pose_estimation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pose_estimation",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/uprestel/AutoNeRF/blob/master/pose_estimation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZh3aklt3ZET"
      },
      "source": [
        "# Pose estimation\n",
        "In this Notebook we \n",
        "* load a trained VAE, a trained NeRF to generate images\n",
        "* Next we generate a pose and an image at that pose \n",
        "* Finally, we learn to translate between a pose and a prior given the latent vector of our image\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghRFiG5zKvko"
      },
      "source": [
        "### Load imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptTYjWao3VsM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0c4d18b-788e-44a0-a427-51f65b37c664"
      },
      "source": [
        "import sys\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "!git clone https://github.com/uprestel/AutoNeRF.git\n",
        "\n",
        "import AutoNeRF.data.data\n",
        "import AutoNeRF.models.cinn as cinn\n",
        "import AutoNeRF.models.blocks as blocks\n",
        "import AutoNeRF.models.loss as cinn_loss\n",
        "import AutoNeRF.models.nerf as nerf\n",
        "import AutoNeRF.models.vae\n",
        "import AutoNeRF.util.utils\n",
        "from AutoNeRF.util.utils import show\n",
        "#import AutoNeRF.cam_util\n",
        "from AutoNeRF.util.transforms import random_rotation, look_at_rotation\n",
        "\n",
        "import time\n",
        "import os\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torchvision.utils import make_grid\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/gdrive', force_remount=True)\n",
        "\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'AutoNeRF'...\n",
            "remote: Enumerating objects: 75, done.\u001b[K\n",
            "remote: Counting objects: 100% (75/75), done.\u001b[K\n",
            "remote: Compressing objects: 100% (75/75), done.\u001b[K\n",
            "remote: Total 445 (delta 33), reused 0 (delta 0), pack-reused 370\u001b[K\n",
            "Receiving objects: 100% (445/445), 41.90 MiB | 28.83 MiB/s, done.\n",
            "Resolving deltas: 100% (229/229), done.\n",
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSjNg9hx9rjl"
      },
      "source": [
        "## Load trained NeRF model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtXqR_0nOyGV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "152cb4fc-8b65-4b07-f55b-636c49f2c139"
      },
      "source": [
        "model = nerf.TinyNerfModel().to(device)\n",
        "model.load_state_dict(torch.load(\"/gdrive/My Drive/nerf_lego.pt\"))\n",
        "model.eval()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TinyNerfModel(\n",
              "  (layer1): Linear(in_features=39, out_features=128, bias=True)\n",
              "  (layer2): Linear(in_features=128, out_features=128, bias=True)\n",
              "  (layer3): Linear(in_features=128, out_features=64, bias=True)\n",
              "  (layer4): Linear(in_features=64, out_features=32, bias=True)\n",
              "  (layer5): Linear(in_features=32, out_features=4, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJ7EZmcradiX"
      },
      "source": [
        "## cINN training\n",
        "Now that our VAE is ready, we are now learning a normalizing flow to generate new samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcfTAQI26icc"
      },
      "source": [
        "latent_space = 64\n",
        "batch_size = 32\n",
        "epochs = 40\n",
        "\n",
        "\n",
        "def transform(image):\n",
        "    image = (AutoNeRF.util.utils.swap_channels(image))\n",
        "    #image = F.interpolate(image, size=64)\n",
        "    return image\n",
        "\n",
        "\n",
        "# We create an invertible neural Network with the following properties:\n",
        "# * Our conditional input (the latent space vector) is 64-dimensional\n",
