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utils.py
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utils.py
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import os
import time
import logging
import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom
import torch.nn as nn
import SimpleITK as sitk
from functools import partial
from multiprocessing import Pool
class DiceLoss(nn.Module):
def __init__(self, n_classes):
super(DiceLoss, 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
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 calculate_metric_list_percase(pred, gt, classes=9):
metric_list = []
for i in range(1, classes):
metric_list.append(calculate_metric_percase(pred == i, gt == i))
return np.array(metric_list)
def calculate_metric_multicases(preds, gts, classes=9, num_workers=12):
with Pool(num_workers) as p:
metrics_list = p.starmap(partial(calculate_metric_list_percase, classes=classes), zip(preds, gts))
metrics_list = np.array(metrics_list)
metrics_list = metrics_list.mean(axis=0) # 8x2
return metrics_list
def test_single_volume(image, label, net, classes, patch_size=[256, 256], test_save_path=None, case=None, z_spacing=1):
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:
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")
return metric_list
def get_logger(filename, verbosity=1, name=None):
level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
formatter = logging.Formatter(
"[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
)
logger = logging.getLogger(name)
logger.setLevel(level_dict[verbosity])
fh = logging.FileHandler(filename, "w")
fh.setFormatter(formatter)
logger.addHandler(fh)
sh = logging.StreamHandler()
sh.setFormatter(formatter)
logger.addHandler(sh)
return logger
def maybe_mkdir_p(directory: str) -> None:
os.makedirs(directory, exist_ok=True)
def make_dirs_by_time(save_dir):
version = str(time.time())
save_dir = join(save_dir, f"exp_{version}")
if not os.path.exists(save_dir):
maybe_mkdir_p(save_dir)
return save_dir
join = os.path.join