tiny_nerf_update_model(nerfpl).ipynb

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                "data": {
                  "text/plain": "<Figure size 2000x600 with 1 Axes>",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BoyoungH/2D-to-3D-Nerf/blob/main/tiny_nerf_update_model(nerfpl).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eIfAMiQblpi",
        "outputId": "10cabea5-259a-48d3-a0d7-2d2d84aeac71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/aliutkus/torchsearchsorted\n",
        "import torchsearchsorted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAD0WUU_2b9g",
        "outputId": "35d95bc3-f0f1-44a9-e0d8-43563c50d189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/aliutkus/torchsearchsorted\n",
            "  Cloning https://github.com/aliutkus/torchsearchsorted to /tmp/pip-req-build-ip09s2ri\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/aliutkus/torchsearchsorted /tmp/pip-req-build-ip09s2ri\n",
            "  Resolved https://github.com/aliutkus/torchsearchsorted to commit 22b628aa348abb854aef8660fed67d98b7676fc9\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: torchsearchsorted\n",
            "  Building wheel for torchsearchsorted (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchsearchsorted: filename=torchsearchsorted-1.1-cp310-cp310-linux_x86_64.whl size=6547255 sha256=69989b37af13910bd08d69c99f6a852639c9bb82a11fea61481228c1f8b8dde2\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-6nffvzrj/wheels/66/03/bf/287777e30536367f12403b7ecbb6eb92dc5045053c9132cece\n",
            "Successfully built torchsearchsorted\n",
            "Installing collected packages: torchsearchsorted\n",
            "Successfully installed torchsearchsorted-1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8hHZ0LnoFh_c"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from torch import nn, optim\n",
        "\n",
        "def get_coarse_query_points(ds, N_c, t_i_c_bin_edges, t_i_c_gap, os):\n",
        "    u_is_c = torch.rand(*list(ds.shape[:2]) + [N_c]).to(ds)\n",
        "    t_is_c = t_i_c_bin_edges + u_is_c * t_i_c_gap\n",
        "    r_ts_c = os[..., None, :] + t_is_c[..., :, None] * ds[..., None, :]\n",
        "    return (r_ts_c, t_is_c)\n",
        "\n",
        "\n",
        "def get_fine_query_points(w_is_c, N_f, t_is_c, t_f, os, ds):\n",
        "    w_is_c = w_is_c + 1e-5\n",
        "    pdfs = w_is_c / torch.sum(w_is_c, dim=-1, keepdim=True)\n",
        "    cdfs = torch.cumsum(pdfs, dim=-1)\n",
        "    cdfs = torch.cat([torch.zeros_like(cdfs[..., :1]), cdfs[..., :-1]], dim=-1)\n",
        "\n",
        "    us = torch.rand(list(cdfs.shape[:-1]) + [N_f]).to(w_is_c)\n",
        "\n",
        "    idxs = torch.searchsorted(cdfs, us, right=True)\n",
        "    t_i_f_bottom_edges = torch.gather(t_is_c, 2, idxs - 1)\n",
        "    idxs_capped = idxs.clone()\n",
        "    max_ind = cdfs.shape[-1]\n",
        "    idxs_capped[idxs_capped == max_ind] = max_ind - 1\n",
        "    t_i_f_top_edges = torch.gather(t_is_c, 2, idxs_capped)\n",
        "    t_i_f_top_edges[idxs == max_ind] = t_f\n",
        "    t_i_f_gaps = t_i_f_top_edges - t_i_f_bottom_edges\n",
        "    u_is_f = torch.rand_like(t_i_f_gaps).to(os)\n",
        "    t_is_f = t_i_f_bottom_edges + u_is_f * t_i_f_gaps\n",
        "\n",
        "    (t_is_f, _) = torch.sort(torch.cat([t_is_c, t_is_f.detach()], dim=-1), dim=-1)\n",
        "    r_ts_f = os[..., None, :] + t_is_f[..., :, None] * ds[..., None, :]\n",
        "    return (r_ts_f, t_is_f)\n",
        "\n",
        "\n",
        "def render_radiance_volume(r_ts, ds, chunk_size, F, t_is):\n",
        "    r_ts_flat = r_ts.reshape((-1, 3))\n",
        "    ds_rep = ds.unsqueeze(2).repeat(1, 1, r_ts.shape[-2], 1)\n",
        "    ds_flat = ds_rep.reshape((-1, 3))\n",
        "    c_is = []\n",
        "    sigma_is = []\n",
        "    for chunk_start in range(0, r_ts_flat.shape[0], chunk_size):\n",
        "        r_ts_batch = r_ts_flat[chunk_start : chunk_start + chunk_size]\n",
        "        ds_batch = ds_flat[chunk_start : chunk_start + chunk_size]\n",
        "        preds = F(r_ts_batch, ds_batch)\n",
        "        c_is.append(preds[\"c_is\"])\n",
        "        sigma_is.append(preds[\"sigma_is\"])\n",
        "\n",
        "    c_is = torch.cat(c_is).reshape(r_ts.shape)\n",
        "    sigma_is = torch.cat(sigma_is).reshape(r_ts.shape[:-1])\n",
        "    delta_is = t_is[..., 1:] - t_is[..., :-1]\n",
        "    one_e_10 = torch.Tensor([1e10]).expand(delta_is[..., :1].shape)\n",
        "    delta_is = torch.cat([delta_is, one_e_10.to(delta_is)], dim=-1)\n",
        "    delta_is = delta_is * ds.norm(dim=-1).unsqueeze(-1)\n",
        "    alpha_is = 1.0 - torch.exp(-sigma_is * delta_is)\n",
        "    T_is = torch.cumprod(1.0 - alpha_is + 1e-10, -1)\n",
        "    T_is = torch.roll(T_is, 1, -1)\n",
        "    T_is[..., 0] = 1.0\n",
