1408 lines
59 KiB
Python
1408 lines
59 KiB
Python
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# coding=utf-8
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# Copyright 2021 Microsoft Research The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch LayoutLMv2 model."""
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import math
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward
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from ...utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_detectron2_available,
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logging,
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replace_return_docstrings,
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requires_backends,
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)
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from .configuration_layoutlmv2 import LayoutLMv2Config
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# soft dependency
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if is_detectron2_available():
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import detectron2
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from detectron2.modeling import META_ARCH_REGISTRY
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "microsoft/layoutlmv2-base-uncased"
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_CONFIG_FOR_DOC = "LayoutLMv2Config"
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from ..deprecated._archive_maps import LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class LayoutLMv2Embeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super(LayoutLMv2Embeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
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self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
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self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
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self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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def _calc_spatial_position_embeddings(self, bbox):
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try:
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left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
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upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
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right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
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lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
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except IndexError as e:
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raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
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h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
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w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
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spatial_position_embeddings = torch.cat(
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[
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left_position_embeddings,
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upper_position_embeddings,
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right_position_embeddings,
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lower_position_embeddings,
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h_position_embeddings,
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w_position_embeddings,
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],
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dim=-1,
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)
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return spatial_position_embeddings
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class LayoutLMv2SelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.fast_qkv = config.fast_qkv
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.has_relative_attention_bias = config.has_relative_attention_bias
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self.has_spatial_attention_bias = config.has_spatial_attention_bias
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if config.fast_qkv:
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self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
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self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
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self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
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else:
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def compute_qkv(self, hidden_states):
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if self.fast_qkv:
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qkv = self.qkv_linear(hidden_states)
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q, k, v = torch.chunk(qkv, 3, dim=-1)
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if q.ndimension() == self.q_bias.ndimension():
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q = q + self.q_bias
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v = v + self.v_bias
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else:
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_sz = (1,) * (q.ndimension() - 1) + (-1,)
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q = q + self.q_bias.view(*_sz)
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v = v + self.v_bias.view(*_sz)
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else:
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q = self.query(hidden_states)
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k = self.key(hidden_states)
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v = self.value(hidden_states)
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return q, k, v
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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rel_pos=None,
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rel_2d_pos=None,
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):
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q, k, v = self.compute_qkv(hidden_states)
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# (B, L, H*D) -> (B, H, L, D)
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query_layer = self.transpose_for_scores(q)
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key_layer = self.transpose_for_scores(k)
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value_layer = self.transpose_for_scores(v)
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query_layer = query_layer / math.sqrt(self.attention_head_size)
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# [BSZ, NAT, L, L]
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.has_relative_attention_bias:
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attention_scores += rel_pos
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if self.has_spatial_attention_bias:
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attention_scores += rel_2d_pos
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attention_scores = attention_scores.float().masked_fill_(
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attention_mask.to(torch.bool), torch.finfo(attention_scores.dtype).min
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)
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attention_probs = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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class LayoutLMv2Attention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = LayoutLMv2SelfAttention(config)
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self.output = LayoutLMv2SelfOutput(config)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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rel_pos=None,
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rel_2d_pos=None,
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):
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self_outputs = self.self(
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions,
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rel_pos=rel_pos,
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rel_2d_pos=rel_2d_pos,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class LayoutLMv2SelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->LayoutLMv2
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class LayoutLMv2Intermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
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class LayoutLMv2Output(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class LayoutLMv2Layer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = LayoutLMv2Attention(config)
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self.intermediate = LayoutLMv2Intermediate(config)
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self.output = LayoutLMv2Output(config)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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rel_pos=None,
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rel_2d_pos=None,
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):
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self_attention_outputs = self.attention(
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions=output_attentions,
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rel_pos=rel_pos,
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rel_2d_pos=rel_2d_pos,
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)
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attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
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)
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outputs = (layer_output,) + outputs
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return outputs
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def feed_forward_chunk(self, attention_output):
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
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"""
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Adapted from Mesh Tensorflow:
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https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
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Translate relative position to a bucket number for relative attention. The relative position is defined as
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memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
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position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small
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absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions
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>=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should
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allow for more graceful generalization to longer sequences than the model has been trained on.
