code_diffusion_synthetic.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This code is modified from the tutorial code in \n",
        "Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. \"[Score-Based Generative Modeling through Stochastic Differential Equations.](https://arxiv.org/pdf/2011.13456.pdf)\" Internation Conference on Learning Representations, 2021"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "cellView": "form",
        "id": "YyQtV7155Nht"
      },
      "outputs": [],
      "source": [
        "#@title Define a time-dependent score-based model \n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "\n",
        "class GaussianFourierProjection(nn.Module):\n",
        "  \"\"\"Gaussian random features for encoding time steps.\"\"\"  \n",
        "  def __init__(self, embed_dim, scale=30.):\n",
        "    super().__init__()\n",
        "    # Randomly sample weights during initialization. These weights are fixed \n",
        "    # during optimization and are not trainable.\n",
        "    self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)\n",
        "  def forward(self, x):\n",
        "    x_proj = x[:, None] * self.W[None, :] * 2 * np.pi\n",
        "    return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)\n",
        "\n",
        "\n",
        "class Dense(nn.Module):\n",
        "  \"\"\"A fully connected layer that reshapes outputs to feature maps.\"\"\"\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super().__init__()\n",
        "    self.dense = nn.Linear(input_dim, output_dim)\n",
        "  def forward(self, x):\n",
        "    return self.dense(x)[..., None, None]\n",
        "\n",
        "\n",
        "class ScoreNet(nn.Module):\n",
        "  \"\"\"A time-dependent score-based model built upon U-Net architecture.\"\"\"\n",
        "\n",
        "  def __init__(self, marginal_prob_std, channels=[32, 64, 128, 256], embed_dim=256):\n",
        "    \"\"\"Initialize a time-dependent score-based network.\n",
        "\n",
        "    Args:\n",
        "      marginal_prob_std: A function that takes time t and gives the standard\n",
        "        deviation of the perturbation kernel p_{0t}(x(t) | x(0)).\n",
        "      channels: The number of channels for feature maps of each resolution.\n",
        "      embed_dim: The dimensionality of Gaussian random feature embeddings.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    # Gaussian random feature embedding layer for time\n",
        "    self.embed = nn.Sequential(GaussianFourierProjection(embed_dim=embed_dim),\n",
        "         nn.Linear(embed_dim, embed_dim))\n",
        "    # Encoding layers where the resolution decreases\n",
        "    self.conv1 = nn.Conv2d(1, channels[0], 3, stride=1, bias=False)\n",
        "    self.dense1 = Dense(embed_dim, channels[0])\n",
        "    self.gnorm1 = nn.GroupNorm(4, num_channels=channels[0])\n",
        "    self.conv2 = nn.Conv2d(channels[0], channels[1], 3, stride=2, bias=False)\n",
        "    self.dense2 = Dense(embed_dim, channels[1])\n",
        "    self.gnorm2 = nn.GroupNorm(32, num_channels=channels[1])\n",
        "    self.conv3 = nn.Conv2d(channels[1], channels[2], 3, stride=2, bias=False)\n",
        "    self.dense3 = Dense(embed_dim, channels[2])\n",
        "    self.gnorm3 = nn.GroupNorm(32, num_channels=channels[2])\n",
        "    self.conv4 = nn.Conv2d(channels[2], channels[3], 3, stride=2, bias=False)\n",
        "    self.dense4 = Dense(embed_dim, channels[3])\n",
        "    self.gnorm4 = nn.GroupNorm(32, num_channels=channels[3])    \n",
        "\n",
        "    # Decoding layers where the resolution increases\n",
        "    self.tconv4 = nn.ConvTranspose2d(channels[3], channels[2], 3, stride=2, bias=False)\n",
        "    self.dense5 = Dense(embed_dim, channels[2])\n",
        "    self.tgnorm4 = nn.GroupNorm(32, num_channels=channels[2])\n",
        "    self.tconv3 = nn.ConvTranspose2d(channels[2] + channels[2], channels[1], 3, stride=2, bias=False, output_padding=1)    \n",
