def main(): parser = argparse.ArgumentParser() parser.add_argument("--paragraph", default=None, type=str) parser.add_argument("--model", default=None, type=str) parser.add_argument("--max_seq_length", default=384, type=int) parser.add_argument("--doc_stride", default=128, type=int) parser.add_argument("--max_query_length", default=64, type=int) parser.add_argument("--config_file", default=None, type=str) parser.add_argument("--max_answer_length", default=30, type=int) args = parser.parse_args() para_file = args.paragraph model_path = args.model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() ### Loading Pretrained model for QnA print("Loading BERT-model...\n\n") config = BertConfig(args.config_file) model = BertForQuestionAnswering(config) model.load_state_dict( torch.load(model_path, map_location=torch.device("cpu"))) model.to(device) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True) while True: print("Please specify paragraph: \n " "1: Assisted Time Holdover \n " "2: Semcon short version \n " "3: Semcon long version") choice = input() if choice == "1": break elif choice == "2": para_file = "bert/input/semcon_short.txt" break elif choice == "3": para_file = "bert/input/semcon.txt" break else: print("I did not understand that, please type in 1, 2 or 3. \n") ### Reading paragraph f = open(para_file, "r") para = f.read() f.close() print("\nParagraph:\n", para) while True: input_data = [] paragraphs = {} paragraphs["id"] = 1 # paragraphs["text"] = splits[0].replace("Paragraph:", "").strip("\n") paragraphs["text"] = para paragraphs["ques"] = [input("\n What is your question?\n")] if paragraphs["ques"] == ["exit"]: exit() start = time.time() input_data.append(paragraphs) ## input_data is a list of dictionary which has a paragraph and questions examples = read_squad_examples(input_data) eval_features = convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, ) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) pred_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data pred_sampler = SequentialSampler(pred_data) pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=9) predictions = [] for input_ids, input_mask, segment_ids, example_indices in pred_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) features = [] example = [] all_results = [] for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() feature = eval_features[example_index.item()] unique_id = int(feature.unique_id) features.append(feature) all_results.append( RawResult( unique_id=unique_id, start_logits=start_logits, end_logits=end_logits, )) output = predict(examples, features, all_results, args.max_answer_length) predictions.append(output) prediction = colored( predictions[math.floor(examples[0].unique_id / 12)][examples[0]], "green", attrs=["reverse"], ) print(prediction, "\n") print("Time: ", time.time() - start) """
def start(): app = Flask(__name__) host = "0.0.0.0" port = 8000 debug = True parser = argparse.ArgumentParser() parser.add_argument( "--max_query_length", default=64, type=int, help= "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument( "--verbose_logging", action='store_true', help= "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument( '--null_score_diff_threshold', type=float, default=0.0, help= "If null_score - best_non_null is greater than the threshold predict null." ) args = parser.parse_args() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True) config = BertConfig("./output/config.json") model = BertForQuestionAnswering(config) model.load_state_dict( torch.load("./output/pytorch_model.bin", map_location='cpu')) model.to(device) @app.route('/', methods=['POST']) def filter(): dat_in = { "index": 2, "original_sentence": "existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects . this limitation severely hinders the use of these models in real world applications dealing with images in the wild . we address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time , without re-training . our method uses constrained beam search to force the inclusion of selected tag words in the output , and fixed , pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words . using this approach we achieve state of the art results for out-of-domain captioning on mscoco -LRB- and improved results for in-domain captioning -RRB- . perhaps surprisingly , our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm . we also show that we can significantly improve the quality of generated imagenet captions by leveraging ground-truth labels . ", "tagged_sentence": "existing│O_ANS image│O_ANS captioning│O_ANS models│O_ANS do│O_ANS not│O_ANS generalize│O_ANS well│O_ANS to│O_ANS out-of-domain│O_ANS images│O_ANS containing│O_ANS novel│O_ANS scenes│O_ANS or│O_ANS objects│O_ANS .│O_ANS this│O_ANS limitation│O_ANS severely│O_ANS hinders│O_ANS the│O_ANS use│O_ANS of│O_ANS these│O_ANS models│O_ANS in│O_ANS real│O_ANS world│O_ANS applications│O_ANS dealing│O_ANS with│O_ANS images│O_ANS in│O_ANS the│O_ANS wild│O_ANS .│O_ANS we│O_ANS address│O_ANS this│O_ANS problem│O_ANS using│O_ANS a│O_ANS flexible│O_ANS approach│O_ANS that│O_ANS enables│O_ANS existing│O_ANS deep│O_ANS captioning│O_ANS architectures│O_ANS to│O_ANS take│O_ANS advantage│O_ANS of│O_ANS image│O_ANS taggers│O_ANS at│O_ANS test│O_ANS time│O_ANS ,│O_ANS without│O_ANS re-training│O_ANS .│O_ANS our│O_ANS method│O_ANS uses│O_ANS constrained│O_ANS beam│O_ANS search│O_ANS to│O_ANS force│O_ANS the│O_ANS inclusion│O_ANS of│O_ANS selected│O_ANS tag│O_ANS words│O_ANS in│O_ANS the│O_ANS output│O_ANS ,│O_ANS and│O_ANS fixed│O_ANS ,│O_ANS pretrained│O_ANS word│B_ANS embeddings│I_ANS to│O_ANS facilitate│O_ANS vocabulary│O_ANS expansion│O_ANS to│O_ANS previously│O_ANS unseen│O_ANS tag│O_ANS words│O_ANS .│O_ANS using│O_ANS this│O_ANS approach│O_ANS we│O_ANS achieve│O_ANS state│O_ANS of│O_ANS the│O_ANS art│O_ANS results│O_ANS for│O_ANS out-of-domain│O_ANS captioning│O_ANS on│O_ANS mscoco│O_ANS -LRB-│O_ANS and│O_ANS improved│O_ANS results│O_ANS for│O_ANS in-domain│O_ANS captioning│O_ANS -RRB-│O_ANS .│O_ANS perhaps│O_ANS surprisingly│O_ANS ,│O_ANS our│O_ANS results│O_ANS significantly│O_ANS outperform│O_ANS approaches│O_ANS that│O_ANS incorporate│O_ANS the│O_ANS same│O_ANS tag│O_ANS predictions│O_ANS into│O_ANS the│O_ANS learning│O_ANS algorithm│O_ANS .│O_ANS we│O_ANS also│O_ANS show│O_ANS that│O_ANS we│O_ANS can│O_ANS significantly│O_ANS improve│O_ANS the│O_ANS quality│O_ANS of│O_ANS generated│O_ANS imagenet│O_ANS captions│O_ANS by│O_ANS leveraging│O_ANS ground-truth│O_ANS labels│O_ANS .│O_ANS ", "answer": "word embeddings", "question": [ "What does pretrained stand for ?", "What is pretrained ?", "What does re-training stand for ?", "What is the pretrained ?", "What is the term for pretrained ?" ], "score": [ -2.3564553260803223, -3.8269970417022705, -4.229936122894287, -5.298074722290039, -5.689377307891846 ] } eval_examples = read_squad_examples(input_data=dat_in, is_training=False, version_2_with_negative=True) eval_features = convert_examples_to_features( examples=eval_examples, tokenizer=tokenizer, max_seq_length=384, doc_stride=128, max_query_length=args.max_query_length, is_training=False) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) model.eval() all_results = [] logger.info("Start evaluating") for input_ids, input_mask, segment_ids, example_indices in tqdm( eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]): if len(all_results) % 1000 == 0: logger.info("Processing example: %d" % (len(all_results))) input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) result = write_predictions(eval_examples, eval_features, all_results, 20, 30, True, args.verbose_logging, True, args.null_score_diff_threshold) # inputs = request.get_json(force=True) return result app.run(debug=debug, host=host, port=port, use_reloader=False, threaded=True)
