249 lines
9.9 KiB
Python
Executable File
249 lines
9.9 KiB
Python
Executable File
import os
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import torch
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from torch import nn
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from transformers import LayoutLMv2Model, LayoutLMv2FeatureExtractor
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from transformers import LayoutXLMTokenizer
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from lightning_modules.utils import merged_token_embeddings, merged_token_embeddings2
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from model.relation_extractor import RelationExtractor
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from utils import load_checkpoint
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class KVUModel(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.device = 'cuda'
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self.model_cfg = cfg.model
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self.freeze = cfg.train.freeze
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self.finetune_only = cfg.train.finetune_only
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# if cfg.stage == 2:
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# self.freeze = True
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self._get_backbones(self.model_cfg.backbone)
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self._create_head()
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if (cfg.stage == 2) and (os.path.exists(self.model_cfg.ckpt_model_file)):
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self.backbone_layoutxlm = load_checkpoint(self.model_cfg.ckpt_model_file, self.backbone_layoutxlm, 'backbone_layoutxlm')
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self._create_head()
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self.loss_func = nn.CrossEntropyLoss()
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if self.freeze:
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for name, param in self.named_parameters():
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if 'backbone' in name:
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param.requires_grad = False
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def _create_head(self):
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self.backbone_hidden_size = 768
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self.head_hidden_size = self.model_cfg.head_hidden_size
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self.head_p_dropout = self.model_cfg.head_p_dropout
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self.n_classes = self.model_cfg.n_classes + 1
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# (1) Initial token classification
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self.itc_layer = nn.Sequential(
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nn.Dropout(self.head_p_dropout),
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nn.Linear(self.backbone_hidden_size, self.backbone_hidden_size),
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nn.Dropout(self.head_p_dropout),
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nn.Linear(self.backbone_hidden_size, self.n_classes),
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)
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# (2) Subsequent token classification
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self.stc_layer = RelationExtractor(
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n_relations=1, #1
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backbone_hidden_size=self.backbone_hidden_size,
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head_hidden_size=self.head_hidden_size,
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head_p_dropout=self.head_p_dropout,
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)
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# (3) Linking token classification
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self.relation_layer = RelationExtractor(
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n_relations=1, #1
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backbone_hidden_size=self.backbone_hidden_size,
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head_hidden_size=self.head_hidden_size,
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head_p_dropout=self.head_p_dropout,
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)
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# (4) Linking token classification
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self.relation_layer_from_key = RelationExtractor(
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n_relations=1, #1
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backbone_hidden_size=self.backbone_hidden_size,
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head_hidden_size=self.head_hidden_size,
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head_p_dropout=self.head_p_dropout,
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)
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self.itc_layer.apply(self._init_weight)
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self.stc_layer.apply(self._init_weight)
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self.relation_layer.apply(self._init_weight)
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def _get_backbones(self, config_type):
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self.tokenizer_layoutxlm = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base')
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self.feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
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self.backbone_layoutxlm = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base')
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@staticmethod
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def _init_weight(module):
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init_std = 0.02
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if isinstance(module, nn.Linear):
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nn.init.normal_(module.weight, 0.0, init_std)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0.0)
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elif isinstance(module, nn.LayerNorm):
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nn.init.normal_(module.weight, 1.0, init_std)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0.0)
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# def forward(self, inputs):
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# token_embeddings = inputs['embeddings'].transpose(0, 1).contiguous().cuda()
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# itc_outputs = self.itc_layer(token_embeddings).transpose(0, 1).contiguous()
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# stc_outputs = self.stc_layer(token_embeddings, token_embeddings).squeeze(0)
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# el_outputs = self.relation_layer(token_embeddings, token_embeddings).squeeze(0)
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# el_outputs_from_key = self.relation_layer_from_key(token_embeddings, token_embeddings).squeeze(0)
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# head_outputs = {"itc_outputs": itc_outputs, "stc_outputs": stc_outputs,
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# "el_outputs": el_outputs, "el_outputs_from_key": el_outputs_from_key}
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# loss = self._get_loss(head_outputs, inputs)
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# return head_outputs, loss
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# def forward_single_doccument(self, lbatches):
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def forward(self, lbatches):
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windows = lbatches['windows']
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token_embeddings_windows = []
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lvalids = []
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loverlaps = []
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for i, batch in enumerate(windows):
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batch = {k: v.cuda() for k, v in batch.items() if k not in ('img_path', 'words')}
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image = batch["image"]
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input_ids_layoutxlm = batch["input_ids_layoutxlm"]
