.
# If we wish to restart the model training, we can set --restart and then we will store data in a new
# directory: 'args.expname:001' after the first restart, then 'args.expname:002' after the second, etc.
# Find the latest directory that matches the experiment
exp_dir = sorted(glob.glob("{}/{}:*".format(args.result_dir,
args.expname)))
run_id = 0
if len(exp_dir) > 0:
run_id = int(exp_dir[-1].split(":")[-1])
# If restart, then work over a new directory
if args.restart:
run_id += 1
run_name = "{}:{:03d}".format(args.expname, run_id)
train.printname = "{} ".format(misc.bold(args.expname))
snapshot, kimg, resume = None, 0, False
pkls = sorted(
glob.glob("{}/{}/network*.pkl".format(args.result_dir, run_name)))
# Load a particular snapshot is specified
if args.pretrained_pkl:
# Soft links support
snapshot = glob.glob(args.pretrained_pkl)[0]
if os.path.islink(snapshot):
snapshot = os.readlink(snapshot)
# Extract training step from the snapshot if specified
try:
kimg = int(snapshot.split("-")[-1].split(".")[0])
except:
pass
# Find latest snapshot in the directory
elif len(pkls) > 0:
snapshot = pkls[-1]
kimg = int(snapshot.split("-")[-1].split(".")[0])
resume = True
if snapshot:
print(
misc.bcolored("Resuming {}, kimg {}".format(snapshot, kimg),
"white"))
train.resume_pkl = snapshot
train.resume_kimg = kimg
else:
print(misc.bcolored("Start model training from scratch.", "white"))
# Run environment configuration
sc.run_dir_root = args.result_dir
sc.run_desc = args.expname
sc.run_id = run_id
sc.run_name = run_name
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(cG=cG, cD=cD)
kwargs.update(dataset_args=dataset_args,
vis_args=vis,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.resume = resume
kwargs.load_config = args.reload
dnnlib.submit_run(**kwargs)
def training_loop(
# General configuration
train = False, # Training mode
eval = False, # Evaluation mode
vis = False, # Visualization mode
run_dir = ".", # Output directory
num_gpus = 1, # Number of GPUs participating in the training
rank = 0, # Rank of the current process in [0, num_gpus]
cG = {}, # Options for generator network
cD = {}, # Options for discriminator network
# Data
dataset_args = {}, # Options for training set
drange_net = [-1,1], # Dynamic range used when feeding image data to the networks
# Optimization
loss_args = {}, # Options for loss function
total_kimg = 25000, # Total length of the training, measured in thousands of real images
batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus
batch_gpu = 4, # Number of samples processed at a time by one GPU
ema_kimg = 10.0, # Half-life of the exponential moving average (EMA) of generator weights
ema_rampup = None, # EMA ramp-up coefficient
cudnn_benchmark = True, # Enable torch.backends.cudnnbenchmark?
allow_tf32 = False, # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnnallow_tf32?
# Logging
resume_pkl = None, # Network pickle to resume training from
resume_kimg = 0.0, # Assumed training progress at the beginning
# Affects reporting and training schedule
kimg_per_tick = 8, # Progress snapshot interval
img_snapshot_ticks = 3, # How often to save image snapshots? None = disable
network_snapshot_ticks = 3, # How often to save network snapshots? None = disable
last_snapshots = 10, # Maximal number of prior snapshots to save
printname = "", # Experiment name for logging
# Evaluation
vis_args = {}, # Options for vis.vis
metrics = [], # Metrics to evaluate during training
eval_images_num = 50000, # Sample size for the metrics
truncation_psi = 0.7 # Style strength multiplier for the truncation trick (used for visualizations only)
):
# Initialize
start_time = time.time()
device = init_cuda(rank, cudnn_benchmark, allow_tf32)
log = (rank == 0)
dataset, dataset_iter = load_dataset(dataset_args, batch_size, rank, num_gpus, log) # Load training set
nets = construct_nets(cG, cD, dataset, device, log) if train else None # Construct networks
G, D, Gs = load_nets(resume_pkl, nets, device, log) # Resume from existing pickle
print_nets(G, D, batch_gpu, device, log) # Print network summary tables
if eval:
misc.log("Run evaluation...", log = log)
evaluate(Gs, resume_pkl, metrics, eval_images_num, dataset_args, num_gpus, rank, device, log)
if vis and log:
misc.log("Produce visualizations...")
