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| # 要添加一个新单元，输入 '# %%'
# 要添加一个新的标记单元，输入 '# %% [markdown]'
# %%
from IPython import get_ipython
# %%
get_ipython().system('nvidia-smi')
# %%
# %%
import json
import random
import os
import pickle
import time
import gc
import copy
from tqdm import tqdm
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torch import nn
from torch import optim
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
# %%
torch.__version__
# %%
torch.cuda.is_available()
# %%
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.enabled = False
# %%
processed_expose_test_path = "data/processed_test_expose.json"
processed_expose_train_labeled_path = "data/processed_train_expose_labeled.json"
processed_expose_train_unlabel_predict_other_path = "data/processed_train_expose_unlabel_predict_other_0.5.json"
processed_expose_train_valid_path = "data/processed_train_valid.json"
processed_expose_train_train_path = "data/processed_train_train.json"
pretrained_bert_path = "bert-base-chinese/"
category_encoder_path = 'model/category_encoder_second.pickle'
paragraphs_num_encoder_path = 'model/paragraphs_num_encoder_second.pickle'
source_encoder_path = 'model/source_encoder_second.pickle'
doctype_encoder_path = 'model/doctype_encoder_second.pickle'
words_len_encoder_path = 'model/words_len_encoder_second.pickle'
model_train_mutil_model_path = 'model/model_train_mutil_target.pt'
submission_path = "submission_train_mutil_target_predict.csv"
num_workers = 0
# %%
# %%
def get_feature_list(input_path, feature_name):
feature_list = []
with open(input_path, 'r', encoding="utf-8") as input_file:
for line in tqdm(input_file):
json_data = json.loads(line)
feature_list.append(json_data[feature_name])
return feature_list
# %%
def get_category_encoder():
if os.path.exists(category_encoder_path):
with open (category_encoder_path, 'rb') as category_encoder_file:
return pickle.load(category_encoder_file)
category_list = get_feature_list(processed_expose_train_labeled_path, 'category') + get_feature_list(processed_expose_test_path, 'category') + get_feature_list(processed_expose_train_unlabel_predict_other_path, 'category')
category_list = np.array(category_list).reshape(-1, 1)
encoder = OneHotEncoder(categories='auto', handle_unknown='ignore').fit(category_list)
with open (category_encoder_path, 'wb') as category_encoder_file:
pickle.dump(encoder, category_encoder_file)
return encoder
# %%
category_encoder = get_category_encoder()
category_encoder.transform(np.array([1]).reshape(-1, 1)).toarray()[0]
# %%
len(category_encoder.categories_[0])
# %%
def get_paragraphs_num_encoder():
if os.path.exists(paragraphs_num_encoder_path):
with open (paragraphs_num_encoder_path, 'rb') as paragraphs_num_encoder_file:
return pickle.load(paragraphs_num_encoder_file)
paragraphs_num_list = get_feature_list(processed_expose_train_labeled_path, 'paragraphs_num') + get_feature_list(processed_expose_test_path, 'paragraphs_num') + get_feature_list(processed_expose_train_unlabel_predict_other_path, 'paragraphs_num')
paragraphs_num_list = np.array(paragraphs_num_list).reshape(-1, 1)
encoder = StandardScaler().fit(paragraphs_num_list)
with open (paragraphs_num_encoder_path, 'wb') as paragraphs_num_encoder_file:
pickle.dump(encoder, paragraphs_num_encoder_file)
return encoder
# %%
paragraphs_num_encoder = get_paragraphs_num_encoder()
paragraphs_num_encoder.transform(np.array([100]).reshape(-1, 1))[0][0]
# %%
paragraphs_num_encoder.transform(np.array([99999999999]).reshape(-1, 1))[0][0]
# %%
def get_words_len_encoder():
if os.path.exists(words_len_encoder_path):
with open (words_len_encoder_path, 'rb') as words_len_encoder_file:
return pickle.load(words_len_encoder_file)
words_len_list = get_feature_list(processed_expose_train_labeled_path, 'words_len') + get_feature_list(processed_expose_test_path, 'words_len') + get_feature_list(processed_expose_train_unlabel_predict_other_path, 'words_len')
words_len_list = np.array(words_len_list).reshape(-1, 1)
encoder = StandardScaler().fit(words_len_list)
with open (words_len_encoder_path, 'wb') as words_len_encoder_file:
pickle.dump(encoder, words_len_encoder_file)
