experiment-ONet.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Experiment ONet\n",
    "#### Setup:\n",
    "**Dataset**: 3 different types of brain tumors <br>\n",
    "**Loss function**: Dice loss <br>\n",
    "**Optimizer**: Adam <br>\n",
    "**Model**: ONet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Imports and training parameters\n",
    "import torch\n",
    "from torchvision.transforms import transforms\n",
    "from src.datasets import BrainTumorDatasetMaskAndBoundingBox\n",
    "from src.loss import  dice_loss\n",
    "from src.metrics import dice_coeff\n",
    "from src.models import ONet\n",
    "from src.trainers import Trainer\n",
    "torch.manual_seed(0)\n",
    "\n",
    "num_epochs = 100\n",
    "batch_size = 2\n",
    "learning_rate = 0.001\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data directory already exists.  if from some reason the data directory structure is wrong please remove the data dir and rerun this script\n",
      "Data directory already exists.  if from some reason the data directory structure is wrong please remove the data dir and rerun this script\n"
     ]
    }
   ],
   "source": [
    "# Data preparation\n",
    "# load the data see data_preprocessing notebook for more explanation\n",
    "\n",
    "def normalize(x,  mean=470, std=None):\n",
    "    mean_tansor = torch.ones_like(x) * mean\n",
    "    x -= mean_tansor\n",
    "    if std:\n",
    "        x /= std\n",
    "    return x\n",
    "\n",
    "def preprocessing(image, mask, bounding_box):\n",
    "    mask_transformer = transforms.Compose([\n",
    "        transforms.ToTensor()    \n",
    "    ])\n",
    "    mask_tensor = mask_transformer(mask).float()\n",
    "    bounding_box_tensor = torch.zeros_like(mask_tensor).squeeze()\n",
    "    bounding_box_tensor[bounding_box[0]:bounding_box[1],\n",
    "                      bounding_box[2]:bounding_box[3]] = 1\n",
    "    bounding_box_tensor = bounding_box_tensor.unsqueeze(0)\n",
    "    image_transformer = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Lambda(lambda x: normalize(x))\n",
    "    ])\n",
    "    return (image_transformer(image).float(), bounding_box_tensor), mask_tensor\n",
    "\n",
    "target_folder = './data/'\n",
    "ds_train = BrainTumorDatasetMaskAndBoundingBox(root=target_folder, train=True, transform=preprocessing)\n",
    "ds_test = BrainTumorDatasetMaskAndBoundingBox(root=target_folder, train=False, transform=preprocessing)\n",
    "dl_train = torch.utils.data.DataLoader(ds_train, batch_size, shuffle=True)\n",
    "dl_test = torch.utils.data.DataLoader(ds_test, batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- EPOCH 1/100 ---\n",
      "train_batch (Avg. Loss 0.828, Min 0.306, Max 1.000), (Avg. Score 0.1785, Min 0.0000, Max 0.7340): 100%|██████████| 1296/1296 [09:50<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.827, Min 0.425, Max 1.000), (Avg. Score 0.1869, Min 0.0000, Max 0.6647): 100%|██████████| 229/229 [00:38<00:00,  6.37it/s]\n",
      "--- EPOCH 2/100 ---\n",
      "train_batch (Avg. Loss 0.724, Min 0.203, Max 1.000), (Avg. Score 0.2791, Min 0.0000, Max 0.8234): 100%|██████████| 1296/1296 [09:51<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.819, Min 0.386, Max 1.000), (Avg. Score 0.1829, Min 0.0000, Max 0.6182): 100%|██████████| 229/229 [00:39<00:00,  5.83it/s]\n",
      "--- EPOCH 3/100 ---\n",
      "train_batch (Avg. Loss 0.649, Min 0.122, Max 1.000), (Avg. Score 0.3517, Min 0.0000, Max 0.8788): 100%|██████████| 1296/1296 [09:52<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.740, Min 0.118, Max 1.000), (Avg. Score 0.2631, Min 0.0000, Max 0.8817): 100%|██████████| 229/229 [00:39<00:00,  5.86it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 4/100 ---\n",
      "train_batch (Avg. Loss 0.582, Min 0.079, Max 1.000), (Avg. Score 0.4185, Min 0.0000, Max 0.9211): 100%|██████████| 1296/1296 [09:52<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.679, Min 0.143, Max 1.000), (Avg. Score 0.3202, Min 0.0000, Max 0.8586): 100%|██████████| 229/229 [00:38<00:00,  5.88it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 5/100 ---\n",
      "train_batch (Avg. Loss 0.499, Min 0.061, Max 1.000), (Avg. Score 0.5009, Min 0.0000, Max 0.9407): 100%|██████████| 1296/1296 [09:52<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.569, Min 0.112, Max 1.000), (Avg. Score 0.4305, Min 0.0000, Max 0.8882): 100%|██████████| 229/229 [00:38<00:00,  5.95it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 6/100 ---\n",
      "train_batch (Avg. Loss 0.464, Min 0.053, Max 1.000), (Avg. Score 0.5363, Min 0.0000, Max 0.9480): 100%|██████████| 1296/1296 [09:53<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.610, Min 0.093, Max 1.000), (Avg. Score 0.3895, Min 0.0000, Max 0.9063): 100%|██████████| 229/229 [00:39<00:00,  5.85it/s]\n",
