def get_grad(self, batch, attack_Ins): """ :param batch: a batch of things including a tensor of word vector indices (B, len) :param attack_Ins: if attack the Ins transformation, if True, we will return other two grads instead (x_iou, c) :return: if attack_Ins is False, return np array with shape (len, word_vec_size), Otherwise, return np arrays with shape (len, h_size * 3), (len, h_size) """ self.adv_attack = True if attack_Ins: self.adv_attack_Ins = True g = batch['trees'] n = g.number_of_nodes() h = th.zeros((n, self.h_size)).to(self.device) c = th.zeros((n, self.h_size)).to(self.device) logits = self.forward(batch, g, h, c, compute_bounds=False) loss = CrossEntropyLoss()(logits, batch['y']).sum() loss.backward() self.adv_attack = False if attack_Ins: self.adv_attack_Ins = False return self.out_ioux_c[0].grad[0].cpu().numpy(), self.out_ioux_c[1].grad[0].cpu().numpy() return self.out_x_vecs.grad[0].cpu().numpy()
def train(self, epochs, pretrain_file=None): logging.info( "%s INFO: Begin training", time.strftime("%m/%d/%Y %I:%M:%S %p", time.localtime()), ) iter_ = 0 start_epoch, accu, iou, f1, train_loss, test_loss, losses = self._load_init( pretrain_file ) loss_weights = torch.ones( self.cfg.N_CLASSES, dtype=torch.float32, device=self.device ) if self.cfg.WEIGHTED_LOSS or self.cfg.REVOLVER_WEIGHTED: weights = self.gt_dataset.compute_frequency() if self.cfg.REVOLVER_WEIGHTED: self.train_dataset.set_sparsifier_weights(weights) if self.cfg.WEIGHTED_LOSS: loss_weights = ( torch.from_numpy(weights).type(torch.FloatTensor).to(self.device) ) train_loader = self.train_dataset.get_loader( self.cfg.BATCH_SIZE, self.cfg.WORKERS ) for e in tqdm(range(start_epoch, epochs + 1), total=epochs + 1 - start_epoch): logging.info( "\n%s Epoch %s", time.strftime("%m/%d/%Y %I:%M:%S %p", time.localtime()), e, ) self.net.train() steps_pbar = tqdm( train_loader, total=self.cfg.EPOCH_SIZE // self.cfg.BATCH_SIZE ) for data in steps_pbar: features, labels = data self.optimizer.zero_grad() features = features.float().to(self.device) labels = labels.float().to(self.device) output = self.net(features) loss = CrossEntropyLoss(loss_weights)(output, labels.long()) loss.backward() self.optimizer.step() losses.append(loss.item()) iter_ += 1 steps_pbar.set_postfix({"loss": loss.item()}) train_loss.append(np.mean(losses[-1 * self.cfg.EPOCH_SIZE :])) loss, iou_, acc_, f1_ = self.test() test_loss.append(loss) accu.append(acc_) iou.append(iou_ * 100) f1.append(f1_ * 100) del (loss, iou_, acc_) if e % 5 == 0: self._save_net(e, accu, iou, f1, train_loss, test_loss, losses) self.scheduler.step() # Save final state self._save_net(epochs, accu, iou, f1, train_loss, test_loss, losses, False)
def train(model, optimizer, dataset, args): model = model.to(device) optimizer = opt(optimizer, model, args) # scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=args.training_steps) model.train() loss_hist = [] acc_hist = [] accuracy = 0 print("Begin Training...") for epoch in range(args.epochs): print(f"Epoch {epoch}") ctime = time.time() for data_e, (inp, tar) in enumerate(dataset): tar = tar.to(device) input_ids, token_type_ids, attention_mask = inp["input_ids"].to(device), \ inp["token_type_ids"].to(device), \ inp["attention_mask"].to(device) output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, cls_pos=args.cls_pos) loss = CrossEntropyLoss()(output, tar) loss = loss / args.gradient_accumulation_steps loss.backward() accuracy += (tar == output.argmax(1)).type(torch.float).mean() / args.gradient_accumulation_steps if not data_e % args.gradient_accumulation_steps: loss_hist.append(loss.item()) optimizer.step() # scheduler.step() optimizer.zero_grad() acc_hist.append(accuracy) accuracy = 0 if not data_e % 2000: print(f"Batch {data_e} Loss : {loss.item()}") print(f"Ground Truth: {tar.tolist()} \t Predicted: {output.argmax(1).tolist()}") print( f"Time taken for epoch{epoch+1} : {round( (time.time() - ctime) / 60, 2 )} MINUTES" ) torch.save(model, args.pretrainedPATH + f"saved_checkpoint_{args.save_checkpoint}.pt") # model.save_pretrained( # args.pretrainedPATH + f"saved_checkpoint_{args.save_checkpoint}" # ) print( f"Model saved at {args.pretrainedPATH}saved_checkpoint_{args.save_checkpoint}" ) args.save_checkpoint += 1 return loss_hist, acc_hist
