def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
return cfg
def main(args):
config.merge_from_list(args.opts)
cfg, logger = default_setup(config, args)
model = build_model(cfg)
logger.info(f"Model structure: {model}")
file_sys = os.statvfs(cfg.OUTPUT_DIR)
free_space_Gb = (file_sys.f_bfree * file_sys.f_frsize) / 2**30
# We assume that a single dumped model is 700Mb
eval_space_Gb = (cfg.SOLVER.LR_SCHEDULER.MAX_ITER //
cfg.SOLVER.CHECKPOINT_PERIOD) * 700 / 2**10
if eval_space_Gb > free_space_Gb:
logger.warning(f"{Fore.RED}Remaining space({free_space_Gb}GB) "
f"is less than ({eval_space_Gb}GB){Style.RESET_ALL}")
if args.eval_only:
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
return res
"""
If you'd like to do anything fancier than the standard training logic,
consider writing your own training loop or subclassing the trainer.
"""
trainer = Trainer(cfg, model)
trainer.resume_or_load(resume=args.resume)
if cfg.TEST.AUG.ENABLED:
trainer.register_hooks([
hooks.EvalHook(0,
lambda: trainer.test_with_TTA(cfg, trainer.model))
])
return trainer.train()
def main(args):
config.merge_from_list(args.opts)
cfg, logger = default_setup(config, args)
if args.debug:
batches = int(cfg.SOLVER.IMS_PER_BATCH / 8 * args.num_gpus)
if cfg.SOLVER.IMS_PER_BATCH != batches:
cfg.SOLVER.IMS_PER_BATCH = batches
logger.warning(
"SOLVER.IMS_PER_BATCH is changed to {}".format(batches))
if "MODEL.WEIGHTS" in args.opts:
valid_files = [cfg.MODEL.WEIGHTS]
else:
list_of_files = glob.glob(os.path.join(cfg.OUTPUT_DIR, '*.pth'))
assert list_of_files, "no pth file found in {}".format(cfg.OUTPUT_DIR)
list_of_files.sort(key=os.path.getctime)
latest_file = list_of_files[-1]
if not args.end_iter:
valid_files = [latest_file]
else:
files = [f for f in list_of_files if str(f) <= str(latest_file)]
valid_files = []
for f in files:
try:
model_iter = int(re.split(r'(model_|\.pth)', f)[-3])
except Exception:
logger.warning("remove {}".format(f))
continue
if args.start_iter <= model_iter <= args.end_iter:
valid_files.append(f)
assert valid_files, "No .pth files satisfy your requirement"
# * means all if need specific format then *.csv
for current_file in valid_files:
cfg.MODEL.WEIGHTS = current_file
model = build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))