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在进入网络自适应配置之前，我们先学习一下nnUNet的基础训练框架，该模块对于大部分的视觉任务都通用，不管是分割、检测还是分类，因此可更具自身需要将此模块进行修改，添加至自己任务中。该模块包括train_val 循环、跟踪trian、val损失以及metirc，使用早停法提前终止训练，使用移动平均得到更加平滑的实验结果。nnUNet的后续训练都是再此基础进行延申。该模块位于nnunet/training/network_training/network_trainer.py中。
该部分需要了解的知识点：
class NetworkTrainer(object):
def __init__(self, deterministic=True, fp16=False):
"""
A generic class that can train almost any neural network (RNNs excluded). It provides basic functionality such
as the training loop, tracking of training and validation losses (and the target metric if you implement it)
Training can be terminated early if the validation loss (or the target metric if implemented) do not improve
anymore. This is based on a moving average (MA) of the loss/metric instead of the raw values to get more smooth
results.
What you need to override:
- __init__
- initialize
- run_online_evaluation (optional)
- finish_online_evaluation (optional)
- validate
- predict_test_case
"""
self.fp16 = fp16
self.amp_grad_scaler = None
if deterministic:
np.random.seed(12345)
torch.manual_seed(12345)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(12345)
# 设置cuda随机数种子，保证每次运行网络时同样的输入输出固定
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
################# SET THESE IN self.initialize() ###################################
# 这个在initialize() 中进行重写
self.network: Tuple[SegmentationNetwork, nn.DataParallel] = None
self.optimizer = None
self.lr_scheduler = None
self.tr_gen = self.val_gen = None # 这两个是什么
self.was_initialized = False
################# SET THESE IN INIT ################################################
# 相关路径设置
self.output_folder = None
self.fold = None
self.loss = None
self.dataset_directory = None
################# SET THESE IN LOAD_DATASET OR DO_SPLIT ############################
self.dataset = None # these can be None for inference mode
self.dataset_tr = self.dataset_val = None # do not need to be used, they just appear if you are using the suggested load_dataset_and_do_split
################# THESE DO NOT NECESSARILY NEED TO BE MODIFIED #####################
self.patience = 50
# 这里前后比重差异有点大呀
self.val_eval_criterion_alpha = 0.9 # alpha * old + (1-alpha) * new
# if this is too low then the moving average will be too noisy and the training may terminate early. If it is
# too high the training will take forever
self.train_loss_MA_alpha = 0.93 # alpha * old + (1-alpha) * new
self.train_loss_MA_eps = 5e-4 # new MA must be at least this much better (smaller)
# 难怪之前都是训练了那么久
self.max_num_epochs = 1000
self.num_batches_per_epoch = 250
self.num_val_batches_per_epoch = 50
self.also_val_in_tr_mode = False
self.lr_threshold = 1e-6 # the network will not terminate training if the lr is still above this threshold
################# LEAVE THESE ALONE ################################################
self.val_eval_criterion_MA = None
self.train_loss_MA = None
self.best_val_eval_criterion_MA = None
self.best_MA_tr_loss_for_patience = None
self.best_epoch_based_on_MA_tr_loss = None
self.all_tr_losses = []
self.all_val_losses = []
self.all_val_losses_tr_mode = []
self.all_val_eval_metrics = [] # does not have to be used
self.epoch = 0
self.log_file = None
self.deterministic = deterministic
# 这个进程条用到了tqdm模块
self.use_progress_bar = False
if 'nnunet_use_progress_bar' in os.environ.keys():
self.use_progress_bar = bool(int(os.environ['nnunet_use_progress_bar']))
################# Settings for saving checkpoints ##################################
self.save_every = 50
self.save_latest_only = True # if false it will not store/overwrite _latest but separate files each
# time an intermediate checkpoint is created
self.save_intermediate_checkpoints = True # whether or not to save checkpoint_latest
self.save_best_checkpoint = True # whether or not to save the best checkpoint according to self.best_val_eval_criterion_MA
self.save_final_checkpoint = True # whether or not to save the final checkpoint
@abstractmethod
def initialize(self, training=True):
"""
create self.output_folder
modify self.output_folder if you are doing cross-validation (one folder per fold)
set self.tr_gen and self.val_gen
call self.initialize_network and self.initialize_optimizer_and_scheduler (important!)
