Pytorch实战 | 猴痘病识别
参考：
Pytorch实战 | 第4天：猴痘病识别
数据集：
https://www.aliyundrive.com/s/dGsVQS8G7Vf
提取码: 8nq9
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import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import matplotlib.pyplot as plt
import os, PIL, pathlib
from torch.autograd import Variable
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 获取标签名
data_dir = 'data/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
# 导入数据
transforms = transforms.Compose([transforms.ToTensor(),
transforms.Resize([224, 224]),
transforms.Normalize(mean=[0.5], std=[0.5])])
data = datasets.ImageFolder(root=data_dir, transform=transforms)
classes = list(data.class_to_idx)
# 划分数据
from torch.utils.data import random_split
data_train, data_test = random_split(data, lengths=[int(0.8 * len(data)), len(data) - int(0.8 * len(data))],
generator=torch.Generator().manual_seed(0))
# 加载数据
batch_size = 16
data_loader_train = torch.utils.data.DataLoader(dataset=data_train, batch_size=batch_size, shuffle=True)
data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=batch_size, shuffle=True)
# 建立模型
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5),
torch.nn.BatchNorm2d(12),
torch.nn.ReLU(),
torch.nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5),
torch.nn.BatchNorm2d(12),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2, 2),
torch.nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5),
torch.nn.BatchNorm2d(24),
torch.nn.ReLU(),
torch.nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5),
torch.nn.BatchNorm2d(24),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2, 2)
)
self.fc = torch.nn.Linear(50 * 50 * 24, len(classeNames))
# 前向传播
def forward(self, x):
x = self.conv1(x)
x = x.view(-1, 50 * 50 * 24)
x = self.fc(x)
return x
from PIL import Image
# 预测图片
def predict_one_image(image_path, model, transform, classes):
test_img = Image.open(image_path).convert('RGB')
plt.imshow(test_img) # 展示预测的图片
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_, pred = torch.max(output, 1)
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')
# 模型训练和测试
model = Model()
model = model.to(device)
# epochs = 100
# optimizer = torch.optim.Adam(model.parameters())
# cost = torch.nn.CrossEntropyLoss()
#
# train_loss = []
# train_acc = []
# test_loss = []
# test_acc = []
#
# for i in range(epochs):
# running_loss = 0.0
# running_correct = 0
# testing_correct = 0
# testing_loss = 0.0
# print("Epoch{}/{}".format(i + 1, epochs))
# print("-" * 20)
# size = len(data_train) # 测试集的大小，一共10000张图片
# num_batches = len(data_loader_train)
#
# for data in data_loader_train:
# x_train, y_train = data
# x_train, y_train = x_train.to(device), y_train.to(device)
# x_train, y_train = Variable(x_train), Variable(y_train)
# outputs = model(x_train)
# optimizer.zero_grad()
# _, pred = torch.max(outputs.data, 1)
# loss = cost(outputs, y_train)
# loss.backward()
# optimizer.step()
# running_loss += loss
# running_correct += torch.sum(pred == y_train)
# train_acc.append(running_correct / size)
# train_loss.append(running_loss / num_batches)
#
# size = len(data_test) # 测试集的大小
# num_batches = len(data_loader_test) # 批次数目
#
# for data in data_loader_test:
# x_test, y_test = data
# x_test, y_test = x_test.to(device), y_test.to(device)
# x_test, y_test = Variable(x_test), Variable(y_test)
# with torch.no_grad():
# outputs = model(x_test)
# _, pred = torch.max(outputs.data, 1)
# loss = cost(outputs, y_test)
# testing_loss += loss
# testing_correct += torch.sum(pred.data == y_test.data)
#
# print("Loss is:{:.4f},Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}".format(
# running_loss / len(data_train),
# 100 * running_correct / len(
# data_train),
# 100 * testing_correct / len(
# data_test)))
# test_acc.append(testing_correct / size)
# test_loss.append(testing_loss / num_batches)
# PATH = './model.pth' # 保存的参数文件名
# model.load_state_dict(torch.load(PATH, map_location=device))
#
# import matplotlib.pyplot as plt
# # 预测训练集中的某张照片
# predict_one_image(image_path='./data/Monkeypox/M01_01_00.jpg',
# model=model,
# transform=transforms,
# classes=classes)
# Loss与Accuracy图
# 隐藏警告
# import warnings
#
# warnings.filterwarnings("ignore") # 忽略警告信息
# plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
# plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# plt.rcParams['figure.dpi'] = 100 # 分辨率
#
# epochs_range = range(epochs)
#
# plt.figure(figsize=(12, 3))
# plt.subplot(1, 2, 1)
#
# plt.plot(epochs_range, train_acc, label='Training Accuracy')
# plt.plot(epochs_range, test_acc, label='Test Accuracy')
# plt.legend(loc='lower right')
# plt.title('Training and Validation Accuracy')
#
# plt.subplot(1, 2, 2)
# plt.plot(epochs_range, train_loss, label='Training Loss')
# plt.plot(epochs_range, test_loss, label='Test Loss')
# plt.legend(loc='upper right')
# plt.title('Training and Validation Loss')
# plt.show()
遇到的问题：
transforms.ToTensor没加括号，遇到上面这种问题就去检查检查前面的函数括号有没有漏写或者多写