notebooks/2022-06-06-get-predictions-pl.ipynb

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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# Make sure we're in the right directory\n",
    "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
    "    os.chdir(\"..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference with ClimART data and models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pytorch_lightning as pl\n",
    "from climart.utils.config_utils import get_config_from_hydra_compose_overrides\n",
    "from climart.interface import get_model_and_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_workers=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['WANDB_SILENT']=\"true\"\n",
    "np.set_printoptions(suppress=True, threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocessing is handled by SLURM.\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
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   ],
   "source": [
    "exp_type='pristine'\n",
    "predict_years = \"2012-14\"\n",
    "overrides = [f'model=mlp',\n",
    "             f'datamodule.exp_type={exp_type}',\n",
    "             f'datamodule.num_workers={num_workers}', \n",
    "             f'++datamodule.predict_years={predict_years}']\n",
    "cfg = get_config_from_hydra_compose_overrides(overrides)\n",
    "model, dm = get_model_and_data(cfg)\n",
    "trainer = pl.Trainer(gpus=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": "90e0261b55f04cccaf82bcb90e3494f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results1 = trainer.predict(model=model,datamodule=dm, return_predictions=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(122880, 49)"
      ]
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     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "results1 = model.aggregate_predictions(results1)\n",
    "sw_hr_preds_2014 = results1[2014]['preds']['hrsc']\n",
    "sw_hr_preds_2014.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternatively to command line/config-directed changing of the predict_years, you can also do this programmatically to an existing datamodule as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
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       "model_id": "0c78d34a8ed94ced997186cdffb4d0e5",
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      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.predict_years = \"2010\"\n",
    "results2 = trainer.predict(model, dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([512, 50])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2[0]['preds']['rsuc'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(122880, 49)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2 = model.aggregate_predictions(results2)\n",
    "sw_hr_preds_2010 = results2[2010]['preds']['hrsc']\n",
    "sw_hr_preds_2010.shape"
   ]
  }
 ],
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