        "    self.dense6 = Dense(embed_dim, channels[1])\n",
        "    self.tgnorm3 = nn.GroupNorm(32, num_channels=channels[1])\n",
        "    self.tconv2 = nn.ConvTranspose2d(channels[1] + channels[1], channels[0], 3, stride=2, bias=False, output_padding=1)    \n",
        "    self.dense7 = Dense(embed_dim, channels[0])\n",
        "    self.tgnorm2 = nn.GroupNorm(32, num_channels=channels[0])\n",
        "    self.tconv1 = nn.ConvTranspose2d(channels[0] + channels[0], 1, 3, stride=1)\n",
        "    \n",
        "    # The swish activation function\n",
        "    self.act = lambda x: x * torch.sigmoid(x)\n",
        "    self.marginal_prob_std = marginal_prob_std\n",
        "  \n",
        "  def forward(self, x, t): \n",
        "    # Obtain the Gaussian random feature embedding for t   \n",
        "    embed = self.act(self.embed(t))    \n",
        "    # Encoding path\n",
        "    h1 = self.conv1(x)    \n",
        "    # Incorporate information from t\n",
        "    h1 += self.dense1(embed)\n",
        "    # Group normalization\n",
        "    h1 = self.gnorm1(h1)\n",
        "    h1 = self.act(h1)\n",
        "    h2 = self.conv2(h1)\n",
        "    h2 += self.dense2(embed)\n",
        "    h2 = self.gnorm2(h2)\n",
        "    h2 = self.act(h2)\n",
        "    h3 = self.conv3(h2)\n",
        "    h3 += self.dense3(embed)\n",
        "    h3 = self.gnorm3(h3)\n",
        "    h3 = self.act(h3)\n",
        "    h4 = self.conv4(h3)\n",
        "    h4 += self.dense4(embed)\n",
        "    h4 = self.gnorm4(h4)\n",
        "    h4 = self.act(h4)\n",
        "\n",
        "    # Decoding path\n",
        "    h = self.tconv4(h4)\n",
        "    # Skip connection from the encoding path\n",
        "    h += self.dense5(embed)\n",
        "    h = self.tgnorm4(h)\n",
        "    h = self.act(h)\n",
        "    h = self.tconv3(torch.cat([h, h3], dim=1))\n",
        "    h += self.dense6(embed)\n",
        "    h = self.tgnorm3(h)\n",
        "    h = self.act(h)\n",
        "    h = self.tconv2(torch.cat([h, h2], dim=1))\n",
        "    h += self.dense7(embed)\n",
        "    h = self.tgnorm2(h)\n",
        "    h = self.act(h)\n",
        "    h = self.tconv1(torch.cat([h, h1], dim=1))\n",
        "\n",
        "    # Normalize output\n",
        "    h = h / self.marginal_prob_std(t)[:, None, None, None]\n",
        "    return h"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "BeMWzgPzZSJy"
      },
      "outputs": [],
      "source": [
        "#@title Define OUR score-based models \n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "\n",
        "class GaussianFourierProjection(nn.Module):\n",
        "  \"\"\"Gaussian random features for encoding time steps.\"\"\"  \n",
        "  def __init__(self, embed_dim, scale=30.):\n",
        "    super().__init__()\n",
        "    # Randomly sample weights during initialization. These weights are fixed \n",
        "    # during optimization and are not trainable.\n",
        "    self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)\n",
        "  def forward(self, x):\n",
        "    x_proj = x[:, None] * self.W[None, :] * 2 * np.pi\n",
        "    return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)\n",
        "\n",
        "\n",
        "class Dense(nn.Module):\n",
        "  \"\"\"A fully connected layer that reshapes outputs to feature maps.\"\"\"\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super().__init__()\n",
        "    self.dense = nn.Linear(input_dim, output_dim)\n",
        "  def forward(self, x):\n",
        "    return self.dense(x)\n",
        "\n",
        "\n",
        "class ScoreNet_1HiddenLayerFC(nn.Module):\n",
        "  \"\"\"A time-dependent score-based model built upon 1-hidden-layer fully-connected NN architecture.\"\"\"\n",
        "\n",
        "  def __init__(self, marginal_prob_std, hidden_dim= 16, embed_dim=4):\n",
        "    \"\"\"Initialize a time-dependent score-based network.\n",
        "\n",
        "    Args:\n",
        "      marginal_prob_std: A function that takes time t and gives the standard\n",