return encoder
# %%
words_len_encoder = get_words_len_encoder()
words_len_encoder.transform(np.array([1000]).reshape(-1, 1))[0][0]
# %%
words_len_encoder.transform(np.array([2000]).reshape(-1, 1))[0][0]
# %%
def get_source_encoder():
if os.path.exists(source_encoder_path):
with open (source_encoder_path, 'rb') as source_encoder_file:
return pickle.load(source_encoder_file)
source_list = get_feature_list(processed_expose_train_labeled_path, 'source') + get_feature_list(processed_expose_test_path, 'source') + get_feature_list(processed_expose_train_unlabel_predict_other_path, 'source')
source_list = np.array(source_list).reshape(-1, 1)
encoder = OneHotEncoder(categories='auto', handle_unknown='ignore').fit(source_list)
with open (source_encoder_path, 'wb') as source_encoder_file:
pickle.dump(encoder, source_encoder_file)
return encoder
# %%
source_encoder = get_source_encoder()
source_encoder.transform(np.array(['中国经济周刊']).reshape(-1, 1)).toarray()[0]
# %%
source_encoder.transform(np.array(['hg']).reshape(-1, 1)).toarray()[0]
# %%
len(source_encoder.categories_[0])
# %%
def get_doctype_encoder():
if os.path.exists(doctype_encoder_path):
with open (doctype_encoder_path, 'rb') as doctype_encoder_file:
return pickle.load(doctype_encoder_file)
doctype_list = get_feature_list(processed_expose_train_labeled_path, 'doctype') + get_feature_list(processed_expose_train_unlabel_predict_other_path, 'doctype')
doctype_set = set(doctype_list)
doctype_set.remove('其他')
doctype_list = list(doctype_set)
doctype_list.append('其他')
with open (doctype_encoder_path, 'wb') as doctype_encoder_file:
pickle.dump(doctype_list, doctype_encoder_file)
return doctype_list
# %%
doctype_list = get_doctype_encoder()
doctype_list, len(doctype_list)
# %%
# %%
# with open(processed_expose_train_labeled_path, 'r', encoding="utf-8") as input_file, \
# open(processed_expose_train_unlabel_predict_other_path, 'r', encoding="utf-8") as input1_file, \
# open(processed_expose_train_train_path, 'w', encoding="utf-8") as train_file, \
# open(processed_expose_train_valid_path, 'w', encoding="utf-8") as valid_file:
# for line in tqdm(input_file):
# json_data = json.loads(line)
# if random.random() < 0.1:
# valid_file.write(f"{json.dumps(json_data, ensure_ascii=False)}\n")
# else:
# train_file.write(f"{json.dumps(json_data, ensure_ascii=False)}\n")
# for line in tqdm(input1_file):
# json_data = json.loads(line)
# if random.random() < 0.1:
# valid_file.write(f"{json.dumps(json_data, ensure_ascii=False)}\n")
# else:
# train_file.write(f"{json.dumps(json_data, ensure_ascii=False)}\n")
# %%
# %%
class MyDataset(Dataset):
def __init__(self, input_paths, dataset_type):
self.dataset_type = dataset_type
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_bert_path)
self.data_list = self.load_data(input_paths)
@classmethod
def get_data_label(cls, tokenizer, item, dataset_type):
token = tokenizer(item['text'], add_special_tokens=True,
max_length=512,
truncation=True,
padding='max_length',
return_tensors="pt")
del item['text']
# item = dict(item, **token)
item['input_ids'] = token['input_ids'][0]
item['token_type_ids'] = token['token_type_ids'][0]
item['attention_mask'] = token['attention_mask'][0]
item['category'] = category_encoder.transform(np.array([item['category']]).reshape(-1, 1)).toarray()[0]
item['paragraphs_num'] = paragraphs_num_encoder.transform(np.array([item['paragraphs_num']]).reshape(-1, 1))[0]
item['words_len'] = words_len_encoder.transform(np.array([item['words_len']]).reshape(-1, 1))[0]
del item['pic_num']
item['source'] = source_encoder.transform(np.array([item['source']]).reshape(-1, 1)).toarray()[0]
# del item['id']
if dataset_type == 'test':
item['doctype'] = -1
else:
item['doctype'] = doctype_list.index(item['doctype'])
label = item['doctype']
del item['doctype']
# print(item)
return item, label
def __getitem__(self, index):
item = self.data_list[index]
item, label = MyDataset.get_data_label(self.tokenizer, item, self.dataset_type)
return item, label
def __len__(self):
return len(self.data_list)
def load_data(self, input_paths):
if type(input_paths) != list:
print("input_paths is not list!")