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Args:
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relative_position: an int32 Tensor
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bidirectional: a boolean - whether the attention is bidirectional
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num_buckets: an integer
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max_distance: an integer
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Returns:
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a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
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"""
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ret = 0
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if bidirectional:
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num_buckets //= 2
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|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
class LayoutLMv2Encoder(nn.Module):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)])
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
self.gradient_checkpointing = False
|
||
|
|
|
||
|
|
def _calculate_1d_position_embeddings(self, position_ids):
|
||
|
|
rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
|
||
|
|
rel_pos = 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 _calculate_2d_position_embeddings(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 = 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 = 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,
|
||
|
|
attention_mask=None,
|
||
|
|
head_mask=None,
|
||
|
|
output_attentions=False,
|
||
|
|
output_hidden_states=False,
|
||
|
|
return_dict=True,
|
||
|
|
bbox=None,
|
||
|
|
position_ids=None,
|
||
|
|
):
|
||
|
|
all_hidden_states = () if output_hidden_states else None
|
||
|
|
all_self_attentions = () if output_attentions else None
|
||
|
|
|
||
|
|
rel_pos = self._calculate_1d_position_embeddings(position_ids) if self.has_relative_attention_bias else None
|
||
|
|
rel_2d_pos = self._calculate_2d_position_embeddings(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_pos,
|
||
|
|
rel_2d_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,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class LayoutLMv2PreTrainedModel(PreTrainedModel):
|
||
|
|
"""
|
||
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
|
|
models.
|
||
|
|
"""
|
||
|
|
|
||
|
|
config_class = LayoutLMv2Config
|
||
|
|
base_model_prefix = "layoutlmv2"
|
||
|
|
|
||
|
|
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=0.0, 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)
|
||
|
|
|
||
|
|
|
||
|
|
def my_convert_sync_batchnorm(module, process_group=None):
|
||
|
|
# same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d`
|
||
|
|
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
|
||
|
|
return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
|
||
|
|
module_output = module
|
||
|
|
if isinstance(module, detectron2.layers.FrozenBatchNorm2d):
|
||
|
|
module_output = torch.nn.SyncBatchNorm(
|
||
|
|
num_features=module.num_features,
|
||
|
|
eps=module.eps,
|
||
|
|
affine=True,
|
||
|
|
track_running_stats=True,
|
||
|
|
process_group=process_group,
|
||
|
|
)
|
||
|
|
module_output.weight = torch.nn.Parameter(module.weight)
|
||
|
|
module_output.bias = torch.nn.Parameter(module.bias)
|
||
|
|
module_output.running_mean = module.running_mean
|
||
|
|
module_output.running_var = module.running_var
|
||
|
|
module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device)
|
||
|
|
for name, child in module.named_children():
|
||
|
|
module_output.add_module(name, my_convert_sync_batchnorm(child, process_group))
|
||
|
|
del module
|
||
|
|
return module_output
|
||
|
|
|
||
|
|
|
||
|
|
class LayoutLMv2VisualBackbone(nn.Module):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__()
|
||
|
|
self.cfg = config.get_detectron2_config()
|
||
|
|
meta_arch = self.cfg.MODEL.META_ARCHITECTURE
|
||
|
|
model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg)
|
||
|
|
assert isinstance(model.backbone, detectron2.modeling.backbone.FPN)
|
||
|
|
self.backbone = model.backbone
|
||
|
|
|
||
|
|
assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD)
|
||
|
|
num_channels = len(self.cfg.MODEL.PIXEL_MEAN)
|
||
|
|
self.register_buffer(
|
||
|
|
"pixel_mean",
|
||
|
|
torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1),
|
||
|
|
persistent=False,
|
||
|
|
)
|
||
|
|
self.register_buffer(
|
||
|
|
"pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1), persistent=False
|
||
|
|
)
|
||
|
|
self.out_feature_key = "p2"
|
||
|
|
if torch.are_deterministic_algorithms_enabled():
|
||
|
|
logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`")
|
||
|
|
input_shape = (224, 224)
|
||
|
|
backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride
|
||
|
|
self.pool = nn.AvgPool2d(
|
||
|
|
(
|
||
|
|
math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]),
|
||
|
|
math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2])
|
||
|
|
if len(config.image_feature_pool_shape) == 2:
|
||
|
|
config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels)
|
||
|
|
assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2]
|
||
|
|
|
||
|
|
def forward(self, images):
|
||
|
|
images_input = ((images if torch.is_tensor(images) else images.tensor) - self.pixel_mean) / self.pixel_std
|
||
|
|
features = self.backbone(images_input)
|
||
|
|
features = features[self.out_feature_key]
|
||
|
|
features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous()
|
||
|
|
return features
|
||
|
|
|
||
|
|
def synchronize_batch_norm(self):
|
||
|
|
if not (
|
||
|
|
torch.distributed.is_available()
|
||
|
|
and torch.distributed.is_initialized()
|
||
|
|
and torch.distributed.get_rank() > -1
|
||
|
|
):
|
||
|
|
raise RuntimeError("Make sure torch.distributed is set up properly.")