finally set self.was_initialized to True
:param training:
:return:
"""
@abstractmethod
def load_dataset(self):
pass
def do_split(self):
"""
This is a suggestion for if your dataset is a dictionary (my personal standard)
:return:
"""
splits_file = join(self.dataset_directory, "splits_final.pkl") # pkl中存放了整个数据集
if not isfile(splits_file):
self.print_to_log_file("Creating new split...")
splits = []
all_keys_sorted = np.sort(list(self.dataset.keys()))
kfold = KFold(n_splits=5, shuffle=True, random_state=12345)
# 使用sklearn获取数据5折交叉验证所需的train-idx,test-idx,使用for循环依次获取，并将所有的idx使用pkl进行保存
for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)):
train_keys = np.array(all_keys_sorted)[train_idx]
test_keys = np.array(all_keys_sorted)[test_idx]
splits.append(OrderedDict())
splits[-1]['train'] = train_keys
splits[-1]['val'] = test_keys
save_pickle(splits, splits_file)
splits = load_pickle(splits_file)
if self.fold == "all":
tr_keys = val_keys = list(self.dataset.keys())
else:
tr_keys = splits[self.fold]['train']
val_keys = splits[self.fold]['val']
tr_keys.sort()
val_keys.sort()
# 获取训练、测试数据集
self.dataset_tr = OrderedDict()
for i in tr_keys:
self.dataset_tr[i] = self.dataset[i]
self.dataset_val = OrderedDict()
for i in val_keys:
self.dataset_val[i] = self.dataset[i]
def plot_progress(self):
"""
Should probably by improved
:return:
"""
try:
font = {'weight': 'normal',
'size': 18}
matplotlib.rc('font', **font)
fig = plt.figure(figsize=(30, 24))
ax = fig.add_subplot(111)
ax2 = ax.twinx()
x_values = list(range(self.epoch + 1))
ax.plot(x_values, self.all_tr_losses, color='b', ls='-', label="loss_tr")
ax.plot(x_values, self.all_val_losses, color='r', ls='-', label="loss_val, train=False")
# 这个是什么用途？
if len(self.all_val_losses_tr_mode) > 0:
ax.plot(x_values, self.all_val_losses_tr_mode, color='g', ls='-', label="loss_val, train=True")
if len(self.all_val_eval_metrics) == len(x_values):
ax2.plot(x_values, self.all_val_eval_metrics, color='g', ls='--', label="evaluation metric")
ax.set_xlabel("epoch")
ax.set_ylabel("loss")
ax2.set_ylabel("evaluation metric")
ax.legend()
ax2.legend(loc=9)
fig.savefig(join(self.output_folder, "progress.png"))
plt.close()
except IOError:
self.print_to_log_file("failed to plot: ", sys.exc_info())
def print_to_log_file(self, *args, also_print_to_console=True, add_timestamp=True):
timestamp = time() # 获取当前时间
dt_object = datetime.fromtimestamp(timestamp) # 返回tuple，当前的时间
if add_timestamp:
args = ("%s:" % dt_object, *args) # 需要打印的语句
# 如果之前没有log文件，则创建log文件
if self.log_file is None:
maybe_mkdir_p(self.output_folder)
timestamp = datetime.now() # 更新时间
# 创建 log 文件
self.log_file = join(self.output_folder, "training_log_%d_%d_%d_%02.0d_%02.0d_%02.0d.txt" %
(timestamp.year, timestamp.month, timestamp.day, timestamp.hour, timestamp.minute,
timestamp.second))
with open(self.log_file, 'w') as f:
f.write("Starting... \n")
successful = False
max_attempts = 5
ctr = 0
while not successful and ctr < max_attempts:
try:
# 写入log文件
with open(self.log_file, 'a+') as f:
for a in args:
f.write(str(a))
f.write(" ")
f.write("\n")
successful = True
except IOError:
print("%s: failed to log: " % datetime.fromtimestamp(timestamp), sys.exc_info()) # 之前的时间
sleep(0.5)
ctr += 1 # 记录语句
# 将信息也打印到终端中
if also_print_to_console:
print(*args)
def save_checkpoint(self, fname, save_optimizer=True):
start_time = time()
# 保存模型参数
state_dict = self.network.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].cpu()
# 保存当前的学习率设置
lr_sched_state_dct = None
if self.lr_scheduler is not None and hasattr(self.lr_scheduler,
'state_dict'): # not isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau):
lr_sched_state_dct = self.lr_scheduler.state_dict()
# WTF is this!?
# for key in lr_sched_state_dct.keys():
# lr_sched_state_dct[key] = lr_sched_state_dct[key]
# 保存优化器参数
if save_optimizer:
optimizer_state_dict = self.optimizer.state_dict()
else:
optimizer_state_dict = None
self.print_to_log_file("saving checkpoint...")
