text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
if isinstance(table, pd.DataFrame) and isinstance(query, (list, tuple)):
# single table, many queries case
# duplicate table for every query
table = [table] * len(query)
if isinstance(table, (list, tuple)) and isinstance(query, str):
# many tables, single query ca... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
batch_outputs = self._batch_prepare_for_model(
table=table,
query=query,
answer=answer,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
table: Union["pd.DataFrame", List["pd.DataFrame"]],
query: Optional[Union[TextInput, List[TextInput]]] = None,
answer: Optional[Union[str, List[str]]] = Non... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
This method 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.
"""
batch_outputs = {}
if answer is None:
answer = [None] * len(table)
for _... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
if self.do_lower_case:
text = text.lower() | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
tokens = self.tokenize(text)
outputs = self.prepare_for_model(
ids=self.convert_tokens_to_ids(tokens),
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards
truncation=truncation_strategy... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.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,
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
def encode(
self,
table: "pd.DataFrame",
query: Optional[TextInput] = None,
answer: Optional[str] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, ... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
**kwargs,
) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
return encoded_inputs["input_ids"] | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
table: "pd.DataFrame",
query: Optional[TextInput] = None,
answer: Optional[str] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, Paddin... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
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_multiple_of,
verbose=verbose,
**kwargs,
) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
return self._encode_plus(
table=table,
query=query,
answer=answer,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
pad_to_multiple_of=... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def _encode_plus(
self,
table: "pd.DataFrame",
query: Optional[TextInput] = None,
answer: Optional[str] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStr... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More information on available tokenizers at "
"https://github.com/huggingfa... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
text = self.prepare_table_query(
table, query, answer, truncation_strategy=truncation_strategy, max_length=max_length
)
# if necessary, perform lower case
if self.do_lower_case:
text = text.lower()
tokens = self.tokenize(text) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
return self.prepare_for_model(
ids=self.convert_tokens_to_ids(tokens),
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def target_call_func(
self,
answer: Union[str, List[str]],
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
Args:
answer (`str` or `List[str]`):
Corresponding answer supervision to the queries for training the model.
"""
is_batched = isinstance(answer, (list, tuple)) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
if is_batched:
return self.target_batch_encode_plus(
answer=answer,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def target_batch_encode_plus(
self,
answer: List[str],
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
ret... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
Args:
answer `List[str]`:
Corresponding answer supervision to the queries for training the model.
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strat... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
return self._target_batch_encode_plus(
answer=answer,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
retur... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def _target_batch_encode_plus(
self,
answer: List[str],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
tokens = self.tokenize(text)
outputs = self.prepare_for_model(
ids=self.convert_tokens_to_ids(tokens),
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards
truncation=truncation_strategy... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.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,
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def target_encode(
self,
answer: str,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None,
max_length: Optional[int] = None,
return_tensors: Optional[... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
Args:
answer `str`:
Corresponding answer supervision to the queries for training the model
"""
encoded_outputs = self.target_encode_plus(
answer=answer,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncati... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def target_encode_plus(
self,
answer: str,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors:... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
Args:
answer `str`:
Corresponding answer supervision to the queries for training the model.
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
return self._target_encode_plus(
answer=answer,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tens... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def _target_encode_plus(
self,
answer: str,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
text = answer
# if necessary, perform lower case
if self.do_lower_case:
text = text.lower()
tokens = self.tokenize(text)
return self.prepare_for_model(
ids=self.convert_tokens_to_ids(tokens),
add_special_tokens=add_special_tokens,
paddin... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def prepare_table_query(
self,
table,
query,
answer=None,
truncation_strategy=Union[str, TruncationStrategy, TapexTruncationStrategy],
max_length=None,
):
"""
This method can be used to linearize a table and add a corresponding query.
Optional... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
# step 2: modify table internally
# always truncate table cells based on self.max_cell_length
# optionally truncate rows if truncation_strategy is set to it
self.truncate_table_cells(table_content, query, answer)
if truncation_strategy == TapexTruncationStrategy.DROP_ROWS... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
if linear_table == "":
logger.warning(
"You provide an empty table, or all cells contain much tokens (e.g., >= 1024 tokens). "
+ f"Please carefully check the corresponding table with the query : {query}."
)
if query == "":
logger.warning("You p... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def truncate_table_cells(self, table_content: Dict, question: str, answer: List):
# TODO (Qian): is it possible to revert the original cell if it is in the final answer?
cell_mapping = {}
for row in table_content["rows"]:
for i, cell in enumerate(row):
truncate_cell =... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def truncate_cell(self, cell_value):
# do not process on these cases
if isinstance(cell_value, int) or isinstance(cell_value, float):
return cell_value
if cell_value.strip() != "":
try_tokens = self.tokenize(cell_value)
if len(try_tokens) >= self.max_cell_leng... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
answer:
if for training, is the supervision; otherwise will be empty
"""
delete_ratio, remain_token_len = self.estimate_delete_ratio(table_content, question, max_length)
# randomly delete unrelated rows
self.delete_unrelated_rows(table_content, question, answer, delete_ratio)... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def estimate_delete_ratio(self, table_content: Dict, question: str, max_length=None):
if "header" not in table_content or "rows" not in table_content:
raise ValueError("The table content should contain both 'header' and 'rows' keys.")