        "    w_is = T_is * alpha_is\n",
        "    C_rs = (w_is[..., None] * c_is).sum(dim=-2)\n",
        "\n",
        "    return (C_rs, w_is)\n",
        "\n",
        "\n",
        "def run_one_iter_of_nerf(\n",
        "    ds, N_c, t_i_c_bin_edges, t_i_c_gap, os, chunk_size, F_c, N_f, t_f, F_f\n",
        "):\n",
        "    (r_ts_c, t_is_c) = get_coarse_query_points(ds, N_c, t_i_c_bin_edges, t_i_c_gap, os)\n",
        "    (C_rs_c, w_is_c) = render_radiance_volume(r_ts_c, ds, chunk_size, F_c, t_is_c)\n",
        "\n",
        "    (r_ts_f, t_is_f) = get_fine_query_points(w_is_c, N_f, t_is_c, t_f, os, ds)\n",
        "    (C_rs_f, _) = render_radiance_volume(r_ts_f, ds, chunk_size, F_f, t_is_f)\n",
        "\n",
        "    return (C_rs_c, C_rs_f)\n",
        "\n",
        "class NeRFMLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.L_pos = 10\n",
        "        self.L_dir = 4\n",
        "        pos_enc_feats = 3 + 3 * 2 * self.L_pos\n",
        "        dir_enc_feats = 3 + 3 * 2 * self.L_dir\n",
        "\n",
        "        in_feats = pos_enc_feats\n",
        "        net_width = 256\n",
        "        early_mlp_layers = 1\n",
        "        early_mlp = []\n",
        "        for layer_idx in range(early_mlp_layers):\n",
        "            early_mlp.append(nn.Linear(in_feats, net_width))\n",
        "            early_mlp.append(nn.ReLU())\n",
        "            in_feats = net_width\n",
        "\n",
        "        self.early_mlp = nn.Sequential(*early_mlp)\n",
        "\n",
        "        in_feats = pos_enc_feats + net_width\n",
        "        late_mlp_layers = 1\n",
        "        late_mlp = []\n",
        "        for layer_idx in range(late_mlp_layers):\n",
        "            late_mlp.append(nn.Linear(in_feats, net_width))\n",
        "            late_mlp.append(nn.ReLU())\n",
        "            in_feats = net_width\n",
        "\n",
        "        self.late_mlp = nn.Sequential(*late_mlp)\n",
        "        self.sigma_layer = nn.Linear(net_width, net_width + 1)\n",
        "        self.pre_final_layer = nn.Sequential(\n",
        "            nn.Linear(dir_enc_feats + net_width, net_width // 2), nn.ReLU()\n",
        "        )\n",
        "        self.final_layer = nn.Sequential(nn.Linear(net_width // 2, 3), nn.Sigmoid())\n",
        "\n",
        "    def forward(self, xs, ds):\n",
        "        xs_encoded = [xs]\n",
        "        for l_pos in range(self.L_pos):\n",
        "            xs_encoded.append(torch.sin(2 ** l_pos * torch.pi * xs))\n",
        "            xs_encoded.append(torch.cos(2 ** l_pos * torch.pi * xs))\n",
        "\n",
        "        xs_encoded = torch.cat(xs_encoded, dim=-1)\n",
        "\n",
        "        ds = ds / ds.norm(p=2, dim=-1).unsqueeze(-1)\n",
        "        ds_encoded = [ds]\n",
        "        for l_dir in range(self.L_dir):\n",
        "            ds_encoded.append(torch.sin(2 ** l_dir * torch.pi * ds))\n",
        "            ds_encoded.append(torch.cos(2 ** l_dir * torch.pi * ds))\n",
        "\n",
        "        ds_encoded = torch.cat(ds_encoded, dim=-1)\n",
        "        outputs = self.early_mlp(xs_encoded)\n",
        "        outputs = self.late_mlp(torch.cat([xs_encoded, outputs], dim=-1))\n",
        "        outputs = self.sigma_layer(outputs)\n",
        "        sigma_is = torch.relu(outputs[:, 0])\n",
        "        outputs = self.pre_final_layer(torch.cat([ds_encoded, outputs[:, 1:]], dim=-1))\n",
        "        c_is = self.final_layer(outputs)\n",
        "        return {\"c_is\": c_is, \"sigma_is\": sigma_is}\n",
        "\n",
        "'''import torch\n",
        "from torchsearchsorted import searchsorted\n",
        "\n",
        "__all__ = ['render_rays']\n",
        "\n",
        "def sample_pdf(bins, weights, N_importance, det=False, eps=1e-5):\n",
        "    N_rays, N_samples_ = weights.shape\n",
        "    weights = weights + eps # prevent division by zero (don't do inplace op!)\n",
        "    pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)\n",
        "    cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function\n",
        "    cdf = torch.cat([torch.zeros_like(cdf[: ,:1]), cdf], -1)  # (N_rays, N_samples_+1)\n",
        "                                                               # padded to 0~1 inclusive\n",
        "\n",
        "    if det:\n",
        "        u = torch.linspace(0, 1, N_importance, device=bins.device)\n",
        "        u = u.expand(N_rays, N_importance)\n",
        "    else:\n",
        "        u = torch.rand(N_rays, N_importance, device=bins.device)\n",
        "    u = u.contiguous()\n",
        "\n",
        "    inds = searchsorted(cdf, u, side='right')\n",
        "    below = torch.clamp_min(inds-1, 0)\n",
        "    above = torch.clamp_max(inds, N_samples_)\n",
        "\n",
        "    inds_sampled = torch.stack([below, above], -1).view(N_rays, 2*N_importance)\n",