      "--- EPOCH 7/100 ---\n",
      "train_batch (Avg. Loss 0.438, Min 0.046, Max 1.000), (Avg. Score 0.5622, Min 0.0000, Max 0.9553): 100%|██████████| 1296/1296 [09:53<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.544, Min 0.065, Max 1.000), (Avg. Score 0.4577, Min 0.0000, Max 0.9363): 100%|██████████| 229/229 [00:39<00:00,  6.77it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 8/100 ---\n",
      "train_batch (Avg. Loss 0.395, Min 0.043, Max 1.000), (Avg. Score 0.6053, Min 0.0000, Max 0.9572): 100%|██████████| 1296/1296 [09:53<00:00,  2.23it/s]\n",
      "test_batch (Avg. Loss 0.417, Min 0.097, Max 0.999), (Avg. Score 0.5831, Min 0.0000, Max 0.9033): 100%|██████████| 229/229 [00:39<00:00,  5.78it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 9/100 ---\n",
      "train_batch (Avg. Loss 0.364, Min 0.035, Max 1.000), (Avg. Score 0.6363, Min 0.0000, Max 0.9661): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.416, Min 0.071, Max 1.000), (Avg. Score 0.5852, Min 0.0000, Max 0.9295): 100%|██████████| 229/229 [00:39<00:00,  5.82it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 10/100 ---\n",
      "train_batch (Avg. Loss 0.349, Min 0.032, Max 1.000), (Avg. Score 0.6518, Min 0.0000, Max 0.9684): 100%|██████████| 1296/1296 [09:55<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.369, Min 0.060, Max 1.000), (Avg. Score 0.6313, Min 0.0000, Max 0.9392): 100%|██████████| 229/229 [00:40<00:00,  5.68it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 11/100 ---\n",
      "train_batch (Avg. Loss 0.324, Min 0.039, Max 1.000), (Avg. Score 0.6759, Min 0.0000, Max 0.9613): 100%|██████████| 1296/1296 [09:55<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.319, Min 0.063, Max 0.978), (Avg. Score 0.6814, Min 0.0211, Max 0.9366): 100%|██████████| 229/229 [00:39<00:00,  5.76it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 12/100 ---\n",
      "train_batch (Avg. Loss 0.311, Min 0.031, Max 0.970), (Avg. Score 0.6897, Min 0.0287, Max 0.9690): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.542, Min 0.064, Max 1.000), (Avg. Score 0.4581, Min 0.0000, Max 0.9356): 100%|██████████| 229/229 [00:39<00:00,  5.79it/s]\n",
      "--- EPOCH 13/100 ---\n",
      "train_batch (Avg. Loss 0.293, Min 0.030, Max 1.000), (Avg. Score 0.7073, Min 0.0000, Max 0.9701): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.358, Min 0.049, Max 0.857), (Avg. Score 0.6429, Min 0.1439, Max 0.9513): 100%|██████████| 229/229 [00:39<00:00,  5.74it/s]\n",
      "--- EPOCH 14/100 ---\n",
      "train_batch (Avg. Loss 0.279, Min 0.027, Max 1.000), (Avg. Score 0.7213, Min 0.0000, Max 0.9743): 100%|██████████| 1296/1296 [09:55<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.231, Min 0.049, Max 0.979), (Avg. Score 0.7689, Min 0.0207, Max 0.9505): 100%|██████████| 229/229 [00:40<00:00,  5.71it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 15/100 ---\n",
      "train_batch (Avg. Loss 0.270, Min 0.032, Max 1.000), (Avg. Score 0.7303, Min 0.0000, Max 0.9684): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.253, Min 0.063, Max 0.957), (Avg. Score 0.7476, Min 0.0419, Max 0.9364): 100%|██████████| 229/229 [00:39<00:00,  5.74it/s]\n",
      "--- EPOCH 16/100 ---\n",
      "train_batch (Avg. Loss 0.260, Min 0.026, Max 0.946), (Avg. Score 0.7404, Min 0.0539, Max 0.9737): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.274, Min 0.052, Max 0.981), (Avg. Score 0.7263, Min 0.0185, Max 0.9469): 100%|██████████| 229/229 [00:39<00:00,  5.73it/s]\n",
      "--- EPOCH 17/100 ---\n",
      "train_batch (Avg. Loss 0.247, Min 0.022, Max 0.893), (Avg. Score 0.7532, Min 0.1075, Max 0.9783): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.270, Min 0.042, Max 0.972), (Avg. Score 0.7296, Min 0.0277, Max 0.9580): 100%|██████████| 229/229 [00:39<00:00,  5.79it/s]\n",
      "--- EPOCH 18/100 ---\n",
      "train_batch (Avg. Loss 0.236, Min 0.030, Max 0.917), (Avg. Score 0.7641, Min 0.0833, Max 0.9698): 100%|██████████| 1296/1296 [09:56<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.238, Min 0.040, Max 1.000), (Avg. Score 0.7622, Min 0.0000, Max 0.9597): 100%|██████████| 229/229 [00:39<00:00,  5.78it/s]\n",
      "--- EPOCH 19/100 ---\n",
      "train_batch (Avg. Loss 0.220, Min 0.027, Max 0.947), (Avg. Score 0.7806, Min 0.0524, Max 0.9728): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.234, Min 0.049, Max 0.947), (Avg. Score 0.7658, Min 0.0512, Max 0.9508): 100%|██████████| 229/229 [00:40<00:00,  5.70it/s]\n",
      "--- EPOCH 20/100 ---\n",
      "train_batch (Avg. Loss 0.219, Min 0.020, Max 1.000), (Avg. Score 0.7813, Min 0.0000, Max 0.9800): 100%|██████████| 1296/1296 [09:56<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.208, Min 0.045, Max 0.816), (Avg. Score 0.7925, Min 0.1834, Max 0.9561): 100%|██████████| 229/229 [00:39<00:00,  5.82it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 21/100 ---\n",
