def main(**kwargs):
args = DefaultConfig()
args.parse(kwargs)
train_iter, val_iter, test_iter, args.vocab_size, vectors = util.load_data(
args, args.text_type)
args.print_config()
# model
if args.model_path:
# 加载模型
saved_model = torch.load(args.model_path)
config = saved_model['config']
config.device = args.device
model = getattr(models, args.model)(args, vectors)
model.load_state_dict(saved_model['state_dict'])
best_score = saved_model['best_score']
print('Load model from {}!'.format(args.model_path))
else:
print("No trained model!")
if not torch.cuda.is_available():
config.cuda = False
config.device = None
if args.cuda:
torch.cuda.set_device(args.device)
model.cuda()
probs = infer(model, test_iter, config)
result_path = 'result/' + '{}_{}_{}'.format(args.model, args.id,
args.best_score)
np.save('{}.npy'.format(result_path), probs)
print('Prob result {}.npy saved!'.format(result_path))
def train(**kwargs):
# 根据命令行参数更新配置
opt = DefaultConfig()
opt.parse(kwargs)
print("参数配置完成")
# step1: 模型
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_num
model = getattr(models, opt.model)(opt, 512) # TODO:512这个值后期可能需要用变量代替
if opt.model == "StatisticModel" and model.PCA_state is False:
model.pretrainpca()
if opt.load_model_path:
model.load(opt.load_model_path)
if opt.use_gpu:
model.cuda()
print("模型加载完成")
# step2: 数据
train_data = VideoSet(opt, state='train')
train_dataloader = DataLoader(train_data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
print("数据集准备就绪")
# step3: 目标函数和优化器
criterion = torch.nn.BCELoss()
lr = opt.lr
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=opt.weight_decay)
# step4: 统计指标
print("开始训练")
# 训练
for epoch in range(opt.max_epoch):
for ii, (data, label) in enumerate(train_dataloader):
# 训练模型参数
input_data = Variable(data)
target = Variable(label)
# ipdb.set_trace()
if opt.use_gpu:
input_data = input_data.cuda()
target = target.cuda()
optimizer.zero_grad()
score = model(input_data)
loss = criterion(score, target)
loss.backward()
optimizer.step()
# 最后保存一次模型参数
model.save()
def train(**kwargs):
"""
训练
"""
#根据命令行参数更新配置
opt.parse(kwargs)
#可视化
#vis = Visualizer(opt.env)
#step1 加载模型:
model = getattr(models,opt.model)()
if opt.load_model_path:
model.load(opt.load_model_path)#加载训练好的参数
if opt.use_gpu:model.cuda()
#step2 数据：
train_data = DogCat(opt.train_data_root,train=True)
val_data = DogCat(opt.train_data_root,train=False)
train_dataloader = DataLoader(train_data,opt.batch_size,shuffle=True,num_workers=opt.num_workers)
val_dataloader = DataLoader(val_data,opt.batch_size,shuffle=True,num_workers=opt.num_workers)
#step3:目标函数和优化器
criterion = torch.nn.CrossEntropyLoss()
lr = opt.lr
optimizer = torch.optim.Adam(model.parameters(),lr=lr,weight_decay=opt.weight_decay)
#统计指标：平滑处理之后的损失，还有混淆矩阵
#######################待补充#########################
#训练
for epoch in range(opt.max_epoch):
for ii,(data,label) in enumerate(train_dataloader):
#训练模型
if opt.use_gpu:
input = input.cuda()
target = input.cuda()
optimizer.zero_grad()
score = model(input)
loss = criterion(score,target)
loss.backward()
optimizer.step()
#更新统计指标及可视化
if ii%opt.print_freq == opt.print_freq - 1:
print('ii:{},loss:{}'.format(ii,loss))
model.save()
def inference(**kwargs):
# 根据命令行参数更新配置
opt = DefaultConfig()
opt.parse(kwargs)
print("参数配置完成")
if opt.model_type == 1:
model_type = "model_1"
elif opt.model_type == 2:
model_type = "model_2_" + opt.model_2_layers
elif opt.model_type == 3:
model_type = "model_3"
# 加载静态图
model_files = opt.model_path + model_type + "\\data_SNR_" + str(opt.SNR)
saver = tf.train.import_meta_graph(model_files + "\\data_SNR_" +
str(opt.SNR) + ".meta")
# 开始测试
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = opt.per_process_gpu_memory_fraction
with tf.Session(config=config) as sess:
# 加载参数值
saver.restore(sess, tf.train.latest_checkpoint(model_files))
# 定义测试集dataset
test_dataset = CSISet(opt.test_data_path, opt.batch_size, False,
"test")
data_loss = [] # 保存每个batch的发送信号和预测的发送信号之间的误差
print("开始预测过程！")
start_time = time.time()
for ii, (batch_x, batch_tx,
batch_rx) in enumerate(test_dataset.get_data()):
inputs = tf.get_collection("input_batch")[0]
predictions = tf.get_collection("predictions")[0]
# pred_H是模型预测的信道完整特性，维度是[batch, 72, 14, 2]
# 再利用公式 H=rx/tx 和(pred_H, batch_rx)就可以得到pred_rx
pred_H = np.squeeze(np.array(
sess.run([predictions], feed_dict={inputs: batch_x})),
axis=0)
complex_pred_H = pred_H[:, :, :, 0] + pred_H[:, :, :, 1] * 1j
# ipdb.set_trace()
pred_batch_tx = np.divide(batch_rx, complex_pred_H)
pred_batch_tx[:, :5, 5:7] = 1.
