Skip to content

GitLab

Unverified Commit 9947dd07 authored by Gunjan Chhablani's avatar Gunjan Chhablani Committed by GitHub
Browse files

Add VisualBert type hints (#16544)

parent 59a9c83e
Show whitespace changes
Inline Side-by-side
Showing with 92 additions and 92 deletions
src/transformers/models/visual_bert/modeling_visual_bert.py
View file @ 9947dd07
......@@ -17,7 +17,7 @@
import math
from dataclasses import dataclass
from typing import Optional, Tuple
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
......@@ -720,20 +720,20 @@ class VisualBertModel(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
r"""
Returns:
......@@ -893,22 +893,22 @@ class VisualBertForPreTraining(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
sentence_image_labels=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
sentence_image_labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], VisualBertForPreTrainingOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
......@@ -1039,21 +1039,21 @@ class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
......@@ -1191,21 +1191,21 @@ class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
......@@ -1317,21 +1317,21 @@ class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
......@@ -1477,22 +1477,22 @@ class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
visual_embeds=None,
visual_attention_mask=None,
visual_token_type_ids=None,
image_text_alignment=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
region_to_phrase_position=None,
labels=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
region_to_phrase_position: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
region_to_phrase_position (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
The positions depicting the position of the image embedding corresponding to the textual tokens.
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment