text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return self._batch_encode_plus_boxes(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strateg... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def encode_boxes(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
`tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method).
text_pair (`str`, `List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return encoded_inputs["input_ids"] | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def encode_plus_boxes(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
text (`str`, `List[str]` or (for non-fast tokenizers) `List[int]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_mu... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return self._encode_plus_boxes(
text=text,
text_pair=text_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_len... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def _batch_encode_plus_boxes(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[List[List[... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokeniz... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
batch_outputs = self._batch_prepare_for_model_boxes(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
tru... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
@add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING)
def _batch_prepare_for_model_boxes(
self,
batch_text_or_text_pairs,
is_pair: bool = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
""" | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
batch_outputs = {}
for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
batch_text_or_text_pair, boxes_example = example
outputs = self.prepare_for_model_boxes(
batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
batch_text_... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verb... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def _encode_plus_boxes(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStra... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. ... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return self.prepare_for_model_boxes(
text=text,
text_pair=text_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_leng... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
@add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING)
def prepare_for_model_boxes(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
truncates sequences if overflowing while taking into account the special tokens and manages a moving window
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
labeled with -100, such that they will be ignored by the loss function.
Args:
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_mu... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if text_pair is None:
if word_labels is None:
# CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
for word, box in zip(text, boxes):
if len(word) < 1: # skip empty words
continu... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
else:
labels.extend([label] * len(word_tokens))
else:
# CASE ... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
for word, box in zip(text_pair, boxes):
if len(word) < 1: # skip empty words
continue
word_tokens = self.tokenize(word)
pair_tokens.extend(word_tokens)
pair_token_boxes.extend([box] * len(word_tokens))
# Create ids + pair_ids
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Truncation: Handle max sequence length
overflowing_tokens = []
overflowing_token_boxes = []
overflowing_labels = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
(
ids,
token_boxes,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Add special tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
token_boxes = token_boxes + [self.sep_token_box]
if pair_token_boxes:
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Build output dictionary
encoded_inputs["input_ids"] = sequence
encoded_inputs["bbox"] = token_boxes + pair_token_boxes
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.layoutxlm.tokenization_layoutxlm.LayoutXLMTokenizer.truncate_sequences
def truncate_sequences(
self,
ids: List[int],
token_boxes: List[List[int]],
pair_ids: Optional[List[int]] = None,
pair_token_boxes: Optional[List[List[int]]] = None,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
token_boxes (`List[List[int]]`):
Bounding boxes of the first sequence.
pai... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
The strategy to follow for truncation. Can be: | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
- `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of ... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
than the model maximum admissible input size).
stride (`int`, *optional*, de... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Returns:
`Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
overflowing tokens.
"""
if num_tokens_to_remove <= 0:
return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
if not isinstance(tr... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
overflowing_tokens = []
overflowing_token_boxes = []
overflowing_labels = []
if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
if not overflowing_token... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
overflowing_tokens.extend(pair_ids[-window_len:])
overflowing_token_boxes.extend(pair_token_boxes[-window_len:])
pair_ids = pair_ids[:-1]
pair_token_boxes = pair_token_boxes[:-1]
elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
if... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
f"Please select another truncation strategy than {truncation_strategy}, "
"for instance 'longest_first' or 'only_second'."
)
elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
if len(pair_ids) > num_tokens_to_remove:
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
"for instance 'longest_first' or 'only_first'."
) | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return (
ids,
token_boxes,
pair_ids,
pair_token_boxes,
labels,
overflowing_tokens,
overflowing_token_boxes,
overflowing_labels,
)
# Copied from transformers.models.layoutxlm.tokenization_layoutxlm.LayoutXLMToken... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_side if padding_side is not None else self.padding_side
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inp... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif padding_side == "left":
if return_attention_mask:
encoded_in... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(padding_side)) | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return encoded_inputs | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
class Blip2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a
BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield... | 3,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
Args:
hidden_size (`int`, *optional*, defaults to 1408):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hid... | 3,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults
to 1e-5): The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
ini... | 3,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
Example:
```python
>>> from transformers import Blip2VisionConfig, Blip2VisionModel
>>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration
>>> configuration = Blip2VisionConfig()
>>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/bli... | 3,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
... | 3,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
class Blip2QFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a
BLIP-2 Querying Transformer (Q-Former) model according to the specified arguments, defining the model architecture.
