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        "author": "OIdiotLin",
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        "description": "# torchtracer\n\n[![Build Status](https://travis-ci.com/OIdiotLin/torchtracer.svg?branch=master)](https://travis-ci.com/OIdiotLin/torchtracer)\n![](https://img.shields.io/badge/python-3.6-blue.svg)\n![](https://img.shields.io/badge/pytorch-0.4.1-orange.svg)\n\n`torchtracer` is a tool package for visualization and storage management in pytorch AI task.\n\n## Getting Started\n\n### PyTorch Required\n\nThis tool is developed for PyTorch AI task. Thus, PyTorch is needed of course.\n\n### Installing\n\nYou can use `pip` to install `torchtracer`.\n\n```bash\npip install torchtracer\n``` \n\n## How to use?\n\n### Import `torchtracer`\n\n```python\nfrom torchtracer import Tracer\n```\n\n### Create an instance of `Tracer`\n\nAssume that the root is `./checkpoints` and current task id is `lmmnb`.\n\n***Avoiding messing working directory, you should make root directory manually.***\n\n```python\ntracer = Tracer('checkpoints').attach('lmmnb')\n```\n\nThis step will create a directory `checkpoints` inside which is a directory `lmmnb` for current AI task.\n\nAlso, you could call `.attach()` without task id. **Datetime will be used as task id.**\n\n```python\ntracer = Tracer('checkpoints').attach()\n```\n\n### Saving config\n\nRaw config should be a `dict` like this:\n\n```python\n# `net` is a defined nn.Module\nargs = {'epoch_n': 120,\n        'batch_size': 10,\n        'criterion': nn.MSELoss(),\n        'optimizer': torch.optim.RMSprop(net.parameters(), lr=1e-3)}\n```\n\nThe config dict should be wrapped with `torchtracer.data.Config`\n\n```python\ncfg = Config(args)\ntracer.store(cfg)\n```\n\nThis step will create `config.json` in `./checkpoints/lmmnb/`, which contains JSON information like this:\n\n```json\n{\n  \"epoch_n\": 120,\n  \"batch_size\": 10,\n  \"criterion\": \"MSELoss\",\n  \"optimizer\": {\n    \"lr\": 0.001,\n    \"momentum\": 0,\n    \"alpha\": 0.99,\n    \"eps\": 1e-08,\n    \"centered\": false,\n    \"weight_decay\": 0,\n    \"name\": \"RMSprop\"\n  }\n}\n```\n\n### Logging\n\nDuring the training iteration, you could print any information you want by using `Tracer.log(msg, file)`.\n\nIf `file` not specified, it will output `msg` to `./checkpoints/lmmnb/log`. Otherwise, it will be `./checkpoints/lmmnb/something.log`.\n\n```python\ntracer.log(msg='Epoch #{:03d}\\ttrain_loss: {:.4f}\\tvalid_loss: {:.4f}'.format(epoch, train_loss, valid_loss),\n           file='losses')\n```\n\nThis step will create a log file `losses.log` in `./checkpoints/lmmnb/`, which contains logs like:\n\n```text\nEpoch #001\ttrain_loss: 18.6356\tvalid_loss: 21.3882\nEpoch #002\ttrain_loss: 19.1731\tvalid_loss: 17.8482\nEpoch #003\ttrain_loss: 19.6756\tvalid_loss: 19.1418\nEpoch #004\ttrain_loss: 20.0638\tvalid_loss: 18.3875\nEpoch #005\ttrain_loss: 18.4679\tvalid_loss: 19.6304\n...\n```\n\n### Saving model\n\nThe model object should be wrapped with `torchtracer.data.Model`\n\nIf `file` not specified, it will generates model files `model.txt`. Otherwise, it will be `somename.txt`\n\n```python\ntracer.store(Model(model), file='somename')\n```\n\nThis step will create 2 files: \n\n- **description**: `somename.txt`\n\n```text\nSequential\nSequential(\n  (0): Linear(in_features=1, out_features=6, bias=True)\n  (1): ReLU()\n  (2): Linear(in_features=6, out_features=12, bias=True)\n  (3): ReLU()\n  (4): Linear(in_features=12, out_features=12, bias=True)\n  (5): ReLU()\n  (6): Linear(in_features=12, out_features=1, bias=True)\n)\n```\n\n- **parameters**: `somename.pth`\n\n### Saving matplotlib images\n\nUse `tracer.store(figure, file)` to save matplotlib figure in `images/`\n\n```python\n# assume that `train_losses` and `valid_losses` are lists of losses. \n# create figure manually.\nplt.plot(train_losses, label='train loss', c='b')\nplt.plot(valid_losses, label='valid loss', c='r')\nplt.title('Demo Learning on SQRT')\nplt.legend()\n# save figure. remember to call `plt.gcf()`\ntracer.store(plt.gcf(), 'losses.png')\n```\n\nThis step will save a png file `losses.png` representing losses curves.\n\n### Progress bar for epochs\n\nUse `tracer.epoch_bar_init(total)` to initialize a progress bar.\n\n```python\ntracer.epoch_bar_init(epoch_n)\n```\n\nUse `tracer.epoch_bar.update(n=1, **params)` to update postfix of the progress bar.\n\n```python\ntracer.epoch_bar.update(train_loss=train_loss, valid_loss=train_loss)\n```\n\n```plain\n(THIS IS A DEMO) \nTracer start at /home/oidiotlin/projects/torchtracer/checkpoints\nTracer attached with task: rabbit\nEpoch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 120/120 [00:02<00:00, 41.75it/s, train_loss=0.417, valid_loss=0.417]\n```\n\n**DO NOT FORGET TO CALL** `tracer.epoch_bar.close()` to finish the bar.\n\n## Contribute\n\nIf you like this project, welcome to pull request & create issues.",
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