notebooks/segmentation_pytorch_lightning.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20496,
     "status": "ok",
     "timestamp": 1645030510268,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "JnIWCiPGfviG",
    "outputId": "851a697e-1dce-4ead-e96f-fdfd3bba1e96",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31201,
     "status": "ok",
     "timestamp": 1645030541466,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "EyOohIbnSQR5",
    "outputId": "d4913514-1954-4d27-d3b1-0f9c13f16722",
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# Install required libs\n",
    "\n",
    "### please update Albumentations to version>=0.3.0 for `Lambda` transform support\n",
    "!pip install -U git+https://github.com/albu/albumentations --no-cache-dir\n",
    "\n",
    "!pip uninstall -y opencv-python\n",
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 527,
     "status": "ok",
     "timestamp": 1645030541987,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "2Gf65DtIj_uS",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# !git clone https://github.com/qubvel/segmentation_models.pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 28505,
     "status": "ok",
     "timestamp": 1645030570483,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "qEl3C_OGSnir",
    "outputId": "18e96053-a5c0-4857-9da8-a83a17e6b7de",
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "%cd ./gdrive/MyDrive/Colab Notebooks/Solar Panels\n",
    "\n",
    "!pip install -r requirements.txt\n",
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mA87d65ySQR9",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 61761,
     "status": "ok",
     "timestamp": 1645030632237,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "_ca6no_ISQR_",
    "outputId": "912e0ec8-8f3d-47ba-a014-bc4343ad37e5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.2.1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "\n",
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "print(smp.__version__)\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EvwrRpc1SQSC",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dataloader and utility functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 2997,
     "status": "ok",
     "timestamp": 1645030635219,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "C8HpZzGlSQSC",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# from src.models.segmentation.datasets import *\n",
    "from src.dataloader import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 2027,
     "status": "ok",
     "timestamp": 1645030637241,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "nnzfqi86SQSJ",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from src.transformers import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SNH8F04tSQSK",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Segmentation model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 390,
     "status": "ok",
     "timestamp": 1645030637622,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "571wgIYGYzp6",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "from src.model import SolarPanelsModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1645030637622,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "AlABdmSKSCWo",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_model_info(model_name):\n",
    "    info, ext = model_name.split('.')\n",
    "    arch, *enc, epochs = info.split('_')\n",
    "\n",
    "    enc = '_'.join(enc[:-1])\n",
    "    raw_name = arch + '_' + enc\n",
    "    return raw_name, enc, int(epochs)\n",
    "\n",
    "def model_exists(model_name):\n",
    "    parent = Path(model_name).parent\n",
    "    name, _, _ = get_model_info(model_name)\n",
    "    for model in os.listdir(parent):\n",
    "        if model.startswith(name):\n",
    "            return os.path.join(parent, model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1645030637623,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "hZv7hpCsN7Wb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def train(params, device, verbose=True):\n",
    "    model = SolarPanelsModel(\n",
    "        arch=params['architecture'],\n",
    "        encoder=params['encoder'],\n",
    "        in_channels=3,\n",
    "        out_classes=len(params['classes']),\n",
    "        model_params=params\n",
    "    )\n",
    "    sp_module = SolarPanelsDataModule(\n",
    "        data_dir=params['data_dir'],\n",
    "        classes=params['classes'],\n",
    "        train_augmentation=params['train_augmentation'],\n",
    "        valid_augmentation=params['valid_augmentation'],\n",
    "        preprocessing=params['preprocessing'],\n",
    "        batch_size=params['batch_size'],\n",
    "        num_workers=params['num_workers'],\n",
    "    )\n",
    "    trainer = pl.Trainer(gpus=-1, max_epochs=params['epochs'])\n",
    "    trainer.fit(model, datamodule=sp_module)\n",
    "\n",
    "    valid_metrics = trainer.validate(model, datamodule=sp_module, verbose=False)\n",
    "\n",
    "    return trainer, valid_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1645030637624,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "uZzIUwwrSCWq",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def gen_params(arch, encoder, epochs, data_dir, results_dir):\n",
    "    return {\n",
    "        'data_dir': data_dir,\n",
    "        'results_dir': results_dir,\n",
    "        'model_name': f'{results_dir}/{arch.lower()}_{encoder}_e{epochs}.pth',\n",
    "\n",
    "        'architecture': arch,\n",
    "        'encoder': encoder,\n",
    "        'classes': CLASSES,\n",
    "        'lr': LR,\n",
    "        'epochs': epochs,\n",
    "        'batch_size': BATCH_SIZE,\n",
    "\n",
    "        'train_augmentation': get_training_augmentation,\n",
    "        'valid_augmentation': get_validation_augmentation,\n",
    "        'preprocessing': get_preprocessing,\n",
    "        'num_workers': 2,\n",
    "\n",
    "        'loss': LOSS,\n",
    "        'optimizer': torch.optim.Adam\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 406,
     "status": "ok",
     "timestamp": 1645030638023,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     },
     "user_tz": -60
    },
    "id": "5W8ta1T7SCWq",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "DEVICE = 'cuda'\n",
    "\n",
    "CLASSES = ['solar_panel']\n",
    "BATCH_SIZE = 16\n",
    "LR = 0.0001\n",
    "LOSS = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)\n",
    "\n",
    "DATA_DIR = 'data'\n",
    "RESULTS_DIR = 'models_pytorch'\n",
    "\n",
    "ARCHITECTURE = \"UnetPlusPlus\"\n",
