text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence
pair mask has the following format:
... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | 9,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 9,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 9,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
class LayoutLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`LayoutLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.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,... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
The value used to pad inp... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value t... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
Examples:
```python
>>> from transformers import LayoutLMConfig, LayoutLMModel
>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = LayoutLMModel(configuration)
>>> # Accessing... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.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,
... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.max_2d_posi... | 9,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
class LayoutLMOnnxConfig(OnnxConfig):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.max_2d_positions = config.max_2d_positi... | 9,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for ... | 9,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
input_dict = super().generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# Generate a dummy bbox
box = [48, 84, 73, 128]
if not framework == TensorType.PYTORCH:
raise NotImplementedError("Exporti... | 9,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py |
class LayoutLMTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" LayoutLM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass fo... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is no... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used f... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original LayoutLM).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = LayoutLMTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_t... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", toke... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LayoutLM sequence has the following format:
- single sequence: `[CLS] X... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence
pair mask has the following format:
... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 9,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py |
class LayoutLMEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(LayoutLMEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
... | 9,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
se... | 9,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
words_embeddings = inputs_embeds
position_embeddings = self.position_embeddings(position_ids)
try:
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_em... | 9,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
embeddings = (
words_embeddings
+ position_embeddings
+ left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
+ h_position_embeddings
+ w_position_embeddings
... | 9,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all ... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.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 LayoutLMModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention ... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.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,)
i... | 9,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMSelfOutput(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)
d... | 9,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LAYOUTLM_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = LayoutLMSelfO... | 9,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.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) | 9,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMIntermediate(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.in... | 9,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMOutput(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)
... | 9,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMLayer(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 = LayoutLMAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | 9,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 9,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 9,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.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
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 9,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LayoutLMLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_ca... | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
... | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attent... | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 9,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMPooler(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 h... | 9,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMPredictionHeadTransform(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:
self.... | 9,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LayoutLMPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.... | 9,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LayoutLMLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scor... | 9,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
base_model_prefix = "layoutlm"
supports_gradient_checkpointing = True | 9,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.... | 9,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMModel(LayoutLMPreTrainedModel):
def __init__(self, config):
super(LayoutLMModel, self).__init__(config)
self.config = config
self.embeddings = LayoutLMEmbeddings(config)
self.encoder = LayoutLMEncoder(config)
self.pooler = LayoutLMPooler(config)
# Init... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
Returns: | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["He... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> outputs = model(
... input_... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).uns... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
embedding_output = self.embeddings(
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
extended_atte... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
... | 9,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.layoutlm = LayoutLMModel(config)
self.cls = LayoutLMOnlyMLMHead(config)
# Initi... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] =... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
l... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncas... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> labels = tokenizer("Hello world", retur... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
outputs = self.layoutlm(
input_ids,
bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 9,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMForSequenceClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(c... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
@add_start_docstrings_to_model_forward(LAYOUTLM_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,
bbox: Optional[torch.Lon... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForSequenceClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForSequenceClassification.from_pretrained("m... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> sequence_label = torch.tensor([1])
... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_att... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 9,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(conf... | 9,618 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py |
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