nb-train-optuna.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import optuna\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from src.data import MovingMnistDataset\n",
    "import torch.optim as optim\n",
    "import time\n",
    "\n",
    "from src.dl_models.unet import UNet\n",
    "from src import model, visualization, evaluate, data, preprocessing,train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001b[32m[I 2021-05-31 21:32:43,303]\u001b[0m A new study created in memory with name: no-name-1163e290-90ba-415f-9c75-aa15f83228c6\u001b[0m\n",
      "Using cuda\n",
      "EPOCH: 1 \u001b[32m[I 2021-05-31 21:33:49,837]\u001b[0m Trial 0 finished with value: 0.018770927796140313 and parameters: {'lr': 0.00744267075149259, 'optimizer': 'SGD'}. Best is trial 0 with value: 0.018770927796140313.\u001b[0m\n",
      "Time elapsed: 63.86 secs.\n",
      "EPOCH: 1 \u001b[32m[I 2021-05-31 21:34:58,959]\u001b[0m Trial 1 finished with value: 0.018296240363270044 and parameters: {'lr': 0.023316677633055713, 'optimizer': 'Adam'}. Best is trial 1 with value: 0.018296240363270044.\u001b[0m\n",
      "Time elapsed: 68.98 secs.\n",
      "\n",
      "Best trial:\n",
      "  Value:  0.018296240363270044\n",
      "  Params: \n",
      "    lr: 0.023316677633055713\n",
      "    optimizer: Adam\n"
     ]
    }
   ],
   "source": [
    "# Optuna MovingMnistDataset\n",
    "class Objective_optuna(object):\n",
    "    def __init__(self, path, path_val, device):\n",
    "        # Called when the study is created\n",
    "        self.device = device\n",
    "        self.path = path\n",
    "        self.path_val = path_val\n",
    "        self.loss_history = []\n",
    "        self.loss_history_val = []\n",
    "        \n",
    "    def __call__(self, trial):\n",
    "        # Called on each trial\n",
    "        # Hyperparameters\n",
    "        n_channels=3\n",
    "        n_classes=1\n",
    "\n",
    "        # Load data\n",
    "        mnist_dataset = MovingMnistDataset(path=self.path, n_frames=4, shuffle=True)\n",
    "        loader = DataLoader(mnist_dataset, batch_size=10)\n",
    "        mnist_dataset_val = MovingMnistDataset(path=self.path_val, n_frames=4, shuffle=False)\n",
    "        loader_val = DataLoader(mnist_dataset_val, batch_size=10)\n",
    "\n",
    "        # Initialize network\n",
    "        model = UNet(n_channels=n_channels, n_classes=n_classes, bilinear=True).to(device)\n",
    "        model.train()\n",
    "\n",
    "        # Hyperparameters\n",
    "        # lr = 0.001\n",
    "        # optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "        lr = trial.suggest_float(\"lr\", 1e-5, 1e-1)\n",
    "        optimizer_name = trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"])\n",
    "        optimizer = getattr(optim, optimizer_name)(model.parameters(), lr=lr)\n",
    "\n",
    "        # criterion_name =  trial.suggest_categorical(\"criterion\", [\"MSELoss\", \"L1Loss\"])\n",
    "        # criterion = getattr(nn, criterion_name)()\n",
    "        criterion = nn.MSELoss()\n",
    "\n",
    "        loss_history, loss_history_val = train.train_model(\n",
    "            model,\n",
    "            loader,\n",
    "            criterion,\n",
    "            optimizer,\n",
    "            self.device,\n",
    "            curr_epoch=0,\n",
    "            loss_history=[],\n",
    "            train_for=1,\n",
    "            verbose=True,\n",
    "            checkpoint_every=0,\n",
    "            print_cuda_mem=False,\n",
    "            loader_val=loader_val,\n",
    "            trial=trial\n",
    "        )\n",
    "        self.loss_history_val.append(loss_history_val)\n",
    "        self.loss_history.append(loss_history)\n",
    "        return loss_history_val[-1]\n",
    "\n",
    "path = 'data/moving_mnist/train_1000/'\n",
    "path_val = 'data/moving_mnist/val_5/'\n",
    "\n",
    "# Set device\n",
    "USE_CUDA = True\n",
    "device = torch.device('cuda' if torch.cuda.is_available()\n",
    "                    and USE_CUDA else 'cpu')\n",
    "print(f'Using {device}')\n",
    "\n",
    "# Execute an optimization by using an `Objective` instance.\n",
    "study = optuna.create_study(sampler=optuna.samplers.TPESampler()) #default sampler\n",
    "obj_optuna = Objective_optuna(path, path_val, device)\n",
    "study.optimize(obj_optuna, n_trials=2)\n",
    "\n",
    "print(\"\\nBest trial:\")\n",
    "trial = study.best_trial\n",
