def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint',
type=str,
required=True,
help='The path to the checkpoint. '
'This can be a relative path (relative to cfg.INTERACTIVE_MODELS_PATH) '
'or an absolute path. The file extension can be omitted.')
parser.add_argument('--n-clicks',
type=int,
default=100,
help='Maximum number of input clicks for the model.')
parser.add_argument('--gpu', type=int, default=0, help='Id of GPU to use.')
parser.add_argument('--cfg',
type=str,
default="config.yml",
help='The path to the config file.')
args = parser.parse_args()
args.device = torch.device(f'cuda:{args.gpu}')
cfg = exp.load_config_file(args.cfg, return_edict=True)
return args, cfg
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, required=True,
help='The path to the checkpoint. '
'This can be a relative path (relative to cfg.INTERACTIVE_MODELS_PATH) '
'or an absolute path. The file extension can be omitted.')
parser.add_argument('--gpu', type=int, default=0,
help='Id of GPU to use.')
parser.add_argument('--cpu', action='store_true', default=False,
help='Use only CPU for inference.')
parser.add_argument('--limit-longest-size', type=int, default=800,
help='If the largest side of an image exceeds this value, '
'it is resized so that its largest side is equal to this value.')
parser.add_argument('--norm-radius', type=int, default=260)
parser.add_argument('--cfg', type=str, default="config.yml",
help='The path to the config file.')
args = parser.parse_args()
if args.cpu:
args.device =torch.device('cpu')
else:
args.device = torch.device(f'cuda:{args.gpu}')
cfg = exp.load_config_file(args.cfg, return_edict=True)
return args, cfg
def parse_args():
parser = argparse.ArgumentParser()
group_pkl_path = parser.add_mutually_exclusive_group(required=True)
group_pkl_path.add_argument('--folder', type=str, default=None,
help='Path to folder with .pickle files.')
group_pkl_path.add_argument('--files', nargs='+', default=None,
help='List of paths to .pickle files separated by space.')
group_pkl_path.add_argument('--model-dirs', nargs='+', default=None,
help="List of paths to model directories with 'plots' folder "
"containing .pickle files separated by space.")
group_pkl_path.add_argument('--exp-models', nargs='+', default=None,
help='List of experiments paths suffixes (relative to cfg.EXPS_PATH/evaluation_logs). '
'For each experiment, the checkpoint prefix must be specified '
'by using the ":" delimiter at the end.')
parser.add_argument('--mode', choices=['NoBRS', 'RGB-BRS', 'DistMap-BRS',
'f-BRS-A', 'f-BRS-B', 'f-BRS-C'],
default=None, nargs='*', help='')
parser.add_argument('--datasets', type=str, default='GrabCut,Berkeley,DAVIS,COCO_MVal,SBD',
help='List of datasets for plotting the iou analysis'
'Datasets are separated by a comma. Possible choices: '
'GrabCut, Berkeley, DAVIS, COCO_MVal, SBD')
parser.add_argument('--config-path', type=str, default='./config.yml',
help='The path to the config file.')
parser.add_argument('--n-clicks', type=int, default=-1,
help='Maximum number of clicks to plot.')
parser.add_argument('--plots-path', type=str, default='',
help='The path to the evaluation logs. '
'Default path: cfg.EXPS_PATH/evaluation_logs/iou_analysis.')
args = parser.parse_args()
cfg = load_config_file(args.config_path, return_edict=True)
cfg.EXPS_PATH = Path(cfg.EXPS_PATH)
args.datasets = args.datasets.split(',')
if args.plots_path == '':
args.plots_path = cfg.EXPS_PATH / 'evaluation_logs/iou_analysis'
else:
args.plots_path = Path(args.plots_path)
print(args.plots_path)
args.plots_path.mkdir(parents=True, exist_ok=True)
return args, cfg
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('mode', choices=['NoBRS', 'RGB-BRS', 'DistMap-BRS',
'f-BRS-A', 'f-BRS-B', 'f-BRS-C'],
help='')
parser.add_argument('--checkpoint', type=str, required=True,
help='The path to the checkpoint. '
'This can be a relative path (relative to cfg.INTERACTIVE_MODELS_PATH) '
'or an absolute path. The file extension can be omitted.')
parser.add_argument('--datasets', type=str, default='GrabCut,Berkeley,DAVIS,COCO_MVal,SBD',
help='List of datasets on which the model should be tested. '
'Datasets are separated by a comma. Possible choices: '
'GrabCut, Berkeley, DAVIS, COCO_MVal, SBD')
parser.add_argument('--n-clicks', type=int, default=20,
help='Maximum number of clicks for the NoC metric.')
parser.add_argument('--gpus', type=str, default='0',
help='ID of used GPU.')
parser.add_argument('--cpu', action='store_true', default=False,
help='Use only CPU for inference.')
parser.add_argument('--thresh', type=float, required=False, default=0.49,
help='The segmentation mask is obtained from the probability outputs using this threshold.')
