这个包中有四个大类：
- torchvision.datasets
- torchvision.models
- torchvision.transforms
- torchvision.utils
1. torchvision.datasets
- CelebA
- CIFAR
- Cityscapes
- COCO
- DatasetFolder
- EMNIST
- FakeData
- Fashion-MNIST
- Flickr
- HMDB51
- ImageFolder
- ImageNet
- Kinetics-400
- KMNIST
- LSUN
- MNIST
- Omniglot
- PhotoTour
- Places365
- QMNIST
- SBD
- SBU
- STL10
- SVHN
- UCF101
- USPS
- VOC
torch.utils.data.DataLoader(coco_cap, batch_size=args.batchSize, shuffle=True, num_workers=args.nThreads)
2. torchvision.io
- Video
- Fine-grained video API
- Image
3. torchvision.models
- Classification
- AlexNet
- VGG
- ResNet
- SqueezeNet
- DenseNet
- Inception v3
- GoogLeNet
- ShuffleNet v2
- MobileNet v2
- ResNeXt
- Wide ResNet
- MNASNet
- Semantic Segmentation
- FCN ResNet50
- ResNet101
- DeepLabV3 ResNet50
- ResNet101
- Object Detection, Instance Segmentation and Person Keypoint Detection
- Faster R-CNN ResNet-50 FPN
- Mask R-CNN ResNet-50 FPN
- Video classification
载入模型示例
import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
densenet = models.densenet161()
inception = models.inception_v3()
googlenet = models.googlenet()
shufflenet = models.shufflenet_v2_x1_0()
mobilenet = models.mobilenet_v2()
resnext50_32x4d = models.resnext50_32x4d()
wide_resnet50_2 = models.wide_resnet50_2()
mnasnet = models.mnasnet1_0()
你也可以选择使用 pretrained=True 参数来加载预训练参数。
以下罗列了常见的几种模型的 Top-1/5 error ：ImageNet 1-crop error rates (224x224)
| Network | Top-1 error | Top-5 error |
|---|---|---|
| AlexNet | 43.45 | 20.91 |
| VGG-16 | 28.41 | 9.62 |
| VGG-19 | 27.62 | 9.12 |
| VGG-16 with batch normalization | 26.63 | 8.50 |
| VGG-19 with batch normalization | 25.76 | 8.15 |
| ResNet-18 | 30.24 | 10.92 |
| ResNet-34 | 26.70 | 8.58 |
| ResNet-50 | 23.85 | 7.13 |
| ResNet-101 | 22.63 | 6.44 |
| ResNet-152 | 21.69 | 5.94 |
| Densenet-161 | 22.35 | 6.20 |
| MobileNet V2 | 28.12 | 9.71 |
| ResNeXt-50-32x4d | 22.38 | 6.30 |
| ResNeXt-101-32x8d | 20.69 | 5.47 |
| Wide ResNet-50-2 | 21.49 | 5.91 |
| Wide ResNet-101-2 | 21.16 | 5.72 |
4. torchvision.transforms
- Scriptable transforms
- Compositions of transforms
- Transforms on PIL Image and torch.*Tensor
- Transforms on PIL Image only
- Transforms on torch.*Tensor only
- Conversion Transforms
- Generic Transforms
- Functional Transforms
4.1. 针对 PIL.Image 的图像处理
- Scale(size, interpolation=Image.BILINEAR)
- CenterCrop(size) - center-crops the image to the given size
- RandomCrop(size, padding=0)
- RandomHorizontalFlip()
- RandomSizedCrop(size, interpolation=Image.BILINEAR)
- Pad(padding, fill=0)
4.2. 针对张量 torch.*Tensor 的操作
- Normalize(mean, std)
4.3. 数据格式转换操作
- ToTensor()
4.4. 其他操作
例如：
transforms.Lambda(lambda x: x.add(10)) # 将每个像素值加10