pred_batch_tx[:, 67:72, 5:7] = 1.
batch_tx[:, :5, 5:7] = 1.
batch_tx[:, 67:72, 5:7] = 1.
batch_data_loss_ratio = np.mean(
np.divide(abs(pred_batch_tx - batch_tx), abs(batch_tx)))
# print(batch_data_loss)
print("第%d个batch的发送信息预测平均误差是%.6f" %
(ii + 1, batch_data_loss_ratio))
data_loss.append(batch_data_loss_ratio)
result = np.mean(data_loss)
print("信噪比为%d时模型在测试集上的平均估计误差为%.2f" % (opt.SNR, result))
end_time = time.time()
print_time(start_time, end_time, "整个测试过程")
result_path = opt.result_path + model_type + "\\data_SNR_" + str(
opt.SNR) + "\\test\\result.npy"
np.save(result_path, data_loss)
results.extend(probability.cpu().numpy())
write_csv(ids, results, breed, opt.result_file)
if __name__ == '__main__':
opt = DefaultConfig()
# opt.parse()
# m = getattr(model, opt.model)()
# # print(m)
# for param in m.parameters():
# print(param.requires_grad)
opt.parse({'model': 'ResNet50','max_epoch': 30, 'load_model_path': './checkpoints/ResNet50_19'})
# train(opt)
test(opt)
'''
m = getattr(model, opt.model)().cuda()
train_dataloader = dataloader(opt.train_data_root,
train=True,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
for (data, label) in train_dataloader:
m.train()
data = data.cuda()
label = label.cuda()
score = m(data)
print('train: ', score.size())
def train(**kwargs):
# step1 : customize config
opt = DefaultConfig()
opt.parse(kwargs)
# step2 : model
# TODO: make the initiation correspond to the data
# Problem : when node_num is greater than 5M, the depth of encoder hidden layers can not be greater than 3
# the number of parameters will be very large
print('Initiate model')
model = getattr(models, opt.model)(opt.num_units, 1, opt.d)
print(model)
# if opt.load_model_path:
# model.load(opt.load_model_path)
if opt.use_gpu:
model.cuda()
model.train()
print('Initiate Train_Data')
# step3 : prepare data. In this case, only training data is needed
train_data = DataLoader(SDNEData(opt.train_data_root, opt.file_name,
opt.beta),
shuffle=True,
num_workers=1,
pin_memory=False)
print('Initiate Optimizer and Loss function')
# step4 : optimizer
optimizer = torch.optim.SGD(model.parameters(),
opt.lr,
momentum=0.99,
nesterov=True,
weight_decay=1e-5)
loss_func = MyLoss(1, 1, 1e-5, opt.node_num).cuda()
total_time = 0.0
for epoch in range(0, opt.max_epoch):
epoch_st_time = time.time()
# Dimension: x1,x2, num_node ; a1,a2, num_node+1 , X_ij, num_node
for ii, (x1, x2, a1, a2, x_ij) in enumerate(train_data):
if ii == 10:
break
if opt.use_gpu:
x1 = x1.cuda()
x2 = x2.cuda()
a1 = a1.cuda()
a2 = a2.cuda()
x_ij = x_ij.cuda()
x_diff1, y1 = model(x1)
x_diff2, y2 = model(x2)
y_diff = y2 - y1
optimizer.zero_grad()
loss = loss_func(x_diff1, x_diff2, y_diff, a1, a2, x_ij)
# print(torch.cuda.memory_allocated())
# print(torch.cuda.max_memory_allocated())
# # if(ii%4000000 == 0):
# print(loss)
start_time = time.time()
loss.backward(retain_graph=False)
torch.cuda.synchronize()
end_time = time.time()
print('backward time = ' + str(end_time - start_time))
optimizer.step()
epoch_end_time = time.time()
total_time += epoch_end_time - epoch_st_time
print("total_time is " + str(total_time) + 's\n')
def main(**kwargs):
config = DefaultConfig()
config.parse(kwargs)
config.env = str(config.id)
vis = Visualizer
# set random seed
# cpu and gpu both need to set
torch.manual_seed(config.seed)
torch.cuda.manual_seed(config.seed)
np.random.seed(config.seed)
random.seed(config.seed)
if not torch.cuda.is_available():
config.cuda = False
config.device = None
train_iter, test_iter, emb_vectors = utils.load_data(config)
config.print_config()
model = getattr(models, config.model)(config, emb_vectors)
print(model)
if config.cuda:
torch.cuda.set_device(config.device)
model.cuda()
# 目标函数和优化器
loss_f = F.cross_entropy
lr1, lr2 = config.lr1, config.lr2
optimizer = model.get_optimizer(lr1, lr2)
model.train()
for epoch in range(config.max_epochs):
start_time = time.time()
total_loss = 0.0
correct = 0
total = 0
for batch_i, batch in enumerate(train_iter):
text, label = batch.text[0], batch.label
if config.cuda:
text, label = text.cuda(), label.cuda()
optimizer.zero_grad()
pred = model(text)
loss = loss_f(pred, label)