      "train_batch (Avg. Loss 0.207, Min 0.021, Max 1.000), (Avg. Score 0.7935, Min 0.0000, Max 0.9786): 100%|██████████| 1296/1296 [09:56<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.224, Min 0.050, Max 1.000), (Avg. Score 0.7762, Min 0.0000, Max 0.9492): 100%|██████████| 229/229 [00:40<00:00,  6.27it/s]\n",
      "--- EPOCH 22/100 ---\n",
      "train_batch (Avg. Loss 0.202, Min 0.025, Max 0.846), (Avg. Score 0.7978, Min 0.1454, Max 0.9754): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.243, Min 0.036, Max 1.000), (Avg. Score 0.7571, Min 0.0000, Max 0.9644): 100%|██████████| 229/229 [00:40<00:00,  6.06it/s]\n",
      "--- EPOCH 23/100 ---\n",
      "train_batch (Avg. Loss 0.188, Min 0.026, Max 0.772), (Avg. Score 0.8122, Min 0.2285, Max 0.9736): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.211, Min 0.045, Max 0.990), (Avg. Score 0.7890, Min 0.0000, Max 0.9549): 100%|██████████| 229/229 [00:39<00:00,  5.77it/s]\n",
      "--- EPOCH 24/100 ---\n",
      "train_batch (Avg. Loss 0.188, Min 0.027, Max 0.878), (Avg. Score 0.8118, Min 0.1225, Max 0.9733): 100%|██████████| 1296/1296 [09:55<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.200, Min 0.043, Max 1.000), (Avg. Score 0.7996, Min 0.0000, Max 0.9578): 100%|██████████| 229/229 [00:39<00:00,  5.79it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 25/100 ---\n",
      "train_batch (Avg. Loss 0.180, Min 0.027, Max 1.000), (Avg. Score 0.8204, Min 0.0000, Max 0.9735): 100%|██████████| 1296/1296 [09:56<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.257, Min 0.040, Max 1.000), (Avg. Score 0.7430, Min 0.0000, Max 0.9595): 100%|██████████| 229/229 [00:39<00:00,  6.44it/s]\n",
      "--- EPOCH 26/100 ---\n",
      "train_batch (Avg. Loss 0.170, Min 0.022, Max 0.875), (Avg. Score 0.8300, Min 0.1247, Max 0.9783): 100%|██████████| 1296/1296 [09:55<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.191, Min 0.036, Max 0.669), (Avg. Score 0.8093, Min 0.3307, Max 0.9640): 100%|██████████| 229/229 [00:40<00:00,  6.10it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 27/100 ---\n",
      "train_batch (Avg. Loss 0.166, Min 0.026, Max 0.731), (Avg. Score 0.8336, Min 0.2692, Max 0.9742): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.243, Min 0.043, Max 1.000), (Avg. Score 0.7567, Min 0.0000, Max 0.9573): 100%|██████████| 229/229 [00:39<00:00,  6.03it/s]\n",
      "--- EPOCH 28/100 ---\n",
      "train_batch (Avg. Loss 0.164, Min 0.023, Max 0.870), (Avg. Score 0.8363, Min 0.1300, Max 0.9773): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.226, Min 0.035, Max 0.790), (Avg. Score 0.7743, Min 0.2096, Max 0.9647): 100%|██████████| 229/229 [00:41<00:00,  5.57it/s]\n",
      "--- EPOCH 29/100 ---\n",
      "train_batch (Avg. Loss 0.162, Min 0.019, Max 0.708), (Avg. Score 0.8380, Min 0.2917, Max 0.9807): 100%|██████████| 1296/1296 [09:55<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.205, Min 0.032, Max 1.000), (Avg. Score 0.7947, Min 0.0000, Max 0.9680): 100%|██████████| 229/229 [00:39<00:00,  5.83it/s]\n",
      "--- EPOCH 30/100 ---\n",
      "train_batch (Avg. Loss 0.154, Min 0.022, Max 0.769), (Avg. Score 0.8461, Min 0.2308, Max 0.9783): 100%|██████████| 1296/1296 [09:55<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.185, Min 0.037, Max 0.976), (Avg. Score 0.8153, Min 0.0206, Max 0.9631): 100%|██████████| 229/229 [00:40<00:00,  5.68it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 31/100 ---\n",
      "train_batch (Avg. Loss 0.151, Min 0.026, Max 0.691), (Avg. Score 0.8493, Min 0.3086, Max 0.9743): 100%|██████████| 1296/1296 [09:55<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.184, Min 0.037, Max 0.764), (Avg. Score 0.8157, Min 0.2364, Max 0.9627): 100%|██████████| 229/229 [00:40<00:00,  5.72it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 32/100 ---\n",
      "train_batch (Avg. Loss 0.145, Min 0.024, Max 0.688), (Avg. Score 0.8550, Min 0.3122, Max 0.9757): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.199, Min 0.036, Max 1.000), (Avg. Score 0.8006, Min 0.0000, Max 0.9644): 100%|██████████| 229/229 [00:39<00:00,  6.31it/s]\n",
      "--- EPOCH 33/100 ---\n",
      "train_batch (Avg. Loss 0.142, Min 0.024, Max 0.680), (Avg. Score 0.8584, Min 0.3203, Max 0.9761): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.178, Min 0.044, Max 0.912), (Avg. Score 0.8217, Min 0.0885, Max 0.9555): 100%|██████████| 229/229 [00:40<00:00,  6.58it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 34/100 ---\n",