# 每次需要保存完整的训练信息，学习
save_this = {
'epoch': self.epoch + 1,
'state_dict': state_dict,
'optimizer_state_dict': optimizer_state_dict,
'lr_scheduler_state_dict': lr_sched_state_dct,
'plot_stuff': (self.all_tr_losses, self.all_val_losses, self.all_val_losses_tr_mode,
self.all_val_eval_metrics),
'best_stuff' : (self.best_epoch_based_on_MA_tr_loss, self.best_MA_tr_loss_for_patience, self.best_val_eval_criterion_MA)}
if self.amp_grad_scaler is not None:
save_this['amp_grad_scaler'] = self.amp_grad_scaler.state_dict()
torch.save(save_this, fname)
self.print_to_log_file("done, saving took %.2f seconds" % (time() - start_time))
# 如果存在bestmodel则加载此model，否则加载最新model
def load_best_checkpoint(self, train=True):
if self.fold is None:
raise RuntimeError("Cannot load best checkpoint if self.fold is None")
if isfile(join(self.output_folder, "model_best.model")):
self.load_checkpoint(join(self.output_folder, "model_best.model"), train=train)
else:
self.print_to_log_file("WARNING! model_best.model does not exist! Cannot load best checkpoint. Falling "
"back to load_latest_checkpoint")
self.load_latest_checkpoint(train)
def load_latest_checkpoint(self, train=True):
if isfile(join(self.output_folder, "model_final_checkpoint.model")):
return self.load_checkpoint(join(self.output_folder, "model_final_checkpoint.model"), train=train)
if isfile(join(self.output_folder, "model_latest.model")):
return self.load_checkpoint(join(self.output_folder, "model_latest.model"), train=train)
if isfile(join(self.output_folder, "model_best.model")):
return self.load_best_checkpoint(train)
raise RuntimeError("No checkpoint found")
def load_final_checkpoint(self, train=False):
filename = join(self.output_folder, "model_final_checkpoint.model")
if not isfile(filename):
raise RuntimeError("Final checkpoint not found. Expected: %s. Please finish the training first." % filename)
return self.load_checkpoint(filename, train=train)
def load_checkpoint(self, fname, train=True):
self.print_to_log_file("loading checkpoint", fname, "train=", train)
if not self.was_initialized:
self.initialize(train) # 需要自己定义
# saved_model = torch.load(fname, map_location=torch.device('cuda', torch.cuda.current_device()))
saved_model = torch.load(fname, map_location=torch.device('cpu'))
self.load_checkpoint_ram(saved_model, train) # 模型加载核心代码
@abstractmethod
def initialize_network(self):
"""
initialize self.network here
:return:
"""
pass
@abstractmethod
def initialize_optimizer_and_scheduler(self):
"""
initialize self.optimizer and self.lr_scheduler (if applicable) here
:return:
"""
pass
def load_checkpoint_ram(self, checkpoint, train=True):
"""
used for if the checkpoint is already in ram
:param checkpoint:
:param train:
:return:
"""
if not self.was_initialized:
self.initialize(train)
new_state_dict = OrderedDict()
curr_state_dict_keys = list(self.network.state_dict().keys())
# if state dict comes form nn.DataParallel but we use non-parallel model here then the state dict keys do not
# match. Use heuristic to make it match
for k, value in checkpoint['state_dict'].items():
key = k
# 这里防止出现多GPU保存模型不能加载情况
if key not in curr_state_dict_keys and key.startswith('module.'):
key = key[7:]
new_state_dict[key] = value
# 半精度模型
if self.fp16:
self._maybe_init_amp()
if train:
if 'amp_grad_scaler' in checkpoint.keys():
self.amp_grad_scaler.load_state_dict(checkpoint['amp_grad_scaler'])
self.network.load_state_dict(new_state_dict) # 加载模型
self.epoch = checkpoint['epoch']
if train:
optimizer_state_dict = checkpoint['optimizer_state_dict']
if optimizer_state_dict is not None:
self.optimizer.load_state_dict(optimizer_state_dict)
if self.lr_scheduler is not None and hasattr(self.lr_scheduler, 'load_state_dict') and checkpoint[
'lr_scheduler_state_dict'] is not None:
self.lr_scheduler.load_state_dict(checkpoint['lr_scheduler_state_dict'])
if issubclass(self.lr_scheduler.__class__, _LRScheduler):
self.lr_scheduler.step(self.epoch)
self.all_tr_losses, self.all_val_losses, self.all_val_losses_tr_mode, self.all_val_eval_metrics = checkpoint[
'plot_stuff']