return
data_list = []
for input_path in input_paths:
with open(input_path, 'r', encoding='utf-8') as input_file:
for line in tqdm(input_file):
json_data = json.loads(line)
data_list.append(json_data)
if self.dataset_type != 'test':
random.shuffle(data_list)
return data_list
# %%
# train_dataset = MyDataset([processed_expose_train_train_path], 'train')
# train_loader = DataLoader(dataset=train_dataset, batch_size=2, shuffle=True, num_workers=num_workers)
# for batch_idx,data_ in enumerate(train_loader, 0):
# print(data_)
# break
# %%
# torch.tensor([])
# %%
train_dataset = MyDataset([processed_expose_train_train_path], 'train')
train_loader = DataLoader(dataset=train_dataset, batch_size=1, shuffle=True, num_workers=num_workers)
data_tmp = {'id':[],
'category':torch.tensor([]),
'paragraphs_num':torch.tensor([]),
'source':torch.tensor([]),
'words_len':torch.tensor([]),
'input_ids':torch.tensor([]),
'token_type_ids':torch.tensor([]),
'attention_mask':torch.tensor([]),}
label_tmp = torch.tensor([], dtype=torch.int)
count = 0
for data_ in train_loader:
# print(data_)
data, label = data_
# print(label)
data_tmp['id'].extend(data['id'])
data_tmp['category'] = torch.cat((data_tmp['category'], data['category']), 0)
data_tmp['paragraphs_num'] = torch.cat((data_tmp['paragraphs_num'], data['paragraphs_num']), 0)
data_tmp['source'] = torch.cat((data_tmp['source'], data['source']), 0)
data_tmp['words_len'] = torch.cat((data_tmp['words_len'], data['words_len']), 0)
data_tmp['input_ids'] = torch.cat((data_tmp['input_ids'], data['input_ids']), 0)
data_tmp['token_type_ids'] = torch.cat((data_tmp['token_type_ids'], data['token_type_ids']), 0)
data_tmp['attention_mask'] = torch.cat((data_tmp['attention_mask'], data['attention_mask']), 0)
label_tmp = torch.cat((label_tmp, label), 0)
count += 1
if count > 1:
print(data_tmp)
print(label_tmp)
print(label_tmp.shape[0])
print(torch.where(label_tmp == 1, 2, label_tmp))
data_tmp = {'id':[],
'category':torch.tensor([]),
'paragraphs_num':torch.tensor([]),
'source':torch.tensor([]),
'words_len':torch.tensor([]),
'input_ids':torch.tensor([]),
'token_type_ids':torch.tensor([]),
'attention_mask':torch.tensor([]),}
label_tmp = torch.tensor([], dtype=torch.int64)
break
# %%
# %%
class MutilTargetModel(nn.Module):
"""Bert分类器模型"""
def __init__(self, hidden_size=768):
super(MutilTargetModel, self).__init__()
# self.expert = AutoModel.from_pretrained(pretrained_bert_path)
self.bert = AutoModel.from_pretrained(pretrained_bert_path)
category_size = len(category_encoder.categories_[0])
paragraphs_num_size = 1
words_len_size = 1
source_size = len(source_encoder.categories_[0])
expert_size = 10
linear_size = hidden_size + category_size + paragraphs_num_size + source_size + words_len_size + expert_size
self.all_net = nn.Sequential(
nn.Linear(linear_size, 512),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(512, 64),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(64, len(doctype_list)),
)
self.expert_net = nn.Sequential(
nn.Linear(hidden_size, 256),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(256, 64),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(64, expert_size),
)
def forward(self, batch_data):
input_ids = batch_data['input_ids'].cuda()
token_type_ids = batch_data['token_type_ids'].cuda()
attention_mask = batch_data['attention_mask'].cuda()
bert_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
bert_cls_hidden_state = bert_output[0][:, 0, :]
# expert = self.expert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)[0][:, 0, :]
expert_output = self.expert_net(bert_cls_hidden_state)
cat_layer = torch.cat((bert_cls_hidden_state,
batch_data['category'].cuda(),
batch_data['paragraphs_num'].cuda(),
batch_data['source'].cuda(),
batch_data['words_len'].cuda(),
expert_output),
1)
# print(cat_layer.shape)
all_output = self.all_net(cat_layer.to(torch.float32))
return all_output, expert_output
def freeze(self, layer):
for child in layer.children():
# print(child)
for param in child.parameters():
param.requires_grad = False
# print(param)
# %%
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 7
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
delta (float): Minimum change in the monitored quantity to qualify as an improvement.
