├── .gitignore ├── LICENSE ├── README.md ├── dcgan ├── README.md ├── main.py └── requirements.txt ├── docs ├── _config.yml ├── _modules │ ├── index.html │ └── tensorboard │ │ ├── embedding.html │ │ └── writer.html ├── _sources │ ├── index.rst.txt │ └── tensorboard.rst.txt ├── _static │ ├── ajax-loader.gif │ ├── basic.css │ ├── comment-bright.png │ ├── comment-close.png │ ├── comment.png │ ├── css │ │ ├── badge_only.css │ │ └── theme.css │ ├── doctools.js │ ├── down-pressed.png │ ├── down.png │ ├── file.png │ ├── fonts │ │ ├── Inconsolata-Bold.ttf │ │ ├── Inconsolata-Regular.ttf │ │ ├── Lato-Bold.ttf │ │ ├── Lato-Regular.ttf │ │ ├── RobotoSlab-Bold.ttf │ │ ├── RobotoSlab-Regular.ttf │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.svg │ │ ├── fontawesome-webfont.ttf │ │ └── fontawesome-webfont.woff │ ├── jquery-3.1.0.js │ ├── jquery.js │ ├── js │ │ ├── modernizr.min.js │ │ └── theme.js │ ├── minus.png │ ├── plus.png │ ├── pygments.css │ ├── searchtools.js │ ├── underscore-1.3.1.js │ ├── underscore.js │ ├── up-pressed.png │ ├── up.png │ └── websupport.js ├── genindex.html ├── html │ ├── .buildinfo │ ├── .nojekyll │ ├── _modules │ │ ├── index.html │ │ └── tensorboard │ │ │ ├── embedding.html │ │ │ └── writer.html │ ├── _sources │ │ ├── index.rst.txt │ │ └── tensorboard.rst.txt │ ├── _static │ │ ├── ajax-loader.gif │ │ ├── basic.css │ │ ├── comment-bright.png │ │ ├── comment-close.png │ │ ├── comment.png │ │ ├── css │ │ │ ├── badge_only.css │ │ │ └── theme.css │ │ ├── doctools.js │ │ ├── down-pressed.png │ │ ├── down.png │ │ ├── file.png │ │ ├── fonts │ │ │ ├── Inconsolata-Bold.ttf │ │ │ ├── Inconsolata-Regular.ttf │ │ │ ├── Lato-Bold.ttf │ │ │ ├── Lato-Regular.ttf │ │ │ ├── RobotoSlab-Bold.ttf │ │ │ ├── RobotoSlab-Regular.ttf │ │ │ ├── fontawesome-webfont.eot │ │ │ ├── fontawesome-webfont.svg │ │ │ ├── fontawesome-webfont.ttf │ │ │ └── fontawesome-webfont.woff │ │ ├── jquery-3.1.0.js │ │ ├── jquery.js │ │ ├── js │ │ │ ├── modernizr.min.js │ │ │ └── theme.js │ │ ├── minus.png │ │ ├── plus.png │ │ ├── pygments.css │ │ ├── searchtools.js │ │ ├── underscore-1.3.1.js │ │ ├── underscore.js │ │ ├── up-pressed.png │ │ ├── up.png │ │ └── websupport.js │ ├── genindex.html │ ├── index.html │ ├── objects.inv │ ├── py-modindex.html │ ├── search.html │ ├── searchindex.js │ └── tensorboard.html ├── index.html ├── objects.inv ├── py-modindex.html ├── search.html ├── searchindex.js └── tensorboard.html ├── fast_neural_style ├── README.md ├── download_saved_models.sh ├── images │ ├── content-images │ │ └── amber.jpg │ ├── output-images │ │ ├── amber-candy.jpg │ │ ├── amber-mosaic.jpg │ │ ├── amber-rain-princess.jpg │ │ └── amber-udnie.jpg │ └── style-images │ │ ├── candy.jpg │ │ ├── mosaic.jpg │ │ ├── rain-princess-cropped.jpg │ │ ├── rain-princess.jpg │ │ └── udnie.jpg └── neural_style │ ├── __init__.py │ ├── neural_style.py │ ├── transformer_net.py │ ├── utils.py │ └── vgg.py ├── imagenet ├── README.md ├── main.py └── requirements.txt ├── index.html ├── mnist ├── README.md ├── main.py └── requirements.txt ├── mnist_hogwild ├── main.py ├── requirements.txt └── train.py ├── regression ├── README.md └── main.py ├── reinforcement_learning ├── README.md ├── actor_critic.py ├── reinforce.py └── requirements.txt ├── snli ├── model.py ├── requirements.txt ├── train.py └── util.py ├── super_resolution ├── README.md ├── data.py ├── dataset.py ├── main.py ├── model.py └── super_resolve.py ├── time_sequence_prediction ├── README.md ├── generate_sine_wave.py └── train.py ├── vae ├── README.md ├── main.py └── requirements.txt └── word_language_model ├── README.md ├── data.py ├── data └── penn │ ├── test.txt │ ├── train.txt │ └── valid.txt ├── generate.py ├── main.py ├── model.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | dcgan/data 2 | data 3 | *.pyc 4 | OpenNMT/data 5 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2017, 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | * Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyTorch Examples with tensorboard 2 | 3 | A repository showcasing examples of using [PyTorch](https://github.com/pytorch/pytorch) with tensorboard(https://github.com/lanpa/tensorboardX) 4 | 5 | - [X] MNIST Convnets 6 | - [ ] Word level Language Modeling using LSTM RNNs 7 | - [X] Training Imagenet Classifiers with Residual Networks 8 | - [X] Generative Adversarial Networks (DCGAN) 9 | - [ ] Variational Auto-Encoders 10 | - [X] Superresolution using an efficient sub-pixel convolutional neural network 11 | - [ ] Hogwild training of shared ConvNets across multiple processes on MNIST 12 | - [ ] Training a CartPole to balance in OpenAI Gym with actor-critic 13 | - [ ] Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext 14 | - [ ] Time sequence prediction - create an LSTM to learn Sine waves 15 | 16 | Additionally, a list of good examples hosted in their own repositories: 17 | 18 | - [Neural Machine Translation using sequence-to-sequence RNN with attention (OpenNMT)](https://github.com/OpenNMT/OpenNMT-py) 19 | -------------------------------------------------------------------------------- /dcgan/README.md: -------------------------------------------------------------------------------- 1 | # Deep Convolution Generative Adversarial Networks 2 | 3 | This example implements the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) 4 | 5 | The implementation is very close to the Torch implementation [dcgan.torch](https://github.com/soumith/dcgan.torch) 6 | 7 | After every 100 training iterations, the files `real_samples.png` and `fake_samples.png` are written to disk 8 | with the samples from the generative model. 9 | 10 | After every epoch, models are saved to: `netG_epoch_%d.pth` and `netD_epoch_%d.pth` 11 | 12 | ## Downloading the dataset 13 | You can download the LSUN dataset by cloning [this repo](https://github.com/fyu/lsun) and running 14 | ``` 15 | python download.py -c bedroom 16 | ``` 17 | 18 | ## Usage 19 | ``` 20 | usage: main.py [-h] --dataset DATASET --dataroot DATAROOT [--workers WORKERS] 21 | [--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--nz NZ] 22 | [--ngf NGF] [--ndf NDF] [--niter NITER] [--lr LR] 23 | [--beta1 BETA1] [--cuda] [--ngpu NGPU] [--netG NETG] 24 | [--netD NETD] 25 | 26 | optional arguments: 27 | -h, --help show this help message and exit 28 | --dataset DATASET cifar10 | lsun | imagenet | folder | lfw 29 | --dataroot DATAROOT path to dataset 30 | --workers WORKERS number of data loading workers 31 | --batchSize BATCHSIZE 32 | input batch size 33 | --imageSize IMAGESIZE 34 | the height / width of the input image to network 35 | --nz NZ size of the latent z vector 36 | --ngf NGF 37 | --ndf NDF 38 | --niter NITER number of epochs to train for 39 | --lr LR learning rate, default=0.0002 40 | --beta1 BETA1 beta1 for adam. default=0.5 41 | --cuda enables cuda 42 | --ngpu NGPU number of GPUs to use 43 | --netG NETG path to netG (to continue training) 44 | --netD NETD path to netD (to continue training) 45 | ``` 46 | -------------------------------------------------------------------------------- /dcgan/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchvision 3 | lmdb 4 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /docs/_modules/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Overview: module code — tensorboard-pytorch documentation 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 |
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154 | 155 | 156 | 157 | 158 | 159 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 187 | 188 | 189 | 190 | -------------------------------------------------------------------------------- /docs/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. tensorboard-pytorch documentation master file, created by 2 | sphinx-quickstart on Wed Aug 9 01:38:01 2017. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to tensorboard-pytorch's documentation! 7 | =============================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 2 11 | :caption: Contents: 12 | 13 | tensorboard 14 | 15 | 16 | Indices and tables 17 | ================== 18 | 19 | * :ref:`genindex` 20 | * :ref:`modindex` 21 | * :ref:`search` 22 | -------------------------------------------------------------------------------- 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| Index — tensorboard-pytorch documentation 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 |
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194 | © Copyright 2017, tensorboard-pytorch Contributors. 195 | 196 |

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140 | © Copyright 2017, tensorboard-pytorch Contributors. 141 | 142 |

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154 | 155 | 156 | 157 | 158 | 159 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 187 | 188 | 189 | 190 | -------------------------------------------------------------------------------- /docs/html/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. tensorboard-pytorch documentation master file, created by 2 | sphinx-quickstart on Wed Aug 9 01:38:01 2017. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to tensorboard-pytorch's documentation! 7 | =============================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 2 11 | :caption: Contents: 12 | 13 | tensorboard 14 | 15 | 16 | Indices and tables 17 | ================== 18 | 19 | * :ref:`genindex` 20 | * :ref:`modindex` 21 | * :ref:`search` 22 | -------------------------------------------------------------------------------- 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11 | 12 | ## Requirements 13 | The program is written in Python, and uses [pytorch](http://pytorch.org/), [scipy](https://www.scipy.org). A GPU is not necessary, but can provide a significant speed up especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models. 14 | 15 | ## Usage 16 | Stylize image 17 | ``` 18 | python neural_style/neural_style.py eval --content-image --model --output-image --cuda 0 19 | ``` 20 | * `--content-image`: path to content image you want to stylize. 21 | * `--model`: saved model to be used for stylizing the image (eg: `mosaic.pth`) 22 | * `--output-image`: path for saving the output image. 23 | * `--content-scale`: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image) 24 | * `--cuda`: set it to 1 for running on GPU, 0 for CPU. 25 | 26 | Train model 27 | ```bash 28 | python neural_style/neural_style.py train --dataset --style-image --save-model-dir --epochs 2 --cuda 1 29 | ``` 30 | 31 | There are several command line arguments, the important ones are listed below 32 | * `--dataset`: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] [(download)](http://mscoco.org/dataset/#download). 33 | * `--style-image`: path to style-image. 34 | * `--save-model-dir`: path to folder where trained model will be saved. 35 | * `--cuda`: set it to 1 for running on GPU, 0 for CPU. 36 | 37 | Refer to ``neural_style/neural_style.py`` for other command line arguments. For training new models you might have to tune the values of `--content-weight` and `--style-weight`. The mosaic style model shown above was trained with `--content-weight 1e5` and `--style-weight 1e10`. The remaining 3 models were also trained with similar order of weight parameters with slight variation in the `--style-weight` (`5e10` or `1e11`). 38 | 39 | ## Models 40 | 41 | Models for the examples shown below can be downloaded from [here](https://www.dropbox.com/s/lrvwfehqdcxoza8/saved_models.zip?dl=0) or by running the script ``download_saved_models.sh``. 42 | 43 |