        "    cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, N_importance, 2)\n",
        "    bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)\n",
        "\n",
        "    denom = cdf_g[...,1]-cdf_g[...,0]\n",
        "    denom[denom<eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled\n",
        "                         # anyway, therefore any value for it is fine (set to 1 here)\n",
        "\n",
        "    samples = bins_g[...,0] + (u-cdf_g[...,0])/denom * (bins_g[...,1]-bins_g[...,0])\n",
        "    return samples\n",
        "\n",
        "\n",
        "def render_rays(models,\n",
        "                embeddings,\n",
        "                rays,\n",
        "                N_samples=64,\n",
        "                use_disp=False,\n",
        "                perturb=0,\n",
        "                noise_std=1,\n",
        "                N_importance=0,\n",
        "                chunk=1024*32,\n",
        "                white_back=False,\n",
        "                test_time=False\n",
        "                ):\n",
        "\n",
        "    def inference(model, embedding_xyz, xyz_, dir_, dir_embedded, z_vals, weights_only=False):\n",
        "        N_samples_ = xyz_.shape[1]\n",
        "        # Embed directions\n",
        "        xyz_ = xyz_.view(-1, 3) # (N_rays*N_samples_, 3)\n",
        "        if not weights_only:\n",
        "            dir_embedded = torch.repeat_interleave(dir_embedded, repeats=N_samples_, dim=0)\n",
        "                           # (N_rays*N_samples_, embed_dir_channels)\n",
        "\n",
        "        # Perform model inference to get rgb and raw sigma\n",
        "        B = xyz_.shape[0]\n",
        "        out_chunks = []\n",
        "        for i in range(0, B, chunk):\n",
        "            # Embed positions by chunk\n",
        "            xyz_embedded = embedding_xyz(xyz_[i:i+chunk])\n",
        "            if not weights_only:\n",
        "                xyzdir_embedded = torch.cat([xyz_embedded,\n",
        "                                             dir_embedded[i:i+chunk]], 1)\n",
        "            else:\n",
        "                xyzdir_embedded = xyz_embedded\n",
        "            out_chunks += [model(xyzdir_embedded, sigma_only=weights_only)]\n",
        "\n",
        "        out = torch.cat(out_chunks, 0)\n",
        "        if weights_only:\n",
        "            sigmas = out.view(N_rays, N_samples_)\n",
        "        else:\n",
        "            rgbsigma = out.view(N_rays, N_samples_, 4)\n",
        "            rgbs = rgbsigma[..., :3] # (N_rays, N_samples_, 3)\n",
        "            sigmas = rgbsigma[..., 3] # (N_rays, N_samples_)\n",
        "\n",
        "        # Convert these values using volume rendering (Section 4)\n",
        "        deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples_-1)\n",
        "        delta_inf = 1e10 * torch.ones_like(deltas[:, :1]) # (N_rays, 1) the last delta is infinity\n",
        "        deltas = torch.cat([deltas, delta_inf], -1)  # (N_rays, N_samples_)\n",
        "\n",
        "        # Multiply each distance by the norm of its corresponding direction ray\n",
        "        # to convert to real world distance (accounts for non-unit directions).\n",
        "        deltas = deltas * torch.norm(dir_.unsqueeze(1), dim=-1)\n",
        "\n",
        "        noise = torch.randn(sigmas.shape, device=sigmas.device) * noise_std\n",
        "\n",
        "        # compute alpha by the formula (3)\n",
        "        alphas = 1-torch.exp(-deltas*torch.relu(sigmas+noise)) # (N_rays, N_samples_)\n",
        "        alphas_shifted = \\\n",
        "            torch.cat([torch.ones_like(alphas[:, :1]), 1-alphas+1e-10], -1) # [1, a1, a2, ...]\n",
        "        weights = \\\n",
        "            alphas * torch.cumprod(alphas_shifted, -1)[:, :-1] # (N_rays, N_samples_)\n",
        "        weights_sum = weights.sum(1) # (N_rays), the accumulated opacity along the rays\n",
        "                                     # equals \"1 - (1-a1)(1-a2)...(1-an)\" mathematically\n",
        "        if weights_only:\n",
        "            return weights\n",
        "\n",
        "        # compute final weighted outputs\n",
        "        rgb_final = torch.sum(weights.unsqueeze(-1)*rgbs, -2) # (N_rays, 3)\n",
        "        depth_final = torch.sum(weights*z_vals, -1) # (N_rays)\n",
        "\n",
        "        if white_back:\n",
        "            rgb_final = rgb_final + 1-weights_sum.unsqueeze(-1)\n",
        "\n",
        "        return rgb_final, depth_final, weights\n",
        "\n",
        "\n",
        "    # Extract models from lists\n",
        "    model_coarse = models[0]\n",
        "    embedding_xyz = embeddings[0]\n",
        "    embedding_dir = embeddings[1]\n",
        "\n",
        "    # Decompose the inputs\n",