loss.backward()
optimizer.step()
total_loss += loss.item()
predicted = pred.max(dim=1)[1]
total += label.size(0)
correct += predicted.eq(label).sum().item()
if (batch_i + 1) % (10000 // config.batch_size) == 0:
# 10000条训练数据输出一次统计指标
print('[Epoch {}] loss: {:.5f} | Acc: {:.3f}%({}/{})'.format(
epoch + 1, total_loss, 100.0 * correct / total, correct,
total))
train_acc, train_acc_n, train_n = val(model, train_iter, config)
print('Epoch {} time spends : {:.1f}s'.format(epoch + 1,
time.time() -
start_time))
print('Epoch {} Train Acc: {:.2f}%({}/{})'.format(
epoch + 1, train_acc, train_acc_n, train_n))
test_acc, test_acc_n, test_n = val(model, test_iter, config)
print('Epoch {} Test Acc: {:.2f}%({}/{})\n'.format(
epoch + 1, test_acc, test_acc_n, test_n))
def main(**kwargs):
args = DefaultConfig()
args.parse(kwargs)
args.model = 'LSTM'
args.device = 0
args.id = 'word4'
if not torch.cuda.is_available():
args.cuda = False
args.device = None
torch.manual_seed(args.seed) # set random seed for cpu
train_iter, val_iter, test_iter, args.vocab_size, vectors = data.load_data(args)
args.print_config()
global best_score
# init model
model = getattr(models, args.model)(args, vectors)
print(model)
# 模型保存位置
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
save_path = os.path.join(args.save_dir, '{}_{}.pth'.format(args.model, args.id))
if args.cuda:
torch.cuda.set_device(args.device)
torch.cuda.manual_seed(args.seed) # set random seed for gpu
model.cuda()
# 目标函数和优化器
criterion = F.cross_entropy
lr1, lr2 = args.lr1, args.lr2
optimizer = model.get_optimizer(lr1, lr2, args.weight_decay)
for i in range(args.max_epochs):
total_loss = 0.0
correct = 0
total = 0
model.train()
for idx, batch in enumerate(train_iter):
# 训练模型参数
# 使用BatchNorm层时，batch size不能为1
if len(batch) == 1:
continue
text, label = batch.text, batch.label
if args.cuda:
text, label = text.cuda(), label.cuda()
optimizer.zero_grad()
pred = model(text)
loss = criterion(pred, label)
loss.backward()
optimizer.step()
# 更新统计指标
total_loss += loss.item()
predicted = pred.max(1)[1]
total += label.size(0)
correct += predicted.eq(label).sum().item()
if idx % 80 == 79:
print('[{}, {}] loss: {:.3f} | Acc: {:.3f}%({}/{})'.format(i + 1, idx + 1, total_loss / 20,
100. * correct / total, correct, total))
total_loss = 0.0
# 计算再验证集上的分数，并相应调整学习率
f1score = val(model, val_iter, args)
if f1score > best_score:
best_score = f1score
checkpoint = {
'state_dict': model.state_dict(),
'config': args
}
torch.save(checkpoint, save_path)
print('Best tmp model f1score: {}'.format(best_score))
if f1score < best_score:
model.load_state_dict(torch.load(save_path, map_location={'cuda:5':'cuda:0'})['state_dict'])
lr1 *= args.lr_decay
lr2 = 2e-4 if lr2 == 0 else lr2 * 0.8
optimizer = model.get_optimizer(lr1, lr2, 0)
print('* load previous best model: {}'.format(best_score))
print('* model lr:{} emb lr:{}'.format(lr1, lr2))
if lr1 < args.min_lr:
print('* training over, best f1 score: {}'.format(best_score))
break
# 保存训练最终的模型
args.best_score = best_score
final_model = {
'state_dict': model.state_dict(),
'config': args
}
best_model_path = os.path.join(args.save_dir, '{}_{}_{}.pth'.format(args.model, args.text_type, best_score))
torch.save(final_model, best_model_path)
print('Best Final Model saved in {}'.format(best_model_path))
# 在测试集上运行模型并生成概率结果和提交结果
if not os.path.exists('result/'):
os.mkdir('result/')
probs, test_pred = test(model, test_iter, args)
result_path = 'result/' + '{}_{}_{}'.format(args.model, args.id, args.best_score)
np.save('{}.npy'.format(result_path), probs)
print('Prob result {}.npy saved!'.format(result_path))
test_pred[['id', 'class']].to_csv('{}.csv'.format(result_path), index=None)
print('Result {}.csv saved!'.format(result_path))
t2 = time.time()
print('time use: {}'.format(t2 - t1))
def main(**kwargs):
start_time = time.time()
config = DefaultConfig()
config.parse(kwargs)
vis = Visualizer(config.env)
if not torch.cuda.is_available():
config.cuda = False
config.device = None
torch.manual_seed(config.seed)
train_iter, val_iter, test_iter, config.vocab_size, config.target_vocab_size, config.aspect_vocab_size, \
