def main():
# config
parser = argparse.ArgumentParser()
parser.add_argument('-s',
'--save',
default='./save',
help='保存的文件夹路径，如果有重名，会在其后加-来区别')
parser.add_argument('-is',
'--image_size',
default=224,
type=int,
help='patch会被resize到多大，默认时224 x 224')
parser.add_argument('-vts',
'--valid_test_size',
default=(0.1, 0.1),
type=float,
nargs=2,
help='验证集、测试集的大小，默认时0.1, 0.1')
parser.add_argument('-bs',
'--batch_size',
default=64,
type=int,
help='batch size，默认时64')
parser.add_argument('-nw',
'--num_workers',
default=12,
type=int,
help='多进程数目，默认时12')
parser.add_argument('-lr',
'--learning_rate',
default=0.0001,
type=float,
help='学习率大小，默认时0.0001')
parser.add_argument('-e',
'--epoch',
default=10,
type=int,
help='epoch 数量，默认是10')
parser.add_argument('-tp',
'--test_patches',
default=None,
type=int,
help=('测试时随机从每个patient中抽取的patches的数量，默认是None，'
'即使用全部的patches进行测试'))
parser.add_argument('--cindex_reduction',
default='mean',
help='聚合同一张slide的patches时的聚合方式，默认时mean')
parser.add_argument('--loss_type',
default='cox',
help='使用的loss的类型，默认是cox，也可以是svmloss')
parser.add_argument('--zoom', default='40.0', help="使用的放大倍数，默认是40.0")
parser.add_argument('--rank_ratio',
default=1.0,
type=float,
help="svmloss的rank_ratio，默认是1.0")
args = parser.parse_args()
save = args.save
image_size = (args.image_size, args.image_size)
valid_size, test_size = args.test_size
batch_size = args.batch_size
num_workers = args.num_workers
lr = args.learning_rate
epoch = args.epoch
test_patches = args.test_patches
cindex_reduction = args.cindex_reduction
zoom = args.zoom
rank_ratio = args.rank_ratio
# ----- 读取数据 -----
demographic_file = '/home/dl/NewDisk/Slides/TCGA-OV/demographic.csv'
tiles_dir = '/home/dl/NewDisk/Slides/TCGA-OV/Tiles'
dat = SlidePatchData.from_demographic(demographic_file,
tiles_dir,
transfer=transforms.ToTensor(),
zoom=zoom)
train_transfer = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transfer = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_dat, valid_dat = dat.split_by_patients(valid_size + test_size,
train_transfer=train_transfer,
test_transfer=test_transfer)
valid_dat, test_dat = valid_dat.split_by_patients(test_size /
(valid_size + test_size))
train_sampler = OneEveryPatientSampler(train_dat)
if test_patches is not None:
test_sampler = OneEveryPatientSampler(valid_dat,
num_per_patients=test_patches)
else:
test_sampler = None
dataloaders = {
'train':
data.DataLoader(train_dat,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers),
'valid':
data.DataLoader(valid_dat,
batch_size=batch_size,
sampler=test_sampler,
num_workers=num_workers),
'test':
data.DataLoader(test_dat,
batch_size=batch_size,
sampler=test_sampler,
num_workers=num_workers),
}
# ----- 构建网络和优化器 -----
net = SurvivalPatchCNN()
if args.loss_type == 'cox':
criterion = NegativeLogLikelihood()
elif args.loss_type == 'svmloss':
criterion = SvmLoss(rank_ratio=rank_ratio)
optimizer = optim.Adam(net.parameters(), lr=lr)
scorings = [mm.Loss(), mm.CIndexForSlide(reduction=cindex_reduction)]
# ----- 训练网络 -----
net, hist = train(net,
criterion,
optimizer,
dataloaders,
epoch=epoch,
metrics=scorings)
print('')
# ----- 最后的测试 -----
test_hist = evaluate(net, dataloaders['test'], criterion, metrics=scorings)
# 保存结果
dirname = check_update_dirname(save)
torch.save(net.state_dict(), os.path.join(dirname, 'model.pth'))
pd.DataFrame(hist).to_csv(os.path.join(dirname, 'train.csv'))
with open(os.path.join(dirname, 'test.json'), 'w') as f:
json.dump(test_hist, f)
with open(os.path.join(dirname, 'config.json'), 'w') as f:
json.dump(args.__dict__, f)
def train(model,
criterion,
optimizer,
dataloaders,
scheduler=NoneScheduler(None),
epoch=100,
device=torch.device('cuda:0'),
l2=0.0,
metrics=(mm.Loss(), mm.CIndexForSlide()),
standard_metric_index=1,
clip_grad=False):
best_model_wts = copy.deepcopy(model.state_dict())
best_metric = 0.0
best_metric_name = metrics[standard_metric_index].__class__.__name__ + \
'_valid'
history = {
m.__class__.__name__ + p: []
for p in ['_train', '_valid'] for m in metrics
}
model.to(device)
for e in range(epoch):
for phase in ['train', 'valid']:
if phase == 'train':
scheduler.step()
model.train()
prefix = "Train: "
else:
model.eval()
prefix = "Valid: "
# progressbar
format_custom_text = pb.FormatCustomText('Loss: %(loss).4f',
dict(loss=0.))
