1372 lines
58 KiB
Python
1372 lines
58 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 Microsoft Research and The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""PyTorch LayoutLMv3 model."""
|
|
|
|
import collections
|
|
import math
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.utils.checkpoint
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_outputs import (
|
|
BaseModelOutput,
|
|
QuestionAnsweringModelOutput,
|
|
SequenceClassifierOutput,
|
|
TokenClassifierOutput,
|
|
)
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...pytorch_utils import apply_chunking_to_forward
|
|
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
|
from .configuration_layoutlmv3 import LayoutLMv3Config
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
_CONFIG_FOR_DOC = "LayoutLMv3Config"
|
|
|
|
|
|
from ..deprecated._archive_maps import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
|
|
|
|
|
|
LAYOUTLMV3_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
|
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`LayoutLMv3Config`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
LAYOUTLMV3_MODEL_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
|
|
token. See `pixel_values` for `patch_sequence_length`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
|
|
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
|
|
Bounding boxes of each input sequence tokens. Selected in the range `[0,
|
|
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
|
|
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
|
|
y1) represents the position of the lower right corner.
|
|
|
|
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
|
|
token. See `pixel_values` for `patch_sequence_length`.
|
|
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
|
|
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
|
|
config.patch_size) * (width / config.patch_size))`.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
|
|
token. See `pixel_values` for `patch_sequence_length`.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
|
|
token. See `pixel_values` for `patch_sequence_length`.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
|
|
token. See `pixel_values` for `patch_sequence_length`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
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`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
|
|
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
|
|
Bounding boxes of each input sequence tokens. Selected in the range `[0,
|
|
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
|
|
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
|
|
y1) represents the position of the lower right corner.
|
|
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
|
|
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
|
|
config.patch_size) * (width / config.patch_size))`.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
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`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
class LayoutLMv3PatchEmbeddings(nn.Module):
|
|
"""LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying
|
|
image sizes."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
image_size = (
|
|
config.input_size
|
|
if isinstance(config.input_size, collections.abc.Iterable)
|
|
else (config.input_size, config.input_size)
|
|
)
|
|
patch_size = (
|
|
config.patch_size
|
|
if isinstance(config.patch_size, collections.abc.Iterable)
|
|
else (config.patch_size, config.patch_size)
|
|
)
|
|
self.patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
|
|
self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size)
|
|
|
|
def forward(self, pixel_values, position_embedding=None):
|
|
embeddings = self.proj(pixel_values)
|
|
|
|
if position_embedding is not None:
|
|
# interpolate the position embedding to the corresponding size
|
|
position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1)
|
|
position_embedding = position_embedding.permute(0, 3, 1, 2)
|
|
patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
|
|
position_embedding = F.interpolate(position_embedding, size=(patch_height, patch_width), mode="bicubic")
|
|
embeddings = embeddings + position_embedding
|
|
|
|
embeddings = embeddings.flatten(2).transpose(1, 2)
|
|
return embeddings
|
|
|
|
|
|
class LayoutLMv3TextEmbeddings(nn.Module):
|
|
"""
|
|
LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
self.position_embeddings = nn.Embedding(
|
|
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
|
|
)
|
|
|
|
self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
|
|
self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
|
|
self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
|
|
self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
|
|
|
|
def calculate_spatial_position_embeddings(self, bbox):
