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.max_images, 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.component_num
if args.latent_size == 0:
print(bcolored("Error: latent-size is too small. Must best a multiply of component-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"]:
cset(train, arg, args[arg])
# Visualization
args.imgs = args.images
args.ltnts = args.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[arg]}
vis_args = {
"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, cD.args], "resnet_mlp", args.resnet_mlp)
cset(cG.args, "tanh", args.tanh)
# Latent sizes
if args.component_num > 1
if not (args.attention or args.merge):
print(bcolored("Error: component-num > 1 but the model is not using components.", "red"))
print(bcolored("Either add --attention for GANsformer or --merge for k-GAN).", "red"))
exit()
args.latent_size = int(args.latent_size / args.component_num)
cD.args.latent_size = cG.args.latent_size = cG.args.dlatent_size = args.latent_size
cset([cG.args, cD.args, train, vis], "component_num", args.component_num)
# Mapping network
for arg in ["layersnum", "lrmul", "dim", "shared"]:
cset(cG.args, arg, args["mapping_{}".formt(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, train], "attention", args.transformer)
cset(cD.args, "attention", args.d_transformer)
cset([cG.args, cD.args], "num_heads", args.num_heads)
args.norm = args.normalize
for arg in ["norm", "integration", "ltnt_gate", "img_gate", "kmeans",
"kmeans_iters", "asgn_direct", "mapping_ltnt2ltnt"]:
cset(cG.args, arg, args[arg])
for arg in ["attention_inputs", "use_pos"]:
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", "channelwise"]:
field = "merge_{}".format(arg)
cset(cG.args, field, args[field])
cset([cG.args, train], "merge", args.merge)
# 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)))[-1]
run_id = int(exp_dir.split(":")[-1])
# If restart, then work over a new directory
if args.restart:
run_id += 1
run_name = "{}:{0:03d}".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("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
# If reload new options from the command line, no need to load the original configuration file
kwargs.load_config = not args.reload
dnnlib.submit_run(**kwargs)