# load best loss (if present)
if 'best_stuff' in checkpoint.keys():
self.best_epoch_based_on_MA_tr_loss, self.best_MA_tr_loss_for_patience, self.best_val_eval_criterion_MA = checkpoint[
'best_stuff']
# after the training is done, the epoch is incremented one more time in my old code. This results in
# self.epoch = 1001 for old trained models when the epoch is actually 1000. This causes issues because
# len(self.all_tr_losses) = 1000 and the plot function will fail. We can easily detect and correct that here
# 修正之前版本的错误，之前版本epoch从1开始
if self.epoch != len(self.all_tr_losses):
self.print_to_log_file("WARNING in loading checkpoint: self.epoch != len(self.all_tr_losses). This is "
"due to an old bug and should only appear when you are loading old models. New "
"models should have this fixed! self.epoch is now set to len(self.all_tr_losses)")
self.epoch = len(self.all_tr_losses)
self.all_tr_losses = self.all_tr_losses[:self.epoch]
self.all_val_losses = self.all_val_losses[:self.epoch]
self.all_val_losses_tr_mode = self.all_val_losses_tr_mode[:self.epoch]
self.all_val_eval_metrics = self.all_val_eval_metrics[:self.epoch]
self._maybe_init_amp()
def _maybe_init_amp(self):
if self.fp16 and self.amp_grad_scaler is None:
self.amp_grad_scaler = GradScaler()
def plot_network_architecture(self):
"""
can be implemented (see nnUNetTrainer) but does not have to. Not implemented here because it imposes stronger
assumptions on the presence of class variables
:return:
"""
pass
def run_training(self):
if not torch.cuda.is_available():
self.print_to_log_file("WARNING!!! You are attempting to run training on a CPU (torch.cuda.is_available() is False). This can be VERY slow!")
_ = self.tr_gen.next()
_ = self.val_gen.next()
if torch.cuda.is_available():
torch.cuda.empty_cache()
self._maybe_init_amp()
maybe_mkdir_p(self.output_folder)
self.plot_network_architecture()
if cudnn.benchmark and cudnn.deterministic:
warn("torch.backends.cudnn.deterministic is True indicating a deterministic training is desired. "
"But torch.backends.cudnn.benchmark is True as well and this will prevent deterministic training! "
"If you want deterministic then set benchmark=False")
if not self.was_initialized:
self.initialize(True)
while self.epoch < self.max_num_epochs:
self.print_to_log_file("\nepoch: ", self.epoch)
epoch_start_time = time()
train_losses_epoch = []
# train one epoch
self.network.train()
if self.use_progress_bar: # 使用tqdm显示训练进度
with trange(self.num_batches_per_epoch) as tbar:
for b in tbar:
tbar.set_description("Epoch {}/{}".format(self.epoch+1, self.max_num_epochs))
l = self.run_iteration(self.tr_gen, True) # 得到训练的损失函数
tbar.set_postfix(loss=l)
train_losses_epoch.append(l)
else:
for _ in range(self.num_batches_per_epoch):
l = self.run_iteration(self.tr_gen, True)
train_losses_epoch.append(l)
self.all_tr_losses.append(np.mean(train_losses_epoch)) # 当前epoch的平均损失
self.print_to_log_file("train loss : %.4f" % self.all_tr_losses[-1])
# 在完成一个epoch的训练后，进行推理
with torch.no_grad():
# validation with train=False
self.network.eval()
val_losses = []
for b in range(self.num_val_batches_per_epoch):
l = self.run_iteration(self.val_gen, False, True) #
val_losses.append(l)
self.all_val_losses.append(np.mean(val_losses))
self.print_to_log_file("validation loss: %.4f" % self.all_val_losses[-1])
if self.also_val_in_tr_mode:
self.network.train()
# validation with train=True
val_losses = []
for b in range(self.num_val_batches_per_epoch):
l = self.run_iteration(self.val_gen, False) # 不进行梯度反传
val_losses.append(l)
self.all_val_losses_tr_mode.append(np.mean(val_losses)) # 验证损失
self.print_to_log_file("validation loss (train=True): %.4f" % self.all_val_losses_tr_mode[-1])
self.update_train_loss_MA() # needed for lr scheduler and stopping of training
continue_training = self.on_epoch_end()
epoch_end_time = time()
if not continue_training:
# allows for early stopping
break
self.epoch += 1
self.print_to_log_file("This epoch took %f s\n" % (epoch_end_time - epoch_start_time))
self.epoch -= 1 # if we don't do this we can get a problem with loading model_final_checkpoint.