def train(model, device, train_loader, optimizer, epoch, log_interval=1): model.to(device) model.train() for batch_idx, (data, target) in enumerate(train_loader): print(data.shape) data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) print(target) loss = CrossEntropyLoss(output, target) loss.backward() optimizer.step() if batch_idx % log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item()))
def train(model, device, loader, optimiser): num_batches = len(loader) / BATCH_SIZE total_loss = 0.0 y_true = [] y_pred = [] for step, batch in enumerate(loader): batch = batch.to(device) # TRAIN model.train() out = model(batch) optimiser.zero_grad() loss = CrossEntropyLoss()(out, batch.y) total_loss += loss loss.backward() optimiser.step() # EVAL model.eval() with torch.no_grad(): pred = model(batch).argmax(dim=-1) y_true.append(batch.y.detach().cpu()) y_pred.append(pred.detach().cpu()) y_true = torch.cat(y_true, dim=0).numpy() y_pred = torch.cat(y_pred, dim=0).numpy() avg_loss = total_loss / num_batches acc = f1_score(y_true=y_true, y_pred=y_pred, average='micro') return avg_loss.item(), acc
def do_training(train_fs, train_exs): """Runs BERT fine-tuning.""" # Allows to write to enclosed variables global_step nonlocal global_step # Create the batched training data out of the features. train_data = create_tensor_dataset(train_fs) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) # Calculate the number of optimization steps. num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer. param_optimizer = list(model.named_parameters()) 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} ] optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) # Log some information about the training. logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_exs)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_optimization_steps) # Set the model to training mode and train for X epochs. model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 # Iterate over all batches. for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch # Get the Logits and calculate the loss. logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask) loss = CrossEntropyLoss()(logits.view(-1, num_labels), label_ids.view(-1)) # Scale the loss in gradient accumulation mode. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps # Calculate the gradients. loss.backward() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 # Update the weights every gradient_accumulation_steps steps. if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() optimizer.zero_grad() global_step += 1 tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step) tb_writer.add_scalar('loss', loss.item(), global_step)
def distill(args, output_model_file, processor, label_list, tokenizer, device, n_gpu, tensorboard_logger, eval_data=None): assert args.kd_policy is not None model = args.kd_policy.student args.kd_policy.teacher.eval() num_labels = len(args.labels) global_step = 0 nb_tr_steps = 0 tr_loss = 0 save_best_model = eval_data is not None and args.eval_interval > 0 train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) optimizer, t_total = get_optimizer(args, model, num_train_steps) train_data = prepare(args, processor, label_list, tokenizer, 'train') logger.info("***** Running distillation *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_steps) 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) train_steps = 0 best_eval_accuracy = 0 for epoch in trange(int(args.num_train_epochs), desc="Epoch", dynamic_ncols=True): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 args.kd_policy.on_epoch_begin(model, None, None) for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", dynamic_ncols=True)): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch model.train() logits = args.kd_policy.forward(input_ids, segment_ids, input_mask) loss = CrossEntropyLoss()(logits.view(-1, num_labels), label_ids.view(-1)) loss = args.kd_policy.before_backward_pass(model, epoch, None, None, loss, None).overall_loss 