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        "author": "Guanglei Ding",
        "author_email": "dingguanglei.bupt@qq.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: Implementation",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "![Jdit](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/logo.png)\n\n---\n\n[![](http://img.shields.io/travis/dingguanglei/jdit.svg)](https://github.com/dingguanglei/jdit)\n[![Documentation Status](https://readthedocs.org/projects/jdit/badge/?version=latest)](https://jdit.readthedocs.io/en/latest/?badge=latest)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/1a5aa0721e2f44ebb61b0b73627bbc90)](https://www.codacy.com/app/dingguanglei/jdit?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=dingguanglei/jdit&amp;utm_campaign=Badge_Grade)\n![Packagist](https://img.shields.io/hexpm/l/plug.svg)\n![pypi download](https://img.shields.io/pypi/dm/jdit.svg)\n\n**Jdit** is a research processing oriented framework based on pytorch.\nOnly care about your ideas. You don't need to build a long boring code\nto run a deep learning project to verify your ideas.\n\nYou only need to implement you ideas and don't do anything with training\nframework, multiply-gpus, checkpoint, process visualization, performance\nevaluation and so on.\n\nGuide: [https://dingguanglei.com/tag/jdit](https://dingguanglei.com/tag/jdit)\n\nDocs: [https://jdit.readthedocs.io/en/latest/index.html](https://jdit.readthedocs.io/en/latest/index.html)\n\nIf you have any problems, or you find bugs you can contact the author.\n\nE-mail: dingguanglei.bupt@qq.com\n\n## Install \nRequires:\n``` {.sourceCode .bash}\ntensorboard >= 1.14.0\npytorch >= 1.1.0\n``` \nInstall requirement.\n``` {.sourceCode .bash}\npip install -r requirements.txt\n```\n### From source\nThis method is recommended, because you can keep the newest version.\n1.  Clone from github\n    ``` {.sourceCode .bash}\n    git clone https://github.com/dingguanglei/jdit\n    ```\n\n2.  Setup\n    By using `setup.py` to install the package.\n    ``` {.sourceCode .bash}\n    python setup.py bdist_wheel\n    ```\n\n3.  Install\n    You will find packages in `jdit/dist/`. Use pip to install.\n    ``` {.sourceCode .bash}\n    pip install dist/jdit-0.x.0-py3-none-any.whl\n    ```\n\n### From pip\n``` {.sourceCode .bash}\npip install jdit\n```\n\n## Quick start\n\nAfter building and installing jdit package, you can make a new directory\nfor a quick test. Assuming that you get a new directory example. run\nthis code in ipython cmd.(Create a main.py file is also acceptable.)\n\n``` {.sourceCode .python}\nfrom jdit.trainer.instances.fashingClassification import start_fashingClassTrainer\nstart_fashingClassTrainer()\n```\nThe following is the accomplishment of ``start_fashingClassTrainer()``\n\n``` {.sourceCode .python}\n# coding=utf-8\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom jdit.trainer.classification import ClassificationTrainer\nfrom jdit import Model\nfrom jdit.optimizer import Optimizer\nfrom jdit.dataset import FashionMNIST\n\n# This is your model. Defined by torch.nn.Module\nclass SimpleModel(nn.Module):\n    def __init__(self, depth=64, num_class=10):\n        super(SimpleModel, self).__init__()\n        self.num_class = num_class\n        self.layer1 = nn.Conv2d(1, depth, 3, 1, 1)\n        self.layer2 = nn.Conv2d(depth, depth * 2, 4, 2, 1)\n        self.layer3 = nn.Conv2d(depth * 2, depth * 4, 4, 2, 1)\n        self.layer4 = nn.Conv2d(depth * 4, depth * 8, 4, 2, 1)\n        self.layer5 = nn.Conv2d(depth * 8, num_class, 4, 1, 0)\n\n    def forward(self, input):\n        out = F.relu(self.layer1(input))\n        out = F.relu(self.layer2(out))\n        out = F.relu(self.layer3(out))\n        out = F.relu(self.layer4(out))\n        out = self.layer5(out)\n        out = out.view(-1, self.num_class)\n        return out\n\n# A trainer, you need to rewrite the loss and valid function.\nclass FashingClassTrainer(ClassificationTrainer):\n    def __init__(self, logdir, nepochs, gpu_ids, net, opt, datasets, num_class):\n        super(FashingClassTrainer, self).__init__(logdir, nepochs, gpu_ids, net, opt, datasets, num_class)\n        data, label = self.datasets.samples_train\n        # plot samples of dataset in tensorboard.\n        self.watcher.embedding(data, data, label, 1)\n\n    def compute_loss(self):\n        var_dic = {}\n        var_dic[\"CEP\"] = loss = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())\n\n        _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2\n        total = predict.size(0) * 1.0\n        labels = self.labels.squeeze().long()\n        correct = predict.eq(labels).cpu().sum().float()\n        acc = correct / total\n        var_dic[\"ACC\"] = acc\n        return loss, var_dic\n\n    def compute_valid(self):\n        var_dic = {}\n        var_dic[\"CEP\"] = cep = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())\n\n        _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2\n        total = predict.size(0) * 1.0\n        labels = self.labels.squeeze().long()\n        correct = predict.eq(labels).cpu().sum().float()\n        acc = correct / total\n        var_dic[\"ACC\"] = acc\n        return var_dic\n\n\ndef start_fashingClassTrainer(gpus=(), nepochs=10, run_type=\"train\"):\n    num_class = 10\n    