|
|
try:
|
|
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
|
|
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
|
|
right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
|
|
lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
|
|
except IndexError as e:
|
|
raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
|
|
|
|
h_position_embeddings = self.h_position_embeddings(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023))
|
|
w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023))
|
|
|
|
# below is the difference between LayoutLMEmbeddingsV2 (torch.cat) and LayoutLMEmbeddingsV1 (add)
|
|
spatial_position_embeddings = torch.cat(
|
|
[
|
|
left_position_embeddings,
|
|
upper_position_embeddings,
|
|
right_position_embeddings,
|
|
lower_position_embeddings,
|
|
h_position_embeddings,
|
|
w_position_embeddings,
|
|
],
|
|
dim=-1,
|
|
)
|
|
return spatial_position_embeddings
|
|
|
|
def create_position_ids_from_input_ids(self, input_ids, padding_idx):
|
|
"""
|
|
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
|
|
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
|
|
"""
|
|
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
|
|
mask = input_ids.ne(padding_idx).int()
|
|
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
|
|
return incremental_indices.long() + padding_idx
|
|
|
|
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
|
"""
|
|
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
|
"""
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
sequence_length = input_shape[1]
|
|
|
|
position_ids = torch.arange(
|
|
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
|
)
|
|
return position_ids.unsqueeze(0).expand(input_shape)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
bbox=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
):
|
|
if position_ids is None:
|
|
if input_ids is not None:
|
|
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
|
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
|
|
input_ids.device
|
|
)
|
|
else:
|
|
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
|
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
|
|
spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox)
|
|
|
|
embeddings = embeddings + spatial_position_embeddings
|
|
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
class LayoutLMv3PreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LayoutLMv3Config
|
|
base_model_prefix = "layoutlmv3"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
# 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=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
class LayoutLMv3SelfAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.has_relative_attention_bias = config.has_relative_attention_bias
|
|
self.has_spatial_attention_bias = config.has_spatial_attention_bias
|
|
|
|
def transpose_for_scores(self, x):
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(*new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def cogview_attention(self, attention_scores, alpha=32):
|
|
"""
|
|
https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
|
|
(PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs
|
|
will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs,
|
|
cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better.
|
|
"""
|
|
scaled_attention_scores = attention_scores / alpha
|
|
max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1)
|
|
new_attention_scores = (scaled_attention_scores - max_value) * alpha
|
|
return nn.Softmax(dim=-1)(new_attention_scores)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
# The attention scores QT K/√d could be significantly larger than input elements, and result in overflow.
|
|
# Changing the computational order into QT(K/√d) alleviates the problem. (https://arxiv.org/pdf/2105.13290.pdf)
|
|
attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2))
|
|
|
|
if self.has_relative_attention_bias and self.has_spatial_attention_bias:
|
|
attention_scores += (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size)
|
|
elif self.has_relative_attention_bias:
|
|
attention_scores += rel_pos / math.sqrt(self.attention_head_size)
|
|
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
# Use the trick of the CogView paper to stablize training
|
|
attention_probs = self.cogview_attention(attention_scores)
|
|
|
|
# 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.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
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,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput
|
|
class LayoutLMv3SelfOutput(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)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Attention with LayoutLMv2->LayoutLMv3
|
|
