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utils.py
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utils.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.transforms.functional as TF
import numpy as np
import os
import math
import random
import logging
import logging.handlers
from matplotlib import pyplot as plt
from scipy.ndimage import zoom
import SimpleITK as sitk
from medpy import metric
def set_seed(seed):
# for hash
os.environ['PYTHONHASHSEED'] = str(seed)
# for python and numpy
random.seed(seed)
np.random.seed(seed)
# for cpu gpu
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# for cudnn
cudnn.benchmark = False
cudnn.deterministic = True
def get_logger(name, log_dir):
'''
Args:
name(str): name of logger
log_dir(str): path of log
'''
if not os.path.exists(log_dir):
os.makedirs(log_dir)
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
info_name = os.path.join(log_dir, '{}.info.log'.format(name))
info_handler = logging.handlers.TimedRotatingFileHandler(info_name,
when='D',
encoding='utf-8')
info_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
info_handler.setFormatter(formatter)
logger.addHandler(info_handler)
return logger
def log_config_info(config, logger):
config_dict = config.__dict__
log_info = f'#----------Config info----------#'
logger.info(log_info)
for k, v in config_dict.items():
if k[0] == '_':
continue
else:
log_info = f'{k}: {v},'
logger.info(log_info)
def get_optimizer(config, model):
assert config.opt in ['Adadelta', 'Adagrad', 'Adam', 'AdamW', 'Adamax', 'ASGD', 'RMSprop', 'Rprop', 'SGD'], 'Unsupported optimizer!'
if config.opt == 'Adadelta':
return torch.optim.Adadelta(
model.parameters(),
lr = config.lr,
rho = config.rho,
eps = config.eps,
weight_decay = config.weight_decay
)
elif config.opt == 'Adagrad':
return torch.optim.Adagrad(
model.parameters(),
lr = config.lr,
lr_decay = config.lr_decay,
eps = config.eps,
weight_decay = config.weight_decay
)
elif config.opt == 'Adam':
return torch.optim.Adam(
model.parameters(),
lr = config.lr,
betas = config.betas,
eps = config.eps,
weight_decay = config.weight_decay,
amsgrad = config.amsgrad
)
elif config.opt == 'AdamW':
return torch.optim.AdamW(
model.parameters(),
lr = config.lr,
betas = config.betas,
eps = config.eps,
weight_decay = config.weight_decay,
amsgrad = config.amsgrad
)
elif config.opt == 'Adamax':
return torch.optim.Adamax(
model.parameters(),
lr = config.lr,
betas = config.betas,
eps = config.eps,
weight_decay = config.weight_decay
)
elif config.opt == 'ASGD':
return torch.optim.ASGD(
model.parameters(),
lr = config.lr,
lambd = config.lambd,
alpha = config.alpha,
t0 = config.t0,
weight_decay = config.weight_decay
)
elif config.opt == 'RMSprop':
return torch.optim.RMSprop(
model.parameters(),
lr = config.lr,
momentum = config.momentum,
alpha = config.alpha,
eps = config.eps,
centered = config.centered,
weight_decay = config.weight_decay
)
elif config.opt == 'Rprop':
return torch.optim.Rprop(
model.parameters(),
lr = config.lr,
etas = config.etas,
step_sizes = config.step_sizes,
)
elif config.opt == 'SGD':
return torch.optim.SGD(
model.parameters(),
lr = config.lr,
momentum = config.momentum,
weight_decay = config.weight_decay,
dampening = config.dampening,
nesterov = config.nesterov
)
else: # default opt is SGD
return torch.optim.SGD(
model.parameters(),
lr = 0.01,
momentum = 0.9,
weight_decay = 0.05,
)
def get_scheduler(config, optimizer):
assert config.sch in ['StepLR', 'MultiStepLR', 'ExponentialLR', 'CosineAnnealingLR', 'ReduceLROnPlateau',
'CosineAnnealingWarmRestarts', 'WP_MultiStepLR', 'WP_CosineLR'], 'Unsupported scheduler!'
