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Functions214 in github.com/airsplay/lxmert

⨍ Functions 214◇ Types & classes 53

Method__getitem__

(self, item: int)

src/tasks/nlvr2_data.py:93

Method__getitem__

(self, item: int)

src/tasks/gqa_data.py:124

Method__getitem__

(self, item: int)

src/tasks/vqa_data.py:120

Method__getitem__

(self, item: int)

src/pretrain/lxmert_data.py:144

Constructs a BertTokenizer. Args: vocab_file: Path to a one-wordpiece-per-line vocabulary file do_lower_case: Whether to

src/lxrt/tokenization.py:75

Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input.

src/lxrt/tokenization.py:177

(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100)

src/lxrt/tokenization.py:294

(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999

src/lxrt/optimization.py:66

(self, l_layers=12, x_layers=5, r_layers=0)

src/lxrt/modeling.py:143

Constructs BertConfig. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. hidden_s

src/lxrt/modeling.py:175

src/lxrt/modeling.py:267

(self, config, ctx_dim=None)

src/lxrt/modeling.py:296

src/lxrt/modeling.py:351

src/lxrt/modeling.py:365

src/lxrt/modeling.py:377

src/lxrt/modeling.py:390

src/lxrt/modeling.py:405

src/lxrt/modeling.py:419

src/lxrt/modeling.py:440

src/lxrt/modeling.py:492

src/lxrt/modeling.py:521

src/lxrt/modeling.py:570

src/lxrt/modeling.py:585

(self, config, bert_model_embedding_weights)

src/lxrt/modeling.py:602

(self, config, num_answers)

src/lxrt/modeling.py:621

(self, config, visual_losses)

src/lxrt/modeling.py:636

(self, config, bert_model_embedding_weights)

src/lxrt/modeling.py:662

(self, config, *inputs, **kwargs)

src/lxrt/modeling.py:677

src/lxrt/modeling.py:838

(self, config, task_mask_lm=True, task_matched=True,

src/lxrt/modeling.py:890

:param config: :param mode: Number of visual layers

src/lxrt/modeling.py:996

(self, input_ids, input_mask, segment_ids)

src/lxrt/entry.py:30

(self, args, max_seq_length, mode='x')

src/lxrt/entry.py:81

(self, dataset: NLVR2Dataset)

src/tasks/nlvr2_data.py:59

(self, dataset: NLVR2Dataset)

src/tasks/nlvr2_data.py:130

src/tasks/gqa.py:35

(self, num_answers)

src/tasks/gqa_model.py:15

src/tasks/nlvr2_model.py:11

src/tasks/gqa_data.py:64

(self, dataset: GQADataset)

src/tasks/gqa_data.py:90

(self, dataset: GQADataset)

src/tasks/gqa_data.py:159

(self, num_answers)

src/tasks/vqa_model.py:15

src/tasks/nlvr2.py:33

(self, dataset: VQADataset)

src/tasks/vqa_data.py:83

(self, dataset: VQADataset)

src/tasks/vqa_data.py:154

src/tasks/vqa.py:34

(self, input_ids, input_mask, segment_ids, lm_label_ids, visual_feats, obj_l

src/pretrain/lxmert_pretrain.py:54

(self, max_seq_length)

src/pretrain/lxmert_pretrain.py:215

(self, dsets=None)

src/pretrain/qa_answer_table.py:27

:param splits: The data sources to be loaded :param qa_sets: if None, no action o.w., only takes the answers

src/pretrain/lxmert_data.py:41

(self, dataset: LXMERTDataset, topk=-1)

src/pretrain/lxmert_data.py:88

(self, dataset: LXMERTDataset)

src/pretrain/lxmert_data.py:199

src/tasks/nlvr2_data.py:48

src/tasks/nlvr2_data.py:90

src/tasks/gqa_data.py:59

src/tasks/gqa_data.py:121

src/tasks/vqa_data.py:72

src/tasks/vqa_data.py:117

src/pretrain/lxmert_data.py:74

src/pretrain/lxmert_data.py:133

src/lxrt/modeling.py:248

Methodall_answers

src/pretrain/qa_answer_table.py:76

Methodconvert_ids_to_tokens

Converts a sequence of ids in wordpiece tokens using the vocab.

