pytorch最全预训练模型下载与调用_pytorch预训练模型下载-程序员宅基地

技术标签：深度学习 pytorch 人工智能

pytorch预训练模型下载与调用

torchvision.models

官方预训练模型调用代码：https://github.com/pytorch/vision/tree/master/torchvision/models

官方文档地址：https://pytorch.org/docs/master/torchvision/models.html

预训练模型下载:

Classification 分类

AlexNet

model_urls = {
    
    'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}

VGG

model_urls = {
    
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}

ResNet

model_urls = {
    
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

ResNeXt

model_urls = {
    
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'resnext101_32x16d': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'
}

Wide ResNet

model_urls = {
    
  'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
  'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'
  }

SqueezeNet

model_urls = {
    
    'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
    'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
}

DenseNet

model_urls = {
    
    'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
    'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
    'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
    'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}

Inception v3

model_urls = {
    
    ## Inception v3 ported from TensorFlow
    'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}

GoogLeNet

model_urls = {
    
    # GoogLeNet ported from TensorFlow
    'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}

ShuffleNet v2

model_urls = {
    
    'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
    'shufflenetv2_x1.0': 'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
    'shufflenetv2_x1.5': None,
    'shufflenetv2_x2.0': None,
}

MobileNet v2

model_urls = {
    
    'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}

MobileNet v3

model_urls = {
    
    'mobilenet_v3_small': "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth",
    'mobilenet_v3_large': "https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth"
    
}

MNASNet

_MODEL_URLS = {
    
    "mnasnet0_5":
    "https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
    "mnasnet0_75": None,
    "mnasnet1_0":
    "https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
    "mnasnet1_3": None
}

CONVNEXT

   _MODEL_URLS = {
    
    "convnext_tiny": "https://download.pytorch.org/models/convnext_tiny-983f1562.pth",
    "convnext_small": "https://download.pytorch.org/models/convnext_small-0c510722.pth",
    "convnext_base": "https://download.pytorch.org/models/convnext_base-6075fbad.pth",
    "convnext_large": "https://download.pytorch.org/models/convnext_large-ea097f82.pth",
}

REGNET

   _MODEL_URLS = {
    
    "regnet_y_400mf": "https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth",
    "regnet_y_800mf": "https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth",
    "regnet_y_1_6gf": "https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth",
    "regnet_y_3_2gf": "https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth",
    "regnet_y_8gf": "https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth",
    "regnet_y_16gf": "https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth",
    "regnet_y_32gf": "https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth",
    "regnet_y_128gf": "https://download.pytorch.org/models/regnet_y_128gf_swag-c8ce3e52.pth",
    "regnet_x_400mf": "https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth",
    "regnet_x_800mf": "https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth",
    "regnet_x_1_6gf": "https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth",
    "regnet_x_3_2gf": "https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth",
    "regnet_x_8gf": "https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth",
    "regnet_x_16gf": "https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth",
    "regnet_x_32gf": "https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth",  
}

Semantic Segmentation 语义分割

FCN ResNet50, ResNet101

model_urls = {
    
    'fcn_resnet50_coco': 'https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth',
    'fcn_resnet101_coco': 'https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth'
}

DeepLabV3 ResNet50, ResNet101

model_urls = {
    
    'deeplabv3_resnet50_coco': 'https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth',
    'deeplabv3_resnet101_coco': 'https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth'
}

Object Detection 目标检测

Faster R-CNN ResNet-50 FPN

model_urls = {
    
    'fasterrcnn_resnet50_fpn_coco':
    'https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth',
}

Instance Segmentation 实例分割

Mask R-CNN ResNet-50 FPN

model_urls = {
    
    'maskrcnn_resnet50_fpn_coco':
    'https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth',
}

Person Keypoint Detection 人体关键点

Keypoint R-CNN ResNet-50 FPN

model_urls = {
    
    # legacy model for BC reasons, see https://github.com/pytorch/vision/issues/1606
    'keypointrcnn_resnet50_fpn_coco_legacy':
    'https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth',
    'keypointrcnn_resnet50_fpn_coco':
    'https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth',
}

Video classification 视频分类

ResNet 3D

model_urls = {
    
	## ResNet 3D
    'r3d_18': 'https://download.pytorch.org/models/r3d_18-b3b3357e.pth'
}

ResNet Mixed Convolution

model_urls = {
    
    ## ResNet Mixed Convolution
    'mc3_18': 'https://download.pytorch.org/models/mc3_18-a90a0ba3.pth'
}

ResNet (2+1)D

model_urls = {
    
    ## ResNet (2+1)D
    'r2plus1d_18': 'https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth'
}

预训练模型加载调用:

Classification 分类

import torchvision.models as models
## 如果只需要网络结构，不需要用预训练模型的参数来初始化，pretrained=False：
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(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 = models.mobilenet_v2(pretrained=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=True)
mnasnet = models.mnasnet1_0(pretrained=True)

Semantic Segmentation 语义分割

fcn_resnet50 = torchvision.models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
fcn_resnet101 = torchvision.models.segmentation.fcn_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet50 = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet101 = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)

Object Detection 目标检测

fasterrcnn_resnet50_fpn = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=3)

Instance Segmentation 实例分割

maskrcnn_resnet50_fpn = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True)

Person Keypoint Detection 人体关键点

keypointrcnn_resnet50_fpn = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True)

Video classification 视频分类

r3d_18 = torchvision.models.video.r3d_18(pretrained=False, progress=True)

完整调用代码请移步一>官方预训练模型调用代码
完整理解代码请移步一>官方文档地址

持续更新中…

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