| from model_LXM2T5 import T5tokenizer, LXMT52T5, LXMtokenizer |
| import tqdm |
| from dataset_val4LXMT5 import KgDatasetVal |
| model = LXMT52T5() |
| model.module.load_state_dict(torch.load("xxxx.pth")) |
| test_dataset = KgDatasetVal(val=False) |
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| test_dataloader = DataLoader(dataset=test_dataset, batch_size=args.batch_size, shuffle=False, |
| num_workers=0, collate_fn=my_val_collate) |
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| model.eval() |
| answers = [] |
| preds = [] |
| preds_list = [] |
| answers_list = [] |
| id2pred_list = {} |
| for i, batch_data in enumerate(tqdm(test_dataloader)): |
| with torch.no_grad(): |
| val_T5_input_id = torch.stack(batch_data['T5_input_ids']).to(device) |
| val_T5_input_mask = torch.stack(batch_data['T5_input_masks']).to(device) |
| val_visual_faetures = torch.tensor(np.array(batch_data['img'])).float().to(device) |
| val_spatial_features = torch.tensor(np.array(batch_data['spatial'])).float().to(device) |
| |
| val_LXM_input_id = torch.stack(batch_data['LXM_input_ids']).to(device) |
| val_LXM_input_mask = torch.stack(batch_data['LXM_input_masks']).to(device) |
| val_LXM_token_type_ids = torch.stack(batch_data['LXM_token_type_ids']).to(device) |
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| val_outputs = model(train=False, LXM_source_ids=val_LXM_input_id, LXM_source_masks=val_LXM_input_mask,T5_source_ids=val_T5_input_id, T5_source_masks=val_T5_input_mask,token_type_ids=val_LXM_token_type_ids, visual_features=val_visual_faetures, spatial_features=val_spatial_features,T5_target_ids=None) |
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| val_list_predict = T5tokenizer.batch_decode(val_outputs, skip_special_tokens=True) |
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| for i, pre in enumerate(batch_data['ans']): |
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| preds_list.append(val_list_predict[i]) |
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| answers_list.append(batch_data['ans'][i]) |
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| id2pred_list[str(batch_data['id'][i])]=val_list_predict[i] |
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| f=open("file_to_save.json", 'w') |
| json.dump(id2pred_list, f) |
| f.close() |
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