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58 | -------------------------------------------------------------------------------- /fast_neural_style/download_saved_models.sh: -------------------------------------------------------------------------------- 1 | wget https://www.dropbox.com/s/lrvwfehqdcxoza8/saved_models.zip?dl=1 2 | unzip saved_models.zip?dl=1 3 | -------------------------------------------------------------------------------- /fast_neural_style/images/content-images/amber.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/content-images/amber.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/output-images/amber-candy.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/output-images/amber-candy.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/output-images/amber-mosaic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/output-images/amber-mosaic.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/output-images/amber-rain-princess.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/output-images/amber-rain-princess.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/output-images/amber-udnie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/output-images/amber-udnie.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/style-images/candy.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/style-images/candy.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/style-images/mosaic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/style-images/mosaic.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/style-images/rain-princess-cropped.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/style-images/rain-princess-cropped.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/style-images/rain-princess.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/style-images/rain-princess.jpg -------------------------------------------------------------------------------- /fast_neural_style/images/style-images/udnie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/images/style-images/udnie.jpg -------------------------------------------------------------------------------- /fast_neural_style/neural_style/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lanpa/tensorboard-pytorch-examples/92c56fbcf44628930a8ae8b896133dff6a1fb857/fast_neural_style/neural_style/__init__.py -------------------------------------------------------------------------------- /fast_neural_style/neural_style/transformer_net.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class TransformerNet(torch.nn.Module): 5 | def __init__(self): 6 | super(TransformerNet, self).__init__() 7 | # Initial convolution layers 8 | self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1) 9 | self.in1 = torch.nn.InstanceNorm2d(32, affine=True) 10 | self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2) 11 | self.in2 = torch.nn.InstanceNorm2d(64, affine=True) 12 | self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2) 13 | self.in3 = torch.nn.InstanceNorm2d(128, affine=True) 14 | # Residual layers 15 | self.res1 = ResidualBlock(128) 16 | self.res2 = ResidualBlock(128) 17 | self.res3 = ResidualBlock(128) 18 | self.res4 = ResidualBlock(128) 19 | self.res5 = ResidualBlock(128) 20 | # Upsampling Layers 21 | self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2) 22 | self.in4 = torch.nn.InstanceNorm2d(64, affine=True) 23 | self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2) 24 | self.in5 = torch.nn.InstanceNorm2d(32, affine=True) 25 | self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1) 26 | # Non-linearities 27 | self.relu = torch.nn.ReLU() 28 | 29 | def forward(self, X): 30 | y = self.relu(self.in1(self.conv1(X))) 31 | y = self.relu(self.in2(self.conv2(y))) 32 | y = self.relu(self.in3(self.conv3(y))) 33 | y = self.res1(y) 34 | y = self.res2(y) 35 | y = self.res3(y) 36 | y = self.res4(y) 37 | y = self.res5(y) 38 | y = self.relu(self.in4(self.deconv1(y))) 39 | y = self.relu(self.in5(self.deconv2(y))) 40 | y = self.deconv3(y) 41 | return y 42 | 43 | 44 | class ConvLayer(torch.nn.Module): 45 | def __init__(self, in_channels, out_channels, kernel_size, stride): 46 | super(ConvLayer, self).__init__() 47 | reflection_padding = kernel_size // 2 48 | self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding) 49 | self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride) 50 | 51 | def forward(self, x): 52 | out = self.reflection_pad(x) 53 | out = self.conv2d(out) 54 | return out 55 | 56 | 57 | class ResidualBlock(torch.nn.Module): 58 | """ResidualBlock 59 | introduced in: https://arxiv.org/abs/1512.03385 60 | recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html 61 | """ 62 | 63 | def __init__(self, channels): 64 | super(ResidualBlock, self).__init__() 65 | self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1) 66 | self.in1 = torch.nn.InstanceNorm2d(channels, affine=True) 67 | self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1) 68 | self.in2 = torch.nn.InstanceNorm2d(channels, affine=True) 69 | self.relu = torch.nn.ReLU() 70 | 71 | def forward(self, x): 72 | residual = x 73 | out = self.relu(self.in1(self.conv1(x))) 74 | out = self.in2(self.conv2(out)) 75 | out = out + residual 76 | return out 77 | 78 | 79 | class UpsampleConvLayer(torch.nn.Module): 80 | """UpsampleConvLayer 81 | Upsamples the input and then does a convolution. This method gives better results 82 | compared to ConvTranspose2d. 83 | ref: http://distill.pub/2016/deconv-checkerboard/ 84 | """ 85 | 86 | def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None): 87 | super(UpsampleConvLayer, self).__init__() 88 | self.upsample = upsample 89 | if upsample: 90 | self.upsample_layer = torch.nn.UpsamplingNearest2d(scale_factor=upsample) 91 | reflection_padding = kernel_size // 2 92 | self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding) 93 | self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride) 94 | 95 | def forward(self, x): 96 | x_in = x 97 | if self.upsample: 98 | x_in = self.upsample_layer(x_in) 99 | out = self.reflection_pad(x_in) 100 | out = self.conv2d(out) 101 | return out 102 | -------------------------------------------------------------------------------- /fast_neural_style/neural_style/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from PIL import Image 3 | from torch.autograd import Variable 4 | 5 | 6 | def load_image(filename, size=None, scale=None): 7 | img = Image.open(filename) 8 | if size is not None: 9 | img = img.resize((size, size), Image.ANTIALIAS) 10 | elif scale is not None: 11 | img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS) 12 | return img 13 | 14 | 15 | def save_image(filename, data): 16 | img = data.clone().clamp(0, 255).numpy() 17 | img = img.transpose(1, 2, 0).astype("uint8") 18 | img = Image.fromarray(img) 19 | img.save(filename) 20 | 21 | 22 | def gram_matrix(y): 23 | (b, ch, h, w) = y.size() 24 | features = y.view(b, ch, w * h) 25 | features_t = features.transpose(1, 2) 26 | gram = features.bmm(features_t) / (ch * h * w) 27 | return gram 28 | 29 | 30 | def normalize_batch(batch): 31 | # normalize using imagenet mean and std 32 | mean = batch.data.new(batch.data.size()) 33 | std = batch.data.new(batch.data.size()) 34 | mean[:, 0, :, :] = 0.485 35 | mean[:, 1, :, :] = 0.456 36 | mean[:, 2, :, :] = 0.406 37 | std[:, 0, :, :] = 0.229 38 | std[:, 1, :, :] = 0.224 39 | std[:, 2, :, :] = 0.225 40 | batch = torch.div(batch, 255.0) 41 | batch -= Variable(mean) 42 | batch = batch / Variable(std) 43 | return batch 44 | -------------------------------------------------------------------------------- /fast_neural_style/neural_style/vgg.py: -------------------------------------------------------------------------------- 1 | from collections import namedtuple 2 | 3 | import torch 4 | from torchvision import models 5 | 6 | 7 | class Vgg16(torch.nn.Module): 8 | def __init__(self, requires_grad=False): 9 | super(Vgg16, self).__init__() 10 | vgg_pretrained_features = models.vgg16(pretrained=True).features 11 | self.slice1 = torch.nn.Sequential() 12 | self.slice2 = torch.nn.Sequential() 13 | self.slice3 = torch.nn.Sequential() 14 | self.slice4 = torch.nn.Sequential() 15 | for x in range(4): 16 | self.slice1.add_module(str(x), vgg_pretrained_features[x]) 17 | for x in range(4, 9): 18 | self.slice2.add_module(str(x), vgg_pretrained_features[x]) 19 | for x in range(9, 16): 20 | self.slice3.add_module(str(x), vgg_pretrained_features[x]) 21 | for x in range(16, 23): 22 | self.slice4.add_module(str(x), vgg_pretrained_features[x]) 23 | if not requires_grad: 24 | for param in self.parameters(): 25 | param.requires_grad = False 26 | 27 | def forward(self, X): 28 | h = self.slice1(X) 29 | h_relu1_2 = h 30 | h = self.slice2(h) 31 | h_relu2_2 = h 32 | h = self.slice3(h) 33 | h_relu3_3 = h 34 | h = self.slice4(h) 35 | h_relu4_3 = h 36 | vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3']) 37 | out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3) 38 | return out 39 | -------------------------------------------------------------------------------- /imagenet/README.md: -------------------------------------------------------------------------------- 1 | # ImageNet training in PyTorch 2 | 3 | This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset. 4 | 5 | ## Requirements 6 | 7 | - Install PyTorch ([pytorch.org](http://pytorch.org)) 8 | - `pip install -r requirements.txt` 9 | - Download the ImageNet dataset and move validation images to labeled subfolders 10 | 11 | ## Training 12 | 13 | To train a model, run `main.py` with the desired model architecture and the path to the ImageNet dataset: 14 | 15 | ```bash 16 | python main.py -a resnet18 [imagenet-folder with train and val folders] 17 | ``` 18 | 19 | The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs. This is appropriate for ResNet and models with batch normalization, but too high for AlexNet and VGG. Use 0.01 as the initial learning rate for AlexNet or VGG: 20 | 21 | ```bash 22 | python main.py -a alexnet --lr 0.01 [imagenet-folder with train and val folders] 23 | ``` 24 | 25 | ## Usage 26 | 27 | ``` 28 | usage: main.py [-h] [--arch ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] 29 | [--lr LR] [--momentum M] [--weight-decay W] [--print-freq N] 30 | [--resume PATH] [-e] [--pretrained] 31 | DIR 32 | 33 | PyTorch ImageNet Training 34 | 35 | positional arguments: 36 | DIR path to dataset 37 | 38 | optional arguments: 39 | -h, --help show this help message and exit 40 | --arch ARCH, -a ARCH model architecture: alexnet | resnet | resnet101 | 41 | resnet152 | resnet18 | resnet34 | resnet50 | vgg | 42 | vgg11 | vgg11_bn | vgg13 | vgg13_bn | vgg16 | vgg16_bn 43 | | vgg19 | vgg19_bn (default: resnet18) 44 | -j N, --workers N number of data loading workers (default: 4) 45 | --epochs N number of total epochs to run 46 | --start-epoch N manual epoch number (useful on restarts) 47 | -b N, --batch-size N mini-batch size (default: 256) 48 | --lr LR, --learning-rate LR 49 | initial learning rate 50 | --momentum M momentum 51 | --weight-decay W, --wd W 52 | weight decay (default: 1e-4) 53 | --print-freq N, -p N print frequency (default: 10) 54 | --resume PATH path to latest checkpoint (default: none) 55 | -e, --evaluate evaluate model on validation set 56 | --pretrained use pre-trained model 57 | ``` 58 | -------------------------------------------------------------------------------- /imagenet/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchvision 3 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /mnist/README.md: -------------------------------------------------------------------------------- 1 | # Basic MNIST Example 2 | 3 | ```bash 4 | pip install -r requirements.txt 5 | python main.py 6 | # CUDA_VISIBLE_DEVICES=2 python main.py # to specify GPU id to ex. 2 7 | ``` 8 | -------------------------------------------------------------------------------- /mnist/main.