widgets = [
prefix, " ",
pb.Counter(), ' ',
pb.Bar(), ' ',
pb.Timer(), ' ',
pb.AdaptiveETA(), ' ', format_custom_text
]
iterator = pb.progressbar(dataloaders[phase], widgets=widgets)
for m in metrics:
m.reset()
for batch_x, batch_y, (batch_ids, batch_files) in iterator:
batch_x = batch_x.to(device)
batch_y = batch_y.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
logit = model(batch_x)
loss = criterion(logit, batch_y)
# 只给weight加l2正则化
if l2 > 0.0:
for p_n, p_v in model.named_parameters():
if p_n == 'weight':
loss += l2 * p_v.norm()
if phase == 'train':
loss.backward()
if clip_grad:
nn.utils.clip_grad_norm_(model.parameters(),
max_norm=1)
optimizer.step()
with torch.no_grad():
for m in metrics:
if isinstance(m, mm.Loss):
m.add(loss.cpu().item(), batch_x.size(0))
format_custom_text.update_mapping(loss=m.value())
else:
m.add(logit.squeeze(), batch_y, batch_ids)
for m in metrics:
history[m.__class__.__name__ + '_' + phase].append(m.value())
print("Epoch: %d, Phase:%s, " % (e, phase) + ", ".join([
'%s: %.4f' % (m.__class__.__name__,
history[m.__class__.__name__ + '_' + phase][-1])
for m in metrics
]))
if phase == 'valid':
epoch_metric = history[best_metric_name][-1]
if epoch_metric > best_metric:
best_metric = epoch_metric
best_model_wts = copy.deepcopy(model.state_dict())
print("Best metric: %.4f" % best_metric)
model.load_state_dict(best_model_wts)
return model, history
def main():
warnings.filterwarnings('ignore')
# config
parser = argparse.ArgumentParser()
parser.add_argument('-s',
'--save',
default='./save',
help='保存的文件夹路径，如果有重名，会在其后加-来区别')
parser.add_argument('-is',
'--image_size',
default=224,
type=int,
help='patch会被resize到多大，默认时224 x 224')
parser.add_argument('-vts',
'--valid_test_size',
default=(0.1, 0.1),
type=float,
nargs=2,
help='训练集和测试集的大小，默认时0.1, 0.1')
parser.add_argument('-bs',
'--batch_size',
default=32,
type=int,
help='batch size，默认时32')
parser.add_argument('-nw',
'--num_workers',
default=12,
type=int,
help='多进程数目，默认时12')
parser.add_argument('-lr',
'--learning_rate',
default=0.0001,
type=float,
help='学习率大小，默认时0.0001')
parser.add_argument('-e',
'--epoch',
default=10,
type=int,
help='epoch 数量，默认是10')
parser.add_argument('--reduction',
default='mean',
help='聚合同一bag的instances时的聚合方式，默认时mean')
parser.add_argument('--multipler',
default=2.0,
type=float,
help="为了平衡pos和neg，在weight再乘以一个大于1的数，默认是2.0")
args = parser.parse_args()
save = args.save
image_size = (args.image_size, args.image_size)
valid_size, test_size = args.valid_test_size
batch_size = args.batch_size
num_workers = args.num_workers
lr = args.learning_rate
epoch = args.epoch
reduction = args.reduction
multipler = args.multipler
