huggingface-(2)

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# File model.py
# -*- coding: utf-8 -*-
import torch
import os
from torch import nn
from transformers import BertForSequenceClassification, BertConfig
​
class BertModel(nn.Module):
    def __init__(self, num_labels):
        super(BertModel, self).__init__()
        self.bert = BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext", num_labels=num_labels)
        self.device = torch.device("cuda")
        for param in self.bert.parameters():
            param.requires_grad = True  # 每个参数都要求梯度,也可以冻结一些层
​
    def forward(self, batch_seqs, batch_seq_masks, batch_seq_segments, labels):
        loss, logits = self.bert(input_ids = batch_seqs, attention_mask = batch_seq_masks, 
                              token_type_ids=batch_seq_segments, labels = labels)[:2]
        probabilities = nn.functional.softmax(logits, dim=-1)
        prob, pre_label = torch.max(probabilities, 1)
        return loss, pre_label, probfrom torch.utils.data import Dataset
from hanziconv import HanziConv
import pandas as pd
import torch
​
class DataPrecessForCLF(Dataset):
    def __init__(self, bert_tokenizer, df, max_char_len):
       
        self.y = torch.LongTensor(df["id"])
        self.max_seq_len = max_char_len
        df["sentence"] = df["sentence"].apply(lambda i: HanziConv.toSimplified(i))
        self.encoded_inputs = bert_tokenizer(df["sentence"].tolist(), padding="max_length", truncation=True,
                                             max_length=max_char_len, return_tensors="pt")
​
    def __len__(self):
        return len(self.y)
​
    def __getitem__(self, idx):
        assert len(self.encoded_inputs["input_ids"][idx]) == len(self.encoded_inputs["attention_mask"][idx]) == len(self.encoded_inputs["token_type_ids"][idx])
        return self.encoded_inputs["input_ids"][idx], self.encoded_inputs["attention_mask"][idx], self.encoded_inputs["token_type_ids"][idx], self.y[idx]
import torch
import torch.nn as nn
import time
from tqdm import tqdm
​
def validate(model, dataloader):
    model.eval()
    device = model.device
    epoch_start = time.time()
    batch_time_avg = 0.0
    running_loss = 0.0
    correct_preds = 0
     