def answer_prediction(paras,question,model,config_file,max_seq_length=384,doc_stride=128,max_query_length=64,max_answer_length=60): #para_file = 'Input_file.txt' model_path = model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() ### Raeding paragraph ## Reading question # f = open(ques_file, 'r') # ques = f.read() # f.close() ## input_data is a list of dictionary which has a paragraph and questions #para_list = para.split('\n\n') #print(paras) input_data = [] i = 1 for i,para in enumerate(paras): # print(para) paragraphs = {} #splits = para.split('\nQuestions:') paragraphs['id'] = i paragraphs['text'] = para paragraphs['ques']= question input_data.append(paragraphs) examples = read_paragraphs(input_data,question) tokenizer = AutoTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad', do_lower_case=True) eval_features = convert_examples_to_features( examples = examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=doc_stride, max_query_length=max_query_length) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) ### Loading Pretrained model for QnA config = BertConfig(config_file) model = BertForQuestionAnswering(config) model.load_state_dict(torch.load(model_path,map_location='cpu')) model.to(device) pred_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data pred_sampler = SequentialSampler(pred_data) pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=10) predictions = [] for input_ids, input_mask, segment_ids, example_indices in tqdm(pred_dataloader): model.eval() input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask) features=[] example = [] all_results = [] for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() feature = eval_features[example_index.item()] unique_id = int(feature.unique_id) features.append(feature) all_results.append(RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output = predict(examples, features, all_results,max_answer_length) predictions.append(output) ### For printing the results #### final_preds = [] final_paras = [] final_probs = [] final_scores = [] final_ques = [] index = None for i,example in enumerate(examples): if index!= example.example_id: index = example.example_id # ques_text = colored(example.question_text, 'blue') prediction = predictions[math.floor(example.unique_id/12)][example] prob = predictions[math.floor(example.unique_id/12)]['prob'+str(example)] final_ques.append(ques_text) final_preds.append(prediction) final_paras.append(example.para_text) final_probs.append(prob) return final_ques,final_preds,final_paras,final_probs
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model checkpoints and predictions will be written." ) ## Other parameters parser.add_argument("--model", default=None, type=str) parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") parser.add_argument( "--max_seq_length", default=384, type=int, help= "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ) parser.add_argument( "--doc_stride", default=128, type=int, help= "When splitting up a long document into chunks, how much stride to take between chunks." ) parser.add_argument( "--max_query_length", default=64, type=int, help= "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% " "of training.") parser.add_argument( "--n_best_size", default=20, type=int, help= "The total number of n-best predictions to generate in the nbest_predictions.json " "output file.") parser.add_argument( "--max_answer_length", default=30, type=int, help= "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument( "--verbose_logging", action='store_true', help= "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument( "--do_lower_case", action='store_true', help= "Whether to lower case the input text. True for uncased models, False for cased models." ) parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument( '--version_2_with_negative', action='store_true', help= 'If true, the SQuAD examples contain some that do not have an answer.') parser.add_argument( '--null_score_diff_threshold', type=float, default=0.0, help= "If null_score - best_non_null is greater than the threshold predict null." ) parser.add_argument("--config_file", default=None, type=str) args = parser.parse_args() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if os.path.exists(args.output_dir) and os.listdir( args.output_dir) and args.do_train: raise ValueError( "Output directory () already exists and is not empty.") if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) config = BertConfig(args.config_file) model = BertForQuestionAnswering(config) model.load_state_dict(torch.load(args.model, map_location='cpu')) model.to(device) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_squad_examples( is_training=False, version_2_with_negative=args.version_2_with_negative) eval_features = convert_examples_to_features( examples=eval_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=False) logger.info("***** Running predictions *****") logger.info(" Num orig examples = %d", len(eval_examples)) logger.info(" Num split examples = %d", len(eval_features)) logger.info(" Batch size = %d", args.predict_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) model.eval() all_results = [] logger.info("Start evaluating") for input_ids, input_mask, segment_ids, example_indices in tqdm( eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]): if len(all_results) % 1000 == 0: logger.info("Processing example: %d" % (len(all_results))) input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output_prediction_file = os.path.join(args.output_dir, "predictions.json") output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json") write_predictions(eval_examples, eval_features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold)