        "# * The hidden dimension is 3 * the size of the number of components in our pose matrix (12 * 3)\n",
        "# * The translations and scalings have the depth 5\n",
        "# * We have 14 alternating coupling layers\n",
        "tau = cinn.ConditionalTransformer(\n",
        "    in_channels = 12,\n",
        "    cond_channels = latent_space,\n",
        "    hidden_dim = 12 * 3,\n",
        "    hidden_depth = 5,\n",
        "    n_flows = 14\n",
        ").to(device)\n",
        "\n",
        "\n",
        "\n",
        "# OPTIMIZER: AMSGRAD OR ADAM WITH 1e-2\n",
        "optimizer = torch.optim.Adam(params=tau.parameters(), lr=1e-4)\n",
        "\n",
        "\n",
        "pth=\"/gdrive/My Drive/temp_dataset_lego_new3.pt.npz\"\n",
        "dataset = AutoNeRF.data.data.AutoNeRF_Dataset(pth)\n",
        "vae = AutoNeRF.models.vae.VAE(in_channels = 3, latent_dim=latent_space).to(device)\n",
        "vae.load_state_dict(torch.load(\"/gdrive/My Drive/vae_lego.pt\"))\n",
        "vae.eval()\n",
        "\n",
        "\n",
        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=True)\n",
        "loss = cinn_loss.Loss(None)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    print(\"--- starting epoch %s ---\"%epoch)\n",
        "    for i, sample in enumerate(dataloader):\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        images, poses = sample\n",
        "        images = transform(images).to(device)\n",
        "\n",
        "        poses = poses.view(batch_size, -1)\n",
        "        poses_red = poses[:, :12].to(device)\n",
        "        poses_red = poses_red[:,:,None,None]\n",
        "        _,_,_,z = vae(images)\n",
        "        z = z[:,:,None,None]\n",
        "        #print(poses, poses_red)\n",
        "        #print(z.shape, poses_red.shape)\n",
        "        zz, logdet = tau(poses_red, z)\n",
        "        l = loss(zz, logdet)\n",
        "        l.backward()\n",
        "        \n",
        "        optimizer.step()\n",
        "        if i % 100 == 0:\n",
        "            print(l.item())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV0PIXQ0lwtR"
      },
      "source": [
        "# Save / Load the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIKT-IN6Jp1F"
      },
      "source": [
        "torch.save(tau.state_dict(), \"/gdrive/My Drive/cinn_lego_pose.pt\")"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEqTGdGWC-Qd"
      },
      "source": [
        "latent_space = 64\n",
        "tau = cinn.ConditionalTransformer(\n",
        "    in_channels = 12,\n",
        "    cond_channels = latent_space,\n",
        "    hidden_dim = 12 * 3,\n",
        "    hidden_depth = 5,\n",
        "    n_flows = 14\n",
        ").to(device)\n",
        "tau.load_state_dict(torch.load(\"/gdrive/My Drive/cinn_lego_pose.pt\"))\n",
        "tau.eval()\n",
        "\n",
        "\n",
        "vae = AutoNeRF.models.vae.VAE(in_channels = 3, latent_dim=latent_space).to(device)\n",
        "vae.load_state_dict(torch.load(\"/gdrive/My Drive/vae_lego.pt\"))\n",
        "vae.eval()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNhBe1P_yHkc"
      },
      "source": [
        "# Pose estimation example\n",
        "In this example we will do the following:\n",
        "* Generate a new pose and render a corresponding image using NeRF\n",
        "* Estimate the pose using the cINN \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "Q9J9aDnwvYqZ",
        "outputId": "745da41f-145b-447d-b2a5-1960b09300c0"
      },
      "source": [
        "dataset = AutoNeRF.data.data.AutoNeRF_Dataset(\"/gdrive/My Drive/chair.npz\")\n",
        "\n",
        "def get_new_pose(center, radius):\n",
        "    \"\"\"\n",
        "    generates a new pose / perspective for the NeRF model\n",
        "    \"\"\"\n",
        "\n",
        "    rot = random_rotation().to(device)\n",
        "    unit_x = torch.tensor([1.,0.,0.]).to(device)\n",
        "    new_t = center + radius*(rot @ unit_x)\n",
        "\n",
        "    new_t[2] = torch.abs(new_t[2])\n",
