def main(args):
load_config(cfg, args.config)
local_rank = -1
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
cfg.defrost()
timestr = datetime.datetime.now().__format__("%Y%m%d%H%M%S")
cfg.save_dir = os.path.join(cfg.save_dir, timestr)
mkdir(local_rank, cfg.save_dir)
logger = NanoDetLightningLogger(cfg.save_dir)
assert args.task in ["val", "test"]
cfg.update({"test_mode": args.task})
logger.info("Setting up data...")
val_dataset = build_dataset(cfg.data.val, args.task)
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=cfg.device.batchsize_per_gpu,
shuffle=False,
num_workers=cfg.device.workers_per_gpu,
pin_memory=True,
collate_fn=naive_collate,
drop_last=False,
)
evaluator = build_evaluator(cfg.evaluator, val_dataset)
logger.info("Creating model...")
task = TrainingTask(cfg, evaluator)
ckpt = torch.load(args.model)
if "pytorch-lightning_version" not in ckpt:
warnings.warn(
"Warning! Old .pth checkpoint is deprecated. "
"Convert the checkpoint with tools/convert_old_checkpoint.py ")
ckpt = convert_old_model(ckpt)
task.load_state_dict(ckpt["state_dict"])
if cfg.device.gpu_ids == -1:
logger.info("Using CPU training")
accelerator, devices = "cpu", None
else:
accelerator, devices = "gpu", cfg.device.gpu_ids
trainer = pl.Trainer(
default_root_dir=cfg.save_dir,
accelerator=accelerator,
devices=devices,
log_every_n_steps=cfg.log.interval,
num_sanity_val_steps=0,
logger=logger,
)
logger.info("Starting testing...")
trainer.test(task, val_dataloader)
def main(args):
warnings.warn(
'Warning! Old testing code is deprecated and will be deleted '
'in next version. Please use tools/test.py')
load_config(cfg, args.config)
local_rank = -1
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
cfg.defrost()
timestr = datetime.datetime.now().__format__('%Y%m%d%H%M%S')
cfg.save_dir = os.path.join(cfg.save_dir, timestr)
cfg.freeze()
mkdir(local_rank, cfg.save_dir)
logger = Logger(local_rank, cfg.save_dir)
logger.log('Creating model...')
model = build_model(cfg.model)
logger.log('Setting up data...')
val_dataset = build_dataset(cfg.data.val, args.task)
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=cfg.device.batchsize_per_gpu,
shuffle=False,
num_workers=cfg.device.workers_per_gpu,
pin_memory=True,
collate_fn=collate_function,
drop_last=True)
trainer = build_trainer(local_rank, cfg, model, logger)
cfg.schedule.update({'load_model': args.model})
trainer.load_model(cfg)
evaluator = build_evaluator(cfg, val_dataset)
logger.log('Starting testing...')
with torch.no_grad():
results, val_loss_dict = trainer.run_epoch(0,
val_dataloader,
mode=args.task)
if args.task == 'test':
res_json = evaluator.results2json(results)
json_path = os.path.join(cfg.save_dir,
'results{}.json'.format(timestr))
json.dump(res_json, open(json_path, 'w'))
elif args.task == 'val':
eval_results = evaluator.evaluate(results,
cfg.save_dir,
rank=local_rank)
if args.save_result:
txt_path = os.path.join(cfg.save_dir,
"eval_results{}.txt".format(timestr))
with open(txt_path, "a") as f:
for k, v in eval_results.items():
f.write("{}: {}\n".format(k, v))
def main(args):
load_config(cfg, args.config)
local_rank = -1
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
cfg.defrost()
timestr = datetime.datetime.now().__format__('%Y%m%d%H%M%S')
cfg.save_dir = os.path.join(cfg.save_dir, timestr)
mkdir(local_rank, cfg.save_dir)
logger = Logger(local_rank, cfg.save_dir)
assert args.task in ['val', 'test']
cfg.update({'test_mode': args.task})
logger.log('Setting up data...')
val_dataset = build_dataset(cfg.data.val, args.task)
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=cfg.device.batchsize_per_gpu,
shuffle=False,
num_workers=cfg.device.workers_per_gpu,
pin_memory=True,
collate_fn=collate_function,
drop_last=True)
evaluator = build_evaluator(cfg, val_dataset)
logger.log('Creating model...')
task = TrainingTask(cfg, evaluator)
ckpt = torch.load(args.model)
if 'pytorch-lightning_version' not in ckpt:
warnings.warn(
'Warning! Old .pth checkpoint is deprecated. '
'Convert the checkpoint with tools/convert_old_checkpoint.py ')
ckpt = convert_old_model(ckpt)
task.load_state_dict(ckpt['state_dict'])
trainer = pl.Trainer(
default_root_dir=cfg.save_dir,
gpus=cfg.device.gpu_ids,
accelerator='ddp',
log_every_n_steps=cfg.log.interval,
num_sanity_val_steps=0,
)
logger.log('Starting testing...')
trainer.test(task, val_dataloader)