Returns:
argparse.ArgumentParser:
"""
parser.add_argument(
'-T',
"--eval-type",
dest='eval_type',
default="final",
choices=['p', 'checkpoints', 'inference', 'loc_predict', 'final'],
help="select an evaluation type")
return parser
if __name__ == '__main__':
parser = default_argument_parser()
args = custom_argparse(parser).parse_known_args()[0]
cfg = setup(args)
print('ready to run 0')
print('ready to run 1')
# torch.set_default_dtype(torch.float16)f
out_channels = 1 if cfg.MODEL.BINARY_CLASSIFICATION else cfg.MODEL.OUT_CHANNELS
net = UNet(cfg)
print('ready to run 2')
if args.resume_from: # TODO Remove this
full_model_path = os.path.join(cfg.OUTPUT_DIR, args.resume_from)
net.load_state_dict(torch.load(full_model_path))
print('Model loaded from {}'.format(full_model_path))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cfg.merge_from_list(args.opts)
cfg.NAME = args.config_file
if args.log_dir: # Override Output dir
cfg.OUTPUT_DIR = path.join(args.log_dir, args.config_file)
else:
cfg.OUTPUT_DIR = path.join(cfg.OUTPUT_BASE_DIR, args.config_file)
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
if args.data_dir:
cfg.DATASETS.TRAIN = (args.data_dir, )
return cfg
if __name__ == '__main__':
args = default_argument_parser().parse_known_args()[0]
cfg = setup(args)
out_channels = cfg.MODEL.OUT_CHANNELS
if cfg.MODEL.BACKBONE.ENABLED:
net = smp.Unet(
cfg.MODEL.BACKBONE.TYPE,
encoder_weights=None,
decoder_channels=[512, 256, 128, 64, 32],
)
else:
net = UNet(cfg)
if args.resume and args.resume_from:
full_model_path = path.join(cfg.OUTPUT_DIR, args.model_path)
net.load_state_dict(torch.load(full_model_path))
f'{run_type} argmax F1': argmaxF1,
f'{run_type} false positive rate': best_fpr,
f'{run_type} false negative rate': best_fnr,
'step': step,
'epoch': epoch,
})
print(f'{maxF1.item():.3f}', flush=True)
return maxF1.item(), best_thresh.item()
if __name__ == '__main__':
# setting up config based on parsed argument
parser = args.default_argument_parser()
args = parser.parse_known_args()[0]
cfg = setup(args)
torch.manual_seed(cfg.SEED)
np.random.seed(cfg.SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# loading network
net = load_network(cfg)
# tracking land with w&b
if not cfg.DEBUG:
wandb.init(
name=cfg.NAME,