        "        deviation of the perturbation kernel p_{0t}(x(t) | x(0)).\n",
        "      embed_dim: The dimensionality of Gaussian random feature embeddings.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    # Gaussian random feature embedding layer for time\n",
        "    self.embed = nn.Sequential(GaussianFourierProjection(embed_dim=embed_dim),\n",
        "         nn.Linear(embed_dim, hidden_dim))\n",
        "    self.dense1 = Dense(1, hidden_dim)\n",
        "    self.dense2 = Dense(hidden_dim, 1)\n",
        "\n",
        "    \n",
        "    # The swish activation function\n",
        "    self.act = lambda x: x * torch.sigmoid(x)\n",
        "    self.marginal_prob_std = marginal_prob_std\n",
        "  \n",
        "  def forward(self, x, t): \n",
        "    # Obtain the Gaussian random feature embedding for t   \n",
        "    embed = self.embed(t) \n",
        "    # Feature map\n",
        "    h = self.dense1(x)   \n",
        "    # Incorporate information from t\n",
        "    h += embed\n",
        "    h = self.dense2(self.act(h))\n",
        "    h = h / 16 # Normalize by the power of hidden_dim (=2048)\n",
        "\n",
        "    # Normalize output\n",
        "    h = h / self.marginal_prob_std(t)[:, None]\n",
        "    return h"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "FCBaH6yyt0Sj"
      },
      "outputs": [],
      "source": [
        "#@title Auxilary codes to test substituted score models, can ignore\n",
        "\n",
        "import torch\n",
        "# from torchsummary import summary\n",
        "\n",
        "x = torch.randn(32, 1)\n",
        "t = torch.rand(32)\n",
        "# GFP = GaussianFourierProjection(embed_dim=8)\n",
        "# res = GFP.forward(t)\n",
        "# DS = Dense(input_dim=4, output_dim=6)\n",
        "# res = DS.forward(x=torch.randn(16, 2, 4))\n",
        "SN = ScoreNet_1HiddenLayerFC(marginal_prob_std=1.2)\n",
        "print(SN)\n",
        "res = SN.forward(x, t)\n",
        "res.size()\n",
        "# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# summary(ScoreNet(marginal_prob_std=1.).to(device), [(1, 28, 28), (None, 1, 1)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "cellView": "form",
        "id": "LZC7wrOvxLdL"
      },
      "outputs": [],
      "source": [
        "#@title Set up the SDE\n",
        "\n",
        "import functools\n",
        "\n",
        "device = 'cuda' #@param ['cuda', 'cpu'] {'type':'string'}\n",
        "\n",
        "def marginal_prob_std(t, sigma):\n",
        "  \"\"\"Compute the mean and standard deviation of $p_{0t}(x(t) | x(0))$.\n",
        "\n",
        "  Args:    \n",
        "    t: A vector of time steps.\n",
        "    sigma: The $\\sigma$ in our SDE.  \n",
        "  \n",
        "  Returns:\n",
        "    The standard deviation.\n",
        "  \"\"\"    \n",
        "  t = torch.tensor(t, device=device)\n",
        "  return torch.sqrt((sigma**(2 * t) - 1.) / 2. / np.log(sigma))\n",
        "\n",
        "def diffusion_coeff(t, sigma):\n",
        "  \"\"\"Compute the diffusion coefficient of our SDE.\n",
        "\n",
        "  Args:\n",
        "    t: A vector of time steps.\n",
        "    sigma: The $\\sigma$ in our SDE.\n",
        "  \n",
        "  Returns:\n",
        "    The vector of diffusion coefficients.\n",
        "  \"\"\"\n",
        "  return torch.tensor(sigma**t, device=device)\n",
        "  \n",
        "sigma = 25.0#@param {'type':'number'}\n",
        "marginal_prob_std_fn = functools.partial(marginal_prob_std, sigma=sigma)\n",
        "diffusion_coeff_fn = functools.partial(diffusion_coeff, sigma=sigma)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "cellView": "form",
        "id": "zOsoqPdXHuL5"
      },
      "outputs": [],
      "source": [
        "#@title Define the loss function\n",
        "\n",
        "def loss_fn(model, x, marginal_prob_std, eps=1e-5):\n",
        "  \"\"\"The loss function for training score-based generative models.\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model instance that represents a \n",
