4DVarNet_L63.ipynb

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          "text": [
            "Collecting pytorch_lightning\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch_lightning) (3.1.1)\n",
            "Collecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-5.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (160 kB)\n",
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            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
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            "\u001b[?25hCollecting aiosignal>=1.1.2\n",
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            "Collecting frozenlist>=1.1.1\n",
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            "Collecting asynctest==0.13.0\n",
            "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
            "Building wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=a9fd98bfebffdb43eab1cffec116ab2d24bd19eb399e1299663def22a6b42a94\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "Successfully built future\n",
            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, torchmetrics, PyYAML, pyDeprecate, future, pytorch-lightning\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.1 asynctest-0.13.0 frozenlist-1.2.0 fsspec-2021.11.1 future-0.18.2 multidict-5.2.0 pyDeprecate-0.3.1 pytorch-lightning-1.5.4 torchmetrics-0.6.1 yarl-1.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEDUuvB92-Sr"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import os\n",
        "import tensorflow.keras as keras\n",
        "\n",
        "import time\n",
        "import copy\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from sklearn import decomposition\n",
        "from sklearn.feature_extraction import image\n",
        "from utils import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZj5LMIyN62A",
        "outputId": "233d046d-1703-4bea-8eae-4fdaffd48d5b"
      },
      "source": [
        "!git clone https://github.com/CIA-Oceanix/DLGD2021.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'DLGD2021'...\n",
            "remote: Enumerating objects: 294, done.\u001b[K\n",
            "remote: Counting objects: 100% (294/294), done.\u001b[K\n",
            "remote: Compressing objects: 100% (271/271), done.\u001b[K\n",
            "remote: Total 294 (delta 136), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (294/294), 101.86 MiB | 14.11 MiB/s, done.\n",
            "Resolving deltas: 100% (136/136), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HFAjJ_CVpR2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e940087b-164e-4e0c-c275-80e5c2e15790"
      },
      "source": [
        "!pwd\n",
        "os.chdir('DLGD2021/lecture-5-inverse-problems')\n",
        "import solver as solver_4DVarNet\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "rJwF-t6D6UFw",
        "outputId": "27ff06a3-caae-4595-dfe5-7414e16770f8"
      },
      "source": [
        "os.chdir('content')\n",
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-668aaec445bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'content'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pwd'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'content'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1FjHV_K3W9s"
      },
      "source": [
        "# L63 Data simulation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ium9S5mQdcHS"
      },
      "source": [
        "from importlib import reload\n",
        "import utils\n",
        "reload(utils) \n",
        "from utils import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZSjh6fTsHRf"
      },
      "source": [
        "Training_dataset,Val_dataset,Test_dataset = L63PatchDataExtraction(sparsity=0.5,sigma_noise=np.sqrt(2),num_variables=3)\n",
        "\n",
        "\n",
        "meanTr          = np.mean(Training_dataset['Missing']) / np.mean(Training_dataset['Mask']) \n",
        "stdTr           = np.sqrt( np.mean( Training_dataset['Missing']**2 ) / np.mean(Training_dataset['Mask']) )\n",
        "#visualisation_data(Training_dataset['Truth'],Training_dataset['Obs'],Training_dataset['Init'],125)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKaiNQwURhFo",
        "outputId": "65f95ebe-d571-48f9-f298-cd4779ae3323"
      },
      "source": [
        "# reshape to 2D tensors to use \n",
        "# the 2D implementation of the 4DVarNet\n",
        "dT = 200\n",