    for batch_idx, (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in enumerate(dataloader):
        batch_start = time.time()
        seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(
            device), batch_labels.to(device)
        loss, pre_label, prob = model(seqs, masks, segments, labels)
        batch_time_avg += time.time() - batch_start
        running_loss += loss.item()
        correct_preds += (pre_label == labels).sum().item()
    epoch_time = time.time() - epoch_start
    epoch_loss = running_loss / len(dataloader)
    epoch_accuracy = correct_preds / len(dataloader.dataset)
    return epoch_time, epoch_loss, epoch_accuracy
​
def test(model, dataloader, inference=False):
    model.eval()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备
    time_start = time.time()
    batch_time = 0.0
    correct_preds = 0
    all_prob = []
    all_pred_label = []
    all_labels = []
    # Deactivate autograd for evaluation.
    with torch.no_grad():
        for (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in dataloader:
            batch_start = time.time()
            seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(device), batch_labels.to(device)
            loss, pre_label, prob = model(seqs, masks, segments, labels)
            correct_preds += (pre_label == labels).sum().item()
            batch_time += time.time() - batch_start
            all_prob.extend(prob.cpu().numpy())
            all_pred_label.extend(pre_label.cpu().numpy())
            all_labels.extend(batch_labels)
​
    batch_time /= len(dataloader)
    total_time = time.time() - time_start
    if inference:
        return all_pred_label, all_prob, total_time
    accuracy = correct_preds / len(dataloader.dataset)
    return batch_time, total_time, accuracy
​
def train(model, dataloader, optimizer, epoch_number, max_gradient_norm):
    model.train()
    device = model.device
    epoch_start = time.time()
    batch_time_avg = 0.0
    running_loss = 0.0
    correct_preds = 0
    tqdm_batch_iterator = tqdm(dataloader)
    for batch_index, (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in enumerate(tqdm_batch_iterator):
        batch_start = time.time()
        seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(device), batch_labels.to(device)
        optimizer.zero_grad()
        loss, pre_label, prob = model(seqs, masks, segments, labels)
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), max_gradient_norm)
        optimizer.step()
        batch_time_avg += time.time() - batch_start
        running_loss += loss.item()
        correct_preds += (pre_label == labels).sum().item()
        description = "Avg. batch proc. time: {:.4f}s, loss: {:.4f}"\
                      .format(batch_time_avg/(batch_index+1), running_loss/(batch_index+1))
        tqdm_batch_iterator.set_description(description)
    epoch_time = time.time() - epoch_start
    epoch_loss = running_loss / len(dataloader)
    epoch_accuracy = correct_preds / len(dataloader.dataset)
    return epoch_time, epoch_loss, epoch_accuracy# -*- coding: utf-8 -*-
import os
import sys
import torch
from torch import nn
from torch.utils.data import DataLoader
from data import DataPrecessForCLF
from utils import train, validate
from model import BertModel
from transformers import BertTokenizer
from transformers.optimization import AdamW
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import json
from sklearn.model_selection import train_test_split
le = LabelEncoder()
​
def main(train_file, target_dir,
         epochs=25,
         batch_size=16,
         lr=2e-05,
         patience=5,
         max_char_len=512,
         max_grad_norm=10.0,
         checkpoint=None):
    bert_tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext", do_lower_case=True)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备
    print(20 * "=", " Preparing for training ", 20 * "=")
    # 保存模型的路径
    if not os.path.exists(target_dir):
        os.makedirs(target_dir)
    # -------------------- Data loading ------------------- #
    data = pd.read_csv(train_file)
    data = data[["sentence", "label"]]
    data["sentence"] = data["sentence"].apply(lambda i: str(i))
    data["label"] = data["label"].apply(lambda i: str(i))
    data["id"] = torch.LongTensor(le.fit_transform(data["label"]))
    label = data[["id", "label"]]
    label = label.drop_duplicates()
    label_dict = {}
    for index, row in label.iterrows():
        label_dict[row["label"]] = row["id"]
    refund_map = dict(sorted(label_dict.items(), key=lambda d: d[0]))
    label_num = len(refund_map)
    with open(target_dir + '/label2id.json', "w", encoding="utf-8") as f:
        json.dump(refund_map, f, ensure_ascii=False, indent=4)
    print("the classification label num is {}".format(label_num))
    # train_dev_split
    df_train, df_dev, y_train, y_test = train_test_split(data, data["label"], test_size=0.2,
                                                    stratify=data["label"], random_state=666)
    df_train.reset_index(inplace=True, drop=True)
    df_dev.reset_index(inplace=True, drop=True)
    print("\t* Loading training data... , dataset size is {}".format(len(df_train)))
    train_data = DataPrecessForCLF(bert_tokenizer, df=df_train, max_char_len=max_char_len)
    train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
    print("\t* Loading validation data... , dataset size is {}".format(len(df_dev)))
    dev_data = DataPrecessForCLF(bert_tokenizer, df=df_dev, max_char_len=max_char_len)
    dev_loader = DataLoader(dev_data, shuffle=True, batch_size=batch_size)
    # -------------------- Model definition ------------------- #
    print("\t* Building model...")
    model = BertModel(label_num).to(device)
    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 = AdamW(optimizer_grouped_parameters, lr=lr)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", 
                                                           factor=0.85, patience=0)
    best_score = 0.0
    start_epoch = 1
    # Data for loss curves plot
    epochs_count = []
    train_losses = []
    valid_losses = []
    # Continuing training from a checkpoint if one was given as argument
    if checkpoint:
        checkpoint = torch.load(checkpoint)
        start_epoch = checkpoint["epoch"] + 1
        best_score = checkpoint["best_score"]
        print("\t* Training will continue on existing model from epoch {}...".format(start_epoch))
        model.load_state_dict(checkpoint["model"])
        optimizer.load_state_dict(checkpoint["optimizer"])
        epochs_count = checkpoint["epochs_count"]
        train_losses = checkpoint["train_losses"]
        valid_losses = checkpoint["valid_losses"]
     # Compute loss and accuracy before starting (or resuming) training.
    _, valid_loss, valid_accuracy = validate(model, dev_loader)
    print("\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%".format(valid_loss, (valid_accuracy*100)))
    # -------------------- Training epochs ------------------- #
    print("\n", 20 * "=", "Training Bert model on device: {}".format(device), 20 * "=")
    patience_counter = 0
    for epoch in range(start_epoch, epochs + 1):
        epochs_count.append(epoch)
        print("* Training epoch {}:".format(epoch))
        epoch_time, epoch_loss, epoch_accuracy = train(model, train_loader, optimizer, epoch, max_grad_norm)
        train_losses.append(epoch_loss)
        print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%"
              .format(epoch_time, epoch_loss, (epoch_accuracy*100)))
        print("* Validation for epoch {}:".format(epoch))
        epoch_time, epoch_loss, epoch_accuracy = validate(model, dev_loader)
        valid_losses.append(epoch_loss)
        print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
              .format(epoch_time, epoch_loss, (epoch_accuracy*100)))
        # Update the optimizer's learning rate with the scheduler.
        scheduler.step(epoch_accuracy)
        # Early stopping on validation accuracy.
        if epoch_accuracy < best_score:
            patience_counter += 1
        else:
            best_score = epoch_accuracy
            patience_counter = 0
            torch.save({"epoch": epoch, 
                        "model": model.state_dict(),
                        "best_score": best_score,
                        "epochs_count": epochs_count,
                        "train_losses": train_losses,
                        "valid_losses": valid_losses},
                        os.path.join(target_dir, "0704_nre.pth.tar"))
        if patience_counter >= patience:
            print("-> Early stopping: patience limit reached, stopping...")
            break
​
if __name__ == "__main__":
    main(os.path.join(base_path, "model_data/train.csv"),
     os.path.join(base_path, "checkpoint"))
​