adversarial_VLNDUET/map_nav_src/models/vlnbert_init.py
Shizhe Chen 747cf0587b init
2021-11-24 13:29:08 +01:00

65 lines
2.1 KiB
Python

import torch
def get_tokenizer(args):
from transformers import AutoTokenizer
if args.tokenizer == 'xlm':
cfg_name = 'xlm-roberta-base'
else:
cfg_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(cfg_name)
return tokenizer
def get_vlnbert_models(args, config=None):
from transformers import PretrainedConfig
from models.vilmodel import GlocalTextPathNavCMT
model_name_or_path = args.bert_ckpt_file
new_ckpt_weights = {}
if model_name_or_path is not None:
ckpt_weights = torch.load(model_name_or_path)
for k, v in ckpt_weights.items():
if k.startswith('module'):
k = k[7:]
if '_head' in k or 'sap_fuse' in k:
new_ckpt_weights['bert.' + k] = v
else:
new_ckpt_weights[k] = v
if args.tokenizer == 'xlm':
cfg_name = 'xlm-roberta-base'
else:
cfg_name = 'bert-base-uncased'
vis_config = PretrainedConfig.from_pretrained(cfg_name)
if args.tokenizer == 'xlm':
vis_config.type_vocab_size = 2
vis_config.max_action_steps = 100
vis_config.image_feat_size = args.image_feat_size
vis_config.angle_feat_size = args.angle_feat_size
vis_config.obj_feat_size = args.obj_feat_size
vis_config.obj_loc_size = 3
vis_config.num_l_layers = args.num_l_layers
vis_config.num_pano_layers = args.num_pano_layers
vis_config.num_x_layers = args.num_x_layers
vis_config.graph_sprels = args.graph_sprels
vis_config.glocal_fuse = args.fusion == 'dynamic'
vis_config.fix_lang_embedding = args.fix_lang_embedding
vis_config.fix_pano_embedding = args.fix_pano_embedding
vis_config.fix_local_branch = args.fix_local_branch
vis_config.update_lang_bert = not args.fix_lang_embedding
vis_config.output_attentions = True
vis_config.pred_head_dropout_prob = 0.1
vis_config.use_lang2visn_attn = False
visual_model = GlocalTextPathNavCMT.from_pretrained(
pretrained_model_name_or_path=None,
config=vis_config,
state_dict=new_ckpt_weights)
return visual_model