目录ROC曲线PR曲线Pytorch多分类模型绘制ROC,PR曲线（代码亲测可用）ROC曲线示例代码importtorchimporttorch.nnasnnimportosim...
目录
ROC曲线PR曲线
Pytorch 多分类模型绘制 ROC, PR 曲线（代码 亲测 可用）
ROC曲线
示例代码
import torch
import torch.nn as nn
import os
import numpy as np
from torchvision.datasets import ImageFolder
from utils.transform import get_transform_for_test
from senet.se_resnet import FineTuneSEResnet50
from scipy import interp
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn.metrics import roc_curve, auc, f1_score, precision_recall_curve, average_precision_score
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
data_root = r'D:\TJU\GBDB\set113\set113_images\test1' # 测试集路径
test_weights_path = r"C:\Users\admin\Desktop\fsdownload\epoch_0278_top1_70.565_'checkpoint.pth.tar'" # 预训练模型参数
num_class = 113 # 类别数量
gpu = "cuda:0"
# mean=[0.948078, 0.93855226, 0.9332005], var=[0.14589554, 0.17054074, 0.18254866]
def test(model, test_path):
# 加载测试集和预训练模型参数
test_dir = os.path.join(data_root,www.cppcns.com 'test_images')
class_list = list(os.listdir(test_dir))
class_list.sort()
transform_test = get_transform_for_test(mean=[0.948078, 0.93855226, 0.9332005],
var=[0.14589554, 0.17054074, 0.18254866])
test_dataset = ImageFolder(test_dir, transform=transform_test)
test_loader = torch.utils.data.DataLoader(
test_dataset, BATch_size=1, shuffle=False, drop_last=False, pin_memory=True, num_workers=1)
checkpoint = torch.load(test_path)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
score_list = [] # 存储预测得分
label_list = [] # 存储真实标签
for i, (inputs, labels) in enumerate(test_loader):
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
# prob_tmp = torch.nn.Softmax(dim=1)(outputs) # (batchsize, nclass)
score_tmp = outputs # (batchsize, nclass)
score_list.extend(score_tmp.detach().cpu().numpy())
label_list.extend(labels.cpu().numpy())
score_array = np.array(score_list)
# 将label转换成onehot形式
label_tensor = torch.tensor(label_list)
label_tensor = label_tensor.reshape((label_tensor.shape[0], 1))
label_onehot = torch.zeros(label_tensor.shape[0], num_class)
label_onehot.scatter_(dim=1, index=label_tensor, value=1)
label_onehot = np.array(label_onehot)
print("score_array:", score_array.shape) # (batchsize, classnum)
print("label_onehot:", label_onehot.shape) # torch.Size([batchsize, classnum])
# 调用sklearn库，计算每个类别对应的fpr和tpr
fpr_dict = dict()
tpr_dict = dict()
roc_auc_dict = dict()
for i in range(num_class):
fpr_dict[i], tpr_dict[i], _ = roc_curve(label_onehot[:, i], score_array[:, i])
roc_auc_dict[i] = auc(fpr_dict[i], tpr_dict[i])
# micro
fpr_dict["micro"], tpr_dict["micro"], _ = roc_curve(label_onehot.ravel(), score_array.ravel())
roc_auc_dict["micro"] = auc(fpr_dict["micro"], tpr_dict["micro"])
# MACro
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr_dict[i] for i in range(num_class)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(num_class):
mean_tpr += interp(all_fpr, fpr_dict[i], tpr_dict[i])
# Finally average it and compute AUC
mean_tpr /= num_class
fpr_dict["macro"] = all_fpr
tpr_dict["macro"] = mean_tpr
roc_auc_dict["macro"] = auc(fpr_dict["macro"], tpr_dict["macro"])
# 绘制所有类别平均的roc曲线
plt.figure()
lw = 2
plt.plot(fpr_dict["micro"], tpr_dict["micro"],
label='micro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc_dict["micro"]),
color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr_dict["macro"], tpr_dict["macro"],
label='macro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc_dict["macro"]),
color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(num_class), colors):
plt.plot(fpr_dict[i], tpr_dict[i], color=color, lw=lw,
label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc_dict[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.savefig('set113_roc.jpg')