if config.sch == 'StepLR':
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size = config.step_size,
gamma = config.gamma,
last_epoch = config.last_epoch
)
elif config.sch == 'MultiStepLR':
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones = config.milestones,
gamma = config.gamma,
last_epoch = config.last_epoch
)
elif config.sch == 'ExponentialLR':
scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer,
gamma = config.gamma,
last_epoch = config.last_epoch
)
elif config.sch == 'CosineAnnealingLR':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max = config.T_max,
eta_min = config.eta_min,
last_epoch = config.last_epoch
)
elif config.sch == 'ReduceLROnPlateau':
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode = config.mode,
factor = config.factor,
patience = config.patience,
threshold = config.threshold,
threshold_mode = config.threshold_mode,
cooldown = config.cooldown,
min_lr = config.min_lr,
eps = config.eps
)
elif config.sch == 'CosineAnnealingWarmRestarts':
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer,
T_0 = config.T_0,
T_mult = config.T_mult,
eta_min = config.eta_min,
last_epoch = config.last_epoch
)
elif config.sch == 'WP_MultiStepLR':
lr_func = lambda epoch: epoch / config.warm_up_epochs if epoch <= config.warm_up_epochs else config.gamma**len(
[m for m in config.milestones if m <= epoch])
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_func)
elif config.sch == 'WP_CosineLR':
lr_func = lambda epoch: epoch / config.warm_up_epochs if epoch <= config.warm_up_epochs else 0.5 * (
math.cos((epoch - config.warm_up_epochs) / (config.epochs - config.warm_up_epochs) * math.pi) + 1)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_func)
return scheduler
def save_imgs(img, msk, msk_pred, i, save_path, datasets, threshold=0.5, test_data_name=None):
img = img.squeeze(0).permute(1,2,0).detach().cpu().numpy()
img = img / 255. if img.max() > 1.1 else img
if datasets == 'retinal':
msk = np.squeeze(msk, axis=0)
msk_pred = np.squeeze(msk_pred, axis=0)
else:
msk = np.where(np.squeeze(msk, axis=0) > 0.5, 1, 0)
msk_pred = np.where(np.squeeze(msk_pred, axis=0) > threshold, 1, 0)
plt.figure(figsize=(7,15))
plt.subplot(3,1,1)
plt.imshow(img)
plt.axis('off')
plt.subplot(3,1,2)
plt.imshow(msk, cmap= 'gray')
plt.axis('off')
plt.subplot(3,1,3)
plt.imshow(msk_pred, cmap = 'gray')
plt.axis('off')
if test_data_name is not None:
save_path = save_path + test_data_name + '_'
plt.savefig(save_path + str(i) +'.png')
plt.close()
class BCELoss(nn.Module):
def __init__(self):
super(BCELoss, self).__init__()
self.bceloss = nn.BCELoss()
def forward(self, pred, target):
size = pred.size(0)
pred_ = pred.view(size, -1)
target_ = target.view(size, -1)
return self.bceloss(pred_, target_)
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, pred, target):
smooth = 1
size = pred.size(0)
pred_ = pred.view(size, -1)
target_ = target.view(size, -1)
intersection = pred_ * target_
dice_score = (2 * intersection.sum(1) + smooth)/(pred_.sum(1) + target_.sum(1) + smooth)
dice_loss = 1 - dice_score.sum()/size
return dice_loss
class nDiceLoss(nn.Module):
def __init__(self, n_classes):
super(nDiceLoss, self).__init__()
self.n_classes = n_classes
def _one_hot_encoder(self, input_tensor):
tensor_list = []
for i in range(self.n_classes):
temp_prob = input_tensor == i # * torch.ones_like(input_tensor)
tensor_list.append(temp_prob.unsqueeze(1))
output_tensor = torch.cat(tensor_list, dim=1)
return output_tensor.float()
def _dice_loss(self, score, target):
target = target.float()
smooth = 1e-5
intersect = torch.sum(score * target)
y_sum = torch.sum(target * target)
z_sum = torch.sum(score * score)
loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
loss = 1 - loss
return loss
def forward(self, inputs, target, weight=None, softmax=False):
if softmax:
inputs = torch.softmax(inputs, dim=1)
target = self._one_hot_encoder(target)
if weight is None:
weight = [1] * self.n_classes
assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size())
class_wise_dice = []
loss = 0.0
for i in range(0, self.n_classes):
dice = self._dice_loss(inputs[:, i], target[:, i])
class_wise_dice.append(1.0 - dice.item())
loss += dice * weight[i]
return loss / self.n_classes
class CeDiceLoss(nn.Module):
def __init__(self, num_classes, loss_weight=[0.4, 0.6]):
super(CeDiceLoss, self).__init__()
self.celoss = nn.CrossEntropyLoss()
self.diceloss = nDiceLoss(num_classes)
self.loss_weight = loss_weight
def forward(self, pred, target):
loss_ce = self.celoss(pred, target[:].long())
loss_dice = self.diceloss(pred, target, softmax=True)
loss = self.loss_weight[0] * loss_ce + self.loss_weight[1] * loss_dice
return loss
class BceDiceLoss(nn.Module):
def __init__(self, wb=1, wd=1):
super(BceDiceLoss, self).__init__()
self.bce = BCELoss()
self.dice = DiceLoss()
self.wb = wb
self.wd = wd
def forward(self, pred, target):
