def main():
args, cfg = parse_args()
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH, args.checkpoint)
model = utils.load_is_model(checkpoint_path, args.device, num_max_clicks=args.n_clicks)
eval_exp_name = get_eval_exp_name(args)
eval_exp_path = args.logs_path / eval_exp_name
eval_exp_path.mkdir(parents=True, exist_ok=True)
print_header = True
for dataset_name in args.datasets.split(','):
dataset = utils.get_dataset(dataset_name, cfg)
zoom_in_target_size = 600 if dataset_name == 'DAVIS' else 400
predictor = get_predictor(model, args.mode, args.device,
prob_thresh=args.thresh,
predictor_params={'num_max_points': args.n_clicks},
zoom_in_params={'target_size': zoom_in_target_size})
dataset_results = evaluate_dataset(dataset, predictor, pred_thr=args.thresh,
max_iou_thr=args.target_iou,
max_clicks=args.n_clicks)
save_results(args, dataset_name, eval_exp_path, dataset_results,
print_header=print_header)
print_header = False
def main():
args, cfg = parse_args()
checkpoints_list, logs_path, logs_prefix = get_checkpoints_list_and_logs_path(
args, cfg)
logs_path.mkdir(parents=True, exist_ok=True)
single_model_eval = len(checkpoints_list) == 1
assert not args.iou_analysis if not single_model_eval else True, \
"Can't perform IoU analysis for multiple checkpoints"
print_header = single_model_eval
for dataset_name in args.datasets.split(','):
dataset = utils.get_dataset(dataset_name, cfg)
for checkpoint_path in checkpoints_list:
model = utils.load_is_model(checkpoint_path, args.device)
predictor_params, zoomin_params = get_predictor_and_zoomin_params(
args, dataset_name)
predictor = get_predictor(model,
args.mode,
args.device,
prob_thresh=args.thresh,
predictor_params=predictor_params,
zoom_in_params=zoomin_params)
vis_callback = get_prediction_vis_callback(
logs_path, dataset_name,
args.thresh) if args.vis_preds else None
dataset_results = evaluate_dataset(dataset,
predictor,
pred_thr=args.thresh,
max_iou_thr=args.target_iou,
min_clicks=args.min_n_clicks,
max_clicks=args.n_clicks,
callback=vis_callback)
row_name = args.mode if single_model_eval else checkpoint_path.stem
if args.iou_analysis:
save_iou_analysis_data(args,
dataset_name,
logs_path,
logs_prefix,
dataset_results,
model_name=args.model_name)
save_results(args,
row_name,
dataset_name,
logs_path,
logs_prefix,
dataset_results,
save_ious=single_model_eval and args.save_ious,
single_model_eval=single_model_eval,
print_header=print_header)
print_header = False
import numpy as np
import torch
sys.path.insert(0, '..')
from isegm.utils import vis, exp
from isegm.inference import utils
from isegm.inference.evaluation import evaluate_dataset, evaluate_sample
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cfg = exp.load_config_file('./config.yml', return_edict=True)
## init dateset
# Possible choices: 'GrabCut', 'Berkeley', 'DAVIS', 'COCO_MVal', 'SBD'
DATASET = 'Berkeley'
dataset = utils.get_dataset(DATASET, cfg)
## init model
from isegm.inference.predictors import get_predictor
EVAL_MAX_CLICKS = 20
MODEL_THRESH = 0.49
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH,
'coco_lvis_h18_itermask')
model = utils.load_is_model(checkpoint_path, device)
# Possible choices: 'NoBRS', 'f-BRS-A', 'f-BRS-B', 'f-BRS-C', 'RGB-BRS', 'DistMap-BRS'
brs_mode = 'f-BRS-B'
predictor = get_predictor(model, brs_mode, device, prob_thresh=MODEL_THRESH)