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bbox = batch["bbox"]
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attention_mask_layoutxlm = batch["attention_mask_layoutxlm"]
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backbone_outputs_layoutxlm = self.backbone_layoutxlm(
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image=image, input_ids=input_ids_layoutxlm, bbox=bbox, attention_mask=attention_mask_layoutxlm)
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last_hidden_states_layoutxlm = backbone_outputs_layoutxlm.last_hidden_state[:, :512, :]
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lvalids.append(batch['len_valid_tokens'])
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loverlaps.append(batch['len_overlap_tokens'])
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token_embeddings_windows.append(last_hidden_states_layoutxlm)
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token_embeddings = merged_token_embeddings2(token_embeddings_windows, loverlaps, lvalids, average=False)
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# token_embeddings = merged_token_embeddings(token_embeddings_windows, loverlaps, lvalids, average=True)
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token_embeddings = token_embeddings.transpose(0, 1).contiguous().cuda()
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itc_outputs = self.itc_layer(token_embeddings).transpose(0, 1).contiguous()
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stc_outputs = self.stc_layer(token_embeddings, token_embeddings).squeeze(0)
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el_outputs = self.relation_layer(token_embeddings, token_embeddings).squeeze(0)
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el_outputs_from_key = self.relation_layer_from_key(token_embeddings, token_embeddings).squeeze(0)
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head_outputs = {"itc_outputs": itc_outputs, "stc_outputs": stc_outputs,
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"el_outputs": el_outputs, "el_outputs_from_key": el_outputs_from_key,
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'embedding_tokens': token_embeddings.transpose(0, 1).contiguous().detach().cpu().numpy()}
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loss = 0.0
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if any(['labels' in key for key in lbatches.keys()]):
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labels = {k: v.cuda() for k, v in lbatches["documents"].items() if k not in ('img_path')}
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loss = self._get_loss(head_outputs, labels)
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return head_outputs, loss
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def _get_loss(self, head_outputs, batch):
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itc_outputs = head_outputs["itc_outputs"]
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stc_outputs = head_outputs["stc_outputs"]
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el_outputs = head_outputs["el_outputs"]
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el_outputs_from_key = head_outputs["el_outputs_from_key"]
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itc_loss = self._get_itc_loss(itc_outputs, batch)
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stc_loss = self._get_stc_loss(stc_outputs, batch)
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el_loss = self._get_el_loss(el_outputs, batch)
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el_loss_from_key = self._get_el_loss(el_outputs_from_key, batch, from_key=True)
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loss = itc_loss + stc_loss + el_loss + el_loss_from_key
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return loss
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def _get_itc_loss(self, itc_outputs, batch):
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itc_mask = batch["are_box_first_tokens"].view(-1)
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itc_logits = itc_outputs.view(-1, self.model_cfg.n_classes + 1)
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itc_logits = itc_logits[itc_mask]
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itc_labels = batch["itc_labels"].view(-1)
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itc_labels = itc_labels[itc_mask]
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itc_loss = self.loss_func(itc_logits, itc_labels)
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return itc_loss
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def _get_stc_loss(self, stc_outputs, batch):
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inv_attention_mask = 1 - batch["attention_mask_layoutxlm"]
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bsz, max_seq_length = inv_attention_mask.shape
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device = inv_attention_mask.device
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invalid_token_mask = torch.cat(
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[inv_attention_mask, torch.zeros([bsz, 1]).to(device)], axis=1
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).bool()
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stc_outputs.masked_fill_(invalid_token_mask[:, None, :], -10000.0)
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self_token_mask = (
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torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()
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)
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stc_outputs.masked_fill_(self_token_mask[None, :, :], -10000.0)
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stc_mask = batch["attention_mask_layoutxlm"].view(-1).bool()
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stc_logits = stc_outputs.view(-1, max_seq_length + 1)
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stc_logits = stc_logits[stc_mask]
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stc_labels = batch["stc_labels"].view(-1)
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stc_labels = stc_labels[stc_mask]
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stc_loss = self.loss_func(stc_logits, stc_labels)
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return stc_loss
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def _get_el_loss(self, el_outputs, batch, from_key=False):
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bsz, max_seq_length = batch["attention_mask_layoutxlm"].shape
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device = batch["attention_mask_layoutxlm"].device
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self_token_mask = (
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torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()
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)
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box_first_token_mask = torch.cat(
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[
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(batch["are_box_first_tokens"] == False),
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torch.zeros([bsz, 1], dtype=torch.bool).to(device),
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],
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axis=1,
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)
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el_outputs.masked_fill_(box_first_token_mask[:, None, :], -10000.0)
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el_outputs.masked_fill_(self_token_mask[None, :, :], -10000.0)
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mask = batch["are_box_first_tokens"].view(-1)
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logits = el_outputs.view(-1, max_seq_length + 1)
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logits = logits[mask]
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if from_key:
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el_labels = batch["el_labels_from_key"]
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else:
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el_labels = batch["el_labels"]
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labels = el_labels.view(-1)
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labels = labels[mask]
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loss = self.loss_func(logits, labels)
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return loss
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