import argparse import os import torch class Param: def __init__(self): self.parser = argparse.ArgumentParser(description="") # General self.parser.add_argument('--iters', type=int, default=100000) self.parser.add_argument('--name', type=str, default='default') self.parser.add_argument('--train', type=str, default='speaker') self.parser.add_argument("--load_pretrain", default=None) # --load_pretrain snap/vlnp-v3.0.0/state_dict/Iter_100000 self.parser.add_argument('--test_only', type=int, default=0) self.parser.add_argument('--description', type=str, default='no description\n') # Data preparation self.parser.add_argument('--maxInput', type=int, default=80, help="max input instruction") # self.parser.add_argument('--maxDecode', type=int, default=120, help="max input instruction") self.parser.add_argument('--maxAction', type=int, default=20, help='Max Action sequence') self.parser.add_argument('--batchSize', type=int, default=64) self.parser.add_argument('--ignoreid', type=int, default=-100) self.parser.add_argument('--directions', type=int, default=4, help='agent-centered visual directions') # fix to 4 for now self.parser.add_argument('--feature_size', type=int, default=2048) self.parser.add_argument("--loadOptim",action="store_const", default=False, const=True) # Load the model from self.parser.add_argument("--speaker", default=None) self.parser.add_argument("--listener", default=None) self.parser.add_argument("--load", default=None) # snap/vlnb-v3.0.0/state_dict/Iter_100000 # More Paths from self.parser.add_argument("--aug", default=None) # Listener Model Config self.parser.add_argument("--zeroInit", dest='zero_init', action='store_const', default=False, const=True) self.parser.add_argument("--mlWeight", dest='ml_weight', type=float, default=0.05) self.parser.add_argument("--teacherWeight", dest='teacher_weight', type=float, default=1.) self.parser.add_argument("--accumulateGrad", dest='accumulate_grad', type=int, default=0) self.parser.add_argument("--features", type=str, default='imagenet') # Env Dropout Param self.parser.add_argument('--featdropout', type=float, default=0.3) # SSL configuration self.parser.add_argument("--selfTrain", dest='self_train', action='store_const', default=False, const=True) # Submision configuration self.parser.add_argument("--candidates", type=int, default=1) self.parser.add_argument("--paramSearch", dest='param_search', action='store_const', default=False, const=True) self.parser.add_argument("--submit", action='store_const', default=False, const=True) self.parser.add_argument("--beam", action="store_const", default=False, const=True) self.parser.add_argument("--alpha", type=float, default=0.5) # Training Configurations self.parser.add_argument('--optim', type=str, default='rms') # rms, adam self.parser.add_argument('--lr', type=float, default=0.0001, help="The learning rate") self.parser.add_argument('--decay', dest='weight_decay', type=float, default=0.) self.parser.add_argument('--dropout', type=float, default=0.5) self.parser.add_argument('--feedback', type=str, default='sample', help='How to choose next position, one of ``teacher``, ``sample`` and ``argmax``') self.parser.add_argument('--teacher', type=str, default='final', help="How to get supervision. one of ``next`` and ``final`` ") self.parser.add_argument('--epsilon', type=float, default=0.1) # Model hyper params: self.parser.add_argument('--rnnDim', dest="rnn_dim", type=int, default=512) self.parser.add_argument('--wemb', type=int, default=256) self.parser.add_argument('--aemb', type=int, default=64) self.parser.add_argument('--proj', type=int, default=512) self.parser.add_argument("--fast", dest="fast_train", action="store_const", default=False, const=True) self.parser.add_argument("--valid", action="store_const", default=False, const=True) self.parser.add_argument("--candidate", dest="candidate_mask", action="store_const", default=False, const=True) self.parser.add_argument("--bidir", type=bool, default=True) # This is not full option self.parser.add_argument("--encode", type=str, default="word") # sub, word, sub_ctx self.parser.add_argument("--subout", dest="sub_out", type=str, default="tanh") # tanh, max self.parser.add_argument("--attn", type=str, default="soft") # soft, mono, shift, dis_shift self.parser.add_argument("--angleFeatSize", dest="angle_feat_size", type=int, default=4) # A2C self.parser.add_argument("--gamma", default=0.9, type=float) self.parser.add_argument("--normalize", dest="normalize_loss", default="total", type=str, help='batch or total') self.args = self.parser.parse_args() if self.args.optim == 'rms': print("Optimizer: Using RMSProp") self.args.optimizer = torch.optim.RMSprop elif self.args.optim == 'adam': print("Optimizer: Using Adam") self.args.optimizer = torch.optim.Adam elif self.args.optim == 'adamW': print("Optimizer: Using AdamW") self.args.optimizer = torch.optim.AdamW elif self.args.optim == 'sgd': print("Optimizer: sgd") self.args.optimizer = torch.optim.SGD else: assert False param = Param() args = param.args args.TRAIN_VOCAB = 'tasks/R2R/data/train_vocab.txt' args.TRAINVAL_VOCAB = 'tasks/R2R/data/trainval_vocab.txt' args.IMAGENET_FEATURES = 'img_features/ResNet-152-imagenet.tsv' args.CANDIDATE_FEATURES = 'img_features/ResNet-152-candidate.tsv' args.features_fast = 'img_features/ResNet-152-imagenet-fast.tsv' args.log_dir = 'snap/%s' % args.name args.directions = args.directions * 3 # times 3 for up, horizon and bottom if not os.path.exists(args.log_dir): os.makedirs(args.log_dir) DEBUG_FILE = open(os.path.join('snap', args.name, "debug.log"), 'w')