EfficientNet-PyTorch：EfficientNet的PyTorch实现

# EfficientNet PyTorch ### Quickstart Install with `pip install efficientnet_pytorch` and load a pretrained EfficientNet with: ```python from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained('efficientnet-b0') ``` ### Updates #### Update (Aug 25, 2020) This update adds: * A new `include_top` (default: `True`) option ([#208](https://github.com/lukemelas/EfficientNet-PyTorch/pull/208)) * Continuous testing with [sotabench](https://sotabench.com/) * Code quality improvements and fixes ([#215](https://github.com/lukemelas/EfficientNet-PyTorch/pull/215) [#223](https://github.com/lukemelas/EfficientNet-PyTorch/pull/223)) #### Update (May 14, 2020) This update adds comprehensive comments and documentation (thanks to @workingcoder). #### Update (January 23, 2020) This update adds a new category of pre-trained model based on adversarial training, called _advprop_. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. As a result, by default, advprop models are not used. To load a model with advprop, use: ```python model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True) ``` There is also a new, large `efficientnet-b8` pretrained model that is only available in advprop form. When using these models, replace ImageNet preprocessing code as follows: ```python if advprop: # for models using advprop pretrained weights normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0) else: normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ``` This update also addresses multiple other issues ([#115](https://github.com/lukemelas/EfficientNet-PyTorch/issues/115), [#128](https://github.com/lukemelas/EfficientNet-PyTorch/issues/128)). #### Update (October 15, 2019) This update allows you to choose whether to use a memory-efficient Swish activation. The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. For this purpose, we have also included a standard (export-friendly) swish activation function. To switch to the export-friendly version, simply call `model.set_swish(memory_efficient=False)` after loading your desired model. This update addresses issues [#88](https://github.com/lukemelas/EfficientNet-PyTorch/pull/88) and [#89](https://github.com/lukemelas/EfficientNet-PyTorch/pull/89). #### Update (October 12, 2019) This update makes the Swish activation function more memory-efficient. It also addresses pull requests [#72](https://github.com/lukemelas/EfficientNet-PyTorch/pull/72), [#73](https://github.com/lukemelas/EfficientNet-PyTorch/pull/73), [#85](https://github.com/lukemelas/EfficientNet-PyTorch/pull/85), and [#86](https://github.com/lukemelas/EfficientNet-PyTorch/pull/86). Thanks to the authors of all the pull requests! #### Update (July 31, 2019) _Upgrade the pip package with_ `pip install --upgrade efficientnet-pytorch` The B6 and B7 models are now available. Additionally, _all_ pretrained models have been updated to use AutoAugment preprocessing, which translates to better performance across the board. Usage is the same as before: ```python from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained('efficientnet-b7') ``` #### Update (June 29, 2019) This update adds easy model exporting ([#20](https://github.com/lukemelas/EfficientNet-PyTorch/issues/20)) and feature extraction ([#38](https://github.com/lukemelas/EfficientNet-PyTorch/issues/38)). * [Example: Export to ONNX](#example-export) * [Example: Extract features](#example-feature-extraction) * Also: fixed a CUDA/CPU bug ([#32](https://github.com/lukemelas/EfficientNet-PyTorch/issues/32)) It is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning: ```python model = EfficientNet.from_pretrained('efficientnet-b1', num_classes=23) ``` #### Update (June 23, 2019) The B4 and B5 models are now available. Their usage is identical to the other models: ```python from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained('efficientnet-b4') ``` ### Overview This repository contains an op-for-op PyTorch reimplementation of [EfficientNet](https://arxiv.org/abs/1905.11946), along with pre-trained models and examples. The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented. At the moment, you can easily: * Load pretrained EfficientNet models * Use EfficientNet models for classification or feature extraction * Evaluate EfficientNet models on ImageNet or your own images _Upcoming features_: In the next few days, you will be able to: * Train new models from scratch on ImageNet with a simple command * Quickly finetune an EfficientNet on your own dataset * Export EfficientNet models for production ### Table of contents 1. [About EfficientNet](#about-efficientnet) 2. [About EfficientNet-PyTorch](#about-efficientnet-pytorch) 3. [Installation](#installation) 4. [Usage](#usage) * [Load pretrained models](#loading-pretrained-models) * [Example: Classify](#example-classification) * [Example: Extract features](#example-feature-extraction) * [Example: Export to ONNX](#example-export) 6. [Contributing](#contributing) ### About EfficientNet If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation: EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models. We develop EfficientNets based on AutoML and Compound Scaling. In particular, we first use [AutoML Mobile framework](https://ai.googleblog.com/2018/08/mnasnet-towards-automating-design-of.html) to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to B7. <table border="0"> <tr> <td> <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" width="100%" /> </td> <td> <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png", width="90%" /> </td> </tr> </table> EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency: * In high-accuracy regime, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS, being 8.4x smaller and 6.1x faster on CPU inference than previous best [Gpipe](https://arxiv.org/abs/1811.06965). * In middle-accuracy regime, our EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than [ResNet-152](https://arxiv.org/abs/1512.03385), with similar ImageNet accuracy. * Compared with the widely used [ResNet-50](https://arxiv.org/abs/1512.03385), our EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint. ### About EfficientNet PyTorch EfficientNet PyTorch is a PyTorch re-implementation of EfficientNet. It is consistent with the [original TensorFlow implementation](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet), such that it is easy to load weights from a TensorFlow checkpoint. At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible. If you have any feature requests or questions, feel free to leave them as GitHub issues! ### Installation Install via pip: ```bash pip install efficientnet_pytorch ``` Or install from source: ```bash git clone https://github.com/lukemelas/EfficientNet-PyTorch cd EfficientNet-Pytorch pip install -e . ``` ##