experiment.ja.json

{
 "<h1><a href=\"index.html\">Denoising Diffusion Probabilistic Models (DDPM)</a> training</h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p>This trains a DDPM based model on CelebA HQ dataset. You can find the download instruction in this <a href=\"https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3\">discussion on fast.ai</a>. Save the images inside <a href=\"#dataset_path\"><span translate=no>_^_1_^_</span> folder</a>.</p>\n<p>The paper had used a exponential moving average of the model with a decay of <span translate=no>_^_2_^_</span>. We have skipped this for simplicity.</p>\n": "<h1><a href=\"index.html\">\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb</a> (DDPM) \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001CeleBA HQ \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067 DDPM \u30d9\u30fc\u30b9\u306e\u30e2\u30c7\u30eb\u304c\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u307e\u3059\u3002\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u8aac\u660e\u306f\u3001<a href=\"https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3\">fast.ai \u306e\u3053\u306e\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u306b\u3042\u308a\u307e\u3059</a>\u3002<a href=\"#dataset_path\"><span translate=no>_^_1_^_</span>\u753b\u50cf\u3092\u30d5\u30a9\u30eb\u30c0\u30fc\u306b\u4fdd\u5b58\u3057\u307e\u3059</a>\u3002</p>\n<p>\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001\u30e2\u30c7\u30eb\u306e\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3092\u6e1b\u8870\u3055\u305b\u3066\u4f7f\u7528\u3057\u3066\u3044\u307e\u3057\u305f\u3002<span translate=no>_^_2_^_</span>\u7c21\u7565\u5316\u306e\u305f\u3081\u3001\u3053\u3053\u3067\u306f\u7701\u7565\u3057\u3066\u3044\u307e\u3059</p>\u3002\n",
 "<h2>Configurations</h2>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n",
 "<h3>CelebA HQ dataset</h3>\n": "<h3>CeleBA \u672c\u793e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</h3>\n",
 "<h3>MNIST dataset</h3>\n": "<h3>MNIST \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</h3>\n",
 "<h3>Sample images</h3>\n": "<h3>\u30b5\u30f3\u30d7\u30eb\u753b\u50cf</h3>\n",
 "<h3>Train</h3>\n": "<h3>\u5217\u8eca</h3>\n",
 "<h3>Training loop</h3>\n": "<h3>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n",
 "<p> </p>\n": "<p></p>\n",
 "<p> Create CelebA dataset</p>\n": "<p>CeleBA \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f5c\u6210</p>\n",
 "<p> Create MNIST dataset</p>\n": "<p>MNIST \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f5c\u6210</p>\n",
 "<p> Get an image</p>\n": "<p>\u753b\u50cf\u3092\u53d6\u5f97</p>\n",
 "<p> Size of the dataset</p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30a4\u30ba</p>\n",
 "<p><a href=\"index.html\">DDPM algorithm</a> </p>\n": "<p><a href=\"index.html\">DDPM \u30a2\u30eb\u30b4\u30ea\u30ba\u30e0</a></p>\n",
 "<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
 "<p>Adam optimizer </p>\n": "<p>\u30a2\u30c0\u30e0\u30fb\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",
 "<p>Batch size </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba</p>\n",
 "<p>Calculate loss </p>\n": "<p>\u640d\u5931\u306e\u8a08\u7b97</p>\n",
 "<p>CelebA images folder </p>\n": "<p>\u30bb\u30ec\u30d0\u753b\u50cf\u30d5\u30a9\u30eb\u30c0\u30fc</p>\n",
 "<p>Compute gradients </p>\n": "<p>\u52fe\u914d\u306e\u8a08\u7b97</p>\n",
 "<p>Create <a href=\"index.html\">DDPM class</a> </p>\n": "<p><a href=\"index.html\">DDPM \u30af\u30e9\u30b9\u306e\u4f5c\u6210</a></p>\n",
 "<p>Create <span translate=no>_^_0_^_</span> model </p>\n": "<p><span translate=no>_^_0_^_</span>\u30e2\u30c7\u30eb\u4f5c\u6210</p>\n",
 "<p>Create configurations </p>\n": "<p>\u69cb\u6210\u306e\u4f5c\u6210</p>\n",
 "<p>Create dataloader </p>\n": "<p>\u30c7\u30fc\u30bf\u30ed\u30fc\u30c0\u30fc\u306e\u4f5c\u6210</p>\n",
 "<p>Create experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u4f5c\u6210</p>\n",
 "<p>Create optimizer </p>\n": "<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u3092\u4f5c\u6210</p>\n",
 "<p>Dataloader </p>\n": "<p>\u30c7\u30fc\u30bf\u30ed\u30fc\u30c0\u30fc</p>\n",
 "<p>Dataset </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</p>\n",
 "<p>Device to train the model on. <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a>  picks up an available CUDA device or defaults to CPU. </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u30c7\u30d0\u30a4\u30b9\u3002<a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a>\u4f7f\u7528\u53ef\u80fd\u306a CUDA \u30c7\u30d0\u30a4\u30b9\u3092\u9078\u629e\u3059\u308b\u304b\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u3067 CPU \u306b\u8a2d\u5b9a\u3057\u307e\u3059</p>\u3002\n",
