wehub-resource-sync
938 lines
38 KiB
Python
938 lines
38 KiB
Python
# coding=utf-8
|
|
import math
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from torch.nn import CrossEntropyLoss
|
|
|
|
import detectron2
|
|
from detectron2.modeling import META_ARCH_REGISTRY
|
|
from transformers import PreTrainedModel
|
|
from transformers.modeling_outputs import (
|
|
BaseModelOutputWithPastAndCrossAttentions,
|
|
BaseModelOutputWithPoolingAndCrossAttentions,
|
|
TokenClassifierOutput,
|
|
)
|
|
from transformers.modeling_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
|
|
from transformers.models.layoutlm.modeling_layoutlm import LayoutLMIntermediate as LayoutLMv2Intermediate
|
|
from transformers.models.layoutlm.modeling_layoutlm import LayoutLMOutput as LayoutLMv2Output
|
|
from transformers.models.layoutlm.modeling_layoutlm import LayoutLMPooler as LayoutLMv2Pooler
|
|
from transformers.models.layoutlm.modeling_layoutlm import LayoutLMSelfOutput as LayoutLMv2SelfOutput
|
|
from transformers.utils import logging
|
|
|
|
from ...modules.decoders.re import REDecoder
|
|
from ...utils import ReOutput
|
|
from .configuration_layoutlmv2 import LayoutLMv2Config
|
|
from .detectron2_config import add_layoutlmv2_config
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
|
"layoutlmv2-base-uncased",
|
|
"layoutlmv2-large-uncased",
|
|
]
|
|
|
|
|
|
LayoutLMv2LayerNorm = torch.nn.LayerNorm
|
|
|
|
|
|
class LayoutLMv2Embeddings(nn.Module):
|
|
"""Construct the embeddings from word, position and token_type embeddings."""
|
|
|
|
def __init__(self, config):
|
|
super(LayoutLMv2Embeddings, self).__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
|
|
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)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
self.LayerNorm = LayoutLMv2LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
|
|
|
|
def _cal_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 :obj:`bbox`coordinate values should be within 0-1000 range.") from e
|
|
|
|
h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
|
|
w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
|
|
|
|
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
|
|
|
|
|
|
class LayoutLMv2SelfAttention(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.fast_qkv = config.fast_qkv
|
|
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.has_relative_attention_bias = config.has_relative_attention_bias
|
|
self.has_spatial_attention_bias = config.has_spatial_attention_bias
|
|
|
|
if config.fast_qkv:
|
|
self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
|
|
self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
|
|
self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
|
|
else:
|
|
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)
|
|
|
|
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 compute_qkv(self, hidden_states):
|
|
if self.fast_qkv:
|
|
qkv = self.qkv_linear(hidden_states)
|
|
q, k, v = torch.chunk(qkv, 3, dim=-1)
|
|
if q.ndimension() == self.q_bias.ndimension():
|
|
q = q + self.q_bias
|
|
v = v + self.v_bias
|
|
else:
|
|
_sz = (1,) * (q.ndimension() - 1) + (-1,)
|
|
q = q + self.q_bias.view(*_sz)
|
|
v = v + self.v_bias.view(*_sz)
|
|
else:
|
|
q = self.query(hidden_states)
|
|
k = self.key(hidden_states)
|
|
v = self.value(hidden_states)
|
|
return q, k, v
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
q, k, v = self.compute_qkv(hidden_states)
|
|
|
|
# (B, L, H*D) -> (B, H, L, D)
|
|
query_layer = self.transpose_for_scores(q)
|
|
key_layer = self.transpose_for_scores(k)
|
|
value_layer = self.transpose_for_scores(v)
|
|
|
|
query_layer = query_layer / math.sqrt(self.attention_head_size)
|
|
# [BSZ, NAT, L, L]
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
if self.has_relative_attention_bias:
|
|
attention_scores += rel_pos
|
|
if self.has_spatial_attention_bias:
|
|
attention_scores += rel_2d_pos
|
|
attention_scores = attention_scores.float().masked_fill_(attention_mask.to(torch.bool), float("-inf"))
|
|
attention_probs = F.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer)
|
|
# 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)
|
|
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
|
|
|
|
|
|
class LayoutLMv2Attention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = LayoutLMv2SelfAttention(config)
|
|
self.output = LayoutLMv2SelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
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
|
|
|
|
|
|
class LayoutLMv2Layer(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 = LayoutLMv2Attention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
|
|
self.crossattention = LayoutLMv2Attention(config)
|
|
self.intermediate = LayoutLMv2Intermediate(config)
|
|
self.output = LayoutLMv2Output(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
rel_pos=None,
|
|
rel_2d_pos=None,
|
|
):
|
|
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
|
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=self_attn_past_key_value,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
present_key_value = self_attention_outputs[-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
cross_attn_present_key_value = None
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
assert hasattr(
|
|
self, "crossattention"
|
|
), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
|
|
|
|
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
cross_attn_past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
|
cross_attn_present_key_value = cross_attention_outputs[-1]
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (present_key_value,)
|
|
|
|
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
|
|
|
|
|
|
def relative_position_bucket(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
|
|
|
|
|
|
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_onehot_size = config.rel_pos_bins
|
|
self.rel_pos_bias = nn.Linear(self.rel_pos_onehot_size, 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_2d_pos_onehot_size = config.rel_2d_pos_bins
|
|
self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
|
|
self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
|
|
|
|
def _cal_1d_pos_emb(self, hidden_states, 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 = F.one_hot(rel_pos, num_classes=self.rel_pos_onehot_size).type_as(hidden_states)
|
|
rel_pos = self.rel_pos_bias(rel_pos).permute(0, 3, 1, 2)
|
|
rel_pos = rel_pos.contiguous()
|
|
return rel_pos
|
|
|
|
def _cal_2d_pos_emb(self, hidden_states, 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 = F.one_hot(rel_pos_x, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