Default: 0
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
self.delta = delta
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score + self.delta:
self.counter += 1
print(f'EarlyStopping counter: {self.counter} out of {self.patience}. score:{-score}')
with open("early_stop.log", 'a+', encoding="utf-8") as log_file:
log_file.write(f'{localtime} EarlyStopping counter: {self.counter} out of {self.patience}. score:{-score}\n')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
localtime = time.asctime( time.localtime(time.time()) )
with open("early_stop.log", 'a+', encoding="utf-8") as log_file:
log_file.write(f'{localtime} Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...\n')
# torch.save(model.state_dict(), model_train_mutil_model_path) # 这里会存储迄今最优模型的参数
torch.save(model, model_train_mutil_model_path)
self.val_loss_min = val_loss
# %%
batch_size = 3
# torch.cuda.set_device(0)
model = MutilTargetModel()
# torch.set_default_tensor_type(torch.DoubleTensor)
model = model.cuda()
# print(model)
# 不同子网络设定不同的学习率
Bert_model_param = []
Bert_downstream_param = []
for items, param in model.named_parameters():
if not param.requires_grad:
# print(items)
continue
if items.startswith('expert.') or items.startswith('expert_net.'):
continue
if items.startswith('bert.'):
Bert_model_param.append(param)
else:
Bert_downstream_param.append(param)
param_groups = [{"params": Bert_model_param, "lr": 1e-5},
{"params": Bert_downstream_param, "lr": 1e-4}]
all_optimizer = optim.Adam(param_groups, eps=1e-7, weight_decay=0.001)
Bert_model_param = []
Bert_downstream_param = []
for items, param in model.named_parameters():
if not param.requires_grad:
continue
if not items.startswith('expert.') or items.startswith('expert_net.'):
continue
if items.startswith('bert.'):
Bert_model_param.append(param)
else:
Bert_downstream_param.append(param)
param_groups = [{"params": Bert_model_param, "lr": 1e-5},
{"params": Bert_downstream_param, "lr": 1e-4}]
expert_optimizer = optim.Adam(param_groups, eps=1e-7, weight_decay=0.001)
# 初始化 early_stopping 对象
patience = 2 # 当验证集损失在连续n次训练周期中都没有得到降低时，停止模型训练，以防止模型过拟合
early_stopping = EarlyStopping(patience, verbose=True)
criterion = nn.CrossEntropyLoss()
# %%
epoch_num = 5
batch_loss_num = 100
for epoch in range(epoch_num):
model.train() # 设置模型为训练模式
train_dataset = MyDataset([processed_expose_train_train_path], 'train')
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
with tqdm(total=len(train_loader)) as t:
loss_sum = 0.0
batch_loss = 1.0
for batch_idx,data_ in enumerate(train_loader, 0):
data, label = data_
# 清空梯度
all_optimizer.zero_grad()
expert_optimizer.zero_grad()
all_output, expert_output = model(data)
all_loss = criterion(all_output, label.cuda())
# all_loss = all_loss / 2
# print(f"label:{label}")
# print(f"expert_output:{expert_output}")
# print(all_output)
# expert_predict = np.argmax(expert_output.cpu(), 1)
other_index = doctype_list.index('其他')
expert_predict = torch.tensor(np.argmax(expert_output.cpu().detach().numpy(), 1))
# print(f"expert_predict:{expert_predict}")
# expert_label = torch.where(label > other_index, label - 1, label)
# print(expert_label)
# expert_label = torch.where(label == other_index, expert_predict, expert_label)
expert_index = label != other_index
# print(f"expert_index:{expert_index}")
expert_label = label[expert_index]