depth = 32\n    gpus = gpus\n    batch_size = 64\n    nepochs = nepochs\n    logdir = \"log/fashion_classify\"\n    opt_hpm = {\"optimizer\": \"Adam\",\n               \"lr_decay\": 0.94,\n               \"decay_position\": 10,\n               \"decay_type\": \"epoch\",\n               \"lr\": 1e-3,\n               \"weight_decay\": 2e-5,\n               \"betas\": (0.9, 0.99)}\n\n    print('===> Build dataset')\n    mnist = FashionMNIST(batch_size=batch_size)\n    torch.backends.cudnn.benchmark = True\n    print('===> Building model')\n    net = Model(SimpleModel(depth=depth), gpu_ids_abs=gpus, init_method=\"kaiming\", check_point_pos=1)\n    print('===> Building optimizer')\n    opt = Optimizer(net.parameters(), **opt_hpm)\n    print('===> Training')\n    print(\"using `tensorboard --logdir=log` to see learning curves and net structure.\"\n          \"training and valid_epoch data, configures info and checkpoint were save in `log` directory.\")\n    Trainer = FashingClassTrainer(logdir, nepochs, gpus, net, opt, mnist, num_class)\n    if run_type == \"train\":\n        Trainer.train()\n    elif run_type == \"debug\":\n        Trainer.debug()\n\nif __name__ == '__main__':\n    start_fashingClassTrainer()\n```\n\nThen you will see something like this as following.\n\n``` {.sourceCode .python}\n===> Build dataset\nuse 8 thread\nDownloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\nDownloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\nDownloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\nDownloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\nProcessing...\nDone\n===> Building model\nResNet Total number of parameters: 2776522\nResNet model use CPU\napply kaiming weight init\n===> Building optimizer\n===> Training\nusing `tensorboard --logdir=log` to see learning curves and net structure.\ntraining and valid_epoch data, configures info and checkpoint were save in `log` directory.\n  0%|            | 0/10 [00:00<?, ?epoch/s]\n0step [00:00, step?/s]\n```\nTo see learning curves in tensorboard. Pay attention to your code about ``var_dic[\"ACC\"], var_dic[\"CEP\"]``.\nThis will be shown in the tensorboard.\n\n### Learning curves\n\n![tb_scalars](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/tb_scalars.png\n)\n\n### Model structure\n\n![tb_graphs](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/tb_graphs.png)\n\n### Dataset\n\nYou need to apply ``self.watcher.embedding(data, data, label)``)\n\n![tb_projector](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/tb_projector.png)\n\n### Log\nAll of these will be saved in ``log/fashion_classify``, because of parameter ``logdir = \"log/fashion_classify\"``.\n\n![Log list](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_log.jpg)\n\n#### Process data\nFor the most thing that we care about are training process and valid process data. \nThey are saved in ``Train.csv`` and ``Valid.csv``. The following are the content.\n\n##### Train.csv\n\n![Valid data](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_valid.png)\n\n##### Valid.csv\n\n![Training data](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_train.png)\n\n#### Model\nThe info of model will be saved in ``net.csv``. (The file name is given by your variable name(``net``).)\nIf your model changes during the process, it will be recorded in this file.\n\n![Model info](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_net.png)\n\n#### Learning rate trace\nFrom file ``opt.csv`` you can see the learning rate variation. It will be saved only feature changed.\n\n![Optimizer info](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_opt.png)\n\n#### Dataset info\nFrom file ``datasets.csv`` you can see the information of your dataset.\n\n![Dataset info](https://raw.githubusercontent.com/dingguanglei/jdit/master/resources/class_dataset.png)\n\n#### Others\n\n*   For file ``performance.csv``, it saves the memory cost during the training.\n*   For file ``FashingClassTrainer.csv``, it saves some parameters, such as the amount of epochs. \n*   Model checkpoint in the ``checkpoint`` directory.\n\n### Conclusion\nDue to this simple classification example, there are many interesting features not showed here.\nBut you can get a intuitive feeling from this simple example. \nIt seems that your code doesn't have these functions. So, this is what Jdit did.\n\nAlthough it is just an example, you still can build your own project\neasily by using jdit framework. Jdit framework can deal with \n*   Data visualization. (learning curves, images in pilot process) \n*   CPU, GPU or GPUs. (Training your model on specify devices) \n*   Intermediate data storage. (Saving training data into a csv file) \n*   Model checkpoint automatically. \n*   Flexible templates can be used to integrate and custom overrides. \n*   ...\n\n## More\nFor other templates, you can see and learn form here. \n\nGuide List:\n\n*   [Classification](https://dingguanglei.com/jdit_start)\n\n*   [Generative Adversarial Networks](https://dingguanglei.com/jdit_gan) \n\n*   [Gan for Generation](https://dingguanglei.com/jdit_gan_generataion) \n\n*   [Gan for Pix2pix](https://dingguanglei.com/jdit_gan_pix2pix)\n\n*   [Parallel Task](https://dingguanglei.com/jdit_parallel)\n\n*   ......\n\n\n",
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