        "    N_rays = rays.shape[0]\n",
        "    rays_o, rays_d = rays[:, 0:3], rays[:, 3:6] # both (N_rays, 3)\n",
        "    near, far = rays[:, 6:7], rays[:, 7:8] # both (N_rays, 1)\n",
        "\n",
        "    # Embed direction\n",
        "    dir_embedded = embedding_dir(rays_d) # (N_rays, embed_dir_channels)\n",
        "\n",
        "    # Sample depth points\n",
        "    z_steps = torch.linspace(0, 1, N_samples, device=rays.device) # (N_samples)\n",
        "    if not use_disp: # use linear sampling in depth space\n",
        "        z_vals = near * (1-z_steps) + far * z_steps\n",
        "    else: # use linear sampling in disparity space\n",
        "        z_vals = 1/(1/near * (1-z_steps) + 1/far * z_steps)\n",
        "\n",
        "    z_vals = z_vals.expand(N_rays, N_samples)\n",
        "\n",
        "    if perturb > 0: # perturb sampling depths (z_vals)\n",
        "        z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:]) # (N_rays, N_samples-1) interval mid points\n",
        "        # get intervals between samples\n",
        "        upper = torch.cat([z_vals_mid, z_vals[: ,-1:]], -1)\n",
        "        lower = torch.cat([z_vals[: ,:1], z_vals_mid], -1)\n",
        "\n",
        "        perturb_rand = perturb * torch.rand(z_vals.shape, device=rays.device)\n",
        "        z_vals = lower + (upper - lower) * perturb_rand\n",
        "\n",
        "    xyz_coarse_sampled = rays_o.unsqueeze(1) + \\\n",
        "                         rays_d.unsqueeze(1) * z_vals.unsqueeze(2) # (N_rays, N_samples, 3)\n",
        "\n",
        "    if test_time:\n",
        "        weights_coarse = \\\n",
        "            inference(model_coarse, embedding_xyz, xyz_coarse_sampled, rays_d,\n",
        "                      dir_embedded, z_vals, weights_only=True)\n",
        "        result = {'opacity_coarse': weights_coarse.sum(1)}\n",
        "    else:\n",
        "        rgb_coarse, depth_coarse, weights_coarse = \\\n",
        "            inference(model_coarse, embedding_xyz, xyz_coarse_sampled, rays_d,\n",
        "                      dir_embedded, z_vals, weights_only=False)\n",
        "        result = {'rgb_coarse': rgb_coarse,\n",
        "                  'depth_coarse': depth_coarse,\n",
        "                  'opacity_coarse': weights_coarse.sum(1)\n",
        "                 }\n",
        "\n",
        "    if N_importance > 0: # sample points for fine model\n",
        "        z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:]) # (N_rays, N_samples-1) interval mid points\n",
        "        z_vals_ = sample_pdf(z_vals_mid, weights_coarse[:, 1:-1],\n",
        "                             N_importance, det=(perturb==0)).detach()\n",
        "                  # detach so that grad doesn't propogate to weights_coarse from here\n",
        "\n",
        "        z_vals, _ = torch.sort(torch.cat([z_vals, z_vals_], -1), -1)\n",
        "\n",
        "        xyz_fine_sampled = rays_o.unsqueeze(1) + \\\n",
        "                           rays_d.unsqueeze(1) * z_vals.unsqueeze(2)\n",
        "                           # (N_rays, N_samples+N_importance, 3)\n",
        "\n",
        "        model_fine = models[1]\n",
        "        rgb_fine, depth_fine, weights_fine = \\\n",
        "            inference(model_fine, embedding_xyz, xyz_fine_sampled, rays_d,\n",
        "                      dir_embedded, z_vals, weights_only=False)\n",
        "\n",
        "        result['rgb_fine'] = rgb_fine\n",
        "        result['depth_fine'] = depth_fine\n",
        "        result['opacity_fine'] = weights_fine.sum(1)\n",
        "        C_rs_c = result['rgb_coarse']\n",
        "        C_rs_f = result['rgb_fine']\n",
        "\n",
        "    return (C_rs_c, C_rs_f)\n",
        "\n",
        "class Embedding(nn.Module):\n",
        "    def __init__(self, in_channels, N_freqs, logscale=True):\n",
        "\n",
        "        super(Embedding, self).__init__()\n",
        "        self.N_freqs = N_freqs\n",
        "        self.in_channels = in_channels\n",
        "        self.funcs = [torch.sin, torch.cos]\n",
        "        self.out_channels = in_channels*(len(self.funcs)*N_freqs+1)\n",
        "\n",
        "        if logscale:\n",
        "            self.freq_bands = 2**torch.linspace(0, N_freqs-1, N_freqs)\n",
        "        else:\n",
        "            self.freq_bands = torch.linspace(1, 2**(N_freqs-1), N_freqs)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = [x]\n",
        "        for freq in self.freq_bands:\n",
        "            for func in self.funcs:\n",
        "                out += [func(freq*x)]\n",
        "\n",
        "        return torch.cat(out, -1)\n",
        "\n",
        "\n",
        "class NeRF(nn.Module):\n",
        "    def __init__(self,\n",
        "                 D=8, W=256,\n",