bceloss = self.bce(pred, target)
diceloss = self.dice(pred, target)
loss = self.wd * diceloss + self.wb * bceloss
return loss
class GT_BceDiceLoss(nn.Module):
def __init__(self, wb=1, wd=1):
super(GT_BceDiceLoss, self).__init__()
self.bcedice = BceDiceLoss(wb, wd)
def forward(self, gt_pre, out, target):
bcediceloss = self.bcedice(out, target)
gt_pre5, gt_pre4, gt_pre3, gt_pre2, gt_pre1 = gt_pre
gt_loss = self.bcedice(gt_pre5, target) * 0.1 + self.bcedice(gt_pre4, target) * 0.2 + self.bcedice(gt_pre3, target) * 0.3 + self.bcedice(gt_pre2, target) * 0.4 + self.bcedice(gt_pre1, target) * 0.5
return bcediceloss + gt_loss
class myToTensor:
def __init__(self):
pass
def __call__(self, data):
image, mask = data
return torch.tensor(image).permute(2,0,1), torch.tensor(mask).permute(2,0,1)
class myResize:
def __init__(self, size_h=256, size_w=256):
self.size_h = size_h
self.size_w = size_w
def __call__(self, data):
image, mask = data
return TF.resize(image, [self.size_h, self.size_w]), TF.resize(mask, [self.size_h, self.size_w])
class myRandomHorizontalFlip:
def __init__(self, p=0.5):
self.p = p
def __call__(self, data):
image, mask = data
if random.random() < self.p: return TF.hflip(image), TF.hflip(mask)
else: return image, mask
class myRandomVerticalFlip:
def __init__(self, p=0.5):
self.p = p
def __call__(self, data):
image, mask = data
if random.random() < self.p: return TF.vflip(image), TF.vflip(mask)
else: return image, mask
class myRandomRotation:
def __init__(self, p=0.5, degree=[0,360]):
self.angle = random.uniform(degree[0], degree[1])
self.p = p
def __call__(self, data):
image, mask = data
if random.random() < self.p: return TF.rotate(image,self.angle), TF.rotate(mask,self.angle)
else: return image, mask
class myNormalize:
def __init__(self, data_name, train=True):
if data_name == 'isic18':
if train:
self.mean = 157.561
self.std = 26.706
else:
self.mean = 149.034
self.std = 32.022
elif data_name == 'isic17':
if train:
self.mean = 159.922
self.std = 28.871
else:
self.mean = 148.429
self.std = 25.748
elif data_name == 'isic18_82':
if train:
self.mean = 156.2899
self.std = 26.5457
else:
self.mean = 149.8485
self.std = 35.3346
def __call__(self, data):
img, msk = data
img_normalized = (img-self.mean)/self.std
img_normalized = ((img_normalized - np.min(img_normalized))
/ (np.max(img_normalized)-np.min(img_normalized))) * 255.
return img_normalized, msk
from thop import profile ## 导入thop模块
def cal_params_flops(model, size, logger):
input = torch.randn(1, 3, size, size).cuda()
flops, params = profile(model, inputs=(input,))
print('flops',flops/1e9) ## 打印计算量
print('params',params/1e6) ## 打印参数量
total = sum(p.numel() for p in model.parameters())
print("Total params: %.2fM" % (total/1e6))
logger.info(f'flops: {flops/1e9}, params: {params/1e6}, Total params: : {total/1e6:.4f}')
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0 and gt.sum()>0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt)
return dice, hd95
elif pred.sum() > 0 and gt.sum()==0:
return 1, 0
else:
return 0, 0
def test_single_volume(image, label, net, classes, patch_size=[256, 256],
test_save_path=None, case=None, z_spacing=1, val_or_test=False):
image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy()
if len(image.shape) == 3:
prediction = np.zeros_like(label)
for ind in range(image.shape[0]):
slice = image[ind, :, :]
x, y = slice.shape[0], slice.shape[1]
if x != patch_size[0] or y != patch_size[1]:
slice = zoom(slice, (patch_size[0] / x, patch_size[1] / y), order=3) # previous using 0
input = torch.from_numpy(slice).unsqueeze(0).unsqueeze(0).float().cuda()
net.eval()
with torch.no_grad():
outputs = net(input)
out = torch.argmax(torch.softmax(outputs, dim=1), dim=1).squeeze(0)
out = out.cpu().detach().numpy()
if x != patch_size[0] or y != patch_size[1]:
pred = zoom(out, (x / patch_size[0], y / patch_size[1]), order=0)
else:
pred = out
prediction[ind] = pred
else:
input = torch.from_numpy(image).unsqueeze(
0).unsqueeze(0).float().cuda()
net.eval()
with torch.no_grad():
out = torch.argmax(torch.softmax(net(input), dim=1), dim=1).squeeze(0)
prediction = out.cpu().detach().numpy()
metric_list = []
for i in range(1, classes):
metric_list.append(calculate_metric_percase(prediction == i, label == i))
if test_save_path is not None and val_or_test is True:
img_itk = sitk.GetImageFromArray(image.astype(np.float32))
prd_itk = sitk.GetImageFromArray(prediction.astype(np.float32))
lab_itk = sitk.GetImageFromArray(label.astype(np.float32))
img_itk.SetSpacing((1, 1, z_spacing))
prd_itk.SetSpacing((1, 1, z_spacing))
lab_itk.SetSpacing((1, 1, z_spacing))
sitk.WriteImage(prd_itk, test_save_path + '/'+case + "_pred.nii.gz")
sitk.WriteImage(img_itk, test_save_path + '/'+ case + "_img.nii.gz")
sitk.WriteImage(lab_itk, test_save_path + '/'+ case + "_gt.nii.gz")
# cv2.imwrite(test_save_path + '/'+case + '.png', prediction*255)
return metric_list