# # expert_label = label.clone().detach().float().requires_grad_()
# # for i in range(label.shape[0]):
# # if label[i] == doctype_list.index('其他'):
# # expert_label[i] = torch.max(expert_output[i])
# print(f"expert_label:{expert_label}")
expert_output = expert_output[expert_index]
# print(f"expert_output:{expert_output}")
expert_loss = criterion(expert_output, expert_label.cuda())
# expert_loss = expert_loss / 2
all_loss.backward(retain_graph=True)
# all_loss.backward()
expert_loss.backward()
# 更新模型参数
all_optimizer.step()
expert_optimizer.step()
loss_sum += all_loss.item()
if batch_idx % batch_loss_num == batch_loss_num - 1:
batch_loss = loss_sum / batch_loss_num
localtime = time.asctime( time.localtime(time.time()) )
with open("early_stop.log", 'a+', encoding="utf-8") as log_file:
log_file.write(f'{localtime} train_loss={all_loss.item():.6f} batch_loss={batch_loss:.6f}\n')
loss_sum = 0.0
t.set_postfix_str(f'train_loss={all_loss.item():.6f} batch_loss={batch_loss:.6f}')
t.update()
del data, label, all_output, expert_output, other_index, expert_predict, expert_index, expert_label
gc.collect()
torch.cuda.empty_cache()
#----------------------------------------------------
model.eval() # 设置模型为评估/测试模式
valid_dataset = MyDataset([processed_expose_train_valid_path], 'valid')
valid_loader = DataLoader(dataset=valid_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
valid_loss_sum = 0.0
with tqdm(total=len(valid_loader)) as t:
with torch.no_grad():
for data, label in valid_loader:
# 一般如果验证集不是很大的话，模型验证就不需要按批量进行了，但要注意输入参数的维度不能错
all_output, _ = model(data)
all_loss = criterion(all_output, label.cuda())
valid_loss_sum += all_loss.item()
t.set_postfix_str(f'valid loss={all_loss.item()}')
t.update()
early_stopping(valid_loss_sum, model)
# 若满足 early stopping 要求
if early_stopping.early_stop:
print("Early stopping")
# 结束模型训练
break
# %%
# 保存完整的 BERT 分类器模型
# torch.save(model, model_train_mutil_model_path)
# %%
# 获得 early stopping 时的模型参数
# model.load_state_dict(torch.load(model_train_mutil_model_path))
# %%
epoch_num = 1
batch_loss_num = 100
for epoch in range(epoch_num):
model.train() # 设置模型为训练模式
train_dataset = MyDataset([processed_expose_train_labeled_path, processed_expose_train_unlabel_predict_other_path], 'train')
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
with tqdm(total=len(train_loader)) as t:
loss_sum = 0.0
batch_loss = 1.0
for batch_idx,data_ in enumerate(train_loader, 0):
data, label = data_
# 清空梯度
all_optimizer.zero_grad()
expert_optimizer.zero_grad()
all_output, expert_output = model(data)
all_loss = criterion(all_output, label.cuda())
# all_loss = all_loss / 2
other_index = doctype_list.index('其他')
# print(expert_output)
expert_predict = torch.tensor(np.argmax(expert_output.cpu().detach().numpy(), 1))
# print(expert_predict)
# print(label)
# expert_label = torch.where(label > other_index, label - 1, label)
# expert_label = torch.where(label == other_index, expert_predict, expert_label)
expert_label = label.clone().detach()
for i in range(label.shape[0]):
if label[i] == other_index:
expert_label[i] = expert_predict[i]
elif label[i] > other_index:
expert_label[i] -= 1
# print(expert_label)
expert_loss = criterion(expert_output, expert_label.cuda())
# expert_loss = expert_loss / 2
all_loss.backward(retain_graph=True)
expert_loss.backward()
# 更新模型参数
all_optimizer.step()
expert_optimizer.step()
loss_sum += all_loss.item()
if batch_idx % batch_loss_num == batch_loss_num - 1:
batch_loss = loss_sum / batch_loss_num
