pytorch 图片识别,pytorch调用训练好的模型识别新的图片
这篇文章主要介绍了框架实现图像识别（实战),文章主要分享实现代码，但也具有一定的参考价值，需要的小伙伴可以才可以一下，希望对你有所帮助
目录
1.代码讲解1.1 导库1.2 标准化、转换、设置GPU1.3预处理数据1.4 建立模型1.5 训练模型1.6 测试模型1.7结果
1. 代码讲解
1.1 导库
导入os.path
从操作系统导入列表目录
将数组作为铭牌导入
进口熊猫作为螺纹中径
从太平航运进口图片
进口火炬
将torch.nn作为神经网络导入
导入火炬. nn .功能为F
将火炬. optim作为使最优化导入
从torch.nn导入AdaptiveAvgPool2d
从火炬. utils.data.sampler导入水下取样器
从torch.utils.data导入数据集
将火炬视觉.转换作为转换导入
从sklearn.model_selection导入训练_测试_拆分
1.2 标准化、transform、设置GPU
设备=手电筒。设备( cuda :0 如果火炬。cuda。is _ available()else CPU )
正常化=转换。正常化（
均值=[0.485,0.456,0.406],
std=[0.229，0.224，0.225]
)
转换=转换。撰写([转换ToTensor()、normalize]) #转换
1.3 预处理数据
类狗数据集(数据集):
# 定义变量
def __init__(self，img_paths，img_labels，size_of_images):
self.img _ paths=img _ paths
self.img _ labels=img _ labels
self.size _ of _ images=size的大小
# 多少长图片
def __len__(self):
返回len(self.img_paths)
# 打开每组图片并处理每张图片
def __getitem__(self，index):
PIL _图像=图像。开放(自我。img _ paths[index]).调整大小(图片大小)
张量图像=变换(PIL图像)
label=self.img_labels[index]
返回张量_图像，标签
print(len(listdir(r c : \ Users \ ai axit \ Desktop \ Deep Learning project \ Deep _ Learning _ Data \ dog-breed-identificati on \ train ))
打印(镜头(PD。read _ CSV(r c : \ Users \ ai axit \ Desktop \ Deep Learning project \ Deep _ Learning _ Data \ dog-breed-identificati on \ labels。CSV’))
print(len(listdir(r c : \ Users \ ai axit \ Desktop \ Deep Learning project \ Deep _ Learning _ Data \ dog-breed-identificati on \ test ))
train_paths=[]
测试路径=[]
标签=[]
# 训练集图片路径
train_paths_lir = rC:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\train
for path in listdir(train_paths_lir):
train_paths.append(os.path.join(train_paths_lir, path))
# 测试集图片路径
labels_data = pd.read_csv(rC:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\labels.csv)
labels_data = pd.DataFrame(labels_data)
# 把字符标签离散化，因为数据有120种狗，不离散化后面把数据给模型时会报错：字符标签过多。把字符标签从0-119编号
size_mapping = {}
value = 0
size_mapping = dict(labels_data[breed].value_counts())
for kay in size_mapping:
size_mapping[kay] = value
value += 1
# print(size_mapping)
labels = labels_data[breed].map(size_mapping)
labels = list(labels)
# print(labels)
print(len(labels))
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(train_paths, labels, test_size=0.2)
train_set = DogDataset(X_train, y_train, (32, 32))
test_set = DogDataset(X_test, y_test, (32, 32))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64)
1.4 建立模型
class LeNet(nn.Module):def __init__(self):
super(LeNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5),
nn.ReLU(),
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),
nn.ReLU(),
nn.AvgPool2d(kernel_size=2, stride=2)
)
self.classifier = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84, 120)
)
def forward(self, x):
batch_size = x.shape[0]
x = self.features(x)
x = x.view(batch_size, -1)
x = self.classifier(x)
return x
model = LeNet().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters())
TRAIN_LOSS = [] # 损失
TRAIN_ACCURACY = [] # 准确率
1.5 训练模型
def train(epoch):model.train()
epoch_loss = 0.0 # 损失
correct = 0 # 精确率
for batch_index, (Data, Label) in enumerate(train_loader):
# 扔到GPU中
Data = Data.to(device)
Label = Label.to(device)
output_train = model(Data)
# 计算损失
loss_train = criterion(output_train, Label)
epoch_loss = epoch_loss + loss_train.item()
# 计算精确率
pred = torch.max(output_train, 1)[1]
train_correct = (pred == Label).sum()
correct = correct + train_correct.item()
# 梯度归零、反向传播、更新参数
optimizer.zero_grad()
loss_train.backward()
optimizer.step()
print(Epoch: , epoch, Train_loss: , epoch_loss / len(train_set), Train correct: , correct / len(train_set))
1.6 测试模型
和训练集差不多。
def test():model.eval()
correct = 0.0
test_loss = 0.0
with torch.no_grad():
for Data, Label in test_loader:
Data = Data.to(device)
Label = Label.to(device)
test_output = model(Data)
loss = criterion(test_output, Label)
pred = torch.max(test_output, 1)[1]
test_correct = (pred == Label).sum()
correct = correct + test_correct.item()
test_loss = test_loss + loss.item()
print(Test_loss: , test_loss / len(test_set), Test correct: , correct / len(test_set))
1.7结果
epoch = 10for n_epoch in range(epoch):
train(n_epoch)
test()
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