class LayoutLMv3Attention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = LayoutLMv3SelfAttention(config)
|
|
self.output = LayoutLMv3SelfOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Layer with LayoutLMv2->LayoutLMv3
|
|
class LayoutLMv3Layer(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 = LayoutLMv3Attention(config)
|
|
self.intermediate = LayoutLMv3Intermediate(config)
|
|
self.output = LayoutLMv3Output(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class LayoutLMv3Encoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
self.has_relative_attention_bias = config.has_relative_attention_bias
|
|
self.has_spatial_attention_bias = config.has_spatial_attention_bias
|
|
|
|
if self.has_relative_attention_bias:
|
|
self.rel_pos_bins = config.rel_pos_bins
|
|
self.max_rel_pos = config.max_rel_pos
|
|
self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False)
|
|
|
|
if self.has_spatial_attention_bias:
|
|
self.max_rel_2d_pos = config.max_rel_2d_pos
|
|
self.rel_2d_pos_bins = config.rel_2d_pos_bins
|
|
self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
|
|
self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
|
|
|
|
def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
|
|
ret = 0
|
|
if bidirectional:
|
|
num_buckets //= 2
|
|
ret += (relative_position > 0).long() * num_buckets
|
|
n = torch.abs(relative_position)
|
|
else:
|
|
n = torch.max(-relative_position, torch.zeros_like(relative_position))
|
|
# now n is in the range [0, inf)
|
|
|
|
# half of the buckets are for exact increments in positions
|
|
max_exact = num_buckets // 2
|
|
is_small = n < max_exact
|
|
|
|
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
|
|
val_if_large = max_exact + (
|
|
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
|
|
).to(torch.long)
|
|
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
|
|
|
|
ret += torch.where(is_small, n, val_if_large)
|
|
return ret
|
|
|
|
def _cal_1d_pos_emb(self, position_ids):
|
|
rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
|
|
|
|
rel_pos = self.relative_position_bucket(
|
|
rel_pos_mat,
|
|
num_buckets=self.rel_pos_bins,
|
|
max_distance=self.max_rel_pos,
|
|
)
|
|
rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2)
|
|
rel_pos = rel_pos.contiguous()
|
|
return rel_pos
|
|
|
|
def _cal_2d_pos_emb(self, bbox):
|
|
position_coord_x = bbox[:, :, 0]
|
|
position_coord_y = bbox[:, :, 3]
|
|
rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
|
|
rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
|
|
rel_pos_x = self.relative_position_bucket(
|
|
rel_pos_x_2d_mat,
|
|
num_buckets=self.rel_2d_pos_bins,
|
|
max_distance=self.max_rel_2d_pos,
|
|
)
|
|
rel_pos_y = self.relative_position_bucket(
|
|
rel_pos_y_2d_mat,
|
|
num_buckets=self.rel_2d_pos_bins,
|
|
max_distance=self.max_rel_2d_pos,
|
|
)
|
|
rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2)
|
|
rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2)
|
|
rel_pos_x = rel_pos_x.contiguous()
|
|
rel_pos_y = rel_pos_y.contiguous()
|
|
rel_2d_pos = rel_pos_x + rel_pos_y
|
|
return rel_2d_pos
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
bbox=None,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
position_ids=None,
|
|
patch_height=None,
|
|
patch_width=None,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None
|
|
rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
rel_pos,
|
|
rel_2d_pos,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate
|
|
class LayoutLMv3Intermediate(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.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaOutput
|
|
class LayoutLMv3Output(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)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.",
|
|
LAYOUTLMV3_START_DOCSTRING,
|
|
)
|
|
class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
if config.text_embed:
|
|
self.embeddings = LayoutLMv3TextEmbeddings(config)
|
|
|
|
if config.visual_embed:
|
|
# use the default pre-training parameters for fine-tuning (e.g., input_size)
|
|
# when the input_size is larger in fine-tuning, we will interpolate the position embeddings in forward
|
|
self.patch_embed = LayoutLMv3PatchEmbeddings(config)
|
|
|
|
size = int(config.input_size / config.patch_size)
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
|
|
self.pos_embed = nn.Parameter(torch.zeros(1, size * size + 1, config.hidden_size))
|
|
self.pos_drop = nn.Dropout(p=0.0)
|
|
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
|
|
self.init_visual_bbox(image_size=(size, size))
|
|
|
|
self.norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
|
|
|
|
self.encoder = LayoutLMv3Encoder(config)
|
|
|
|
self.init_weights()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
def init_visual_bbox(self, image_size=(14, 14), max_len=1000):
|
|
"""
|
|
Create the bounding boxes for the visual (patch) tokens.
