default='/mnt/mercury-fast/datasets/', help='Location to root directory fo dataset') # /mnt/mars-fast/datasets/ parser.add_argument( '--save_root', default='/mnt/mercury-alpha/', help='Location to save checkpoint models') # /mnt/sun-gamma/datasets/ # parser.add_argument('--model_dir', default='/mnt/sun-beta/vivek/weights/', help='Location to where imagenet pretrained models exists') # /mnt/mars-fast/datasets/ parser.add_argument('--model_dir', default='/mnt/mars-gamma/global-models/pytorch-imagenet/', help='Location to where imagenet pretrained models exists' ) # /mnt/mars-fast/datasets/ # args.model_dir = '' ## Parse arguments args = parser.parse_args() args = utils.set_args(args) # set directories and subsets fo datasets if args.tensorboard: from tensorboardX import SummaryWriter ## set random seeds and global settings np.random.seed(args.man_seed) torch.manual_seed(args.man_seed) torch.cuda.manual_seed_all(args.man_seed) torch.set_default_tensor_type('torch.FloatTensor') def main(): args.exp_name = utils.create_exp_name(args) args.save_root += args.dataset + '/'
def main(): parser = argparse.ArgumentParser( description='Training single stage FPN with OHEM, resnet as backbone') parser.add_argument('DATA_ROOT', help='Location to root directory for dataset reading' ) # /mnt/mars-fast/datasets/ parser.add_argument( 'SAVE_ROOT', help='Location to root directory for saving checkpoint models' ) # /mnt/mars-alpha/ parser.add_argument( 'MODEL_PATH', help= 'Location to root directory where kinetics pretrained models are stored' ) parser.add_argument( '--MODE', default='train', help= 'MODE can be train, gen_dets, eval_frames, eval_tubes define SUBSETS accordingly, build tubes' ) # Name of backbone network, e.g. resnet18, resnet34, resnet50, resnet101 resnet152 are supported parser.add_argument('--ARCH', default='resnet50', type=str, help=' base arch') parser.add_argument('--MODEL_TYPE', default='I3D', type=str, help=' base model') parser.add_argument('--ANCHOR_TYPE', default='RETINA', type=str, help='type of anchors to be used in model') parser.add_argument('--SEQ_LEN', default=8, type=int, help='NUmber of input frames') parser.add_argument('--TEST_SEQ_LEN', default=8, type=int, help='NUmber of input frames') parser.add_argument( '--MIN_SEQ_STEP', default=1, type=int, help='DIFFERENCE of gap between the frames of sequence') parser.add_argument( '--MAX_SEQ_STEP', default=1, type=int, help='DIFFERENCE of gap between the frames of sequence') # if output heads are have shared features or not: 0 is no-shareing else sharining enabled # parser.add_argument('--MULIT_SCALE', default=False, type=str2bool,help='perfrom multiscale training') parser.add_argument('--HEAD_LAYERS', default=3, type=int, help='0 mean no shareding more than 0 means shareing') parser.add_argument('--NUM_FEATURE_MAPS', default=5, type=int, help='0 mean no shareding more than 0 means shareing') parser.add_argument('--CLS_HEAD_TIME_SIZE', default=3, type=int, help='Temporal kernel size of classification head') parser.add_argument('--REG_HEAD_TIME_SIZE', default=3, type=int, help='Temporal kernel size of regression head') # Name of the dataset only voc or coco are supported parser.add_argument('--DATASET', default='road', type=str, help='dataset being used') parser.add_argument('--TRAIN_SUBSETS', default='train_3,', type=str, help='Training SUBSETS seprated by ,') parser.add_argument('--VAL_SUBSETS', default='', type=str, help='Validation SUBSETS seprated by ,') parser.add_argument('--TEST_SUBSETS', default='', type=str, help='Testing SUBSETS seprated by ,') # Input size of image only 600 is supprted at the moment parser.add_argument('--MIN_SIZE', default=512, type=int, help='Input Size for FPN') # data loading argumnets parser.add_argument('-b', '--BATCH_SIZE', default=4, type=int, help='Batch size for training') parser.add_argument('--TEST_BATCH_SIZE', default=1, type=int, help='Batch size for testing') # Number of worker to load data in parllel parser.add_argument('--NUM_WORKERS', '-j', default=8, type=int, help='Number of workers used in dataloading') # optimiser hyperparameters