# ----- 读取数据 -----
neg_dir = './DATA/TCT/negative'
pos_dir = './DATA/TCT/positive'
dat = MilData.from2dir(neg_dir, pos_dir)
train_transfer = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transfer = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_dat, valid_dat, test_dat = dat.split_by_bag(
test_size,
valid_size,
train_transfer=train_transfer,
valid_transfer=test_transfer,
test_transfer=test_transfer)
dataloaders = {
'train':
data.DataLoader(train_dat,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True),
'valid':
data.DataLoader(
valid_dat,
batch_size=batch_size,
num_workers=num_workers,
),
'test':
data.DataLoader(
test_dat,
batch_size=batch_size,
num_workers=num_workers,
)
}
# ----- 构建网络和优化器 -----
net = NormalCnn()
criterion = nn.BCELoss(reduction='none')
optimizer = optim.Adam(net.parameters(), lr=lr)
scorings = [
mm.Loss(),
mm.Recall(reduction=reduction),
mm.ROCAUC(reduction=reduction),
mm.BalancedAccuracy(reduction=reduction),
mm.F1Score(reduction=reduction),
mm.Precision(reduction=reduction),
mm.Accuracy(reduction=reduction)
]
# ----- 训练网络 -----
try:
net, hist, weighter = train(net,
criterion,
optimizer,
dataloaders,
epoch=epoch,
metrics=scorings,
weighter_multipler=multipler)
test_hist = evaluate(net, dataloaders['test'], criterion, scorings)
except Exception as e:
import ipdb
ipdb.set_trace() # XXX BREAKPOINT
# 保存结果
dirname = check_update_dirname(save)
torch.save(net.state_dict(), os.path.join(dirname, 'model.pth'))
torch.save(weighter, os.path.join(dirname, 'weigher.pth'))
pd.DataFrame(hist).to_csv(os.path.join(dirname, 'train.csv'))
with open(os.path.join(dirname, 'config.json'), 'w') as f:
json.dump(args.__dict__, f)
with open(os.path.join(dirname, 'test.json'), 'w') as f:
json.dump(test_hist, f)
def train(model,
criterion,
optimizer,
dataloaders,
scheduler=NoneScheduler(None),
epoch=100,
device=torch.device('cuda:0'),
l2=0.0,
metrics=(mm.Loss(), ),
standard_metric_index=1,
clip_grad=False,
weighter_multipler=1.0):
weighter = Weighter(dataloaders['train'].dataset,
device,
multipler=weighter_multipler)
# 构建几个变量来储存最好的模型
best_model_wts = copy.deepcopy(model.state_dict())
best_metric = 0.0
best_metric_name = metrics[standard_metric_index].__class__.__name__ + \
'_valid'
best_weighter = copy.deepcopy(weighter) # 储存最好模型对应的weighter
# 构建dict来储存训练过程中的结果
history = {
m.__class__.__name__ + p: []
for p in ['_train', '_valid'] for m in metrics
}
model.to(device)
for e in range(epoch):
for phase in ['train', 'valid']:
if phase == 'train':
scheduler.step()
model.train()
prefix = "Train: "
else:
model.eval()
prefix = "Valid: "
# progressbar
format_custom_text = pb.FormatCustomText('Loss: %(loss).4f',
dict(loss=0.))
