# import torch.nn as nn # import torch import argparse from torch.utils.data import DataLoader import torch.nn as nn from torch.optim import Adam, SGD import torch from sklearn.metrics import precision_score, recall_score, f1_score from src.pretrainer import BERTFineTuneTrainer1 from src.dataset import TokenizerDataset from src.vocab import Vocab import tqdm import numpy as np import time from src.bert import BERT from hint_fine_tuning import CustomBERTModel # from vocab import Vocab # class BERTForSequenceClassification(nn.Module): # """ # Since its classification, # n_labels = 2 # """ # def __init__(self, vocab_size, n_labels, layers=None, hidden=768, n_layers=12, attn_heads=12, dropout=0.1): # super().__init__() # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # print(device) # # model_ep0 = torch.load("output_1/bert_trained.model.ep0", map_location=device) # self.bert = torch.load("output_1/bert_trained.model.ep0", map_location=device) # self.dropout = nn.Dropout(dropout) # # add an output layer # self. # def forward(self, x, segment_info): # return x class BERTFineTunedTrainer: def __init__(self, bert: CustomBERTModel, vocab_size: int, train_dataloader: DataLoader = None, test_dataloader: DataLoader = None, lr: float = 1e-4, betas=(0.9, 0.999), weight_decay: float = 0.01, warmup_steps=10000, with_cuda: bool = True, cuda_devices=None, log_freq: int = 10, workspace_name=None, num_labels=2): """ :param bert: BERT model which you want to train :param vocab_size: total word vocab size :param train_dataloader: train dataset data loader :param test_dataloader: test dataset data loader [can be None] :param lr: learning rate of optimizer :param betas: Adam optimizer betas :param weight_decay: Adam optimizer weight decay param :param with_cuda: traning with cuda :param log_freq: logging frequency of the batch iteration """ self.device = "cpu" self.model = bert self.test_data = test_dataloader self.log_freq = log_freq self.workspace_name = workspace_name # print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()])) def test(self, epoch): self.iteration(epoch, self.test_data, train=False) def iteration(self, epoch, data_loader, train=True): """ loop over the data_loader for training or testing if on train status, backward operation is activated and also auto save the model every peoch :param epoch: current epoch index :param data_loader: torch.utils.data.DataLoader for iteration :param train: boolean value of is train or test :return: None """ str_code = "train" if train else "test" # Setting the tqdm progress bar data_iter = tqdm.tqdm(enumerate(data_loader), desc="EP_%s:%d" % (str_code, epoch), total=len(data_loader), bar_format="{l_bar}{r_bar}") avg_loss = 0.0 total_correct = 0 total_element = 0 plabels = [] tlabels = [] logits_list = [] labels_list = [] positive_class_probs = [] self.model.eval() for i, data in data_iter: data = {key: value.to(self.device) for key, value in data.items()} with torch.no_grad(): h_rep, logits = self.model.forward(data["input"], data["segment_label"]) # print(logits, logits.shape) logits_list.append(logits.cpu()) labels_list.append(data["label"].cpu()) probs = F.Softmax(dim=-1)(logits) predicted_labels = torch.argmax(probs, dim=-1) true_labels = torch.argmax(data["label"], dim=-1) positive_class_probs.extend(probs[:, 1]) plabels.extend(predicted_labels.cpu().numpy()) tlabels.extend(true_labels.cpu().numpy()) # print(">>>>>>>>>>>>>>", predicted_labels, true_labels) # Compare predicted labels to true labels and calculate accuracy correct = (predicted_labels == true_labels).sum().item() total_correct += correct total_element += data["label"].nelement() precisions = precision_score(tlabels, plabels, average="weighted") recalls = recall_score(tlabels, plabels, average="weighted") f1_scores = f1_score(tlabels, plabels, average="weighted") accuracy = total_correct * 100.0 / total_element auc_score = roc_auc_score(tlabels.cpu(), plabels.cpu()) final_msg = { "epoch": f"EP{epoch}_{str_code}", "accuracy": accuracy, "avg_loss": avg_loss / len(data_iter), "precisions": precisions, "recalls": recalls, "f1_scores": f1_scores } print(final_msg) # print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_acc=", total_correct * 100.0 / total_element) if __name__ == "__main__": # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # print(device) # is_model = torch.load("ratio_proportion_change4/output/bert_fine_tuned.IS.model.ep40", map_location=device) # learned_parameters = model_ep0.state_dict() # for param_name, param_tensor in learned_parameters.items(): # print(param_name) # print(param_tensor) # # print(model_ep0.state_dict()) # # model_ep0.add_module("out", nn.Linear(10,2)) # # print(model_ep0) # seq_vocab = Vocab("pretraining/vocab_file.txt") # seq_vocab.load_vocab() # classifier = BERTForSequenceClassification(len(seq_vocab.vocab), 