docs/tutorials/trainers.ipynb

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   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# PyTorch Lightning trainers\n",
    "\n",
    "In this tutorial, we demonstrate TorchGeo trainers to train and test a model. Specifically, we use the [Tropical Cyclone dataset](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#tropical-cyclone-wind-estimation-competition) and train models to predict cyclone windspeed given imagery of the cyclone. \n",
    "\n",
    "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started."
   ]
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   "source": [
    "## Setup\n",
    "\n",
    "First, we install TorchGeo."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "%pip install torchgeo"
   ]
  },
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   "source": [
    "## Imports\n",
    "\n",
    "Next, we import TorchGeo and any other libraries we need."
   ]
  },
  {
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   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import csv\n",
    "import tempfile\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "from torchgeo.datamodules import CycloneDataModule\n",
    "from torchgeo.trainers import RegressionTask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8bc0d83",
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   "source": [
    "# we set a flag to check to see whether the notebook is currently being run by PyTest, if this is the case then we'll\n",
    "# skip the expensive training.\n",
    "in_tests = (\"PYTEST_CURRENT_TEST\" in os.environ)"
   ]
  },
  {
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   "source": [
    "## Lightning modules\n",
    "\n",
    "Our trainers use [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/) to organize both the training code, and the dataloader setup code. This makes it easy to create and share reproducible experiments and results.\n",
    "\n",
    "First we'll create a `CycloneDataModule` object which is simply a wrapper around the [TropicalCycloneWindEstimation](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#tropical-cyclone-wind-estimation-competition) dataset. This object 1.) ensures that the data is downloaded*, 2.) sets up PyTorch `DataLoader` objects for the train, validation, and test splits, and 3.) ensures that data from the same cyclone **is not** shared between the training and validation sets so that you can properly evaluate the generalization performance of your model.\n",
    "\n",
    "\n",
    "*To automatically download the dataset, you need an API key from the [Radiant Earth MLHub](https://mlhub.earth/). This is completely free, and will give you access to a growing catalog of ML-ready remote sensing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b9c9591",
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   "source": [
    "# Set this to your API key (available for free at https://mlhub.earth/)\n",
    "RADIANT_EARTH_API_KEY = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f2a04c7",
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   "outputs": [],
   "source": [
    "data_dir = os.path.join(tempfile.gettempdir(), \"cyclone_data\")\n",
    "\n",
    "datamodule = CycloneDataModule(\n",
    "    root_dir=data_dir,\n",
    "    seed=1337,\n",
    "    batch_size=64,\n",
    "    num_workers=6,\n",
    "    api_key=RADIANT_EARTH_API_KEY\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056b7b4c",
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   "source": [
    "Next, we create a `RegressionTask` object that holds the model object, optimizer object, and training logic."
   ]
  },
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   "execution_count": 5,
   "id": "ba5c5442",
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    "colab": {
     "base_uri": "https://localhost:8080/"
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   "source": [
    "task = RegressionTask(\n",
    "    model=\"resnet18\",\n",
    "    pretrained=True,\n",
    "    learning_rate=0.1,\n",
    "    learning_rate_schedule_patience=5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Training\n",
    "\n",
    "Now that we have the Lightning modules set up, we can use a PyTorch Lightning [Trainer](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) to run the the training and evaluation loops. There are many useful pieces of configuration that can be set in the `Trainer` -- below we set up model checkpointing based on the validation loss, early stopping based on the validation loss, and a CSV based logger. We encourage you to see the [PyTorch Lightning docs](https://pytorch-lightning.readthedocs.io/) for other options that can be set here, e.g. Tensorboard logging, automatically selecting your optimizer's learning rate, and easy multi-GPU training."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "experiment_dir = os.path.join(tempfile.gettempdir(), \"cyclone_results\")\n",
    "\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    monitor=\"val_loss\",\n",
    "    dirpath=experiment_dir,\n",
    "    save_top_k=1,\n",
    "    save_last=True,\n",
    ")\n",
    "\n",
    "early_stopping_callback = EarlyStopping(\n",
    "    monitor=\"val_loss\",\n",
    "    min_delta=0.00,\n",
    "    patience=10,\n",
    ")\n",
    "\n",
    "csv_logger = CSVLogger(\n",
    "    save_dir=experiment_dir,\n",
    "    name=\"tutorial_logs\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06afd8c7",
   "metadata": {},
   "source": [
    "For tutorial purposes we deliberately lower the maximum number of training epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "225a6d36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(\n",
    "    callbacks=[checkpoint_callback, early_stopping_callback],\n",
    "    logger=[csv_logger],\n",
    "    default_root_dir=experiment_dir,\n",
    "    min_epochs=1,\n",
    "    max_epochs=10,\n",
    "    fast_dev_run=in_tests\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d71e8f",
   "metadata": {},
   "source": [
    "When we first call `.fit(...)` the dataset will be downloaded and checksummed (if it hasn't already). This can take 5–10 minutes. After this, the training process will kick off, and results will be saved to a CSV file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00e08790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name  | Type   | Params\n",
      "---------------------------------\n",
      "0 | model | ResNet | 11.2 M\n",
      "---------------------------------\n",
      "11.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.2 M    Total params\n",
      "44.708    Total estimated model params size (MB)\n"
     ]
    },
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      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/torchgeo/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /tmp/pip-req-build-19kunu9c/c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
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      "text/plain": [
       "Training: -1it [00:00, ?it/s]"
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   "source": [
    "trainer.fit(model=task, datamodule=datamodule)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73700fb5",
   "metadata": {},
   "source": [
    "We load the log files and plot the training RMSE over batches, and the validation RMSE over epochs. We can see that our model is just starting to converge, and would probably benefit from additional training time and a lower initial learning rate."
   ]
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  {
   "cell_type": "code",
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    "if not in_tests:\n",
    "    train_steps = []\n",
    "    train_rmse = []\n",
    "\n",
    "    val_steps = []\n",
    "    val_rmse = []\n",
    "    with open(os.path.join(experiment_dir, \"tutorial_logs\", \"version_0\", \"metrics.csv\"), \"r\") as f:\n",
    "        csv_reader = csv.DictReader(f, delimiter=',')\n",
    "        for i, row in enumerate(csv_reader):\n",
    "            try:\n",
    "                train_rmse.append(float(row[\"train_RMSE\"]))\n",
    "                train_steps.append(i)\n",
    "            except ValueError: # Ignore rows where train RMSE is empty\n",
    "                pass\n",
    "\n",
    "            try:\n",
    "                val_rmse.append(float(row[\"val_RMSE\"]))\n",
    "                val_steps.append(i)\n",
    "            except ValueError: # Ignore rows where val RMSE is empty\n",
    "                pass"
   ]
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not in_tests:\n",
    "    plt.figure()\n",
    "    plt.plot(train_steps, train_rmse, label=\"Train RMSE\")\n",
    "    plt.plot(val_steps, val_rmse, label=\"Validation RMSE\")\n",
    "    plt.legend(fontsize=15)\n",
    "    plt.xlabel(\"Batches\", fontsize=15)\n",
    "    plt.ylabel(\"RMSE\", fontsize=15)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cfc7a8",
   "metadata": {},
   "source": [
    "Finally, after the model has been trained, we can easily evaluate it on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "604a3b2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6351593c51e48a9baffdaa048417e65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': 227.86709594726562, 'test_rmse': 13.240558624267578}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 227.86709594726562, 'test_rmse': 13.240558624267578}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=task, datamodule=datamodule)"
   ]
  }
 ],
 "metadata": {
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