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import argparse 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | import torch.optim as optim 7 | from torchvision import datasets, transforms 8 | from torch.autograd import Variable 9 | from tensorboardX import SummaryWriter 10 | writer = SummaryWriter('runs') 11 | # Training settings 12 | parser = argparse.ArgumentParser(description='PyTorch MNIST Example') 13 | parser.add_argument('--batch-size', type=int, default=64, metavar='N', 14 | help='input batch size for training (default: 64)') 15 | parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', 16 | help='input batch size for testing (default: 1000)') 17 | parser.add_argument('--epochs', type=int, default=10, metavar='N', 18 | help='number of epochs to train (default: 10)') 19 | parser.add_argument('--lr', type=float, default=0.01, metavar='LR', 20 | help='learning rate (default: 0.01)') 21 | parser.add_argument('--momentum', type=float, default=0.5, metavar='M', 22 | help='SGD momentum (default: 0.5)') 23 | parser.add_argument('--no-cuda', action='store_true', default=False, 24 | help='disables CUDA training') 25 | parser.add_argument('--seed', type=int, default=1, metavar='S', 26 | help='random seed (default: 1)') 27 | parser.add_argument('--log-interval', type=int, default=10, metavar='N', 28 | help='how many batches to wait before logging training status') 29 | args = parser.parse_args() 30 | args.cuda = not args.no_cuda and torch.cuda.is_available() 31 | 32 | torch.manual_seed(args.seed) 33 | if args.cuda: 34 | torch.cuda.manual_seed(args.seed) 35 | 36 | 37 | kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} 38 | train_loader = torch.utils.data.DataLoader( 39 | datasets.MNIST('../data', train=True, download=True, 40 | transform=transforms.Compose([ 41 | transforms.ToTensor(), 42 | transforms.Normalize((0.1307,), (0.3081,)) 43 | ])), 44 | batch_size=args.batch_size, shuffle=True, **kwargs) 45 | test_loader = torch.utils.data.DataLoader( 46 | datasets.MNIST('../data', train=False, transform=transforms.Compose([ 47 | transforms.ToTensor(), 48 | transforms.Normalize((0.1307,), (0.3081,)) 49 | ])), 50 | batch_size=args.batch_size, shuffle=True, **kwargs) 51 | 52 | 53 | class Net(nn.Module): 54 | def __init__(self): 55 | super(Net, self).__init__() 56 | self.conv1 = nn.Conv2d(1, 10, kernel_size=5) 57 | self.conv2 = nn.Conv2d(10, 20, kernel_size=5) 58 | self.conv2_drop = nn.Dropout2d() 59 | self.fc1 = nn.Linear(320, 50) 60 | self.fc2 = nn.Linear(50, 10) 61 | 62 | def forward(self, x): 63 | x = F.relu(F.max_pool2d(self.conv1(x), 2)) 64 | x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) 65 | x = x.view(-1, 320) 66 | x = F.relu(self.fc1(x)) 67 | x = F.dropout(x, training=self.training) 68 | x = self.fc2(x) 69 | return F.log_softmax(x) 70 | 71 | model = Net() 72 | if args.cuda: 73 | model.cuda() 74 | 75 | optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) 76 | 77 | def train(epoch): 78 | model.train() 79 | for batch_idx, (data, target) in enumerate(train_loader): 80 | if args.cuda: 81 | data, target = data.cuda(), target.cuda() 82 | data, target = Variable(data), Variable(target) 83 | optimizer.zero_grad() 84 | output = model(data) 85 | loss = F.nll_loss(output, target) 86 | loss.backward() 87 | optimizer.step() 88 | if batch_idx % args.log_interval == 0: 89 | print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( 90 | epoch, batch_idx * len(data), len(train_loader.dataset), 91 | 100. * batch_idx / len(train_loader), loss.data[0])) 92 | niter = epoch*len(train_loader)+batch_idx 93 | writer.add_scalar('Train/Loss', loss.data[0], niter) 94 | 95 | def test(): 96 | model.eval() 97 | test_loss = 0 98 | correct = 0 99 | for data, target in test_loader: 100 | if args.cuda: 101 | data, target = data.cuda(), target.cuda() 102 | data, target = Variable(data, volatile=True), Variable(target) 103 | output = model(data) 104 | test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss 105 | pred = output.data.max(1)[1] # get the index of the max log-probability 106 | correct += pred.eq(target.data).cpu().sum() 107 | 108 | test_loss /= len(test_loader.dataset) 109 | print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( 110 | test_loss, correct, len(test_loader.dataset), 111 | 100. * correct / len(test_loader.dataset))) 112 | 113 | 114 | for epoch in range(1, args.epochs + 1): 115 | train(epoch) 116 | test() 117 | -------------------------------------------------------------------------------- /mnist/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchvision 3 | -------------------------------------------------------------------------------- /mnist_hogwild/main.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import argparse 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | import torch.multiprocessing as mp 7 | 8 | from train import train 9 | 10 | # Training settings 11 | parser = argparse.ArgumentParser(description='PyTorch MNIST Example') 12 | parser.add_argument('--batch-size', type=int, default=64, metavar='N', 13 | help='input batch size for training (default: 64)') 14 | parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', 15 | help='input batch size for testing (default: 1000)') 16 | parser.add_argument('--epochs', type=int, default=10, metavar='N', 17 | help='number of epochs to train (default: 2)') 18 | parser.add_argument('--lr', type=float, default=0.01, metavar='LR', 19 | help='learning rate (default: 0.01)') 20 | parser.add_argument('--momentum', type=float, default=0.5, metavar='M', 21 | help='SGD momentum (default: 0.5)') 22 | parser.add_argument('--seed', type=int, default=1, metavar='S', 23 | help='random seed (default: 1)') 24 | parser.add_argument('--log-interval', type=int, default=10, metavar='N', 25 | help='how many batches to wait before logging training status') 26 | parser.add_argument('--num-processes', type=int, default=2, metavar='N', 27 | help='how many training processes to use (default: 2)') 28 | 29 | class Net(nn.Module): 30 | def __init__(self): 31 | super(Net, self).__init__() 32 | self.conv1 = nn.Conv2d(1, 10, kernel_size=5) 33 | self.conv2 = nn.Conv2d(10, 20, kernel_size=5) 34 | self.conv2_drop = nn.Dropout2d() 35 | self.fc1 = nn.Linear(320, 50) 36 | self.fc2 = nn.Linear(50, 10) 37 | 38 | def forward(self, x): 39 | x = F.relu(F.max_pool2d(self.conv1(x), 2)) 40 | x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) 41 | x = x.view(-1, 320) 42 | x = F.relu(self.fc1(x)) 43 | x = F.dropout(x, training=self.training) 44 | x = self.fc2(x) 45 | return F.log_softmax(x) 46 | 47 | if __name__ == '__main__': 48 | args = parser.parse_args() 49 | 50 | torch.manual_seed(args.seed) 51 | 52 | model = Net() 53 | model.share_memory() # gradients are allocated lazily, so they are not shared here 54 | 55 | processes = [] 56 | for rank in range(args.num_processes): 57 | p = mp.Process(target=train, args=(rank, args, model)) 58 | p.start() 59 | processes.append(p) 60 | for p in processes: 61 | p.join() 62 | -------------------------------------------------------------------------------- /mnist_hogwild/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchvision 3 | -------------------------------------------------------------------------------- /mnist_hogwild/train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.optim as optim 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | from torchvision import datasets, transforms 7 | 8 | def train(rank, args, model): 9 | torch.manual_seed(args.seed + rank) 10 | 11 | train_loader = torch.utils.data.DataLoader( 12 | datasets.MNIST('../data', train=True, download=True, 13 | transform=transforms.Compose([ 14 | transforms.ToTensor(), 15 | transforms.Normalize((0.1307,), (0.3081,)) 16 | ])), 17 | batch_size=args.batch_size, shuffle=True, num_workers=1) 18 | test_loader = torch.utils.data.DataLoader( 19 | datasets.MNIST('../data', train=False, transform=transforms.Compose([ 20 | transforms.ToTensor(), 21 | transforms.Normalize((0.1307,), (0.3081,)) 22 | ])), 23 | batch_size=args.batch_size, shuffle=True, num_workers=1) 24 | 25 | optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) 26 | for epoch in range(1, args.epochs + 1): 27 | train_epoch(epoch, args, model, train_loader, optimizer) 28 | test_epoch(model, test_loader) 29 | 30 | 31 | def train_epoch(epoch, args, model, data_loader, optimizer): 32 | model.train() 33 | pid = os.getpid() 34 | for batch_idx, (data, target) in enumerate(data_loader): 35 | data, target = Variable(data), Variable(target) 36 | optimizer.zero_grad() 37 | output = model(data) 38 | loss = F.nll_loss(output, target) 39 | loss.backward() 40 | optimizer.step() 41 | if batch_idx % args.log_interval == 0: 42 | print('{}\tTrain Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( 43 | pid, epoch, batch_idx * len(data), len(data_loader.dataset), 44 | 100. * batch_idx / len(data_loader), loss.data[0])) 45 | 46 | 47 | def test_epoch(model, data_loader): 48 | model.eval() 49 | test_loss = 0 50 | correct = 0 51 | for data, target in data_loader: 52 | data, target = Variable(data, volatile=True), Variable(target) 53 | output = model(data) 54 | test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss 55 | pred = output.data.max(1)[1] # get the index of the max log-probability 56 | correct += pred.eq(target.data).cpu().sum() 57 | 58 | test_loss /= len(data_loader.dataset) 59 | print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( 60 | test_loss, correct, len(data_loader.dataset), 61 | 100. * correct / len(data_loader.dataset))) 62 | -------------------------------------------------------------------------------- /regression/README.md: -------------------------------------------------------------------------------- 1 | # Linear regression example 2 | 3 | Trains a single fully-connected layer to fit a 4th degree polynomial. 4 | -------------------------------------------------------------------------------- /regression/main.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from __future__ import print_function 3 | from itertools import count 4 | 5 | import torch 6 | import torch.autograd 7 | import torch.nn.functional as F 8 | from torch.autograd import Variable 9 | 10 | POLY_DEGREE = 4 11 | W_target = torch.randn(POLY_DEGREE, 1) * 5 12 | b_target = torch.randn(1) * 5 13 | 14 | 15 | def make_features(x): 16 | """Builds features i.e. a matrix with columns [x, x^2, x^3, x^4].""" 17 | x = x.unsqueeze(1) 18 | return torch.cat([x ** i for i in range(1, POLY_DEGREE+1)], 1) 19 | 20 | 21 | def f(x): 22 | """Approximated function.""" 23 | return x.mm(W_target) + b_target[0] 24 | 25 | 26 | def poly_desc(W, b): 27 | """Creates a string description of a polynomial.""" 