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        "author": "Victor Turrisi",
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            "License :: OSI Approved :: MIT License",
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        "description": "# torchfuel\n[![Build Status](https://travis-ci.org/vturrisi/torchfuel.svg?branch=master)](https://travis-ci.org/vturrisi/torchfuel)\n[![codecov](https://codecov.io/gh/vturrisi/torchfuel/branch/master/graph/badge.svg)](https://codecov.io/gh/vturrisi/torchfuel)\n\nBuild on top of pytorch to fuel productivity.\n\n# Features\n\n- Generic Trainer\n- Classification Trainer (with cross-entropy loss)\n- MSE Trainer\n- Additional utility layers\n- Better dataloaders (currently only for image datasets)\n\n# Classification Example\n\n```python\nimport os\nimport time\nfrom collections import namedtuple\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torchvision import datasets, models, transforms\n\nfrom torchfuel.data_loaders.image import ImageDataLoader\nfrom torchfuel.trainers.classification import ClassificationTrainer\nfrom torchfuel.transforms.noise import DropPixelNoiser\n\n\ndl = ImageDataLoader(\n    train_data_folder='imgs/train',\n    eval_data_folder='imgs/eval',\n    pil_transformations=[transforms.RandomHorizontalFlip()]\n    tensor_transformations=[DropPixelNoiser()],\n    batch_size=64,\n    imagenet_format=True,\n)\n\ntrain_dataloader, eval_dataloader, n_classes = dl.prepare()\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\nmodel = Model(...).to(device)\n\noptimiser = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n\nscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimiser, 'min', patience=20)\n\ntrainer = ClassificationTrainer(device, model, optimiser, scheduler)\n\nfitted_model = trainer.fit(epochs, train_dataloader, eval_dataloader)\n\n```\n\n# How to install\nClone repository and run:\n```bash\npip install .\n```\n\nOptionally (not up to date):\n```bash\npip install torchfuel\n```\n\n\n\n",
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