      "train_batch (Avg. Loss 0.128, Min 0.024, Max 0.707), (Avg. Score 0.8720, Min 0.2927, Max 0.9758): 100%|██████████| 1296/1296 [09:55<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.184, Min 0.035, Max 1.000), (Avg. Score 0.8157, Min 0.0000, Max 0.9649): 100%|██████████| 229/229 [00:39<00:00,  5.82it/s]\n",
      "--- EPOCH 35/100 ---\n",
      "train_batch (Avg. Loss 0.133, Min 0.019, Max 0.718), (Avg. Score 0.8671, Min 0.2819, Max 0.9807): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.179, Min 0.040, Max 1.000), (Avg. Score 0.8212, Min 0.0000, Max 0.9602): 100%|██████████| 229/229 [00:39<00:00,  5.73it/s]\n",
      "--- EPOCH 36/100 ---\n",
      "train_batch (Avg. Loss 0.128, Min 0.027, Max 0.625), (Avg. Score 0.8725, Min 0.3749, Max 0.9734): 100%|██████████| 1296/1296 [09:55<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.176, Min 0.040, Max 1.000), (Avg. Score 0.8242, Min 0.0000, Max 0.9598): 100%|██████████| 229/229 [00:40<00:00,  5.72it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 37/100 ---\n",
      "train_batch (Avg. Loss 0.123, Min 0.018, Max 0.726), (Avg. Score 0.8770, Min 0.2739, Max 0.9821): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.223, Min 0.045, Max 0.659), (Avg. Score 0.7772, Min 0.3411, Max 0.9550): 100%|██████████| 229/229 [00:40<00:00,  6.67it/s]\n",
      "--- EPOCH 38/100 ---\n",
      "train_batch (Avg. Loss 0.122, Min 0.024, Max 0.661), (Avg. Score 0.8782, Min 0.3405, Max 0.9763): 100%|██████████| 1296/1296 [09:55<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.192, Min 0.035, Max 1.000), (Avg. Score 0.8085, Min 0.0000, Max 0.9646): 100%|██████████| 229/229 [00:40<00:00,  5.65it/s]\n",
      "--- EPOCH 39/100 ---\n",
      "train_batch (Avg. Loss 0.118, Min 0.021, Max 0.602), (Avg. Score 0.8817, Min 0.3983, Max 0.9786): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.172, Min 0.037, Max 1.000), (Avg. Score 0.8285, Min 0.0000, Max 0.9626): 100%|██████████| 229/229 [00:40<00:00,  5.69it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 40/100 ---\n",
      "train_batch (Avg. Loss 0.118, Min 0.018, Max 0.695), (Avg. Score 0.8823, Min 0.3052, Max 0.9820): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.182, Min 0.036, Max 1.000), (Avg. Score 0.8185, Min 0.0000, Max 0.9643): 100%|██████████| 229/229 [00:40<00:00,  5.79it/s]\n",
      "--- EPOCH 41/100 ---\n",
      "train_batch (Avg. Loss 0.110, Min 0.022, Max 0.601), (Avg. Score 0.8896, Min 0.3992, Max 0.9782): 100%|██████████| 1296/1296 [09:55<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.173, Min 0.041, Max 1.000), (Avg. Score 0.8266, Min 0.0000, Max 0.9592): 100%|██████████| 229/229 [00:40<00:00,  5.71it/s]\n",
      "--- EPOCH 42/100 ---\n",
      "train_batch (Avg. Loss 0.107, Min 0.018, Max 0.755), (Avg. Score 0.8928, Min 0.2453, Max 0.9817): 100%|██████████| 1296/1296 [09:55<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.223, Min 0.036, Max 1.000), (Avg. Score 0.7770, Min 0.0000, Max 0.9643): 100%|██████████| 229/229 [00:39<00:00,  5.74it/s]\n",
      "--- EPOCH 43/100 ---\n",
      "train_batch (Avg. Loss 0.109, Min 0.022, Max 0.754), (Avg. Score 0.8906, Min 0.2457, Max 0.9781): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.175, Min 0.038, Max 1.000), (Avg. Score 0.8245, Min 0.0000, Max 0.9618): 100%|██████████| 229/229 [00:39<00:00,  6.50it/s]\n",
      "--- EPOCH 44/100 ---\n",
      "train_batch (Avg. Loss 0.102, Min 0.022, Max 0.680), (Avg. Score 0.8982, Min 0.3203, Max 0.9781): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.188, Min 0.040, Max 0.892), (Avg. Score 0.8119, Min 0.1032, Max 0.9604): 100%|██████████| 229/229 [00:39<00:00,  6.85it/s]\n",
      "--- EPOCH 45/100 ---\n",
      "train_batch (Avg. Loss 0.096, Min 0.021, Max 0.613), (Avg. Score 0.9036, Min 0.3869, Max 0.9789): 100%|██████████| 1296/1296 [09:57<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.159, Min 0.041, Max 0.944), (Avg. Score 0.8407, Min 0.0557, Max 0.9587): 100%|██████████| 229/229 [00:39<00:00,  5.74it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 46/100 ---\n",
      "train_batch (Avg. Loss 0.104, Min 0.019, Max 0.644), (Avg. Score 0.8958, Min 0.3550, Max 0.9810): 100%|██████████| 1296/1296 [09:56<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.170, Min 0.031, Max 0.839), (Avg. Score 0.8304, Min 0.1609, Max 0.9688): 100%|██████████| 229/229 [00:40<00:00,  5.66it/s]\n",
      "--- EPOCH 47/100 ---\n",
      "train_batch (Avg. Loss 0.096, Min 0.021, Max 0.580), (Avg. Score 0.9040, Min 0.4197, Max 0.9794): 100%|██████████| 1296/1296 [09:56<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.287, Min 0.029, Max 1.000), (Avg. Score 0.7131, Min 0.0000, Max 0.9716): 100%|██████████| 229/229 [00:40<00:00,  5.72it/s]\n",
      "--- EPOCH 48/100 ---\n",