        "Training_dataset['Truth'] = Training_dataset['Truth'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Training_dataset['Mask']  = Training_dataset['Mask'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Training_dataset['Init']  = Training_dataset['Init'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Training_dataset['Obs']   = Training_dataset['Obs'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "\n",
        "Val_dataset['Truth']      = Val_dataset['Truth'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Val_dataset['Mask']       = Val_dataset['Mask'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Val_dataset['Init']       = Val_dataset['Init'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Val_dataset['Obs']        = Val_dataset['Obs'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "\n",
        "Test_dataset['Truth']     = Test_dataset['Truth'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Test_dataset['Mask']      = Test_dataset['Mask'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Test_dataset['Init']      = Test_dataset['Init'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "Test_dataset['Obs']       = Test_dataset['Obs'].reshape((-1,dT,3,1)).transpose((0,2,1,3))\n",
        "\n",
        "print('..... Training dataset: %dx%dx%dx%d'%(Training_dataset['Truth'].shape[0],Training_dataset['Truth'].shape[1],Training_dataset['Truth'].shape[2],Training_dataset['Truth'].shape[3]))\n",
        "print('..... Test dataset    : %dx%dx%dx%d'%(Test_dataset['Truth'].shape[0],Test_dataset['Truth'].shape[1],Test_dataset['Truth'].shape[2],Test_dataset['Truth'].shape[3]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "..... Training dataset: 12800x3x200x1\n",
            "..... Test dataset    : 2560x3x200x1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "g2AU2yv8YSnw",
        "outputId": "8d18d955-59a9-4e1e-f862-737c0028616c"
      },
      "source": [
        "plt.plot(Test_dataset['Truth'][1,0,:,:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0997251550>]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhKugE5VpyZc"
      },
      "source": [
        "# Trainable dynamical prior $\\Phi$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJEhltDDu1Nm"
      },
      "source": [
        "flagAEType = 'unet'\n",
        "DimAE = 10\n",
        "shapeData  = Training_dataset['Truth'].shape[1:]\n",
        "\n",
        "\n",
        "\n",
        "dW = 5\n",
        "class Phi_r(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Phi_r, self).__init__()\n",
        "        self.pool1  = torch.nn.AvgPool2d((4,1))\n",
        "        #self.conv1  = ConstrainedConv2d(shapeData[0],2*shapeData[0]*DimAE,(2*dW+1,1),padding=(dW,0),bias=False)\n",
        "        self.conv1  = torch.nn.Conv2d(shapeData[0],2*shapeData[0]*DimAE,(2*dW+1,1),padding=(dW,0),bias=False)\n",
        "        self.conv2  = torch.nn.Conv2d(2*shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "          \n",
        "        self.conv21 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.conv22 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.conv23 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.conv3  = torch.nn.Conv2d(2*shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "          \n",
        "        self.conv2Tr = torch.nn.ConvTranspose2d(shapeData[0]*DimAE,shapeData[0],(4,1),stride=(4,1),bias=False)          \n",
        "        \n",
        "        self.convHR1  = torch.nn.Conv2d(shapeData[0],2*shapeData[0]*DimAE,(2*dW+1,1),padding=(dW,0),bias=False)\n",
        "        self.convHR2  = torch.nn.Conv2d(2*shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "          \n",
        "        self.convHR21 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.convHR22 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.convHR23 = torch.nn.Conv2d(shapeData[0]*DimAE,shapeData[0]*DimAE,1,padding=0,bias=False)\n",
        "        self.convHR3  = torch.nn.Conv2d(2*shapeData[0]*DimAE,shapeData[0],1,padding=0,bias=False)\n",
        "\n",
        "    def forward(self, xinp):\n",
        "        x = self.pool1( xinp )\n",
        "        x = self.conv1( x )\n",
        "        x = self.conv2( F.relu(x) )\n",
        "        x = torch.cat((self.conv21(x), self.conv22(x) * self.conv23(x)),dim=1)\n",
        "        x = self.conv3( x )\n",
        "        x = self.conv2Tr( x )\n",
        "          \n",
        "          \n",