        "      time-dependent score-based model.\n",
        "    x: A mini-batch of training data.    \n",
        "    marginal_prob_std: A function that gives the standard deviation of \n",
        "      the perturbation kernel.\n",
        "    eps: A tolerance value for numerical stability.\n",
        "  \"\"\"\n",
        "  random_t = torch.rand(x.shape[0], device=x.device) * (1. - eps) + eps  \n",
        "  z = torch.randn_like(x)\n",
        "  std = marginal_prob_std(random_t)\n",
        "  perturbed_x = x + z * std[:, None]\n",
        "  score = model(perturbed_x, random_t)\n",
        "  loss = torch.mean(torch.sum((score * std[:, None] + z)**2, dim=(1)))\n",
        "  return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "u-AOmtqVAXZ4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "第 0 个Batch \n",
            "tensor([ 3.4545,  4.2307,  3.4537, -5.1684,  2.5124, -0.9774,  2.9295, -2.2165,\n",
            "         4.1086, -2.3678,  1.8971, -1.9056,  3.8853,  2.8749, -2.8030,  3.8626,\n",
            "        -2.3626, -3.7237,  3.4676, -2.9863,  1.0915, -4.7500,  2.5474,  3.6417,\n",
            "         1.8369, -2.7469,  2.9156, -2.9955, -2.3876, -4.6952,  4.2452, -2.4893])\n",
            "第 1 个Batch \n",
            "tensor([-3.4227,  2.2794, -4.7184, -4.1803,  3.1782,  2.4326,  4.1751, -3.3122,\n",
            "        -2.5830,  3.2893, -2.9894,  2.6886,  3.1617,  1.7049,  3.0762, -4.0633,\n",
            "        -4.3795, -2.8614,  3.3156, -3.6159, -3.6630,  0.5622,  1.0092,  2.2229,\n",
            "         3.8862,  3.1689, -5.2860,  4.1451,  2.1583,  2.0636,  4.4552,  1.6712])\n",
            "第 2 个Batch \n",
            "tensor([-2.3817, -1.6967, -3.6747, -3.0554,  3.0285, -2.4978, -3.6327, -2.7464,\n",
            "         2.4673,  1.5665,  4.0536, -2.7231, -3.5562,  1.4634, -3.2955,  2.4066,\n",
            "        -3.5431,  3.1838, -2.1958, -2.4230,  2.7110,  3.2057,  3.0468,  2.0105,\n",
            "        -3.0200, -2.8328,  1.8880, -4.7602,  3.6504, -3.4159, -3.6731, -2.7953])\n",
            "第 3 个Batch \n",
            "tensor([ 0.8171, -3.6093,  2.5493,  3.8557, -3.5690, -0.7133,  3.6338, -3.0074,\n",
            "         1.8602, -1.8574,  2.9905, -3.1261, -2.2674,  4.2875, -1.1586, -2.8882,\n",
            "         1.7178, -2.8005,  2.5152,  3.6051, -2.7394,  3.8530,  2.2040,  2.8889,\n",
            "        -4.4153,  4.4424,  2.5701,  3.4765, -2.0093, -4.1266, -1.8539,  3.1531])\n",
            "第 4 个Batch \n",
            "tensor([ 1.5028,  3.9468, -2.4741,  4.0715,  2.4088, -2.0016, -2.6375, -3.0335,\n",
            "        -3.4205,  1.7783, -3.2165, -2.2342,  4.2588, -4.3066, -1.7804,  4.2035,\n",
            "        -1.2169, -5.0898,  4.5414, -3.2493,  2.1230,  2.8770,  2.0640, -3.9643,\n",
            "        -3.1797, -3.6162, -2.6296, -1.9293,  2.0350,  3.1233,  1.3462,  2.0563])\n",
            "第 5 个Batch \n",
            "tensor([-3.2931, -3.5504,  1.6558,  2.1897, -4.3716,  4.0760, -4.3748,  1.7601,\n",
            "         5.5353, -2.5669,  2.8817, -1.5999, -4.0466,  4.0921, -3.8188, -3.1250,\n",
            "        -3.1610,  4.4799,  4.4881, -2.8608, -4.4550,  1.7578,  2.8237,  2.5916,\n",
            "         1.9397,  3.0483, -3.7067,  2.2443, -1.3097,  3.3116, -4.2857, -2.0746])\n",
            "第 6 个Batch \n",
            "tensor([-5.2584,  4.0802,  3.5030,  3.4811,  3.1065, -3.1909, -3.5472,  2.6388,\n",
            "        -3.0065, -1.7897,  2.7154, -2.5943,  3.8700,  2.9317, -3.1157,  3.3051,\n",
            "         2.9023, -3.5271,  2.2348,  4.4925, -1.3869,  3.3811, -2.8654, -3.7398,\n",
            "        -1.8729,  2.3803, -2.7586, -3.8260,  3.8569, -4.5063,  3.7172,  1.9510])\n",
            "第 7 个Batch \n",
            "tensor([-4.1904,  2.7038, -4.2098, -3.2246, -2.7263,  4.0648, -2.1188,  1.8089,\n",
            "         3.7867, -2.7421,  4.3668,  3.5864,  2.1012, -2.4505, -3.3029,  3.8609,\n",
            "         4.0488, -3.5060,  4.2506,  1.9572, -1.8077, -3.2851, -2.6896, -4.6316,\n",
