def Test():
from Metric.Dice import Dice
import matplotlib.pyplot as plt
dice = Dice()
test_loader = LoadTestData(is_test=True)
train_loader, validation_loader = LoadTVData(is_test=True)
model = MultiSegModel(in_channels=3, out_channels=1).to(device)
model.load_state_dict(torch.load(model_path))
name_list = ['Train', 'Validation', 'Test']
loader_list = [train_loader, validation_loader, test_loader]
# with torch.no_grad():
model.eval()
for name_num, loader in enumerate(loader_list):
class_list, class_pred_list = [], []
prostate_list, prostate_pred_list = [], []
roi_list, roi_pred_list = [], []
prostate_dice, roi_dice = [], []
for i, (inputs, outputs) in enumerate(loader):
t2, dwi, adc = inputs[0], inputs[1], inputs[2],
roi, prostate = outputs[0].to(device), outputs[1].to(device)
inputs = torch.cat([t2, dwi, adc], dim=1)
inputs = inputs.type(torch.FloatTensor).to(device)
roi_out, prostate_out = model(inputs)
prostate_out = BinaryPred(prostate_out).cpu().detach()
prostate = prostate.cpu()
prostate_pred_list.extend(list(prostate_out))
prostate_list.extend(list(prostate))
roi_out = BinaryPred(roi_out).cpu().detach()
roi = roi.cpu()
roi_pred_list.extend(list(roi_out))
roi_list.extend(list(roi))
for idx in range(len(roi_list)):
roi_dice.append(dice(roi_list[idx], roi_pred_list[idx]).numpy())
prostate_dice.append(dice(prostate_list[idx], prostate_pred_list[idx]).numpy())
print('average dice of roi in', name_list[name_num], ':', sum(roi_dice)/len(roi_dice))
print('average dice of prostate in', name_list[name_num], ':', sum(prostate_dice) / len(prostate_dice))
plt.hist(roi_dice)
plt.title('Dice of Prostate Cancer in ' + name_list[name_num])
plt.show()
plt.hist(prostate_dice)
plt.title('Dice of Prostate in ' + name_list[name_num])
plt.show()
def ShowPicture():
from Metric.Dice import Dice
dice = Dice()
train_loader, validation_loader = LoadTVData(is_test=True)
test_loader = LoadTestData()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = MultiTaskModel(in_channels=3, out_channels=1).to(device)
model.load_state_dict(torch.load(model_path))
ece_pre_list = []
ece_list = []
for i, (inputs, outputs) in enumerate(test_loader):
t2, dwi, adc = inputs[0], inputs[1], inputs[2],
ece, roi, prostate = np.squeeze(
outputs[0], axis=1), outputs[1].to(device), outputs[2].to(device)
inputs = torch.cat([t2, dwi, adc], axis=1)
inputs = inputs.type(torch.FloatTensor).to(device)
ece = ece.type(torch.FloatTensor).to(device)
roi_out, prostate_out, class_out, _ = model(inputs)
class_out_softmax = nn.functional.softmax(class_out, dim=1)
ece_pre_list.append(class_out_softmax.cpu().detach().numpy()[0][0])
ece_list.append(ece.cpu().numpy()[0][0])
# prostate_out = np.squeeze(BinaryPred(prostate_out).cpu().detach().numpy())
# prostate = np.squeeze(prostate.cpu().numpy())
# roi_out = np.squeeze(BinaryPred(roi_out).cpu().detach().numpy())
# roi = np.squeeze(roi.cpu().numpy())
# t2_data = np.squeeze(t2.cpu().numpy())
# plt.imshow(t2_data, cmap='gray')
# plt.contour(prostate, colors='r')
# plt.contour(prostate_out, colors='y')
# plt.contour(roi, colors='g')
# plt.contour(roi_out, colors='b')
# plt.title('ECE: ' + str(ece.cpu().numpy()[0][0])
# + ', predict ece: ' + str(class_out.cpu().detach().numpy()[0][0]))
# plt.axis('off')
# plt.show()
plt.hist(ece_list)
plt.show()
def Test():
from Metric.Dice import Dice
import matplotlib.pyplot as plt
dice = Dice()
test_loader = LoadTestData()
train_loader, validation_loader = LoadTVData(is_test=True)
model = MultiTaskModel(in_channels=3, out_channels=1).to(device)
model.load_state_dict(torch.load(model_path))
fpr_list, tpr_list, auc_list = [], [], []
name_list = ['Train', 'Validation', 'Test']
loader_list = [train_loader, validation_loader, test_loader]
# with torch.no_grad():
model.eval()
for name_num, loader in enumerate(loader_list):
class_list, class_pred_list = [], []
prostate_list, prostate_pred_list = [], []
roi_list, roi_pred_list = [], []
prostate_dice, roi_dice = [], []
for i, (inputs, outputs) in enumerate(loader):
t2, dwi, adc = inputs[0], inputs[1], inputs[2],
ece, roi, prostate = np.squeeze(
outputs[0],
axis=1), outputs[1].to(device), outputs[2].to(device)
inputs = torch.cat([t2, dwi, adc], axis=1)
inputs = inputs.type(torch.FloatTensor).to(device)
ece = np.argmax(ece, axis=1)
ece = ece.type(torch.LongTensor).to(device)
roi_out, prostate_out, class_out, _ = model(inputs)
class_out_softmax = nn.functional.softmax(class_out, dim=1)
class_list.extend(list(ece.cpu().numpy()))
class_pred_list.extend(
list(class_out_softmax.cpu().detach().numpy()[..., 1]))
prostate_out = BinaryPred(prostate_out).cpu().detach()
prostate = prostate.cpu()
prostate_pred_list.extend(list(prostate_out))
prostate_list.extend(list(prostate))
roi_out = BinaryPred(roi_out).cpu().detach()
roi = roi.cpu()
roi_pred_list.extend(list(roi_out))
roi_list.extend(list(roi))
for idx in range(len(roi_list)):
roi_dice.append(dice(roi_list[idx], roi_pred_list[idx]).numpy())
prostate_dice.append(
dice(prostate_list[idx], prostate_pred_list[idx]).numpy())
print('average dice of roi:', sum(roi_dice) / len(roi_dice))
print('average dice of prostate:',
sum(prostate_dice) / len(prostate_dice))
plt.hist(roi_dice)
plt.title('Dice of Prostate Cancer in ' + name_list[name_num])
plt.show()
plt.hist(prostate_dice)
plt.title('Dice of Prostate in ' + name_list[name_num])
plt.show()
fpr, tpr, auc = get_auc(class_pred_list, class_list)
fpr_list.append(fpr)
tpr_list.append(tpr)
auc_list.append(auc)
draw_roc(fpr_list, tpr_list, auc_list, name_list)