|
||
|
|
|
||
|
|
self_rank = torch.distributed.get_rank()
|
||
|
|
node_size = torch.cuda.device_count()
|
||
|
|
world_size = torch.distributed.get_world_size()
|
||
|
|
if not (world_size % node_size == 0):
|
||
|
|
raise RuntimeError("Make sure the number of processes can be divided by the number of nodes")
|
||
|
|
|
||
|
|
node_global_ranks = [list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)]
|
||
|
|
sync_bn_groups = [
|
||
|
|
torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size)
|
||
|
|
]
|
||
|
|
node_rank = self_rank // node_size
|
||
|
|
|
||
|
|
self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank])
|
||
|
|
|
||
|
|
|
||
|
|
LAYOUTLMV2_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 ([`LayoutLMv2Config`]): 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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
LAYOUTLMV2_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.
|
||
|
|
|
||
|
|
image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`):
|
||
|
|
Batch of document images.
|
||
|
|
|
||
|
|
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 `(batch_size, sequence_length, 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 LayoutLMv2Pooler(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):
|
||
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
||
|
|
# to the first token.
|
||
|
|
first_token_tensor = hidden_states[:, 0]
|
||
|
|
pooled_output = self.dense(first_token_tensor)
|
||
|
|
pooled_output = self.activation(pooled_output)
|
||
|
|
return pooled_output
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"The bare LayoutLMv2 Model transformer outputting raw hidden-states without any specific head on top.",
|
||
|
|
LAYOUTLMV2_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
requires_backends(self, "detectron2")
|
||
|
|
super().__init__(config)
|
||
|
|
self.config = config
|
||
|
|
self.has_visual_segment_embedding = config.has_visual_segment_embedding
|
||
|
|
self.embeddings = LayoutLMv2Embeddings(config)
|
||
|
|
|
||
|
|
self.visual = LayoutLMv2VisualBackbone(config)
|
||
|
|
self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size)
|
||
|
|
if self.has_visual_segment_embedding:
|
||
|
|
self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0])
|
||
|
|
self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||
|
|
self.visual_dropout = nn.Dropout(config.hidden_dropout_prob)
|
||
|
|
|
||
|
|
self.encoder = LayoutLMv2Encoder(config)
|
||
|
|
self.pooler = LayoutLMv2Pooler(config)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.embeddings.word_embeddings
|
||
|
|
|
||
|
|
def set_input_embeddings(self, value):
|
||
|
|
self.embeddings.word_embeddings = value
|
||
|
|
|
||
|
|
def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids, inputs_embeds=None):
|
||
|
|
if input_ids is not None:
|
||
|
|
input_shape = input_ids.size()
|
||
|
|
else:
|
||
|
|
input_shape = inputs_embeds.size()[:-1]
|
||
|
|
|
||
|
|
seq_length = input_shape[1]
|
||
|
|
|
||
|
|
if position_ids is None:
|
||
|
|
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
|
||
|
|
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
|
||
|
|
if token_type_ids is None:
|
||
|
|
token_type_ids = torch.zeros_like(input_ids)
|
||
|
|
|
||
|
|
if inputs_embeds is None:
|
||
|
|
inputs_embeds = self.embeddings.word_embeddings(input_ids)
|
||
|
|
position_embeddings = self.embeddings.position_embeddings(position_ids)
|
||
|
|
spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
|
||
|
|
token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)
|
||
|
|
|
||
|
|
embeddings = inputs_embeds + position_embeddings + spatial_position_embeddings + token_type_embeddings
|
||
|
|
embeddings = self.embeddings.LayerNorm(embeddings)