widgets = [
prefix, " ",
pb.Percentage(), ' ',
pb.SimpleProgress(format='(%s)' %
pb.SimpleProgress.DEFAULT_FORMAT), ' ',
pb.Bar(), ' ',
pb.Timer(), ' ',
pb.AdaptiveETA(), ' ', format_custom_text
]
iterator = pb.progressbar(dataloaders[phase], widgets=widgets)
for m in metrics:
m.reset()
for batch_x, batch_y, bag_ids, inst_ids in iterator:
batch_x = batch_x.to(device)
batch_y = batch_y.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
proba = model(batch_x).squeeze() # 注意模型输出的需要时proba
# 计算每个样本的权重
w = weighter(proba, batch_y, bag_ids, inst_ids)
# 这个criterion不能reduction
loss_es = criterion(proba, batch_y.float())
# 使用计算的权重
loss = (loss_es * w).mean()
# 只给weight加l2正则化
if l2 > 0.0:
for p_n, p_v in model.named_parameters():
if p_n == 'weight':
loss += l2 * p_v.norm()
if phase == 'train':
loss.backward()
if clip_grad:
nn.utils.clip_grad_norm_(model.parameters(),
max_norm=1)
optimizer.step()
with torch.no_grad():
for m in metrics:
if isinstance(m, mm.Loss):
m.add(loss.cpu().item(), batch_x.size(0))
format_custom_text.update_mapping(loss=m.value())
else:
m.add(proba.squeeze(), batch_y, bag_ids)
for m in metrics:
history[m.__class__.__name__ + '_' + phase].append(m.value())
print("Epoch: %d, Phase:%s, " % (e, phase) + ", ".join([
'%s: %.4f' % (m.__class__.__name__,
history[m.__class__.__name__ + '_' + phase][-1])
for m in metrics
]))
if phase == 'valid':
epoch_metric = history[best_metric_name][-1]
if epoch_metric > best_metric:
best_metric = epoch_metric
best_model_wts = copy.deepcopy(model.state_dict())
best_weighter = copy.deepcopy(weighter)
print("Best metric: %.4f" % best_metric)
model.load_state_dict(best_model_wts)
return model, history, best_weighter
def fit(self, datas):
# get dataloaders
train_data = data.ConcatDataset([datas['subject'], datas['qc']]) \
if self.train_with_qc else datas['subject']
dataloaders = {
'train': data.DataLoader(train_data, batch_size=self.bs,
num_workers=self.nw, shuffle=True),
'qc': data.DataLoader(datas['qc'], batch_size=self.bs,
num_workers=self.nw)
}
# begin training
pbar = tqdm(total=self.epoch)
for e in range(self.epoch):
self.e = e
if e < self.rec_epoch:
self.phase = 'rec_pretrain'
elif e < self.rec_epoch + self.disc_epoch:
self.phase = 'disc_pretrain'
else:
self.phase = 'iter_train'
pbar.set_description(self.phase)
# --- train phase ---
for model in self.models.values():
model.train()
disc_b_loss_obj = mm.Loss()
disc_o_loss_obj = mm.Loss()
adv_b_loss_obj = mm.Loss()
adv_o_loss_obj = mm.Loss()
rec_loss_obj = mm.Loss()
for batch_x, batch_y in tqdm(dataloaders['train'], 'Batch: '):
batch_x = batch_x.to(self.device).float()
batch_y = batch_y.to(self.device).float()
bs0 = batch_x.size(0)
for optimizer in self.optimizers.values():
optimizer.zero_grad()
if self.phase in ['disc_pretrain', 'iter_train']:
disc_b_loss, disc_o_loss = \
self._forward_discriminate(batch_x, batch_y)
disc_b_loss_obj.add(disc_b_loss, bs0)
disc_o_loss_obj.add(disc_o_loss, bs0)
if self.phase in ['rec_pretrain', 'iter_train']:
rec_loss, adv_b_loss, adv_o_loss = \
self._forward_autoencode(batch_x, batch_y)
rec_loss_obj.add(rec_loss, bs0)
adv_b_loss_obj.add(adv_b_loss, bs0)
