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病灶分类test.py
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病灶分类test.py
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import os
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
import cv2
from torchvision import models, transforms
import pandas as pd
import torch.nn as nn
print('————病灶分类test————')
# test_root = r'D:\lab_picture\test'
# test_root = r'D:\8.3\test_new'
test_root = '/Users/shaotianyu01/Desktop/school/11.4/test_new'
cate2label = {}
list_img = []
list_dir = []
list_label = []
list_cate = os.listdir(test_root)
for i in range(len(list_cate)):
if list_cate[i] not in cate2label:
cate2label[list_cate[i]] = i
for cate in list_cate:
list_name = os.listdir(os.path.join(test_root, cate))
for name in list_name:
img_dir = os.path.join(test_root, cate, name)
img = cv2.imread(img_dir)
list_dir.append(img_dir)
img = cv2.resize(img, (128, 128))
# cv2.imshow('img', img)
# cv2.waitKey(1)
list_img.append(img)
list_label.append(cate2label[cate])
print('测试集共有{}张图片'.format(len(list_img)))
# transform = transforms.Compose([
# transforms.ToTensor(),
# ])
# class Mydata(Dataset):
# def __init__(self, x, y, transform):
# self.X = x
# self.Y = y
# self.transform = transform
#
# def __len__(self):
# return len(self.X)
#
# def __getitem__(self, idx):
# x_idx = self.X[idx]
# y_idx = np.array(self.Y[idx])
# return self.transform(x_idx).type(torch.FloatTensor), torch.from_numpy(y_idx).type(torch.LongTensor)
class Mydata(Dataset):
def __init__(self, x, y):
self.X = x
self.Y = y
self.as_tensor = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.625, 0.448, 0.688], [0.131, 0.177, 0.101]),
])
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
x_idx = self.X[idx]
y_idx = self.Y[idx]
x_idx = self.as_tensor(x_idx)
y_idx = np.array(y_idx)
y_idx = torch.from_numpy(y_idx).type(torch.LongTensor)
x_idx = x_idx.type(torch.FloatTensor)
return x_idx, y_idx
test_set = Mydata(list_img, list_label)
test_loader = DataLoader(
test_set,
batch_size=32,
shuffle=False,
num_workers=0,
)
# class Net(nn.Module):
# def __init__(self, model):
# super(Net, self).__init__()
# self.res_layers = nn.Sequential(*list(model.children())[: -2])
# self.se = nn.Sequential(
# nn.AdaptiveAvgPool2d((1, 1)),
# nn.Conv2d(512, 256, 1),
# nn.ReLU(),
# nn.Conv2d(256, 512, 1),
# nn.Sigmoid(),
# )
# self.pool = nn.AdaptiveAvgPool2d((1, 1))
# self.fc = nn.Linear(512, 3)
#
# def forward(self, x):
# x1 = self.res_layers(x)
# x = self.se(x1)
# y = x1 * x
# y = self.pool(y).view((x1.shape[0], -1))
# y = self.fc(y)
# return y
model = models.resnet18(pretrained=False)
model.fc = nn.Linear(512, 3)
# net = Net(model)
model.load_state_dict(torch.load('/Users/shaotianyu01/Desktop/school/11.4/11.11.py_best_acc.pth'), False)
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = model.to(DEVICE)
print('开始预测')
correct = 0
total = 0
list_pred = []
with torch.no_grad():
net.eval()
for i, (data, label) in enumerate(test_loader):
data = data.to(DEVICE)
label = label.to(DEVICE)
y_pred = net(data)
# print(y_pred.shape)
pred = y_pred.argmax(dim=1)
correct += pred.eq(label.view_as(pred)).sum().item()
# print(pred)
# correct += pred.eq(label.view_as(pred)).sum().item()
list_pred += pred.cpu().numpy().tolist()
total += label.shape[0]
print('测试集准确率为:{}'.format(correct / total))
label2cate = {}
for i in range(len(list_cate)):
if i not in label2cate:
label2cate[i] = list_cate[i]
list_error = []
print('显示预测错误图片')
for i in range(len(list_label)):
if list_label[i] != list_pred[i]:
img = cv2.imread(list_dir[i])
# cv2.imshow('True_label:{} Pred_label:{}'.format(list_label[i], list_pred[i]), img)
# cv2.waitKey(0)
list_error.append([list_dir[i], label2cate[list_label[i]], label2cate[list_pred[i]]])
names = ['图片地址', '真实类别', '预测类别']
print('保存预测错误结果')
ans_df = pd.DataFrame(columns=names, data=list_error)
ans_df.to_csv('/Users/shaotianyu01/Desktop/school/11.4/error.csv')
print('运行完毕')