text_vectors, target_vectors, aspect_vectors = data.load_data(config)
# 需要进一步处理样本不均衡
config.print_config()
# init model
model = getattr(models, config.model)(config, text_vectors, target_vectors, aspect_vectors)
print(model)
# 模型保存位置
if not os.path.exists(config.save_dir):
os.mkdir(config.save_dir)
tmp_save_path = os.path.join(config.save_dir, 'entnet_{}.pth'.format(config.id))
if config.cuda:
torch.cuda.set_device(config.device)
torch.cuda.manual_seed(config.seed) # set random seed for gpu
model.cuda()
# 目标函数和优化器
criterion = F.cross_entropy
lr1, lr2 = config.lr1, config.lr2
optimizer = model.get_optimizer(lr1, lr2)
global best_acc
best_acc = 0.0
# 开始训练
for i in range(config.max_epoch):
total_loss = 0.0
correct = 0
total = 0
model.train()
for idx, batch in enumerate(train_iter):
text, target, aspect, label = batch.text, batch.target, batch.aspect, batch.label
if config.cuda:
text, target, aspect, label = text.cuda(), target.cuda(), aspect.cuda(), label.cuda()
optimizer.zero_grad()
pred = model(text, target, aspect)
loss = criterion(pred, label)
loss.backward(retain_graph=True)
optimizer.step()
# 更新统计指标
total_loss += loss.item()
predicted = pred.max(dim=1)[1]
total += label.size(0)
correct += predicted.eq(label).sum().item()
# 每个batch之后计算测试集上的准确率
print('[Epoch {}] loss: {:.5f} | Acc: {:.3f}%({}/{})'.format(i + 1, total_loss,
100. * correct / total, correct, total))
vis.plot('loss', total_loss)
# 每5个epoch计算验证集上的准确率，并相应调整学习率
if i % 5 == 4:
acc, acc_n, val_n = val(model, val_iter, config)
vis.plot('val_acc', acc)
print('Epoch {} Val Acc: {:.3f}%({}/{})'.format(i + 1, acc, acc_n, val_n))
# 100个epoch之后模型接近收敛，此时开始调整学习率
# 因为数据集偏小，100个epoch之前虽然整体呈下降趋势，但会有小幅度波动，此时调整学习率可能会影响模型收敛
if i > 100:
if acc >= best_acc:
best_acc = acc
checkpoint = {
'state_dict': model.state_dict(),
'config': config
}
torch.save(checkpoint, tmp_save_path)
# if acc < best_acc:
# model.load_state_dict(torch.load(tmp_save_path)['state_dict'])
# lr1 *= config.lr_delay
# optimizer = model.get_optimizer(lr1, lr2)
# print('## load previous best model: {:.3f}%'.format(best_acc))
# print('## set model lr1 to {}'.format(lr1))
# if lr1 < config.min_lr:
# print('## training over, best f1 acc : {:.3f}'.format(best_acc))
# break
# 计算测试集上分数(准确率)
test_acc, test_acc_n, test_n = val(model, test_iter, config)
vis.plot('test_acc', test_acc)
print('Epoch {} Test Acc: {:.3f}%({}/{})\n'.format(i + 1, test_acc, test_acc_n, test_n))
# 加载训练过程中保存的验证集最佳模型
# 计算最终训练模型的测试集准确率
model.load_state_dict(torch.load(tmp_save_path)['state_dict'])
print('Load tmp best model from {}'.format(tmp_save_path))
test_acc, test_acc_n, test_n = val(model, test_iter, config)
print('Finally Test Acc: {:.3f}%({}/{})'.format(test_acc, test_acc_n, test_n))
# print('Best final model saved in {}'.format('{:.3f}_{}'.format(test_acc, tmp_save_path)))
print('Final cost time : {}s'.format(time.time() - start_time))
gt_bboxes = y.slice_axis(axis=-1, begin=0, end=4)
gt_bboxes *= im_scale
gt_difficulty = y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None
val_metric.update(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficulty)
return val_metric.get()
if __name__ == '__main__':
opt = DefaultConfig()
opt.parse({'model': 'vgg16_faster_rcnn',
'env': 'vgg16',
'lr_decay_epoch': '8, 16',
'preload': False,
'special_load': True,
'lr': 0.001,
'start_epoch': 0,
'max_epoch': 20,
'load_file_path': '',
'log_file_path': './log/vgg16_faster_rcnn.log'})
logger = get_logger(opt)
if opt.special_load and opt.special_load_path is not None:
model_train = getattr(models, opt.model)(True, opt.special_load_path)
else:
model_train = getattr(models, opt.model)()
initialize_model_gpu(model_train)
train_dataloader, val_dataloader, val_metric = dataloader.DogDataLoader(model_train)
tag2label = {
"O": 0,
"B-W": 1,
"I-W": 2,
}
tag2label[START_TAG] = len(tag2label)
tag2label[STOP_TAG] = len(tag2label)
print("映射word and tag to id")
sentence = []
for idx, data in enumerate(data):
sent = list(data[0])
sent = tokens_to_ids(sent, vocab)
sentence.append(sent)
tags = [[tag2label[tag] for tag in x[1]] for x in data]
opt.parse({'vocab_size': len(words), 'embedding_length': 300})