28 | result = 'y = ' 29 | for i, w in enumerate(W): 30 | result += '{:+.2f} x^{} '.format(w, len(W) - i) 31 | result += '{:+.2f}'.format(b[0]) 32 | return result 33 | 34 | 35 | def get_batch(batch_size=32): 36 | """Builds a batch i.e. (x, f(x)) pair.""" 37 | random = torch.randn(batch_size) 38 | x = make_features(random) 39 | y = f(x) 40 | return Variable(x), Variable(y) 41 | 42 | 43 | # Define model 44 | fc = torch.nn.Linear(W_target.size(0), 1) 45 | 46 | for batch_idx in count(1): 47 | # Get data 48 | batch_x, batch_y = get_batch() 49 | 50 | # Reset gradients 51 | fc.zero_grad() 52 | 53 | # Forward pass 54 | output = F.smooth_l1_loss(fc(batch_x), batch_y) 55 | loss = output.data[0] 56 | 57 | # Backward pass 58 | output.backward() 59 | 60 | # Apply gradients 61 | for param in fc.parameters(): 62 | param.data.add_(-0.1 * param.grad.data) 63 | 64 | # Stop criterion 65 | if loss < 1e-3: 66 | break 67 | 68 | print('Loss: {:.6f} after {} batches'.format(loss, batch_idx)) 69 | print('==> Learned function:\t' + poly_desc(fc.weight.data.view(-1), fc.bias.data)) 70 | print('==> Actual function:\t' + poly_desc(W_target.view(-1), b_target)) 71 | -------------------------------------------------------------------------------- /reinforcement_learning/README.md: -------------------------------------------------------------------------------- 1 | # Reinforcement learning training example 2 | 3 | ```bash 4 | pip install -r requirements.txt 5 | # For REINFORCE: 6 | python reinforce.py 7 | # For actor critic: 8 | python actor_critic.py 9 | ``` 10 | -------------------------------------------------------------------------------- /reinforcement_learning/actor_critic.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import gym 3 | import numpy as np 4 | from itertools import count 5 | from collections import namedtuple 6 | 7 | import torch 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | import torch.optim as optim 11 | import torch.autograd as autograd 12 | from torch.autograd import Variable 13 | 14 | 15 | parser = argparse.ArgumentParser(description='PyTorch actor-critic example') 16 | parser.add_argument('--gamma', type=float, default=0.99, metavar='G', 17 | help='discount factor (default: 0.99)') 18 | parser.add_argument('--seed', type=int, default=543, metavar='N', 19 | help='random seed (default: 1)') 20 | parser.add_argument('--render', action='store_true', 21 | help='render the environment') 22 | parser.add_argument('--log-interval', type=int, default=10, metavar='N', 23 | help='interval between training status logs (default: 10)') 24 | args = parser.parse_args() 25 | 26 | 27 | env = gym.make('CartPole-v0') 28 | env.seed(args.seed) 29 | torch.manual_seed(args.seed) 30 | 31 | 32 | SavedAction = namedtuple('SavedAction', ['action', 'value']) 33 | class Policy(nn.Module): 34 | def __init__(self): 35 | super(Policy, self).__init__() 36 | self.affine1 = nn.Linear(4, 128) 37 | self.action_head = nn.Linear(128, 2) 38 | self.value_head = nn.Linear(128, 1) 39 | 40 | self.saved_actions = [] 41 | self.rewards = [] 42 | 43 | def forward(self, x): 44 | x = F.relu(self.affine1(x)) 45 | action_scores = self.action_head(x) 46 | state_values = self.value_head(x) 47 | return F.softmax(action_scores), state_values 48 | 49 | 50 | model = Policy() 51 | optimizer = optim.Adam(model.parameters(), lr=3e-2) 52 | 53 | 54 | def select_action(state): 55 | state = torch.from_numpy(state).float().unsqueeze(0) 56 | probs, state_value = model(Variable(state)) 57 | action = probs.multinomial() 58 | model.saved_actions.append(SavedAction(action, state_value)) 59 | return action.data 60 | 61 | 62 | def finish_episode(): 63 | R = 0 64 | saved_actions = model.saved_actions 65 | value_loss = 0 66 | rewards = [] 67 | for r in model.rewards[::-1]: 68 | R = r + args.gamma * R 69 | rewards.insert(0, R) 70 | rewards = torch.Tensor(rewards) 71 | rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps) 72 | for (action, value), r in zip(saved_actions, rewards): 73 | reward = r - value.data[0,0] 74 | action.reinforce(reward) 75 | value_loss += F.smooth_l1_loss(value, Variable(torch.Tensor([r]))) 76 | optimizer.zero_grad() 77 | final_nodes = [value_loss] + list(map(lambda p: p.action, saved_actions)) 78 | gradients = [torch.ones(1)] + [None] * len(saved_actions) 79 | autograd.backward(final_nodes, gradients) 80 | optimizer.step() 81 | del model.rewards[:] 82 | del model.saved_actions[:] 83 | 84 | 85 | running_reward = 10 86 | for i_episode in count(1): 87 | state = env.reset() 88 | for t in range(10000): # Don't infinite loop while learning 89 | action = select_action(state) 90 | state, reward, done, _ = env.step(action[0,0]) 91 | if args.render: 92 | env.render() 93 | model.rewards.append(reward) 94 | if done: 95 | break 96 | 97 | running_reward = running_reward * 0.99 + t * 0.01 98 | finish_episode() 99 | if i_episode % args.log_interval == 0: 100 | print('Episode {}\tLast length: {:5d}\tAverage length: {:.2f}'.format( 101 | i_episode, t, running_reward)) 102 | if running_reward > 200: 103 | print("Solved! Running reward is now {} and " 104 | "the last episode runs to {} time steps!".format(running_reward, t)) 105 | break 106 | -------------------------------------------------------------------------------- /reinforcement_learning/reinforce.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import gym 3 | import numpy as np 4 | from itertools import count 5 | 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | import torch.optim as optim 10 | import torch.autograd as autograd 11 | from torch.autograd import Variable 12 | 13 | 14 | parser = argparse.ArgumentParser(description='PyTorch REINFORCE example') 15 | parser.add_argument('--gamma', type=float, default=0.99, metavar='G', 16 | help='discount factor (default: 0.99)') 17 | parser.add_argument('--seed', type=int, default=543, metavar='N', 18 | help='random seed (default: 543)') 19 | parser.add_argument('--render', action='store_true', 20 | help='render the environment') 21 | parser.add_argument('--log_interval', type=int, default=10, metavar='N', 22 | help='interval between training status logs (default: 10)') 23 | args = parser.parse_args() 24 | 25 | 26 | env = gym.make('CartPole-v0') 27 | env.seed(args.seed) 28 | torch.manual_seed(args.seed) 29 | 30 | 31 | class Policy(nn.Module): 32 | def __init__(self): 33 | super(Policy, self).__init__() 34 | self.affine1 = nn.Linear(4, 128) 35 | self.affine2 = nn.Linear(128, 2) 36 | 37 | self.saved_actions = [] 38 | self.rewards = [] 39 | 40 | def forward(self, x): 41 | x = F.relu(self.affine1(x)) 42 | action_scores = self.affine2(x) 43 | return F.softmax(action_scores) 44 | 45 | 46 | policy = Policy() 47 | optimizer = optim.Adam(policy.parameters(), lr=1e-2) 48 | 49 | 50 | def select_action(state): 51 | state = torch.from_numpy(state).float().unsqueeze(0) 52 | probs = policy(Variable(state)) 53 | action = probs.multinomial() 54 | policy.saved_actions.append(action) 55 | return action.data 56 | 57 | 58 | def finish_episode(): 59 | R = 0 60 | rewards = [] 61 | for r in policy.rewards[::-1]: 62 | R = r + args.gamma * R 63 | rewards.insert(0, R) 64 | rewards = torch.Tensor(rewards) 65 | rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps) 66 | for action, r in zip(policy.saved_actions, rewards): 67 | action.reinforce(r) 68 | optimizer.zero_grad() 69 | autograd.backward(policy.saved_actions, [None for _ in policy.saved_actions]) 70 | optimizer.step() 71 | del policy.rewards[:] 72 | del policy.saved_actions[:] 73 | 74 | 75 | running_reward = 10 76 | for i_episode in count(1): 77 | state = env.reset() 78 | for t in range(10000): # Don't infinite loop while learning 79 | action = select_action(state) 80 | state, reward, done, _ = env.step(action[0,0]) 81 | if args.render: 82 | env.render() 83 | policy.rewards.append(reward) 84 | if done: 85 | break 86 | 87 | running_reward = running_reward * 0.99 + t * 0.01 88 | finish_episode() 89 | if i_episode % args.log_interval == 0: 90 | print('Episode {}\tLast length: {:5d}\tAverage length: {:.2f}'.format( 91 | i_episode, t, running_reward)) 92 | if running_reward > 200: 93 | print("Solved! Running reward is now {} and " 94 | "the last episode runs to {} time steps!".format(running_reward, t)) 95 | break 96 | -------------------------------------------------------------------------------- /reinforcement_learning/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | numpy 3 | gym 4 | -------------------------------------------------------------------------------- /snli/model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Variable 4 | 5 | 6 | class Bottle(nn.Module): 7 | 8 | def forward(self, input): 9 | if len(input.size()) <= 2: 10 | return super(Bottle, self).forward(input) 11 | size = input.size()[:2] 12 | out = super(Bottle, self).forward(input.view(size[0]*size[1], -1)) 13 | return out.view(size[0], size[1], -1) 14 | 15 | 16 | class Linear(Bottle, nn.Linear): 17 | pass 18 | 19 | 20 | class Encoder(nn.Module): 21 | 22 | def __init__(self, config): 23 | super(Encoder, self).__init__() 24 | self.config = config 25 | input_size = config.d_proj if config.projection else config.d_embed 26 | self.rnn = nn.LSTM(input_size=input_size, hidden_size=config.d_hidden, 27 | num_layers=config.n_layers, dropout=config.dp_ratio, 28 | bidirectional=config.birnn) 29 | 30 | def forward(self, inputs): 31 | batch_size = inputs.size()[1] 32 | state_shape = self.config.n_cells, batch_size, self.config.d_hidden 33 | h0 = c0 = Variable(inputs.data.new(*state_shape).zero_()) 34 | outputs, (ht, ct) = self.rnn(inputs, (h0, c0)) 35 | return ht[-1] if not self.config.birnn else ht[-2:].transpose(0, 1).contiguous().view(batch_size, -1) 36 | 37 | 38 | class SNLIClassifier(nn.Module): 39 | 40 | def __init__(self, config): 41 | super(SNLIClassifier, self).__init__() 42 | self.config = config 43 | self.embed = nn.Embedding(config.n_embed, config.d_embed) 44 | self.projection = Linear(config.d_embed, config.d_proj) 45 | self.encoder = Encoder(config) 46 | self.dropout = nn.Dropout(p=config.dp_ratio) 47 | self.relu = nn.ReLU() 48 | seq_in_size = 2*config.d_hidden 49 | if self.config.birnn: 50 | seq_in_size *= 2 51 | lin_config = [seq_in_size]*2 52 | self.out = nn.Sequential( 53 | Linear(*lin_config), 54 | self.relu, 55 | self.dropout, 56 | Linear(*lin_config), 57 | self.relu, 58 | self.dropout, 59 | Linear(*lin_config), 60 | self.relu, 61 | self.dropout, 62 | Linear(seq_in_size, config.d_out)) 63 | 64 | def forward(self, batch): 65 | prem_embed = self.embed(batch.premise) 66 | hypo_embed = self.embed(batch.hypothesis) 67 | if self.config.fix_emb: 68 | prem_embed = Variable(prem_embed.data) 69 | hypo_embed = Variable(hypo_embed.data) 70 | if self.config.projection: 71 | prem_embed = self.relu(self.projection(prem_embed)) 72 | hypo_embed = self.relu(self.projection(hypo_embed)) 73 | premise = self.encoder(prem_embed) 74 | hypothesis = self.encoder(hypo_embed) 75 | scores = self.out(torch.cat([premise, hypothesis], 1)) 76 | return scores 77 | -------------------------------------------------------------------------------- /snli/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchtext 3 | -------------------------------------------------------------------------------- /snli/train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import time 3 | import glob 4 | 5 | import torch 6 | import torch.optim as O 7 | import torch.nn as nn 8 | 9 | from torchtext import data 10 | from torchtext import datasets 11 | 12 | from model import SNLIClassifier 13 | from util import get_args, makedirs 14 | 15 | 16 | args = get_args() 17 | torch.cuda.set_device(args.gpu) 18 | 19 | inputs = data.Field(lower=args.lower) 20 | answers = data.Field(sequential=False) 21 | 22 | train, dev, test = datasets.SNLI.splits(inputs, answers) 23 | 24 | inputs.build_vocab(train, dev, test) 25 | if args.word_vectors: 26 | if os.path.isfile(args.vector_cache): 27 | inputs.vocab.vectors = torch.load(args.vector_cache) 28 | else: 29 | inputs.vocab.load_vectors(wv_dir=args.data_cache, wv_type=args.word_vectors, wv_dim=args.d_embed) 30 | makedirs(os.path.dirname(args.vector_cache)) 