      "train_batch (Avg. Loss 0.091, Min 0.020, Max 0.551), (Avg. Score 0.9090, Min 0.4496, Max 0.9796): 100%|██████████| 1296/1296 [09:56<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.189, Min 0.041, Max 0.943), (Avg. Score 0.8113, Min 0.0583, Max 0.9589): 100%|██████████| 229/229 [00:40<00:00,  6.00it/s]\n",
      "--- EPOCH 49/100 ---\n",
      "train_batch (Avg. Loss 0.092, Min 0.021, Max 0.628), (Avg. Score 0.9080, Min 0.3718, Max 0.9790): 100%|██████████| 1296/1296 [09:57<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.183, Min 0.038, Max 1.000), (Avg. Score 0.8167, Min 0.0000, Max 0.9613): 100%|██████████| 229/229 [00:39<00:00,  5.76it/s]\n",
      "--- EPOCH 50/100 ---\n",
      "train_batch (Avg. Loss 0.086, Min 0.021, Max 0.575), (Avg. Score 0.9136, Min 0.4254, Max 0.9795): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.170, Min 0.039, Max 0.967), (Avg. Score 0.8300, Min 0.0342, Max 0.9606): 100%|██████████| 229/229 [00:40<00:00,  5.61it/s]\n",
      "--- EPOCH 51/100 ---\n",
      "train_batch (Avg. Loss 0.089, Min 0.021, Max 0.597), (Avg. Score 0.9105, Min 0.4027, Max 0.9794): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.165, Min 0.039, Max 1.000), (Avg. Score 0.8349, Min 0.0000, Max 0.9610): 100%|██████████| 229/229 [00:40<00:00,  5.59it/s]\n",
      "--- EPOCH 52/100 ---\n",
      "train_batch (Avg. Loss 0.091, Min 0.020, Max 0.756), (Avg. Score 0.9090, Min 0.2438, Max 0.9801): 100%|██████████| 1296/1296 [09:56<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.171, Min 0.037, Max 0.684), (Avg. Score 0.8291, Min 0.3149, Max 0.9631): 100%|██████████| 229/229 [00:40<00:00,  5.63it/s]\n",
      "--- EPOCH 53/100 ---\n",
      "train_batch (Avg. Loss 0.084, Min 0.021, Max 0.617), (Avg. Score 0.9160, Min 0.3831, Max 0.9792): 100%|██████████| 1296/1296 [09:58<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.181, Min 0.037, Max 1.000), (Avg. Score 0.8189, Min 0.0000, Max 0.9624): 100%|██████████| 229/229 [00:40<00:00,  5.70it/s]\n",
      "--- EPOCH 54/100 ---\n",
      "train_batch (Avg. Loss 0.079, Min 0.020, Max 0.553), (Avg. Score 0.9206, Min 0.4471, Max 0.9801): 100%|██████████| 1296/1296 [09:57<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.173, Min 0.038, Max 0.800), (Avg. Score 0.8275, Min 0.2054, Max 0.9624): 100%|██████████| 229/229 [00:40<00:00,  5.71it/s]\n",
      "--- EPOCH 55/100 ---\n",
      "train_batch (Avg. Loss 0.079, Min 0.022, Max 0.659), (Avg. Score 0.9215, Min 0.3412, Max 0.9783): 100%|██████████| 1296/1296 [09:57<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.202, Min 0.039, Max 1.000), (Avg. Score 0.7978, Min 0.0000, Max 0.9612): 100%|██████████| 229/229 [00:40<00:00,  5.66it/s]\n",
      "--- EPOCH 56/100 ---\n",
      "train_batch (Avg. Loss 0.074, Min 0.018, Max 0.594), (Avg. Score 0.9260, Min 0.4061, Max 0.9824): 100%|██████████| 1296/1296 [09:58<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.160, Min 0.036, Max 1.000), (Avg. Score 0.8405, Min 0.0000, Max 0.9646): 100%|██████████| 229/229 [00:40<00:00,  5.66it/s]\n",
      "--- EPOCH 57/100 ---\n",
      "train_batch (Avg. Loss 0.084, Min 0.018, Max 0.644), (Avg. Score 0.9162, Min 0.3557, Max 0.9818): 100%|██████████| 1296/1296 [09:55<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.150, Min 0.031, Max 0.873), (Avg. Score 0.8496, Min 0.1285, Max 0.9689): 100%|██████████| 229/229 [00:40<00:00,  5.69it/s]\n",
      "**** Checkpoint saved ****\n",
      "--- EPOCH 58/100 ---\n",
      "train_batch (Avg. Loss 0.071, Min 0.019, Max 0.608), (Avg. Score 0.9292, Min 0.3919, Max 0.9808): 100%|██████████| 1296/1296 [09:55<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.152, Min 0.037, Max 0.656), (Avg. Score 0.8483, Min 0.3448, Max 0.9629): 100%|██████████| 229/229 [00:40<00:00,  6.07it/s]\n",
      "--- EPOCH 59/100 ---\n",
      "train_batch (Avg. Loss 0.070, Min 0.018, Max 0.556), (Avg. Score 0.9300, Min 0.4445, Max 0.9821): 100%|██████████| 1296/1296 [09:55<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.171, Min 0.036, Max 1.000), (Avg. Score 0.8287, Min 0.0000, Max 0.9642): 100%|██████████| 229/229 [00:39<00:00,  5.73it/s]\n",
      "--- EPOCH 60/100 ---\n",
      "train_batch (Avg. Loss 0.068, Min 0.017, Max 0.634), (Avg. Score 0.9322, Min 0.3662, Max 0.9829): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.167, Min 0.039, Max 1.000), (Avg. Score 0.8328, Min 0.0000, Max 0.9612): 100%|██████████| 229/229 [00:40<00:00,  5.65it/s]\n",
      "--- EPOCH 61/100 ---\n",
      "train_batch (Avg. Loss 0.072, Min 0.017, Max 0.553), (Avg. Score 0.9280, Min 0.4473, Max 0.9829): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.189, Min 0.037, Max 1.000), (Avg. Score 0.8107, Min 0.0000, Max 0.9626): 100%|██████████| 229/229 [00:41<00:00,  5.59it/s]\n",
      "--- EPOCH 62/100 ---\n",