 "<p>Image logging </p>\n": "<p>\u753b\u50cf\u30ed\u30ae\u30f3\u30b0</p>\n",
 "<p>Image size </p>\n": "<p>\u753b\u50cf\u30b5\u30a4\u30ba</p>\n",
 "<p>Increment global step </p>\n": "<p>\u30b0\u30ed\u30fc\u30d0\u30eb\u30b9\u30c6\u30c3\u30d7\u3092\u30a4\u30f3\u30af\u30ea\u30e1\u30f3\u30c8</p>\n",
 "<p>Initialize </p>\n": "<p>[\u521d\u671f\u5316]</p>\n",
 "<p>Iterate through the dataset </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53cd\u5fa9\u51e6\u7406</p>\n",
 "<p>Learning rate </p>\n": "<p>\u5b66\u7fd2\u7387</p>\n",
 "<p>List of files </p>\n": "<p>\u30d5\u30a1\u30a4\u30eb\u30ea\u30b9\u30c8</p>\n",
 "<p>Log samples </p>\n": "<p>\u30ed\u30b0\u30b5\u30f3\u30d7\u30eb</p>\n",
 "<p>Make the gradients zero </p>\n": "<p>\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3\u3092\u30bc\u30ed\u306b\u3059\u308b</p>\n",
 "<p>Move data to device </p>\n": "<p>\u30c7\u30fc\u30bf\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
 "<p>New line in the console </p>\n": "<p>\u30b3\u30f3\u30bd\u30fc\u30eb\u306e\u65b0\u3057\u3044\u884c</p>\n",
 "<p>Number of channels in the image. <span translate=no>_^_0_^_</span> for RGB. </p>\n": "<p>\u753b\u50cf\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3002<span translate=no>_^_0_^_</span>RGB \u7528\u3067\u3059\u3002</p>\n",
 "<p>Number of channels in the initial feature map </p>\n": "<p>\u521d\u671f\u6a5f\u80fd\u30de\u30c3\u30d7\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
 "<p>Number of samples to generate </p>\n": "<p>\u751f\u6210\u3059\u308b\u30b5\u30f3\u30d7\u30eb\u306e\u6570</p>\n",
 "<p>Number of time steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u6570 <span translate=no>_^_0_^_</span></p>\n",
 "<p>Number of training epochs </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30a8\u30dd\u30c3\u30af\u306e\u6570</p>\n",
 "<p>Remove noise for <span translate=no>_^_0_^_</span> steps </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b9\u30c6\u30c3\u30d7\u306e\u30ce\u30a4\u30ba\u9664\u53bb</p>\n",
 "<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></p>\n",
 "<p>Sample some images </p>\n": "<p>\u3044\u304f\u3064\u304b\u306e\u753b\u50cf\u306e\u30b5\u30f3\u30d7\u30eb</p>\n",
 "<p>Save the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u4fdd\u5b58\u3059\u308b</p>\n",
 "<p>Set configurations. You can override the defaults by passing the values in the dictionary. </p>\n": "<p>\u69cb\u6210\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u30c7\u30a3\u30af\u30b7\u30e7\u30ca\u30ea\u306b\u5024\u3092\u6e21\u3059\u3053\u3068\u3067\u30c7\u30d5\u30a9\u30eb\u30c8\u3092\u30aa\u30fc\u30d0\u30fc\u30e9\u30a4\u30c9\u3067\u304d\u307e\u3059\u3002</p>\n",
 "<p>Set models for saving and loading </p>\n": "<p>\u4fdd\u5b58\u304a\u3088\u3073\u8aad\u307f\u8fbc\u307f\u7528\u306e\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b</p>\n",
 "<p>Start and run the training loop </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3092\u958b\u59cb\u3057\u3066\u5b9f\u884c\u3059\u308b</p>\n",
 "<p>Take an optimization step </p>\n": "<p>\u6700\u9069\u5316\u306e\u4e00\u6b69\u3092\u8e0f\u307f\u51fa\u3059</p>\n",
 "<p>The list of booleans that indicate whether to use attention at each resolution </p>\n": "<p>\u5404\u89e3\u50cf\u5ea6\u3067\u6ce8\u610f\u3092\u5411\u3051\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3059\u30d6\u30fc\u30ea\u30a2\u30f3\u306e\u30ea\u30b9\u30c8</p>\n",
 "<p>The list of channel numbers at each resolution. The number of channels is <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5404\u89e3\u50cf\u5ea6\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u756a\u53f7\u306e\u30ea\u30b9\u30c8\u3002\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306f <span translate=no>_^_0_^_</span></p>\n",
 "<p>Track the loss </p>\n": "<p>\u640d\u5931\u3092\u30c8\u30e9\u30c3\u30ad\u30f3\u30b0</p>\n",
 "<p>Train the model </p>\n": "<p>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</p>\n",
 "<p>Transformations to resize the image and convert to tensor </p>\n": "<p>\u753b\u50cf\u306e\u30b5\u30a4\u30ba\u3092\u5909\u66f4\u3057\u3066\u30c6\u30f3\u30bd\u30eb\u306b\u5909\u63db\u3059\u308b\u5909\u63db</p>\n",
 "<p>U-Net model for <span translate=no>_^_0_^_</span> </p>\n": "<p>\u7528\u306e U-Net \u30e2\u30c7\u30eb <span translate=no>_^_0_^_</span></p>\n",
 "Denoising Diffusion Probabilistic Models (DDPM) training": "\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM) \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0",
 "Training code for Denoising Diffusion Probabilistic Model.": "\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b3\u30fc\u30c9"
}