31 | torch.save(inputs.vocab.vectors, args.vector_cache) 32 | answers.build_vocab(train) 33 | 34 | train_iter, dev_iter, test_iter = data.BucketIterator.splits( 35 | (train, dev, test), batch_size=args.batch_size, device=args.gpu) 36 | 37 | config = args 38 | config.n_embed = len(inputs.vocab) 39 | config.d_out = len(answers.vocab) 40 | config.n_cells = config.n_layers 41 | 42 | # double the number of cells for bidirectional networks 43 | if config.birnn: 44 | config.n_cells *= 2 45 | 46 | if args.resume_snapshot: 47 | model = torch.load(args.resume_snapshot, map_location=lambda storage, locatoin: storage.cuda(args.gpu)) 48 | else: 49 | model = SNLIClassifier(config) 50 | if args.word_vectors: 51 | model.embed.weight.data = inputs.vocab.vectors 52 | model.cuda() 53 | 54 | criterion = nn.CrossEntropyLoss() 55 | opt = O.Adam(model.parameters(), lr=args.lr) 56 | 57 | iterations = 0 58 | start = time.time() 59 | best_dev_acc = -1 60 | train_iter.repeat = False 61 | header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy' 62 | dev_log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'.split(',')) 63 | log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'.split(',')) 64 | makedirs(args.save_path) 65 | print(header) 66 | 67 | for epoch in range(args.epochs): 68 | train_iter.init_epoch() 69 | n_correct, n_total = 0, 0 70 | for batch_idx, batch in enumerate(train_iter): 71 | 72 | # switch model to training mode, clear gradient accumulators 73 | model.train(); opt.zero_grad() 74 | 75 | iterations += 1 76 | 77 | # forward pass 78 | answer = model(batch) 79 | 80 | # calculate accuracy of predictions in the current batch 81 | n_correct += (torch.max(answer, 1)[1].view(batch.label.size()).data == batch.label.data).sum() 82 | n_total += batch.batch_size 83 | train_acc = 100. * n_correct/n_total 84 | 85 | # calculate loss of the network output with respect to training labels 86 | loss = criterion(answer, batch.label) 87 | 88 | # backpropagate and update optimizer learning rate 89 | loss.backward(); opt.step() 90 | 91 | # checkpoint model periodically 92 | if iterations % args.save_every == 0: 93 | snapshot_prefix = os.path.join(args.save_path, 'snapshot') 94 | snapshot_path = snapshot_prefix + '_acc_{:.4f}_loss_{:.6f}_iter_{}_model.pt'.format(train_acc, loss.data[0], iterations) 95 | torch.save(model, snapshot_path) 96 | for f in glob.glob(snapshot_prefix + '*'): 97 | if f != snapshot_path: 98 | os.remove(f) 99 | 100 | # evaluate performance on validation set periodically 101 | if iterations % args.dev_every == 0: 102 | 103 | # switch model to evaluation mode 104 | model.eval(); dev_iter.init_epoch() 105 | 106 | # calculate accuracy on validation set 107 | n_dev_correct, dev_loss = 0, 0 108 | for dev_batch_idx, dev_batch in enumerate(dev_iter): 109 | answer = model(dev_batch) 110 | n_dev_correct += (torch.max(answer, 1)[1].view(dev_batch.label.size()).data == dev_batch.label.data).sum() 111 | dev_loss = criterion(answer, dev_batch.label) 112 | dev_acc = 100. * n_dev_correct / len(dev) 113 | 114 | print(dev_log_template.format(time.time()-start, 115 | epoch, iterations, 1+batch_idx, len(train_iter), 116 | 100. * (1+batch_idx) / len(train_iter), loss.data[0], dev_loss.data[0], train_acc, dev_acc)) 117 | 118 | # update best valiation set accuracy 119 | if dev_acc > best_dev_acc: 120 | 121 | # found a model with better validation set accuracy 122 | 123 | best_dev_acc = dev_acc 124 | snapshot_prefix = os.path.join(args.save_path, 'best_snapshot') 125 | snapshot_path = snapshot_prefix + '_devacc_{}_devloss_{}__iter_{}_model.pt'.format(dev_acc, dev_loss.data[0], iterations) 126 | 127 | # save model, delete previous 'best_snapshot' files 128 | torch.save(model, snapshot_path) 129 | for f in glob.glob(snapshot_prefix + '*'): 130 | if f != snapshot_path: 131 | os.remove(f) 132 | 133 | elif iterations % args.log_every == 0: 134 | 135 | # print progress message 136 | print(log_template.format(time.time()-start, 137 | epoch, iterations, 1+batch_idx, len(train_iter), 138 | 100. * (1+batch_idx) / len(train_iter), loss.data[0], ' '*8, n_correct/n_total*100, ' '*12)) 139 | 140 | 141 | -------------------------------------------------------------------------------- /snli/util.py: -------------------------------------------------------------------------------- 1 | import os 2 | from argparse import ArgumentParser 3 | 4 | def makedirs(name): 5 | """helper function for python 2 and 3 to call os.makedirs() 6 | avoiding an error if the directory to be created already exists""" 7 | 8 | import os, errno 9 | 10 | try: 11 | os.makedirs(name) 12 | except OSError as ex: 13 | if ex.errno == errno.EEXIST and os.path.isdir(name): 14 | # ignore existing directory 15 | pass 16 | else: 17 | # a different error happened 18 | raise 19 | 20 | 21 | def get_args(): 22 | parser = ArgumentParser(description='PyTorch/torchtext SNLI example') 23 | parser.add_argument('--epochs', type=int, default=50) 24 | parser.add_argument('--batch_size', type=int, default=128) 25 | parser.add_argument('--d_embed', type=int, default=300) 26 | parser.add_argument('--d_proj', type=int, default=300) 27 | parser.add_argument('--d_hidden', type=int, default=300) 28 | parser.add_argument('--n_layers', type=int, default=1) 29 | parser.add_argument('--log_every', type=int, default=50) 30 | parser.add_argument('--lr', type=float, default=.001) 31 | parser.add_argument('--dev_every', type=int, default=1000) 32 | parser.add_argument('--save_every', type=int, default=1000) 33 | parser.add_argument('--dp_ratio', type=int, default=0.2) 34 | parser.add_argument('--no-bidirectional', action='store_false', dest='birnn') 35 | parser.add_argument('--preserve-case', action='store_false', dest='lower') 36 | parser.add_argument('--no-projection', action='store_false', dest='projection') 37 | parser.add_argument('--train_embed', action='store_false', dest='fix_emb') 38 | parser.add_argument('--gpu', type=int, default=0) 39 | parser.add_argument('--save_path', type=str, default='results') 40 | parser.add_argument('--data_cache', type=str, default=os.path.join(os.getcwd(), '.data_cache')) 41 | parser.add_argument('--vector_cache', type=str, default=os.path.join(os.getcwd(), '.vector_cache/input_vectors.pt')) 42 | parser.add_argument('--word_vectors', type=str, default='glove.42B') 43 | parser.add_argument('--resume_snapshot', type=str, default='') 44 | args = parser.parse_args() 45 | return args 46 | -------------------------------------------------------------------------------- /super_resolution/README.md: -------------------------------------------------------------------------------- 1 | # Superresolution using an efficient sub-pixel convolutional neural network 2 | 3 | This example illustrates how to use the efficient sub-pixel convolution layer described in ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al.](https://arxiv.org/abs/1609.05158) for increasing spatial resolution within your network for tasks such as superresolution. 4 | 5 | ``` 6 | usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--batchSize BATCHSIZE] 7 | [--testBatchSize TESTBATCHSIZE] [--nEpochs NEPOCHS] [--lr LR] 8 | [--cuda] [--threads THREADS] [--seed SEED] 9 | 10 | PyTorch Super Res Example 11 | 12 | optional arguments: 13 | -h, --help show this help message and exit 14 | --upscale_factor super resolution upscale factor 15 | --batchSize training batch size 16 | --testBatchSize testing batch size 17 | --nEpochs number of epochs to train for 18 | --lr Learning Rate. Default=0.01 19 | --cuda use cuda 20 | --threads number of threads for data loader to use Default=4 21 | --seed random seed to use. Default=123 22 | ``` 23 | This example trains a super-resolution network on the [BSD300 dataset](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), using crops from the 200 training images, and evaluating on crops of the 100 test images. A snapshot of the model after every epoch with filename model_epoch_.pth 24 | 25 | ## Example Usage: 26 | 27 | ### Train 28 | 29 | `python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001` 30 | 31 | ### Super Resolve 32 | `python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png` 33 | -------------------------------------------------------------------------------- /super_resolution/data.py: -------------------------------------------------------------------------------- 1 | from os.path import exists, join, basename 2 | from os import makedirs, remove 3 | from six.moves import urllib 4 | import tarfile 5 | from torchvision.transforms import Compose, CenterCrop, ToTensor, Scale 6 | 7 | from dataset import DatasetFromFolder 8 | 9 | 10 | def download_bsd300(dest="dataset"): 11 | output_image_dir = join(dest, "BSDS300/images") 12 | 13 | if not exists(output_image_dir): 14 | makedirs(dest) 15 | url = "http://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz" 16 | print("downloading url ", url) 17 | 18 | data = urllib.request.urlopen(url) 19 | 20 | file_path = join(dest, basename(url)) 21 | with open(file_path, 'wb') as f: 22 | f.write(data.read()) 23 | 24 | print("Extracting data") 25 | with tarfile.open(file_path) as tar: 26 | for item in tar: 27 | tar.extract(item, dest) 28 | 29 | remove(file_path) 30 | 31 | return output_image_dir 32 | 33 | 34 | def calculate_valid_crop_size(crop_size, upscale_factor): 35 | return crop_size - (crop_size % upscale_factor) 36 | 37 | 38 | def input_transform(crop_size, upscale_factor): 39 | return Compose([ 40 | CenterCrop(crop_size), 41 | Scale(crop_size // upscale_factor), 42 | ToTensor(), 43 | ]) 44 | 45 | 46 | def target_transform(crop_size): 47 | return Compose([ 48 | CenterCrop(crop_size), 49 | ToTensor(), 50 | ]) 51 | 52 | 53 | def get_training_set(upscale_factor): 54 | root_dir = download_bsd300() 55 | train_dir = join(root_dir, "train") 56 | crop_size = calculate_valid_crop_size(256, upscale_factor) 57 | 58 | return DatasetFromFolder(train_dir, 59 | input_transform=input_transform(crop_size, upscale_factor), 60 | target_transform=target_transform(crop_size)) 61 | 62 | 63 | def get_test_set(upscale_factor): 64 | root_dir = download_bsd300() 65 | test_dir = join(root_dir, "test") 66 | crop_size = calculate_valid_crop_size(256, upscale_factor) 67 | 68 | return DatasetFromFolder(test_dir, 69 | input_transform=input_transform(crop_size, upscale_factor), 70 | target_transform=target_transform(crop_size)) 71 | -------------------------------------------------------------------------------- /super_resolution/dataset.py: -------------------------------------------------------------------------------- 1 | import torch.utils.data as data 2 | 3 | from os import listdir 4 | from os.path import join 5 | from PIL import Image 6 | 7 | 8 | def is_image_file(filename): 9 | return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"]) 10 | 11 | 12 | def load_img(filepath): 13 | img = Image.open(filepath).convert('YCbCr') 14 | y, _, _ = img.split() 15 | return y 16 | 17 | 18 | class DatasetFromFolder(data.Dataset): 19 | def __init__(self, image_dir, input_transform=None, target_transform=None): 20 | super(DatasetFromFolder, self).__init__() 21 | self.image_filenames = [join(image_dir, x) for x in listdir(image_dir) if is_image_file(x)] 22 | 23 | self.input_transform = input_transform 24 | self.target_transform = target_transform 25 | 26 | def __getitem__(self, index): 27 | input = load_img(self.image_filenames[index]) 28 | target = input.copy() 29 | if self.input_transform: 30 | input = self.input_transform(input) 31 | if self.target_transform: 32 | target = self.target_transform(target) 33 | 34 | return input, target 35 | 36 | def __len__(self): 37 | return len(self.image_filenames) 38 | -------------------------------------------------------------------------------- /super_resolution/main.