│ └─coatnet.py
│
└─train.py
│
└─test.py
==============================================================
数据集选用植物幼苗分类，总共12类。数据集连接如下：
链接：https://pan.baidu.com/s/1TOLSNj9JE4-MFhU0Yv8TNQ
提取码：syng
在工程的根目录新建data文件夹，获取数据集后，将trian和test解压放到data文件夹下面，如下图：
======================================================================
安装完成后，导入到项目中。
import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
from dataset.dataset import SeedlingData
from torch.autograd import Variable
from models.coatnet import coatnet_0
=================================================================
设置使用GPU，设置学习率、BatchSize、epoch等参数
设置全局参数
modellr = 1e-4
BATCH_SIZE = 16
EPOCHS = 50
DEVICE = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
================================================================
数据处理比较简单，没有做复杂的尝试，有兴趣的可以加入一些处理。
数据预处理
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
transform_test = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
===============================================================
然后我们在dataset文件夹下面新建 init.py和dataset.py，在mydatasets.py文件夹写入下面的代码：
说一下代码的核心逻辑。
第一步 建立字典，定义类别对应的ID,用数字代替类别。
第二步 在__init__里面编写获取图片路径的方法。测试集只有一层路径直接读取，训练集在train文件夹下面是类别文件夹，先获取到类别，再获取到具体的图片路径。然后使用sklearn中切分数据集的方法，按照7：3的比例切分训练集和验证集。
第三步 在__getitem__方法中定义读取单个图片和类别的方法，由于图像中有位深度32位的，所以我在读取图像的时候做了转换。
代码如下：
coding:utf8
import os
from PIL import Image
from torch.utils import data
from torchvision import transforms as T
from sklearn.model_selection import train_test_split
Labels = {‘Black-grass’: 0, ‘Charlock’: 1, ‘Cleavers’: 2, ‘Common Chickweed’: 3,
‘Common wheat’: 4, ‘Fat Hen’: 5, ‘Loose Silky-bent’: 6, ‘Maize’: 7, ‘Scentless Mayweed’: 8,
‘Shepherds Purse’: 9, ‘Small-flowered Cranesbill’: 10, ‘Sugar beet’: 11}
class SeedlingData (data.Dataset):
def init(self, root, transforms=None, train=True, test=False):
“”"
主要目标： 获取所有图片的地址，并根据训练，验证，测试划分数据
“”"
self.test = test
self.transforms = transforms
if self.test:
imgs = [os.path.join(root, img) for img in os.listdir(root)]
self.imgs = imgs
else:
imgs_labels = [os.path.join(root, img) for img in os.listdir(root)]
imgs = []
for imglable in imgs_labels:
for imgname in os.listdir(imglable):
imgpath = os.path.join(imglable, imgname)
imgs.append(imgpath)
trainval_files, val_files = train_test_split(imgs, test_size=0.3, random_state=42)
if train:
self.imgs = trainval_files
else:
self.imgs = val_files
def getitem(self, index):
“”"
一次返回一张图片的数据
“”"
img_path = self.imgs[index]
img_path=img_path.replace(“\”,‘/’)
if self.test:
label = -1
else:
labelname = img_path.split(‘/’)[-2]
label = Labels[labelname]
data = Image.open(img_path).convert(‘RGB’)
data = self.transforms(data)
return data, label
def len(self):
return len(self.imgs)
然后我们在train.py调用SeedlingData读取数据 ，记着导入刚才写的dataset.py(from mydatasets import SeedlingData)
读取数据
dataset_train = SeedlingData(‘data/train’, transforms=transform, train=True)
dataset_test = SeedlingData(“data/train”, transforms=transform_test, train=False)
导入数据
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)
===============================================================
-
设置loss函数为nn.CrossEntropyLoss()。
-
设置模型为coatnet_0，修改最后一层全连接输出改为12。
-
优化器设置为adam。
-
学习率调整策略改为余弦退火
实例化模型并且移动到GPU
criterion = nn.CrossEntropyLoss()
model_ft = coatnet_0()
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 12)
model_ft.to(DEVICE)
选择简单暴力的Adam优化器，学习率调低
optimizer = optim.Adam(model_ft.parameters(), lr=modellr)
cosine_schedule = optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer,T_max=20,eta_min=1e-9)
定义训练过程
def train(model, device, train_loader, optimizer, epoch):
model.train()
sum_loss = 0
total_num = len(train_loader.dataset)
print(total_num, len(train_loader))
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data).to(device), Variable(target).to(device)
output = model(data)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print_loss = loss.data.item()
sum_loss += print_loss
if (batch_idx + 1) % 10 == 0:
print(‘Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}’.format(
epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
-
- (batch_idx + 1) / len(train_loader), loss.item()))
ave_loss = sum_loss / len(train_loader)
print(‘epoch:{},loss:{}’.format(epoch, ave_loss))
验证过程
def val(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
total_num = len(test_loader.dataset)
print(total_num, len(test_loader))
with torch.no_grad():
for data, target in test_loader:
data, target = Variable(data).to(device), Variable(target).to(device)
output = model(data)
loss = criterion(output, target)
_, pred = torch.max(output.data, 1)
correct += torch.sum(pred == target)
print_loss = loss.data.item()
test_loss += print_loss
correct = correct.data.item()
acc = correct / total_num
avgloss = test_loss / len(test_loader)
print(‘\nVal set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n’.format(
avgloss, correct, len(test_loader.dataset), 100 * acc))
训练
for epoch in range(1, EPOCHS + 1):
train(model_ft, DEVICE, train_loader, optimizer, epoch)
cosine_schedule.step()
val(model_ft, DEVICE, test_loader)
torch.save(model_ft, ‘model.pth’)
=============================================================
测试集存放的目录如下图：
第一步 定义类别，这个类别的顺序和训练时的类别顺序对应，一定不要改变顺序！！！！
classes = (‘Black-grass’, ‘Charlock’, ‘Cleavers’, ‘Common Chickweed’,
‘Common wheat’, ‘Fat Hen’, ‘Loose Silky-bent’,
‘Maize’, ‘Scentless Mayweed’, ‘Shepherds Purse’, ‘Small-flowered Cranesbill’, ‘Sugar beet’)
第二步 定义transforms，transforms和验证集的transforms一样即可，别做数据增强。
transform_test = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
第三步 加载model，并将模型放在DEVICE里。
DEVICE = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
model = torch.load(“model.pth”)
model.eval()
model.to(DEVICE)
第四步 读取图片并预测图片的类别，在这里注意，读取图片用PIL库的Image。不要用cv2，transforms不支持。
path = ‘data/test/’
testList = os.listdir(path)
for file in testList:
img = Image.open(path + file)
img = transform_test(img)
img.unsqueeze_(0)
img = Variable(img).to(DEVICE)
out = model(img)
Predict
_, pred = torch.max(out.data, 1)
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