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 EnsembleInference(data_type, weights_list=None):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
input_shape = (192, 192)
batch_size = 12
model_folder = model_root + '/PAGNet_MaxPred'
bc = BinaryClassification()
# spliter = DataSpliter()
# sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(data_type))
data = DataManager()
if data_type == 'test':
data.AddOne(Image2D(data_root + '/Test/T2Slice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Test/AdcSlice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Test/DwiSlice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Test/DistanceMap', shape=input_shape, is_roi=True))
else:
data.AddOne(Image2D(data_root + '/Train/T2Slice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Train/AdcSlice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Train/DwiSlice', shape=input_shape))
data.AddOne(Image2D(data_root + '/Train/DistanceMap', shape=input_shape, is_roi=True))
data.AddOne(Feature(data_root + '/clinical.csv'), is_input=True)
data.AddOne(Label(data_root + '/ece.csv'), 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_pred1_list, cv_pred2_list, cv_label_list = [], [], []
for cv_index, cv_folder in enumerate(cv_folder_list):
model = ResNeXt(3, 2, 7).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)))
pred1_list, pred2_list, label_list = [], [], []
model.eval()
for inputs, outputs in data_loader:
inputs = MoveTensorsToDevice(inputs, device)
outputs = MoveTensorsToDevice(outputs, device)
pred1, pred2 = model(*inputs)
pred1_list.extend(pred1[:, 1].cpu().data.numpy().squeeze().tolist())
pred2_list.extend((pred2[:, 1]).cpu().data.numpy().squeeze().tolist())
label_list.extend((outputs).cpu().data.numpy().astype(int).squeeze().tolist())
cv_pred1_list.append(pred1_list)
cv_pred2_list.append(pred2_list)
cv_label_list.append(label_list)
del model, weights_path
cv_pred1 = np.array(cv_pred1_list)
cv_label = np.array(cv_label_list)
cv_pred2 = np.array(cv_pred2_list)
mean_pred1 = np.mean(cv_pred1, axis=0)
mean_label = np.mean(cv_label, axis=0)
mean_pred2 = np.mean(cv_pred2, axis=0)
# if data_type == 'test':
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_test_pred1.npy', mean_pred1)
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_test_pred2.npy', mean_pred2)
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_test_label.npy', mean_label)
# else:
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_train_pred1.npy', mean_pred1)
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_train_pred2.npy', mean_pred2)
# np.save(r'/home/zhangyihong/Documents/ProstateECE/NPYMaxPred/Result/PAGNet_train_label.npy', mean_label)
print(mean_label)
bc.Run(mean_pred1.tolist(), mean_label.astype(int).tolist())
bc.Run(mean_pred2.tolist(), mean_label.astype(int).tolist())
def ModelSUH(weights_list=None):
from Metric.classification_statistics import get_auc
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_20200820'
bc = BinaryClassification()
fpr_list, tpr_list, auc_list = [], [], []
data = DataManager()
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(Label(data_root + '/label_negative.csv', label_tag='Negative'), is_input=False)
data.AddOne(Label(data_root + '/label_negative.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 = [], []
cv_fcn_out_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 = [], []
fcn_out_list = []
model.eval()
for inputs, outputs in data_loader:
inputs = MoveTensorsToDevice(inputs, device)
outputs = MoveTensorsToDevice(outputs, device)
# preds = model(*inputs)[:, 1]
model_pred = model(*inputs)
preds = model_pred[0][:, 1]
if isinstance((1 - preds).cpu().data.numpy().squeeze().tolist(), float):
# pred_list.append((1 - preds).cpu().data.numpy().squeeze().tolist())
# label_list.append((1 - outputs).cpu().data.numpy().astype(int).squeeze().tolist())
pred_list.append((preds).cpu().data.numpy().squeeze().tolist())
label_list.append((outputs).cpu().data.numpy().astype(int).squeeze().tolist())
fcn_out_list.append(model_pred[1].cpu().data.numpy().squeeze().tolist())
else:
# 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())
fcn_out_list.extend(model_pred[1].cpu().data.numpy().squeeze().tolist())
cv_pred_list.append(pred_list)
cv_label_list.append(label_list)
cv_fcn_out_list.append(fcn_out_list)
del model, weights_path
cv_pred = np.array(cv_pred_list)
cv_label = np.array(cv_label_list)
cv_fcn = np.array(cv_fcn_out_list)
mean_pred = np.mean(cv_pred, axis=0)
mean_label = np.mean(cv_label, axis=0)
mean_fcn_out = np.mean(cv_fcn, axis=0)
