模型測試

import cv2
from torchvision import transforms, datasets, models
from torch.utils.data import  DataLoader
import torch
import numpy as np
import os
from sklearn import metrics 
import matplotlib.pyplot as plt

device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu")
print(device)
num_class = 3
model_path = 模型路徑
model = 模型(num_class).to(device)
model.load_state_dict(torch.load(model_path))
model.eval()   # Set model to evaluate mode

test_dataset = 資料集讀取(train=False)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=2)

def turn(l):
    l = l.data.cpu().numpy()
    l = l.squeeze()
    l = np.swapaxes(l, 0, 2)
    l = np.swapaxes(l, 0, 1)
    return l

for inputs, labels in test_loader:
    model.to(device)
    inputs = inputs.to(device)
    labels = labels.to(device)

    pred = model(inputs)
    # pred = torch.relu(pred)
    pred = turn(pred)
    gt = turn(labels)
 

評價指標

混淆矩陣

以分割為例，經過.flatten()處理。

def acc(pred, gt):
    tp = 0
    tn = 0
    fp = 0
    fn = 0
    num = len(pred)
    for i in range(num):
        if pred[i] > 0 and gt[i] == 1:
            tp += 1
        if pred[i] > 0 and gt[i] == 0:
            fp += 1
        if pred[i] == 0 and gt[i] == 1:
            fn += 1
        if pred[i] == 0 and gt[i] == 0:
            tn += 1
    acc = (tp + tn) / num
    iou = tp / (tp + fp + fn)
    rec = tp / (tp + fn)
    pre = tp / (tp + fp)
    f1 = 2 * pre * rec / (pre + rec)
    print("mAcc is :{},  mIou is :{}, recall is :{}, precision is :{}, f1 is :{}".format(acc, iou, rec, pre, f1))
 

ROC曲線圖

def draw_roc(pred, gt, name):
    tpr, fpr, thresholds = metrics.roc_curve(gt, pred, pos_label=0)
    plt.figure
    plt.plot(fpr, tpr, label = name)
    plt.xlabel('FPR')
    plt.ylabel('TPR')
    plt.legend(loc = 'lower right')
    plt.title(name)
    plt.savefig('路徑/{}.png'.format(name))
    # plt.close()  如果有多個類別，不close()就會畫在一張圖上