用PyTorch微调预训练卷积神经网络

2023年4月8日上午3:29 • 卷积神经网络

转自:http://ruby.ctolib.com/article/wiki/77331

Fine-tune pretrained Convolutional Neural Networks with PyTorch.

Features

Gives access to the most popular CNN architectures pretrained on ImageNet.
Automatically replaces classifier on top of the network, which allows you to train a network with a dataset that has a different number of classes.
Allows you to use images with any resolution (and not only the resolution that was used for training the original model on ImageNet).
Allows adding a Dropout layer or a custom pooling layer.

Supported architectures and models

torchvision package:

ResNet (resnet18, resnet34, resnet50, resnet101, resnet152)
DenseNet (densenet121, densenet169, densenet201, densenet161)
Inception v3 (inception_v3)
VGG (vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn)
SqueezeNet (squeezenet1_0, squeezenet1_1)
AlexNet (alexnet)

Pretrained models for PyTorch package:

ResNeXt (resnext101_32x4d, resnext101_64x4d)
NASNet-A Large (nasnetalarge)
NASNet-A Mobile (nasnetamobile)
Inception-ResNet v2 (inceptionresnetv2)
Dual Path Networks (dpn68, dpn68b, dpn92, dpn98, dpn131, dpn107)
Inception v4 (inception_v4)
Xception (xception)
Squeeze-and-Excitation Networks (senet154, se_resnet50, se_resnet101, se_resnet152, se_resnext50_32x4d, se_resnext101_32x4d)

Requirements

Python 3.5+
PyTorch 0.3+

Installation

pip install cnn_finetune

Example usage:

Make a model with ImageNet weights for 10 classes

from cnn_finetune import make_model

model = make_model('resnet18', num_classes=10, pretrained=True)

Make a model with Dropout

model = make_model('nasnetalarge', num_classes=10, pretrained=True, dropout_p=0.5)

Make a model with Global Max Pooling instead of Global Average Pooling

import torch.nn as nn

model = make_model('inceptionresnetv2', num_classes=10, pretrained=True, pool=nn.AdaptiveMaxPool2d(1))

Make a VGG16 model that takes images of size 256x256 pixels

VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers.

model = make_model('vgg16', num_classes=10, pretrained=True, input_size=(256, 256))

Make a VGG16 model that takes images of size 256x256 pixels and uses a custom classifier

import torch.nn as nn

def make_classifier(in_features, num_classes):
    return nn.Sequential(
        nn.Linear(in_features, 4096),
        nn.ReLU(inplace=True),
        nn.Linear(4096, num_classes),
    )

model = make_model('vgg16', num_classes=10, pretrained=True, input_size=(256, 256), classifier_factory=make_classifier)

Show preprocessing that was used to train the original model on ImageNet

>> model = make_model('resnext101_64x4d', num_classes=10, pretrained=True)
>> print(model.original_model_info)
ModelInfo(input_space='RGB', input_size=[3, 224, 224], input_range=[0, 1], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
>> print(model.original_model_info.mean)
[0.485, 0.456, 0.406]