# 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