if self.save_final_checkpoint: self.save_checkpoint(join(self.output_folder, "model_final_checkpoint.model"))
# now we can delete latest as it will be identical with final
if isfile(join(self.output_folder, "model_latest.model")):
os.remove(join(self.output_folder, "model_latest.model"))
if isfile(join(self.output_folder, "model_latest.model.pkl")):
os.remove(join(self.output_folder, "model_latest.model.pkl"))
def maybe_update_lr(self):
# maybe update learning rate
if self.lr_scheduler is not None:
# 只有两种指定的学习率衰减方法
assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler))
if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau):
# lr scheduler is updated with moving average val loss. should be more robust
self.lr_scheduler.step(self.train_loss_MA)
else:
self.lr_scheduler.step(self.epoch + 1)
self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr']))
def maybe_save_checkpoint(self):
"""
Saves a checkpoint every save_ever epochs.
:return:
"""
# 是否保存中间的cp
if self.save_intermediate_checkpoints and (self.epoch % self.save_every == (self.save_every - 1)):
self.print_to_log_file("saving scheduled checkpoint file...")
if not self.save_latest_only:
self.save_checkpoint(join(self.output_folder, "model_ep_%03.0d.model" % (self.epoch + 1)))
self.save_checkpoint(join(self.output_folder, "model_latest.model"))
self.print_to_log_file("done")
def update_eval_criterion_MA(self):
"""
If self.all_val_eval_metrics is unused (len=0) then we fall back to using -self.all_val_losses for the MA to determine early stopping
(not a minimization, but a maximization of a metric and therefore the - in the latter case)
:return:
"""
if self.val_eval_criterion_MA is None:
if len(self.all_val_eval_metrics) == 0:
self.val_eval_criterion_MA = - self.all_val_losses[-1]
else:
self.val_eval_criterion_MA = self.all_val_eval_metrics[-1]
else:
if len(self.all_val_eval_metrics) == 0:
"""
We here use alpha * old - (1 - alpha) * new because new in this case is the vlaidation loss and lower
is better, so we need to negate it.
"""
self.val_eval_criterion_MA = self.val_eval_criterion_alpha * self.val_eval_criterion_MA - (
1 - self.val_eval_criterion_alpha) * \
self.all_val_losses[-1]
else:
self.val_eval_criterion_MA = self.val_eval_criterion_alpha * self.val_eval_criterion_MA + (
1 - self.val_eval_criterion_alpha) * \
self.all_val_eval_metrics[-1]
# 早停法
def manage_patience(self):
# update patience
continue_training = True
if self.patience is not None:
# if best_MA_tr_loss_for_patience and best_epoch_based_on_MA_tr_loss were not yet initialized,
# initialize them
if self.best_MA_tr_loss_for_patience is None:
self.best_MA_tr_loss_for_patience = self.train_loss_MA
if self.best_epoch_based_on_MA_tr_loss is None:
self.best_epoch_based_on_MA_tr_loss = self.epoch
if self.best_val_eval_criterion_MA is None:
self.best_val_eval_criterion_MA = self.val_eval_criterion_MA
# check if the current epoch is the best one according to moving average of validation criterion. If so
# then save 'best' model
# Do not use this for validation. This is intended for test set prediction only.
#self.print_to_log_file("current best_val_eval_criterion_MA is %.4f0" % self.best_val_eval_criterion_MA)
#self.print_to_log_file("current val_eval_criterion_MA is %.4f" % self.val_eval_criterion_MA)
if self.val_eval_criterion_MA > self.best_val_eval_criterion_MA:
self.best_val_eval_criterion_MA = self.val_eval_criterion_MA
#self.print_to_log_file("saving best epoch checkpoint...")
