notebook.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.model.pl_modules import RacingF1Detector\n",
    "from src.data.datasets import RacingF1Dataset\n",
    "from src.data.pl_data_modules import RacingF1DataModule\n",
    "from src.data.custom_transforms import CustomRescale\n",
    "from pytorch_lightning import Trainer\n",
    "from src.utils import draw_bounding_box, tensor_to_image\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import pytorch_lightning as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pl.seed_everything(42, workers=True)\n",
    "\n",
    "hparams = {\n",
    "\t\"lr\": 0.005,\n",
    "\t\"momentum\": 0.9,\n",
    "\t\"weight_decay\": 0.0005,\n",
    "\t\"batch_size\": 32\n",
    "}\n",
    "\n",
    "detector = RacingF1Detector(hparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "augmentation = transforms.Compose([\n",
    "\tCustomRescale((350, 350)),\n",
    "])\n",
    "\n",
    "data_module = RacingF1DataModule.from_directories(hparams[\"batch_size\"], \"dataset\",\n",
    "                                                  data_transform=augmentation)\n",
    "\n",
    "data_module.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = TensorBoardLogger('tb_logs', name='racing_detector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "      <iframe id=\"tensorboard-frame-6953e5e3f1e33525\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-6953e5e3f1e33525\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
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       "          frame.src = url;\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir tb_logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    }
   ],
   "source": [
    "early_stop_callback = EarlyStopping(\n",
    "\tmonitor=\"val_mAP\",\n",
    "\tpatience=3,\n",
    "\tverbose=False,\n",
    "\tmode=\"max\",\n",
    ")\n",
    "\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    dirpath=\"./savedcheckpoints\",\n",
    "    filename=\"{epoch}{val_mAP:.3f}\",\n",
    "    monitor=\"val_mAP\",\n",
    "    save_top_k=2,\n",
    "    save_last=True,\n",
    "    mode=\"max\",\n",
    ")\n",
    "\n",
    "trainer = Trainer(gpus=0, max_epochs=1, logger=logger, callbacks=[early_stop_callback, checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name     | Type       | Params\n",
      "----------------------------------------\n",
      "0 | model    | FasterRCNN | 41.3 M\n",
      "1 | test_mAP | MAP        | 0     \n",
      "----------------------------------------\n",
      "41.1 M    Trainable params\n",
      "222 K     Non-trainable params\n",
      "41.3 M    Total params\n",
      "165.197   Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation sanity check:   0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "trainer.fit(detector, datamodule=data_module)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = pl.Trainer(gpus=1)\n",
    "\n",
    "trainer.test(model=model, dataloaders=test_dataloader)"
   ]
  }
 ],
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