        "        xHR = self.convHR1( xinp )\n",
        "        xHR = self.convHR2( F.relu(xHR) )\n",
        "        xHR = torch.cat((self.convHR21(xHR), self.convHR22(xHR) * self.convHR23(xHR)),dim=1)\n",
        "        xHR = self.convHR3( xHR )\n",
        "          \n",
        "        x   = torch.add(x,1.,xHR)\n",
        "          \n",
        "        x = x.view(-1,shapeData[0],shapeData[1],1)\n",
        "        return x\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rg1i7L9OEaTd"
      },
      "source": [
        "# Observation model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-hQ_izp3kSb"
      },
      "source": [
        "# Observation model\n",
        "class Model_H(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Model_H, self).__init__()\n",
        "        self.dim_obs = 1\n",
        "        self.dim_obs_channel = np.array([shapeData[0]])\n",
        "\n",
        "        self.DimObs = 1\n",
        "        self.dimObsChannel = np.array([shapeData[0]])\n",
        "\n",
        "    def forward(self, x, y, mask):\n",
        "        dyout = (x - y) * mask\n",
        "        return dyout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sha96boMElBa"
      },
      "source": [
        "# Lightning Class for 4DVarNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQR3PVR_39hD"
      },
      "source": [
        "class HParam:\n",
        "    def __init__(self):\n",
        "        self.iter_update     = []\n",
        "        self.nb_grad_update  = []\n",
        "        self.lr_update       = []\n",
        "        self.n_grad          = 1\n",
        "        self.dim_grad_solver = 10\n",
        "        self.dropout         = 0.25\n",
        "        self.w_loss          = []\n",
        "        self.automatic_optimization = True\n",
        "\n",
        "        self.alpha_proj    = 0.5\n",
        "        self.alpha_mse = 10.\n",
        "\n",
        "        self.k_batch = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTkxCRNq32d1"
      },
      "source": [
        "EPS_NORM_GRAD = 0. * 1.e-20  \n",
        "import pytorch_lightning as pl\n",
        "\n",
        "class LitModel(pl.LightningModule):\n",
        "    def __init__(self,conf=HParam(),*args, **kwargs):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "\n",
        "        # hyperparameters\n",
        "        self.hparams.iter_update     = [0, 20, 50, 70, 100, 150, 800]  # [0,2,4,6,9,15]\n",
        "        self.hparams.nb_grad_update  = [5, 5, 10, 10, 15, 15, 20, 20, 20]  # [0,0,1,2,3,3]#[0,2,2,4,5,5]#\n",
        "        self.hparams.lr_update       = [1e-3, 1e-4, 1e-4, 1e-5, 1e-4, 1e-5, 1e-5, 1e-6, 1e-7]\n",
        "        \n",
        "        self.hparams.n_grad          = self.hparams.nb_grad_update[0]\n",
        "        self.hparams.k_n_grad        = 1\n",
        "        self.hparams.dim_grad_solver = dimGradSolver\n",
        "        self.hparams.dropout         = rateDropout\n",
        "        \n",
        "        self.hparams.k_batch         = 1\n",
        "        \n",
        "        self.hparams.alpha_prior    = 0.5\n",
        "        self.hparams.alpha_mse = 1.e1        \n",
        "\n",
        "        self.hparams.w_loss          = torch.nn.Parameter(torch.Tensor(w_loss), requires_grad=False)\n",
        "        self.hparams.automatic_optimization = False#True#\n",
        "\n",
        "        # main model\n",
        "        self.model        = solver_4DVarNet.Solver_Grad_4DVarNN(Phi_r(), \n",
        "                                                            Model_H(), \n",
        "                                                            solver_4DVarNet.model_GradUpdateLSTM(shapeData, UsePriodicBoundary, self.hparams.dim_grad_solver, self.hparams.dropout), \n",
        "                                                            None, None, shapeData, self.hparams.n_grad, EPS_NORM_GRAD)#, self.hparams.eps_norm_grad)\n",
        "        self.w_loss       = self.hparams.w_loss # duplicate for automatic upload to gpu\n",
        "        self.x_rec    = None # variable to store output of test method\n",
        "        self.x_rec_obs = None\n",
        "        self.curr = 0\n",
        "\n",
        "        self.automatic_optimization = self.hparams.automatic_optimization\n",
        "        \n",
        "\n",
        "    def forward(self):\n",