model = Bilstm_crf(opt, tag2label)
optim = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()))
if torch.cuda.is_available():
model = model.cuda()
x_train, x_valid, y_train, y_valid = train_test_split(sentence,
tags,
test_size=0.2)
train_data = data_process.batch_iter(x_train,
y_train,
opt.batch_size,
num_epochs=opt.num_epochs)
steps = 0
min_delta = opt.min_delta
correct_num = 0
for i, (data, label) in enumerate(dataloader):
if opt.use_gpu:
data = data.cuda()
score = model_test(data)
_, predict = torch.max(score.data, 1)
total_num += label.size(0)
correct_num += (predict.cpu() == label).sum()
return 100 * float(correct_num) / float(total_num)
if __name__ == '__main__':
print("Initialize starting options")
opt = DefaultConfig()
opt.parse({'batch_size': 128, 'num_workers': 4})
model_test = getattr(model, opt.model)()
model_test.load("model/SuleymanNET_model_state_dict.pkl")
testloader = data_loader(opt.root, opt.batch_size, opt.num_workers)
accuracy = test(model_test, testloader, opt)
print("Accuracy of Test Set: %.3f" % accuracy)
optimizer_g.zero_grad()
# train by fake image
# refresh the value of noises
noises.data.copy_(torch.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = model_G(noises)
output = model_D(fake_img)
loss_g = criterion(output, true_labels)
loss_g.backward()
optimizer_g.step()
loss_G_meter.add(loss_g.item())
if ii % opt.print_freq:
vis.plot('loss_d', loss_D_meter.value()[0])
vis.plot('loss_g', loss_G_meter.value()[0])
fix_fake_img = model_G(fix_noises)
vis.images(fix_fake_img.data.cpu().numpy()[:64] * 0.5 + 0.5,
win='fixfake')
if (epoch + 1) % 20 == 0:
model_G.save(opt.save_model_path + opt.G_model + '_' + str(epoch))
model_D.save(opt.save_model_path + opt.D_model + '_' + str(epoch))
if __name__ == '__main__':
opt = DefaultConfig()
opt.parse({'max_epoch': 100})
train(opt)
def main(**kwargs):
args = DefaultConfig()
args.parse(kwargs)
# boost模型
args.max_epochs = 5
if not torch.cuda.is_available():
args.cuda = False
args.device = None
torch.manual_seed(args.seed) # set random seed for cpu
train_iter, val_iter, test_iter, args.vocab_size, vectors = util.load_data(
args)
args.print_config()
# 模型保存位置
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
save_path = os.path.join(
args.save_dir, '{}_{}_{}.pth'.format(args.model, args.text_type,
args.id))
if args.cuda:
torch.cuda.set_device(args.device)
torch.cuda.manual_seed(args.seed) # set random seed for gpu
for lay_i in range(args.bo_layers):
print('-------------- lay {} ---------------'.format(lay_i))
model = getattr(models, args.model)(args, vectors)
model = model.cuda()
print(model)
best_score = 0.0
# 目标函数和优化器
criterion = F.cross_entropy
lr1 = args.lr1
lr2 = args.lr2
optimizer = model.get_optimizer(lr1, lr2, args.weight_decay)
if lay_i != 0:
# 加载上一层模型的loss weight
saved_model = torch.load(args.model_path)
loss_weight = saved_model['loss_weight']
print(list(enumerate(loss_weight)))
loss_weight = loss_weight.cuda()
for i in range(args.max_epochs):
total_loss = 0.0
correct = 0
total = 0
model.train()
for idx, batch in enumerate(train_iter):
# 训练模型参数
# 使用BatchNorm层时，batch size不能为1
if len(batch) == 1:
continue
text, label = batch.text, batch.label
if args.cuda:
text, label = text.cuda(), label.cuda()
optimizer.zero_grad()
pred = model(text)
if lay_i != 0:
loss = criterion(pred,
label,
weight=loss_weight + 1 -
loss_weight.mean())
else:
loss = criterion(pred, label)
loss.backward()
optimizer.step()
# 更新统计指标
total_loss += loss.item()
predicted = pred.max(1)[1]
total += label.size(0)
correct += predicted.eq(label).sum().item()
if idx % 80 == 79:
print('[{}, {}] loss: {:.3f} | Acc: {:.3f}%({}/{})'.format(
i + 1, idx + 1, total_loss / 20,
100. * correct / total, correct, total))
total_loss = 0.0
# 计算再验证集上的分数，并相应调整学习率
f1score, tmp_loss_weight = val(model, val_iter, args)
if f1score > best_score:
best_score = f1score
checkpoint = {'state_dict': model.state_dict(), 'config': args}
torch.save(checkpoint, save_path)
print('Best tmp model f1score: {}'.format(best_score))
if f1score < best_score:
model.load_state_dict(torch.load(save_path)['state_dict'])