src/lxrt/tokenization.py:128

Methoddump_result

Dump result to a CSV file, which is compatible with NLVR2 evaluation system. NLVR2 CSV file requirement: Each line contai

src/tasks/nlvr2_data.py:142

Methoddump_result

Dump results to a json file, which could be submitted to the VQA online evaluation. VQA json file submission requirement:

src/tasks/vqa_data.py:166

Methoddump_result

(self, uid2ans: dict, path)

src/pretrain/lxmert_data.py:254

(self, quesid2ans: dict)

src/tasks/nlvr2_data.py:133

(self, quesid2ans: dict)

src/tasks/gqa_data.py:162

(self, quesid2ans: dict)

src/tasks/vqa_data.py:157

Functionfilename_to_url

Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not

src/lxrt/file_utils.py:55

src/lxrt/modeling.py:130

(self, input_ids, token_type_ids=None)

src/lxrt/modeling.py:278

(self, hidden_states, context, attention_mask=None)

src/lxrt/modeling.py:320

(self, hidden_states, input_tensor)

src/lxrt/modeling.py:357

(self, input_tensor, ctx_tensor, ctx_att_mask=None)

src/lxrt/modeling.py:370

(self, input_tensor, attention_mask)

src/lxrt/modeling.py:382

(self, hidden_states)

src/lxrt/modeling.py:398

(self, hidden_states, input_tensor)

src/lxrt/modeling.py:411

(self, hidden_states, attention_mask)

src/lxrt/modeling.py:425

(self, lang_feats, lang_attention_mask, visn_feats, visn_attention_mask)

src/lxrt/modeling.py:477

(self, visn_input)

src/lxrt/modeling.py:507

(self, lang_feats, lang_attention_mask, visn_feats, visn_attention_mask=None)

src/lxrt/modeling.py:546

(self, hidden_states)

src/lxrt/modeling.py:575

(self, hidden_states)

src/lxrt/modeling.py:594

(self, hidden_states)

src/lxrt/modeling.py:614

(self, hidden_states)

src/lxrt/modeling.py:631

(self, hidden_states)

src/lxrt/modeling.py:653

(self, sequence_output, pooled_output)

src/lxrt/modeling.py:667

(self, input_ids, token_type_ids=None, attention_mask=None, visual_feats=None, visual_attentio

src/lxrt/modeling.py:845

(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, visual_feat

src/lxrt/modeling.py:922

(self, input_ids, token_type_ids=None, attention_mask=None, visual_feats=None, visual_attentio

src/lxrt/modeling.py:1007

(self, sents, feats, visual_attention_mask=None)

src/lxrt/entry.py:109

b -- batch_size, o -- object_number, f -- visual_feature_size :param feat: (b, o, f) :param pos: (b, o, 4) :param s

src/tasks/gqa_model.py:30

:param feat: b, 2, o, f :param pos: b, 2, o, 4 :param sent: b, (string) :param leng: b, (numpy, int) :return

src/tasks/nlvr2_model.py:26

b -- batch_size, o -- object_number, f -- visual_feature_size :param feat: (b, o, f) :param pos: (b, o, 4) :param s

src/tasks/vqa_model.py:34

Methodfrom_pretrained

Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model fil

src/lxrt/modeling.py:702

Functionget_file_extension

(path, dot=True, lower=True)

src/lxrt/file_utils.py:244

src/lxrt/optimization.py:86

Methodid2ans_map

src/pretrain/qa_answer_table.py:70

Methodinit_bert_weights

Initialize the weights.

src/lxrt/modeling.py:688

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