2) parser = argparse.ArgumentParser() parser.add_argument('-workspace_name', type=str, default="ratio_proportion_change3_1920") # parser.add_argument("-t", "--test_dataset", type=str, default="finetuning/before_June/train_in.txt", help="test set for evaluate fine tune train set") # parser.add_argument("-tlabel", "--test_label", type=str, default="finetuning/before_June/train_in_label.txt", help="test set for evaluate fine tune train set") # ##### change Checkpoint # parser.add_argument("-c", "--finetuned_bert_checkpoint", type=str, default="ratio_proportion_change3/output/before_June/bert_fine_tuned.FS.model.ep30", help="checkpoint of saved pretrained bert model") # parser.add_argument("-v", "--vocab_path", type=str, default="pretraining/vocab.txt", help="built vocab model path with bert-vocab") parser.add_argument("-t", "--test_dataset", type=str, default="/home/jupyter/bert/dataset/hint_based/ratio_proportion_change_3/er/er_test_dataset.csv", help="test set for evaluate fine tune train set") parser.add_argument("-tlabel", "--test_label", type=str, default="/home/jupyter/bert/dataset/hint_based/ratio_proportion_change_3/er/test_infos_only.csv", help="test set for evaluate fine tune train set") ##### change Checkpoint parser.add_argument("-c", "--finetuned_bert_checkpoint", type=str, default="/home/jupyter/bert/ratio_proportion_change3_1920/_Aug23/output/hint_classification/fine_tuned_model_2.pth", help="checkpoint of saved pretrained bert model") parser.add_argument("-v", "--vocab_path", type=str, default="/home/jupyter/bert/ratio_proportion_change3_1920/_Aug23/pretraining/vocab.txt", help="built vocab model path with bert-vocab") parser.add_argument("-num_labels", type=int, default=2, help="Number of labels") parser.add_argument("-hs", "--hidden", type=int, default=64, help="hidden size of transformer model") parser.add_argument("-l", "--layers", type=int, default=4, help="number of layers") parser.add_argument("-a", "--attn_heads", type=int, default=8, help="number of attention heads") parser.add_argument("-s", "--seq_len", type=int, default=100, help="maximum sequence length") parser.add_argument("-b", "--batch_size", type=int, default=32, help="number of batch_size") parser.add_argument("-e", "--epochs", type=int, default=1, help="number of epochs") # Use 50 for pretrain, and 10 for fine tune parser.add_argument("-w", "--num_workers", type=int, default=4, help="dataloader worker size") # Later run with cuda parser.add_argument("--with_cuda", type=bool, default=False, help="training with CUDA: true, or false") parser.add_argument("--log_freq", type=int, default=10, help="printing loss every n iter: setting n") parser.add_argument("--corpus_lines", type=int, default=None, help="total number of lines in corpus") parser.add_argument("--cuda_devices", type=int, nargs='+', default=None, help="CUDA device ids") parser.add_argument("--on_memory", type=bool, default=True, help="Loading on memory: true or false") parser.add_argument("--dropout", type=float, default=0.1, help="dropout of network") parser.add_argument("--lr", type=float, default=1e-3, help="learning rate of adam") parser.add_argument("--adam_weight_decay", type=float, default=0.01, help="weight_decay of adam") parser.add_argument("--adam_beta1", type=float, default=0.9, help="adam first beta value") parser.add_argument("--adam_beta2", type=float, default=0.999, help="adam first beta value") args = parser.parse_args() for k,v in vars(args).items(): if ('dataset' in k) or ('path' in k) or ('label' in k): if v: # setattr(args, f"{k}", args.workspace_name+"/"+v) print(f"args.{k} : {getattr(args, f'{k}')}") print("Loading Vocab", args.vocab_path) vocab_obj = Vocab(args.vocab_path) vocab_obj.load_vocab() print("Vocab Size: ", len(vocab_obj.vocab)) print("Loading Test Dataset", args.test_dataset) test_dataset = TokenizerDataset(args.test_dataset, args.test_label, vocab_obj, seq_len=args.seq_len) print("Creating Dataloader") test_data_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers) bert = torch.load(args.finetuned_bert_checkpoint, map_location="cpu") num_labels = 2 print(f"Number of Labels : {num_labels}") print("Creating BERT Fine Tune Trainer") trainer = BERTFineTuneTrainer1(bert, len(vocab_obj.vocab), train_dataloader=None, test_dataloader=test_data_loader, lr=args.lr, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, with_cuda=args.with_cuda, cuda_devices=args.cuda_devices, log_freq=args.log_freq, workspace_name = args.workspace_name, num_labels=args.num_labels) print("Testing Start....") start_time = time.time() for epoch in range(args.epochs): trainer.test(epoch) end_time = time.time() print("Time Taken to fine tune dataset = ", end_time - start_time) # bert/ratio_proportion_change3_2223/sch_largest_100-coded/output/Opts/bert_fine_tuned.model.ep22