    "ENCODER = 'se_resnext101_32x4d'\n",
    "EPOCHS = 5"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model_params = gen_params(ARCHITECTURE, ENCODER, EPOCHS, DATA_DIR, RESULTS_DIR)"
   ],
   "metadata": {
    "id": "tnAkFRqsLP_k",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1645030638023,
     "user_tz": -60,
     "elapsed": 7,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     }
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 13,
   "outputs": []
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Downloading: \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth\" to /root/.cache/torch/hub/checkpoints/se_resnext101_32x4d-3b2fe3d8.pth\n"
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       "  0%|          | 0.00/187M [00:00<?, ?B/s]"
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     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
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     "text": [
      "<function get_training_augmentation at 0x7f4551c37ef0>\n"
     ]
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     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | model   | UnetPlusPlus | 72.4 M\n",
      "1 | loss_fn | DiceLoss     | 0     \n",
      "-----------------------------------------\n",
      "72.4 M    Trainable params\n",
      "0         Non-trainable params\n",
      "72.4 M    Total params\n",
      "289.537   Total estimated model params size (MB)\n"
     ]
    },
    {
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       "model_id": "55deb783af4f4036ac241c2d2977b2d7",
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       "Validation sanity check: 0it [00:00, ?it/s]"
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     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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     "name": "stdout",
     "text": [
      "<function get_training_augmentation at 0x7f4551c37ef0>\n"
     ]
    },
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       "Validating: 0it [00:00, ?it/s]"
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   ],
   "source": [
    "trainer, valid_metrics = train(model_params, device='cuda')\n",
    "\n",
    "if not os.path.exists(model_params['model_name']):\n",
    "    torch.save(trainer.model.state_dict(), model_params['model_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 67,
     "status": "ok",
     "timestamp": 1645016885732,
     "user": {
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     },
     "user_tz": -60
    },
    "id": "YwtcEeeYnqVT",
    "outputId": "49d8f1e1-6733-4cb4-9e8c-9a1ba0f3197e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[{'valid_per_image_iou': 0.8144766688346863, 'valid_dataset_iou': 0.9060141444206238, 'valid_per_image_f1': 0.8639066815376282, 'valid_dataset_f1': 0.9506899118423462}]\n"
     ]
    }
   ],
   "source": [
    "print(valid_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xoTlE0PPSCWr",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# from itertools import product\n",
    "\n",
    "# epochs = 25\n",
    "# architectures = [smp.Unet, smp.UnetPlusPlus, smp.MAnet, smp.Linknet, smp.FPN, smp.PSPNet, smp.PAN, smp.DeepLabV3, smp.DeepLabV3Plus]\n",
    "\n",
    "# encoders = [\n",
    "#     # 'resnet50',\n",
    "#     # 'resnext50_32x4d',\n",
    "#     # 'timm-resnest50d_4s2x40d',\n",
    "#     # 'timm-res2next50', 'timm-regnetx_064', 'timm-gernet_m',\n",
    "#     # 'se_resnext101_32x4d',\n",
    "#     # 'densenet201',\n",
    "#     # 'xception',\n",
    "#     'efficientnet-b2',\n",
    "#     # 'timm-efficientnet-b3',\n",
    "#     'timm-mobilenetv3_large_100',\n",
    "#     'vgg16_bn', 'vgg19_bn'\n",
    "# ]\n",
    "# count = 0\n",
    "# for arch, encoder in product(architectures, encoders):\n",
    "#     train_params = gen_params(arch, encoder, epochs)\n",
    "#     print('ARCH:', train_params['architecture'].__name__)\n",
    "#     print('ENCODER:', train_params['encoder'])\n",
    "\n",
    "#     train(train_params, DEVICE, verbose=False)\n",
    "\n",
    "#     best_model = torch.load(train_params['model_name'])\n",
    "#     _, logs = test(best_model, train_params, DEVICE)\n",
    "\n",
    "#     if logs.get('fscore') > 0.92:\n",
    "#         break\n",
    "\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TU1g4n1nSQSP",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def load_model(model_path, params):\n",
    "    model = SolarPanelsModel(\n",
    "        arch=params['architecture'],\n",
    "        encoder=params['encoder'],\n",
    "        in_channels=3,\n",
    "        out_classes=len(params['classes']),\n",
    "        model_params=params\n",
    "    )\n",
    "    model.load_state_dict(torch.load(model_path), strict=False)\n",
    "    return model"
   ],
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    "id": "Ufr7hCSNuhsl",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1645030638024,
     "user_tz": -60,
     "elapsed": 7,
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      "displayName": "Sergio Aizcorbe",
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     }
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "YvBC4h33SCWs",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1645030638024,
     "user_tz": -60,
     "elapsed": 6,
     "user": {
      "displayName": "Sergio Aizcorbe",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06957247140334446809"
     }
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def test(model_path, params):\n",
    "\n",
    "    model = load_model(model_path, params)\n",
    "\n",
    "    datamodule = SolarPanelsDataModule(\n",
    "        data_dir=params['data_dir'],\n",
    "        classes=params['classes'],\n",
    "        train_augmentation=params['train_augmentation'],\n",
    "        valid_augmentation=params['valid_augmentation'],\n",
    "        preprocessing=params['preprocessing'],\n",
    "        batch_size=params['batch_size'],\n",
    "        num_workers=params['num_workers'],\n",
    "    )\n",
    "    trainer = pl.Trainer(gpus=-1)\n",
    "    test_metrics = trainer.test(model, datamodule=datamodule, verbose=False)\n",
    "\n",
    "    return trainer, test_metrics"
   ]
  },
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   "cell_type": "code",
   "source": [
    "_, metrics = test('models_pytorch/unetplusplus_se_resnext101_32x4d_e5.pth', model_params)\n",
    "print(metrics)"
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      "dm\n",
      "hi\n",
      "<function get_training_augmentation at 0x7fe494563ef0>\n"
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