text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if (image_text_alignment_mask == 0).sum() != 0:
image_text_alignment_mask[image_text_alignment_mask == 0] = 1 # Avoid divide by zero error
logger.warning(
"Found 0 values in `image_text_alignment_mask`. Setting them to 1 to avoid divide-by-zero"
... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
# When fine-tuning the detector , the image_text_alignment is sometimes padded too long.
if visual_position_embeddings.size(1) != visual_embeds.size(1):
if visual_position_embeddings.size(1) < visual_embeds.size(1):
raise ValueError(
... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
visual_position_embeddings = visual_position_embeddings + self.visual_position_embeddings(
visual_position_ids
)
else:
visual_position_ids = torch.zeros(
*visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device
... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the ... | 2,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_a... | 2,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in VisualBertSelfAttentionModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize... | 2,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
... | 2,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = VisualBertSelfAttention(config)
self.output = VisualBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
... | 3,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
... | 3,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.... | 3,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,003 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VisualBertAttention(config)
self.intermediate = VisualBertIntermediate(config)
self.out... | 3,004 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermedia... | 3,004 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_st... | 3,005 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
... | 3,005 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
all_hidden_states,
all_self_attentions,
]
if v is not None
)
return BaseModelOutput(
last_... | 3,005 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the... | 3,006 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
sel... | 3,007 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = VisualBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder =... | 3,008 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = VisualBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.... | 3,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VisualBertConfig
base_model_prefix = "visual_bert"
supports_gradient_checkpointing = True
de... | 3,010 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`VisualBertForPreTraining`]. | 3,011 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the sentence-image prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequ... | 3,011 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
shape `(batch_size, sequence_length, hidden_size)`. | 3,011 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 3,011 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertModel(VisualBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. ... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for laye... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Opt... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
r""" | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image.
from transformers import AutoTokenizer, VisualBertModel
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
mod... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if visual_embeds is not None:
visual_input_shape = visual_embeds.size()[:-1]
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=devi... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
else:
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, (batch_size, input_shape)
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if self.bypass_transformer and visual_embeds is not None:
text_length = input_ids.size(1)
text_embedding_output = embedding_output[:, :text_length, :]
visual_embedding_output = embedding_output[:, text_length:, :]
text_extended_attention_mask = extended_attention_mask[:,... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
else:
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=ret... | 3,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForPreTraining(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.cls = VisualBertPreTrainingHeads(confi... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask:... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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 ... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
- 0 indicates sequence B is a matching pair of sequence A for the given image,
- 1 indicates sequence B is a random sequence w.r.t A for the given image.
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
inputs.update(
{
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
max_length = inputs["input_ids"].shape[-1] + visual_embeds.shape[-2]
labels... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if labels is not None:
total_size = attention_mask.size(-1) + visual_attention_mask.size(-1)
if labels.size(-1) != total_size:
raise ValueError(
"The labels provided should have same sequence length as total attention mask. "
f"Found labels... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
total_loss = None
if labels is not None and sentence_image_labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
sentence_image_loss = loss_fct(seq_relationship_score.view(-1, 2), sente... | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
return VisualBertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(config.hidden_size, 1)
# Initialize weigh... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(
VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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, ...,
num_choic... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForMultipleChoice
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-b... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
visual_embeds = get_visual_embeddings(image)
# (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
loss = outputs.loss
logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if in... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
visual_embeds = (
visual_embeds.view(-1, visual_embeds.size(-2), visual_embeds.size(-1))
if visual_embeds is not None
else None
)
visual_attention_mask = (
visual_attention_mask.view(-1, visual_attention_mask.size(-1))
if visual_attention_mask ... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=ou... | 3,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(con... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optio... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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, .... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForQuestionAnswering
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/ber... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
labels = torch.tensor([[0.0, 1.0]]).unsqueeze(0) # Batch size 1, Num labels 2
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get the ind... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
pooled_output = self.dropout(pooled_output)
logits = self.cls(pooled_output)
reshaped_logits = logits.view(-1, self.num_labels)
loss = None
if labels is not None:
loss_fct = nn.KLDivLoss(reduction="batchmean")
log_softmax = nn.LogSoftmax(dim=-1)
resha... | 3,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(confi... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optio... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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, ...,
config... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForVisualReasoning
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
labels = torch.tensor(1).unsqueeze(0) # Batch size 1, Num choices 2
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.visual_b... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
# sequence_output = outputs[0]
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.cls(pooled_output)
reshaped_logits = logits.contiguous()
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = lo... | 3,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertRegionToPhraseAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
... | 3,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, query, key, attention_mask):
attention_mask = attention_mask.to(query.dtype)
attentio... | 3,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
sel... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
@add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optio... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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 ... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length, visual_sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. KLDivLoss is computed against these labels and the
outputs from the attention layer.