    "print(\"  Value: \", trial.value)\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using cuda\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1008x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "error_average 0.014405526100285808\n"
     ]
    }
   ],
   "source": [
    "# Test Model\n",
    "\n",
    "# Set device\n",
    "USE_CUDA = True\n",
    "device = torch.device('cuda' if torch.cuda.is_available()\n",
    "                    and USE_CUDA else 'cpu')\n",
    "print(f'Using {device}')\n",
    "\n",
    "path_test = 'data/moving_mnist/moving_mnist_test/'\n",
    "csv_test = 'data/moving_mnist/moving_mnist_test.csv'\n",
    "load_path = 'checkpoints/model_epoch10.pt'\n",
    "\n",
    "n_channels=3\n",
    "n_classes=1\n",
    "\n",
    "model_test = UNet(n_channels=n_channels, n_classes=n_classes, bilinear=True).to(device)\n",
    "model_test.load_state_dict(torch.load(load_path)[\"model_state_dict\"])\n",
    "model_test.eval()\n",
    "\n",
    "mnist_dataset_test = MovingMnistDataset(path=path_test, csv=csv_test, shuffle=True)\n",
    "loader_test = DataLoader(mnist_dataset_test, batch_size=10)\n",
    "\n",
    "# Test one sample\n",
    "(curr_seq, idxs, inputs, targets) = iter(loader_test).next()\n",
    "\n",
    "with torch.no_grad():\n",
    "  prueba = model_test(inputs.to(device=device, dtype=torch.float32)).cpu()\n",
    "\n",
    "L = [inputs[0][0], inputs[0][1], inputs[0][2], targets[0][0], prueba[0][0]]\n",
    "visualization.show_image_list(L, 1)\n",
    "visualization.show_images_diff(targets[0][0], prueba[0][0])\n",
    "\n",
    "# Test model\n",
    "criterion = nn.MSELoss()\n",
    "error_average = evaluate.test_model(model_test, loader_test, criterion, device)\n",
    "print(\"error_average\", error_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optuna.visualization import plot_contour\n",
    "from optuna.visualization import plot_edf\n",
    "from optuna.visualization import plot_intermediate_values\n",
    "from optuna.visualization import plot_optimization_history\n",
    "from optuna.visualization import plot_parallel_coordinate\n",
    "from optuna.visualization import plot_param_importances\n",
    "from optuna.visualization import plot_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "markers",
         "name": "Objective Value",
         "type": "scatter",
         "x": [
          0
         ],
         "y": [
          0.01699612214643922
         ]
        },
        {
         "name": "Best Value",
         "type": "scatter",
         "x": [
          0
         ],
         "y": [
          0.01699612214643922
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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           "bgcolor": "#E5ECF6",
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            "linecolor": "white",
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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        },
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     "metadata": {}
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   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimize cmv with optuna MontevideoFoldersDataset\n",
    "def objective(trial):\n",
    "    #https://docs.opencv.org/3.4/dc/d6b/group__video__track.html#ga5d10ebbd59fe09c5f650289ec0ece5af\n",
    "    dcfg_optuna = {'algorithm': {'cmv': {\n",
    "        'pyr_scale' : trial.suggest_float(\"pyr_scale\", 0.2, 0.8),\n",
    "        'levels' : trial.suggest_int(\"levels\", 2, 5),\n",
    "        'winsize' : trial.suggest_int(\"winsize\", 15, 25),\n",
    "        'iterations' : trial.suggest_int(\"iterations\", 1, 5),\n",
    "        'poly_n' : trial.suggest_int(\"poly_n\", 4, 8), # typically poly_n =5 or 7.\n",
    "        'poly_sigma' : trial.suggest_float(\"poly_sigma\", 0.8, 1.8)}}}\n",
    "\n",
    "    val_mvd = MontevideoFoldersDataset(path = 'data/mvd/val/', in_channel = 2, out_channel=6)\n",
    "    val_loader = DataLoader(val_mvd)\n",
    "\n",
    "    cmv = model.Cmv2(dcfg = dcfg_optuna)\n",
    "    error = evaluate.evaluate_model(cmv, val_loader, 6, metric='RMSE')\n",
    "    print(np.mean(error, axis=0))\n",
    "    return np.mean(error)\n",
    "\n",
    "study = optuna.create_study()\n",
    "study.optimize(objective, n_trials=1)\n",
    "\n",
    "print(\"\\nBest trial:\")\n",
    "trial = study.best_trial\n",
    "print(\"  Value: \", trial.value)\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))\n"
   ]
  }
 ]
}