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model checkpoints and predictions will be written." ) ## Other parameters parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") parser.add_argument( "--predict_file", default=None, type=str, help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json" ) parser.add_argument( "--max_seq_length", default=384, type=int, help= "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ) parser.add_argument( "--doc_stride", default=128, type=int, help= "When splitting up a long document into chunks, how much stride to take between chunks." ) parser.add_argument( "--max_query_length", default=64, type=int, help= "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% " "of training.") parser.add_argument( "--n_best_size", default=20, type=int, help= "The total number of n-best predictions to generate in the nbest_predictions.json " "output file.") parser.add_argument( "--max_answer_length", default=30, type=int, help= "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument( "--verbose_logging", action='store_true', help= "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument( "--do_lower_case", action='store_true', help= "Whether to lower case the input text. True for uncased models, False for cased models." ) parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--overwrite_output_dir', action='store_true', help="Overwrite the content of the output directory") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument( '--version_2_with_negative', action='store_true', help= 'If true, the SQuAD examples contain some that do not have an answer.') parser.add_argument( '--null_score_diff_threshold', type=float, default=0.0, help= "If null_score - best_non_null is greater than the threshold predict null." ) parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--checkpoint_dir', type=str, default='', help="Checkpoint directory used for prediction.") parser.add_argument('--checkpoint_filename', type=str, default='', help="Checkpoint binary filename used for prediction.") parser.add_argument('--output_result_file', type=str, default='', help="Result files will be save in this directory.") args = parser.parse_args() print(args) if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.info( "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}". format(device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_predict: raise ValueError( "At least one of `do_train` or `do_predict` must be True.") if args.do_train: if not args.train_file: raise ValueError( "If `do_train` is True, then `train_file` must be specified.") if args.do_predict: if not args.predict_file: raise ValueError( "If `do_predict` is True, then `predict_file` must be specified." ) if os.path.exists(args.output_dir) and os.listdir( args.output_dir ) and args.do_train and not args.overwrite_output_dir: raise ValueError( "Output directory () already exists and is not empty.") if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) if args.local_rank not in [-1, 0]: torch.distributed.barrier( ) # Make sure only the first process in distributed training will download model & vocab tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) model = BertForQuestionAnswering.from_pretrained(args.bert_model) if args.local_rank == 0: torch.distributed.barrier() if args.fp16: model.half() model.to(device) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) elif n_gpu > 1: model = torch.nn.DataParallel(model) if args.do_train: if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() # Prepare data loader logger.info("***** Preparing Data *****") train_examples = read_thai_qa_examples(input_file=args.train_file, is_training=True) cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}_cache'.format( list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length)) try: with open(cached_train_features_file, "rb") as reader: train_features = pickle.load(reader) except: train_features = convert_examples_to_features( examples=train_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=True) if args.local_rank == -1 or torch.distributed.get_rank() == 0: logger.info(" Saving train features into cached file %s", cached_train_features_file) with open(cached_train_features_file, "wb") as writer: pickle.dump(train_features, writer) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) all_start_positions = torch.tensor( [f.start_position for f in train_features], dtype=torch.long) all_end_positions = torch.tensor( [f.end_position for f in train_features], dtype=torch.long) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) num_train_optimization_steps = len( train_dataloader ) // args.gradient_accumulation_steps * args.num_train_epochs # if args.local_rank != -1: # num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() logger.info("***** Finished Preparing Data *****") # Prepare optimizer param_optimizer = list(model.named_parameters()) # hack to remove pooler, which is not used # thus it produce None grad that break apex param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [ p for n, p in param_optimizer if not any(nd in n for nd in no_decay) ], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) warmup_linear = WarmupLinearSchedule( warmup=args.warmup_proportion, t_total=num_train_optimization_steps) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 logger.info("***** Running training *****") logger.info(" Num orig examples = %d", len(train_examples)) logger.info(" Num split examples = %d", len(train_features)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_optimization_steps) model.train() lowest_training_loss = float('inf') for epoch in trange(int(args.num_train_epochs), desc="Epoch"): for step, batch in enumerate( tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])): if n_gpu == 1: batch = tuple( t.to(device) for t in batch) # multi-gpu does scattering it-self input_ids, input_mask, segment_ids, start_positions, end_positions = batch loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: loss.backward() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used and handles this automatically lr_this_step = args.learning_rate * warmup_linear.get_lr( global_step, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 if args.local_rank in [-1, 0]: tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step) tb_writer.add_scalar('loss', loss.item(), global_step) # Save the best model aka lowest training loss in this case loss_scalar = loss.item() if loss_scalar < lowest_training_loss: lowest_training_loss = loss_scalar model_to_save = model.module if hasattr( model, 'module') else model output_model_file = os.path.join(args.output_dir, f"best_weight.bin") torch.save(model_to_save.state_dict(), output_model_file) # Save the model every epoch model_to_save = model.module if hasattr(model, 'module') else model output_model_file = os.path.join(args.output_dir, f"epoch_{epoch}.bin") torch.save(model_to_save.state_dict(), output_model_file) if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Save a trained model, configuration and tokenizer model_to_save = model.module if hasattr( model, 'module') else model # Only save the model it-self # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # Good practice: save your training arguments together with the trained model output_args_file = os.path.join(args.output_dir, 'training_args.bin') torch.save(args, output_args_file) else: model = BertForQuestionAnswering.from_pretrained(args.bert_model) model.to(device) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Load checkpoint logger.info( f'Loading checkpoint from {args.checkpoint_dir}/{args.checkpoint_filename}' ) config = BertConfig.from_json_file( f'{args.checkpoint_dir}/config.json') model = BertForQuestionAnswering(config) state_dict = torch.load( f'{args.checkpoint_dir}/{args.checkpoint_filename}') model.load_state_dict(state_dict) tokenizer = BertTokenizer(f'{args.checkpoint_dir}/vocab.txt', do_lower_case=args.do_lower_case) model.to(device) eval_examples = read_thai_qa_examples(input_file=args.predict_file, is_training=False) eval_features = convert_examples_to_features( examples=eval_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=False) logger.info("***** Running predictions *****") logger.info(" Num orig examples = %d", len(eval_examples)) logger.info(" Num split examples = %d", len(eval_features)) logger.info(" Batch size = %d", args.predict_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) model.eval() all_results = [] logger.info("Start evaluating") for input_ids, input_mask, segment_ids, example_indices in tqdm( eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]): # if len(all_results) % 1000 == 0: # logger.info("Processing example: %d" % (len(all_results))) input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) # output_prediction_file = os.path.join(args.output_dir, "predictions.json") # output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") # output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json") write_predictions(eval_examples, eval_features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, args.output_result_file, args.verbose_logging, args.null_score_diff_threshold, tokenizer)