lr1 *= args.lr_decay
lr2 = 2e-4 if lr2 == 0 else lr2 * 0.8
optimizer = model.get_optimizer(lr1, lr2, 0)
print('* load previous best model: {}'.format(best_score))
print('* model lr:{} emb lr:{}'.format(lr1, lr2))
if lr1 < args.min_lr:
print('* training over, best f1 score: {}'.format(
best_score))
break
# 保存训练最终的模型
# 保存当前层的loss weight
loss_weight = tmp_loss_weight
args.best_score = best_score
final_model = {
'state_dict': model.state_dict(),
'config': args,
'loss_weight': loss_weight
}
args.model_path = os.path.join(
args.save_dir,
'{}_{}_lay{}_{}.pth'.format(args.model, args.text_type, lay_i,
best_score))
torch.save(final_model, args.model_path)
print('Best Final Model saved in {}'.format(args.model_path))
t2 = time.time()
print('time use: {}'.format(t2 - t1))
sum(val_loss_his) / len(val_loss_his) / opt.batch_size)
return val_acc, val_mean_loss
if __name__ == '__main__':
opt = DefaultConfig()
opt.parse({
'model': 'VGG16',
'env': 'VGG16',
'lr': 0.001,
'train_dir':
'/home/qinliang/dataset/stanford_dog_dataset/cut_images_train',
'valid_dir':
'/home/qinliang/dataset/stanford_dog_dataset/cut_images_val',
'save_path': './cut_image_checkpoints/',
'lr_decay': 0.5,
'preload': True,
'start_epoch': 0,
'max_epoch': 50,
'batch_size': 32,
'wd': 15e-4,
'load_file_path':
'/home/qinliang/Desktop/kaggle/dog_recognition_gluon/checkpoints/epoch16_acc_99.31.params',
'log_file_path': './log/VGG16_cut_image.log'
})
logger = get_logger(opt)
model_train = getattr(models, opt.model)()
model_train.initialize()
convert_model_gpu(model_train)
model_train.hybridize()
data = data.cuda()
label = label.cuda()
optimizer.zero_grad()
score = model_train(data)
loss = criterion(score, label)
loss.backward()
optimizer.step()
loss_meter.add(loss.item())
model_train.train()
# update learning rate
if loss_meter.value()[0] > previous_loss:
lr = lr * opt.lr_decay
for param in optimizer.param_groups:
param['lr'] = lr
print("Changing learning rate to %.19f" % lr)
previous_loss = loss_meter.value()[0]
torch.save(model_train.state_dict(), "model/SuleymanNET_model_state_dict.pkl")
if __name__ == '__main__':
opt = DefaultConfig()
opt.parse({'max_epoch': 110, 'weight_decay': 15e-4, 'lr': 0.1})
train(opt)
def main(**kwargs):
args = DefaultConfig()
args.parse(kwargs)
if not torch.cuda.is_available():
args.cuda = False
args.device = None
torch.manual_seed(args.seed) # set random seed for cpu
train = pd.read_csv(args.train_path, sep='\t', encoding='utf-8', header=0)
test_df = pd.read_csv(args.test_path, sep='\t', encoding='utf-8', header=0)
corpus_all = pd.concat([train, test_df], axis=0)
vocab = get_dictionary(corpus_all.text)
args.vocab_size = len(vocab)
train = list(zip(train.label, train.text))
test = list(zip(test_df.label, test_df.text))
train_data, val_data = train_test_split(train,
test_size=0.1,
random_state=1)
train_iter = get_iter(train_data, vocab, args.batch_size, True, max_len=32)
val_iter = get_iter(val_data, vocab, args.batch_size, True, max_len=32)
test_iter = get_iter(test, vocab, args.batch_size, True, max_len=32)
if args.pretrain_embeds_path is None:
vectors = None
else:
vectors = pickle.load(args.pretrain_embeds_path)
assert len(
vectors) == args.vocab_size, '预训练的词向量shape[0]为%d,而字典大小为%d' % (
len(vectors), args.vocab_size)
assert vectors.shape[
1] == args.embedding_dim, '预训练词向量的shape[1]为%d，而设置的embedding_dim为%d' % (
vectors.shape[1], args.embedding_dim)
args.print_config()
global best_score
# init model
model = getattr(models, args.model)(args, vectors)
print(model)
# 模型保存位置
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
save_path = os.path.join(args.save_dir,
'{}_{}.pth'.format(args.model, args.id))
if args.cuda:
torch.cuda.current_device()
torch.cuda.set_device(args.device)
torch.cuda.manual_seed(args.seed) # set random seed for gpu
model.cuda()
# 目标函数和优化器
criterion = F.cross_entropy
lr1, lr2 = args.lr1, args.lr2
optimizer = model.get_optimizer(lr1, lr2, args.weight_decay)
for i in range(args.max_epochs):
total_loss = 0.0
pred_labels = []
labels = []
model.train()
for idx, (b_x, b_y) in enumerate(train_iter):
# 训练模型参数
# 使用BatchNorm层时，batch size不能为1
if len(b_x) == 1:
