def Test():
test_loader = LoadTestData(is_test=True)
train_loader, validation_loader = LoadTVData(is_test=True)
model = ResNet(Bottleneck, [3, 4, 6, 3]).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]
model.eval()
# with torch.no_grad():
for name_num, loader in enumerate(loader_list):
class_list, class_pred_list = [], []
for i, (inputs, outputs) in enumerate(loader):
t2, dwi, adc, roi, prostate = inputs[0], inputs[1], inputs[
2], inputs[3], inputs[4]
ece = np.argmax(outputs, axis=1)
inputs = torch.cat([t2, dwi, adc, roi, prostate], axis=1)
inputs = inputs.type(torch.FloatTensor).to(device)
ece = ece.type(torch.LongTensor).to(device)
class_out = model(inputs)
class_out = torch.squeeze(class_out, dim=1)
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]))
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)
def Test():
test_loader = LoadTestData(data_folder)
train_loader, validation_loader = LoadTVData(data_folder, is_test=True)
model = ResNeXt(5, 1, [3, 4, 6, 3]).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 = [], []
for i, (inputs, outputs) in enumerate(loader):
t2, dwi, adc, pca, prostate = inputs[0], inputs[1], inputs[
2], inputs[3], inputs[4]
ece = outputs.to(device)
inputs = torch.cat([t2, dwi, adc, pca, prostate], dim=1)
inputs = inputs.float().to(device)
class_out = model(inputs)
class_out_sigmoid = class_out.sigmoid()
class_list.extend(list(ece.cpu().numpy()))
class_pred_list.extend(
list(class_out_sigmoid.cpu().detach().numpy()))
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)
def ModelTest(weights_list=None, data_type=None):
if data_type is None:
data_type = ['alltrain', 'test']
from Metric.classification_statistics import get_auc, draw_roc
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
input_shape = (192, 192)
batch_size = 2
model_folder = model_root + '/PAGNet_MaxPred'
bc = BinaryClassification()
fpr_list, tpr_list, auc_list = [], [], []
train_result, test_result = {}, {}
for type in data_type:
spliter = DataSpliter()
data_folder = os.path.join(data_root, type)
sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(type))
data = DataManager(sub_list=sub_list)
data.AddOne(Image2D(data_folder + '/T2Slice', shape=input_shape))
data.AddOne(Image2D(data_folder + '/AdcSlice', shape=input_shape))
data.AddOne(Image2D(data_folder + '/DwiSlice', shape=input_shape))
data.AddOne(Image2D(data_folder + '/DistanceMap', shape=input_shape, is_roi=True))
data.AddOne(Label(data_root + '/label.csv', label_tag='Positive'), is_input=False)
data_loader = DataLoader(data, batch_size=batch_size, shuffle=False)
cv_folder_list = [one for one in IterateCase(model_folder, only_folder=True, verbose=0)]
cv_pred_list, cv_label_list = [], []
for cv_index, cv_folder in enumerate(cv_folder_list):
model = ResNeXt(3, 2).to(device)
if weights_list is None:
one_fold_weights_list = [one for one in IterateCase(cv_folder, only_folder=False, verbose=0) if
one.is_file()]
one_fold_weights_list = sorted(one_fold_weights_list, key=lambda x: os.path.getctime(str(x)))
weights_path = one_fold_weights_list[-1]
else:
weights_path = weights_list[cv_index]
print(weights_path.name)
model.load_state_dict(torch.load(str(weights_path)))
pred_list, label_list = [], []
model.eval()
for inputs, outputs in data_loader:
inputs = MoveTensorsToDevice(inputs, device)
outputs = MoveTensorsToDevice(outputs, device)
preds = model(*inputs)[:, 1]
pred_list.extend((1 - preds).cpu().data.numpy().squeeze().tolist())
label_list.extend((1 - outputs).cpu().data.numpy().astype(int).squeeze().tolist())
# pred_list.extend((preds).cpu().data.numpy().squeeze().tolist())
# label_list.extend((outputs).cpu().data.numpy().astype(int).squeeze().tolist())
cv_pred_list.append(pred_list)
cv_label_list.append(label_list)
del model, weights_path
cv_pred = np.array(cv_pred_list)
cv_label = np.array(cv_label_list)
mean_pred = np.mean(cv_pred, axis=0)
mean_label = np.mean(cv_label, axis=0)
