{ "info": { "author": "ProGamerGov", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Artistic Software", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "# neural-style-pt\n\nThis is a PyTorch implementation of the paper [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)\nby Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. The code is based on Justin Johnson's [Neural-Style](https://github.com/jcjohnson/neural-style).\n\nThe paper presents an algorithm for combining the content of one image with the style of another image using\nconvolutional neural networks. Here's an example that maps the artistic style of\n[The Starry Night](https://en.wikipedia.org/wiki/The_Starry_Night)\nonto a night-time photograph of the Stanford campus:\n\n
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\n\nApplying the style of different images to the same content image gives interesting results.\nHere we reproduce Figure 2 from the paper, which renders a photograph of the Tubingen in Germany in a\nvariety of styles:\n\n
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\n\nHere are the results of applying the style of various pieces of artwork to this photograph of the\ngolden gate bridge:\n\n\n
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\n\n### Content / Style Tradeoff\n\nThe algorithm allows the user to trade-off the relative weight of the style and content reconstruction terms,\nas shown in this example where we port the style of [Picasso's 1907 self-portrait](http://www.wikiart.org/en/pablo-picasso/self-portrait-1907) onto Brad Pitt:\n\n
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\n\n### Style Scale\n\nBy resizing the style image before extracting style features, we can control the types of artistic\nfeatures that are transfered from the style image; you can control this behavior with the `-style_scale` flag.\nBelow we see three examples of rendering the Golden Gate Bridge in the style of The Starry Night.\nFrom left to right, `-style_scale` is 2.0, 1.0, and 0.5.\n\n
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\n\n### Multiple Style Images\nYou can use more than one style image to blend multiple artistic styles.\n\nClockwise from upper left: \"The Starry Night\" + \"The Scream\", \"The Scream\" + \"Composition VII\",\n\"Seated Nude\" + \"Composition VII\", and \"Seated Nude\" + \"The Starry Night\"\n\n
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\n\n\n### Style Interpolation\nWhen using multiple style images, you can control the degree to which they are blended:\n\n
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\n\n### Transfer style but not color\nIf you add the flag `-original_colors 1` then the output image will retain the colors of the original image.\n\n
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\n\n## Setup:\n\nWhile you can use Python 2's pip, it's recommended that you use Python 3's pip:\n\n```\n# in a terminal, run the command\npip3 install neural-style\n```\n\n\nAfter installing neural-style-pt, you'll need to run the following script to download the default VGG and NIN models:\n\n\n```\nneural-style -download_models\n```\n\nBy default the models are downloaded to your home directory, but you can specify a download location with:\n\n```\nneural-style -download_models \n```\n\nThis will download the original [VGG-19 model](https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md).\nThe original [VGG-16 model](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md) will also be downloaded. By default the original VGG-19 model is used.\n\nIf you have a smaller memory GPU then using NIN Imagenet model will be better and gives slightly worse yet comparable results. You can get the details on the model from [BVLC Caffe ModelZoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). The NIN model is downloaded when you run `neural-style -download_models`.\n\n## Usage\nBasic usage:\n```\nneural-style -style_image -content_image \n```\n\ncuDNN usage with NIN Model:\n```\nneural-style -style_image examples/inputs/picasso_selfport1907.jpg -content_image examples/inputs/brad_pitt.jpg -output_image profile.png -model_file models/nin_imagenet.pth -gpu 0 -backend cudnn -num_iterations 1000 -seed 123 -content_layers relu0,relu3,relu7,relu12 -style_layers relu0,relu3,relu7,relu12 -content_weight 10 -style_weight 500 -image_size 512 -optimizer adam\n```\n\n![cuDNN NIN Model Picasso Brad Pitt](https://raw.githubusercontent.com/ProGamerGov/neural-style-pt/master/examples/outputs/pitt_picasso_nin_cudnn.png)\n\n\nTo use multiple style images, pass a comma-separated list like this:\n\n`-style_image starry_night.jpg,the_scream.jpg`.