      "train_batch (Avg. Loss 0.071, Min 0.021, Max 0.583), (Avg. Score 0.9286, Min 0.4174, Max 0.9787): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.162, Min 0.042, Max 0.914), (Avg. Score 0.8384, Min 0.0887, Max 0.9581): 100%|██████████| 229/229 [00:40<00:00,  5.69it/s]\n",
      "--- EPOCH 63/100 ---\n",
      "train_batch (Avg. Loss 0.064, Min 0.018, Max 0.536), (Avg. Score 0.9360, Min 0.4643, Max 0.9816): 100%|██████████| 1296/1296 [09:57<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.167, Min 0.036, Max 0.770), (Avg. Score 0.8325, Min 0.2300, Max 0.9638): 100%|██████████| 229/229 [00:39<00:00,  5.77it/s]\n",
      "--- EPOCH 64/100 ---\n",
      "train_batch (Avg. Loss 0.065, Min 0.019, Max 1.000), (Avg. Score 0.9351, Min 0.0000, Max 0.9814): 100%|██████████| 1296/1296 [09:57<00:00,  2.21it/s]\n",
      "test_batch (Avg. Loss 0.322, Min 0.038, Max 1.000), (Avg. Score 0.6776, Min 0.0000, Max 0.9621): 100%|██████████| 229/229 [00:39<00:00,  5.82it/s]\n",
      "--- EPOCH 65/100 ---\n",
      "train_batch (Avg. Loss 0.069, Min 0.019, Max 0.572), (Avg. Score 0.9308, Min 0.4278, Max 0.9810): 100%|██████████| 1296/1296 [09:56<00:00,  2.15it/s]\n",
      "test_batch (Avg. Loss 0.163, Min 0.039, Max 1.000), (Avg. Score 0.8367, Min 0.0000, Max 0.9610): 100%|██████████| 229/229 [00:40<00:00,  5.66it/s]\n",
      "--- EPOCH 66/100 ---\n",
      "train_batch (Avg. Loss 0.061, Min 0.018, Max 0.634), (Avg. Score 0.9385, Min 0.3657, Max 0.9822): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.166, Min 0.039, Max 1.000), (Avg. Score 0.8338, Min 0.0000, Max 0.9605): 100%|██████████| 229/229 [00:40<00:00,  6.27it/s]\n",
      "--- EPOCH 67/100 ---\n",
      "train_batch (Avg. Loss 0.059, Min 0.014, Max 0.553), (Avg. Score 0.9410, Min 0.4467, Max 0.9858): 100%|██████████| 1296/1296 [09:56<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.184, Min 0.038, Max 1.000), (Avg. Score 0.8158, Min 0.0000, Max 0.9623): 100%|██████████| 229/229 [00:39<00:00,  5.75it/s]\n",
      "--- EPOCH 68/100 ---\n",
      "train_batch (Avg. Loss 0.059, Min 0.017, Max 0.541), (Avg. Score 0.9406, Min 0.4594, Max 0.9829): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.179, Min 0.042, Max 1.000), (Avg. Score 0.8210, Min 0.0000, Max 0.9586): 100%|██████████| 229/229 [00:39<00:00,  5.77it/s]\n",
      "--- EPOCH 69/100 ---\n",
      "train_batch (Avg. Loss 0.059, Min 0.018, Max 0.625), (Avg. Score 0.9408, Min 0.3748, Max 0.9816): 100%|██████████| 1296/1296 [09:55<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.174, Min 0.035, Max 1.000), (Avg. Score 0.8264, Min 0.0000, Max 0.9648): 100%|██████████| 229/229 [00:40<00:00,  5.59it/s]\n",
      "--- EPOCH 70/100 ---\n",
      "train_batch (Avg. Loss 0.062, Min 0.018, Max 0.565), (Avg. Score 0.9384, Min 0.4348, Max 0.9823): 100%|██████████| 1296/1296 [09:55<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.155, Min 0.036, Max 1.000), (Avg. Score 0.8455, Min 0.0000, Max 0.9640): 100%|██████████| 229/229 [00:41<00:00,  5.53it/s]\n",
      "--- EPOCH 71/100 ---\n",
      "train_batch (Avg. Loss 0.056, Min 0.017, Max 0.534), (Avg. Score 0.9443, Min 0.4662, Max 0.9833): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.210, Min 0.033, Max 0.889), (Avg. Score 0.7897, Min 0.1113, Max 0.9674): 100%|██████████| 229/229 [00:40<00:00,  5.61it/s]\n",
      "--- EPOCH 72/100 ---\n",
      "train_batch (Avg. Loss 0.056, Min 0.018, Max 0.547), (Avg. Score 0.9438, Min 0.4534, Max 0.9822): 100%|██████████| 1296/1296 [09:55<00:00,  2.14it/s]\n",
      "test_batch (Avg. Loss 0.186, Min 0.038, Max 1.000), (Avg. Score 0.8137, Min 0.0000, Max 0.9623): 100%|██████████| 229/229 [00:40<00:00,  5.59it/s]\n",
      "--- EPOCH 73/100 ---\n",
      "train_batch (Avg. Loss 0.054, Min 0.017, Max 0.541), (Avg. Score 0.9455, Min 0.4584, Max 0.9834): 100%|██████████| 1296/1296 [09:55<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.153, Min 0.036, Max 1.000), (Avg. Score 0.8470, Min 0.0000, Max 0.9640): 100%|██████████| 229/229 [00:40<00:00,  5.59it/s]\n",
      "--- EPOCH 74/100 ---\n",
      "train_batch (Avg. Loss 0.052, Min 0.017, Max 0.583), (Avg. Score 0.9481, Min 0.4171, Max 0.9830): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.158, Min 0.036, Max 1.000), (Avg. Score 0.8418, Min 0.0000, Max 0.9634): 100%|██████████| 229/229 [00:40<00:00,  5.65it/s]\n",
      "--- EPOCH 75/100 ---\n",
      "train_batch (Avg. Loss 0.053, Min 0.013, Max 0.544), (Avg. Score 0.9466, Min 0.4562, Max 0.9873): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.163, Min 0.044, Max 0.800), (Avg. Score 0.8371, Min 0.2005, Max 0.9562): 100%|██████████| 229/229 [00:40<00:00,  5.92it/s]\n",
      "--- EPOCH 76/100 ---\n",
      "train_batch (Avg. Loss 0.050, Min 0.013, Max 0.534), (Avg. Score 0.9497, Min 0.4658, Max 0.9868): 100%|██████████| 1296/1296 [09:55<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.169, Min 0.037, Max 1.000), (Avg. Score 0.8312, Min 0.0000, Max 0.9630): 100%|██████████| 229/229 [00:40<00:00,  5.62it/s]\n",
