视觉神经网络模型优秀开源工作：PyTorch Image Models（timm）库
时间：2022-12-26 09:30:00
视觉神经网络模型优秀开源工作：PyTorch Image Models（timm）库
PyTorchImageModels，简称timm，是巨大的PyTorch包括一系列代码集：
- image models
- layers
- utilities
- optimizers
- schedulers
- data-loaders / augmentations
- training / validation scripts
旨在将各种SOTA模型集成在一起，复制ImageNet训练结果的能力。
PyTorch Image Models（timm） 优秀的图像分类 Python 它包含了大量的图像模型（Image Models）、Optimizers、Schedulers、Augmentations 等等.
除了使用torchvision.models除了预训练，还有一个常见的预训练模型库，叫做预训练timm，这个库来自加拿大温哥华Ross Wightman创建的。它提供了许多计算机视觉SOTA模型，可视为是torchvision扩展版本，模型的准确性也很高。在本章中，我们主要描述了图书馆预训练模型的使用。如果您感兴趣，可以参考以下链接（数据扩展、优化器等）。
- Github链接：https://github.com/rwightman/pytorch-image-models
- 官网链接：https://fastai.github.io/timmdocs/ https://rwightman.github.io/pytorch-image-models/
- 简略文档：https://rwightman.github.io/pytorch-image-models/
- 详细文档：https://fastai.github.io/timmdocs/
安装
PyTorch Image Models（timm） 优秀的图像分类 Python 它包含了大量的图像模型（Image Models）、Optimizers、Schedulers、Augmentations 等等.
timm 提供参考 training 和 validation 脚本，用于复现 ImageNet 训练结果；以及更多 官方文档 和 timmdocs project.
https://rwightman.github.io/pytorch-image-models/
https://fastai.github.io/timmdocs/
但，由于 timm 有很多功能，所以在定制使用时很难知道如何开始. 这里主要进行概述.
pip install timm==0.5.4 所有的开发和测试都是在 Linux x86-64系统上的 Conda Python 3.在环境中完成，特别是 Python 3.6和3.7 3.8 3.9
PyTorch 版本1.4、1.5. x、1.6、1.7. x 和1.该代码已用于测试。
import timm 加载预训练模型
我们只需要简单create_model如果我们需要使用我们的预训练模型，我们只需要添加参数pretrained=True即可
import timm m = timm.create_model('mobilenetv3_large_100', pretrained=True) m.eval() MobileNetV3( (conv_stem): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): Hardswish() (blocks): Sequential( (0): Sequential( (0): DepthwiseSeparableConv( (conv_dw): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False) (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (se): Identity() (conv_pw): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): Identity() ) ) (1): Sequential( (0): InvertedResidual( (conv_pw): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (conv_dw): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (se): Identity() (conv_pwl): Conv2d(64, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): InvertedResidual( (conv_pw): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (conv_dw): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False) (bn2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (se): Identity() (conv_pwl): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (2): Sequential( (0): InvertedResidual( (conv_pw): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (conv_dw): Conv2d(72, 72, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=72, bias=False) (bn2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (se): SqueezeExcite( (conv_reduce): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1)) (act1): ReLU(inplace=True) (conv_expand): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1)) (gate): Hardsigmoid() ) (conv_pwl): Conv2d(72, 40, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): InvertedResidual( (conv_pw): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (conv_dw): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False) (bn2): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (se): SqueezeExcite( (conv_reduce): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1)) (act1): ReLU(inplace=True) (conv_expand): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1)) (gate): Hardsigmoid() ) (conv_pwl): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): InvertedResidual( (conv_pw): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (conv_dw): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False) (bn2): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (se): SqueezeExcite( (conv_reduce): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1)) (act1): ReLU(inplace=True) (conv_expand): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1)) (gate): Hardsigmoid() ) (conv_pwl): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (3): Sequential( (0): InvertedResidual( (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): Hardswish() (conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False) (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): Hardswish() (se): Identity() (conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): InvertedResidual( (conv_pw): Conv2d(80, 200, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): Hardswish() (conv_dw): Conv2d(200, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=200, bias=False) (bn2): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): Hardswish() (se): Identity() (conv_pwl): Conv2d(200, 80, kernel_size=(1, 1), stride=(1, 1), bias=False) (< 