        "                 in_channels_xyz=63, in_channels_dir=27,\n",
        "                 skips=[4]):\n",
        "        super(NeRF, self).__init__()\n",
        "        self.D = D\n",
        "        self.W = W\n",
        "        self.in_channels_xyz = in_channels_xyz\n",
        "        self.in_channels_dir = in_channels_dir\n",
        "        self.skips = skips\n",
        "\n",
        "        # xyz encoding layers\n",
        "        for i in range(D):\n",
        "            if i == 0:\n",
        "                layer = nn.Linear(in_channels_xyz, W)\n",
        "            elif i in skips:\n",
        "                layer = nn.Linear(W+in_channels_xyz, W)\n",
        "            else:\n",
        "                layer = nn.Linear(W, W)\n",
        "            layer = nn.Sequential(layer, nn.ReLU(True))\n",
        "            setattr(self, f\"xyz_encoding_{i+1}\", layer)\n",
        "        self.xyz_encoding_final = nn.Linear(W, W)\n",
        "\n",
        "        # direction encoding layers\n",
        "        self.dir_encoding = nn.Sequential(\n",
        "                                nn.Linear(W+in_channels_dir, W//2),\n",
        "                                nn.ReLU(True))\n",
        "\n",
        "        # output layers\n",
        "        self.sigma = nn.Linear(W, 1)\n",
        "        self.rgb = nn.Sequential(\n",
        "                        nn.Linear(W//2, 3),\n",
        "                        nn.Sigmoid())\n",
        "\n",
        "    def forward(self, x, sigma_only=False):\n",
        "        if not sigma_only:\n",
        "            input_xyz, input_dir = \\\n",
        "                torch.split(x, [self.in_channels_xyz, self.in_channels_dir], dim=-1)\n",
        "        else:\n",
        "            input_xyz = x\n",
        "\n",
        "        xyz_ = input_xyz\n",
        "        for i in range(self.D):\n",
        "            if i in self.skips:\n",
        "                xyz_ = torch.cat([input_xyz, xyz_], -1)\n",
        "            xyz_ = getattr(self, f\"xyz_encoding_{i+1}\")(xyz_)\n",
        "\n",
        "        sigma = self.sigma(xyz_)\n",
        "        if sigma_only:\n",
        "            return sigma\n",
        "\n",
        "        xyz_encoding_final = self.xyz_encoding_final(xyz_)\n",
        "\n",
        "        dir_encoding_input = torch.cat([xyz_encoding_final, input_dir], -1)\n",
        "        dir_encoding = self.dir_encoding(dir_encoding_input)\n",
        "        rgb = self.rgb(dir_encoding)\n",
        "\n",
        "        out = torch.cat([rgb, sigma], -1)\n",
        "\n",
        "        return out'''"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seed.\n",
        "seed = 9458\n",
        "torch.manual_seed(seed)\n",
        "np.random.seed(seed)\n",
        "\n",
        "device = \"cuda:0\"\n",
        "F_c = NeRF().to(device)\n",
        "F_f = NeRF().to(device)\n",
        "chunk_size = 1024 * 32\n",
        "batch_img_size = 64\n",
        "n_batch_pix = batch_img_size ** 2\n",
        "\n",
        "lr = 5e-4\n",
        "optimizer = optim.Adam(list(F_c.parameters()) + list(F_f.parameters()), lr=lr)\n",
        "criterion = nn.MSELoss()\n",
        "lrate_decay = 250\n",
        "decay_steps = lrate_decay * 1000\n",
        "decay_rate = 0.1\n",
        "\n",
        "# Load dataset.\n",
        "data_f = \"/content/drive/MyDrive/spongee.npz\"\n",
        "data = np.load(data_f)\n",
        "\n",
        "images = data[\"rgb\"]\n",
        "img_size = images.shape[1]\n",
        "xs = torch.arange(img_size) - (img_size / 2 - 0.5)\n",
        "ys = torch.arange(img_size) - (img_size / 2 - 0.5)\n",
        "(xs, ys) = torch.meshgrid(xs, -ys, indexing=\"xy\")\n",
        "focal = float(data[\"focal\"])\n",
        "pixel_coords = torch.stack([xs, ys, torch.full_like(xs, -focal)], dim=-1)\n",
        "camera_coords = pixel_coords / focal\n",
        "init_ds = camera_coords.to(device)\n",
        "init_o = torch.Tensor(np.array([0, 0, 2.25])).to(device)\n",
        "\n",
        "test_idx = 29\n",
        "plt.imshow(images[test_idx])\n",
        "plt.show()\n",
        "test_img = torch.Tensor(images[test_idx]).to(device)\n",
        "poses = data[\"poses\"]\n",
        "test_R = torch.Tensor(poses[test_idx, :3, :3]).to(device)\n",
        "test_ds = torch.einsum(\"ij,hwj->hwi\", test_R, init_ds)\n",
        "test_os = (test_R @ init_o).expand(test_ds.shape)\n",
        "\n",
        "t_n = 1.0\n",
        "t_f = 4.0\n",
        "N_c = 64\n",
        "N_f = 128\n",
        "t_i_c_gap = (t_f - t_n) / N_c\n",
        "t_i_c_bin_edges = (t_n + torch.arange(N_c) * t_i_c_gap).to(device)\n",
        "\n",
        "# Start training model.\n",
        "train_idxs = np.arange(len(images)) != test_idx\n",
        "images = torch.Tensor(images[train_idxs])\n",
        "poses = torch.Tensor(poses[train_idxs])\n",
        "n_pix = img_size ** 2\n",