        "\n",
        "\n",
        "    cam_rot = look_at_rotation(at=new_t.unsqueeze(dim=0), \n",
        "                               camera_position=center.unsqueeze(dim=0),up=UP) #tform_cam2world[0, :3, :3]#\n",
        "\n",
        "    #cam_rot = torch.transpose(cam_rot, 1,2)\n",
        "    transform = torch.zeros((4,4))#.to(device)\n",
        "    \n",
        "    transform[:3, :3] = cam_rot\n",
        "    transform[:3, 3] = new_t\n",
        "    transform[3, 3] = 1\n",
        "    #print(look_at_rotation(center))\n",
        "    return transform\n",
        "\n",
        "def get_thresholds(r):\n",
        "    diff = r - SCENE_RADIUS\n",
        "    tn = diff\n",
        "    tf = diff + 2*SCENE_RADIUS\n",
        "    return tn, tf\n",
        "\n",
        "def show(image, permutation=(0,1,2), title=\"\"):\n",
        "    grid_img = torchvision.utils.make_grid(image, nrow=5)\n",
        "    plt.imshow(grid_img.permute(permutation))\n",
        "    plt.title(title)\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "def to_pose(p):\n",
        "    new_pose = torch.zeros((4,4)).to(device)\n",
        "    Rt = p.reshape((3,4))\n",
        "    new_pose[:3,:4] = Rt\n",
        "    new_pose[3,3] = 1.\n",
        "    return new_pose\n",
        "\n",
        "\n",
        "# ------ First load parameters for NeRF from the dataset ------\n",
        "images = torch.from_numpy(dataset.images).to(device)\n",
        "focal_length = torch.from_numpy(dataset.focal_length).to(device)\n",
        "poses = torch.from_numpy(dataset.poses).to(device)\n",
        "\n",
        "height, width = images.shape[1:3]\n",
        "\n",
        "UP = torch.tensor([0.,0.,1.]).unsqueeze(dim=0).to(device)\n",
        "translation = torch.tensor(dataset.poses[:, :3,3]).to(device)\n",
        "\n",
        "center = torch.zeros(3).to(device)\n",
        "radius = torch.norm(translation[0] - center, p=2)\n",
        "height, width = 100,100\n",
        "print(radius, center)\n",
        "\n",
        "SCENE_RADIUS = 2\n",
        "\n",
        "\n",
        "num_encoding_functions = 6\n",
        "# Specify encoding function.\n",
        "encode = lambda x: nerf.positional_encoding(x, num_encoding_functions=num_encoding_functions)\n",
        "\n",
        "\n",
        "# ------ Now we generate a pose and a corresponding image using NeRF ------\n",
        "depth_samples_per_ray = 240\n",
        "\n",
        "r = torch.tensor(np.random.uniform(radius.item()-1, radius.item()+1)).to(device)\n",
        "near_thresh, far_thresh = get_thresholds(r)\n",
        "\n",
        "new_pose = get_new_pose(center, r).to(device)\n",
        "predicted = nerf.nerf_prediction(model, height, width, focal_length, new_pose,\n",
        "                             near_thresh, far_thresh, depth_samples_per_ray,\n",
        "                             encode, nerf.get_minibatches)\n",
        "\n",
        "show(predicted.cpu(), title=\"NeRF at ground truth pose\")\n",
        "\n",
        "# ------ Next we estimate the pose from our image ------\n",
        "predicted = predicted[None, :,:,:]\n",
        "image = (AutoNeRF.util.utils.swap_channels(predicted))\n",
        "\n",
        "_,_,_,z = vae(image)\n",
        "z = z[:,:,None,None]\n",
        "pose_red = new_pose[:, :12].to(device)\n",
        "pose_red = pose_red[:,:,None,None]\n",
        "\n",
        "zz = torch.randn(batch_size, 12).to(device)\n",
        "zz = zz[:,:, None, None]\n",
        "\n",
        "# ------ Finally, we compare the results ------\n",
        "print(\"Ground truth pose:\\n\",new_pose)\n",
        "pred_pose = tau.reverse(zz, z).squeeze(-1).squeeze(-1)\n",
        "pred_pose = to_pose(pred_pose)\n",
        "print(\"---------------\\n Predicted pose: \\n\", pred_pose)\n",
        "\n",
        "predicted = nerf.nerf_prediction(model, height, width, focal_length, pred_pose,\n",
        "                             near_thresh, far_thresh, depth_samples_per_ray,\n",