fpr, tpr, auc = get_auc(mean_pred, mean_label)
fpr_list.append(fpr)
tpr_list.append(tpr)
auc_list.append(auc)
if type == 'alltrain':
train_result = bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist(), threshould=None)
print(train_result)
elif type == 'test':
test_result = bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist(), threshould=train_result['Youden Index'])
print(test_result)
draw_roc(fpr_list, tpr_list, auc_list, name_list=['alltrian', 'test'])
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)
def ModelTest(weights_list=None, data_type=None):
if data_type is None:
data_type = ['alltrain', 'test']
from Metric.classification_statistics import get_auc, draw_roc
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
input_shape = (192, 192)
batch_size = 2
model_folder = model_root + '/ResNeXt_CBAM_CV_20201116_dis_five_lf'
bc = BinaryClassification()
fpr_list, tpr_list, auc_list = [], [], []
for type in data_type:
spliter = DataSpliter()
sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(type))
if type == 'test':
data = DataManager(sub_list=sub_list)
data.AddOne(Image2D(data_root + '/T2Slice/Test',
shape=input_shape))
data.AddOne(
Image2D(data_root + '/AdcSlice/Test', shape=input_shape))
data.AddOne(
Image2D(data_root + '/DwiSlice/Test', shape=input_shape))
data.AddOne(
Image2D(data_root + '/DistanceMap/Test',
shape=input_shape,
is_roi=True))
data.AddOne(Feature(data_root + '/FiveClinicalbGS.csv'),
is_input=True)
data.AddOne(Label(data_root + '/label.csv', label_tag='Positive'),
is_input=False)
data_loader = DataLoader(data,
batch_size=batch_size,
shuffle=False)
else:
data = DataManager(sub_list=sub_list)
data.AddOne(Image2D(data_root + '/T2Slice', shape=input_shape))
data.AddOne(Image2D(data_root + '/AdcSlice', shape=input_shape))
data.AddOne(Image2D(data_root + '/DwiSlice/', shape=input_shape))
data.AddOne(
Image2D(data_root + '/DistanceMap',
shape=input_shape,
is_roi=True))
data.AddOne(Feature(data_root + '/FiveClinicalbGS.csv'),
is_input=True)
data.AddOne(Label(data_root + '/label.csv', label_tag='Positive'),
is_input=False)
data_loader = DataLoader(data,
batch_size=batch_size,
shuffle=False)
cv_folder_list = [
one
for one in IterateCase(model_folder, only_folder=True, verbose=0)
]
cv_pred_list, cv_label_list = [], []
for cv_index, cv_folder in enumerate(cv_folder_list):
model = ResNeXt(3, 2).to(device)
if weights_list is None:
one_fold_weights_list = [
one for one in IterateCase(
cv_folder, only_folder=False, verbose=0)
if one.is_file()
]
one_fold_weights_list = sorted(
one_fold_weights_list,
key=lambda x: os.path.getctime(str(x)))
weights_path = one_fold_weights_list[-1]
else:
weights_path = weights_list[cv_index]
print(weights_path.name)
model.load_state_dict(torch.load(str(weights_path)))
pred_list, label_list = [], []
model.eval()
for inputs, outputs in data_loader:
inputs = MoveTensorsToDevice(inputs, device)
outputs = MoveTensorsToDevice(outputs, device)
preds = model(*inputs)[:, 1]
pred_list.extend((preds).cpu().data.numpy().squeeze().tolist())
label_list.extend((
outputs).cpu().data.numpy().astype(int).squeeze().tolist())
cv_pred_list.append(pred_list)
cv_label_list.append(label_list)
del model, weights_path
cv_pred = np.array(cv_pred_list)
cv_label = np.array(cv_label_list)
mean_pred = np.mean(cv_pred, axis=0)
mean_label = np.mean(cv_label, axis=0)
np.save(
os.path.join(
r'/home/zhangyihong/Documents/ProstateECE/Result/NPY/AddClinical/FiveFeatureLFbGS',
type + 'JSPH_FiveFeature_label.npy'), mean_label)
np.save(
os.path.join(
r'/home/zhangyihong/Documents/ProstateECE/Result/NPY/AddClinical/FiveFeatureLFbGS',
type + 'JSPH_FiveFeature_pred.npy'), mean_pred)
fpr, tpr, auc = get_auc(mean_pred, mean_label)
fpr_list.append(fpr)
tpr_list.append(tpr)
auc_list.append(auc)
print(type)
bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist())
draw_roc(fpr_list, tpr_list, auc_list, name_list=['alltrian', 'test'])