Returns:
Example:
... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torc... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
if region_to_phrase_position is None:
raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.")
return_dict = return_dict if return_dict is n... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
sequence_output = outputs[0]
region_to_phrase_position_mask = (region_to_phrase_position != -1).long()
# Make the -1 become 0
region_to_phrase_position = region_to_phrase_position * region_to_phrase_position_mask
# Selected_positions = batch x selected position x dim
expanded_... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
if visual_features.size(1) != visual_attention_mask.size(1):
raise ValueError(
f"Visual features length :{visual_features.size(1)} should be the same"
f" as visual attention mask length: {visual_attention_mask.size(1)}."
)
logits = self.attention(selected... | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
class VideoLlavaProcessor(ProcessorMixin):
r"""
Constructs a VideoLlava processor which wraps a VideoLlava image processor and a Llava tokenizer into a single processor.
[`VideoLlavaProcessor`] offers all the functionalities of [`VideoLlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~VideoLl... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
Args:
image_processor ([`VideoLlavaImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`], *optional*):
The tokenizer is a required input.
patch_size (`int`, *optional*, defaults to 14):
Patch size from the vi... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
num_additional_image_tokens (`int`, *optional*, defaults to 1):
Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
extra tokens appended, no need to set this arg.
""" | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
attributes = ["image_processor", "tokenizer"]
valid_kwargs = [
"chat_template",
"patch_size",
"vision_feature_select_strategy",
"image_token",
"video_token",
"num_additional_image_tokens",
]
image_processor_class = "VideoLlavaImageProcessor"
tokenizer_clas... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
def __init__(
self,
image_processor=None,
tokenizer=None,
patch_size=14,
vision_feature_select_strategy="default",
image_token="<image>", # set the default and let users change if they have peculiar special tokens in rare cases
video_token="<video>",
chat... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
videos: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = Non... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
Args:
text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokeni... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
Video frames to preprocess. Expects a single or batch of video frames in NumPy array or PyTorch
tensor. Each video should be of shape (T, C, H, W), where T is number of frames, C is
number of channels, H and W are image height and width.
padding (`bool`, `str` or [`~utils.Pad... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
return_tenso... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the follow... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `t... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
if encoded_images is not None:
if "pixel_values_images" in encoded_images.keys():
height, width = get_image_size(to_numpy_array(encoded_images.get("pixel_values_images")[0]))
num_frames = 1
if "pixel_values_videos" in encoded_images.keys():
one_vi... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
num_image_tokens = (height // self.patch_size) * (
width // self.patch_size
) + self.num_additional_image_tokens
num_video_tokens = num_image_tokens * num_frames
if self.vision_feature_select_strategy == "default":
num_image_tokens -= 1
pr... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method f... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_... | 3,019 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py |
class VideoLlavaCausalLMOutputWithPast(ModelOutput):
"""
Base class for VideoLlava causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | 3,020 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_s... | 3,020 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`torch.FloatTensor`, *optional*):
A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`.
image_hidden_states ... | 3,020 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[torch.FloatTensor] = None
... | 3,020 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
class VideoLlavaMultiModalProjector(nn.Module):
def __init__(self, config: VideoLlavaConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[config.p... | 3,021 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
class VideoLlavaPreTrainedModel(PreTrainedModel):
config_class = VideoLlavaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["VideoLlavaVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_... | 3,022 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding... | 3,022 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
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