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train_keys = wn_keys(train_data)
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coverage = set(train_keys)
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'''
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LOAD TRAINED PROBE
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'''
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probe = torch.nn.Linear(output_dim, len(task_labels))
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probe_path = os.path.join(args.ckpt, 'best_model.ckpt')
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probe.load_state_dict(torch.load(probe_path))
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probe = probe.cuda()
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'''
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LOAD EVAL SET
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'''
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eval_path = os.path.join(args.data_path, 'Evaluation_Datasets/{}/'.format(args.split))
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eval_data = load_data(eval_path, args.split)
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#for backoff
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eval_keys = wn_keys(eval_data)
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eval_data = preprocess(tokenizer, pretrained_model, eval_data, task_labels, label_map)
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eval_data = batchify(eval_data, bsz=1)
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'''
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EVALUATE PROBE w/o backoff
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'''
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eval_preds = _eval(eval_data, probe, task_labels)
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#generate predictions file
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pred_filepath = os.path.join(args.ckpt, './{}_predictions.txt'.format(args.split))
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with open(pred_filepath, 'w') as f:
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for inst, prediction in eval_preds:
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f.write('{} {}\n'.format(inst, prediction))
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#run predictions through scorer
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gold_filepath = os.path.join(eval_path, '{}.gold.key.txt'.format(args.split))
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scorer_path = os.path.join(args.data_path, 'Evaluation_Datasets')
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p, r, f1 = evaluate_output(scorer_path, gold_filepath, pred_filepath)
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print('f1 of WSD probe on {} test set = {}'.format(args.split, f1))
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'''
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EVALUATE PROBE with backoff
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'''
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wn_path = os.path.join(args.data_path, 'Data_Validation/candidatesWN30.txt')
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wn_senses = load_wn_senses(wn_path)
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eval_preds = _eval_with_backoff(eval_data, probe, task_labels, wn_senses, coverage, eval_keys)
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#generate predictions file
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pred_filepath = os.path.join(args.ckpt, './{}_backoff_predictions.txt'.format(args.split))
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with open(pred_filepath, 'w') as f:
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for inst, prediction in eval_preds:
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f.write('{} {}\n'.format(inst, prediction))
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#run predictions through scorer
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gold_filepath = os.path.join(eval_path, '{}.gold.key.txt'.format(args.split))
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scorer_path = os.path.join(args.data_path, 'Evaluation_Datasets')
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p, r, f1 = evaluate_output(scorer_path, gold_filepath, pred_filepath)
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print('f1 of BERT probe (with backoff) = {}'.format(f1))
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return
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if __name__ == "__main__":
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if not torch.cuda.is_available():
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print("Need available GPU(s) to run this model...")
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quit()
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args = parser.parse_args()
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print(args)
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#set random seeds
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torch.manual_seed(args.rand_seed)
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os.environ['PYTHONHASHSEED'] = str(args.rand_seed)
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torch.cuda.manual_seed(args.rand_seed)
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torch.cuda.manual_seed_all(args.rand_seed)
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np.random.seed(args.rand_seed)
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random.seed(args.rand_seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic=True
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if args.eval:
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evaluate_probe(args)
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else:
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train_probe(args)
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# <FILESEP>
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from grabscreen import grab_screen
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import cv2
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import numpy as np
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import time
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import keys as k
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keys = k.Keys()
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def pathing(minimap):
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lower = np.array([75,150,150])
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upper = np.array([150,255,255])
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