# 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 from libs.model.extractor import RoIPool 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(-1e8)) 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]) self.pool = RoIPool(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.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() features = self.pool(features) # notice that self.pool has been modified 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=input_ids.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], )