离线或在线加载pytorch、mmdetection预训练模型vgg、resnet、alexnet等

pytorch预训练模型包含多个经典网络，比如resnet系列、vgg系列和alexnet等，预训练模型可以提高网络提取特征的能力，提升训练模型的性能。下面介绍一下加载预训练模型的两种方式：
第一种是在线的方法，即在代码中采用在线加载模式，

import torch
from torchvision import modelsmodel = models.vgg16(pretrained=True)

这样当代码运行到model时，就会根据pytorch中模型的定义找到该模型，并通过url加载预训练模型放在./cache/checkpoints中，需要时就会加载模型参数。

另一种方法是离线加载方式，这需要提前下载好预训练模型，预训练模型的下载可以进入该网站：
https://github.com/pytorch/vision/tree/master/torchvision/models
里面有多个网络的定义，进入相应网络的py文件即可找到加载与效率模型的网址：

比如vgg网络的vgg.py文件打开后可以看到

import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
from typing import Union, List, Dict, Any, cast__all__ = ['VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn','vgg19_bn', 'vgg19',
]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',
}class VGG(nn.Module):def __init__(self,features: nn.Module,num_classes: int = 1000,init_weights: bool = True) -> None:super(VGG, self).__init__()self.features = featuresself.avgpool = nn.AdaptiveAvgPool2d((7, 7))self.classifier = nn.Sequential(nn.Linear(512 * 7 * 7, 4096),nn.ReLU(True),nn.Dropout(),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(),nn.Linear(4096, num_classes),)

model_urls中注明了多种vgg模型的下载地址，具体下载方法是直接复制网址打开，比如vgg16，复制网址https://download.pytorch.org/models/vgg16-397923af.pth在浏览器中打开，就会自动打开下载界面进行下载，下载好之后放在你当前要运行的文件夹下，其中加载的程序要做一些修改，如下：

....
model = models.vgg16(pretrained=False)#在线模式的True改为False
pre = torch.load('vgg16-397923af.pth')#进行加载
model.load_state_dict(pre)
....

以上就是在线和离线加载预训练模型的两种方法。

使用mmdetection训练时加载预训练模型

**
mmdetection有些预训练模型能在上述pytorch网站中找到，但有些就找不到，看名称很接近，我下载过两个这样的pth模型文件，一个名称一致，一个名称不够细节，结果两个文件大小差了很多，显然不是一样的，如果随便用，就可能报错。
于是查找了mmdetection对应的预训练模型加载方法，结果是所有需求的预训练模型都已经总结在mmcv里面了，有直接的下载网址，可直接拉取。
拉取网址：
https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json

下面是打开的mmcv-master/mmcv/model_zoo/open-mmlab.json的具体内容

{"vgg16_caffe": "https://download.openmmlab.com/pretrain/third_party/vgg16_caffe-292e1171.pth","detectron/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth","detectron2/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth","detectron/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth","detectron2/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_msra-6cc46731.pth","detectron2/resnext101_32x8d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth","resnext50_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext50-32x4d-0ab1a123.pth","resnext101_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d-a5af3160.pth","resnext101_64x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_64x4d-ee2c6f71.pth","contrib/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_thangvubk-ad1730dd.pth","detectron/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn-9186a21c.pth","detectron/resnet101_gn": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn-cac0ab98.pth","jhu/resnet50_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_ws-15beedd8.pth","jhu/resnet101_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn_ws-3e3c308c.pth","jhu/resnext50_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn_ws-0d87ac85.pth","jhu/resnext101_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn_ws-34ac1a9e.pth","jhu/resnext50_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn-c7e8b754.pth","jhu/resnext101_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn-ac3bb84e.pth","msra/hrnetv2_w18_small": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18_small-b5a04e21.pth","msra/hrnetv2_w18": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18-00eb2006.pth","msra/hrnetv2_w32": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w32-dc9eeb4f.pth","msra/hrnetv2_w40": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w40-ed0b031c.pth","msra/hrnetv2_w48": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w48-d2186c55.pth","bninception_caffe": "https://download.openmmlab.com/pretrain/third_party/bn_inception_caffe-ed2e8665.pth","kin400/i3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/i3d_r50_f32s2_k400-2c57e077.pth","kin400/nl3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/nl3d_r50_f32s2_k400-fa7e7caa.pth","res2net101_v1d_26w_4s": "https://download.openmmlab.com/pretrain/third_party/res2net101_v1d_26w_4s_mmdetv2-f0a600f9.pth","regnetx_400mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_400mf-a5b10d96.pth","regnetx_800mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth","regnetx_1.6gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_1.6gf-5791c176.pth","regnetx_3.2gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth","regnetx_4.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_4.0gf-a88f671e.pth","regnetx_6.4gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_6.4gf-006af45d.pth","regnetx_8.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_8.0gf-3c68abe7.pth","regnetx_12gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_12gf-4c2a3350.pth","resnet50_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet50_v1c-2cccc1ad.pth","resnet101_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet101_v1c-e67eebb6.pth","mmedit/vgg16": "https://download.openmmlab.com/mmediting/third_party/vgg_state_dict.pth","mmedit/res34_en_nomixup": "https://download.openmmlab.com/mmediting/third_party/model_best_resnet34_En_nomixup.pth","mmedit/mobilenet_v2": "https://download.openmmlab.com/mmediting/third_party/mobilenet_v2.pth","resnest50": "https://download.openmmlab.com/pretrain/third_party/resnest50_d2-7497a55b.pth","resnest101": "https://download.openmmlab.com/pretrain/third_party/resnest101_d2-f3b931b2.pth","resnest200": "https://download.openmmlab.com/pretrain/third_party/resnest200_d2-ca88e41f.pth","darknet53": "https://download.openmmlab.com/pretrain/third_party/darknet53-a628ea1b.pth"
}

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离线或在线加载pytorch、mmdetection预训练模型vgg、resnet、alexnet等

使用mmdetection训练时加载预训练模型

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