        "                             encode, nerf.get_minibatches)\n",
        "\n",
        "show(predicted.cpu(), title=\"NeRF at predicted pose\")\n",
        "m = torch.nn.functional.mse_loss(pred_pose, new_pose)\n",
        "print(r,m)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(4.0311, device='cuda:0') tensor([0., 0., 0.], device='cuda:0')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Ground truth pose:\n",
            " tensor([[ 0.6173,  0.1473, -0.7728, -2.3924],\n",
            "        [-0.7867,  0.1156, -0.6064, -1.8773],\n",
            "        [ 0.0000,  0.9823,  0.1873,  0.5797],\n",
            "        [ 0.0000,  0.0000,  0.0000,  1.0000]], device='cuda:0')\n",
            "---------------\n",
            " Predicted pose: \n",
            " tensor([[ 6.4013e-01,  1.3830e-01, -7.6191e-01, -2.4775e+00],\n",
            "        [-8.3339e-01,  9.3684e-02, -6.5450e-01, -2.0222e+00],\n",
            "        [-8.8208e-06,  9.9419e-01,  1.2960e-01,  3.7162e-01],\n",
            "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]], device='cuda:0',\n",
            "       grad_fn=<CopySlices>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "tensor(3.0958, device='cuda:0') tensor(0.0050, device='cuda:0', grad_fn=<MseLossBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDcwl9cZnF-K"
      },
      "source": [
        "# Collect pose estimation error\n",
        "Now we will vary vary the radius beyond the interval $[r-1, r+1]$, namely $[r-1.5, r+4]$ and examine how the MSE grows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8PGaDgg1rYz",
        "outputId": "6bb6bc63-7f66-4f99-cc50-7c64cacec322"
      },
      "source": [
        "# ------ First load parameters for NeRF from the dataset ------\n",
        "images = torch.from_numpy(dataset.images).to(device)\n",
        "focal_length = torch.from_numpy(dataset.focal_length).to(device)\n",
        "poses = torch.from_numpy(dataset.poses).to(device)\n",
        "\n",
        "height, width = images.shape[1:3]\n",
        "\n",
        "UP = torch.tensor([0.,0.,1.]).unsqueeze(dim=0).to(device)\n",
        "translation = torch.tensor(dataset.poses[:, :3,3]).to(device)\n",
        "\n",
        "center = torch.zeros(3).to(device)\n",
        "radius = torch.norm(translation[0] - center, p=2)\n",
        "height, width = 100,100\n",
        "print(radius, center)\n",
        "\n",
        "SCENE_RADIUS = 2\n",
        "\n",
        "num_encoding_functions = 6\n",
        "# Specify encoding function.\n",
        "encode = lambda x: nerf.positional_encoding(x, num_encoding_functions=num_encoding_functions)\n",
        "\n",
        "\n",
        "# ------ Now we generate a pose and a corresponding image using NeRF ------\n",
        "depth_samples_per_ray = 240\n",
        "n_samples = 10\n",
        "mses = []\n",
        "\n",
        "radii = np.linspace(radius.item()-1.5, radius.item()+4, 100)\n",
        "for r in radii:\n",
        "\n",
        "    near_thresh, far_thresh = get_thresholds(r)\n",
        "    batch_mse = []\n",
        "    for _ in range(n_samples):\n",
        "        new_pose = get_new_pose(center, r).to(device)\n",
        "        # ------ Next we estimate the pose from our image ------\n",
        "        predicted = nerf.nerf_prediction(model, height, width, focal_length, new_pose,\n",
        "                                near_thresh, far_thresh, depth_samples_per_ray,\n",
        "                                encode, nerf.get_minibatches)\n",
        "        predicted = predicted[None, :,:,:]\n",
        "        image = (AutoNeRF.util.utils.swap_channels(predicted))\n",
        "\n",
        "        _,_,_,z = vae(image)\n",
        "        z = z[:,:,None,None]\n",
        "        pose_red = new_pose[:, :12].to(device)\n",
        "        pose_red = pose_red[:,:,None,None]\n",
        "\n",
        "        zz = torch.randn(batch_size, 12).to(device)\n",
        "        zz = zz[:,:, None, None]\n",
        "\n",
        "        # ------ Finally, we compare the results ------\n",