|
||
|
|
embeddings = self.embeddings.dropout(embeddings)
|
||
|
|
return embeddings
|
||
|
|
|
||
|
|
def _calc_img_embeddings(self, image, bbox, position_ids):
|
||
|
|
visual_embeddings = self.visual_proj(self.visual(image))
|
||
|
|
position_embeddings = self.embeddings.position_embeddings(position_ids)
|
||
|
|
spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
|
||
|
|
embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings
|
||
|
|
if self.has_visual_segment_embedding:
|
||
|
|
embeddings += self.visual_segment_embedding
|
||
|
|
embeddings = self.visual_LayerNorm(embeddings)
|
||
|
|
embeddings = self.visual_dropout(embeddings)
|
||
|
|
return embeddings
|
||
|
|
|
||
|
|
def _calc_visual_bbox(self, image_feature_pool_shape, bbox, device, final_shape):
|
||
|
|
visual_bbox_x = torch.div(
|
||
|
|
torch.arange(
|
||
|
|
0,
|
||
|
|
1000 * (image_feature_pool_shape[1] + 1),
|
||
|
|
1000,
|
||
|
|
device=device,
|
||
|
|
dtype=bbox.dtype,
|
||
|
|
),
|
||
|
|
self.config.image_feature_pool_shape[1],
|
||
|
|
rounding_mode="floor",
|
||
|
|
)
|
||
|
|
visual_bbox_y = torch.div(
|
||
|
|
torch.arange(
|
||
|
|
0,
|
||
|
|
1000 * (self.config.image_feature_pool_shape[0] + 1),
|
||
|
|
1000,
|
||
|
|
device=device,
|
||
|
|
dtype=bbox.dtype,
|
||
|
|
),
|
||
|
|
self.config.image_feature_pool_shape[0],
|
||
|
|
rounding_mode="floor",
|
||
|
|
)
|
||
|
|
visual_bbox = torch.stack(
|
||
|
|
[
|
||
|
|
visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1),
|
||
|
|
visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
|
||
|
|
visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1),
|
||
|
|
visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
|
||
|
|
],
|
||
|
|
dim=-1,
|
||
|
|
).view(-1, bbox.size(-1))
|
||
|
|
|
||
|
|
visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1)
|
||
|
|
|
||
|
|
return visual_bbox
|
||
|
|
|
||
|
|
def _get_input_shape(self, input_ids=None, inputs_embeds=None):
|
||
|
|
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:
|
||
|
|
return input_ids.size()
|
||
|
|
elif inputs_embeds is not None:
|
||
|
|
return inputs_embeds.size()[:-1]
|
||
|
|
else:
|
||
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("(batch_size, 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,
|
||
|
|
image: Optional[torch.FloatTensor] = 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,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
||
|
|
r"""
|
||
|
|
Return:
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoProcessor, LayoutLMv2Model, set_seed
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import torch
|
||
|
|
>>> from datasets import load_dataset
|
||
|
|
|
||
|
|
>>> set_seed(88)
|
||
|
|
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
||
|
|
>>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
||
|
|
|
||
|
|
|
||
|
|
>>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
|
||
|
|
>>> image_path = dataset["test"][0]["file"]
|
||
|
|
>>> image = Image.open(image_path).convert("RGB")
|
||
|
|
|
||
|
|
>>> encoding = processor(image, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> outputs = model(**encoding)
|
||
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
||
|
|
|
||
|
|
>>> last_hidden_states.shape
|
||
|
|
torch.Size([1, 342, 768])
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
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
|
||
|
|
|
||
|
|
input_shape = self._get_input_shape(input_ids, inputs_embeds)
|
||
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
|
|
|
||
|
|
visual_shape = list(input_shape)
|
||
|
|
visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
|
||
|
|
visual_shape = torch.Size(visual_shape)
|
||
|
|
# needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur
|
