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import os
import sys
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'gen'))
import os
import torch
import pprint
import json
from data.preprocess import Dataset
from importlib import import_module
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from models.utils.helper_utils import optimizer_to
if __name__ == '__main__':
# parser
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
# settings
parser.add_argument('--seed', help='random seed', default=123, type=int)
# parser.add_argument('--data', help='dataset folder', default='data/json_feat_2.1.0') # --data data/json_new # json_new/tests_unseen
# parser.add_argument('--splits', help='json file containing train/dev/test splits', default='splits/oct21.json')
parser.add_argument('--data', help='dataset folder', default='data/Re_json_1.3.2') # --data data/json_new # json_new/tests_unseen
parser.add_argument('--splits', help='json file containing train/dev/test splits', default='data/splits/oct18.json')
parser.add_argument('--preprocess', help='store preprocessed data to json files', action='store_true')
parser.add_argument('--pp_folder', help='folder name for preprocessed data', default='pp')
parser.add_argument('--save_every_epoch', help='save model after every epoch (warning: consumes a lot of space)', action='store_true')
parser.add_argument('--model', help='model to use', default='seq2seq_im')
parser.add_argument('--gpu', help='use gpu', action='store_true')
parser.add_argument('--dout', help='where to save model', default='exp/model:{model}')
parser.add_argument('--use_templated_goals', help='use templated goals instead of human-annotated goal descriptions (only available for train set)', action='store_true')
parser.add_argument('--resume', help='load a checkpoint')
# hyper parameters
parser.add_argument('--batch', help='batch size', default=8, type=int)
parser.add_argument('--epoch', help='number of epochs', default=20, type=int)
parser.add_argument('--lr', help='optimizer learning rate', default=1e-4, type=float)
parser.add_argument('--decay_epoch', help='num epoch to adjust learning rate', default=10, type=int)
parser.add_argument('--dhid', help='hidden layer size', default=512, type=int)
parser.add_argument('--dframe', help='image feature vec size', default=2500, type=int)
parser.add_argument('--demb', help='language embedding size', default=100, type=int)
parser.add_argument('--pframe', help='image pixel size (assuming square shape eg: 300x300)', default=300, type=int)
parser.add_argument('--mask_loss_wt', help='weight of mask loss', default=1., type=float)
parser.add_argument('--action_loss_wt', help='weight of action loss', default=1., type=float)
parser.add_argument('--subgoal_aux_loss_wt', help='weight of subgoal completion predictor', default=0., type=float)
parser.add_argument('--pm_aux_loss_wt', help='weight of progress monitor', default=0., type=float)
# dropouts
parser.add_argument('--zero_goal', help='zero out goal language', action='store_true')
parser.add_argument('--zero_instr', help='zero out step-by-step instr language', action='store_true')
parser.add_argument('--lang_dropout', help='dropout rate for language (goal + instr)', default=0., type=float)
parser.add_argument('--input_dropout', help='dropout rate for concatted input feats', default=0., type=float)
parser.add_argument('--vis_dropout', help='dropout rate for Resnet feats', default=0.3, type=float)
parser.add_argument('--hstate_dropout', help='dropout rate for LSTM hidden states during unrolling', default=0.3, type=float)
parser.add_argument('--attn_dropout', help='dropout rate for attention', default=0., type=float)
parser.add_argument('--actor_dropout', help='dropout rate for actor fc', default=0., type=float)
# other settings
parser.add_argument('--dec_teacher_forcing', help='use gpu', action='store_true')
parser.add_argument('--temp_no_history', help='use gpu', action='store_true')
# debugging
parser.add_argument('--fast_epoch', help='fast epoch during debugging', action='store_true')
parser.add_argument('--dataset_fraction', help='use fraction of the dataset for debugging (0 indicates full size)', default=0, type=int)
# args and init
args = parser.parse_args()
args.dout = args.dout.format(**vars(args))
torch.manual_seed(args.seed)
# check if dataset has been preprocessed
if not os.path.exists(os.path.join(args.data, "%s.vocab" % args.pp_folder)) and not args.preprocess:
raise Exception("Dataset not processed; run with --preprocess")
# make output dir
pprint.pprint(args)
if not os.path.isdir(args.dout):
os.makedirs(args.dout)
# load train/valid/tests splits
with open(args.splits) as f:
splits = json.load(f)
pprint.pprint({k: len(v) for k, v in splits.items()})
# preprocess and save
if args.preprocess:
print("\nPreprocessing dataset and saving to %s folders ... This will take a while. Do this once as required." % args.pp_folder)
dataset = Dataset(args, None)
dataset.preprocess_splits(splits)
vocab = torch.load(os.path.join(args.dout, "%s.vocab" % args.pp_folder))
else:
vocab = torch.load(os.path.join(args.data, "%s.vocab" % args.pp_folder))
# load model
M = import_module('model.{}'.format(args.model))
if args.resume:
print("Loading: " + args.resume)
model, optimizer = M.Module.load(args.resume)
else:
model = M.Module(args, vocab)
optimizer = None
# to gpu
if args.gpu:
model = model.to(torch.device('cuda'))
if not optimizer is None:
optimizer_to(optimizer, torch.device('cuda'))
# start train loop
model.run_train(splits, optimizer=optimizer) |