        "        \n",
        "        pred_pose = tau.reverse(zz, z).squeeze(-1).squeeze(-1)\n",
        "        pred_pose = to_pose(pred_pose)\n",
        "\n",
        "        m = torch.nn.functional.mse_loss(pred_pose, new_pose)\n",
        "        #print(r,m.item())\n",
        "        batch_mse.append(m.item())\n",
        "\n",
        "    batch_mse = np.array(batch_mse).mean()\n",
        "    print(\"MSE\", batch_mse, \"at radius \", r)\n",
        "    mses.append(batch_mse)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(4.0311, device='cuda:0') tensor([0., 0., 0.], device='cuda:0')\n",
            "MSE 0.10305003253743053 at radius  2.5311288833618164\n",
            "MSE 0.10437208153307438 at radius  2.5866844389173718\n",
            "MSE 0.07330815065652133 at radius  2.6422399944729276\n",
            "MSE 0.033163945144042375 at radius  2.697795550028483\n",
            "MSE 0.047682674555107954 at radius  2.7533511055840387\n",
            "MSE 0.05491557081695646 at radius  2.808906661139594\n",
            "MSE 0.019311887491494418 at radius  2.86446221669515\n",
            "MSE 0.008749587787315249 at radius  2.9200177722507052\n",
            "MSE 0.006170900881988928 at radius  2.975573327806261\n",
            "MSE 0.009469025279395282 at radius  3.0311288833618164\n",
            "MSE 0.009633661439875141 at radius  3.0866844389173718\n",
            "MSE 0.005924218348809518 at radius  3.1422399944729276\n",
            "MSE 0.010738415678497404 at radius  3.197795550028483\n",
            "MSE 0.004084671183954924 at radius  3.2533511055840387\n",
            "MSE 0.0044416050775907935 at radius  3.308906661139594\n",
            "MSE 0.00660671200894285 at radius  3.3644622166951494\n",
            "MSE 0.0025200778152793648 at radius  3.4200177722507052\n",
            "MSE 0.004649949306622147 at radius  3.475573327806261\n",
            "MSE 0.00785773895913735 at radius  3.5311288833618164\n",
            "MSE 0.005285651786834933 at radius  3.5866844389173718\n",
            "MSE 0.005546371731907129 at radius  3.6422399944729276\n",
            "MSE 0.005586251488421112 at radius  3.697795550028483\n",
            "MSE 0.00933487352449447 at radius  3.7533511055840387\n",
            "MSE 0.008721775142475963 at radius  3.808906661139594\n",
            "MSE 0.007176936906762421 at radius  3.8644622166951494\n",
            "MSE 0.014206985058262945 at radius  3.9200177722507052\n",
            "MSE 0.010498546832241117 at radius  3.975573327806261\n",
            "MSE 0.008809760771691799 at radius  4.031128883361816\n",
            "MSE 0.007042040629312396 at radius  4.086684438917372\n",
            "MSE 0.013389575050678105 at radius  4.142239994472927\n",
            "MSE 0.007472413638606667 at radius  4.1977955500284825\n",
            "MSE 0.010328505450161174 at radius  4.253351105584039\n",
            "MSE 0.0037950080222799443 at radius  4.308906661139594\n",
            "MSE 0.008093437243951484 at radius  4.364462216695149\n",
            "MSE 0.013401693652849645 at radius  4.420017772250706\n",
            "MSE 0.007063824939541519 at radius  4.475573327806261\n",
            "MSE 0.012177504238206894 at radius  4.531128883361816\n",
            "MSE 0.009466185104975012 at radius  4.586684438917372\n",
            "MSE 0.007943194318795577 at radius  4.642239994472927\n",
            "MSE 0.004354097071336582 at radius  4.6977955500284825\n",
            "MSE 0.010999676410574467 at radius  4.753351105584039\n",
            "MSE 0.016487087751738728 at radius  4.808906661139594\n",
            "MSE 0.004736744100227952 at radius  4.864462216695149\n",
            "MSE 0.007938813290093095 at radius  4.920017772250706\n",
            "MSE 0.0157594112213701 at radius  4.975573327806261\n",
            "MSE 0.011167891195509583 at radius  5.031128883361816\n",
            "MSE 0.016466529574245214 at radius  5.086684438917372\n",
            "MSE 0.02084985903929919 at radius  5.142239994472927\n",