continue
if args.cuda:
b_x, b_y = b_x.cuda(), b_y.cuda()
optimizer.zero_grad()
pred = model(b_x)
loss = criterion(pred, b_y)
loss.backward()
optimizer.step()
# 更新统计指标
total_loss += loss.item()
predicted = pred.max(1)[1]
pred_labels.extend(predicted.numpy().tolist())
label = b_y.numpy().tolist()
labels.extend(label)
if idx % 100 == 0:
print('[{}, {}] loss: {:.3f}'.format(i + 1, idx + 1,
total_loss / (idx + 1)))
# total_loss = 0.0
tf1score = metrics.f1_score(labels, pred_labels)
print('[{}, {}] tf1_score:{}'.format(i + 1, idx + 1,
total_loss / (idx + 1), tf1score))
# 计算再验证集上的分数，并相应调整学习率
f1score = val(model, val_iter, args)
if f1score > best_score:
best_score = f1score
checkpoint = {'state_dict': model.state_dict(), 'config': args}
torch.save(checkpoint, save_path)
print('Best tmp model f1score: {}'.format(best_score))
if f1score < best_score:
model.load_state_dict(torch.load(save_path)['state_dict'])
lr1 *= args.lr_decay
lr2 = 2e-4 if lr2 == 0 else lr2 * 0.8
optimizer = model.get_optimizer(lr1, lr2, 0)
print('* load previous best model: {}'.format(best_score))
print('* model lr:{} emb lr:{}'.format(lr1, lr2))
if lr1 < args.min_lr:
print('* training over, best f1 score: {}'.format(best_score))
break
# 保存训练最终的模型
args.best_score = best_score
final_model = {'state_dict': model.state_dict(), 'config': args}
best_model_path = os.path.join(
args.save_dir, '{}_{}_{}.pth'.format(args.model, args.text_type,
best_score))
torch.save(final_model, best_model_path)
print('Best Final Model saved in {}'.format(best_model_path))
# 在测试集上运行模型并生成概率结果和提交结果
if not os.path.exists(args.result_path):
os.mkdir(args.result_path)
probs, pre_labels = predict(model, test_iter, args)
result_path = args.result_path + '{}_{}_{}'.format(args.model, args.id,
args.best_score)
np.save('{}.npy'.format(result_path), probs)
print('Prob result {}.npy saved!'.format(result_path))
test_df['label'] = np.array(pre_labels)
test_df[['idx', 'seq_id', 'label']].to_csv('{}.csv'.format(result_path),
index=None)
print('Result {}.csv saved!'.format(result_path))
t2 = time.time()
print('time use: {}'.format(t2 - t1))
def train(**kwargs):
# 根据命令行参数更新配置
opt = DefaultConfig()
opt.parse(kwargs)
print("参数配置完成")
# 优化器
learning_rate = opt.learning_rate
# optimizer默认是Adam
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
beta2=0.9)
if opt.optimizer_type == "SGD":
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate)
elif opt.optimizer_type == "Momentum":
momentum = opt.momentum
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=momentum)
elif opt.optimizer_type == "Adam":
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
beta2=0.9)
# 建立静态图
with tf.Graph().as_default():
with tf.name_scope("inputs"):
inputs = tf.placeholder("float", [None, 24, 2, 2],
name="model_input")
labels = tf.placeholder("float", [None, 72, 14, 2], name="labels")
# 定义模型，统计并分类需要训练的模型参数
model = []
if opt.model_type == 1: # 反卷积
gmodel = GModel(opt.batch_size, opt.normal_type, True,
"generate_model")
model.append(gmodel)
elif opt.model_type == 2: # 反卷积+可学习pooling
gmodel = GModel(opt.batch_size, opt.normal_type, True,
"generate_model")
model.append(gmodel)
learningpoolingmodel = LearningPoolingModel(
opt.batch_size, opt.normal_type, True, opt.model_2_layers,
"learning_pooling_model")
model.append(learningpoolingmodel)
elif opt.model_type == 3: # 反卷积+GAN
gmodel = GModel(opt.batch_size, opt.normal_type, True,
"generate_model")
model.append(gmodel)
dmodel = DModel(opt.batch_size, opt.normal_type, True,
opt.GAN_type, "discriminate_model")
model.append(dmodel)
# print(model)
# 统计并分类需要训练的参数
# 由于下面加上了对tf.GraphKeys.UPDATE_OPS的依赖，所以get_vars函数要加到calculate_loss函数后面
# 不然就会导致all_vars为空
def get_vars():
all_vars = tf.trainable_variables()
# print(all_vars)
gg_vars = [var for var in all_vars if "generate_model" in var.name]
dd_vars = [
var for var in all_vars if "discriminate_mode" in var.name
]
ll_pp_vars = [
var for var in all_vars if "learning_pooling_model" in var.name
]
return gg_vars, dd_vars, ll_pp_vars
# 加上对update_ops的依赖，不然BN就会出现问题！
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.device(opt.gpu_num):
if opt.model_type == 1: # 反卷积