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import argparse 3 | from math import log10 4 | 5 | import torch 6 | import torch.nn as nn 7 | import torch.optim as optim 8 | from torch.autograd import Variable 9 | from torch.utils.data import DataLoader 10 | from model import Net 11 | from data import get_training_set, get_test_set 12 | import torchvision.utils as vutils 13 | from tensorboardX import SummaryWriter 14 | writer = SummaryWriter('runs') 15 | # Training settings 16 | parser = argparse.ArgumentParser(description='PyTorch Super Res Example') 17 | parser.add_argument('--upscale_factor', type=int, required=True, help="super resolution upscale factor") 18 | parser.add_argument('--batchSize', type=int, default=64, help='training batch size') 19 | parser.add_argument('--testBatchSize', type=int, default=10, help='testing batch size') 20 | parser.add_argument('--nEpochs', type=int, default=200, help='number of epochs to train for') 21 | parser.add_argument('--lr', type=float, default=0.01, help='Learning Rate. Default=0.01') 22 | parser.add_argument('--cuda', action='store_true', help='use cuda?') 23 | parser.add_argument('--threads', type=int, default=4, help='number of threads for data loader to use') 24 | parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') 25 | opt = parser.parse_args() 26 | 27 | print(opt) 28 | 29 | cuda = opt.cuda 30 | if cuda and not torch.cuda.is_available(): 31 | raise Exception("No GPU found, please run without --cuda") 32 | 33 | torch.manual_seed(opt.seed) 34 | if cuda: 35 | torch.cuda.manual_seed(opt.seed) 36 | 37 | print('===> Loading datasets') 38 | train_set = get_training_set(opt.upscale_factor) 39 | test_set = get_test_set(opt.upscale_factor) 40 | training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True) 41 | testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False) 42 | 43 | print('===> Building model') 44 | model = Net(upscale_factor=opt.upscale_factor) 45 | criterion = nn.MSELoss() 46 | 47 | if cuda: 48 | model = model.cuda() 49 | criterion = criterion.cuda() 50 | 51 | optimizer = optim.Adam(model.parameters(), lr=opt.lr) 52 | 53 | 54 | def train(epoch): 55 | epoch_loss = 0 56 | for iteration, batch in enumerate(training_data_loader, 1): 57 | input, target = Variable(batch[0]), Variable(batch[1]) 58 | if cuda: 59 | input = input.cuda() 60 | target = target.cuda() 61 | 62 | optimizer.zero_grad() 63 | output = model(input) 64 | loss = criterion(output, target) 65 | epoch_loss += loss.data[0] 66 | loss.backward() 67 | optimizer.step() 68 | 69 | print("===> Epoch[{}]({}/{}): Loss: {:.4f}".format(epoch, iteration, len(training_data_loader), loss.data[0])) 70 | niter = epoch*len(training_data_loader)+iteration 71 | writer.add_scalar('Loss', loss.data[0], niter) 72 | writer.add_image('Output', vutils.make_grid(output.data, normalize=True, scale_each=True), niter) 73 | print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss / len(training_data_loader))) 74 | 75 | 76 | def test(): 77 | avg_psnr = 0 78 | for batch in testing_data_loader: 79 | input, target = Variable(batch[0]), Variable(batch[1]) 80 | if cuda: 81 | input = input.cuda() 82 | target = target.cuda() 83 | 84 | prediction = model(input) 85 | mse = criterion(prediction, target) 86 | psnr = 10 * log10(1 / mse.data[0]) 87 | avg_psnr += psnr 88 | print("===> Avg. PSNR: {:.4f} dB".format(avg_psnr / len(testing_data_loader))) 89 | 90 | 91 | def checkpoint(epoch): 92 | model_out_path = "model_epoch_{}.pth".format(epoch) 93 | #torch.save(model, model_out_path) 94 | print("Checkpoint saved to {}".format(model_out_path)) 95 | 96 | for epoch in range(1, opt.nEpochs + 1): 97 | train(epoch) 98 | test() 99 | checkpoint(epoch) 100 | -------------------------------------------------------------------------------- /super_resolution/model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.init as init 4 | 5 | 6 | class Net(nn.Module): 7 | def __init__(self, upscale_factor): 8 | super(Net, self).__init__() 9 | 10 | self.relu = nn.ReLU() 11 | self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2)) 12 | self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)) 13 | self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1)) 14 | self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1)) 15 | self.pixel_shuffle = nn.PixelShuffle(upscale_factor) 16 | 17 | self._initialize_weights() 18 | 19 | def forward(self, x): 20 | x = self.relu(self.conv1(x)) 21 | x = self.relu(self.conv2(x)) 22 | x = self.relu(self.conv3(x)) 23 | x = self.pixel_shuffle(self.conv4(x)) 24 | return x 25 | 26 | def _initialize_weights(self): 27 | init.orthogonal(self.conv1.weight, init.calculate_gain('relu')) 28 | init.orthogonal(self.conv2.weight, init.calculate_gain('relu')) 29 | init.orthogonal(self.conv3.weight, init.calculate_gain('relu')) 30 | init.orthogonal(self.conv4.weight) 31 | -------------------------------------------------------------------------------- /super_resolution/super_resolve.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import argparse 3 | import torch 4 | from torch.autograd import Variable 5 | from PIL import Image 6 | from torchvision.transforms import ToTensor 7 | 8 | import numpy as np 9 | 10 | # Training settings 11 | parser = argparse.ArgumentParser(description='PyTorch Super Res Example') 12 | parser.add_argument('--input_image', type=str, required=True, help='input image to use') 13 | parser.add_argument('--model', type=str, required=True, help='model file to use') 14 | parser.add_argument('--output_filename', type=str, help='where to save the output image') 15 | parser.add_argument('--cuda', action='store_true', help='use cuda') 16 | opt = parser.parse_args() 17 | 18 | print(opt) 19 | img = Image.open(opt.input_image).convert('YCbCr') 20 | y, cb, cr = img.split() 21 | 22 | model = torch.load(opt.model) 23 | input = Variable(ToTensor()(y)).view(1, -1, y.size[1], y.size[0]) 24 | 25 | if opt.cuda: 26 | model = model.cuda() 27 | input = input.cuda() 28 | 29 | out = model(input) 30 | out = out.cpu() 31 | out_img_y = out.data[0].numpy() 32 | out_img_y *= 255.0 33 | out_img_y = out_img_y.clip(0, 255) 34 | out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode='L') 35 | 36 | out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC) 37 | out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC) 38 | out_img = Image.merge('YCbCr', [out_img_y, out_img_cb, out_img_cr]).convert('RGB') 39 | 40 | out_img.save(opt.output_filename) 41 | print('output image saved to ', opt.output_filename) 42 | -------------------------------------------------------------------------------- /time_sequence_prediction/README.md: -------------------------------------------------------------------------------- 1 | # Time Sequence Prediction 2 | This is a toy example for beginners to start with. It is helpful for learning both pytorch and time sequence prediction. Two LSTMCell units are used in this example to learn some sine wave signals starting at different phases. After learning the sine waves, the network tries to predict the signal values in the future. The results is shown in the picture below. 3 | 4 | ## Usage 5 | 6 | ``` 7 | python generate_sine_wave.py 8 | python train.py 9 | ``` 10 | 11 | ## Result 12 | The initial signal and the predicted results are shown in the image. We first give some initial signals (full line). The network will subsequently give some predicted results (dash line). It can be concluded that the network can generate new sine waves. 13 | ![image](https://cloud.githubusercontent.com/assets/1419566/24184438/e24f5280-0f08-11e7-8f8b-4d972b527a81.png) 14 | -------------------------------------------------------------------------------- /time_sequence_prediction/generate_sine_wave.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | import torch 4 | T = 20 5 | L = 1000 6 | N = 100 7 | np.random.seed(2) 8 | x = np.empty((N, L), 'int64') 9 | x[:] = np.array(range(L)) + np.random.randint(-4*T, 4*T, N).reshape(N, 1) 10 | data = np.sin(x / 1.0 / T).astype('float64') 11 | torch.save(data, open('traindata.pt', 'wb')) 12 | 13 | -------------------------------------------------------------------------------- /time_sequence_prediction/train.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import torch 3 | import torch.nn as nn 4 | from torch.autograd import Variable 5 | import torch.optim as optim 6 | import numpy as np 7 | import matplotlib 8 | matplotlib.use('Agg') 9 | import matplotlib.pyplot as plt 10 | 11 | class Sequence(nn.Module): 12 | def __init__(self): 13 | super(Sequence, self).__init__() 14 | self.lstm1 = nn.LSTMCell(1, 51) 15 | self.lstm2 = nn.LSTMCell(51, 1) 16 | 17 | def forward(self, input, future = 0): 18 | outputs = [] 19 | h_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False) 20 | c_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False) 21 | h_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False) 22 | c_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False) 23 | 24 | for i, input_t in enumerate(input.chunk(input.size(1), dim=1)): 25 | h_t, c_t = self.lstm1(input_t, (h_t, c_t)) 26 | h_t2, c_t2 = self.lstm2(c_t, (h_t2, c_t2)) 27 | outputs += [c_t2] 28 | for i in range(future):# if we should predict the future 29 | h_t, c_t = self.lstm1(c_t2, (h_t, c_t)) 30 | h_t2, c_t2 = self.lstm2(c_t, (h_t2, c_t2)) 31 | outputs += [c_t2] 32 | outputs = torch.stack(outputs, 1).squeeze(2) 33 | return outputs 34 | 35 | 36 | 37 | if __name__ == '__main__': 38 | # set ramdom seed to 0 39 | np.random.seed(0) 40 | torch.manual_seed(0) 41 | # load data and make training set 42 | data = torch.load('traindata.pt') 43 | input = Variable(torch.from_numpy(data[3:, :-1]), requires_grad=False) 44 | target = Variable(torch.from_numpy(data[3:, 1:]), requires_grad=False) 45 | test_input = Variable(torch.from_numpy(data[:3, :-1]), requires_grad=False) 46 | test_target = Variable(torch.from_numpy(data[:3, 1:]), requires_grad=False) 47 | # build the model 48 | seq = Sequence() 49 | seq.double() 50 | criterion = nn.MSELoss() 51 | # use LBFGS as optimizer since we can load the whole data to train 52 | optimizer = optim.LBFGS(seq.parameters()) 53 | #begin to train 54 | for i in range(15): 55 | print('STEP: ', i) 56 | def closure(): 57 | optimizer.zero_grad() 58 | out = seq(input) 59 | loss = criterion(out, target) 60 | print('loss:', loss.data.numpy()[0]) 61 | loss.backward() 62 | return loss 63 | optimizer.step(closure) 64 | # begin to predict 65 | future = 1000 66 | pred = seq(test_input, future = future) 67 | loss = criterion(pred[:, :-future], test_target) 68 | print('test loss:', loss.data.numpy()[0]) 69 | y = pred.data.numpy() 70 | # draw the result 71 | plt.figure(figsize=(30,10)) 72 | plt.title('Predict future values for time sequences\n(Dashlines are predicted values)', fontsize=30) 73 | plt.xlabel('x', fontsize=20) 74 | plt.ylabel('y', fontsize=20) 75 | plt.xticks(fontsize=20) 76 | plt.yticks(fontsize=20) 77 | def draw(yi, color): 78 | plt.plot(np.arange(input.size(1)), yi[:input.size(1)], color, linewidth = 2.0) 79 | plt.plot(np.arange(input.size(1), input.size(1) + future), yi[input.size(1):], color + ':', linewidth = 2.0) 80 | draw(y[0], 'r') 81 | draw(y[1], 'g') 82 | draw(y[2], 'b') 83 | plt.savefig('predict%d.pdf'%i) 84 | plt.close() 85 | 86 | -------------------------------------------------------------------------------- /vae/README.md: -------------------------------------------------------------------------------- 1 | # Basic VAE Example 2 | 3 | This is an improved implementation of the paper [Stochastic Gradient VB and the 4 | Variational Auto-Encoder](http://arxiv.org/abs/1312.6114) by Kingma and Welling. 