        "pixel_ps = torch.full((n_pix,), 1 / n_pix).to(device)\n",
        "psnrs = []\n",
        "iternums = []\n",
        "# See Section 5.3.\n",
        "num_iters = 300\n",
        "display_every = 100\n",
        "F_c.train()\n",
        "F_f.train()"
      ],
      "metadata": {
        "id": "Z9eVXoDjFoRn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c4c18634-3611-40a3-ac21-1f432bbe8fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NeRF(\n",
              "  (xyz_encoding_1): Sequential(\n",
              "    (0): Linear(in_features=63, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_2): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_3): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_4): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_5): Sequential(\n",
              "    (0): Linear(in_features=319, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_6): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_7): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_8): Sequential(\n",
              "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (xyz_encoding_final): Linear(in_features=256, out_features=256, bias=True)\n",
              "  (dir_encoding): Sequential(\n",
              "    (0): Linear(in_features=283, out_features=128, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (sigma): Linear(in_features=256, out_features=1, bias=True)\n",
              "  (rgb): Sequential(\n",
              "    (0): Linear(in_features=128, out_features=3, bias=True)\n",
              "    (1): Sigmoid()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_xyz = Embedding(3, 10)\n",
        "embedding_dir = Embedding(3, 4)\n",
        "#models = [F_c, F_f]\n",
        "embeddings = [embedding_xyz, embedding_dir]\n",
        "#bins=\n",
        "#(_, weights)=\n",
        "#rays=sample_pdf(bins, weights, N_importance=64, det=False, eps=1e-5)\n",
        "for i in range(num_iters):\n",
        "    target_img_idx = np.random.randint(images.shape[0])\n",
        "    target_pose = poses[target_img_idx].to(device)\n",
        "    R = target_pose[:3, :3]\n",
        "\n",
        "    ds = torch.einsum(\"ij,hwj->hwi\", R, init_ds)\n",
        "    os = (R @ init_o).expand(ds.shape)\n",
        "\n",
        "    pix_idxs = pixel_ps.multinomial(n_batch_pix, False)\n",
        "    pix_idx_rows = pix_idxs // img_size\n",
        "    pix_idx_cols = pix_idxs % img_size\n",
        "    ds_batch = ds[pix_idx_rows, pix_idx_cols].reshape(\n",
        "        batch_img_size, batch_img_size, -1\n",
        "    )\n",
        "    os_batch = os[pix_idx_rows, pix_idx_cols].reshape(\n",
        "        batch_img_size, batch_img_size, -1\n",
        "    )\n",
        "\n",
        "\n",
        "    (C_rs_c, C_rs_f) = render_rays(models,\n",
        "                embeddings,\n",
        "                rays,\n",
        "                N_samples=64,\n",
        "                use_disp=False,\n",
        "                perturb=0,\n",
        "                noise_std=1,\n",
        "                N_importance=0,\n",
        "                chunk=1024*32,\n",
        "                white_back=False,\n",
        "                test_time=False\n",
        "                )\n",
        "\n",
        "    # Run NeRF.\n",
        "    '''(C_rs_c, C_rs_f) = run_one_iter_of_nerf(\n",
        "        ds_batch,\n",
        "        N_c,\n",
        "        t_i_c_bin_edges,\n",
        "        t_i_c_gap,\n",
        "        os_batch,\n",
        "        chunk_size,\n",
        "        F_c,\n",
        "        N_f,\n",
        "        t_f,\n",
        "        F_f,\n",
        "    )'''\n",
        "    target_img = images[target_img_idx].to(device)\n",
        "    target_img_batch = target_img[pix_idx_rows, pix_idx_cols].reshape(F_f.shape)\n",
        "    loss = criterion(F_c, target_img_batch) + criterion(F_f, target_img_batch)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    for g in optimizer.param_groups:\n",
        "        g[\"lr\"] = lr * decay_rate ** (i / decay_steps)\n",
        "\n",
        "    if i % display_every == 0:\n",
        "        F_c.eval()\n",
        "        F_f.eval()\n",
        "        '''with torch.no_grad():\n",
        "            (_, C_rs_f) = run_one_iter_of_nerf(\n",
        "                test_ds,\n",
        "                N_c,\n",
        "                t_i_c_bin_edges,\n",
        "                t_i_c_gap,\n",
        "                test_os,\n",
        "                chunk_size,\n",
        "                F_c,\n",
        "                N_f,\n",
        "                t_f,\n",
        "                F_f,\n",
        "            )'''\n",
        "\n",
        "        loss = criterion(F_f, test_img)\n",
        "        print(f\"Loss: {loss.item()}\")\n",
        "        psnr = -10.0 * torch.log10(loss)\n",
