def baseline_main(args):
config_path = get_config_path()
configs = []
for path, dirs, files in os.walk(config_path):
for file in files:
file = os.path.join(path, file)
match = True
for keyword in args.keywords:
result = re.search(r"[/\\_.]%s[/\\_.]" % keyword, file)
if not result:
match = False
break
if match:
configs.append(file)
if len(configs) == 0:
raise ValueError("Can't find a baseline with keywords: %s" %
", ".join(args.keywords))
if len(configs) > 1:
configs = sorted(configs)
configs = [""] + [
os.path.relpath(config, config_path) for config in configs
]
raise ValueError("Ambiguous keywords. Candidates are:%s" %
"\n ".join(configs))
config = configs[0]
print("running baseline: %s" % os.path.relpath(config, config_path))
cfg = load_config(config)
if args.gpu is not None:
cfg.resource.gpus = range(args.gpu)
if args.cpu is not None:
cfg.resource.cpu_per_gpu = args.cpu
if args.epoch is not None:
cfg.train.num_epoch = args.epoch
app = gap.Application(cfg.application, **cfg.resource)
app.load(**cfg.graph)
app.build(**cfg.build)
if "load" in cfg:
app.load_model(**cfg.load)
app.train(**cfg.train)
if args.eval and "evaluate" in cfg:
if isinstance(cfg.evaluate, dict):
cfg.evaluate = [cfg.evaluate]
for evaluation in cfg.evaluate:
app.evaluate(**evaluation)
if "save" in cfg:
app.save_model(**cfg.save)
def run_main(args):
cfg = load_config(args.config)
if args.gpu:
cfg.resource.gpus = range(args.gpu)
if args.cpu:
cfg.resource.cpu_per_gpu = args.cpu
app = gap.Application(cfg.application, **cfg.resource)
app.load(**cfg.graph)
app.build(**cfg.build)
app.train(**cfg.train)
if args.eval and "evaluate" in cfg:
app.evaluate(**cfg.evaluate)
if "save" in cfg:
app.save(**cfg.save)
def run_main(args):
cfg = load_config(args.config)
if args.gpu is not None:
cfg.resource.gpus = range(args.gpu)
if args.cpu is not None:
cfg.resource.cpu_per_gpu = args.cpu
if args.epoch is not None:
cfg.train.num_epoch = args.epoch
app = gap.Application(cfg.application, **cfg.resource)
if "format" in cfg:
app.set_format(**cfg.format)
app.load(**cfg.graph)
app.build(**cfg.build)
app.train(**cfg.train)
if args.eval and "evaluate" in cfg:
if isinstance(cfg.evaluate, dict):
cfg.evaluate = [cfg.evaluate]
for evaluation in cfg.evaluate:
app.evaluate(**evaluation)
if "save" in cfg:
app.save_model(**cfg.save)