def main(): parser = argparse.ArgumentParser() parser.add_argument("--paragraph", default=None, type=str) parser.add_argument("--model", default=None, type=str) parser.add_argument("--max_seq_length", default=384, type=int) parser.add_argument("--doc_stride", default=128, type=int) parser.add_argument("--max_query_length", default=64, type=int) parser.add_argument("--config_file", default=None, type=str) parser.add_argument("--max_answer_length", default=30, type=int) args = parser.parse_args() para_file = args.paragraph model_path = args.model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(device) n_gpu = torch.cuda.device_count() ### Raeding paragraph f = open(para_file, 'r') para = f.read() f.close() ## Reading question # f = open(ques_file, 'r') # ques = f.read() # f.close() para_list = para.split('\n\n') input_data = [] i = 1 for para in para_list: paragraphs = {} splits = para.split('\nQuestions:') paragraphs['id'] = i paragraphs['text'] = splits[0].replace('Paragraph:', '').strip('\n') paragraphs['ques'] = splits[1].lstrip('\n').split('\n') input_data.append(paragraphs) i += 1 ## input_data is a list of dictionary which has a paragraph and questions examples = read_squad_examples(input_data) tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) eval_features = convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) ### Loading Pretrained model for QnA config = BertConfig(args.config_file) model = BertForQuestionAnswering(config) model.load_state_dict( torch.load(model_path, map_location=torch.device('cpu'))) model.to(device) pred_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data pred_sampler = SequentialSampler(pred_data) pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=9) predictions = [] for input_ids, input_mask, segment_ids, example_indices in tqdm( pred_dataloader): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) features = [] example = [] all_results = [] for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() feature = eval_features[example_index.item()] unique_id = int(feature.unique_id) features.append(feature) all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output = predict(examples, features, all_results, args.max_answer_length) predictions.append(output) ### For printing the results #### index = None for example in examples: if index != example.example_id: print(example.para_text) index = example.example_id print('\n') print( colored('***********Question and Answers *************', 'red')) ques_text = colored(example.question_text, 'blue') print(ques_text) prediction = colored(predictions[math.floor(example.unique_id / 12)][example], 'green', attrs=['reverse', 'blink']) print(prediction) print('\n')
def main(): parser = argparse.ArgumentParser() parser.add_argument("--paragraph", default=None, type=str) parser.add_argument("--question", default=None, type=str) parser.add_argument("--model", default=None, type=str) parser.add_argument("--max_seq_length", default=384, type=int) parser.add_argument("--doc_stride", default=128, type=int) parser.add_argument("--max_query_length", default=64, type=int) parser.add_argument("--config_file", default=None, type=str) parser.add_argument("--max_answer_length", default=30, type=int) args = parser.parse_args() para_file = args.paragraph question_file = args.question model_path = args.model device = torch.device("cpu") ### Raeding paragraph # f = open(para_file, 'r') # para = f.read() # f.close() ## Reading question # f = open(ques_file, 'r') # ques = f.read() # f.close() # para_list = para.split('\n\n') f = open(para_file, "rb") para = f.read() para = para.decode('windows-1252') para = para.strip("\n").replace("\r", " ").replace("\n", "") #print(para) # print(para) f.close() f_ = open(question_file, "r") question = f_.read() question = question.split("\n") while "" in question: question.remove("") for q in question: q = q.strip("\n") f_.close() input_data = [] pfinder = ParaFinder(para) i = 0 for q in question: closest_para = pfinder.closestParagraph(q) paragraphs = {} paragraphs["id"] = i paragraphs["text"] = closest_para paragraphs["ques"] = [q] i += 1 input_data.append(paragraphs) # print(input_data) ## input_data is a list of dictionary which has a paragraph and questions examples = read_squad_examples(input_data) tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) eval_features = convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) ### Loading Pretrained model for QnA config = BertConfig(args.config_file) model = BertForQuestionAnswering(config) model.load_state_dict(torch.load(model_path, map_location=device)) model.to(device) pred_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data pred_sampler = SequentialSampler(pred_data) pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=9) predictions = [] for input_ids, input_mask, segment_ids, example_indices in pred_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model( input_ids, segment_ids, input_mask) features = [] example = [] all_results = [] for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() feature = eval_features[example_index.item()] unique_id = int(feature.unique_id) features.append(feature) all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output = predict(examples, features, all_results, args.max_answer_length) predictions.append(output) ### For printing the results #### index = None for example in examples: if index != example.example_id: # print(example.para_text) index = example.example_id # print('\n') # print(colored('***********Question and Answers *************', 'red')) ques_text = example.question_text print(ques_text) prediction, prob = predictions[math.floor(example.unique_id / 12)][example] if prob > 0.35: print(prediction) #print(type(prediction)) else: print("No result found")