        "\n",
        "        psnrs.append(psnr.item())\n",
        "        iternums.append(i)\n",
        "\n",
        "        plt.figure(figsize=(10, 4))\n",
        "        plt.subplot(121)\n",
        "        plt.imshow(F_f.detach().cpu().numpy())\n",
        "        plt.title(f\"Iteration {i}\")\n",
        "        plt.subplot(122)\n",
        "        plt.plot(iternums, psnrs)\n",
        "        plt.title(\"PSNR\")\n",
        "        plt.show()\n",
        "\n",
        "        F_c.train()\n",
        "        F_f.train()\n",
        "\n",
        "print(\"Done!\")"
      ],
      "metadata": {
        "id": "VWextW26Fvem",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "65620c88-28da-43aa-9c68-04bf3829413b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-ad2a48e252ca>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     52\u001b[0m     )'''\n\u001b[1;32m     53\u001b[0m     \u001b[0mtarget_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget_img_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0mtarget_img_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpix_idx_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpix_idx_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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     55\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_img_batch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_img_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1612\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1613\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1614\u001b[0;31m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m   1615\u001b[0m             type(self).__name__, name))\n\u001b[1;32m   1616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NeRF' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "from ipywidgets import interactive, widgets\n",
        "import numpy as np\n",
        "import torch\n",
        "import torchvision.transforms.functional as F\n",
        "from torchvision.transforms import InterpolationMode\n",
        "from torch.autograd import grad\n",
        "import torch.nn.functional as nnF\n",
        "\n",
        "# checkpoint = torch.load('/content/nerf_model.pth')\n",
        "# F_c.load_state_dict(checkpoint['model_state_dict'])\n",
        "# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "# iteration = checkpoint['iteration']\n",
        "# loss = checkpoint['loss']\n",
        "# psnrs = checkpoint['psnrs']\n",
        "# iternums = checkpoint['iternums']\n",
        "# C_rs_c = checkpoint['C_rs_c']\n",
        "# C_rs_f = checkpoint['C_rs_f']\n",
        "\n",
        "\n",
        "def trans_t(t):\n",
        "  return torch.tensor([\n",
        "  [1,0,0,0],\n",
        "  [0,1,0,0],\n",
        "  [0,0,1,t],\n",
        "  [0,0,0,1],\n",
        "  ], dtype=torch.float32)\n",
        "\n",
        "def rot_phi(phi):\n",
        "    phi = torch.tensor(phi)\n",
        "    return torch.tensor([\n",
        "        [1, 0, 0, 0],\n",
        "        [0, torch.cos(phi), -torch.sin(phi), 0],\n",
        "        [0, torch.sin(phi), torch.cos(phi), 0],\n",
        "        [0, 0, 0, 1],\n",
        "    ], dtype=torch.float32)\n",
        "\n",
        "def rot_theta(th):\n",
        "    th = torch.tensor(th)\n",
        "    return torch.tensor([\n",
        "        [torch.cos(th), 0, -torch.sin(th), 0],\n",
        "        [0, 1, 0, 0],\n",
        "        [torch.sin(th), 0, torch.cos(th), 0],\n",
        "        [0, 0, 0, 1],\n",
        "    ], dtype=torch.float32)\n",
        "\n",
        "def pose_spherical(theta, phi, radius):\n",
        "  c2w = trans_t(radius)\n",
        "  c2w = rot_phi(phi/180.*np.pi) @ c2w\n",
        "  c2w = rot_theta(theta/180.*np.pi) @ c2w\n",
        "  c2w = torch.tensor([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=torch.float32) @ c2w\n",
        "  return c2w\n",
        "\n",
        "def f(**kwargs):\n",
        "  c2w = pose_spherical(**kwargs)\n",
        "  c2w = c2w.to(device)\n",
        "  test_R = torch.Tensor(poses[test_idx, :3, :3]).to(device)\n",
        "  test_R = c2w[:3, :3].to(device)\n",
        "  test_ds = torch.einsum(\"ij,hwj->hwi\", test_R, init_ds)\n",
        "  test_os = (test_R @ init_o).expand(test_ds.shape)\n",
        "  with torch.no_grad():\n",
        "            (_, C_rs_f) = run_one_iter_of_nerf(\n",
        "                test_ds,\n",
        "                N_c,\n",
        "                t_i_c_bin_edges,\n",
        "                t_i_c_gap,\n",
        "                test_os,\n",
        "                chunk_size,\n",
        "                F_c,\n",
        "                N_f,\n",
        "                t_f,\n",
        "                F_f,\n",
        "            )\n",
        "  img = torch.clamp((C_rs_f.detach()), 0, 1)\n",
        "  img = img.permute(2, 0, 1)\n",
        "\n",