plt.show()
if __name__ == '__main__':
# 加载模型
seresnet = FineTuneSEResnet50(num_class=num_class)
device = torch.device(gpu)
seresnet = seresnet.to(device)
test(seresnet, test_weights_path)
运行结果：
PR曲线
示例代码
import torch
import torch.nn as nn
import os
import numpy as np
from torchvision.datasets import ImageFolder
from utils.transform import get_transform_for_test
from senet.se_resnet import FineTuneSEResnet50
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, f1_score, precision_recall_curve, average_precision_score
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
data_root = r'D:\TJU\GBDB\set113\set113_images\test1' # 测试集路径
test_weights_path = r"C:\Users\admin\Desktop\fsdownload\epoch_0278_top1_70.565_'checkpoint.pth.tar'" # 预训练模型参数
num_class = 113 # 类别数量
gpu = "cuda:0"
# mean=[0.948078, 0.93855226, 0.9332005], var=[0.14589554, 0.17054074, 0.18254866]
def test(model, test_path):
# 加载测试集和预训练模型参数
test_dir = os.path.join(data_root, 'test_images')
class_list = list(os.listdir(test_dir))
class_list.sort()
transform_test = get_transform_for_test(mean=[0.948078, 0.93855226, 0.9332005],
var=[0.14589554, 0.17054074, 0.18254866])
test_dataset = ImageFolder(test_dir, transform=transform_test)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=1, shuffle=False, drop_last=False, pin_memory=True, num_workers=1)
checkpoint = torch.load(test_path)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
score_list = [] # 存储预测得分
label_list = [] # 存储真实标签
for i, (inputs, labels) in enumerate(test_loader):
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
# prob_tmp = torch.nn.Softmax(dim=1)(outputs) # (batchsize, nclass)
score_tmp = outputs # (batchsize, nclass)
score_list.extend(score_tmp.detach().cpu().numpy())
label_list.extend(labels.cpu().numpy())
score_array = np.array(score_list)
# 将label转换成onehot形式
label_tensor = torch.tensor(label_list)
label_tensor = label_tensor.reshape((label_tensor.shape[0], 1))
label_onehot = torch.zeros(label_tensor.shape[0], num_class)
label_onehot.scatter_(dim=1, index=label_tensor, value=1)
label_onehot = np.array(label_onehot)
print("score_array:", score_array.shape) # (batchsize, classnum) softmax
print("label_onehot:", label_onehot.shape) # torch.Size([batchsize, classnum]) onehot
# 调用sklearn库，计算每个类别对应的precision和recall
precision_dict = dict()
recall_dict = dict()
average_precision_dict = dict()
for i in range(num_class):
precision_dict[i], recall_dict[i], _ = precision_recall_curve(label_onehot[:, i], score_array[:, i])
average_precision_dict[i] = average_precision_score(label_onehot[:, i], score_array[:, i])
print(precision_dict[i].shape, recall_dict[i].shape, average_precision_dict[i])
# micro
precision_dict["micro"], recall_dict["micro"], _ = precision_recall_curve(label_onehot.ravel(),
score_array.ravel())
average_precision_dict["micro"] = average_precision_score(label_onehot, score_array, average="micro")
print('Average precision score, micro-averaged over all classes: {0:0.2f}'.format(average_precision_dict["micro"]))
# 绘制所有类别平均的pr曲线
plt.figure()
plt.step(recall_dict['micro'], precision_dict['micro'], where='post')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title(
'Average precision score, micro-averaged over all classes: AP={0:0.2f}'
.format(average_precision_dict["micro"]))
plt.savefig("set113_pr_curve.jpg")
# plt.show()
if __name__ == '__main__':
# 加载模型
seresnet = FineTuneSEResnet50(num_class=num_class)
device = torch.device(gpu)
seresnet = seresnet.to(device)
test(seresnet, test_weights_path)
运行结果：
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