\n\nNote that paths to images should not contain the `~` character to represent your home directory; you should instead use a relative\npath or a full absolute path.\n\n**Options**:\n\n* `-image_size`: Maximum side length (in pixels) of the generated image. Default is 512.\n* `-style_blend_weights`: The weight for blending the style of multiple style images, as a\n comma-separated list, such as `-style_blend_weights 3,7`. By default all style images\n are equally weighted.\n* `-gpu`: Zero-indexed ID of the GPU to use; for CPU mode set `-gpu` to `c`.\n\n**Optimization options**:\n\n* `-content_weight`: How much to weight the content reconstruction term. Default is 5e0.\n* `-style_weight`: How much to weight the style reconstruction term. Default is 1e2.\n* `-tv_weight`: Weight of total-variation (TV) regularization; this helps to smooth the image.\n Default is 1e-3. Set to 0 to disable TV regularization.\n* `-num_iterations`: Default is 1000.\n* `-init`: Method for generating the generated image; one of `random` or `image`.\n Default is `random` which uses a noise initialization as in the paper; `image`\n initializes with the content image.\n* `-init_image`: Replaces the initialization image with a user specified image.\n* `-optimizer`: The optimization algorithm to use; either `lbfgs` or `adam`; default is `lbfgs`.\n L-BFGS tends to give better results, but uses more memory. Switching to ADAM will reduce memory usage;\n when using ADAM you will probably need to play with other parameters to get good results, especially\n the style weight, content weight, and learning rate.\n* `-learning_rate`: Learning rate to use with the ADAM optimizer. Default is 1e1.\n\n**Output options**:\n\n* `-output_image`: Name of the output image. Default is `out.png`.\n* `-print_iter`: Print progress every `print_iter` iterations. Set to 0 to disable printing.\n* `-save_iter`: Save the image every `save_iter` iterations. Set to 0 to disable saving intermediate results.\n\n**Layer options**:\n\n* `-content_layers`: Comma-separated list of layer names to use for content reconstruction.\n Default is `relu4_2`.\n* `-style_layers`: Comma-separated list of layer names to use for style reconstruction.\n Default is `relu1_1,relu2_1,relu3_1,relu4_1,relu5_1`.\n\n**Other options**:\n\n* `-style_scale`: Scale at which to extract features from the style image. Default is 1.0.\n* `-original_colors`: If you set this to 1, then the output image will keep the colors of the content image.\n* `-model_file`: Path to the `.pth` file for the VGG Caffe model. Default is the original VGG-19 model; you can also try the original VGG-16 model.\n* `-pooling`: The type of pooling layers to use; one of `max` or `avg`. Default is `max`.\n The VGG-19 models uses max pooling layers, but the paper mentions that replacing these layers with average\n pooling layers can improve the results. I haven't been able to get good results using average pooling, but\n the option is here.\n* `-seed`: An integer value that you can specify for repeatable results. By default this value is random for each run.\n* `-multidevice_strategy`: A comma-separated list of layer indices at which to split the network when using multiple devices. See [Multi-GPU scaling](https://github.com/ProGamerGov/neural-style-pt/tree/pip-master#multi-gpu-scaling) for more details.\n* `-backend`: `nn`, `cudnn`, or `mkl`. Default is `nn`.\n `mkl` requires Intel's MKL backend.\n* `-cudnn_autotune`: When using the cuDNN backend, pass this flag to use the built-in cuDNN autotuner to select\n the best convolution algorithms for your architecture. This will make the first iteration a bit slower and can\n take a bit more memory, but may significantly speed up the cuDNN backend.\n* `-download_models`: Path to where the VGG-19, VGG-16, and NIN models will be downloaded to. If no path is specified, the models will be downloaded to your home directory.\n\n## Frequently Asked Questions\n\n**Problem:** The program runs out of memory and dies\n\n**Solution:** Try reducing the image size: `-image_size 256` (or lower). Note that different image sizes will likely\nrequire non-default values for `-style_weight` and `-content_weight` for optimal results.\nIf you are running on a GPU, you can also try running with `-backend cudnn` to reduce memory usage.\n\n**Problem:** `-backend cudnn` is slower than default NN backend\n\n**Solution:** Add the flag `-cudnn_autotune`; this will use the built-in cuDNN autotuner to select the best convolution algorithms.\n\n**Problem:** Get the following error message:\n\n`Missing key(s) in state_dict: \"classifier.0.bias\", \"classifier.0.weight\", \"classifier.3.bias\", \"classifier.3.weight\". Unexpected key(s) in state_dict: \"classifier.1.weight\", \"classifier.1.bias\", \"classifier.4.weight\", \"classifier.4.bias\".