||
|
|
final_shape = list(self._get_input_shape(input_ids, inputs_embeds))
|
||
|
|
final_shape[1] += visual_shape[1]
|
||
|
|
final_shape = torch.Size(final_shape)
|
||
|
|
|
||
|
|
visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, device, final_shape)
|
||
|
|
final_bbox = torch.cat([bbox, visual_bbox], dim=1)
|
||
|
|
|
||
|
|
if attention_mask is None:
|
||
|
|
attention_mask = torch.ones(input_shape, device=device)
|
||
|
|
|
||
|
|
visual_attention_mask = torch.ones(visual_shape, device=device)
|
||
|
|
final_attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
|
||
|
|
|
||
|
|
if token_type_ids is None:
|
||
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
||
|
|
|
||
|
|
if position_ids is None:
|
||
|
|
seq_length = input_shape[1]
|
||
|
|
position_ids = self.embeddings.position_ids[:, :seq_length]
|
||
|
|
position_ids = position_ids.expand(input_shape)
|
||
|
|
|
||
|
|
visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat(
|
||
|
|
input_shape[0], 1
|
||
|
|
)
|
||
|
|
final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
|
||
|
|
|
||
|
|
if bbox is None:
|
||
|
|
bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
|
||
|
|
|
||
|
|
text_layout_emb = self._calc_text_embeddings(
|
||
|
|
input_ids=input_ids,
|
||
|
|
bbox=bbox,
|
||
|
|
token_type_ids=token_type_ids,
|
||
|
|
position_ids=position_ids,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
)
|
||
|
|
|
||
|
|
visual_emb = self._calc_img_embeddings(
|
||
|
|
image=image,
|
||
|
|
bbox=visual_bbox,
|
||
|
|
position_ids=visual_position_ids,
|
||
|
|
)
|
||
|
|
final_emb = torch.cat([text_layout_emb, visual_emb], dim=1)
|
||
|
|
|
||
|
|
extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)
|
||
|
|
|
||
|
|
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).unsqueeze(-1)
|
||
|
|
head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
|
||
|
|
elif head_mask.dim() == 2:
|
||
|
|
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
|
||
|
|
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
|
||
|
|
else:
|
||
|
|
head_mask = [None] * self.config.num_hidden_layers
|
||
|
|
|
||
|
|
encoder_outputs = self.encoder(
|
||
|
|
final_emb,
|
||
|
|
extended_attention_mask,
|
||
|
|
bbox=final_bbox,
|
||
|
|
position_ids=final_position_ids,
|
||
|
|
head_mask=head_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
sequence_output = encoder_outputs[0]
|
||
|
|
pooled_output = self.pooler(sequence_output)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
||
|
|
|
||
|
|
return BaseModelOutputWithPooling(
|
||
|
|
last_hidden_state=sequence_output,
|
||
|
|
pooler_output=pooled_output,
|
||
|
|
hidden_states=encoder_outputs.hidden_states,
|
||
|
|
attentions=encoder_outputs.attentions,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"""
|
||
|
|
LayoutLMv2 Model with a sequence classification head on top (a linear layer on top of the concatenation of the
|
||
|
|
final hidden state of the [CLS] token, average-pooled initial visual embeddings and average-pooled final visual
|
||
|
|
embeddings, e.g. for document image classification tasks such as the
|
||
|
|
[RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
|
||
|
|
""",
|
||
|
|
LAYOUTLMV2_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class LayoutLMv2ForSequenceClassification(LayoutLMv2PreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
self.layoutlmv2 = LayoutLMv2Model(config)
|
||
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||
|
|
self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.layoutlmv2.embeddings.word_embeddings
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(LAYOUTLMV2_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.LongTensor] = None,
|
||