parser.add_argument('--target-iou', type=float, default=0.90,
help='Target IoU threshold for the NoC metric. (min possible value = 0.8)')
parser.add_argument('--config-path', type=str, default='./config.yml',
help='The path to the config file.')
parser.add_argument('--logs-path', type=str, default='',
help='The path to the evaluation logs. Default path: cfg.EXPS_PATH/evaluation_logs.')
args = parser.parse_args()
if args.cpu:
args.device = torch.device('cpu')
else:
args.device = [torch.device(f'cuda:{x}') for x in args.gpus.split(',')][0]
args.target_iou = max(0.8, args.target_iou)
cfg = load_config_file(args.config_path, return_edict=True)
if args.logs_path == '':
args.logs_path = Path(cfg.EXPS_PATH) / 'evaluation_logs'
else:
args.logs_path = Path(args.logs_path)
return args, cfg
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('dataset',
choices=['openimages', 'ade20k', 'coco_lvis'],
help='')
parser.add_argument('--split',
nargs='+',
choices=['train', 'val', 'test'],
type=str,
default=['train', 'val'],
help='')
parser.add_argument('--n-jobs', type=int, default=10)
parser.add_argument('--config-path',
type=str,
default='./config.yml',
help='The path to the config file.')
args = parser.parse_args()
cfg = load_config_file(args.config_path, return_edict=True)
return args, cfg
import matplotlib.pyplot as plt
import sys, random
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'
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('mode',
choices=[
'NoBRS', 'RGB-BRS', 'DistMap-BRS', 'f-BRS-A',
'f-BRS-B', 'f-BRS-C'
],
help='')
group_checkpoints = parser.add_mutually_exclusive_group(required=True)
group_checkpoints.add_argument(
'--checkpoint',
type=str,
default='',
help='The path to the checkpoint. '
'This can be a relative path (relative to cfg.INTERACTIVE_MODELS_PATH) '
'or an absolute path. The file extension can be omitted.')
group_checkpoints.add_argument(
'--exp-path',
type=str,
default='',
help='The relative path to the experiment with checkpoints.'
'(relative to cfg.EXPS_PATH)')
parser.add_argument(
'--datasets',
type=str,
default='GrabCut,Berkeley,DAVIS,SBD,PascalVOC',
help='List of datasets on which the model should be tested. '
'Datasets are separated by a comma. Possible choices: '
'GrabCut, Berkeley, DAVIS, SBD, PascalVOC')
group_device = parser.add_mutually_exclusive_group()
group_device.add_argument('--gpus',
type=str,
default='0',
help='ID of used GPU.')
group_device.add_argument('--cpu',
action='store_true',
default=False,
help='Use only CPU for inference.')
group_iou_thresh = parser.add_mutually_exclusive_group()
group_iou_thresh.add_argument(
'--target-iou',
type=float,
default=0.90,
help=
'Target IoU threshold for the NoC metric. (min possible value = 0.8)')
group_iou_thresh.add_argument(
'--iou-analysis',
action='store_true',
default=False,
help='Plot mIoU(number of clicks) with target_iou=1.0.')
parser.add_argument('--n-clicks',
type=int,
default=20,
help='Maximum number of clicks for the NoC metric.')
parser.add_argument('--min-n-clicks',
type=int,
default=1,
help='Minimum number of clicks for the evaluation.')
parser.add_argument(
'--thresh',
type=float,
required=False,
default=0.49,
help=
'The segmentation mask is obtained from the probability outputs using this threshold.'
)
parser.add_argument('--clicks-limit', type=int, default=None)
parser.add_argument(
'--eval-mode',
type=str,
default='cvpr',
help='Possible choices: cvpr, fixed<number> (e.g. fixed400, fixed600).'
)
parser.add_argument('--save-ious', action='store_true', default=False)
parser.add_argument('--print-ious', action='store_true', default=False)
parser.add_argument('--vis-preds', action='store_true', default=False)
parser.add_argument('--model-name',
type=str,
default=None,
help='The model name that is used for making plots.')
parser.add_argument('--config-path',
type=str,
default='./config.yml',
help='The path to the config file.')
parser.add_argument(
'--logs-path',
type=str,
default='',
help=
'The path to the evaluation logs. Default path: cfg.EXPS_PATH/evaluation_logs.'
)
args = parser.parse_args()
if args.cpu:
args.device = torch.device('cpu')
else:
args.device = torch.device(f"cuda:{args.gpus.split(',')[0]}")
if (args.iou_analysis or args.print_ious) and args.min_n_clicks <= 1:
args.target_iou = 1.01
else:
args.target_iou = max(0.8, args.target_iou)
cfg = load_config_file(args.config_path, return_edict=True)
cfg.EXPS_PATH = Path(cfg.EXPS_PATH)
if args.logs_path == '':
args.logs_path = cfg.EXPS_PATH / 'evaluation_logs'
else:
args.logs_path = Path(args.logs_path)
return args, cfg