5 | It uses ReLUs and the adam optimizer, instead of sigmoids and adagrad. These changes make the network converge much faster. 6 | 7 | ```bash 8 | pip install -r requirements.txt 9 | python main.py 10 | ``` 11 | -------------------------------------------------------------------------------- /vae/main.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import argparse 3 | import torch 4 | import torch.utils.data 5 | import torch.nn as nn 6 | import torch.optim as optim 7 | from torch.autograd import Variable 8 | from torchvision import datasets, transforms 9 | 10 | parser = argparse.ArgumentParser(description='PyTorch MNIST Example') 11 | parser.add_argument('--batch-size', type=int, default=128, metavar='N', 12 | help='input batch size for training (default: 64)') 13 | parser.add_argument('--epochs', type=int, default=10, metavar='N', 14 | help='number of epochs to train (default: 2)') 15 | parser.add_argument('--no-cuda', action='store_true', default=False, 16 | help='enables CUDA training') 17 | parser.add_argument('--seed', type=int, default=1, metavar='S', 18 | help='random seed (default: 1)') 19 | parser.add_argument('--log-interval', type=int, default=10, metavar='N', 20 | help='how many batches to wait before logging training status') 21 | args = parser.parse_args() 22 | args.cuda = not args.no_cuda and torch.cuda.is_available() 23 | 24 | 25 | torch.manual_seed(args.seed) 26 | if args.cuda: 27 | torch.cuda.manual_seed(args.seed) 28 | 29 | 30 | kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} 31 | train_loader = torch.utils.data.DataLoader( 32 | datasets.MNIST('../data', train=True, download=True, 33 | transform=transforms.ToTensor()), 34 | batch_size=args.batch_size, shuffle=True, **kwargs) 35 | test_loader = torch.utils.data.DataLoader( 36 | datasets.MNIST('../data', train=False, transform=transforms.ToTensor()), 37 | batch_size=args.batch_size, shuffle=True, **kwargs) 38 | 39 | 40 | class VAE(nn.Module): 41 | def __init__(self): 42 | super(VAE, self).__init__() 43 | 44 | self.fc1 = nn.Linear(784, 400) 45 | self.fc21 = nn.Linear(400, 20) 46 | self.fc22 = nn.Linear(400, 20) 47 | self.fc3 = nn.Linear(20, 400) 48 | self.fc4 = nn.Linear(400, 784) 49 | 50 | self.relu = nn.ReLU() 51 | self.sigmoid = nn.Sigmoid() 52 | 53 | def encode(self, x): 54 | h1 = self.relu(self.fc1(x)) 55 | return self.fc21(h1), self.fc22(h1) 56 | 57 | def reparametrize(self, mu, logvar): 58 | std = logvar.mul(0.5).exp_() 59 | if args.cuda: 60 | eps = torch.cuda.FloatTensor(std.size()).normal_() 61 | else: 62 | eps = torch.FloatTensor(std.size()).normal_() 63 | eps = Variable(eps) 64 | return eps.mul(std).add_(mu) 65 | 66 | def decode(self, z): 67 | h3 = self.relu(self.fc3(z)) 68 | return self.sigmoid(self.fc4(h3)) 69 | 70 | def forward(self, x): 71 | mu, logvar = self.encode(x.view(-1, 784)) 72 | z = self.reparametrize(mu, logvar) 73 | return self.decode(z), mu, logvar 74 | 75 | 76 | model = VAE() 77 | if args.cuda: 78 | model.cuda() 79 | 80 | reconstruction_function = nn.BCELoss() 81 | reconstruction_function.size_average = False 82 | 83 | 84 | def loss_function(recon_x, x, mu, logvar): 85 | BCE = reconstruction_function(recon_x, x) 86 | 87 | # see Appendix B from VAE paper: 88 | # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014 89 | # https://arxiv.org/abs/1312.6114 90 | # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) 91 | KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar) 92 | KLD = torch.sum(KLD_element).mul_(-0.5) 93 | 94 | return BCE + KLD 95 | 96 | 97 | optimizer = optim.Adam(model.parameters(), lr=1e-3) 98 | 99 | 100 | def train(epoch): 101 | model.train() 102 | train_loss = 0 103 | for batch_idx, (data, _) in enumerate(train_loader): 104 | data = Variable(data) 105 | if args.cuda: 106 | data = data.cuda() 107 | optimizer.zero_grad() 108 | recon_batch, mu, logvar = model(data) 109 | loss = loss_function(recon_batch, data, mu, logvar) 110 | loss.backward() 111 | train_loss += loss.data[0] 112 | optimizer.step() 113 | if batch_idx % args.log_interval == 0: 114 | print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( 115 | epoch, batch_idx * len(data), len(train_loader.dataset), 116 | 100. * batch_idx / len(train_loader), 117 | loss.data[0] / len(data))) 118 | 119 | print('====> Epoch: {} Average loss: {:.4f}'.format( 120 | epoch, train_loss / len(train_loader.dataset))) 121 | 122 | 123 | def test(epoch): 124 | model.eval() 125 | test_loss = 0 126 | for data, _ in test_loader: 127 | if args.cuda: 128 | data = data.cuda() 129 | data = Variable(data, volatile=True) 130 | recon_batch, mu, logvar = model(data) 131 | test_loss += loss_function(recon_batch, data, mu, logvar).data[0] 132 | 133 | test_loss /= len(test_loader.dataset) 134 | print('====> Test set loss: {:.4f}'.format(test_loss)) 135 | 136 | 137 | for epoch in range(1, args.epochs + 1): 138 | train(epoch) 139 | test(epoch) 140 | -------------------------------------------------------------------------------- /vae/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchvision 3 | tqdm 4 | six 5 | -------------------------------------------------------------------------------- /word_language_model/README.md: -------------------------------------------------------------------------------- 1 | # Word-level language modeling RNN 2 | 3 | This example trains a multi-layer RNN (Elman, GRU, or LSTM) on a language modeling task. 4 | By default, the training script uses the PTB dataset, provided. 5 | The trained model can then be used by the generate script to generate new text. 6 | 7 | ```bash 8 | python main.py --cuda --epochs 6 # Train a LSTM on PTB with CUDA, reaching perplexity of 117.61 9 | python main.py --cuda --epochs 6 --tied # Train a tied LSTM on PTB with CUDA, reaching perplexity of 110.44 10 | python main.py --cuda --tied # Train a tied LSTM on PTB with CUDA for 40 epochs, reaching perplexity of 87.17 11 | python generate.py # Generate samples from the trained LSTM model. 12 | ``` 13 | 14 | The model uses the `nn.RNN` module (and its sister modules `nn.GRU` and `nn.LSTM`) 15 | which will automatically use the cuDNN backend if run on CUDA with cuDNN installed. 16 | 17 | During training, if a keyboard interrupt (Ctrl-C) is received, 18 | training is stopped and the current model is evaluted against the test dataset. 19 | 20 | The `main.py` script accepts the following arguments: 21 | 22 | ```bash 23 | optional arguments: 24 | -h, --help show this help message and exit 25 | --data DATA location of the data corpus 26 | --model MODEL type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU) 27 | --emsize EMSIZE size of word embeddings 28 | --nhid NHID humber of hidden units per layer 29 | --nlayers NLAYERS number of layers 30 | --lr LR initial learning rate 31 | --clip CLIP gradient clipping 32 | --epochs EPOCHS upper epoch limit 33 | --batch-size N batch size 34 | --bptt BPTT sequence length 35 | --dropout DROPOUT dropout applied to layers (0 = no dropout) 36 | --decay DECAY learning rate decay per epoch 37 | --tied tie the word embedding and softmax weights 38 | --seed SEED random seed 39 | --cuda use CUDA 40 | --log-interval N report interval 41 | --save SAVE path to save the final model 42 | ``` 43 | 44 | With these arguments, a variety of models can be tested. 45 | As an example, the following arguments produce slower but better models: 46 | 47 | ```bash 48 | python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 # Test perplexity of 80.97 49 | python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 --tied # Test perplexity of 75.96 50 | python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 # Test perplexity of 77.42 51 | python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 --tied # Test perplexity of 72.30 52 | ``` 53 | 54 | These perplexities are equal or better than 55 | [Recurrent Neural Network Regularization (Zaremba et al. 2014)](https://arxiv.org/pdf/1409.2329.pdf) 56 | and are similar to [Using the Output Embedding to Improve Language Models (Press & Wolf 2016](https://arxiv.org/abs/1608.05859) and [Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling (Inan et al. 2016)](https://arxiv.org/pdf/1611.01462.pdf), though both of these papers have improved perplexities by using a form of recurrent dropout [(variational dropout)](http://papers.nips.cc/paper/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks). 57 | -------------------------------------------------------------------------------- /word_language_model/data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | 4 | class Dictionary(object): 5 | def __init__(self): 6 | self.word2idx = {} 7 | self.idx2word = [] 8 | 9 | def add_word(self, word): 10 | if word not in self.word2idx: 11 | self.idx2word.append(word) 12 | self.word2idx[word] = len(self.idx2word) - 1 13 | return self.word2idx[word] 14 | 15 | def __len__(self): 16 | return len(self.idx2word) 17 | 18 | 19 | class Corpus(object): 20 | def __init__(self, path): 21 | self.dictionary = Dictionary() 22 | self.train = self.tokenize(os.path.join(path, 'train.txt')) 23 | self.valid = self.tokenize(os.path.join(path, 'valid.txt')) 24 | self.test = self.tokenize(os.path.join(path, 'test.txt')) 25 | 26 | def tokenize(self, path): 27 | """Tokenizes a text file.""" 28 | assert os.path.exists(path) 29 | # Add words to the dictionary 30 | with open(path, 'r') as f: 31 | tokens = 0 32 | for line in f: 33 | words = line.split() + [''] 34 | tokens += len(words) 35 | for word in words: 36 | self.dictionary.add_word(word) 37 | 38 | # Tokenize file content 39 | with open(path, 'r') as f: 40 | ids = torch.LongTensor(tokens) 41 | token = 0 42 | for line in f: 43 | words = line.split() + [''] 44 | for word in words: 45 | ids[token] = self.dictionary.word2idx[word] 46 | token += 1 47 | 48 | return ids 49 | -------------------------------------------------------------------------------- /word_language_model/generate.py: -------------------------------------------------------------------------------- 1 | ############################################################################### 2 | # Language Modeling on Penn Tree Bank 3 | # 4 | # This file generates new sentences sampled from the language model 5 | # 6 | ############################################################################### 7 | 8 | import argparse 9 | 10 | import torch 11 | from torch.autograd import Variable 12 | 13 | import data 14 | 15 | parser = argparse.ArgumentParser(description='PyTorch PTB Language Model') 16 | 17 | # Model parameters. 18 | parser.add_argument('--data', type=str, default='./data/penn', 19 | help='location of the data corpus') 20 | parser.add_argument('--checkpoint', type=str, default='./model.pt', 21 | help='model checkpoint to use') 22 | parser.add_argument('--outf', type=str, default='generated.txt', 23 | help='output file for generated text') 24 | parser.add_argument('--words', type=int, default='1000', 25 | help='number of words to generate') 26 | parser.add_argument('--seed', type=int, default=1111, 27 | help='random seed') 28 | parser.add_argument('--cuda', action='store_true', 29 | help='use CUDA') 30 | parser.add_argument('--temperature', type=float, default=1.0, 31 | help='temperature - higher will increase diversity') 32 | parser.add_argument('--log-interval', type=int, default=100, 33 | help='reporting interval') 34 | args = parser.parse_args() 35 | 36 | # Set the random seed manually for reproducibility. 