|
|
"""
|
|
visual_bbox_x = torch.div(
|
|
torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc"
|
|
)
|
|
visual_bbox_y = torch.div(
|
|
torch.arange(0, max_len * (image_size[0] + 1), max_len), image_size[0], rounding_mode="trunc"
|
|
)
|
|
visual_bbox = torch.stack(
|
|
[
|
|
visual_bbox_x[:-1].repeat(image_size[0], 1),
|
|
visual_bbox_y[:-1].repeat(image_size[1], 1).transpose(0, 1),
|
|
visual_bbox_x[1:].repeat(image_size[0], 1),
|
|
visual_bbox_y[1:].repeat(image_size[1], 1).transpose(0, 1),
|
|
],
|
|
dim=-1,
|
|
).view(-1, 4)
|
|
|
|
cls_token_box = torch.tensor([[0 + 1, 0 + 1, max_len - 1, max_len - 1]])
|
|
self.visual_bbox = torch.cat([cls_token_box, visual_bbox], dim=0)
|
|
|
|
def calculate_visual_bbox(self, device, dtype, batch_size):
|
|
visual_bbox = self.visual_bbox.repeat(batch_size, 1, 1)
|
|
visual_bbox = visual_bbox.to(device).type(dtype)
|
|
return visual_bbox
|
|
|
|
def forward_image(self, pixel_values):
|
|
embeddings = self.patch_embed(pixel_values)
|
|
|
|
# add [CLS] token
|
|
batch_size, seq_len, _ = embeddings.size()
|
|
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
|
|
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
|
|
|
|
# add position embeddings
|
|
if self.pos_embed is not None:
|
|
embeddings = embeddings + self.pos_embed
|
|
|
|
embeddings = self.pos_drop(embeddings)
|
|
embeddings = self.norm(embeddings)
|
|
|
|
return embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
LAYOUTLMV3_MODEL_INPUTS_DOCSTRING.format("batch_size, token_sequence_length")
|
|
)
|
|
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
bbox: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModel
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
|
|
>>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> image = example["image"]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
batch_size, seq_length = input_shape
|
|
device = inputs_embeds.device
|
|
elif pixel_values is not None:
|
|
batch_size = len(pixel_values)
|
|
device = pixel_values.device
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values")
|
|
|
|
if input_ids is not None or inputs_embeds is not None:
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
if bbox is None:
|
|
bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
bbox=bbox,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
final_bbox = final_position_ids = None
|
|
patch_height = patch_width = None
|
|
if pixel_values is not None:
|
|
patch_height, patch_width = (
|
|
int(pixel_values.shape[2] / self.config.patch_size),
|
|
int(pixel_values.shape[3] / self.config.patch_size),
|
|
)
|
|
visual_embeddings = self.forward_image(pixel_values)
|
|
visual_attention_mask = torch.ones(
|
|
(batch_size, visual_embeddings.shape[1]), dtype=torch.long, device=device
|
|
)
|
|
if attention_mask is not None:
|
|
attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
|
|
else:
|
|
attention_mask = visual_attention_mask
|
|
|
|
if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
|
|
if self.config.has_spatial_attention_bias:
|
|
visual_bbox = self.calculate_visual_bbox(device, dtype=torch.long, batch_size=batch_size)
|
|
if bbox is not None:
|
|
final_bbox = torch.cat([bbox, visual_bbox], dim=1)
|
|
else:
|
|
final_bbox = visual_bbox
|
|
|
|
visual_position_ids = torch.arange(
|
|
0, visual_embeddings.shape[1], dtype=torch.long, device=device
|
|
).repeat(batch_size, 1)
|
|
if input_ids is not None or inputs_embeds is not None:
|
|
position_ids = torch.arange(0, input_shape[1], device=device).unsqueeze(0)
|
|
position_ids = position_ids.expand(input_shape)
|
|
final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
|
|
else:
|
|
final_position_ids = visual_position_ids
|
|
|
|
if input_ids is not None or inputs_embeds is not None:
|
|
embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)
|
|
else:
|
|
embedding_output = visual_embeddings
|
|
|
|
embedding_output = self.LayerNorm(embedding_output)
|
|
embedding_output = self.dropout(embedding_output)
|
|
elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
|
|
if self.config.has_spatial_attention_bias:
|
|
final_bbox = bbox
|
|
if self.config.has_relative_attention_bias:
|
|
position_ids = self.embeddings.position_ids[:, : input_shape[1]]
|
|
position_ids = position_ids.expand_as(input_ids)
|
|
final_position_ids = position_ids
|
|
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
|
|
attention_mask, None, device, dtype=embedding_output.dtype
|
|
)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
bbox=final_bbox,
|
|
position_ids=final_position_ids,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
patch_height=patch_height,
|
|
patch_width=patch_width,
|
|
)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
if not return_dict:
|
|
return (sequence_output,) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class LayoutLMv3ClassificationHead(nn.Module):