|
|
image: Optional[torch.FloatTensor] = 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,
|
||
|
|
) -> Union[Tuple, SequenceClassifierOutput]:
|
||
|
|
r"""
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
|
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).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import torch
|
||
|
|
>>> from datasets import load_dataset
|
||
|
|
|
||
|
|
>>> set_seed(88)
|
||
|
|
|
||
|
|
>>> dataset = load_dataset("rvl_cdip", split="train", streaming=True)
|
||
|
|
>>> data = next(iter(dataset))
|
||
|
|
>>> image = data["image"].convert("RGB")
|
||
|
|
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
||
|
|
>>> model = LayoutLMv2ForSequenceClassification.from_pretrained(
|
||
|
|
... "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
|
||
|
|
... )
|
||
|
|
|
||
|
|
>>> encoding = processor(image, return_tensors="pt")
|
||
|
|
>>> sequence_label = torch.tensor([data["label"]])
|
||
|
|
|
||
|
|
>>> outputs = model(**encoding, labels=sequence_label)
|
||
|
|
|
||
|
|
>>> loss, logits = outputs.loss, outputs.logits
|
||
|
|
>>> predicted_idx = logits.argmax(dim=-1).item()
|
||
|
|
>>> predicted_answer = dataset.info.features["label"].names[4]
|
||
|
|
>>> predicted_idx, predicted_answer
|
||
|
|
(4, 'advertisement')
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
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()
|
||
|
|
elif inputs_embeds is not None:
|
||
|
|
input_shape = inputs_embeds.size()[:-1]
|
||
|
|
else:
|
||
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
|
|
|
||
|
|
visual_shape = list(input_shape)
|
||
|
|
visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
|
||
|
|
visual_shape = torch.Size(visual_shape)
|
||
|
|
final_shape = list(input_shape)
|
||
|
|
final_shape[1] += visual_shape[1]
|
||
|
|
final_shape = torch.Size(final_shape)
|
||
|
|
|
||
|
|
visual_bbox = self.layoutlmv2._calc_visual_bbox(
|
||
|
|
self.config.image_feature_pool_shape, bbox, device, final_shape
|
||
|
|
)
|
||
|
|
|
||
|
|
visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat(
|
||
|
|
input_shape[0], 1
|
||
|
|
)
|
||
|
|
|
||
|
|
initial_image_embeddings = self.layoutlmv2._calc_img_embeddings(
|
||
|
|
image=image,
|
||
|
|
bbox=visual_bbox,
|
||
|
|
position_ids=visual_position_ids,
|
||
|
|
)
|
||
|
|
|
||
|
|
outputs = self.layoutlmv2(
|
||
|
|
input_ids=input_ids,
|
||
|
|
bbox=bbox,
|
||
|
|
image=image,
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
if input_ids is not None:
|
||
|
|
input_shape = input_ids.size()
|
||
|
|
else:
|
||
|
|
input_shape = inputs_embeds.size()[:-1]
|
||
|
|
|
||
|
|
seq_length = input_shape[1]
|
||
|
|
sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
|
||
|
|
|
||
|
|
cls_final_output = sequence_output[:, 0, :]
|
||
|
|
|
||
|
|
# average-pool the visual embeddings
|
||
|
|
pooled_initial_image_embeddings = initial_image_embeddings.mean(dim=1)
|
||
|
|
pooled_final_image_embeddings = final_image_embeddings.mean(dim=1)
|
||
|
|
# concatenate with cls_final_output
|
||
|
|
sequence_output = torch.cat(
|
||
|
|
[cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1
|
||
|
|
)
|
||
|
|
sequence_output = self.dropout(sequence_output)
|
||
|
|
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[2:]
|
||
|
|
return ((loss,) + output) if loss is not None else output
|
||
|
|
|
||
|
|
return SequenceClassifierOutput(
|
||
|
|
loss=loss,
|
||
|
|
logits=logits,
|
||
|
|
hidden_states=outputs.hidden_states,
|
||
|
|
attentions=outputs.attentions,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"""
|
||
|
|
LayoutLMv2 Model with a token classification head on top (a linear layer on top of the text part of the 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).