        "        return 1\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer   = optim.Adam([{'params': self.model.model_Grad.parameters(), 'lr': self.hparams.lr_update[0]},\n",
        "                                      {'params': self.model.model_VarCost.parameters(), 'lr': self.hparams.lr_update[0]},\n",
        "                                    {'params': self.model.phi_r.parameters(), 'lr': 0.5*self.hparams.lr_update[0]},\n",
        "                                    ], lr=0.)\n",
        "        return optimizer\n",
        "    \n",
        "    def on_epoch_start(self):\n",
        "        self.model.n_grad   = self.hparams.n_grad \n",
        "        \n",
        "    def on_train_epoch_start(self):\n",
        "        self.model.n_grad   = self.hparams.n_grad \n",
        "\n",
        "        opt = self.optimizers()\n",
        "        if (self.current_epoch in self.hparams.iter_update) & (self.current_epoch > 0):\n",
        "            indx             = self.hparams.iter_update.index(self.current_epoch)\n",
        "            print('... Update Iterations number/learning rate #%d: NGrad = %d -- lr = %f'%(self.current_epoch,self.hparams.nb_grad_update[indx],self.hparams.lr_update[indx]))\n",
        "            \n",
        "            self.hparams.n_grad = self.hparams.nb_grad_update[indx]\n",
        "            self.model.n_grad   = self.hparams.n_grad \n",
        "            \n",
        "            mm = 0\n",
        "            lrCurrent = self.hparams.lr_update[indx]\n",
        "            lr = np.array([lrCurrent,lrCurrent,0.5*lrCurrent,0.])            \n",
        "            for pg in opt.param_groups:\n",
        "                pg['lr'] = lr[mm]# * self.hparams.learning_rate\n",
        "                mm += 1\n",
        "        \n",
        "    def training_step(self, train_batch, batch_idx, optimizer_idx=0):\n",
        "        opt = self.optimizers()\n",
        "                    \n",
        "        # compute loss and metrics\n",
        "        loss, out, metrics = self.compute_loss(train_batch, phase='train')\n",
        "        \n",
        "        for kk in range(0,self.hparams.k_n_grad-1):\n",
        "            loss1, out, metrics = self.compute_loss(train_batch, phase='train',batch_init=out[0],hidden=out[1],cell=out[2],normgrad=out[3])\n",
        "            loss = loss + loss1\n",
        "        \n",
        "        self.log(\"tr_mse\", metrics['mse'] , on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)\n",
        "        \n",
        "        # initial grad value\n",
        "        if self.hparams.automatic_optimization == False :\n",
        "            # backward\n",
        "            self.manual_backward(loss)\n",
        "        \n",
        "            if (batch_idx + 1) % self.hparams.k_batch == 0:\n",
        "                # optimisation step\n",
        "                opt.step()\n",
        "                \n",
        "                # grad initialization to zero\n",
        "                opt.zero_grad()\n",
        "         \n",
        "        return loss\n",
        "    \n",
        "    def validation_step(self, val_batch, batch_idx):\n",
        "        loss, out, metrics = self.compute_loss(val_batch, phase='val')\n",
        "        for kk in range(0,self.hparams.k_n_grad-1):\n",
        "            loss1, out, metrics = self.compute_loss(val_batch, phase='val',batch_init=out[0],hidden=out[1],cell=out[2],normgrad=out[3])\n",
        "            loss = loss1\n",
        "\n",
        "        #self.log('val_loss', loss)\n",
        "        self.log('val_loss', metrics['mse'] )\n",
        "        self.log(\"val_mse\",  metrics['mse'] , on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)\n",
        "        return loss\n",
        "\n",
        "    def test_step(self, test_batch, batch_idx):\n",
        "        loss, out, metrics = self.compute_loss(test_batch, phase='test')\n",
        "        \n",
        "        for kk in range(0,self.hparams.k_n_grad-1):\n",
        "            loss1, out, metrics = self.compute_loss(test_batch, phase='test',batch_init=out[0].detach(),hidden=out[1],cell=out[2],normgrad=out[3])\n",
        "\n",
        "        #out_ssh,out_ssh_obs = out\n",
        "        #self.log('test_loss', loss)\n",
        "        self.log(\"test_mse\", stdTr**2 * metrics['mse'] , on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)\n",