# calculate the tokens of header, special tokens will onl... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
value_string = ""
for _, row_example in enumerate(table_content["rows"]):
# use a general index to roughly estimate the overall token len
value_string += self.table_linearize.process_row(row_example, 100) + " "
value_token_len = len(self.tokenize(value_string))
if value_... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
def delete_unrelated_rows(self, table_content: Dict, question: str, answer: List, delete_ratio: float):
"""
The argument answer is used only during training.
"""
truncated_unrelated_indices = []
related_indices = []
if answer is None or len(answer) == 0:
answe... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
related_indices.extend([_row_idx - 2, _row_idx - 1, _row_idx, _row_idx + 1, _row_idx + 2]) | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
# remove the neighbours
truncated_unrelated_indices = [
_row_idx for _row_idx in truncated_unrelated_indices if _row_idx not in related_indices
]
# select some cases to drop
drop_items = min(len(truncated_unrelated_indices), int(len(table_content["rows"]) * delete_ratio))
... | 10,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py |
class ViTHybridConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViTHybridModel`]. It is used to instantiate a ViT
Hybrid model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
Args:
backbone_config (`Union[Dict[str, Any], PretrainedConfig]`, *optional*):
The configuration of the backbone in a dictionary or the config object of the backbone.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbo... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hi... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The d... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
backbone_featmap_shape (`List[int]`, *optional*, defaults to `[1, 1024, 24, 24]`):
Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values. | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
Example:
```python
>>> from transformers import ViTHybridConfig, ViTHybridModel
>>> # Initializing a ViT Hybrid vit-hybrid-base-bit-384 style configuration
>>> configuration = ViTHybridConfig()
>>> # Initializing a model (with random weights) from the vit-hybrid-base-bit-384 style configuration
... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
def __init__(
self,
backbone_config=None,
backbone=None,
use_pretrained_backbone=False,
use_timm_backbone=False,
backbone_kwargs=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
if backbone_config is None and backbone is None:
logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone.")
backbone_config = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
if isinstance(backbone_config, dict):
if "model_type" in backbone_config:
backbone_config_class = CONFIG_MAPPING[backbone_config["model_type"]]
else:
logger.info(
"`model_type` is not found in `backbone_config`. Use `Bit` as the backbone config... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
self.backbone_featmap_shape = backbone_featmap_shape
self.backbone_config = backbone_config
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
self.hidden_size =... | 10,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py |
class ViTHybridImageProcessor(BaseImageProcessor):
r"""
Constructs a ViT Hybrid image processor. | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
crop_size (`Dict[str, int]` *optional*, defaults to 224):
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified sca... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> n... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
output_size = get_resize_output_image_size(
image,
size=size,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: int = None,
do_rescale: bool = None,
rescale_factor: float = None,
d... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
Whether to center crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to r... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: defaults to the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
size = get_size_dict(size, param_name="size", default_to_square=False)
resample = resample if resample is not None else self.resample
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
crop_size = crop_size if crop_size is not None else self.crop_size
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pi... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(
image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
)
... | 10,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py |
class ViTHybridEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: ViTHybridConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.randn(1, 1, c... | 10,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
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 hi... | 10,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 10,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_value... | 10,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddin... | 10,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config... | 10,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
if feature_size is None:
feature_map = config.backbone_featmap_shape
feature_size = feature_map[-2:]
feature_dim = feature_map[1]
else:
feature_size = (
feature_size if isinstance(feature_size, collections.abc.Iterable) else (feature_size, feature... | 10,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match w... | 10,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridSelfAttention(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
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... | 10,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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, head_mask: Optional[torch.Tensor] = None, output... | 10,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dro... | 10,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridSdpaSelfAttention(ViTHybridSelfAttention):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, out... | 10,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.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)
return context_layer, None | 10,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridSelfOutput(nn.Module):
"""
The residual connection is defined in ViTHybridLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.dense =... | 10,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridAttention(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.attention = ViTHybridSelfAttention(config)
self.output = ViTHybridSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
... | 10,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def for... | 10,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridSdpaAttention(ViTHybridAttention):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.attention = ViTHybridSdpaSelfAttention(config) | 10,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridIntermediate(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
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]
... | 10,393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridOutput(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tenso... | 10,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VIT_... | 10,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_sta... | 10,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
# in ViTHybrid, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_ou... | 10,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridEncoder(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViTHybridLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
... | 10,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
... | 10,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
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