- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
目标
马铃薯病害数据集，该数据集包含表现出各种疾病的马铃薯植物的高分辨率图像，包括早期疫病 、晚期疫病 和健康叶子 。它旨在帮助开发和测试图像识别模型，以实现准确的疾病检测和分类，从而促进农业诊断的进步。
具体实现
（一）环境
语言环境 ：Python 3.10
编 译 器: PyCharm
框 架: Pytorch
（二）具体步骤
1. Utils.py
**import torch
import pathlib
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
# 第一步：设置GPU
def USE_GPU():
if torch.cuda.is_available():
print('CUDA is available, will use GPU')
device = torch.device("cuda")
else:
print('CUDA is not available. Will use CPU')
device = torch.device("cpu")
return device
temp_dict = dict()
def recursive_iterate(path):
"""
根据所提供的路径遍历该路径下的所有子目录，列出所有子目录下的文件
:param path: 路径
:return: 返回最后一级目录的数据
""" path = pathlib.Path(path)
for file in path.iterdir():
if file.is_file():
temp_key = str(file).split('\\')[-2]
if temp_key in temp_dict:
temp_dict.update({temp_key: temp_dict[temp_key] + 1})
else:
temp_dict.update({temp_key: 1})
# print(file)
elif file.is_dir():
recursive_iterate(file)
return temp_dict
def data_from_directory(directory, train_dir=None, test_dir=None, show=False):
"""
提供是的数据集是文件形式的，提供目录方式导入数据，简单分析数据并返回数据分类
:param test_dir: 是否设置了测试集目录
:param train_dir: 是否设置了训练集目录
:param directory: 数据集所在目录
:param show: 是否需要以柱状图形式显示数据分类情况,默认显示
:return: 数据分类列表,类型: list
""" global total_image
print("数据目录：{}".format(directory))
data_dir = pathlib.Path(directory)
# for d in data_dir.glob('**/*'): # **/*通配符可以遍历所有子目录
# if d.is_dir():
# print(d) class_name = []
total_image = 0
# temp_sum = 0
if train_dir is None or test_dir is None:
data_path = list(data_dir.glob('*'))
class_name = [str(path).split('\\')[-1] for path in data_path]
print("数据分类: {}, 类别数量：{}".format(class_name, len(list(data_dir.glob('*')))))
total_image = len(list(data_dir.glob('*/*')))
print("图片数据总数: {}".format(total_image))
else:
temp_dict.clear()
train_data_path = directory + '/' + train_dir
train_data_info = recursive_iterate(train_data_path)
print("{}目录:{},{}".format(train_dir, train_data_path, train_data_info))
temp_dict.clear()
test_data_path = directory + '/' + test_dir
print("{}目录:{},{}".format(test_dir, test_data_path, recursive_iterate(test_data_path)))
class_name = temp_dict.keys()
if show:
# 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
for i in class_name:
data = len(list(pathlib.Path((directory + '\\' + i + '\\')).glob('*')))
plt.title('数据分类情况')
plt.grid(ls='--', alpha=0.5)
plt.bar(i, data)
plt.text(i, data, str(data), ha='center', va='bottom')
print("类别-{}:{}".format(i, data))
# temp_sum += data
plt.show()
# if temp_sum == total_image:
# print("图片数据总数检查一致")
# else: # print("数据数据总数检查不一致，请检查数据集是否正确！")
return class_name
def get_transforms_setting(size):
"""
获取transforms的初始设置
:param size: 图片大小
:return: transforms.compose设置
""" transform_setting = {
'train': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
return transform_setting
# 训练循环
def train(dataloader, device, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
def test(dataloader, device, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
from PIL import Image
def predict_one_image(image_path, device, model, transform, classes):
"""
预测单张图片
:param image_path: 图片路径
:param device: CPU or GPU :param model: cnn模型
:param transform: :param classes: :return:
"""
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}')**2. model.py
import torch.nn as nn
import torch
import torch.nn.functional as F
class VGG16(nn.Module):
def __init__(self, num_classes):
super(VGG16, self).__init__()
# 卷积块1
self.block1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积块2
self.block2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积块3
self.block3 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积块4
self.block4 = nn.Sequential(
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积块5
self.block5 = nn.Sequential(
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 全连接网络层，用于分类
self.classifier = nn.Sequential(
nn.Linear( in_features=512 * 7 * 7, out_features=4096),
nn.ReLU(),
nn.Linear(in_features=4096, out_features=4096),
nn.ReLU(),
nn.Linear(in_features=4096, out_features=num_classes)
)
def forward(self, x):
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x3. main.py
import torch.utils.data
from torchvision import datasets
from Utils import USE_GPU, data_from_directory, get_transforms_setting, train, test
from config import get_options
from model import VGG16
# 获取参数配置
opt = get_options()
# 设置GPU
device = USE_GPU()
DATA_DIR = "./data/PotatoPlants"
# 导入数据
classes_name = data_from_directory(DATA_DIR, show=True)
transforms_setting = get_transforms_setting((224, 224))
total_data = datasets.ImageFolder(DATA_DIR, transform=transforms_setting['train'])
print(total_data)
print(total_data.class_to_idx)
# 划分数据集
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset, test_dataset)
train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True)
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=True)
for X, y in train_dl:
print("Shape of X[N, C, H, W]：", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break 
# 加载VGG16模型
model = VGG16(len(classes_name)).to(device)
print(model)
# 统计模型参数量以及其他指标
import torchsummary as summary
summary.summary(model, (3, 224, 224)) 
# 正式训练
import copy
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
loss_fn = torch.nn.CrossEntropyLoss() # 创建损失函数
train_loss, train_acc, test_loss, test_acc = [], [], [], []
best_acc = 0 # 设置了一个最佳准确率，来用作为最佳模型的判别标准
for epoch in range(opt.epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, device, model, loss_fn, optimizer)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, device, model, loss_fn)
# 保存最佳模型
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_model = copy.deepcopy(model)
train_acc.append(epoch_train_acc)
test_acc.append(epoch_test_acc)
train_loss.append(epoch_train_loss)
test_loss.append(epoch_test_loss)
# 获取当前学习率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = 'Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}'
print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss,
epoch_test_acc * 100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH = './models/potato-model.pth'
torch.save(model.state_dict(), PATH)
print("完成")
4. predict.py 预测单张图片
import torch
from PIL import Image
from Utils import predict_one_image, USE_GPU, get_transforms_setting
from model import VGG16
classes = ['Early_blight','Late_blight', 'healthy']
transforms = get_transforms_setting([224,224])
device = USE_GPU()
# 加载VGG16模型
model = VGG16(3)
model.load_state_dict(torch.load('./models/potato-model.pth', map_location=device))
model.to(device)
img_path = "./data/PotatoPlants/Early_blight/0c4f6f72-c7a2-42e1-9671-41ab3bf37fe7___RS_Early.B 6752.JPG"
predict_one_image(img_path, device, model, transforms['train'], classes)