adv_o_loss_obj.add(adv_o_loss, bs0)
# record loss
self.history['disc_b_loss'].append(disc_b_loss_obj.value())
self.history['disc_o_loss'].append(disc_o_loss_obj.value())
self.history['adv_b_loss'].append(adv_b_loss_obj.value())
self.history['adv_o_loss'].append(adv_o_loss_obj.value())
self.history['rec_loss'].append(rec_loss_obj.value())
# visual epoch loss
self.visobj.add_epoch_loss(
winname='disc_losses',
disc_b_loss=self.history['disc_b_loss'][-1],
disc_o_loss=self.history['disc_o_loss'][-1],
adv_b_loss=self.history['adv_b_loss'][-1],
adv_o_loss=self.history['adv_o_loss'][-1],
)
self.visobj.add_epoch_loss(
winname='recon_losses',
recon_loss=self.history['rec_loss'][-1]
)
# --- valid phase ---
all_data = ConcatData(datas['subject'], datas['qc'])
all_reses_dict, qc_loss = self.generate(
all_data, verbose=False, compute_qc_loss=True)
# pca
subject_pca, qc_pca = pca_for_dict(all_reses_dict, 3)
# plot pca
pca_plot(subject_pca, qc_pca)
# display in visdom
self.visobj.vis.matplot(plt, win='PCA', opts={'title': 'PCA'})
plt.close()
# --- early stopping ---
qc_dist = mm.mean_distance(qc_pca['Rec_nobe'])
self.history['qc_rec_loss'].append(qc_loss)
self.history['qc_distance'].append(qc_dist)
self.visobj.add_epoch_loss(winname='qc_rec_loss', qc_loss=qc_loss)
self.visobj.add_epoch_loss(winname='qc_distance', qc_dist=qc_dist)
if e >= (self.epoch - 200):
self._check_qc(qc_dist, qc_loss)
# progressbar
pbar.update(1)
pbar.close()
# early stop information and save visdom env
if self.visdom_env != 'main':
self.visobj.vis.save([self.visdom_env])
print('')
print('The best epoch is %d' % self.early_stop_objs['best_epoch'])
print('The best qc loss is %.4f' %
self.early_stop_objs['best_qc_loss'])
print('The best qc distance is %.4f' %
self.early_stop_objs['best_qc_distance'])
for k, v in self.models.items():
v.load_state_dict(self.early_stop_objs['best_models'][k])
self.early_stop_objs.pop('best_models')
return self.models, self.history, self.early_stop_objs
def generate(self, data_loader, verbose=True, compute_qc_loss=False):
for m in self.models.values():
m.to(self.device).eval()
if isinstance(data_loader, data.Dataset):
data_loader = data.DataLoader(
data_loader, batch_size=self.bs, num_workers=self.nw)
x_ori, x_rec, x_rec_nobe, ys, codes = [], [], [], [], []
qc_loss = mm.Loss()
# encoding
if verbose:
print('----- encoding -----')
iterator = tqdm(data_loader, 'encode and decode: ')
else:
iterator = data_loader
with torch.no_grad():
for batch_x, batch_y in iterator:
# return x and y
x_ori.append(batch_x)
ys.append(batch_y)
# return latent representation
batch_x = batch_x.to(self.device, torch.float)
batch_y = batch_y.to(self.device, torch.float)
hidden = self.models['encoder'](batch_x)
codes.append(hidden)
# return rec with and without batch effects
batch_ys = [
torch.eye(self.batch_label_num)[batch_y[:, 1].long()].to(
hidden),
batch_y[:, [0]]
]
batch_ys = torch.cat(batch_ys, dim=1)
hidden_be = hidden + self.models['map'](batch_ys)
x_rec.append(self.models['decoder'](hidden_be))
x_rec_nobe.append(self.models['decoder'](hidden))
# return qc loss
if compute_qc_loss:
qc_index = batch_y[:, -1] == 0.