parser.add_argument('--OPTIM', default='SGD', type=str, help='Optimiser type') parser.add_argument('--RESUME', default=0, type=int, help='Resume from given epoch') parser.add_argument('--MAX_EPOCHS', default=30, type=int, help='Number of training epoc') parser.add_argument('-l', '--LR', '--learning-rate', default=0.004225, type=float, help='initial learning rate') parser.add_argument('--MOMENTUM', default=0.9, type=float, help='momentum') parser.add_argument('--MILESTONES', default='20,25', type=str, help='Chnage the lr @') parser.add_argument('--GAMMA', default=0.1, type=float, help='Gamma update for SGD') parser.add_argument('--WEIGHT_DECAY', default=1e-4, type=float, help='Weight decay for SGD') # Freeze layers or not parser.add_argument( '--FBN', '--FREEZE_BN', default=True, type=str2bool, help='freeze bn layers if true or else keep updating bn layers') parser.add_argument( '--FREEZE_UPTO', default=1, type=int, help='layer group number in ResNet up to which needs to be frozen') # Loss function matching threshold parser.add_argument('--POSTIVE_THRESHOLD', default=0.5, type=float, help='Min threshold for Jaccard index for matching') parser.add_argument('--NEGTIVE_THRESHOLD', default=0.4, type=float, help='Max threshold Jaccard index for matching') # Evaluation hyperparameters parser.add_argument( '--EVAL_EPOCHS', default='30', type=str, help= 'eval epochs to test network on these epoch checkpoints usually the last epoch is used' ) parser.add_argument('--VAL_STEP', default=2, type=int, help='Number of training epoch before evaluation') parser.add_argument( '--IOU_THRESH', default=0.5, type=float, help='Evaluation threshold for validation and for frame-wise mAP') parser.add_argument( '--CONF_THRESH', default=0.025, type=float, help='Confidence threshold for to remove detection below given number') parser.add_argument( '--NMS_THRESH', default=0.5, type=float, help='NMS threshold to apply nms at the time of validation') parser.add_argument('--TOPK', default=10, type=int, help='topk detection to keep for evaluation') parser.add_argument( '--GEN_CONF_THRESH', default=0.025, type=float, help='Confidence threshold at the time of generation and dumping') parser.add_argument('--GEN_TOPK', default=100, type=int, help='topk at the time of generation') parser.add_argument('--GEN_NMS', default=0.5, type=float, help='NMS at the time of generation') parser.add_argument('--CLASSWISE_NMS', default=False, type=str2bool, help='apply classwise NMS/no tested properly') parser.add_argument( '--JOINT_4M_MARGINALS', default=False, type=str2bool, help= 'generate score of joints i.e. duplexes or triplet by marginals like agents and actions scores' ) ## paths hyper parameters parser.add_argument( '--COMPUTE_PATHS', default=False, type=str2bool, help=' COMPUTE_PATHS if set true then it overwrite existing ones') parser.add_argument( '--PATHS_IOUTH', default=0.5, type=float, help='Iou threshold for building paths to limit neighborhood search') parser.add_argument( '--PATHS_COST_TYPE', default='score', type=str, help= 'cost function type to use for matching, other options are scoreiou, iou' ) parser.add_argument( '--PATHS_JUMP_GAP', default=4, type=int, help= 'GAP allowed for a tube to be kept alive after no matching detection found' ) parser.add_argument('--PATHS_MIN_LEN', default=6, type=int, help='minimum length of generated path') parser.add_argument( '--PATHS_MINSCORE', default=0.1, type=float, help='minimum score a path should have over its length') ## paths hyper parameters parser.add_argument('--COMPUTE_TUBES', default=False, type=str2bool, help='if set true then it overwrite existing tubes') parser.add_argument('--TUBES_ALPHA', default=0, type=float, help='alpha cost for changeing the label') parser.add_argument('--TRIM_METHOD', default='none', type=str, help='other one is indiv which works for UCF24') parser.add_argument('--TUBES_TOPK', default=10, type=int, help='Number of labels to assign for a tube') parser.add_argument('--TUBES_MINLEN', default=5, type=int, help='minimum length of a tube') parser.add_argument( '--TUBES_EVAL_THRESHS', default='0.2,0.5', type=str, help= 'evaluation threshold for checking