visualize.vis(Gs, dataset, device, batch_gpu, drange_net = drange_net, ratio = dataset.ratio,
truncation_psi = truncation_psi, **vis_args)
if not train:
exit()
nets = distribute_nets(G, D, Gs, device, num_gpus, log) # Distribute networks across GPUs
loss, stages = setup_training_stages(loss_args, G, cG, D, cD, nets, device, log) # Setup training stages (losses and optimizers)
grid_size, grid_z, grid_c = init_img_grid(dataset, G.input_shape, device, run_dir, log) # Initialize an image grid
logger = init_logger(run_dir, log) # Initialize logs
# Train
misc.log(f"Training for {total_kimg} kimg...", "white", log)
cur_nimg, cur_tick, batch_idx = int(resume_kimg * 1000), 0, 0
tick_start_nimg, tick_start_time = cur_nimg, time.time()
stats = None
while True:
# Fetch training data
real_img, real_c, gen_zs, gen_cs = fetch_data(dataset, dataset_iter, G.input_shape, drange_net,
device, len(stages), batch_size, batch_gpu)
# Execute training stages
for stage, gen_z, gen_c in zip(stages, gen_zs, gen_cs):
if batch_idx % stage.interval != 0:
continue
run_training_stage(loss, stage, device, real_img, real_c, gen_z, gen_c, batch_size, batch_gpu, num_gpus)
# Update Gs
update_ema_network(G, Gs, batch_size, cur_nimg, ema_kimg, ema_rampup)
# Update state
cur_nimg += batch_size
batch_idx += 1
# Perform maintenance tasks once per tick
done = (cur_nimg >= total_kimg * 1000)
if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
continue
# Print status line and accumulate the info in logger.collector
tick_end_time = time.time()
if stats is not None:
default = dnnlib.EasyDict({'mean': -1})
fields = []
fields.append("tick " + misc.bold(f"{training_stats.report0('Progress/tick', cur_tick):<5d}"))
fields.append("kimg " + misc.bcolored(f"{training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}", "red"))
fields.append("")
fields.append("loss/reg: G (" + misc.bcolored(f"{stats.get('Loss/G/loss', default).mean:>6.3f}", "blue"))
fields.append(misc.bold(f"{stats.get('Loss/G/reg', default).mean:>6.3f}") + ")")
fields.append("D "+ misc.bcolored(f"({stats.get('Loss/D/loss', default).mean:>6.3f}", "blue"))
fields.append(misc.bold(f"{stats.get('Loss/D/reg', default).mean:>6.3f}") + ")")
fields.append("")
fields.append("time " + misc.bold(f"{dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"))
fields.append(f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}")
fields.append(f"mem: GPU {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}")
fields.append(f"CPU {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}")
fields.append(misc.bold(printname))
torch.cuda.reset_peak_memory_stats()
training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60))
training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60))
misc.log(" ".join(fields), log = log)
# Save image snapshot
if log and (img_snapshot_ticks is not None) and (done or cur_tick % img_snapshot_ticks == 0):
visualize.vis(Gs, dataset, device, batch_gpu, training = True,
step = cur_nimg // 1000, grid_size = grid_size, latents = grid_z,
labels = grid_c, drange_net = drange_net, ratio = dataset.ratio, **vis_args)
# Save network snapshot
if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
snapshot_data, snapshot_pkl = save_nets(G, D, Gs, cur_nimg, dataset_args, run_dir, num_gpus > 1, last_snapshots, log)
# Evaluate metrics
evaluate(snapshot_data["Gs"], snapshot_pkl, metrics, eval_images_num,
dataset_args, num_gpus, rank, device, log, logger, run_dir)
del snapshot_data
# Collect stats and update logs
stats = collect_stats(logger, stages)
update_logger(logger, stats, cur_nimg, start_time)
cur_tick += 1
tick_start_nimg, tick_start_time = cur_nimg, time.time()
maintenance_time = tick_start_time - tick_end_time
if done:
break
# Done
misc.log("Done!", "blue")
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()