      "--- EPOCH 77/100 ---\n",
      "train_batch (Avg. Loss 0.055, Min 0.016, Max 0.528), (Avg. Score 0.9454, Min 0.4718, Max 0.9843): 100%|██████████| 1296/1296 [09:55<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.183, Min 0.038, Max 1.000), (Avg. Score 0.8168, Min 0.0000, Max 0.9622): 100%|██████████| 229/229 [00:40<00:00,  5.60it/s]\n",
      "--- EPOCH 78/100 ---\n",
      "train_batch (Avg. Loss 0.055, Min 0.017, Max 0.534), (Avg. Score 0.9450, Min 0.4664, Max 0.9830): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.162, Min 0.034, Max 1.000), (Avg. Score 0.8377, Min 0.0000, Max 0.9664): 100%|██████████| 229/229 [00:40<00:00,  5.68it/s]\n",
      "--- EPOCH 79/100 ---\n",
      "train_batch (Avg. Loss 0.046, Min 0.014, Max 0.542), (Avg. Score 0.9542, Min 0.4584, Max 0.9861): 100%|██████████| 1296/1296 [09:56<00:00,  2.15it/s]\n",
      "test_batch (Avg. Loss 0.158, Min 0.035, Max 1.000), (Avg. Score 0.8422, Min 0.0000, Max 0.9647): 100%|██████████| 229/229 [00:40<00:00,  5.59it/s]\n",
      "--- EPOCH 80/100 ---\n",
      "train_batch (Avg. Loss 0.046, Min 0.016, Max 0.540), (Avg. Score 0.9539, Min 0.4598, Max 0.9844): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.158, Min 0.040, Max 1.000), (Avg. Score 0.8422, Min 0.0000, Max 0.9599): 100%|██████████| 229/229 [00:40<00:00,  6.43it/s]\n",
      "--- EPOCH 81/100 ---\n",
      "train_batch (Avg. Loss 0.048, Min 0.017, Max 0.536), (Avg. Score 0.9524, Min 0.4640, Max 0.9834): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.154, Min 0.040, Max 1.000), (Avg. Score 0.8456, Min 0.0000, Max 0.9598): 100%|██████████| 229/229 [00:40<00:00,  5.67it/s]\n",
      "--- EPOCH 82/100 ---\n",
      "train_batch (Avg. Loss 0.055, Min 0.015, Max 0.572), (Avg. Score 0.9449, Min 0.4281, Max 0.9853): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.163, Min 0.040, Max 1.000), (Avg. Score 0.8368, Min 0.0000, Max 0.9598): 100%|██████████| 229/229 [00:41<00:00,  5.58it/s]\n",
      "--- EPOCH 83/100 ---\n",
      "train_batch (Avg. Loss 0.045, Min 0.015, Max 0.527), (Avg. Score 0.9546, Min 0.4723, Max 0.9853): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.161, Min 0.036, Max 1.000), (Avg. Score 0.8387, Min 0.0000, Max 0.9644): 100%|██████████| 229/229 [00:40<00:00,  5.65it/s]\n",
      "--- EPOCH 84/100 ---\n",
      "train_batch (Avg. Loss 0.044, Min 0.017, Max 0.535), (Avg. Score 0.9559, Min 0.4649, Max 0.9834): 100%|██████████| 1296/1296 [09:56<00:00,  2.20it/s]\n",
      "test_batch (Avg. Loss 0.187, Min 0.032, Max 1.000), (Avg. Score 0.8128, Min 0.0000, Max 0.9676): 100%|██████████| 229/229 [00:40<00:00,  5.66it/s]\n",
      "--- EPOCH 85/100 ---\n",
      "train_batch (Avg. Loss 0.050, Min 0.012, Max 0.526), (Avg. Score 0.9502, Min 0.4742, Max 0.9879): 100%|██████████| 1296/1296 [09:56<00:00,  2.14it/s]\n",
      "test_batch (Avg. Loss 0.163, Min 0.035, Max 1.000), (Avg. Score 0.8368, Min 0.0000, Max 0.9649): 100%|██████████| 229/229 [00:41<00:00,  5.56it/s]\n",
      "--- EPOCH 86/100 ---\n",
      "train_batch (Avg. Loss 0.043, Min 0.015, Max 0.516), (Avg. Score 0.9566, Min 0.4840, Max 0.9854): 100%|██████████| 1296/1296 [09:55<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.170, Min 0.033, Max 1.000), (Avg. Score 0.8299, Min 0.0000, Max 0.9670): 100%|██████████| 229/229 [00:39<00:00,  5.74it/s]\n",
      "--- EPOCH 87/100 ---\n",
      "train_batch (Avg. Loss 0.043, Min 0.015, Max 0.525), (Avg. Score 0.9573, Min 0.4754, Max 0.9850): 100%|██████████| 1296/1296 [09:54<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.151, Min 0.038, Max 0.860), (Avg. Score 0.8495, Min 0.1439, Max 0.9622): 100%|██████████| 229/229 [00:40<00:00,  6.29it/s]\n",
      "--- EPOCH 88/100 ---\n",
      "train_batch (Avg. Loss 0.043, Min 0.013, Max 0.534), (Avg. Score 0.9573, Min 0.4663, Max 0.9871): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.158, Min 0.040, Max 1.000), (Avg. Score 0.8417, Min 0.0000, Max 0.9606): 100%|██████████| 229/229 [00:40<00:00,  5.65it/s]\n",
      "--- EPOCH 89/100 ---\n",
      "train_batch (Avg. Loss 0.042, Min 0.015, Max 0.522), (Avg. Score 0.9578, Min 0.4779, Max 0.9847): 100%|██████████| 1296/1296 [09:56<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.168, Min 0.038, Max 1.000), (Avg. Score 0.8319, Min 0.0000, Max 0.9624): 100%|██████████| 229/229 [00:41<00:00,  5.52it/s]\n",
      "--- EPOCH 90/100 ---\n",
      "train_batch (Avg. Loss 0.042, Min 0.014, Max 0.524), (Avg. Score 0.9578, Min 0.4756, Max 0.9862): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.167, Min 0.036, Max 1.000), (Avg. Score 0.8334, Min 0.0000, Max 0.9639): 100%|██████████| 229/229 [00:41<00:00,  5.49it/s]\n",
      "--- EPOCH 91/100 ---\n",