37 | torch.manual_seed(args.seed) 38 | if torch.cuda.is_available(): 39 | if not args.cuda: 40 | print("WARNING: You have a CUDA device, so you should probably run with --cuda") 41 | else: 42 | torch.cuda.manual_seed(args.seed) 43 | 44 | if args.temperature < 1e-3: 45 | parser.error("--temperature has to be greater or equal 1e-3") 46 | 47 | with open(args.checkpoint, 'rb') as f: 48 | model = torch.load(f) 49 | model.eval() 50 | 51 | if args.cuda: 52 | model.cuda() 53 | else: 54 | model.cpu() 55 | 56 | corpus = data.Corpus(args.data) 57 | ntokens = len(corpus.dictionary) 58 | hidden = model.init_hidden(1) 59 | input = Variable(torch.rand(1, 1).mul(ntokens).long(), volatile=True) 60 | if args.cuda: 61 | input.data = input.data.cuda() 62 | 63 | with open(args.outf, 'w') as outf: 64 | for i in range(args.words): 65 | output, hidden = model(input, hidden) 66 | word_weights = output.squeeze().data.div(args.temperature).exp().cpu() 67 | word_idx = torch.multinomial(word_weights, 1)[0] 68 | input.data.fill_(word_idx) 69 | word = corpus.dictionary.idx2word[word_idx] 70 | 71 | outf.write(word + ('\n' if i % 20 == 19 else ' ')) 72 | 73 | if i % args.log_interval == 0: 74 | print('| Generated {}/{} words'.format(i, args.words)) 75 | -------------------------------------------------------------------------------- /word_language_model/main.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | import math 4 | import torch 5 | import torch.nn as nn 6 | from torch.autograd import Variable 7 | 8 | import data 9 | import model 10 | 11 | parser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model') 12 | parser.add_argument('--data', type=str, default='./data/penn', 13 | help='location of the data corpus') 14 | parser.add_argument('--model', type=str, default='LSTM', 15 | help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)') 16 | parser.add_argument('--emsize', type=int, default=200, 17 | help='size of word embeddings') 18 | parser.add_argument('--nhid', type=int, default=200, 19 | help='number of hidden units per layer') 20 | parser.add_argument('--nlayers', type=int, default=2, 21 | help='number of layers') 22 | parser.add_argument('--lr', type=float, default=20, 23 | help='initial learning rate') 24 | parser.add_argument('--clip', type=float, default=0.25, 25 | help='gradient clipping') 26 | parser.add_argument('--epochs', type=int, default=40, 27 | help='upper epoch limit') 28 | parser.add_argument('--batch_size', type=int, default=20, metavar='N', 29 | help='batch size') 30 | parser.add_argument('--bptt', type=int, default=35, 31 | help='sequence length') 32 | parser.add_argument('--dropout', type=float, default=0.2, 33 | help='dropout applied to layers (0 = no dropout)') 34 | parser.add_argument('--tied', action='store_true', 35 | help='tie the word embedding and softmax weights') 36 | parser.add_argument('--seed', type=int, default=1111, 37 | help='random seed') 38 | parser.add_argument('--cuda', action='store_true', 39 | help='use CUDA') 40 | parser.add_argument('--log-interval', type=int, default=200, metavar='N', 41 | help='report interval') 42 | parser.add_argument('--save', type=str, default='model.pt', 43 | help='path to save the final model') 44 | args = parser.parse_args() 45 | 46 | # Set the random seed manually for reproducibility. 47 | torch.manual_seed(args.seed) 48 | if torch.cuda.is_available(): 49 | if not args.cuda: 50 | print("WARNING: You have a CUDA device, so you should probably run with --cuda") 51 | else: 52 | torch.cuda.manual_seed(args.seed) 53 | 54 | ############################################################################### 55 | # Load data 56 | ############################################################################### 57 | 58 | corpus = data.Corpus(args.data) 59 | 60 | def batchify(data, bsz): 61 | # Work out how cleanly we can divide the dataset into bsz parts. 62 | nbatch = data.size(0) // bsz 63 | # Trim off any extra elements that wouldn't cleanly fit (remainders). 64 | data = data.narrow(0, 0, nbatch * bsz) 65 | # Evenly divide the data across the bsz batches. 66 | data = data.view(bsz, -1).t().contiguous() 67 | if args.cuda: 68 | data = data.cuda() 69 | return data 70 | 71 | eval_batch_size = 10 72 | train_data = batchify(corpus.train, args.batch_size) 73 | val_data = batchify(corpus.valid, eval_batch_size) 74 | test_data = batchify(corpus.test, eval_batch_size) 75 | 76 | ############################################################################### 77 | # Build the model 78 | ############################################################################### 79 | 80 | ntokens = len(corpus.dictionary) 81 | model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.nlayers, args.dropout, args.tied) 82 | if args.cuda: 83 | model.cuda() 84 | 85 | criterion = nn.CrossEntropyLoss() 86 | 87 | ############################################################################### 88 | # Training code 89 | ############################################################################### 90 | 91 | def repackage_hidden(h): 92 | """Wraps hidden states in new Variables, to detach them from their history.""" 93 | if type(h) == Variable: 94 | return Variable(h.data) 95 | else: 96 | return tuple(repackage_hidden(v) for v in h) 97 | 98 | 99 | def get_batch(source, i, evaluation=False): 100 | seq_len = min(args.bptt, len(source) - 1 - i) 101 | data = Variable(source[i:i+seq_len], volatile=evaluation) 102 | target = Variable(source[i+1:i+1+seq_len].view(-1)) 103 | return data, target 104 | 105 | 106 | def evaluate(data_source): 107 | # Turn on evaluation mode which disables dropout. 108 | model.eval() 109 | total_loss = 0 110 | ntokens = len(corpus.dictionary) 111 | hidden = model.init_hidden(eval_batch_size) 112 | for i in range(0, data_source.size(0) - 1, args.bptt): 113 | data, targets = get_batch(data_source, i, evaluation=True) 114 | output, hidden = model(data, hidden) 115 | output_flat = output.view(-1, ntokens) 116 | total_loss += len(data) * criterion(output_flat, targets).data 117 | hidden = repackage_hidden(hidden) 118 | return total_loss[0] / len(data_source) 119 | 120 | 121 | def train(): 122 | # Turn on training mode which enables dropout. 123 | model.train() 124 | total_loss = 0 125 | start_time = time.time() 126 | ntokens = len(corpus.dictionary) 127 | hidden = model.init_hidden(args.batch_size) 128 | for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)): 129 | data, targets = get_batch(train_data, i) 130 | # Starting each batch, we detach the hidden state from how it was previously produced. 131 | # If we didn't, the model would try backpropagating all the way to start of the dataset. 132 | hidden = repackage_hidden(hidden) 133 | model.zero_grad() 134 | output, hidden = model(data, hidden) 135 | loss = criterion(output.view(-1, ntokens), targets) 136 | loss.backward() 137 | 138 | # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs. 139 | torch.nn.utils.clip_grad_norm(model.parameters(), args.clip) 140 | for p in model.parameters(): 141 | p.data.add_(-lr, p.grad.data) 142 | 143 | total_loss += loss.data 144 | 145 | if batch % args.log_interval == 0 and batch > 0: 146 | cur_loss = total_loss[0] / args.log_interval 147 | elapsed = time.time() - start_time 148 | print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | ' 149 | 'loss {:5.2f} | ppl {:8.2f}'.format( 150 | epoch, batch, len(train_data) // args.bptt, lr, 151 | elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss))) 152 | total_loss = 0 153 | start_time = time.time() 154 | 155 | # Loop over epochs. 156 | lr = args.lr 157 | best_val_loss = None 158 | 159 | # At any point you can hit Ctrl + C to break out of training early. 160 | try: 161 | for epoch in range(1, args.epochs+1): 162 | epoch_start_time = time.time() 163 | train() 164 | val_loss = evaluate(val_data) 165 | print('-' * 89) 166 | print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | ' 167 | 'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time), 168 | val_loss, math.exp(val_loss))) 169 | print('-' * 89) 170 | # Save the model if the validation loss is the best we've seen so far. 171 | if not best_val_loss or val_loss < best_val_loss: 172 | with open(args.save, 'wb') as f: 173 | torch.save(model, f) 174 | best_val_loss = val_loss 175 | else: 176 | # Anneal the learning rate if no improvement has been seen in the validation dataset. 177 | lr /= 4.0 178 | except KeyboardInterrupt: 179 | print('-' * 89) 180 | print('Exiting from training early') 181 | 182 | # Load the best saved model. 183 | with open(args.save, 'rb') as f: 184 | model = torch.load(f) 185 | 186 | # Run on test data. 187 | test_loss = evaluate(test_data) 188 | print('=' * 89) 189 | print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format( 190 | test_loss, math.exp(test_loss))) 191 | print('=' * 89) 192 | -------------------------------------------------------------------------------- /word_language_model/model.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from torch.autograd import Variable 3 | 4 | class RNNModel(nn.Module): 5 | """Container module with an encoder, a recurrent module, and a decoder.""" 6 | 7 | def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False): 8 | super(RNNModel, self).__init__() 9 | self.drop = nn.Dropout(dropout) 10 | self.encoder = nn.Embedding(ntoken, ninp) 11 | if rnn_type in ['LSTM', 'GRU']: 12 | self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout) 13 | else: 14 | try: 15 | nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type] 16 | except KeyError: 17 | raise ValueError( """An invalid option for `--model` was supplied, 18 | options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']""") 19 | self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout) 20 | self.decoder = nn.Linear(nhid, ntoken) 21 | 22 | # Optionally tie weights as in: 23 | # "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016) 24 | # https://arxiv.org/abs/1608.05859 25 | # and 26 | # "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016) 27 | # https://arxiv.org/abs/1611.01462 28 | if tie_weights: 29 | if nhid != ninp: 30 | raise ValueError('When using the tied flag, nhid must be equal to emsize') 31 | self.decoder.weight = self.encoder.weight 32 | 33 | self.init_weights() 34 | 35 | self.rnn_type = rnn_type 36 | self.nhid = nhid 37 | self.nlayers = nlayers 38 | 39 | def init_weights(self): 40 | initrange = 0.1 41 | self.encoder.weight.data.uniform_(-initrange, initrange) 42 | self.decoder.bias.data.fill_(0) 43 | self.decoder.weight.data.uniform_(-initrange, initrange) 44 | 45 | def forward(self, input, hidden): 46 | emb = self.drop(self.encoder(input)) 47 | output, hidden = self.rnn(emb, hidden) 48 | output = self.drop(output) 49 | decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2))) 50 | return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden 51 | 52 | def init_hidden(self, bsz): 53 | weight = next(self.parameters()).data 54 | if self.rnn_type == 'LSTM': 55 | return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()), 56 | Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())) 57 | else: 58 | return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()) 59 | -------------------------------------------------------------------------------- /word_language_model/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | --------------------------------------------------------------------------------