def __init__(self, args, max_seq_length, mode='x'):
super().__init__()
self.max_seq_length = max_seq_length
from lxrt.modeling import LXRTFeatureExtraction as VisualBertForLXRFeature, VISUAL_CONFIG
set_visual_config(args, VISUAL_CONFIG)
# Using the bert tokenizer
self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased",
do_lower_case=True)
# Build LXRT Model
self.model = VisualBertForLXRFeature.from_pretrained(
"bert-base-uncased", mode=mode)
if args.from_scratch:
print("Re-initializing all the weights")
self.model.apply(self.model.init_bert_weights)
self.load_pretrain_head = args.get("load_pretrain_head", False)
if self.load_pretrain_head:
from lxmert.src.lxrt.modeling import BertPreTrainingHeads
self.pretrained_head = BertPreTrainingHeads(
self.model.config,
self.model.bert.embeddings.word_embeddings.weight)
def __init__(self, args, mode='x'):
super(LXRTEncoder, self).__init__()
# self.max_seq_length = max_seq_length
# self.max_label_text_length = max_label_text_length
set_visual_config(args)
# Build LXRT Model
self.model = VisualBertForLXRFeature.from_pretrained("../user_data",
mode=mode)
if args.from_scratch:
print("initializing all the weights")
self.model.apply(self.model.init_bert_weights)
def __init__(self, args, max_seq_length, mode='x'):
super().__init__()
self.max_seq_length = max_seq_length
set_visual_config(args)
# Using the bert tokenizer
self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased",
do_lower_case=True)
# Build LXRT Model
self.model = VisualBertForLXRFeature.from_pretrained(
"bert-base-uncased", mode=mode)
if args.from_scratch:
print("initializing all the weights")
self.model.apply(self.model.init_bert_weights)
def __init__(self, args, max_seq_length, mode='x', attention=False):
super().__init__()
print(f"Making {__name__}")
self.max_seq_length = max_seq_length
set_visual_config(args)
# Using the bert tokenizer
self.tokenizer = BertTokenizer.from_pretrained(
"bert-base-uncased",
do_lower_case=True
)
print("Made Tokenizer")
# Build LXRT Model
self.model = VisualBertForLXRFeature.from_pretrained(
"bert-base-uncased",
mode=mode,
attention=attention
)
print("Made VisualBertForLXRFeature")
if args.from_scratch:
print("initializing all the weights")
self.model.apply(self.model.init_bert_weights)
print(f"Done {__name__}")