Instantiating a configur... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensio... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
use_qformer_text_input (`bool`, *optional*, defaults to `False`):
Whether to use BERT-style embeddings. | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
Examples:
```python
>>> from transformers import Blip2QFormerConfig, Blip2QFormerModel
>>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration
>>> configuration = Blip2QFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style confi... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 3,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
class Blip2Config(PretrainedConfig):
r"""
[`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is
used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model
and language model configs. Ins... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Blip2VisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Blip2QFormerConfig`].
text_config (`dict`, *optional*):
... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
```python
>>> from transformers import (
... Blip2VisionConfig,
... Blip2QFormerConfig,
... OPTConfig,
... Blip2Config,
... Blip2ForConditionalGeneration,
... )
>>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration
>>> configuration ... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
>>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config)
```"""
model_type = "blip-2"
sub_configs = {"text_config": AutoConfig, "qformer_config": Blip2QFormerConfig, "vision_config": Blip2VisionConfig}
def __init__(
self,
vision_config=None,
... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")
self.vision_config = Blip2VisionConfig(**vision_config)
self.qformer_config = Blip2QFormerConfig(**qformer_config)
text_model_type... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: Blip2VisionConfig,
qformer_config: Blip2QFormerConfig,
text_config: Optional[PretrainedConfig] = None,
**kwargs,
):
r"""
Instantiate a [`Blip2Config`] (or a derived class) from a BLIP-2... | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
return cls(
vision_config=vision_config.to_dict(),
qformer_config=qformer_config.to_dict(),
text_config=text_config.to_dict() if text_config is not None else None,
**kwargs,
) | 3,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py |
class Blip2ForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`Blip2ForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language modeling loss from the langua... | 3,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
loss: Optional[Tuple[torch.FloatTensor]] = None
logits: Optional[Tuple[torch.FloatTensor]] = None
vision_outputs: Optional[torch.FloatTensor] = None
qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None
language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None
def to_tuple(self) -> Tup... | 3,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2ImageTextMatchingModelOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_siz... | 3,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
The image embeddings obtained by applying the projection layer to the pooled output.
text_model_output (`BaseModelOutputWithPooling`):
The output of the [`Blip2QFormerModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`Blip2VisionModel`].
""" | 3,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
loss: Optional[torch.FloatTensor] = None
logits_per_image: torch.FloatTensor = None
logits_per_text: torch.FloatTensor = None
text_embeds: torch.FloatTensor = None
image_embeds: torch.FloatTensor = None
text_model_output: BaseModelOutputWithPooling = None
vision_model_output: BaseModelOutputWith... | 3,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2TextModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
... | 3,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
Hidden-states of the model at the output of each layer plus the optional 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 shap... | 3,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2VisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_pro... | 3,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
Hidden-states of the model at the output of each layer plus the optional 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 shap... | 3,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2VisionEmbeddings(nn.Module):
def __init__(self, config: Blip2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parame... | 3,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
... | 3,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class_pos_embed = self.position_embedding[:, :1]
patch_pos_embed = self.position_embedding[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed =... | 3,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, w... | 3,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.e... | 3,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if config.qkv_bias:
q_bias = nn.Parameter(torch.zeros(self.embed_dim))
v_bias = nn.Parameter(torch.zeros(self.embed_dim))
else:
q_bias = None
v_bias = None
if q_bias is not None:
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_... | 3,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
mixed_qkv = self.qkv(hidden_states)
mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute(
2, 0, 3, 1, 4
)
query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2]
# Take the dot product between "query" ... | 3,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
context_layer = context_layer.reshape(new_context_layer_shape)
output = self.projection(context_layer)
outputs = (output, attention_p... | 3,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
... | 3,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2EncoderLayer(nn.Module):
def __init__(self, config: Blip2Config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Blip2Attention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Blip2MLP(config)
... | 3,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`... | 3,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
head_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = hidden_states + residual
residual = hidden_state... | 3,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Blip2Config
base_model_prefix = "blip"
supports_gradient_checkpointing = True
_no_split_modules =... | 3,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if isinstance(module, Blip2VisionEmbeddings):
if hasattr(self.config, "vision_config") and not isinstance(self.config, Blip2VisionConfig):
factor = self.config.vision_config.initializer_range
nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor)
nn.in... | 3,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Blip2EncoderLayer`].
Args:
config (`Blip2Config`):
The corresponding vision configuration for the `Blip2Encoder`.
"""
def __init__(self, ... | 3,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
... | 3,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optiona... | 3,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
... | 3,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_st... | 3,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2VisionModel(Blip2PreTrainedModel):
main_input_name = "pixel_values"
config_class = Blip2VisionConfig
def __init__(self, config: Blip2VisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = Blip2VisionEmbedding... | 3,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
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