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--bert_token_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--model_dir", default=None, type=str, required=True, help="학습된 모델이 저장되어 있는 path") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.") ## Other parameters parser.add_argument("--predict_file", default=None, type=str, help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") parser.add_argument("--max_seq_length", default=384, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded.") parser.add_argument("--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.") parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument("--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json " "output file.") parser.add_argument("--max_answer_length", default=30, type=int, help="The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument("--verbose_logging", action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument("--do_lower_case", action='store_true', help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--version_2_with_negative', action='store_true', help='If true, the SQuAD examples contain some that do not have an answer.') parser.add_argument('--null_score_diff_threshold', type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() #python run_triviaqa.py --bert_token_model bert-base-uncased --model_dir bert_triviaQA/ --output_dir result/ --predict_file dev-wiki-triviaqa_m.json --no_cuda --do_lower_case --predict_batch_size 40 print(args) if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if args.do_predict: if not args.predict_file: raise ValueError( "If `do_predict` is True, then `predict_file` must be specified.") if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError("Output directory () already exists and is not empty.") if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) tokenizer = BertTokenizer.from_pretrained(args.bert_token_model, do_lower_case=args.do_lower_case) # Load Pretrained Model config_path = os.path.join(args.model_dir, CONFIG_NAME) model_path = os.path.join(args.model_dir, WEIGHTS_NAME) config = BertConfig(config_path) model = BertForQuestionAnswering(config) model.load_state_dict(torch.load(model_path, map_location='cpu')) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_squad_examples( input_file=args.predict_file, is_training=False, version_2_with_negative=args.version_2_with_negative) eval_features = convert_examples_to_features( examples=eval_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=False) logger.info("***** Running predictions *****") logger.info(" Num orig examples = %d", len(eval_examples)) logger.info(" Num split examples = %d", len(eval_features)) logger.info(" Batch size = %d", args.predict_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) model.eval() all_results = [] logger.info("Start evaluating") for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]): if len(all_results) % 1000 == 0: logger.info("Processing example: %d" % (len(all_results))) input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append(RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output_prediction_file = os.path.join(args.output_dir, "predictions.json") output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json") write_predictions(eval_examples, eval_features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold)
class Bert(object): def __init__(self): # Hyperparameters self.BERT_MODEL = "bert-base-uncased" self.OUTPUT_DIR = "bert-model" self.TRAIN_FILE = "" self.PREDICT_FILE = "squad/test-pred.json" self.MAX_SEQ_LENGTH = 384 self.DOC_STRIDE = 128 self.MAX_QUERY_LENGTH = 64 self.DO_TRAIN = False self.DO_PREDICT = True self.TRAIN_BATCH_SIZE = 12 self.PREDICT_BATCH_SIZE = 8 self.LEARNING_RATE = 3e-5 self.NUM_TRAIN_EPOCHS = 2.0 self.WARMUP_PROPORTION = 0.1 self.N_BEST_SIZE = 20 self.MAX_ANSWER_LENGTH = 30 self.VERBOSE_LOGGING = False self.NO_CUDA = False self.SEED = 42 self.GRADIENT_ACCUMULATION_STEPS = 1 self.DO_LOWER_CASE = True self.LOCAL_RANK = -1 self.FP16 = False self.LOSS_SCALE = 0 self.VERSION_2_WITH_NEGATIVE = True self.NULL_SCORE_DIFF_THRESHOLD = 0.0 if self.LOCAL_RANK == -1 or self.NO_CUDA: self.device = torch.device("cuda" if torch.cuda.is_available() and not self.NO_CUDA else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(self.LOCAL_RANK) self.device = torch.device("cuda", self.LOCAL_RANK) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logger.info( "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}" .format(self.device, n_gpu, bool(self.LOCAL_RANK != -1), self.FP16)) if self.GRADIENT_ACCUMULATION_STEPS < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(self.GRADIENT_ACCUMULATION_STEPS)) self.TRAIN_BATCH_SIZE = self.TRAIN_BATCH_SIZE // self.GRADIENT_ACCUMULATION_STEPS random.seed(self.SEED) np.random.seed(self.SEED) torch.manual_seed(self.SEED) if n_gpu > 0: torch.cuda.manual_seed_all(self.SEED) if not self.DO_TRAIN and not self.DO_PREDICT: raise ValueError( "At least one of `do_train` or `do_predict` must be True.") if self.DO_TRAIN: if not self.TRAIN_FILE: raise ValueError( "If `do_train` is True, then `train_file` must be specified." ) if self.DO_PREDICT: if not self.PREDICT_FILE: raise ValueError( "If `do_predict` is True, then `predict_file` must be specified." ) if os.path.exists(self.OUTPUT_DIR) and os.listdir( self.OUTPUT_DIR) and self.DO_TRAIN: raise ValueError( "Output directory () already exists and is not empty.") if not os.path.exists(self.OUTPUT_DIR): os.makedirs(self.OUTPUT_DIR) self.tokenizer = BertTokenizer.from_pretrained( self.BERT_MODEL, do_lower_case=self.DO_LOWER_CASE) train_examples = None num_train_optimization_steps = None # Prepare model self.model = BertForQuestionAnswering.from_pretrained( self.BERT_MODEL, cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(self.LOCAL_RANK))) if self.FP16: self.model.half() self.model.to(self.device) if self.LOCAL_RANK != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) self.model = DDP(self.model) elif n_gpu > 1: self.model = torch.nn.DataParallel(self.model) # Prepare optimizer param_optimizer = list(self.model.named_parameters()) # hack to remove pooler, which is not used # thus it produce None grad that break apex param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [ p for n, p in param_optimizer if not any(nd in n for nd in no_decay) ], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] if self.FP16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) optimizer = FusedAdam(optimizer_grouped_parameters, lr=self.LEARNING_RATE, bias_correction=False, max_grad_norm=1.0) if self.LOSS_SCALE == 0: optimizer = self.FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = self.FP16_Optimizer( optimizer, static_loss_scale=self.LOSS_SCALE) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=self.LEARNING_RATE, warmup=self.WARMUP_PROPORTION, t_total=num_train_optimization_steps) # self.model = BertForQuestionAnswering.from_pretrained(self.BERT_MODEL) output_model_file = os.path.join(self.OUTPUT_DIR, WEIGHTS_NAME) output_config_file = os.path.join(self.OUTPUT_DIR, CONFIG_NAME) # Load a trained model and config that you have fine-tuned config = BertConfig(output_config_file) self.model = BertForQuestionAnswering(config) if torch.cuda.is_available(): self.model.load_state_dict(torch.load(output_model_file)) else: self.model.load_state_dict( torch.load(output_model_file, map_location='cpu')) self.model.to(self.device) print('\n*** QA MODULE READY [1/3] ***\n') def read_squad_examples(self, input_file, is_training, version_2_with_negative): """Read a SQuAD json file into a list of SquadExample.""" with