            "         3.2619,  1.9019, -2.8306,  2.9458,  4.8380,  2.2974,  4.3613, -3.9079])\n",
            "第 8 个Batch \n",
            "tensor([-4.0823,  2.9332, -4.3024,  3.3853,  4.6148,  4.0626,  3.2211,  2.5203,\n",
            "        -3.5034,  3.7156, -3.3954,  1.4903, -2.7759, -3.2078, -2.7737,  3.8362,\n",
            "         3.2485,  3.2834, -2.1354,  3.6124,  2.3911,  0.0943, -3.9967,  5.0448,\n",
            "        -3.7591, -3.2914,  2.9825,  3.4187, -2.5001,  3.8463,  4.2538,  4.7221])\n",
            "第 9 个Batch \n",
            "tensor([-2.9223, -2.9542, -3.1842,  2.7647, -4.1852, -3.0144, -2.3168,  2.3658,\n",
            "         3.9872,  3.9171, -2.8038,  2.1259,  4.1871, -2.5445, -3.6507, -3.5878,\n",
            "         3.0345,  2.8583, -3.0490,  3.3442, -5.0110,  4.5175, -3.5683,  4.0118,\n",
            "        -4.1725,  2.2600, -2.5436,  3.0334,  3.5265,  2.4322, -2.6680, -2.9930])\n",
            "第 10 个Batch \n",
            "tensor([ 2.1533,  0.9263, -1.4980,  2.7927, -1.0735, -3.6155, -3.0047, -3.8546,\n",
            "        -2.7177, -2.3991, -3.6072,  1.9286,  1.9531, -3.5256,  1.2508,  0.5030,\n",
            "         0.2756,  1.7333, -1.5891, -3.9872,  3.8055,  1.9656,  1.5146, -3.0921,\n",
            "         2.8090,  2.1568, -2.3364, -3.8392, -4.3543, -2.4882, -1.9769,  3.6404])\n",
            "第 11 个Batch \n",
            "tensor([ 3.5856, -2.9192, -4.1704,  4.6263,  1.5397,  2.2775, -2.8106,  3.6347,\n",
            "        -1.9507, -3.1175, -2.2039,  4.3556, -3.9871, -3.0323, -2.4769,  2.0554,\n",
            "        -3.4565,  2.2863, -2.3021, -2.8285, -2.5599,  3.6343, -1.0706, -4.7364,\n",
            "         3.0859, -3.8010, -3.9497,  2.9968,  1.4896, -1.2900,  4.0697,  2.5282])\n",
            "第 12 个Batch \n",
            "tensor([ 2.0607, -2.0016, -2.2926,  3.5728, -3.2889, -3.9618, -1.2715,  3.8979,\n",
            "        -2.1722,  2.3714, -3.7811, -3.7547,  2.0896,  3.6540, -3.8262,  4.0474,\n",
            "        -2.9291,  3.6914, -2.3836, -2.0775,  2.4601, -3.8105, -1.7877, -3.1976,\n",
            "         1.5959, -2.8251, -2.0037, -3.5649, -3.5777, -3.3551, -2.7439,  0.8934])\n",
            "第 13 个Batch \n",
            "tensor([-2.8807, -2.0067, -3.0378,  2.4555,  0.8723, -1.7515,  3.1090, -2.3071,\n",
            "        -3.5172,  4.5221,  2.9911, -4.3887, -2.5684, -1.8982, -4.2101,  1.8003,\n",
            "        -3.1922, -2.5300, -3.6557,  3.6925,  1.4977, -2.6301,  4.0594,  2.5704,\n",
            "         3.5204,  2.6282, -3.5587,  3.7102, -1.7731, -1.3763, -3.6661,  2.5834])\n",
            "第 14 个Batch \n",
            "tensor([-3.4143, -3.1846,  2.4241,  1.4607, -2.7167,  3.8375, -2.0887,  1.9870,\n",
            "        -1.6649, -2.6729,  4.9158,  4.0048,  2.3465, -4.6080,  4.2227,  2.3817,\n",
            "         3.7795,  3.6562, -3.7841,  5.7489, -3.3245, -2.7433,  4.1910, -2.5541,\n",
            "         2.4510, -0.6878,  3.3143,  4.5124,  2.3687, -2.0120, -2.9569, -4.5805])\n",
            "第 15 个Batch \n",
            "tensor([ 4.5927, -3.3139,  2.0189, -2.1950, -1.2848, -3.0653, -3.5914,  2.5903,\n",
            "        -2.7395, -3.5123, -2.6077,  3.6275, -3.0718,  1.7052, -2.4149,  3.0056,\n",
            "        -4.9600, -4.2804,  3.2708,  2.8404, -2.8471, -2.3358, -2.9472,  4.8622,\n",
            "         2.3679, -4.0431, -4.3026,  4.2652,  2.2556, -1.8200,  1.5827,  3.2694])\n",
            "第 16 个Batch \n",
            "tensor([-3.9672,  4.2702,  2.3312,  1.6636, -2.9338, -3.1509, -2.6248, -2.1243,\n",
            "        -4.7444,  2.6632, -1.9939, -4.7213, -2.6759,  3.5561,  2.8744, -2.7415,\n",
            "        -3.1443,  2.8061,  4.8912, -1.0099,  3.3686,  2.9011,  2.2629,  1.8294,\n",
            "        -3.3662, -2.4740, -2.2127,  2.7808, -3.3357, -1.8975,  2.0962,  4.4241])\n",
            "第 17 个Batch \n",
            "tensor([-3.4404,  4.4523,  3.2296, -3.1823,  2.2541,  5.4102,  2.8505, -3.1326,\n",
            "        -2.7231,  3.1032, -3.2451, -3.6680, -1.9293,  3.5404,  4.3694,  5.6132,\n",