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() train_steps += 1 tensorboard_logger.add_scalar('distillation_train_loss', loss.item(), train_steps) tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: # modify learning rate with special warm up BERT uses lr_this_step = args.learning_rate * warmup_linear(global_step / t_total, 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 save_best_model and train_steps % args.eval_interval == 0: eval_loss, eval_accuracy, _ = eval(args, model, eval_data, device, verbose=False) tensorboard_logger.add_scalar('distillation_dev_loss', eval_loss, train_steps) tensorboard_logger.add_scalar('distillation_dev_accuracy', eval_accuracy, train_steps) if eval_accuracy > best_eval_accuracy: save_model(model, output_model_file) best_eval_accuracy = eval_accuracy args.kd_policy.on_epoch_end(model, None, None) if save_best_model: eval_loss, eval_accuracy, _ = eval(args, model, eval_data, device, verbose=False) if eval_accuracy > best_eval_accuracy: save_model(model, output_model_file) else: save_model(model, output_model_file) return global_step, tr_loss / nb_tr_steps
def main(): parser = argparse.ArgumentParser() parser.add_argument("--data_dir", default=None, type=str, required=True, help="输入数据dir。应该包含任务的.tsv文件(或其他数据文件)。") 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("--task_name", default=None, type=str, required=True, help="训练任务的名称") parser.add_argument("--output_dir", default=None, type=str, required=True, help="将写入模型预测和checkpoints的输出目录。 ") parser.add_argument("--cache_dir", default="", type=str, help="您希望将从s3下载的预训练模型存储在何处") parser.add_argument( "--max_seq_length", default=128, type=int, help="WordPiece tokenization 后输入序列的最大总长度,大于这个的序列将被截断,小于的padded") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", action='store_true', help="如果您使用的是uncased模型,请设置此标志。") parser.add_argument("--train_batch_size", default=64, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=256, type=int, help="Total batch size for eval.") 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("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") 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( '--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.0 (default value): dynamic loss scaling.Positive power of 2: static loss scaling value.\n" ) 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() # 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() processors = { # "cola": ColaProcessor, # "mnli": MnliProcessor, # "mnli-mm": MnliMismatchedProcessor, # "mrpc": MrpcProcessor, # "sst-2": Sst2Processor, # "sts-b": StsbProcessor, # "qqp": QqpProcessor, # "qnli": QnliProcessor, "rte": RteProcessor # "wnli": WnliProcessor, } output_modes = { # "cola": "classification", # "mnli": "classification", # "mrpc": "classification", # "sst-2": "classification", # "sts-b": "regression", # "qqp": "classification", # "qnli": "classification", "rte": "classification" # "wnli": "classification", } if args.local_rank == -1 or args.no_cuda: # 未指定GPU,或无GPU device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: # 分布式 torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # ??????????多GPU??????? # Initializes the distributed backend which will take care of sychronizing nodes/GPUs # ?????单GPU没有分布式?????? torch.distributed.init_process_group(backend='nccl') 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)) # 如果显存不足,假设原来的batch size=10,数据总量为1000,那么一共需要100train steps,同时一共进行100次梯度更新。 # 若是显存不够,我们需要减小batch size,我们设置gradient_accumulation_steps=2,那么我们新的batch_size=10/2=5, # 我们需要运行两次,才能在内存中放入10条数据,梯度更新的次数不变为100次,那么我们的train_steps=200 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: # 多GPU torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() # RteProcessor output_mode = output_modes[task_name] # "classification" label_list = processor.get_labels() # ["entailment", "not_entailment"] num_labels = len(label_list) # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join( str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format( args.local_rank)) # model = BertForSequenceClassification.from_pretrained(args.bert_model, # cache_dir=cache_dir, # num_labels=num_labels) # tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) model = BertForSequenceClassification.from_pretrained( 'bert-base-uncased', num_labels=num_labels) # 2个标签 if args.fp16: model.half() model.to(device) if n_gpu > 1: # 多GPU model = torch.nn.DataParallel(model) tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=args.do_lower_case) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] # 不weight_decay 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 }] # nd 在不在 n 中如果在把p放进去 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) else: optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate) if args.do_train: num_train_steps = None # train_examples = processor.get_train_examples_wenpeng('/home/wyin3/Datasets/glue_data/RTE/train.tsv') train_examples, seen_types = processor.get_examples_Wikipedia_train( '/home/zut_csi/tomding/zs/BenchmarkingZeroShotData/tokenized_wiki2categories.txt', 100000) # /export/home/Dataset/wikipedia/parsed_output/tokenized_wiki/tokenized_wiki2categories.txt', 100000) #train_pu_half_v1.txt # seen_classes=[0,2,4,6,8] eval_examples, eval_label_list, eval_hypo_seen_str_indicator, eval_hypo_2_type_index = processor.get_examples_emotion_test( '/home/zut_csi/tomding/zs/BenchmarkingZeroShot/emotion/dev.txt', seen_types) # /export/home/Dataset/Stuttgart_Emotion/unify-emotion-datasets-master/zero-shot-split/dev.txt', seen_types) test_examples, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index = processor.get_examples_emotion_test( '/home/zut_csi/tomding/zs/BenchmarkingZeroShot/emotion/test.txt', seen_types) # /export/home/Dataset/Stuttgart_Emotion/unify-emotion-datasets-master/zero-shot-split/test.txt', seen_types) train_features, eval_features, test_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer, output_mode), convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer, output_mode), convert_examples_to_features( test_examples, label_list, args.max_seq_length, tokenizer, output_mode) all_input_ids, eval_all_input_ids, test_all_input_ids = torch.tensor( [f.input_ids for f in train_features], dtype=torch.long), torch.tensor( [f.input_ids for f in eval_features], dtype=torch.long), torch.tensor( [f.input_ids for f in test_features], dtype=torch.long) all_input_mask, eval_all_input_mask, test_all_input_mask = torch.tensor( [f.input_mask for f in train_features], dtype=torch.long), torch.tensor( [f.input_mask for f in eval_features], dtype=torch.long), torch.tensor( [f.input_mask for f in test_features], dtype=torch.long) all_segment_ids, eval_all_segment_ids, test_all_segment_ids = torch.tensor( [f.segment_ids for f in train_features], dtype=torch.long), torch.tensor( [f.segment_ids for f in eval_features], dtype=torch.long), torch.tensor( [f.segment_ids for f in test_features], dtype=torch.long) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float) eval_all_label_ids, test_all_label_ids = torch.tensor( [f.label_id for f in eval_features], dtype=torch.long), torch.tensor( [f.label_id for f in test_features], dtype=torch.long) train_data, eval_data, test_data = TensorDataset( all_input_ids, all_input_mask, all_segment_ids, all_label_ids), TensorDataset( eval_all_input_ids, eval_all_input_mask, eval_all_segment_ids, eval_all_label_ids), TensorDataset(test_all_input_ids, test_all_input_mask, test_all_segment_ids, test_all_label_ids) train_sampler, eval_sampler, test_sampler = RandomSampler( train_data), SequentialSampler(eval_data), SequentialSampler( test_data) eval_dataloader, test_dataloader, train_dataloader = DataLoader( eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size), DataLoader( test_data, sampler=test_sampler, batch_size=args.eval_batch_size), DataLoader( train_data, sampler=train_sampler, batch_size=args.train_batch_size) # ??????????????batch_size 已经除 args.gradient_accumulation_steps????????????????? num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_steps = num_train_steps // torch.distributed.get_world_size( ) # 全局的整个的进程数 max_test_unseen_acc, max_dev_unseen_acc, max_dev_seen_acc, max_overall_acc = 0.0, 0.0, 0.0, 0.0 # logger.info( '****************************************************** Running_training ***************************************************' ) logger.info("Num_examples:{} Batch_size:{} Num_steps:{}".format( len(train_examples), args.train_batch_size, num_train_steps)) for _ in trange(int(args.num_train_epochs), desc="Epoch"): train_loss = 0 for train_step, batch_data in enumerate( tqdm(train_dataloader, desc="Iteration")): model.train() batch_data = tuple(b.to(device) for b in batch_data) input_ids, input_mask, segment_ids, label_ids = batch_data logits = model(input_ids, segment_ids, input_mask, labels=None)[0] tmp_train_loss = CrossEntropyLoss()(logits.view( -1, num_labels), label_ids.view(-1)) if n_gpu > 1: # 多GPU tmp_train_loss = tmp_train_loss.mean( ) # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: tmp_train_loss = tmp_train_loss / args.gradient_accumulation_steps tmp_train_loss.backward() train_loss += tmp_train_loss.item() optimizer.step() optimizer.zero_grad() if (train_step + 1 ) % 200 == 0: # start evaluate on dev set after this epoch def et(et_dataloader, max_et_unseen_acc, et_label_list, et_hypo_seen_str_indicator, et_hypo_2_type_index): model.eval() et_loss, et_step, preds = 0, 0, [] for input_ids, input_mask, segment_ids, label_ids in et_dataloader: input_ids, input_mask, segment_ids, label_ids = input_ids.to( device), input_mask.to(device), segment_ids.to( device), label_ids.to(device) with torch.no_grad(): logits = model(input_ids, segment_ids, input_mask, labels=None)[0] tmp_et_loss = CrossEntropyLoss()(logits.view( -1, num_labels), label_ids.view(-1)) et_loss += tmp_et_loss.mean().item() et_step += 1 if len(preds) == 0: preds.append(logits.detach().cpu().numpy()) # 进行反向传播时,到该调用detach()的Variable就会停止,不能再继续向前进行传播. # cpu()函数作用是将数据从GPU上复制到memory上,相对应的函数是cuda() else: preds[0] = np.append( preds[0], logits.detach().cpu().numpy(), axis=0) et_loss = et_loss / et_step preds = preds[0] ''' preds: size*2 (entail, not_entail) wenpeng added a softxmax so that each row is a prob vec ''' pred_probs = softmax(preds, axis=1)[:, 0] pred_binary_labels_harsh, pred_binary_labels_loose = [], [] for i in range(preds.shape[0]): pred_binary_labels_harsh.append( 0 ) if preds[i][0] > preds[i][ 1] + 0.1 else pred_binary_labels_harsh.append( 1) pred_binary_labels_loose.append( 0) if preds[i][0] > preds[i][ 1] else pred_binary_labels_loose.append(1) seen_acc, unseen_acc = evaluate_emotion_zeroshot_TwpPhasePred( pred_probs, pred_binary_labels_harsh, pred_binary_labels_loose, et_label_list, et_hypo_seen_str_indicator, et_hypo_2_type_index, seen_types) # result = compute_metrics('F1', preds, all_label_ids.numpy()) loss = train_loss / train_step if args.do_train else None # test_acc = mean_f1#result.get("f1") if unseen_acc > max_et_unseen_acc: max_et_unseen_acc = unseen_acc print( 'seen_f1:{} unseen_f1:{} max_unseen_f1:{}'.format( seen_acc, unseen_acc, max_et_unseen_acc)) return max_et_unseen_acc # if seen_acc+unseen_acc > max_overall_acc: # max_overall_acc = seen_acc + unseen_acc # if seen_acc > max_dev_seen_acc: # max_dev_seen_acc = seen_acc logger.info( '********************* Running evaluation *********************' ) logger.info("Num_examples:{} Batch_size:{}".format( len(eval_examples), args.eval_batch_size)) max_dev_unseen_acc = et(eval_dataloader, max_dev_unseen_acc, eval_label_list, eval_hypo_seen_str_indicator, eval_hypo_2_type_index) logger.info( '********************* Running testing *********************' ) logger.info("Num_examples:{} Batch_size:{}".format( len(test_examples), args.eval_batch_size)) max_test_unseen_acc = et(test_dataloader, max_test_unseen_acc, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index)
#loss를 담을 리스트 losses = [] for i in range(epochs): model.train() y_pred = model(X_train) Y_train = Y_train.squeeze() loss = CrossEntropyLoss()(y_pred, Y_train) losses.append(loss) if i % 10 == 0: print(f'epoch {i}, loss is {loss}') #역전파 수행 optimizer.zero_grad() loss.backward() optimizer.step() #갱신 plt.plot(range(epochs), losses) plt.ylabel('loss') plt.xlabel('Epoch') #plt.show() # 테스트 correct = 0 print("\n\n\n---------------테스트---------------") with torch.no_grad(): for i, data in enumerate(X_test): y_val = model.forward(data)