93 lines
4.5 KiB
Python
93 lines
4.5 KiB
Python
import argparse
|
|
import os
|
|
import torch
|
|
|
|
class Param:
|
|
def __init__(self):
|
|
self.parser = argparse.ArgumentParser(description="")
|
|
|
|
# General
|
|
self.parser.add_argument('--apex', action='store_true', default=False)
|
|
self.parser.add_argument('--test_only', type=int, default=0, help='fast mode for testing')
|
|
|
|
self.parser.add_argument('--iters', type=int, default=300000, help='training iterations')
|
|
self.parser.add_argument('--log_every', type=int, default=2000, help='training iterations')
|
|
|
|
self.parser.add_argument('--name', type=str, default='default', help='experiment id')
|
|
self.parser.add_argument('--vlnbert', type=str, default='oscar', help='oscar or prevalent')
|
|
self.parser.add_argument('--train', type=str, default='listener')
|
|
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('--maxAction', type=int, default=15, help='Max Action sequence')
|
|
self.parser.add_argument('--maxObject', type=int, default=None, help='Max Object per viewpoint')
|
|
self.parser.add_argument('--batchSize', type=int, default=8)
|
|
self.parser.add_argument('--ignoreid', type=int, default=-100)
|
|
|
|
self.parser.add_argument('--feature_size', type=int, default=2048)
|
|
self.parser.add_argument('--directions', type=int, default=4, help='agent-centered visual directions') # fix to 4 for now
|
|
self.parser.add_argument("--angleFeatSize", dest="angle_feat_size", type=int, default=4)
|
|
|
|
# Load the model from
|
|
self.parser.add_argument("--load", default=None, help='path of the trained model')
|
|
self.parser.add_argument("--loadOptim",action="store_const", default=False, const=True)
|
|
|
|
# Augmented 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=0.20)
|
|
self.parser.add_argument("--teacherWeight", dest='teacher_weight', type=float, default=1.)
|
|
self.parser.add_argument('--ref_loss_weight', type=float, default=1.0)
|
|
self.parser.add_argument("--features", type=str, default='places365', choices=['places365'])
|
|
self.parser.add_argument('--init_bert_file', default=None)
|
|
|
|
# Dropout Param
|
|
self.parser.add_argument('--dropout', type=float, default=0.5)
|
|
self.parser.add_argument('--featdropout', type=float, default=0.3)
|
|
|
|
# Submision configuration
|
|
self.parser.add_argument("--submit", action='store_true', default=False)
|
|
|
|
# Training Configurations
|
|
self.parser.add_argument('--optim', type=str, default='rms') # rms, adam
|
|
self.parser.add_argument('--lr', type=float, default=0.00001, help="the learning rate")
|
|
self.parser.add_argument('--decay', dest='weight_decay', type=float, default=0.)
|
|
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)
|
|
|
|
# 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.description = args.name
|
|
args.log_dir = 'snap/%s' % args.name
|
|
|
|
if not os.path.exists(args.log_dir):
|
|
os.makedirs(args.log_dir)
|