def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
if cfg.NUM_GPUS > 1:
torch.multiprocessing.spawn(
mpu.run,
nprocs=cfg.NUM_GPUS,
args=(
cfg.NUM_GPUS,
test,
args.init_method,
cfg.SHARD_ID,
cfg.NUM_SHARDS,
cfg.DIST_BACKEND,
cfg,
),
daemon=False,
)
else:
test(cfg=cfg)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
print('=' * 20)
# print(cfg)
print('Num of GPUs: ', cfg.NUM_GPUS)
print(cfg.TRAIN)
print(cfg.TEST)
print('output dir is: ', cfg.OUTPUT_DIR)
# Perform training.
if cfg.TRAIN.ENABLE:
print("begin to trian the model... ")
if cfg.NUM_GPUS > 1:
print('gpu is over 1')
torch.multiprocessing.spawn(
mpu.run,
nprocs=cfg.NUM_GPUS,
args=(
cfg.NUM_GPUS,
train,
args.init_method,
cfg.SHARD_ID,
cfg.NUM_SHARDS,
cfg.DIST_BACKEND,
cfg,
),
daemon=False,
)
else:
train(cfg=cfg)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
print("begin to test the model... ")
if cfg.NUM_GPUS > 1:
torch.multiprocessing.spawn(
mpu.run,
nprocs=cfg.NUM_GPUS,
args=(
cfg.NUM_GPUS,
test,
args.init_method,
cfg.SHARD_ID,
cfg.NUM_SHARDS,
cfg.DIST_BACKEND,
cfg,
),
daemon=False,
)
else:
test(cfg=cfg)
'loss_rcnn_box': loss_rcnn_box
}
logger.add_scalars("logs_s_{}/losses".format(args.session),
info,
(epoch - 1) * iters_per_epoch + step)
loss_temp = 0
start = time.time()
if (args.val_interval and (epoch + 1) % args.val_interval == 0)\
and (args.dataset_val is not None):
# validate the current model
args_val = copy.deepcopy(args)
args_val.imdbval_name = args.dataset_val
mAP = test(args_val, model=fasterRCNN)
if mAP > best_model_mAP:
# save the model as the current best
print("New best model: mAP={0:.4f}, {1} epochs".format(
mAP, epoch + 1))
best_model_mAP = mAP
best_model_save_name = os.path.join(
output_dir, 'best_model_{0}.pth'.format(args.session))
save_checkpoint(
{
'session':
args.session,
'epoch':
epoch + 1,
'model':
import main import test_net #main.run() #file_name = "_generation-001-specimen-000" file_name = "_generation-007-specimen-000" test_net.test(file_name)