|
||
|
|
""",
|
||
|
|
LAYOUTLMV2_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
self.layoutlmv2 = LayoutLMv2Model(config)
|
||
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.layoutlmv2.embeddings.word_embeddings
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(LAYOUTLMV2_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,
|
||
|
|
image: Optional[torch.FloatTensor] = 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,
|
||
|
|
) -> 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:
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> from datasets import load_dataset
|
||
|
|
|
||
|
|
>>> set_seed(88)
|
||
|
|
|
||
|
|
>>> datasets = load_dataset("nielsr/funsd", split="test")
|
||
|
|
>>> labels = datasets.features["ner_tags"].feature.names
|
||
|
|
>>> id2label = {v: k for v, k in enumerate(labels)}
|
||
|
|
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
|
||
|
|
>>> model = LayoutLMv2ForTokenClassification.from_pretrained(
|
||
|
|
... "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
|
||
|
|
... )
|
||
|
|
|
||
|
|
>>> data = datasets[0]
|
||
|
|
>>> image = Image.open(data["image_path"]).convert("RGB")
|
||
|
|
>>> words = data["words"]
|
||
|
|
>>> boxes = data["bboxes"] # make sure to normalize your bounding boxes
|
||
|
|
>>> word_labels = data["ner_tags"]
|
||
|
|
>>> encoding = processor(
|
||
|
|
... image,
|
||
|
|
... words,
|
||
|
|
... boxes=boxes,
|
||
|
|
... word_labels=word_labels,
|
||
|
|
... padding="max_length",
|
||
|
|
... truncation=True,
|
||
|
|
... return_tensors="pt",
|
||
|
|
... )
|
||
|
|
|
||
|
|
>>> outputs = model(**encoding)
|
||
|
|
>>> logits, loss = outputs.logits, outputs.loss
|
||
|
|
|
||
|
|
>>> predicted_token_class_ids = logits.argmax(-1)
|
||
|
|
>>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
|
||
|
|
>>> predicted_tokens_classes[:5]
|
||
|
|
['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER']
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
outputs = self.layoutlmv2(
|
||
|
|
input_ids=input_ids,
|
||
|
|
bbox=bbox,
|
||
|
|
image=image,
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
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[2:]
|
||
|
|
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(
|
||
|
|
"""
|
||
|
|
LayoutLMv2 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`).
|
||
|
|
""",
|
||
|
|
LAYOUTLMV2_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class LayoutLMv2ForQuestionAnswering(LayoutLMv2PreTrainedModel):
|
||
|
|
def __init__(self, config, has_visual_segment_embedding=True):
|
||
|
|
super().__init__(config)
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
config.has_visual_segment_embedding = has_visual_segment_embedding
|
||
|
|
self.layoutlmv2 = LayoutLMv2Model(config)
|
||
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.layoutlmv2.embeddings.word_embeddings
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(LAYOUTLMV2_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,
|
||
|
|
bbox: Optional[torch.LongTensor] = None,
|
||
|
|
image: Optional[torch.FloatTensor] = 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,
|
||
|
|
) -> 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:
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us
|
||
|
|
a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
|
||
|
|
>>> import torch
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> from datasets import load_dataset
|
||
|
|
|
||
|
|
>>> set_seed(88)
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
||
|
|
>>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
||
|
|
|
||
|
|
>>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
|
||
|
|
>>> image_path = dataset["test"][0]["file"]
|
||
|
|
>>> image = Image.open(image_path).convert("RGB")
|
||
|
|
>>> question = "When is coffee break?"
|
||
|
|
>>> encoding = processor(image, question, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> outputs = model(**encoding)
|
||
|
|
>>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
|
||
|
|
>>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
|
||
|
|
>>> predicted_start_idx, predicted_end_idx
|
||
|
|
(154, 287)
|
||
|
|
|
||
|
|
>>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
|
||
|
|
>>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
|
||
|
|
>>> predicted_answer # results are not very good without further fine-tuning
|
||
|
|
'council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public ...
|
||
|
|
```
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> target_start_index = torch.tensor([7])
|
||
|
|
>>> target_end_index = torch.tensor([14])
|
||
|
|
>>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
|
||
|
|
>>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
|
||
|
|
>>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
|
||
|
|
>>> predicted_answer_span_start, predicted_answer_span_end
|
||
|
|
(154, 287)
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
outputs = self.layoutlmv2(
|
||
|
|
input_ids=input_ids,
|
||
|
|
bbox=bbox,
|
||
|
|
image=image,
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
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]
|
||
|
|
|
||
|
|
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[2:]
|
||
|
|
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,
|
||
|
|
)
|