Построение метрик fpr, tpr

Пытаюсь посчитать fpr, tpr, но получаю ошибку 'DeePixBiS' object has no attribute 'predict_proba' Как можно исправить?

model = DeePixBiS()
model.load_state_dict(torch.load('./DeePixBiS.pth'))

probs = model.predict_proba(X_test)
preds = probs[:,1]
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)

import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

DeePixBiS

import torch
from torch import nn
import torch.nn.functional as F
from torchvision import models
# import torch.sigmoid as sigmoid


class DeePixBiS(nn.Module):
    def __init__(self, pretrained=True):
        super().__init__()
        dense = models.densenet161(pretrained=pretrained)
        features = list(dense.features.children())
        self.enc = nn.Sequential(*features[:8])
        self.dec = nn.Conv2d(384, 1, kernel_size=1, stride=1, padding=0)
        self.linear = nn.Linear(14 * 14, 1)

    def forward(self, x):
        enc = self.enc(x)
        dec = self.dec(enc)
        out_map = F.sigmoid(dec)
        # print(out_map.shape)
        out = self.linear(out_map.view(-1, 14 * 14))
        out = F.sigmoid(out)
        out = torch.flatten(out)
        return out_map, out

Predict

def predict(mask, label, threshold=0.5, score_type='combined'):
    with torch.no_grad():
        if score_type == 'pixel':
            score = torch.mean(mask, axis=(1, 2, 3))
        elif score_type == 'binary':
            score = label
        else:
            score = (torch.mean(mask, axis=(1, 2, 3)) + label) / 2

        preds = (score > threshold).type(torch.FloatTensor)

        return preds, score

UPD:

import sklearn.metrics as metrics
from Model import DeePixBiS
import torch

model = DeePixBiS()
model.load_state_dict(torch.load('./DeePixBiS.pth'))

probs = model.predict(X_test)
preds = probs[:,1]
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)

model = tf.keras.model.load_model(weights_file)

# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()