            "        -2.5036,  2.9900,  3.0542,  2.9399,  2.8191, -4.0253, -5.1654,  4.0658,\n",
            "        -1.6619, -4.5894, -3.5130, -3.7404,  1.5954,  2.1566, -3.1934, -3.7310])\n",
            "第 18 个Batch \n",
            "tensor([-1.6275e+00,  4.1373e-03, -2.6982e+00,  3.0683e+00,  3.4812e+00,\n",
            "        -3.9246e+00, -3.5408e+00, -3.8756e+00, -1.8928e+00,  3.4462e+00,\n",
            "        -3.1262e+00,  4.0410e+00,  2.7956e+00,  1.7176e+00, -9.0954e-01,\n",
            "        -2.6905e+00,  3.6815e+00, -3.8311e+00,  1.7652e+00, -3.0932e+00,\n",
            "        -1.7795e+00,  2.5298e+00,  1.9150e+00,  4.0855e+00,  2.1592e+00,\n",
            "         4.3292e+00, -2.2237e+00, -3.4648e+00, -4.5601e+00,  1.1067e+00,\n",
            "        -2.4284e+00, -2.6973e+00])\n",
            "第 19 个Batch \n",
            "tensor([-1.8645,  3.1322,  1.5017, -2.8507, -2.6818, -3.3485,  3.1597, -3.5341,\n",
            "         5.3129, -1.8499,  1.9192, -2.5648,  5.4333,  3.7448,  4.2717,  3.0917,\n",
            "         4.1865,  3.4273, -4.7557, -3.0457,  2.8323,  2.8403,  2.9434, -2.3500,\n",
            "         2.5905,  2.5152, -1.3053, -1.7711,  2.7956,  2.5031,  4.2492, -2.5166])\n",
            "第 20 个Batch \n",
            "tensor([-2.6054, -1.6081, -3.3872, -3.3746,  2.4622, -4.1530,  2.7163, -2.3040,\n",
            "        -2.0638, -4.0343, -3.9326, -2.7923,  3.8005,  2.5382, -4.5827,  2.9948,\n",
            "        -4.2244, -2.3525, -2.5644,  3.6980,  2.9025, -1.2494,  4.3441, -2.6939,\n",
            "        -1.9283, -2.4366, -1.6891,  2.9931,  2.7036,  3.1138,  3.9287,  1.8789])\n",
            "第 21 个Batch \n",
            "tensor([-2.1617,  3.3125, -1.9180, -3.2034,  3.5156, -2.7662,  2.6485,  1.9754,\n",
            "        -2.5599, -2.9718, -4.0468, -3.0292, -2.5246,  4.5558, -2.4893,  5.1017,\n",
            "         2.8516,  2.3133,  2.8441, -1.1141, -3.2014,  2.6311,  3.3424,  2.7239,\n",
            "         3.1629,  1.5477,  2.1826,  3.8696,  3.4057,  2.9497,  3.2809,  2.1491])\n",
            "第 22 个Batch \n",
            "tensor([ 3.4386,  2.7389, -2.7588, -3.6045, -2.4881,  2.3792, -3.9610,  4.1463,\n",
            "        -1.1130, -3.5255, -2.7088,  4.4374, -3.1233, -1.3957, -4.0539,  3.2442,\n",
            "        -3.6998, -3.1621, -4.4691, -2.2274,  2.7991, -1.3817, -3.4680, -3.8450,\n",
            "        -3.2711, -2.4766,  2.2499,  2.8456,  1.4382, -3.6293,  4.3621,  2.9356])\n",
            "第 23 个Batch \n",
            "tensor([-0.9509,  2.5619, -3.1019,  1.3180,  2.1125,  2.5960,  3.5366,  3.4589,\n",
            "        -3.4380,  2.1606,  3.3381,  1.9311,  3.3514,  3.8129, -2.5113, -2.2333,\n",
            "         2.3964,  3.3561, -2.3980, -2.4645, -1.3245, -3.4693, -4.6552, -3.3421,\n",
            "        -3.7032,  2.4092,  4.0438, -5.4469, -3.0042, -1.6682,  5.9092, -3.6751])\n",
            "第 24 个Batch \n",
            "tensor([-2.1643,  0.9846,  3.5203,  2.6977,  3.0156, -3.0567,  2.9817, -2.4346,\n",
            "         2.4368,  4.0623,  3.0689, -3.7694, -3.9515, -3.2018, -4.4239, -3.4896,\n",
            "        -2.3689, -2.8113, -4.0329,  3.5259, -2.0412,  2.1934,  2.7818,  2.5638,\n",
            "        -0.8993, -3.3192, -3.1014, -3.4262, -2.3791,  3.1994,  4.0485, -2.3648])\n",
            "第 25 个Batch \n",
            "tensor([-2.2269,  2.5817, -4.4445, -3.3415, -2.8606,  4.9860, -2.0205, -4.3407,\n",
            "        -4.0463, -3.2804, -3.0313,  2.0619, -2.5789, -2.3651, -2.8431, -5.2473,\n",
            "        -3.0583, -2.8010,  1.8569,  2.6462,  3.2502, -2.2378, -3.1369,  2.4461,\n",
            "        -4.3236,  2.6573, -1.8649, -3.1592, -3.9836, -5.6085, -2.8605,  3.0696])\n",
            "第 26 个Batch \n",
            "tensor([-2.4611,  2.9516,  2.3293, -3.4985,  1.5748,  3.0957, -4.9656,  3.6047,\n",
            "        -1.6552,  3.0285,  2.6172, -1.6904, -3.7817, -4.2992,  3.0264, -2.8257,\n",
            "         4.0224,  3.7099, -3.9037,  2.7798, -3.0472, -1.5056, -4.9560,  2.7282,\n",