if qc_index.sum() > 0:
batch_qc_loss = self.criterions['rec'](
batch_x[qc_index], x_rec[-1][qc_index])
qc_loss.add(
batch_qc_loss,
qc_index.sum().detach().cpu().item()
)
else:
qc_loss.add(torch.tensor(0.), 0)
# return dataframe
res = {
'Ori': torch.cat(x_ori), 'Ys': torch.cat(ys),
'Codes': torch.cat(codes), 'Rec': torch.cat(x_rec),
'Rec_nobe': torch.cat(x_rec_nobe)
}
for k, v in res.items():
if v is not None:
if k == 'Ys':
res[k] = pd.DataFrame(
v.detach().cpu().numpy(),
index=data_loader.dataset.y_df.index,
columns=data_loader.dataset.y_df.columns
)
elif k != 'Codes':
res[k] = pd.DataFrame(
v.detach().cpu().numpy(),
index=data_loader.dataset.x_df.index,
columns=data_loader.dataset.x_df.columns
)
res[k] = self.pre_transfer.inverse_transform(
res[k], None)[0]
else:
res[k] = pd.DataFrame(
v.detach().cpu().numpy(),
index=data_loader.dataset.x_df.index,
)
if compute_qc_loss:
return res, qc_loss.value()
return res
def main():
# ----- 根据data来读取不同的数据和不同的loss、metrics -----
if config.args.data == 'brca':
rna = RnaData.predicted_data(config.brca_cli, config.brca_rna,
{'PAM50Call_RNAseq': 'pam50'})
rna.transform(tf.LabelMapper(config.brca_label_mapper))
out_shape = len(config.brca_label_mapper)
criterion = nn.CrossEntropyLoss()
scorings = (mm.Loss(), mm.Accuracy(), mm.BalancedAccuracy(),
mm.F1Score(average='macro'), mm.Precision(average='macro'),
mm.Recall(average='macro'), mm.ROCAUC(average='macro'))
elif config.args.data == 'survival':
if os.path.exists('./DATA/temp_pan.pth'):
rna = RnaData.load('./DATA/temp_pan.pth')
else:
rna = RnaData.survival_data(config.pan_cli, config.pan_rna,
'_OS_IND', '_OS')
out_shape = 1
if config.args.loss_type == 'cox':
criterion = NegativeLogLikelihood()
elif config.args.loss_type == 'svm':
criterion = SvmLoss(rank_ratio=config.args.svm_rankratio)
scorings = (mm.Loss(), mm.CIndex())
rna.transform(tf.ZeroFilterCol(0.8))
rna.transform(tf.MeanFilterCol(1))
rna.transform(tf.StdFilterCol(0.5))
norm = tf.Normalization()
rna.transform(norm)
# ----- 构建网络和优化器 -----
inpt_shape = rna.X.shape[1]
if config.args.net_type == 'mlp':
net = MLP(inpt_shape, out_shape, config.args.hidden_num,
config.args.block_num).cuda()
elif config.args.net_type == 'atten':
net = SelfAttentionNet(inpt_shape, out_shape, config.args.hidden_num,
config.args.bottle_num, config.args.block_num,
config.args.no_res, config.act,
config.args.no_head, config.args.no_bottle,
config.args.no_atten,
config.args.dropout_rate).cuda()
elif config.args.net_type == 'resnet':
net = ResidualNet(inpt_shape, out_shape, config.args.hidden_num,
config.args.bottle_num,
config.args.block_num).cuda()
# ----- 训练网络，cross validation -----
split_iterator = rna.split_cv(config.args.test_size,
config.args.cross_valid)
train_hists = []
test_hists = []
for split_index, (train_rna, test_rna) in enumerate(split_iterator):
print('##### save: %s, split: %d #####' %
(config.args.save, split_index))
# 从train中再分出一部分用作验证集，决定停止
train_rna, valid_rna = train_rna.split(0.1)
dats = {
'train': train_rna.to_torchdat(),
'valid': valid_rna.to_torchdat(),
}
dataloaders = {
k: data.DataLoader(v, batch_size=config.args.batch_size)
for k, v in dats.items()
}
test_dataloader = data.DataLoader(test_rna.to_torchdat(),
batch_size=config.args.batch_size)
# 网络训练前都进行一次参数重置，避免之前的训练的影响
net.reset_parameters()
# train
optimizer = optim.Adamax(net.parameters(),
lr=config.args.learning_rate)
lrs = config.lrs(optimizer)
net, hist = train(
net,
criterion,
optimizer,
dataloaders,
epoch=config.args.epoch,
metrics=scorings,
l2=config.args.l2,
standard_metric_index=config.args.standard_metric_index,
scheduler=lrs)
# test
test_res = evaluate(net, criterion, test_dataloader, metrics=scorings)
# 将多次训练的结果保存到一个df中
hist = pd.DataFrame(hist)
hist['split_index'] = split_index
train_hists.append(hist)
# 保存多次test的结果
test_res['split_index'] = split_index
test_hists.append(test_res)
# 每个split训练的模型保存为一个文件
torch.save(net.state_dict(),
os.path.join(config.save_dir, 'model%d.pth' % split_index))
# 保存train的结果
train_hists = pd.concat(train_hists)
train_hists.to_csv(os.path.join(config.save_dir, 'train.csv'))
# 保存test的结果
test_hists = pd.DataFrame(test_hists)
test_hists.to_csv(os.path.join(config.save_dir, 'test.csv'))