open(input_file, "r", encoding='utf-8') as reader: input_data = json.load(reader)["data"] def is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord( c) == 0x202F: return True return False examples = [] for entry in input_data: for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] doc_tokens = [] char_to_word_offset = [] prev_is_whitespace = True for c in paragraph_text: if is_whitespace(c): prev_is_whitespace = True else: if prev_is_whitespace: doc_tokens.append(c) else: doc_tokens[-1] += c prev_is_whitespace = False char_to_word_offset.append(len(doc_tokens) - 1) for qa in paragraph["qas"]: qas_id = qa["id"] question_text = qa["question"] start_position = None end_position = None orig_answer_text = None is_impossible = False if is_training: if version_2_with_negative: is_impossible = qa["is_impossible"] if (len(qa["answers"]) != 1) and (not is_impossible): raise ValueError( "For training, each question should have exactly 1 answer." ) if not is_impossible: answer = qa["answers"][0] orig_answer_text = answer["text"] answer_offset = answer["answer_start"] answer_length = len(orig_answer_text) start_position = char_to_word_offset[answer_offset] end_position = char_to_word_offset[answer_offset + answer_length - 1] # Only add answers where the text can be exactly recovered from the # document. If this CAN'T happen it's likely due to weird Unicode # stuff so we will just skip the example. # # Note that this means for training mode, every example is NOT # guaranteed to be preserved. actual_text = " ".join( doc_tokens[start_position:(end_position + 1)]) cleaned_answer_text = " ".join( whitespace_tokenize(orig_answer_text)) if actual_text.find(cleaned_answer_text) == -1: logger.warning( "Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text) continue else: start_position = -1 end_position = -1 orig_answer_text = "" example = SquadExample(qas_id=qas_id, question_text=question_text, doc_tokens=doc_tokens, orig_answer_text=orig_answer_text, start_position=start_position, end_position=end_position, is_impossible=is_impossible) examples.append(example) return examples def convert_examples_to_features(self, examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training): """Loads a data file into a list of `InputBatch`s.""" unique_id = 1000000000 features = [] for (example_index, example) in enumerate(examples): query_tokens = tokenizer.tokenize(example.question_text) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] tok_to_orig_index = [] orig_to_tok_index = [] all_doc_tokens = [] for (i, token) in enumerate(example.doc_tokens): orig_to_tok_index.append(len(all_doc_tokens)) sub_tokens = tokenizer.tokenize(token) for sub_token in sub_tokens: tok_to_orig_index.append(i) all_doc_tokens.append(sub_token) tok_start_position = None tok_end_position = None if is_training and example.is_impossible: tok_start_position = -1 tok_end_position = -1 if is_training and not example.is_impossible: tok_start_position = orig_to_tok_index[example.start_position] if example.end_position < len(example.doc_tokens) - 1: tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 else: tok_end_position = len(all_doc_tokens) - 1 (tok_start_position, tok_end_position) = self._improve_answer_span( all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.orig_answer_text) # The -3 accounts for [CLS], [SEP] and [SEP] max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 # We can have documents that are longer than the maximum sequence length. # To deal with this we do a sliding window approach, where we take chunks # of the up to our max length with a stride of `doc_stride`. _DocSpan = collections.namedtuple( # pylint: disable=invalid-name "DocSpan", ["start", "length"]) doc_spans = [] start_offset = 0 while start_offset < len(all_doc_tokens): length = len(all_doc_tokens) - start_offset if length > max_tokens_for_doc: length = max_tokens_for_doc doc_spans.append(_DocSpan(start=start_offset, length=length)) if start_offset + length == len(all_doc_tokens): break start_offset += min(length, doc_stride) for (doc_span_index, doc_span) in enumerate(doc_spans): tokens = [] token_to_orig_map = {} token_is_max_context = {} segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in query_tokens: tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) for i in range(doc_span.length): split_token_index = doc_span.start + i token_to_orig_map[len( tokens)] = tok_to_orig_index[split_token_index] is_max_context = self._check_is_max_context( doc_spans, doc_span_index, split_token_index) token_is_max_context[len(tokens)] = is_max_context tokens.append(all_doc_tokens[split_token_index]) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length start_position = None end_position = None if is_training and not example.is_impossible: # For training, if our document chunk does not contain an annotation # we throw it out, since there is nothing to predict. doc_start = doc_span.start doc_end = doc_span.start + doc_span.length - 1 out_of_span = False if not (tok_start_position >= doc_start and tok_end_position <= doc_end): out_of_span = True if out_of_span: start_position = 0 end_position = 0 else: doc_offset = len(query_tokens) + 2 start_position = tok_start_position - doc_start + doc_offset end_position = tok_end_position - doc_start + doc_offset if is_training and example.is_impossible: start_position = 0 end_position = 0 features.append( InputFeatures(unique_id=unique_id, example_index=example_index, doc_span_index=doc_span_index, tokens=tokens, token_to_orig_map=token_to_orig_map, token_is_max_context=token_is_max_context, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, start_position=start_position, end_position=end_position, is_impossible=example.is_impossible)) unique_id += 1 return features def _improve_answer_span(self, doc_tokens, input_start, input_end, tokenizer, orig_answer_text): """Returns tokenized answer spans that better match the annotated answer.""" # The SQuAD annotations are character based. We first project them to # whitespace-tokenized words. But then after WordPiece tokenization, we can # often find a "better match". For example: # # Question: What year was John Smith born? # Context: The leader was John Smith (1895-1943). # Answer: 1895 # # The original whitespace-tokenized answer will be "(1895-1943).". However # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match # the exact answer, 1895. # # However, this is not always possible. Consider the following: # # Question: What country is the top exporter of electornics? # Context: The Japanese electronics industry is the lagest in the world. # Answer: Japan # # In this case, the annotator chose "Japan" as a character sub-span of # the word "Japanese". Since our WordPiece tokenizer does not split # "Japanese", we just use "Japanese" as the annotation. This is fairly rare # in SQuAD, but does happen. tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) for new_start in range(input_start, input_end + 1): for new_end in range(input_end, new_start - 1, -1): text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) if text_span == tok_answer_text: return (new_start, new_end) return (input_start, input_end) def _check_is_max_context(self, doc_spans, cur_span_index, position): """Check