            "MSE 0.02425944252172485 at radius  5.1977955500284825\n",
            "MSE 0.04017279186518863 at radius  5.253351105584038\n",
            "MSE 0.04351930224802345 at radius  5.308906661139594\n",
            "MSE 0.05162389688193798 at radius  5.364462216695149\n",
            "MSE 0.04735530484467745 at radius  5.420017772250706\n",
            "MSE 0.046153257973492144 at radius  5.475573327806261\n",
            "MSE 0.10829929336905479 at radius  5.531128883361816\n",
            "MSE 0.14076077044010163 at radius  5.586684438917372\n",
            "MSE 0.17564972899854184 at radius  5.642239994472927\n",
            "MSE 0.2102814907208085 at radius  5.6977955500284825\n",
            "MSE 0.21172044053673744 at radius  5.753351105584038\n",
            "MSE 0.12156435679644347 at radius  5.808906661139594\n",
            "MSE 0.25598610639572145 at radius  5.864462216695149\n",
            "MSE 0.3280633706599474 at radius  5.920017772250706\n",
            "MSE 0.610991358011961 at radius  5.975573327806261\n",
            "MSE 0.9428461611270904 at radius  6.031128883361816\n",
            "MSE 0.5101155854761601 at radius  6.086684438917372\n",
            "MSE 0.5267667666077613 at radius  6.142239994472927\n",
            "MSE 1.5060699209570885 at radius  6.1977955500284825\n",
            "MSE 0.8308782115578651 at radius  6.253351105584038\n",
            "MSE 0.6545519188046456 at radius  6.308906661139594\n",
            "MSE 1.9773552969098092 at radius  6.364462216695149\n",
            "MSE 1.4539003819227219 at radius  6.420017772250706\n",
            "MSE 1.5739821821451188 at radius  6.475573327806261\n",
            "MSE 1.8571337401866912 at radius  6.531128883361816\n",
            "MSE 2.4136186957359316 at radius  6.586684438917372\n",
            "MSE 1.9610786974430083 at radius  6.642239994472927\n",
            "MSE 2.814133715629578 at radius  6.6977955500284825\n",
            "MSE 1.4101940080523492 at radius  6.753351105584039\n",
            "MSE 1.9595365583896638 at radius  6.808906661139594\n",
            "MSE 2.1692263185977936 at radius  6.864462216695149\n",
            "MSE 2.7954794049263 at radius  6.920017772250705\n",
            "MSE 2.7341695368289947 at radius  6.975573327806261\n",
            "MSE 1.8837181717157363 at radius  7.031128883361816\n",
            "MSE 3.3775684416294096 at radius  7.086684438917372\n",
            "MSE 2.014290687441826 at radius  7.142239994472927\n",
            "MSE 3.202396738529205 at radius  7.1977955500284825\n",
            "MSE 4.0038441002368925 at radius  7.253351105584039\n",
            "MSE 2.618234270811081 at radius  7.308906661139594\n",
            "MSE 3.8661202013492586 at radius  7.364462216695149\n",
            "MSE 3.884061926603317 at radius  7.420017772250705\n",
            "MSE 4.482312619686127 at radius  7.47557332780626\n",
            "MSE 2.689710330963135 at radius  7.531128883361816\n",
            "MSE 2.9545006811618806 at radius  7.586684438917372\n",
            "MSE 2.623051166534424 at radius  7.642239994472927\n",
            "MSE 4.772714710235595 at radius  7.6977955500284825\n",
            "MSE 3.880212163925171 at radius  7.753351105584039\n",
            "MSE 3.5498430132865906 at radius  7.808906661139594\n",
            "MSE 3.198208826780319 at radius  7.864462216695149\n",
            "MSE 4.003277015686035 at radius  7.920017772250705\n",
            "MSE 4.786882483959198 at radius  7.97557332780626\n",
            "MSE 5.1055679559707645 at radius  8.031128883361816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "WpfuMcNdq7-P",
        "outputId": "a1435ad8-f631-471e-c65a-fcd43910c5f0"
      },
      "source": [
        "plt.plot(radii,mses)\n",
        "plt.xlabel(\"radius $r$\")\n",
        "plt.ylabel(\"MSE\")"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MSE')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orRLbqoMuzNP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}