            "        -3.7258,  3.2527, -2.0081, -1.6161, -3.1073,  1.5185, -2.8094, -5.2543])\n",
            "第 27 个Batch \n",
            "tensor([ 4.3174,  2.3397, -3.3972,  4.9758, -4.6934, -1.7695,  3.6498, -4.0854,\n",
            "         2.7664,  2.8353, -4.2697, -3.2195, -1.5464,  2.8479,  3.9128, -2.9152,\n",
            "         2.3899,  3.7088, -3.9071,  3.9296, -3.5342, -3.2755,  0.2882, -2.2244,\n",
            "        -4.2731,  3.2621,  1.9509, -4.4051,  2.6997, -0.2646,  2.2736, -2.6713])\n",
            "第 28 个Batch \n",
            "tensor([ 2.7716, -2.3079, -3.7110, -1.6256,  3.5138, -2.7226,  2.8342, -2.6143,\n",
            "        -1.9165,  1.3766, -0.6868,  3.1577, -2.6305, -2.8581, -3.7018, -3.5445,\n",
            "        -0.9385,  0.9928,  4.0589,  3.0937,  3.4091,  3.6483, -1.3204, -2.3988,\n",
            "        -0.8968, -3.1483,  2.5399,  2.6619, -3.9442, -2.4227,  2.4167, -5.7809])\n",
            "第 29 个Batch \n",
            "tensor([ 4.5232, -1.9267,  3.2545, -3.9150, -3.9358, -2.0421,  3.3757,  4.1097,\n",
            "         3.5263,  3.5014,  4.0153, -3.8966, -3.9090, -2.6311,  2.9898, -1.3534,\n",
            "         2.5757, -3.9234,  3.3443,  1.7927, -4.2352,  3.3414, -3.5406, -1.4305,\n",
            "        -3.6748, -3.0041,  4.7004,  2.8459,  2.5183,  1.6098, -3.7758, -2.6138])\n",
            "第 30 个Batch \n",
            "tensor([ 4.2077,  2.2387,  4.8596, -3.4154,  2.9094,  3.3322,  3.7600,  5.0308,\n",
            "         1.5223,  4.4413,  1.7154, -2.2824,  3.6034,  1.9158,  3.1576, -2.5425,\n",
            "         2.7110, -2.9966, -3.3560,  2.6221,  4.9183, -4.8417, -3.2272, -3.2472,\n",
            "        -2.2930, -2.6555, -3.0441, -5.0697, -3.5690,  1.0062,  2.4736, -3.1909])\n",
            "第 31 个Batch \n",
            "tensor([ 2.6902,  3.3591, -0.9084, -3.5788,  3.4134,  1.7058,  4.5610,  2.8644])\n"
          ]
        }
      ],
      "source": [
        "#@title Sample data from the Gaussian mixture\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "import torch.distributions as D\n",
        "\n",
        "class GetLoader(torch.utils.data.Dataset):\n",
        "    # def __init__(self, data_root, data_label):\n",
        "    #     self.data = data_root\n",
        "    #     self.label = data_label\n",
        "    def __init__(self, data_root):\n",
        "        self.data = data_root\n",
        "    def __getitem__(self, index):\n",
        "        data = self.data[index]\n",
        "        # labels = self.label[index]\n",
        "        # return data, labels\n",
        "        return data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "n_data = 1000 # number of data\n",
        "loc = 3. # location of modes\n",
        "# data size: n_data * 1\n",
        "# data sampled from 2-mode GMM with equal weights\n",
        "mix = D.Categorical(torch.ones(2,)) \n",
        "comp = D.Normal(torch.tensor([-loc, loc]), torch.ones(2,))\n",
        "gmm = D.MixtureSameFamily(mix, comp)\n",
        "source_data = gmm.sample((n_data,))\n",
        "dataset = GetLoader(source_data)\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "datas = DataLoader(dataset, batch_size=32, shuffle=True, drop_last=False)\n",
        "\n",
        "for i, data in enumerate(datas):\n",
        "    print(\"Batch {}\\n{}\".format(i, data))\n",
        "\n",
        "# data.size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Gaussian Mixture Density\n",
        "def GMM_density(x):\n",
        "    return torch.tensor(0.5) / torch.sqrt(2 * torch.tensor(torch.pi)) * torch.exp(-(x - torch.tensor(loc)) ** 2 / 2) + torch.tensor(0.5) / torch.sqrt(2 * torch.tensor(torch.pi)) * torch.exp(-(x + torch.tensor(loc)) ** 2 / 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "import functools\n",
        "from torch.optim import Adam\n",
        "from torch.optim import SGD\n",
        "# from torch.utils.data import DataLoader\n",
        "# import torchvision.transforms as transforms\n",
        "# from torchvision.datasets import MNIST\n",