if this is the 'max context' doc span for the token.""" # Because of the sliding window approach taken to scoring documents, a single # token can appear in multiple documents. E.g. # Doc: the man went to the store and bought a gallon of milk # Span A: the man went to the # Span B: to the store and bought # Span C: and bought a gallon of # ... # # Now the word 'bought' will have two scores from spans B and C. We only # want to consider the score with "maximum context", which we define as # the *minimum* of its left and right context (the *sum* of left and # right context will always be the same, of course). # # In the example the maximum context for 'bought' would be span C since # it has 1 left context and 3 right context, while span B has 4 left context # and 0 right context. best_score = None best_span_index = None for (span_index, doc_span) in enumerate(doc_spans): end = doc_span.start + doc_span.length - 1 if position < doc_span.start: continue if position > end: continue num_left_context = position - doc_span.start num_right_context = end - position score = min(num_left_context, num_right_context) + 0.01 * doc_span.length if best_score is None or score > best_score: best_score = score best_span_index = span_index return cur_span_index == best_span_index RawResult = collections.namedtuple( "RawResult", ["unique_id", "start_logits", "end_logits"]) def write_predictions(self, all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, verbose_logging, version_2_with_negative, null_score_diff_threshold): example_index_to_features = collections.defaultdict(list) for feature in all_features: example_index_to_features[feature.example_index].append(feature) unique_id_to_result = {} for result in all_results: unique_id_to_result[result.unique_id] = result _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name "PrelimPrediction", [ "feature_index", "start_index", "end_index", "start_logit", "end_logit" ]) all_predictions = collections.OrderedDict() all_nbest_json = collections.OrderedDict() scores_diff_json = collections.OrderedDict() for (example_index, example) in enumerate(all_examples): features = example_index_to_features[example_index] prelim_predictions = [] # keep track of the minimum score of null start+end of position 0 score_null = 1000000 # large and positive min_null_feature_index = 0 # the paragraph slice with min null score null_start_logit = 0 # the start logit at the slice with min null score null_end_logit = 0 # the end logit at the slice with min null score for (feature_index, feature) in enumerate(features): result = unique_id_to_result[feature.unique_id] start_indexes = self._get_best_indexes(result.start_logits, n_best_size) end_indexes = self._get_best_indexes(result.end_logits, n_best_size) # if we could have irrelevant answers, get the min score of irrelevant if version_2_with_negative: feature_null_score = result.start_logits[ 0] + result.end_logits[0] if feature_null_score < score_null: score_null = feature_null_score min_null_feature_index = feature_index null_start_logit = result.start_logits[0] null_end_logit = result.end_logits[0] for start_index in start_indexes: for end_index in end_indexes: # We could hypothetically create invalid predictions, e.g., predict # that the start of the span is in the question. We throw out all # invalid predictions. if start_index >= len(feature.tokens): continue if end_index >= len(feature.tokens): continue if start_index not in feature.token_to_orig_map: continue if end_index not in feature.token_to_orig_map: continue if not feature.token_is_max_context.get( start_index, False): continue if end_index < start_index: continue length = end_index - start_index + 1 if length > max_answer_length: continue prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_index, end_index=end_index, start_logit=result.start_logits[start_index], end_logit=result.end_logits[end_index])) if version_2_with_negative: prelim_predictions.append( _PrelimPrediction(feature_index=min_null_feature_index, start_index=0, end_index=0, start_logit=null_start_logit, end_logit=null_end_logit)) prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True) _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name "NbestPrediction", ["text", "start_logit", "end_logit"]) seen_predictions = {} nbest = [] for pred in prelim_predictions: if len(nbest) >= n_best_size: break feature = features[pred.feature_index] if pred.start_index > 0: # this is a non-null prediction tok_tokens = feature.tokens[pred.start_index:( pred.end_index + 1)] orig_doc_start = feature.token_to_orig_map[ pred.start_index] orig_doc_end = feature.token_to_orig_map[pred.end_index] orig_tokens = example.doc_tokens[orig_doc_start:( orig_doc_end + 1)] tok_text = " ".join(tok_tokens) # De-tokenize WordPieces that have been split off. tok_text = tok_text.replace(" ##", "") tok_text = tok_text.replace("##", "") # Clean whitespace tok_text = tok_text.strip() tok_text = " ".join(tok_text.split()) orig_text = " ".join(orig_tokens) final_text = self.get_final_text(tok_text, orig_text, do_lower_case, verbose_logging) if final_text in seen_predictions: continue seen_predictions[final_text] = True else: final_text = "" seen_predictions[final_text] = True nbest.append( _NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit)) # if we didn't include the empty option in the n-best, include it if version_2_with_negative: if "" not in seen_predictions: nbest.append( _NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit)) # In very rare edge cases we could only have single null prediction. # So we just create a nonce prediction in this case to avoid failure. if len(nbest) == 1: nbest.insert( 0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) # In very rare edge cases we could have no valid predictions. So we # just create a nonce prediction in this case to avoid failure. if not nbest: nbest.append( _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) assert len(nbest) >= 1 total_scores = [] best_non_null_entry = None for entry in nbest: total_scores.append(entry.start_logit + entry.end_logit) if not best_non_null_entry: if entry.text: best_non_null_entry = entry probs = self._compute_softmax(total_scores) nbest_json = [] for (i, entry) in enumerate(nbest): output = collections.OrderedDict() output["text"] = entry.text output["probability"] = probs[i] output["start_logit"] = entry.start_logit output["end_logit"] = entry.end_logit nbest_json.append(output) assert len(nbest_json) >= 1 if not version_2_with_negative: all_predictions[example.qas_id] = nbest_json[0]["text"] else: # predict "" iff the null score - the score of best non-null > threshold score_diff = score_null - best_non_null_entry.start_logit - ( best_non_null_entry.end_logit) scores_diff_json[example.qas_id] = score_diff if score_diff > null_score_diff_threshold: all_predictions[example.qas_id] = "" else: all_predictions[example.qas_id] = best_non_null_entry.text all_nbest_json[example.qas_id] = nbest_json ''' with open(output_prediction_file, "w") as writer: writer.write(json.dumps(all_predictions, indent=4) + "\n") with open(output_nbest_file, "w") as writer: writer.write(json.dumps(all_nbest_json, indent=4) + "\n") if version_2_with_negative: with