pytorch集锦(4) |
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pytorch集锦(4)
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目录
加载训练好的模型下载模型权重图像预处理打开要预测的图像传递图像运行模型下载数据打开imagenet_classes.txt预测结果前5个最可能分类
加载训练好的模型
pip3 install pillow
>>> from torchvision import models
>>> dir(models)
['AlexNet', 'DenseNet', 'Inception3', 'ResNet', 'SqueezeNet', 'VGG', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'alexnet', 'densenet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'inception', 'inception_v3', 'resnet', 'resnet101', 'resnet152', 'resnet18', 'resnet34', 'resnet50', 'squeezenet', 'squeezenet1_0', 'squeezenet1_1', 'vgg', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn']
出现 这个错误 ImportError: cannot import name 'PILLOW_VERSION' from 'PIL' (/usr/lib64/python3.11/site-packages/PIL/__init__.py)需要将对应出错文件的PILLOW_VERSION改为__version__ PILLOW_VERSION在Pillow 7.0.0之后的版本被移除了 下载模型权重 >>> resnet=models.resnet101(pretrained=True) Downloading: "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth" to /home/spx/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth 100.0% >>> resnet ResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): Bottleneck( .... 图像预处理 >>>from torchvision import transforms >>> prporocess = transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize(mean=[0.285,0.456,0.406],std=[0.229,0.224,0.225])]1、定义了转换操作，允许快速定义基本预重函数的管道。 2、将输入图像缩放到２５６＊２５６个像素，围绕中心将图像裁剪为２２４*２２４个像素。 3、图像转换为一个张量，对RGB分量进行归一化处理。 打开要预测的图像 >>from PIL import Image >>> img=Image.open('/home/spx/learn/pic/1.jpg') >>> img >>> img.show()
https://image-net.org/里找到 imagenet_classes.txt下载，这是标签文件。 或者 https://gitee.com/lonerlin/classification/blob/master/imagenet_classes.txt https://github.com/ethereon/caffe-tensorflow/blob/master/examples/imagenet/imagenet-classes.txt 这里， 将它复制下来。 新建一个imagenet_classes.txt文件，粘贴进去 打开imagenet_classes.txt >>> with open('/home/spx/learn/pic/imagenet_classes.txt') as f: ... labels=[line.strip() for line in f.readlines()] ... >>> labels ['tench, Tinca tinca', 'goldfish, Carassius auratus', 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 'tiger shark, Galeocerdo cuvieri', 'hammerhead, hammerhead shark', 'electric ray, crampfish, numbfish, torpedo', 'stingray', 'cock', 'hen', 'ostrich, Struthio camelus', 'brambling, Fringilla montifringilla', 'goldfinch, Carduelis carduelis', 'house finch, linnet, Carpodacus mexicanus', 'junco, snowbird', 'indigo bunting, indigo f .... 预测结果 >>> _,index=torch.max(out,1) >>> percentage=torch.nn.functional.softmax(out,dim=1)[0]*100 >>> labels[index[0]] 'hog, pig, grunter, squealer, Sus scrofa' >>> percentage[index[0]].item() 60.67759323120117ｐｉｇ预测正确 前5个最可能分类 >>> _,indices=torch.sort(out,descending=True) >>> [(labels[idx],percentage[idx].item()) for idx in indices[0][:5]] [('hog, pig, grunter, squealer, Sus scrofa', 60.67759323120117), ('weasel', 9.75589656829834), ('guinea pig, Cavia cobaya', 8.112009048461914), ('black-footed ferret, ferret, Mustela nigripes', 5.257884979248047), ('polecat, fitch, foulmart, foumart, Mustela putorius', 4.569345474243164)] |
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