pre_loss, mse, pred = model[0].calculate_loss(inputs, labels)
g_vars, _, _ = get_vars()
with tf.control_dependencies(update_ops):
train_ops = optimizer.minimize(pre_loss, var_list=g_vars)
elif opt.model_type == 2: # 反卷积+可学习pooling
_, mse, pred = model[0].calculate_loss(inputs, labels)
l_p_loss = model[1].calculate_loss(pred, labels,
opt.model_2_scale)
g_vars, _, l_p_vars = get_vars()
with tf.control_dependencies(update_ops):
train_ops = optimizer.minimize(l_p_loss,
var_list=g_vars + l_p_vars)
elif opt.model_type == 3: # 反卷积+GAN
pre_loss, mse, pred = model[0].calculate_loss(inputs, labels)
gen_loss, dis_loss = model[1].calculate_loss(pred, labels)
g_vars, d_vars, _ = get_vars()
with tf.control_dependencies(update_ops):
# D网络的训练 --> G网络的训练 ——> 先验网络（也就是G网络）的训练
d_train_ops = optimizer.minimize(dis_loss, var_list=d_vars)
g_train_ops = optimizer.minimize(gen_loss, var_list=g_vars)
pre_train_ops = optimizer.minimize(pre_loss,
var_list=g_vars)
tf.summary.scalar("MSE", mse)
tf.add_to_collection("input_batch", inputs)
tf.add_to_collection("predictions", pred)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
# 开始训练
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = opt.per_process_gpu_memory_fraction
with tf.Session(config=config) as sess:
# 首先是参数的初始化
sess.run(init)
if opt.model_type == 1:
model_type = "model_1"
elif opt.model_type == 2:
model_type = "model_2_" + str(opt.model_2_layers)
elif opt.model_type == 3:
model_type = "model_3"
summary_path = opt.summary_path + model_type + "\\data_SNR_" + str(
opt.SNR)
writer = tf.summary.FileWriter(summary_path, sess.graph)
merge_ops = tf.summary.merge_all()
start = time.time()
data_path = opt.train_data_path + "data_SNR_" + str(opt.SNR)
# 定义训练集dataset
train_dataset = CSISet(data_path,
opt.batch_size,
True,
state="train")
# 定义验证集dataset
validation_dataset = CSISet(data_path,
opt.batch_size,
True,
state="validation")
# 保存训练集和验证集的中间值，用于后续的画图
train_mse_for_plot = []
valid_mse_for_plot = []
for num in range(opt.num_epoch):
# 判断是否需要改变学习率
if opt.optimizer_type == "Momentum" and (
num % opt.learning_rate_change_epoch) == 0:
learning_rate *= opt.learning_rate_decay
print("第%i个epoch开始，当前学习率是%f" % (num, learning_rate))
for ii, (batch_x,
batch_y) in enumerate(train_dataset.get_data()):
if opt.model_type == 1 or opt.model_type == 2:
_, train_mse, summary = sess.run(
[train_ops, mse, merge_ops],
feed_dict={
inputs: batch_x,
labels: batch_y
})
elif opt.model_type == 3:
_, _, _, train_mse, summary = sess.run([
d_train_ops, g_train_ops, pre_train_ops, mse,
merge_ops
],
feed_dict={
inputs:
batch_x,
labels:
batch_y
})
writer.add_summary(summary)
if (ii + 1) % 1000 == 0:
print("epoch-%d, batch_num-%d: 当前batch训练数据误差是%f" %
(num + 1, ii + 1, train_mse))
# 每1000个batch就在验证集上测试一次
validate_mse = 0
jj = 1
for (validate_x,
validate_y) in validation_dataset.get_data():
temp_mse = sess.run(mse,
feed_dict={
inputs: validate_x,
labels: validate_y
})
validate_mse += temp_mse
jj += 1
validate_mse = validate_mse / (jj + 1)
print("epoch-%d: 当前阶段验证集数据平均误差是%f" %
(num + 1, validate_mse))
train_mse_for_plot.append(train_mse)
valid_mse_for_plot.append(validate_mse)
end = time.time()
utils.print_time(start, end, "跑完" + str(opt.num_epoch) + "个epoch")
plot_path = opt.result_path + model_type + "\\data_SNR_" + str(
opt.SNR) + "\\train"
utils.plot_fig(train_mse_for_plot, valid_mse_for_plot, plot_path)
print("训练过程中最小验证误差是%f" % min(valid_mse_for_plot))
# 保存模型文件
model_file = opt.model_path + model_type + "\\data_SNR_" + str(
opt.SNR) + "\\data_SNR_" + str(opt.SNR)
model_utils.save_model(saver, sess, model_file)
data = data.cuda()
score = model_train(data)
confusion_matrix.add(score.data.squeeze(),
label.type(torch.LongTensor))
_, predict = torch.max(score.data, 1)
total_num += label.size(0)
correct_num += (predict.cpu() == label).sum()
model_train.train()
accuracy = 100 * float(correct_num) / float(total_num)
return confusion_matrix, accuracy
if __name__ == '__main__':
# login itchat
itchat.auto_login()
itchat_send('start!')
opt = DefaultConfig()
opt.parse({
'model': 'VGG19',
'max_epoch': 200,
'weight_decay': 15e-4,
'lr': 0.1
})
train(opt)