Metadata-Version: 2.1
Name: efficientnet-pytorch
Version: 0.6.2
Summary: EfficientNet implemented in PyTorch.
Home-page: https://github.com/lukemelas/efficientnet_pytorch
Author: Luke
Author-email: lmelaskyriazi@college.harvard.edu
License: Apache
Description: 
        # EfficientNet PyTorch
        
        
        _IMPORTANT NOTE_: In the latest update, I switched hosting providers for the pretrained models, as the previous models were becoming extremely expensive to host. This _will_ break old versions of the library. I apologize, but I cannot afford to keep serving the models on the old provider. Everything should work properly if you update the library: 
        ```
        pip install --upgrade efficientnet-pytorch
        ```
        
        ### 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:
        ```
        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:
        ```
        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 .
        ``` 
        
        ### Usage
        
        #### Loading pretrained models
        
        Load an EfficientNet:  
        ```python
        from efficientnet_pytorch import EfficientNet
        model = EfficientNet.from_name('efficientnet-b0')
        ```
        
        Load a pretrained EfficientNet: 
        ```python
        from efficientnet_pytorch import EfficientNet
        model = EfficientNet.from_pretrained('efficientnet-b0')
        ```
        
        Note that pretrained models have only been released for `N=0,1,2,3,4,5` at the current time, so `.from_pretrained` only supports `'efficientnet-b{N}'` for `N=0,1,2,3,4,5`. 
        
        Details about the models are below: 
        
        |    *Name*         |*# Params*|*Top-1 Acc.*|*Pretrained?*|
        |:-----------------:|:--------:|:----------:|:-----------:|
        | `efficientnet-b0` |   5.3M   |    76.3    |      ✓      |
        | `efficientnet-b1` |   7.8M   |    78.8    |      ✓      |
        | `efficientnet-b2` |   9.2M   |    79.8    |      ✓      |
        | `efficientnet-b3` |    12M   |    81.1    |      ✓      |
        | `efficientnet-b4` |    19M   |    82.6    |      ✓      |
        | `efficientnet-b5` |    30M   |    83.3    |      ✓      |
        | `efficientnet-b6` |    43M   |    84.0    |      ✓      |
        | `efficientnet-b7` |    66M   |    84.4    |      ✓      |
        
        
        #### Example: Classification
        
        Below is a simple, complete example. It may also be found as a jupyter notebook in `examples/simple` or as a [Colab Notebook](https://colab.research.google.com/drive/1Jw28xZ1NJq4Cja4jLe6tJ6_F5lCzElb4).
        
        We assume that in your current directory, there is a `img.jpg` file and a `labels_map.txt` file (ImageNet class names). These are both included in `examples/simple`. 
        
        ```python
        import json
        from PIL import Image
        import torch
        from torchvision import transforms
        
        from efficientnet_pytorch import EfficientNet
        model = EfficientNet.from_pretrained('efficientnet-b0')
        
        # Preprocess image
        tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])
        img = tfms(Image.open('img.jpg')).unsqueeze(0)
        print(img.shape) # torch.Size([1, 3, 224, 224])
        
        # Load ImageNet class names
        labels_map = json.load(open('labels_map.txt'))
        labels_map = [labels_map[str(i)] for i in range(1000)]
        
        # Classify
        model.eval()
        with torch.no_grad():
            outputs = model(img)
        
        # Print predictions
        print('-----')
        for idx in torch.topk(outputs, k=5).indices.squeeze(0).tolist():
            prob = torch.softmax(outputs, dim=1)[0, idx].item()
            print('{label:<75} ({p:.2f}%)'.format(label=labels_map[idx], p=prob*100))
        ```
        
        #### Example: Feature Extraction 
        
        You can easily extract features with `model.extract_features`:
        ```python
        from efficientnet_pytorch import EfficientNet
        model = EfficientNet.from_pretrained('efficientnet-b0')
        
        # ... image preprocessing as in the classification example ...
        print(img.shape) # torch.Size([1, 3, 224, 224])
        
        features = model.extract_features(img)
        print(features.shape) # torch.Size([1, 1280, 7, 7])
        ```
        
        #### Example: Export to ONNX  
        
        Exporting to ONNX for deploying to production is now simple: 
        ```python
        import torch 
        from efficientnet_pytorch import EfficientNet
        
        model = EfficientNet.from_pretrained('efficientnet-b1')
        dummy_input = torch.randn(10, 3, 240, 240)
        
        torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True)
        ``` 
        
        [Here](https://colab.research.google.com/drive/1rOAEXeXHaA8uo3aG2YcFDHItlRJMV0VP) is a Colab example. 
        
        
        #### ImageNet
        
        See `examples/imagenet` for details about evaluating on ImageNet.
        
        ### Contributing
        
        If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.   
        
        I look forward to seeing what the community does with these models! 
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