|
|
"""
|
|
Head for sentence-level classification tasks. Reference: RobertaClassificationHead
|
|
"""
|
|
|
|
def __init__(self, config, pool_feature=False):
|
|
super().__init__()
|
|
self.pool_feature = pool_feature
|
|
if pool_feature:
|
|
self.dense = nn.Linear(config.hidden_size * 3, config.hidden_size)
|
|
else:
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, x):
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = torch.tanh(x)
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
|
|
for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
|
|
[SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
|
|
[Kleister-NDA](https://github.com/applicaai/kleister-nda).
|
|
""",
|
|
LAYOUTLMV3_START_DOCSTRING,
|
|
)
|
|
class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.layoutlmv3 = LayoutLMv3Model(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
if config.num_labels < 10:
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
else:
|
|
self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
|
|
)
|
|
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
bbox: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
pixel_values: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForTokenClassification
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
|
|
>>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> image = example["image"]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
>>> word_labels = example["ner_tags"]
|
|
|
|
>>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.layoutlmv3(
|
|
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_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
pixel_values=pixel_values,
|
|
)
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
# only take the text part of the output representations
|
|
sequence_output = outputs[0][:, :seq_length]
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as
|
|
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to
|
|
compute `span start logits` and `span end logits`).
|
|
""",
|
|
LAYOUTLMV3_START_DOCSTRING,
|
|
)
|
|
class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.layoutlmv3 = LayoutLMv3Model(config)
|
|
self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
|
|
)
|
|
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
bbox: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
|
|
>>> from datasets import load_dataset
|
|
>>> import torch
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
|
|
>>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> image = example["image"]
|
|
>>> question = "what's his name?"
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
|
|
>>> start_positions = torch.tensor([1])
|
|
>>> end_positions = torch.tensor([3])
|
|
|
|
>>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
|
|
>>> loss = outputs.loss
|
|
>>> start_scores = outputs.start_logits
|
|
>>> end_scores = outputs.end_logits
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.layoutlmv3(
|
|
input_ids,
|
|
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_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
bbox=bbox,
|
|
pixel_values=pixel_values,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
|
|
[CLS] token) e.g. for document image classification tasks such as the
|
|
[RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
|
|
""",
|
|
LAYOUTLMV3_START_DOCSTRING,
|
|
)
|
|
class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
self.layoutlmv3 = LayoutLMv3Model(config)
|
|
self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
|
|
)
|
|
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
bbox: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutput]:
|
|
"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForSequenceClassification
|
|
>>> from datasets import load_dataset
|
|
>>> import torch
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
|
|
>>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
|
|
|
|
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
|
|
>>> example = dataset[0]
|
|
>>> image = example["image"]
|
|
>>> words = example["tokens"]
|
|
>>> boxes = example["bboxes"]
|
|
|
|
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
|
|
>>> sequence_label = torch.tensor([1])
|
|
|
|
>>> outputs = model(**encoding, labels=sequence_label)
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.layoutlmv3(
|
|
input_ids,
|
|
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_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
bbox=bbox,
|
|
pixel_values=pixel_values,
|
|
)
|
|
|
|
sequence_output = outputs[0][:, 0, :]
|
|
logits = self.classifier(sequence_output)
|
|
|
|
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):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
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 == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|