安装pytorch时我们一般都是会一并选择安装自带的视觉模型库 torchvision , 该库不仅有经典的视觉模型结构同时还提供了对应参数的下载功能,可以说torchvision库是十分方便于研究视觉的pytorch使用者来使用的。
给出pytorch的视觉库torchvision的GitHub地址:
https://github.com/pytorch/vision
该库中提供的模型结构定义文件:
pytorch官方也给出了对应torchvision库的一些介绍和使用说明:
https://pytorch.org/vision/stable/models.html
使用随机权重的torchvision中的视觉模型:
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_v2 = models.mobilenet_v2() mobilenet_v3_large = models.mobilenet_v3_large() mobilenet_v3_small = models.mobilenet_v3_small() resnext50_32x4d = models.resnext50_32x4d() wide_resnet50_2 = models.wide_resnet50_2() mnasnet = models.mnasnet1_0() efficientnet_b0 = models.efficientnet_b0() efficientnet_b1 = models.efficientnet_b1() efficientnet_b2 = models.efficientnet_b2() efficientnet_b3 = models.efficientnet_b3() efficientnet_b4 = models.efficientnet_b4() efficientnet_b5 = models.efficientnet_b5() efficientnet_b6 = models.efficientnet_b6() efficientnet_b7 = models.efficientnet_b7() regnet_y_400mf = models.regnet_y_400mf() regnet_y_800mf = models.regnet_y_800mf() regnet_y_1_6gf = models.regnet_y_1_6gf() regnet_y_3_2gf = models.regnet_y_3_2gf() regnet_y_8gf = models.regnet_y_8gf() regnet_y_16gf = models.regnet_y_16gf() regnet_y_32gf = models.regnet_y_32gf() regnet_x_400mf = models.regnet_x_400mf() regnet_x_800mf = models.regnet_x_800mf() regnet_x_1_6gf = models.regnet_x_1_6gf() regnet_x_3_2gf = models.regnet_x_3_2gf() regnet_x_8gf = models.regnet_x_8gf() regnet_x_16gf = models.regnet_x_16gf() regnet_x_32gf = models.regnet_x_32gf()
使用torchvision给出的权重及torchvision中的视觉模型:
import torchvision.models as models resnet18 = models.resnet18(pretrained=True) alexnet = models.alexnet(pretrained=True) vgg16 = models.vgg16(pretrained=True) squeezenet = models.squeezenet1_0(pretrained=True) densenet = models.densenet161(pretrained=True) inception = models.inception_v3(pretrained=True) googlenet = models.googlenet(pretrained=True) shufflenet = models.shufflenet_v2_x1_0(pretrained=True) mobilenet_v2 = models.mobilenet_v2(pretrained=True) mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True) mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True) resnext50_32x4d = models.resnext50_32x4d(pretrained=True) wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) mnasnet = models.mnasnet1_0(pretrained=True) efficientnet_b0 = models.efficientnet_b0(pretrained=True) efficientnet_b1 = models.efficientnet_b1(pretrained=True) efficientnet_b2 = models.efficientnet_b2(pretrained=True) efficientnet_b3 = models.efficientnet_b3(pretrained=True) efficientnet_b4 = models.efficientnet_b4(pretrained=True) efficientnet_b5 = models.efficientnet_b5(pretrained=True) efficientnet_b6 = models.efficientnet_b6(pretrained=True) efficientnet_b7 = models.efficientnet_b7(pretrained=True) regnet_y_400mf = models.regnet_y_400mf(pretrained=True) regnet_y_800mf = models.regnet_y_800mf(pretrained=True) regnet_y_1_6gf = models.regnet_y_1_6gf(pretrained=True) regnet_y_3_2gf = models.regnet_y_3_2gf(pretrained=True) regnet_y_8gf = models.regnet_y_8gf(pretrained=True) regnet_y_16gf = models.regnet_y_16gf(pretrained=True) regnet_y_32gf = models.regnet_y_32gf(pretrained=True) regnet_x_400mf = models.regnet_x_400mf(pretrained=True) regnet_x_800mf = models.regnet_x_800mf(pretrained=True) regnet_x_1_6gf = models.regnet_x_1_6gf(pretrained=True) regnet_x_3_2gf = models.regnet_x_3_2gf(pretrained=True) regnet_x_8gf = models.regnet_x_8gf(pretrained=True) regnet_x_16gf = models.regnet_x_16gf(pretrained=True) regnet_x_32gf = models.regnet_x_32gf(pretrained=True)