if self.save_best_checkpoint: self.save_checkpoint(join(self.output_folder, "model_best.model"))
# Now see if the moving average of the train loss has improved. If yes then reset patience, else
# increase patience
if self.train_loss_MA + self.train_loss_MA_eps < self.best_MA_tr_loss_for_patience:
self.best_MA_tr_loss_for_patience = self.train_loss_MA
self.best_epoch_based_on_MA_tr_loss = self.epoch
#self.print_to_log_file("New best epoch (train loss MA): %03.4f" % self.best_MA_tr_loss_for_patience)
else:
pass
#self.print_to_log_file("No improvement: current train MA %03.4f, best: %03.4f, eps is %03.4f" %
# (self.train_loss_MA, self.best_MA_tr_loss_for_patience, self.train_loss_MA_eps))
# if patience has reached its maximum then finish training (provided lr is low enough)
if self.epoch - self.best_epoch_based_on_MA_tr_loss > self.patience:
if self.optimizer.param_groups[0]['lr'] > self.lr_threshold:
#self.print_to_log_file("My patience ended, but I believe I need more time (lr > 1e-6)")
self.best_epoch_based_on_MA_tr_loss = self.epoch - self.patience // 2
else:
#self.print_to_log_file("My patience ended")
continue_training = False
else:
pass
#self.print_to_log_file(
# "Patience: %d/%d" % (self.epoch - self.best_epoch_based_on_MA_tr_loss, self.patience))
return continue_training
def on_epoch_end(self):
self.finish_online_evaluation() # does not have to do anything, but can be used to update self.all_val_eval_
# metrics
self.plot_progress()
self.maybe_update_lr()
self.maybe_save_checkpoint()
self.update_eval_criterion_MA()
continue_training = self.manage_patience()
return continue_training
# 更新损失函数
def update_train_loss_MA(self):
if self.train_loss_MA is None:
self.train_loss_MA = self.all_tr_losses[-1]
else:
self.train_loss_MA = self.train_loss_MA_alpha * self.train_loss_MA + (1 - self.train_loss_MA_alpha) * \
self.all_tr_losses[-1]
def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False):
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
# 将数据变为tensor
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
if self.fp16:
with autocast():
output = self.network(data)
del data
l = self.loss(output, target)
if do_backprop:
self.amp_grad_scaler.scale(l).backward()
self.amp_grad_scaler.step(self.optimizer)
self.amp_grad_scaler.update()
else:
output = self.network(data)
del data # 减少内存
l = self.loss(output, target)
if do_backprop:
l.backward()
self.optimizer.step()
if run_online_evaluation:
self.run_online_evaluation(output, target)
del target
return l.detach().cpu().numpy()
def run_online_evaluation(self, *args, **kwargs):
"""
Can be implemented, does not have to
:param output_torch:
:param target_npy:
:return:
"""
pass
def finish_online_evaluation(self):
"""
Can be implemented, does not have to
:return:
"""
pass
@abstractmethod
def validate(self, *args, **kwargs):
pass
# 寻找合适的学习率
def find_lr(self, num_iters=1000, init_value=1e-6, final_value=10., beta=0.98):
"""
stolen and adapted from here: https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html
:param num_iters:
:param init_value:
:param final_value:
:param beta:
:return:
"""
import math
self._maybe_init_amp()
mult = (final_value / init_value) ** (1 / num_iters)
lr = init_value
self.optimizer.param_groups[0]['lr'] = lr
avg_loss = 0.
best_loss = 0.
losses = []
log_lrs = []
for batch_num in range(1, num_iters + 1):
# +1 because this one here is not designed to have negative loss...
loss = self.run_iteration(self.tr_gen, do_backprop=True, run_online_evaluation=False).data.item() + 1
# Compute the smoothed loss
avg_loss = beta * avg_loss + (1 - beta) * loss
smoothed_loss = avg_loss / (1 - beta ** batch_num)
# Stop if the loss is exploding
if batch_num > 1 and smoothed_loss > 4 * best_loss:
break
# Record the best loss
if smoothed_loss < best_loss or batch_num == 1:
best_loss = smoothed_loss
# Store the values
losses.append(smoothed_loss)
log_lrs.append(math.log10(lr))
# Update the lr for the next step
lr *= mult
self.optimizer.param_groups[0]['lr'] = lr
import matplotlib.pyplot as plt
lrs = [10 ** i for i in log_lrs]
fig = plt.figure()
plt.xscale('log')
plt.plot(lrs[10:-5], losses[10:-5])
plt.savefig(join(self.output_folder, "lr_finder.png"))
plt.close()
return log_lrs, losses
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