open(output_null_log_odds_file, "w") as writer: writer.write(json.dumps(scores_diff_json, indent=4) + "\n") ''' return all_predictions[example.qas_id] def get_final_text(self, pred_text, orig_text, do_lower_case, verbose_logging=False): """Project the tokenized prediction back to the original text.""" # When we created the data, we kept track of the alignment between original # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So # now `orig_text` contains the span of our original text corresponding to the # span that we predicted. # # However, `orig_text` may contain extra characters that we don't want in # our prediction. # # For example, let's say: # pred_text = steve smith # orig_text = Steve Smith's # # We don't want to return `orig_text` because it contains the extra "'s". # # We don't want to return `pred_text` because it's already been normalized # (the SQuAD eval script also does punctuation stripping/lower casing but # our tokenizer does additional normalization like stripping accent # characters). # # What we really want to return is "Steve Smith". # # Therefore, we have to apply a semi-complicated alignment heuristic between # `pred_text` and `orig_text` to get a character-to-character alignment. This # can fail in certain cases in which case we just return `orig_text`. def _strip_spaces(text): ns_chars = [] ns_to_s_map = collections.OrderedDict() for (i, c) in enumerate(text): if c == " ": continue ns_to_s_map[len(ns_chars)] = i ns_chars.append(c) ns_text = "".join(ns_chars) return (ns_text, ns_to_s_map) # We first tokenize `orig_text`, strip whitespace from the result # and `pred_text`, and check if they are the same length. If they are # NOT the same length, the heuristic has failed. If they are the same # length, we assume the characters are one-to-one aligned. tokenizer = BasicTokenizer(do_lower_case=do_lower_case) tok_text = " ".join(tokenizer.tokenize(orig_text)) start_position = tok_text.find(pred_text) if start_position == -1: if verbose_logging: logger.info("Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) return orig_text end_position = start_position + len(pred_text) - 1 (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) if len(orig_ns_text) != len(tok_ns_text): if verbose_logging: logger.info( "Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text) return orig_text # We then project the characters in `pred_text` back to `orig_text` using # the character-to-character alignment. tok_s_to_ns_map = {} for (i, tok_index) in tok_ns_to_s_map.items(): tok_s_to_ns_map[tok_index] = i orig_start_position = None if start_position in tok_s_to_ns_map: ns_start_position = tok_s_to_ns_map[start_position] if ns_start_position in orig_ns_to_s_map: orig_start_position = orig_ns_to_s_map[ns_start_position] if orig_start_position is None: if verbose_logging: logger.info("Couldn't map start position") return orig_text orig_end_position = None if end_position in tok_s_to_ns_map: ns_end_position = tok_s_to_ns_map[end_position] if ns_end_position in orig_ns_to_s_map: orig_end_position = orig_ns_to_s_map[ns_end_position] if orig_end_position is None: if verbose_logging: logger.info("Couldn't map end position") return orig_text output_text = orig_text[orig_start_position:(orig_end_position + 1)] return output_text def _get_best_indexes(self, logits, n_best_size): """Get the n-best logits from a list.""" index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) best_indexes = [] for i in range(len(index_and_score)): if i >= n_best_size: break best_indexes.append(index_and_score[i][0]) return best_indexes def _compute_softmax(self, scores): """Compute softmax probability over raw logits.""" if not scores: return [] max_score = None for score in scores: if max_score is None or score > max_score: max_score = score exp_scores = [] total_sum = 0.0 for score in scores: x = math.exp(score - max_score) exp_scores.append(x) total_sum += x probs = [] for score in exp_scores: probs.append(score / total_sum) return probs def get_answer(self, question, article): if self.DO_PREDICT and (self.LOCAL_RANK == -1 or torch.distributed.get_rank() == 0): def is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord( c) == 0x202F: return True return False doc_tokens = [] char_to_word_offset = [] prev_is_whitespace = True for c in article: if is_whitespace(c): prev_is_whitespace = True else: if prev_is_whitespace: doc_tokens.append(c) else: doc_tokens[-1] += c prev_is_whitespace = False char_to_word_offset.append(len(doc_tokens) - 1) eval_examples = [] example = SquadExample(qas_id="1234", question_text=question, doc_tokens=doc_tokens, orig_answer_text=None, start_position=None, end_position=None, is_impossible=False) eval_examples.append(example) eval_features = self.convert_examples_to_features( examples=eval_examples, tokenizer=self.tokenizer, max_seq_length=self.MAX_SEQ_LENGTH, doc_stride=self.DOC_STRIDE, max_query_length=self.MAX_QUERY_LENGTH, is_training=False) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor( [f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor( [f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=self.PREDICT_BATCH_SIZE) self.model.eval() all_results = [] for input_ids, input_mask, segment_ids, example_indices in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(self.device) input_mask = input_mask.to(self.device) segment_ids = segment_ids.to(self.device) with torch.no_grad(): batch_start_logits, batch_end_logits = self.model( input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist( ) end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append( self.RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output_prediction_file = os.path.join(self.OUTPUT_DIR, "predictions.json") output_nbest_file = os.path.join(self.OUTPUT_DIR, "nbest_predictions.json") output_null_log_odds_file = os.path.join(self.OUTPUT_DIR, "null_odds.json") return self.write_predictions( eval_examples, eval_features, all_results, self.N_BEST_SIZE, self.MAX_ANSWER_LENGTH, self.DO_LOWER_CASE, output_prediction_file, output_nbest_file, output_null_log_odds_file, self.VERBOSE_LOGGING, self.VERSION_2_WITH_NEGATIVE, self.NULL_SCORE_DIFF_THRESHOLD)
["unique_id", "start_logits", "end_logits"]) # para_file = "../Input_file.txt" para_file = "/content/drive/My Drive/train-v2.0.json" # TODO: use proper file path model_path = "/content/drive/My Drive/pytorch_model.bin" # TODO: use proper file path tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) ### Loading Pretrained model for QnA config = BertConfig("../Results/bert_config.json") model = BertForQuestionAnswering(config) model.load_state_dict(torch.load(model_path, map_location='cpu')) # model = BertForQuestionAnswering.from_pretrained('bert-base-uncased') model.to(device) print() ### initializing the autoencoder hidden_size = 384 encoder1 = EncoderRNN(384, config.hidden_size, hidden_size).to(device) decoder1 = DecoderRNN(384, config.hidden_size, hidden_size).to(device) encoder_optimizer = optim.Adam(encoder1.parameters()) decoder_optimizer = optim.Adam(decoder1.parameters()) criterion = nn.MSELoss() pp = pprint.PrettyPrinter(indent=4) # input_data is a list of dictionary which has a paragraph and questions with open("/content/drive/My Drive/train-v2.0.json") as f: