Metadata-Version: 2.1
Name: densenet-pytorch
Version: 0.2.0
Summary: Restore the official code 100% and improve it to make it easier to research and facilitate production.
Home-page: https://github.com/Lornatang/DenseNet-PyTorch
Author: Liu Changyu
Author-email: liuchangyu1111@gmail.com
License: Apache
Description: 
        # DenseNet-PyTorch
        
        `Note: Now supports the more efficient DenseNet-BC (DenseNet-Bottleneck-Compressed) networks. Using the DenseNet-BC-190-40 model, it obtaines state of the art performance on CIFAR-10 and CIFAR-100.`
        
        ### Update (Feb 18, 2020)
        
        The update is for ease of use and deployment.
        
         * [Example: Export to ONNX](#example-export-to-onnx)
         * [Example: Extract features](#example-feature-extraction)
         * [Example: Visual](#example-visual)
        
        It is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning:
        
        ```python
        from densenet_pytorch import DenseNet 
        model = DenseNet.from_pretrained('densenet121', num_classes=10)
        ```
        
        ### Update (January 15, 2020)
        
        This update allows you to use NVIDIA's Apex tool for accelerated training. By default choice `hybrid training precision` + `dynamic loss amplified` version, if you need to learn more and details about `apex` tools, please visit https://github.com/NVIDIA/apex.
        
        ### Update (January 6, 2020)
        
        This update adds a modular neural network, making it more flexible in use. It can be deployed to many common dataset classification tasks. Of course, it can also be used in your products.
        
        ### Overview
        This repository contains an op-for-op PyTorch reimplementation of [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf).
        
        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 DenseNet models 
         * Use DenseNet models for classification or feature extraction 
        
        _Upcoming features_: In the next few days, you will be able to:
         * Quickly finetune an DenseNet on your own dataset
         * Export DenseNet models for production
         
        ### Table of contents
        1. [About DenseNet](#about-densenet)
        2. [Installation](#installation)
        3. [Usage](#usage)
            * [Load pretrained models](#loading-pretrained-models)
            * [Example: Classify](#example-classification)
            * [Example: Extract features](#example-feature-extraction)
            * [Example: Export to ONNX](#example-export-to-onnx)
            * [Example: Visual](#example-visual)
        4. [Contributing](#contributing) 
        
        ### About DenseNet
        
        If you're new to DenseNets, here is an explanation straight from the official PyTorch implementation: 
        
        Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
        
        ### Installation
        
        Install from pypi:
        ```bash
        pip install densenet_pytorch
        ```
        
        Install from source:
        ```bash
        git clone https://github.com/Lornatang/DenseNet-PyTorch
        cd DenseNet-PyTorch
        pip install -e .
        ``` 
        
        ### Usage
        
        #### Loading pretrained models
        
        Load an densenet121 network:
        ```python
        from densenet_pytorch import DenseNet
        model = DenseNet.from_name("densenet121")
        ```
        
        Load a pretrained densenet11: 
        ```python
        from densenet_pytorch import DenseNet
        model = DenseNet.from_pretrained("densenet121")
        ```
        
        Their 1-crop error rates on imagenet dataset with pretrained models are listed below.
        
        | Model structure | Top-1 error | Top-5 error |
        | --------------- | ----------- | ----------- |
        |  densenet121    | 25.35       | 7.83        |
        |  densenet169    | 24.00       | 7.00        |
        |  densenet201    | 22.80       | 6.43        |
        |  densenet161    | 22.35       | 6.20        |
        
        #### Example: Classification
        
        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`. 
        
        All pre-trained models expect input images normalized in the same way,
        i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.
        The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`
        and `std = [0.229, 0.224, 0.225]`.
        
        Here's a sample execution.
        
        ```python
        import json
        
        import torch
        import torchvision.transforms as transforms
        from PIL import Image
        
        from densenet_pytorch import DenseNet 
        
        # Open image
        input_image = Image.open("img.jpg")
        
        # Preprocess image
        preprocess = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])
        input_tensor = preprocess(input_image)
        input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model
        
        # Load class names
        labels_map = json.load(open("labels_map.txt"))
        labels_map = [labels_map[str(i)] for i in range(1000)]
        
        # Classify with DenseNet121
        model = DenseNet.from_pretrained("densenet121")
        model.eval()
        
        # move the input and model to GPU for speed if available
        if torch.cuda.is_available():
            input_batch = input_batch.to("cuda")
            model.to("cuda")
        
        with torch.no_grad():
            logits = model(input_batch)
        preds = torch.topk(logits, k=5).indices.squeeze(0).tolist()
        
        print("-----")
        for idx in preds:
            label = labels_map[idx]
            prob = torch.softmax(logits, dim=1)[0, idx].item()
            print(f"{label:<75} ({prob * 100:.2f}%)")
        ```
        
        #### Example: Feature Extraction 
        
        You can easily extract features with `model.extract_features`:
        ```python
        import torch
        from densenet_pytorch import DenseNet 
        model = DenseNet.from_pretrained('densenet121')
        
        # ... image preprocessing as in the classification example ...
        inputs = torch.randn(1, 3, 224, 224)
        print(inputs.shape) # torch.Size([1, 3, 224, 224])
        
        features = model.extract_features(inputs)
        print(features.shape) # torch.Size([1, 1024, 7, 7])
        ```
        
        #### Example: Export to ONNX  
        
        Exporting to ONNX for deploying to production is now simple: 
        ```python
        import torch 
        from densenet_pytorch import DenseNet 
        
        model = DenseNet.from_pretrained('densenet121')
        dummy_input = torch.randn(16, 3, 224, 224)
        
        torch.onnx.export(model, dummy_input, "demo.onnx", verbose=True)
        ```
        
        #### Example: Visual
        
        ```text
        cd $REPO$/framework
        sh start.sh
        ```
        
        Then open the browser and type in the browser address [http://127.0.0.1:10003/](http://127.0.0.1:10003/).
        
        Enjoy it.
        
        #### ImageNet
        
        See `examples/imagenet` for details about evaluating on ImageNet.
        
        For more datasets result. Please see `research/README.md`.
        
        ### 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! 
        
        
        ### Credit
        
        #### Densely Connected Convolutional Networks
        
        *Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger*
        
        ##### Abstract
        
        Recent work has shown that convolutional networks can be substantially deeper, more accurate, 
        and efficient to train if they contain shorter connections between layers close to the input
         and those close to the output. In this paper, we embrace this observation and introduce the 
         Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a 
         feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections
          - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. 
          For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps 
          are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they 
          alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, 
          and substantially reduce the number of parameters. We evaluate our proposed architecture on four 
          highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). 
          DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring 
          less computation to achieve high performance. Code and pre-trained models are available at this [https URL](https://github.com/liuzhuang13/DenseNet) .
        
        [paper](http://arxiv.org/pdf/1608.06993v5) [code](https://github.com/liuzhuang13/DenseNet)
        
        ```text
        @article{DenseNet,
        title:{Densely Connected Convolutional Networks},
        author:{Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger},
        journal={cvpr},
        year={2016}
        }
        ```
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