|
|
rel_pos_y = F.one_hot(rel_pos_y, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
|
|
rel_pos_x = self.rel_pos_x_bias(rel_pos_x).permute(0, 3, 1, 2)
|
|
rel_pos_y = self.rel_pos_y_bias(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,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_values=None,
|
|
use_cache=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
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
rel_pos = self._cal_1d_pos_emb(hidden_states, position_ids) if self.has_relative_attention_bias else None
|
|
rel_2d_pos = self._cal_2d_pos_emb(hidden_states, 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
|
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
|
|
|
if getattr(self.config, "gradient_checkpointing", False) and self.training:
|
|
|
|
if use_cache:
|
|
logger.warn(
|
|
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
|
|
"`use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, past_key_value, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
rel_pos=rel_pos,
|
|
rel_2d_pos=rel_2d_pos,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[-1],)
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class LayoutLMv2PreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LayoutLMv2Config
|
|
pretrained_model_archive_map = LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST
|
|
base_model_prefix = "layoutlmv2"
|
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
|
|
|
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, LayoutLMv2LayerNorm):
|
|
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 VisualBackbone(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.cfg = detectron2.config.get_cfg()
|
|
add_layoutlmv2_config(self.cfg)
|
|
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
|
|
if (
|
|
config.convert_sync_batchnorm
|
|
and torch.distributed.is_available()
|
|
and torch.distributed.is_initialized()
|
|
and torch.distributed.get_rank() > -1
|
|
):
|
|
self_rank = torch.distributed.get_rank()
|
|
node_size = torch.cuda.device_count()
|
|
world_size = torch.distributed.get_world_size()
|
|
assert world_size % node_size == 0
|
|
|
|
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
|
|
assert self_rank in node_global_ranks[node_rank]
|
|
|
|
self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank])
|
|
|
|
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),
|
|
)
|
|
self.register_buffer("pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1))
|
|
self.out_feature_key = "p2"
|
|
if torch.is_deterministic():
|
|
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
|
|
|
|
|
|
class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super(LayoutLMv2Model, self).__init__(config)
|
|
self.config = config
|
|
self.has_visual_segment_embedding = config.has_visual_segment_embedding
|
|
self.embeddings = LayoutLMv2Embeddings(config)
|
|
|
|
self.visual = VisualBackbone(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)
|
|
|
|
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 _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids):
|
|
seq_length = input_ids.size(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)
|
|
|
|
words_embeddings = self.embeddings.word_embeddings(input_ids)
|
|
position_embeddings = self.embeddings.position_embeddings(position_ids)
|
|
spatial_position_embeddings = self.embeddings._cal_spatial_position_embeddings(bbox)
|
|
token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)
|
|
embeddings = words_embeddings + 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._cal_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 forward(
|
|
self,
|
|
input_ids=None,
|
|
bbox=None,
|
|
image=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
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 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:
|
|
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_x = (
|
|
torch.arange(
|
|
0,
|
|
1000 * (self.config.image_feature_pool_shape[1] + 1),
|
|
1000,
|
|
device=device,
|
|
dtype=bbox.dtype,
|
|
)
|
|
// self.config.image_feature_pool_shape[1]
|
|
)
|
|
visual_bbox_y = (
|
|
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]
|
|
)
|
|
visual_bbox = torch.stack(
|
|
[
|
|
visual_bbox_x[:-1].repeat(self.config.image_feature_pool_shape[0], 1),
|
|
visual_bbox_y[:-1].repeat(self.config.image_feature_pool_shape[1], 1).transpose(0, 1),
|
|
visual_bbox_x[1:].repeat(self.config.image_feature_pool_shape[0], 1),
|
|
visual_bbox_y[1:].repeat(self.config.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)
|
|
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_as(input_ids)
|
|
|
|
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,
|
|
)
|
|
|
|
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) * -10000.0
|
|
|
|
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 BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
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)
|
|
|
|
self.init_weights()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.layoutlmv2.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
bbox=None,
|
|
image=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
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,
|
|
)
|
|
seq_length = input_ids.size(1)
|
|
sequence_output, image_output = outputs[0][:, :seq_length], 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()
|
|
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)[active_loss]
|
|
active_labels = labels.view(-1)[active_loss]
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
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,
|
|
)
|
|
|
|
|
|
class LayoutLMv2ForRelationExtraction(LayoutLMv2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.layoutlmv2 = LayoutLMv2Model(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.extractor = REDecoder(config)
|
|
self.init_weights()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
bbox,
|
|
labels=None,
|
|
image=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
entities=None,
|
|
relations=None,
|
|
):
|
|
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,
|
|
)
|
|
|
|
seq_length = input_ids.size(1)
|
|
sequence_output, image_output = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
|
|
sequence_output = self.dropout(sequence_output)
|
|
loss, pred_relations = self.extractor(sequence_output, entities, relations)
|
|
|
|
return ReOutput(
|
|
loss=loss,
|
|
entities=entities,
|
|
relations=relations,
|
|
pred_relations=pred_relations,
|
|
hidden_states=outputs[0],
|
|
)
|