        "        #return {'preds': out_ssh.detach().cpu(),'obs_ssh': out_ssh_obs.detach().cpu()}\n",
        "        return {'preds': out[0].detach().cpu()}\n",
        "\n",
        "    def training_epoch_end(self, training_step_outputs):\n",
        "        # do something with all training_step outputs\n",
        "        print('.. \\n')\n",
        "    \n",
        "    def test_epoch_end(self, outputs):\n",
        "        x_test_rec = torch.cat([chunk['preds'] for chunk in outputs]).numpy()\n",
        "        x_test_rec = x_test_rec      \n",
        "        self.x_rec = x_test_rec.squeeze()\n",
        "\n",
        "        return [{'mse':0.,'preds': 0.}]\n",
        "\n",
        "    def compute_loss(self, batch, phase, batch_init = None , hidden = None , cell = None , normgrad = 0.0):\n",
        "\n",
        "        inputs_init_,inputs_obs,masks,targets_GT = batch\n",
        " \n",
        "        #inputs_init = inputs_init_\n",
        "        if batch_init is None :\n",
        "            inputs_init = inputs_init_\n",
        "        else:\n",
        "            inputs_init = batch_init\n",
        "            \n",
        "        if phase == 'train' :                \n",
        "            inputs_init = inputs_init.detach()\n",
        "            \n",
        "        with torch.set_grad_enabled(True):\n",
        "            # with torch.set_grad_enabled(phase == 'train'):\n",
        "            inputs_init = torch.autograd.Variable(inputs_init, requires_grad=True)\n",
        "\n",
        "            outputs, hidden_new, cell_new, normgrad_ = self.model(inputs_init, inputs_obs, masks, hidden = hidden , cell = cell , normgrad = normgrad )\n",
        "\n",
        "            #loss_mse   = solver_4DVarNet.compute_WeightedLoss((outputs - targets_GT), self.w_loss)\n",
        "            loss_mse = torch.mean((outputs - targets_GT) ** 2)\n",
        "            loss_prior = torch.mean((self.model.phi_r(outputs) - outputs) ** 2)\n",
        "            loss_prior_gt = torch.mean((self.model.phi_r(targets_GT) - targets_GT) ** 2)\n",
        "\n",
        "            loss = self.hparams.alpha_mse * loss_mse\n",
        "            loss += 0.5 * self.hparams.alpha_prior * (loss_prior + loss_prior_gt)\n",
        "            \n",
        "            # metrics\n",
        "            mse       = loss_mse.detach()\n",
        "            metrics   = dict([('mse',mse)])\n",
        "            #print(mse.cpu().detach().numpy())\n",
        "            if (phase == 'val') or (phase == 'test'):                \n",
        "                outputs = outputs.detach()\n",
        "        \n",
        "        out = [outputs,hidden_new, cell_new, normgrad_]\n",
        "        \n",
        "        return loss,out, metrics\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8y3Ajptyk0rT"
      },
      "source": [
        "# Model training "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCiJUyLF92uK",
        "outputId": "eb804741-913f-46e1-977d-76278e4fcf67"
      },
      "source": [
        "# Pytorch dataloadzers\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "\n",
        "UsePriodicBoundary = False # use a periodic boundary for all conv operators in the gradient model (see torch_4DVarNN_dinAE)\n",
        "w_loss = np.ones(dT) / np.float(dT)\n",
        "batch_size = 128\n",
        "idx_val = Training_dataset['Truth'].shape[0]\n",
        "\n",
        "\n",
        "training_dataset     = torch.utils.data.TensorDataset(torch.Tensor(Training_dataset['Init'][:idx_val:,:,:,:]),torch.Tensor(Training_dataset['Obs'][:idx_val:,:,:,:]),torch.Tensor(Training_dataset['Mask'][:idx_val:,:,:,:]),torch.Tensor(Training_dataset['Truth'][:idx_val:,:,:,:])) # create your datset\n",
        "val_dataset         = torch.utils.data.TensorDataset(torch.Tensor(Val_dataset['Init'][:idx_val:,:,:,:]),torch.Tensor(Val_dataset['Obs'][:idx_val:,:,:,:]),torch.Tensor(Val_dataset['Mask'][:idx_val:,:,:,:]),torch.Tensor(Val_dataset['Truth'][:idx_val:,:,:,:]))  # create your datset\n",
        "test_dataset         = torch.utils.data.TensorDataset(torch.Tensor(Test_dataset['Init'][:idx_val:,:,:,:]),torch.Tensor(Test_dataset['Obs'][:idx_val:,:,:,:]),torch.Tensor(Test_dataset['Mask'][:idx_val:,:,:,:]),torch.Tensor(Test_dataset['Truth'][:idx_val:,:,:,:])) # create your datset\n",
        "\n",
        "dataloaders = {\n",