tube overlap at evaluation time, one can provide as many as one wants' ) # parser.add_argument('--TRAIL_ID', default=0, # type=int, help='eval TUBES_Thtrshold at evaluation time') ### parser.add_argument('--LOG_START', default=10, type=int, help='start loging after k steps for text/tensorboard') parser.add_argument('--LOG_STEP', default=10, type=int, help='Log every k steps for text/tensorboard') parser.add_argument( '--TENSORBOARD', default=1, type=str2bool, help='Use tensorboard for loss/evalaution visualization') # Program arguments parser.add_argument('--MAN_SEED', default=123, type=int, help='manualseed for reproduction') parser.add_argument( '--MULTI_GPUS', default=True, type=str2bool, help= 'If more than 0 then use all visible GPUs by default only one GPU used ' ) # Use CUDA_VISIBLE_DEVICES=0,1,4,6 to select GPUs to use ## Parse arguments args = parser.parse_args() args = utils.set_args(args) # set directories and SUBSETS fo datasets args.MULTI_GPUS = False if args.BATCH_SIZE == 1 else args.MULTI_GPUS ## set random seeds and global settings np.random.seed(args.MAN_SEED) torch.manual_seed(args.MAN_SEED) # torch.cuda.manual_seed_all(args.MAN_SEED) torch.set_default_tensor_type('torch.FloatTensor') args = utils.create_exp_name(args) utils.setup_logger(args) logger = utils.get_logger(__name__) logger.info(sys.version) assert args.MODE in [ 'train', 'val', 'gen_dets', 'eval_frames', 'eval_tubes' ], 'MODE must be from ' + ','.join(['train', 'test', 'tubes']) if args.MODE == 'train': args.TEST_SEQ_LEN = args.SEQ_LEN else: args.SEQ_LEN = args.TEST_SEQ_LEN if args.MODE in ['train', 'val']: # args.CONF_THRESH = 0.05 args.SUBSETS = args.TRAIN_SUBSETS train_transform = transforms.Compose([ vtf.ResizeClip(args.MIN_SIZE, args.MAX_SIZE), vtf.ToTensorStack(), vtf.Normalize(mean=args.MEANS, std=args.STDS) ]) # train_skip_step = args.SEQ_LEN # if args.SEQ_LEN>4 and args.SEQ_LEN<=10: # train_skip_step = args.SEQ_LEN-2 if args.SEQ_LEN > 10: train_skip_step = args.SEQ_LEN + (args.MAX_SEQ_STEP - 1) * 2 - 2 else: train_skip_step = args.SEQ_LEN train_dataset = VideoDataset(args, train=True, skip_step=train_skip_step, transform=train_transform) logger.info('Done Loading Dataset Train Dataset') ## For validation set full_test = False args.SUBSETS = args.VAL_SUBSETS skip_step = args.SEQ_LEN * 8 else: args.SEQ_LEN = args.TEST_SEQ_LEN args.MAX_SEQ_STEP = 1 args.SUBSETS = args.TEST_SUBSETS full_test = True #args.MODE != 'train' args.skip_beggning = 0 args.skip_ending = 0 if args.MODEL_TYPE == 'I3D': args.skip_beggning = 2 args.skip_ending = 2 elif args.MODEL_TYPE != 'C2D': args.skip_beggning = 2 skip_step = args.SEQ_LEN - args.skip_beggning val_transform = transforms.Compose([ vtf.ResizeClip(args.MIN_SIZE, args.MAX_SIZE), vtf.ToTensorStack(), vtf.Normalize(mean=args.MEANS, std=args.STDS) ]) val_dataset = VideoDataset(args, train=False, transform=val_transform, skip_step=skip_step, full_test=full_test) logger.info('Done Loading Dataset Validation Dataset') args.num_classes = val_dataset.num_classes # one for objectness args.label_types = val_dataset.label_types args.num_label_types = val_dataset.num_label_types args.all_classes = val_dataset.all_classes args.num_classes_list = val_dataset.num_classes_list args.num_ego_classes = val_dataset.num_ego_classes args.ego_classes = val_dataset.ego_classes args.head_size = 256 if args.MODE in ['train', 'val', 'gen_dets']: net = build_retinanet(args).cuda() if args.MULTI_GPUS: logger.info('\nLets do dataparallel\n') net = torch.nn.DataParallel(net) for arg in sorted(vars(args)): logger.info(str(arg) + ': ' + str(getattr(args, arg))) if args.MODE == 'train': if args.FBN: if args.MULTI_GPUS: net.module.backbone.apply(utils.set_bn_eval) else: net.backbone.apply(utils.set_bn_eval) train(args, net, train_dataset, val_dataset) elif args.MODE == 'val': val(args, net, val_dataset) elif args.MODE == 'gen_dets': gen_dets(args, net, val_dataset) eval_framewise_dets(args, val_dataset) build_eval_tubes(args, val_dataset) elif args.MODE == 'eval_frames': eval_framewise_dets(args, val_dataset) elif args.MODE == 'eval_tubes': build_eval_tubes(args, val_dataset)