        "  img_np = img.cpu().numpy()\n",
        "  plt.figure(2, figsize=(20,6))\n",
        "  plt.imshow(img_np.transpose(1, 2, 0))\n",
        "  plt.show()\n",
        "\n",
        "sldr = lambda v, mi, ma: widgets.FloatSlider(\n",
        "  value=v,\n",
        "  min=mi,\n",
        "  max=ma,\n",
        "  step=.01,\n",
        ")\n",
        "\n",
        "names = [\n",
        "  ['theta', [100., 0., 360]],\n",
        "  ['phi', [-30., 0, 360]],\n",
        "  ['radius', [4., 3., 5.]],\n",
        "]\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "interactive_plot = interactive(f, **{s[0] : sldr(*s[1]) for s in names})\n",
        "output = interactive_plot.children[-1]\n",
        "output.layout.height = '350px'\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "XBMEelEmCcNa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463,
          "referenced_widgets": [
            "4526f5f85f7b4f66ab64a24f52fde3f6",
            "04850b6c86e043c7885f08fde9a8fd8e",
            "596ea2e380ac4c21b4121c18bc874d3c",
            "4774281852314b3c88e908297050a7ef",
            "00ed8627bb534513b556c1d0d5d1afdc",
            "377bc90248564704a6c5239a9a47c8c6",
            "9e4bba314cc141d98cd4aeea780fb3a3",
            "11eddd26e09449be8eaea24b38a47ba9",
            "e7c3a84700374b0ea435cc28a932a690",
            "e26a6b387f524155a46bab6143356ba1",
            "8c779eafd1834096bb943b4c422068fc",
            "ae7eb57633f042419369faeefa8a539d",
            "3c23d1f92b424ecd8d8919f9c8905902"
          ]
        },
        "outputId": "779b1c3b-325a-4cd0-86fc-2ec9659f65df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=100.0, description='theta', max=360.0, step=0.01), FloatSlider(value=0…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4526f5f85f7b4f66ab64a24f52fde3f6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frames = []\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "for th in tqdm(np.linspace(0., 360., 120, endpoint=False)):\n",
        "    c2w = pose_spherical(th, -30., 4.)\n",
        "    test_R = torch.Tensor(poses[test_idx, :3, :3]).to(device)\n",
        "    test_R = c2w[:3, :3].to(device)\n",
        "    test_ds = torch.einsum(\"ij,hwj->hwi\", test_R, init_ds)\n",
        "    test_os = (test_R @ init_o).expand(test_ds.shape)\n",
        "    with torch.no_grad():\n",
        "            (_, C_rs_f) = run_one_iter_of_nerf(\n",
        "                test_ds,\n",
        "                N_c,\n",
        "                t_i_c_bin_edges,\n",
        "                t_i_c_gap,\n",
        "                test_os,\n",
        "                chunk_size,\n",
        "                F_c,\n",
        "                N_f,\n",
        "                t_f,\n",
        "                F_f,\n",
        "            )\n",
        "    frames.append((C_rs_f.detach().cpu().numpy()).astype(np.uint8))\n",
        "\n",
        "import cv2\n",
        "\n",
        "frames_resized = []\n",
        "for frame in frames:\n",
        "    frame_resized = cv2.resize(frame, (304, 304))\n",
        "    frames_resized.append(frame_resized)\n",
        "\n",
        "import imageio\n",
        "f = 'video.mp4'\n",
        "imageio.mimwrite(f, frames_resized, fps=30, quality=7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "8ba2902a21e64bb0aa6ea0fc7bca8d00",
            "9e7049ba73014999815a4d18ffe2c354",
            "aa5dbd470dee4fcdbd5c15cee4fc72aa",
            "754a7770f2aa40db8910f771f5d4858b",
            "191c947c6322438384238a9bb7c5fcb4",
            "0f705c4cd2c744809fd0ef2f22309fd2",
            "b43a7c2328914ff3b9d54d97880f9db3",
            "c7605e24098545cd85244fda7689b2d7",
            "beb066c5408a4b1aa2fc31fc37fdd3de",
            "df2aa0cfe0304168836f835a4cc6ceda",
            "7dca011bf64145c8b95a30b88c566216"
          ]
        },
        "id": "ugRNDkylf5NG",
        "outputId": "4cee9068-6a05-4175-8821-159682cbbd3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-73df42f9facd>:3: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for th in tqdm(np.linspace(0., 360., 120, endpoint=False)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/120 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ba2902a21e64bb0aa6ea0fc7bca8d00"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "mp4 = open('video.mp4','rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"\"\"\n",
        "<video width=400 controls autoplay loop>\n",
        "      <source src=\"%s\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\" % data_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "PMzXkyNSf6CY",
        "outputId": "096bf173-de3f-42c1-b01e-da9453527a03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<video width=400 controls autoplay loop>\n",
              "      <source 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              "</video>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}