        "    'train': torch.utils.data.DataLoader(training_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True),\n",
        "    'val': torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True),\n",
        "    'test': torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True),\n",
        "}            \n",
        "dataset_sizes = {'train': len(training_dataset), 'val': len(val_dataset), 'test': len(test_dataset)}\n",
        "\n",
        "print(dataset_sizes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': 12800, 'val': 2560, 'test': 2560}\n"
          ]
        }
      ]
    },
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        "id": "a__X1p0tEEeQ",
        "outputId": "fd94659f-02ac-4d5e-dbc8-a918d881d6fd"
      },
      "source": [
        "# Training from scratch\n",
        "dimGradSolver = 25\n",
        "rateDropout = 0.2\n",
        "mod = LitModel.load_from_checkpoint(checkpoint_path=\"4DVarNet2.ckpt\")\n",
        "mod.hparams.n_grad          = 5\n",
        "mod.hparams.k_n_grad        = 1\n",
        "mod.hparams.iter_update     = [0, 100, 200, 300, 500, 700, 800]  # [0,2,4,6,9,15]\n",
        "mod.hparams.nb_grad_update  = [5, 5, 5, 5, 15, 15, 20, 20, 20]  # [0,0,1,2,3,3]#[0,2,2,4,5,5]#\n",
        "mod.hparams.lr_update       = [1e-3, 1e-4, 1e-5, 1e-5, 1e-4, 1e-5, 1e-5, 1e-6, 1e-7]\n",
        "        \n",
        "mod.hparams.alpha_prior = 0.1\n",
        "mod.hparams.alpha_mse = 1.\n",
        "\n",
        "profiler_kwargs = {'max_epochs': 400 }\n",
        "\n",
        "trainer = pl.Trainer(gpus=1,  **profiler_kwargs)\n",
        "trainer.fit(mod, dataloaders['train'], dataloaders['val'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name  | Type                | Params\n",
            "----------------------------------------------\n",
            "0 | model | Solver_Grad_4DVarNN | 40.7 K\n",
            "----------------------------------------------\n",
            "40.7 K    Trainable params\n",
            "200       Non-trainable params\n",
            "40.9 K    Total params\n",
            "0.164     Total estimated model params size (MB)\n"
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            ".. \n",
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            ".. \n",
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            ".. \n",
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          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:685: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
            "  rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yarylAdGIOzt"
      },
      "source": [
        "trainer.save_checkpoint(\"4DVarNetL63.ckpt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OubH85fiJ9Aq",
        "outputId": "437acc8b-5e0f-45e9-9b18-a16cc0ec0c4e"
      },
      "source": [
        "\n",
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAATZr_mEH3Y"
      },
      "source": [
        "# Model testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329,
          "referenced_widgets": [
            "abd8897a30f04595972d32826a846d94",
            "00178fcd0d5e4ee18b5921301cc351c6",
            "a3ba2cd5f5574a0684d0524d2b4254aa",
            "89d188d563274fccb8b0d5ced78e553a",
            "94761125c9e64bc69eda2f451a272317",
            "81f127e6358545d3a185b8caffe56926",
            "a5e1082b6d684f6cae5cbc8e28f0c247",
            "ef87f1e9026e476f92ea7124a2564850",
            "a76805e0ef894bb1866d5fefcf23a8d7",
            "cd8a57cb815c4de0b6c1ea545c40a533",
            "439ab29f10ad44fa9be31ff6ddbb1866"
          ]
        },
        "id": "HhBYtJChPg5s",
        "outputId": "bac8efa9-e221-47e8-cab8-ee70173bea37"
      },
      "source": [
        "dimGradSolver = 25\n",
        "rateDropout = 0.2\n",
        "\n",
        "#print(mod.hparams)\n",
        "mod.hparams.n_grad = 5\n",
        "mod.hparams.k_n_grad = 2\n",
        "\n",
        "print(' Ngrad = %d / %d'%(mod.hparams.n_grad,mod.model.n_grad))\n",
        "#trainer = pl.Trainer(gpus=1, accelerator = \"ddp\", **profiler_kwargs)\n",
        "\n",
        "profiler_kwargs = {'max_epochs': 1}\n",
        "trainer = pl.Trainer(gpus=1,  **profiler_kwargs)\n",
        "\n",
        "############################################################\n",
        "# metrics for validation dataset\n",
        "trainer.test(mod, test_dataloaders=dataloaders['test'])\n",
        "\n",
