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utils_map.py
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utils_map.py
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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 torch.nn import functional as F
from torchvision import transforms
import os
import cv2
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
# 注意力可视化
def feature_vis(features,save_path, name, stage): # feaats形状: [b,c,h,w]
output_shape = (224, 224) # 输出形状
# channel_mean = torch.mean(features, dim=1, keepdim=True)
channel_mean,_ = torch.max(features, dim=1, keepdim=True)
channel_mean = F.interpolate(channel_mean, size=output_shape, mode='bilinear', align_corners=False)
channel_mean = channel_mean.squeeze(0).squeeze(0).cpu().detach().numpy() # 四维压缩为二维
channel_mean = (
((channel_mean - np.min(channel_mean)) / (np.max(channel_mean) - np.min(channel_mean))) * 255).astype(
np.uint8)
savedir = os.path.join(save_path, name)
if not os.path.exists(savedir):
os.makedirs(savedir)
channel_mean = cv2.applyColorMap(channel_mean, cv2.COLORMAP_JET)
channel_mean = cv2.rotate(channel_mean, cv2.ROTATE_90_CLOCKWISE)
channel_mean = cv2.flip(channel_mean, 1)
cv2.imwrite(savedir + '/' + stage + '.png', channel_mean)
def test_single_volume(image, label, net, classes, patch_size=[256, 256],
test_save_path=None, case=None, z_spacing=1, attention_map_save_path=None):
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
x_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
print(slice.shape, case, ind, test_save_path)
input = x_transforms(slice).unsqueeze(0).float().cuda()
net.eval()
with torch.no_grad():
outputs, map0, map1, map2, map3, map4 = net(input)
#print(map1.shape, map2.shape, map3.shape)
########################################################
#print(input.shape)
feature_vis(input, attention_map_save_path, case, str(ind) + '_img')
feature_vis(map0, attention_map_save_path, case, str(ind)+'_map0')
feature_vis(map1, attention_map_save_path, case, str(ind)+'_map1')
feature_vis(map2, attention_map_save_path, case, str(ind)+'_map2')
feature_vis(map3, attention_map_save_path, case, str(ind)+'_map3')
feature_vis(map4, attention_map_save_path, case, str(ind)+'_map4')
########################################################
# outputs = F.interpolate(outputs, size=slice.shape[:], mode='bilinear', align_corners=False)
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