如何列出来自 torchvision 的所有 CNN 分类模型?

问题描述 投票:0回答:0

如何列出来自 torchvision 的所有 CNN 分类模型?

我可以做这样的事情:

import torchvision
dir(torchvision.models)

但它不仅产生模型类名称:

['AlexNet',
 'AlexNet_Weights',
 'ConvNeXt',
 'ConvNeXt_Base_Weights',
 'ConvNeXt_Large_Weights',
 'ConvNeXt_Small_Weights',
 'ConvNeXt_Tiny_Weights',
 'DenseNet',
 'DenseNet121_Weights',
 'DenseNet161_Weights',
 'DenseNet169_Weights',
 'DenseNet201_Weights',
 'EfficientNet',
 'EfficientNet_B0_Weights',
 'EfficientNet_B1_Weights',
 'EfficientNet_B2_Weights',
 'EfficientNet_B3_Weights',
 'EfficientNet_B4_Weights',
 'EfficientNet_B5_Weights',
 'EfficientNet_B6_Weights',
 'EfficientNet_B7_Weights',
 'EfficientNet_V2_L_Weights',
 'EfficientNet_V2_M_Weights',
 'EfficientNet_V2_S_Weights',
 'GoogLeNet',
 'GoogLeNetOutputs',
 'GoogLeNet_Weights',
 'Inception3',
 'InceptionOutputs',
 'Inception_V3_Weights',
 'MNASNet',
 'MNASNet0_5_Weights',
 'MNASNet0_75_Weights',
 'MNASNet1_0_Weights',
 'MNASNet1_3_Weights',
 'MaxVit',
 'MaxVit_T_Weights',
 'MobileNetV2',
 'MobileNetV3',
 'MobileNet_V2_Weights',
 'MobileNet_V3_Large_Weights',
 'MobileNet_V3_Small_Weights',
 'RegNet',
 'RegNet_X_16GF_Weights',
 'RegNet_X_1_6GF_Weights',
 'RegNet_X_32GF_Weights',
 'RegNet_X_3_2GF_Weights',
 'RegNet_X_400MF_Weights',
 'RegNet_X_800MF_Weights',
 'RegNet_X_8GF_Weights',
 'RegNet_Y_128GF_Weights',
 'RegNet_Y_16GF_Weights',
 'RegNet_Y_1_6GF_Weights',
 'RegNet_Y_32GF_Weights',
 'RegNet_Y_3_2GF_Weights',
 'RegNet_Y_400MF_Weights',
 'RegNet_Y_800MF_Weights',
 'RegNet_Y_8GF_Weights',
 'ResNeXt101_32X8D_Weights',
 'ResNeXt101_64X4D_Weights',
 'ResNeXt50_32X4D_Weights',
 'ResNet',
 'ResNet101_Weights',
 'ResNet152_Weights',
 'ResNet18_Weights',
 'ResNet34_Weights',
 'ResNet50_Weights',
 'ShuffleNetV2',
 'ShuffleNet_V2_X0_5_Weights',
 'ShuffleNet_V2_X1_0_Weights',
 'ShuffleNet_V2_X1_5_Weights',
 'ShuffleNet_V2_X2_0_Weights',
 'SqueezeNet',
 'SqueezeNet1_0_Weights',
 'SqueezeNet1_1_Weights',
 'SwinTransformer',
 'Swin_B_Weights',
 'Swin_S_Weights',
 'Swin_T_Weights',
 'Swin_V2_B_Weights',
 'Swin_V2_S_Weights',
 'Swin_V2_T_Weights',
 'VGG',
 'VGG11_BN_Weights',
 'VGG11_Weights',
 'VGG13_BN_Weights',
 'VGG13_Weights',
 'VGG16_BN_Weights',
 'VGG16_Weights',
 'VGG19_BN_Weights',
 'VGG19_Weights',
 'ViT_B_16_Weights',
 'ViT_B_32_Weights',
 'ViT_H_14_Weights',
 'ViT_L_16_Weights',
 'ViT_L_32_Weights',
 'VisionTransformer',
 'Weights',
 'WeightsEnum',
 'Wide_ResNet101_2_Weights',
 'Wide_ResNet50_2_Weights',
 '_GoogLeNetOutputs',
 '_InceptionOutputs',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 '_api',
 '_meta',
 '_utils',
 'alexnet',
 'convnext',
 'convnext_base',
 'convnext_large',
 'convnext_small',
 'convnext_tiny',
 'densenet',
 'densenet121',
 'densenet161',
 'densenet169',
 'densenet201',
 'detection',
 'efficientnet',
 'efficientnet_b0',
 'efficientnet_b1',
 'efficientnet_b2',
 'efficientnet_b3',
 'efficientnet_b4',
 'efficientnet_b5',
 'efficientnet_b6',
 'efficientnet_b7',
 'efficientnet_v2_l',
 'efficientnet_v2_m',
 'efficientnet_v2_s',
 'get_model',
 'get_model_builder',
 'get_model_weights',
 'get_weight',
 'googlenet',
 'inception',
 'inception_v3',
 'list_models',
 'maxvit',
 'maxvit_t',
 'mnasnet',
 'mnasnet0_5',
 'mnasnet0_75',
 'mnasnet1_0',
 'mnasnet1_3',
 'mobilenet',
 'mobilenet_v2',
 'mobilenet_v3_large',
 'mobilenet_v3_small',
 'mobilenetv2',
 'mobilenetv3',
 'optical_flow',
 'quantization',
 'regnet',
 'regnet_x_16gf',
 'regnet_x_1_6gf',
 'regnet_x_32gf',
 'regnet_x_3_2gf',
 'regnet_x_400mf',
 'regnet_x_800mf',
 'regnet_x_8gf',
 'regnet_y_128gf',
 'regnet_y_16gf',
 'regnet_y_1_6gf',
 'regnet_y_32gf',
 'regnet_y_3_2gf',
 'regnet_y_400mf',
 'regnet_y_800mf',
 'regnet_y_8gf',
 'resnet',
 'resnet101',
 'resnet152',
 'resnet18',
 'resnet34',
 'resnet50',
 'resnext101_32x8d',
 'resnext101_64x4d',
 'resnext50_32x4d',
 'segmentation',
 'shufflenet_v2_x0_5',
 'shufflenet_v2_x1_0',
 'shufflenet_v2_x1_5',
 'shufflenet_v2_x2_0',
 'shufflenetv2',
 'squeezenet',
 'squeezenet1_0',
 'squeezenet1_1',
 'swin_b',
 'swin_s',
 'swin_t',
 'swin_transformer',
 'swin_v2_b',
 'swin_v2_s',
 'swin_v2_t',
 'vgg',
 'vgg11',
 'vgg11_bn',
 'vgg13',
 'vgg13_bn',
 'vgg16',
 'vgg16_bn',
 'vgg19',
 'vgg19_bn',
 'video',
 'vision_transformer',
 'vit_b_16',
 'vit_b_32',
 'vit_h_14',
 'vit_l_16',
 'vit_l_32',
 'wide_resnet101_2',
 'wide_resnet50_2']
python pytorch
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