        "X_val = Test_dataset['Truth'].squeeze()\n",
        "mask_val = Test_dataset['Mask'].squeeze()\n",
        "var_val  = np.mean( (X_val - np.mean(X_val,axis=0))**2 )\n",
        "mse = np.mean( (mod.x_rec-X_val) **2 ) \n",
        "mse_i   = np.mean( (1.-mask_val.squeeze()) * (mod.x_rec-X_val) **2 ) / np.mean( (1.-mask_val) )\n",
        "mse_r   = np.mean( mask_val.squeeze() * (mod.x_rec-X_val) **2 ) / np.mean( mask_val )\n",
        "\n",
        "nmse = mse / var_val\n",
        "nmse_i = mse_i / var_val\n",
        "nmse_r = mse_r / var_val\n",
        "\n",
        "print(\"..... Assimilation performance (validation data)\")\n",
        "print(\".. MSE ALL.   : %.3f / %.3f\"%(mse,nmse))\n",
        "print(\".. MSE ObsData: %.3f / %.3f\"%(mse_r,nmse_r))\n",
        "print(\".. MSE Interp : %.3f / %.3f\"%(mse_i,nmse_i))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:902: LightningDeprecationWarning: `trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.test(dataloaders)` instead.\n",
            "  \"`trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6.\"\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Ngrad = 5 / 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "abd8897a30f04595972d32826a846d94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'test_mse': 33.036075592041016}\n",
            "--------------------------------------------------------------------------------\n",
            "..... Assimilation performance (validation data)\n",
            ".. MSE ALL.   : 0.128 / 0.002\n",
            ".. MSE ObsData: 0.119 / 0.002\n",
            ".. MSE Interp : 0.131 / 0.002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "TzNNCFGKM-GH",
        "outputId": "ce590bae-f73c-4fe4-906f-ea6135b02ca3"
      },
      "source": [
        "ii = 110\n",
        "\n",
        "mod.x_rec = mod.x_rec.squeeze()\n",
        "x_gt  = Test_dataset['Truth'].squeeze()[ii,:,:]\n",
        "x_rec = mod.x_rec[ii,:,:]\n",
        "y_obs = Test_dataset['Obs'].squeeze()[ii,:,:]\n",
        "\n",
        "## visualization\n",
        "plt.figure(figsize=(10,5))\n",
        "for kk in range(0,3):\n",
        "  plt.subplot(1,3,kk+1)\n",
        "  plt.plot(y_obs[kk,:],'.',label='Observations',ms=4)\n",
        "  plt.plot(x_gt[kk,:],'b-',alpha=0.5,label='Truth')\n",
        "  plt.plot(x_rec[kk,:],'m-',alpha=0.9,label='4DVarNet Prediction')\n",
        "  plt.legend(fontsize=5)\n",
        "  plt.xlabel('Timesteps')\n",
        "  plt.ylabel('Position')\n",
        "\n",
        "# Reconstruction performance\n",
        "X_test = Test_dataset['Truth'].squeeze()\n",
        "mask_test = Test_dataset['Mask'].squeeze()\n",
        "var_test  = np.mean( (X_test - np.mean(X_test,axis=0))**2 )\n",
        "mse = np.mean( (mod.x_rec-X_test) **2 ) \n",
        "mse_i   = np.mean( (1.-mask_test.squeeze()) * (mod.x_rec-X_test) **2 ) / np.mean( (1.-mask_test) )\n",
        "mse_r   = np.mean( mask_test.squeeze() * (mod.x_rec-X_test) **2 ) / np.mean( mask_test )\n",
        "\n",
        "nmse = mse / var_test\n",
        "nmse_i   = mse_i / var_test\n",
        "nmse_r   = mse_r / var_test\n",
        "\n",
        "print(\"..... Assimilation performance\")\n",
        "print(\".. MSE ALL.   : %.3f / %.3f\"%(mse,nmse))\n",
        "print(\".. MSE ObsData: %.3f / %.3f\"%(mse_r,nmse_r))\n",
        "print(\".. MSE Interp : %.3f / %.3f\"%(mse_i,nmse_i))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "..... Assimilation performance\n",
            ".. MSE ALL.   : 0.128 / 0.002\n",
            ".. MSE ObsData: 0.119 / 0.002\n",
            ".. MSE Interp : 0.131 / 0.002\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6p3Z2Ye9Bva",
        "outputId": "8a01da0b-639d-4a3f-e391-26d02a68e37e"
      },
      "source": [
        "R_score = 0\n",
        "mod.x_rec = mod.x_rec.squeeze()\n",
        "x_gt  = Test_dataset['Truth'].squeeze()\n",
        "x_rec = mod.x_rec\n",
        "y_obs = Test_dataset['Obs'].squeeze()\n",
        "\n",
        "R_score = np.sqrt(((x_rec-x_gt)**2).mean(axis=2)).mean(axis = 0)\n",
        "print('Variables reconstruction score : {}'.format(R_score))\n",
        "print('Global reconstruction score : {}'.format(R_score.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variables reconstruction score : [0.24320293 0.3292086  0.40818969]\n",
            "Global reconstruction score : 0.32686707197404835\n"
          ]
        }
      ]
    }
  ]
}