localtime = time.asctime( time.localtime(time.time()) )
with open("early_stop.log", 'a+', encoding="utf-8") as log_file:
log_file.write(f'{localtime} train_loss={all_loss.item():.6f} batch_loss={batch_loss:.6f}\n')
loss_sum = 0.0
t.set_postfix_str(f'train_loss={all_loss.item():.6f} batch_loss={batch_loss:.6f}')
t.update()
del data, label, all_output, expert_output
gc.collect()
torch.cuda.empty_cache()
# 保存完整的 BERT 分类器模型
torch.save(model, model_train_mutil_model_path)
localtime = time.asctime( time.localtime(time.time()) )
with open("early_stop.log", 'a+', encoding="utf-8") as log_file:
log_file.write(f'{localtime} Saving model ...\n')
model.eval() # 设置模型为评估/测试模式
test_dataset = MyDataset([processed_expose_test_path], 'test')
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=True, num_workers=num_workers)
predict_doctype = {}
with torch.no_grad():
for data, label in tqdm(test_loader):
id = data['id'][0]
output, _ = model(data)
predict_list = F.softmax(output, dim=1).tolist()[0]
predict_index = np.argmax(predict_list)
predict_label = doctype_list[predict_index]
predict_doctype[id] = predict_label
predict_data = {'predict_doctype' : predict_doctype}
df = pd.DataFrame(predict_data)
df.index.name = 'id'
df.to_csv(f"submission_train_mutil_target_predict_{epoch}.csv")
# %%
# %%
# model = torch.load(model_train_mutil_model_path)
# model = model.cuda()
# %%
# %%
model.eval() # 设置模型为评估/测试模式
valid_dataset = MyDataset([processed_expose_train_valid_path], 'valid')
valid_loader = DataLoader(dataset=valid_dataset, batch_size=1, shuffle=True, num_workers=num_workers)
with torch.no_grad():
for data, label in tqdm(valid_loader):
print(data, label)
print(data['id'][0])
output, _ = model(data)
# print(output)
# print(torch.sigmoid(output))
# sigmoid = nn.Sigmoid()
# print(sigmoid(output))
predict_list = F.softmax(output, dim=1).tolist()[0]
predict_index = np.argmax(predict_list)
predict_label = doctype_list[predict_index]
print(predict_label)
break
# %%
# with open("data/processed_train_expose_labeled.json", "r", encoding='utf-8') as input_file:
# for line in tqdm(input_file):
# json_data = json.loads(line)
# if json_data['id'] == 'ee137ac3-c2a2-4aba-a517-36840ffd2f1a':
# print(line)
# break
# %%
# model.eval() # 设置模型为评估/测试模式
# valid_dataset = MyDataset("data/test.txt", 'test')
# valid_loader = DataLoader(dataset=valid_dataset, batch_size=1, shuffle=True, num_workers=num_workers)
# with torch.no_grad():
# for data, label in tqdm(valid_loader):
# # print(data, label)
# output = model(data)
# # print(output)
# # print(torch.sigmoid(output))
# print(F.softmax(output, dim=1).tolist()[0])
# break
# %%
# %%
model.eval() # 设置模型为评估/测试模式
test_dataset = MyDataset([processed_expose_test_path], 'test')
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=True, num_workers=num_workers)
predict_doctype = {}
with torch.no_grad():
for data, label in tqdm(test_loader):
id = data['id'][0]
output, _ = model(data)
predict_list = F.softmax(output, dim=1).tolist()[0]
predict_index = np.argmax(predict_list)
predict_label = doctype_list[predict_index]
predict_doctype[id] = predict_label
predict_data = {'predict_doctype' : predict_doctype}
df = pd.DataFrame(predict_data)
df.head()
# %%
df.index.name = 'id'
df.head()
# %%
df.to_csv("submission_train_mutil_target_predict.csv")
# %%
# 看起来，复杂、大模型是有效的(在验证数据集上，第二个epoch的loss还在降低)，所以下一步准备试一下cnn和albert(或roberta)
# %%
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