np.save(r'/home/zhangyihong/Documents/ProstateECE/Result/NPY/FcnOut/aver_fcn_ResNeXt_suh.npy', mean_fcn_out)
fpr, tpr, auc = get_auc(mean_pred, mean_label)
fpr_list.append(fpr)
tpr_list.append(tpr)
auc_list.append(auc)
result1 = bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist())
print(result1)
def ModelJSPH(weights_list=None,
is_dismap=True,
data_type='test',
store_path=r''):
model_root = r'/home/zhangyihong/Documents/ProstateECE/Model'
data_root = r'/home/zhangyihong/Documents/ProstateECE/NPYNoDivide'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
input_shape = (192, 192)
batch_size = 2
bc = BinaryClassification(store_folder=store_path, store_format='eps')
if is_dismap:
from SYECE.model import ResNeXt
model_folder = model_root + '/ResNeXt_CBAM_CV_20200814'
else:
from SYECE.ModelWithoutDis import ResNeXt
model_folder = model_root + '/ResNeXt_CBAM_CV_20200820'
spliter = DataSpliter()
sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(data_type))
if data_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))
if is_dismap:
data.AddOne(
Image2D(data_root + '/DistanceMap/Test',
shape=input_shape,
is_roi=True))
data.AddOne(Label(data_root + '/label.csv', label_tag='Negative'),
is_input=False)
else:
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))
if is_dismap:
data.AddOne(
Image2D(data_root + '/DistanceMap',
shape=input_shape,
is_roi=True))
data.AddOne(Label(data_root + '/label.csv', label_tag='Negative'),
is_input=False)
else:
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]
if is_dismap:
pred_list.extend(
(1 - preds).cpu().data.numpy().squeeze().tolist())
label_list.extend((
1 -
outputs).cpu().data.numpy().astype(int).squeeze().tolist())
else:
pred_list.extend((preds).cpu().data.numpy().squeeze().tolist())
label_list.extend((
outputs).cpu().data.numpy().astype(int).squeeze().tolist())
# if cv_index == 1:
# for num, pred in enumerate(pred_list):
# print(num, pred)
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)
bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist())
return mean_pred, mean_label,
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'])
def ROCofModels(weights_list=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_20201028_dis&age&psa_lastfc'
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())
fpr, tpr, auc = get_auc(pred_list, label_list)
fpr_list.append(fpr)
tpr_list.append(tpr)
auc_list.append(auc)
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)
# draw_roc(fpr_list, tpr_list, auc_list, name_list=['cv0', 'cv1', 'cv2', 'cv3', 'cv4', 'alltrian'])
name_list = [
'model1', 'model2', 'model3', 'model4', 'model5', 'model combined'
]
for idx in range(len(fpr_list)):
label = name_list[idx] + ': ' + '%.3f' % auc_list[idx]
plt.plot(fpr_list[idx], tpr_list[idx], label=label)
plt.plot([0, 1], [0, 1], '--', color='r')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.show()
def EnsembleInference(data_type, weights_list=None):
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_20201028_dis&age&psa_lastfc'
bc = BinaryClassification()
spliter = DataSpliter()
# sub_list = spliter.LoadName(data_root / '{}-name.csv'.format(data_type), contain_label=True)
sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(data_type))
if data_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())
bc.Run(pred_list, label_list)
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)
print(mean_label)
bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist())
def EnsembleInference(data_type, model_name, add_type, weights_list=None):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
input_shape = (192, 192)
batch_size = 2
model_folder = os.path.join(model_root, model_name)
bc = BinaryClassification()
spliter = DataSpliter()
sub_list = spliter.LoadName(data_root + '/{}-name.csv'.format(data_type))
if data_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 + '/' + add_type + '/Test',
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)
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 + '/' + add_type, 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((preds).cpu().data.numpy().squeeze().tolist())
label_list.extend(
(outputs).cpu().data.numpy().astype(int).squeeze().tolist())
# bc.Run(pred_list, label_list)
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/ChangeAtten',
add_type + data_type + '_label.npy'), mean_label)
np.save(
os.path.join(
r'/home/zhangyihong/Documents/ProstateECE/Result/NPY/ChangeAtten',
add_type + data_type + '_pred.npy'), mean_pred)
print(mean_label)
bc.Run(mean_pred.tolist(), mean_label.astype(int).tolist())