      "train_batch (Avg. Loss 0.041, Min 0.015, Max 0.527), (Avg. Score 0.9587, Min 0.4730, Max 0.9853): 100%|██████████| 1296/1296 [09:56<00:00,  2.16it/s]\n",
      "test_batch (Avg. Loss 0.151, Min 0.034, Max 0.758), (Avg. Score 0.8491, Min 0.2433, Max 0.9658): 100%|██████████| 229/229 [00:41<00:00,  5.58it/s]\n",
      "--- EPOCH 92/100 ---\n",
      "train_batch (Avg. Loss 0.041, Min 0.013, Max 0.527), (Avg. Score 0.9586, Min 0.4734, Max 0.9876): 100%|██████████| 1296/1296 [09:56<00:00,  2.17it/s]\n",
      "test_batch (Avg. Loss 0.156, Min 0.037, Max 1.000), (Avg. Score 0.8438, Min 0.0000, Max 0.9626): 100%|██████████| 229/229 [00:41<00:00,  5.52it/s]\n",
      "--- EPOCH 93/100 ---\n",
      "train_batch (Avg. Loss 0.042, Min 0.013, Max 0.543), (Avg. Score 0.9584, Min 0.4571, Max 0.9870): 100%|██████████| 1296/1296 [09:57<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.190, Min 0.027, Max 1.000), (Avg. Score 0.8101, Min 0.0000, Max 0.9733): 100%|██████████| 229/229 [00:41<00:00,  6.41it/s]\n",
      "--- EPOCH 94/100 ---\n",
      "train_batch (Avg. Loss 0.047, Min 0.013, Max 0.777), (Avg. Score 0.9535, Min 0.2232, Max 0.9869): 100%|██████████| 1296/1296 [09:57<00:00,  2.15it/s]\n",
      "test_batch (Avg. Loss 0.219, Min 0.037, Max 1.000), (Avg. Score 0.7812, Min 0.0000, Max 0.9633): 100%|██████████| 229/229 [00:41<00:00,  5.58it/s]\n",
      "--- EPOCH 95/100 ---\n",
      "train_batch (Avg. Loss 0.039, Min 0.014, Max 0.533), (Avg. Score 0.9609, Min 0.4663, Max 0.9857): 100%|██████████| 1296/1296 [09:56<00:00,  2.14it/s]\n",
      "test_batch (Avg. Loss 0.155, Min 0.032, Max 1.000), (Avg. Score 0.8450, Min 0.0000, Max 0.9687): 100%|██████████| 229/229 [00:40<00:00,  5.64it/s]\n",
      "--- EPOCH 96/100 ---\n",
      "train_batch (Avg. Loss 0.037, Min 0.014, Max 0.525), (Avg. Score 0.9632, Min 0.4753, Max 0.9859): 100%|██████████| 1296/1296 [09:57<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.155, Min 0.032, Max 1.000), (Avg. Score 0.8450, Min 0.0000, Max 0.9675): 100%|██████████| 229/229 [00:42<00:00,  5.87it/s]\n",
      "--- EPOCH 97/100 ---\n",
      "train_batch (Avg. Loss 0.038, Min 0.014, Max 0.528), (Avg. Score 0.9621, Min 0.4722, Max 0.9855): 100%|██████████| 1296/1296 [09:57<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.153, Min 0.037, Max 0.938), (Avg. Score 0.8470, Min 0.0619, Max 0.9634): 100%|██████████| 229/229 [00:41<00:00,  6.15it/s]\n",
      "--- EPOCH 98/100 ---\n",
      "train_batch (Avg. Loss 0.039, Min 0.013, Max 0.526), (Avg. Score 0.9611, Min 0.4745, Max 0.9867): 100%|██████████| 1296/1296 [09:57<00:00,  2.19it/s]\n",
      "test_batch (Avg. Loss 0.176, Min 0.039, Max 1.000), (Avg. Score 0.8243, Min 0.0000, Max 0.9610): 100%|██████████| 229/229 [00:41<00:00,  5.50it/s]\n",
      "--- EPOCH 99/100 ---\n",
      "train_batch (Avg. Loss 0.038, Min 0.015, Max 0.518), (Avg. Score 0.9618, Min 0.4823, Max 0.9855): 100%|██████████| 1296/1296 [09:57<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.174, Min 0.034, Max 1.000), (Avg. Score 0.8261, Min 0.0000, Max 0.9660): 100%|██████████| 229/229 [00:40<00:00,  5.62it/s]\n",
      "--- EPOCH 100/100 ---\n",
      "train_batch (Avg. Loss 0.039, Min 0.014, Max 0.529), (Avg. Score 0.9615, Min 0.4710, Max 0.9859): 100%|██████████| 1296/1296 [09:56<00:00,  2.18it/s]\n",
      "test_batch (Avg. Loss 0.168, Min 0.038, Max 0.735), (Avg. Score 0.8323, Min 0.2662, Max 0.9624): 100%|██████████| 229/229 [00:40<00:00,  6.65it/s]\n"
     ]
    }
   ],
   "source": [
    "# Training and evaluation\n",
    "model = ONet()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = dice_loss\n",
    "success_metric = dice_coeff\n",
    "trainer = Trainer(model, criterion, optimizer, dice_coeff, device, None)\n",
    "fit_res = trainer.fit(dl_train, \n",
    "                      dl_test,\n",
    "                      num_epochs= num_epochs,\n",
    "                      checkpoints='src/saved_models/' + model.__class__.__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1152x720 with 4 Axes>,\n",
       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9394423690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f93943dde50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9394392690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9394348cd0>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs236605.plot import plot_fit\n",
    "plot_fit(fit_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best dice score: 0.85\n"
     ]
    }
   ],
   "source": [
    "print(\"Best dice score: %.2f\" % fit_res.best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fit_res.train_acc)\n",
    "plt.plot(fit_res.test_acc)\n",
    "plt.legend(['train score', 'test score'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}