`\n\n**Solution:** Due to a mix up with layer locations, older models require a fix to be compatible with newer versions of PyTorch. Donwloading the models with `neural-style -download_models` will automatically perform these fixes after downloading the models. You can find other compatible models [here](https://github.com/ProGamerGov/neural-style-pt/wiki/Other-Models).\n\n\n\n## Memory Usage\nBy default, `neural-style-pt` uses the `nn` backend for convolutions and L-BFGS for optimization. These give good results, but can both use a lot of memory. You can reduce memory usage with the following:\n\n* **Use cuDNN**: Add the flag `-backend cudnn` to use the cuDNN backend. This will only work in GPU mode.\n* **Use ADAM**: Add the flag `-optimizer adam` to use ADAM instead of L-BFGS. This should significantly\n reduce memory usage, but may require tuning of other parameters for good results; in particular you should\n play with the learning rate, content weight, and style weight.\n This should work in both CPU and GPU modes.\n* **Reduce image size**: If the above tricks are not enough, you can reduce the size of the generated image;\n pass the flag `-image_size 256` to generate an image at half the default size.\n\nWith the default settings, neural-style-pt uses about 3.7 GB of GPU memory on my system; switching to ADAM and cuDNN reduces the GPU memory footprint to about 1GB.\n\n## Speed\nSpeed can vary a lot depending on the backend and the optimizer.\nHere are some times for running 500 iterations with `-image_size=512` on a Tesla K80 with different settings:\n* `-backend nn -optimizer lbfgs`: 117 seconds\n* `-backend nn -optimizer adam`: 100 seconds\n* `-backend cudnn -optimizer lbfgs`: 124 seconds\n* `-backend cudnn -optimizer adam`: 107 seconds\n* `-backend cudnn -cudnn_autotune -optimizer lbfgs`: 109 seconds\n* `-backend cudnn -cudnn_autotune -optimizer adam`: 91 seconds\n\nHere are the same benchmarks on a GTX 1080:\n* `-backend nn -optimizer lbfgs`: 56 seconds\n* `-backend nn -optimizer adam`: 38 seconds\n* `-backend cudnn -optimizer lbfgs`: 40 seconds\n* `-backend cudnn -optimizer adam`: 40 seconds\n* `-backend cudnn -cudnn_autotune -optimizer lbfgs`: 23 seconds\n* `-backend cudnn -cudnn_autotune -optimizer adam`: 24 seconds\n\n## Multi-GPU scaling\nYou can use multiple CPU and GPU devices to process images at higher resolutions; different layers of the network will be\ncomputed on different devices. You can control which GPU and CPU devices are used with the `-gpu` flag, and you can control\nhow to split layers across devices using the `-multidevice_strategy` flag.\n\nFor example in a server with four GPUs, you can give the flag `-gpu 0,1,2,3` to process on GPUs 0, 1, 2, and 3 in that order; by also giving the flag `-multidevice_strategy 3,6,12` you indicate that the first two layers should be computed on GPU 0, layers 3 to 5 should be computed on GPU 1, layers 6 to 11 should be computed on GPU 2, and the remaining layers should be computed on GPU 3. You will need to tune the `-multidevice_strategy` for your setup in order to achieve maximal resolution.\n\nWe can achieve very high quality results at high resolution by combining multi-GPU processing with multiscale\ngeneration as described in the paper\n**Controlling Perceptual Factors in Neural Style Transfer** by Leon A. Gatys,\nAlexander S. Ecker, Matthias Bethge, Aaron Hertzmann and Eli Shechtman.\n\n\nHere is a 4016 x 2213 image generated on a server with eight Tesla K80 GPUs:\n\n\n\nThe script used to generate this image can be found here.\n\n## Implementation details\nImages are initialized with white noise and optimized using L-BFGS.\n\nWe perform style reconstructions using the `conv1_1`, `conv2_1`, `conv3_1`, `conv4_1`, and `conv5_1` layers\nand content reconstructions using the `conv4_2` layer. As in the paper, the five style reconstruction losses have\nequal weights.\n\n## Citation\n\nIf you find this code useful for your research, please cite:\n\n```\n@misc{ProGamerGov2018,\nauthor = {ProGamerGov},\ntitle = {neural-style-pt},\nyear = {2018},\npublisher = {GitHub},\njournal = {GitHub repository},\nhowpublished = {\\url{https://github.com/ProGamerGov/neural-style-pt}},\n}\n```\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/ProGamerGov/neural-style-pt/tree/pip-master/", "keywords": "neural artistic style neural-style neural-style-pt pytorch style transfer style-transfer pytorch-style-transfer neuralart neural-art nst neural-style-transfer deepstyle deep-style mlart machine-learning-art aiart ai-art gatys justin-johnson torch deepdream", "license": "", "maintainer": "", "maintainer_email": "", "name": "neural-style", "package_url": 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