        "# import tqdm\n",
        "# import tqdm.notebook\n",
        "from scipy import integrate\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn\n",
        "torch.manual_seed(42)\n",
        "\n",
        "score_model = torch.nn.DataParallel(ScoreNet_1HiddenLayerFC(marginal_prob_std=marginal_prob_std_fn))\n",
        "score_model = score_model.to(device)\n",
        "\n",
        "n_epochs = 2001#@param {'type':'integer'}\n",
        "# size of a mini-batch\n",
        "# batch_size =  32 #@param {'type':'integer'}\n",
        "# learning rate\n",
        "lr=5e-1 #@param {'type':'number'}\n",
        "\n",
        "# dataset = MNIST('.', train=True, transform=transforms.ToTensor(), download=True)\n",
        "# data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n",
        "\n",
        "eps = 0.065\n",
        "grid_size = 50\n",
        "left = 10.\n",
        "point = [-left + (2 * left / grid_size) * i for i in range(grid_size + 1)]\n",
        "target_dens = torch.zeros(grid_size + 1)\n",
        "for i in range(grid_size + 1):\n",
        "    target_dens[i] = GMM_density(torch.tensor(point[i]))\n",
        "\n",
        "kl = torch.zeros(31)\n",
        "kl_iter = 0\n",
        "def score(x):\n",
        "  return score_model(torch.tensor([[x]]), torch.tensor([eps]))[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {},
      "outputs": [],
      "source": [
        "optimizer = SGD(score_model.parameters(), lr=lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "#@title Training \n",
        "\n",
        "# optimizer = Adam(score_model.parameters(), lr=lr)\n",
        "\n",
        "# tqdm_epoch = tqdm(range(n_epochs))\n",
        "for epoch in tqdm(range(n_epochs)):\n",
        "  # if (epoch > 1000):\n",
        "  #   lr = 1e-4\n",
        "  #   optimizer = Adam(score_model.parameters(), lr=lr)\n",
        "  batch_total_loss = 0.\n",
        "  num_items = 0\n",
        "  # for x, y in data_loader:\n",
        "  for x in datas:\n",
        "    x = x[:,None].to(device)    \n",
        "    loss = loss_fn(score_model, x, marginal_prob_std_fn)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()    \n",
        "    optimizer.step()\n",
        "    batch_total_loss += loss.item() * x.shape[0]\n",
        "    num_items += x.shape[0]\n",
        "  # Print the averaged training loss so far\n",
        "  # tqdm_epoch.set_description('Average Loss: {:.6f}'.format(batch_total_loss / num_items))\n",
        "\n",
        "  ##\n",
        "  # For density\n",
        "  ##\n",
        "\n",
        "  if(epoch % 100 == 0):\n",
        "    print(('Average Loss: {:.6f}'.format(batch_total_loss / num_items)))\n",
        "    density = torch.zeros(grid_size + 1)\n",
        "    for i in tqdm(range(grid_size + 1)):\n",
        "      density[i], err = torch.tensor(integrate.quad(score, -100.0, point[i]))\n",
        "    max = density.max()\n",
        "    dd = density - max\n",
        "    dd = torch. exp(dd)\n",
        "    dd /= (dd.sum() / (1 / (2 * left / grid_size)))\n",
        "    seaborn.kdeplot(source_data, label = 'Target')\n",
        "    plt.plot(point, dd, label = 'Trained')\n",
        "    # plt.title('eps = {:5f}, epoch = {}'.format(eps, epoch))\n",
        "    plt.title('epoch = {}'.format(epoch))\n",
        "    plt.legend()\n",
        "    # plt.savefig('loc_{}epoch_{}.png'.format(loc, epoch), dpi=300)\n",
        "    plt.show()\n",
        "    ddd = dd * (2 * left / grid_size)\n",
        "    target_dens_ = target_dens * (2 * left / grid_size)\n",
        "    print(ddd)\n",
        "    kl[kl_iter] = F.kl_div(ddd.log(), target_dens_, reduction='sum')\n",
        "    print(kl[kl_iter])\n",
        "    # kl[kl_iter] = F.kl_div(dd.softmax(dim=-1).log(), target_dens.softmax(dim=-1), reduction='sum')\n",
        "    kl_iter += 1\n",
        "  # Update the checkpoint after each epoch of training\n",
        "  # torch.save(score_model.state_dict(), 'ckpt_1000.pth')"
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