ode_gan/odegan_mog16.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYWMcJafmrfI"
      },
      "source": [
        "Copyright 2020 DeepMind Technologies Limited.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAHjf0hcm8Az"
      },
      "source": [
        "# **This code implements ODE-GAN for Mixture of Gaussians.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "n8p0WAstrhUT"
      },
      "outputs": [],
      "source": [
        "#@title Imports\n",
        "!pip install dm-haiku\n",
        "import jax\n",
        "from jax import lax\n",
        "import jax.numpy as jnp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import haiku as hk\n",
        "import scipy as sp\n",
        "import functools"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "aoIaRyCysZEs"
      },
      "outputs": [],
      "source": [
        "#@title An MLP Haiku Module \n",
        "\n",
        "class MLP(hk.Module):\n",
        "  def __init__(self, depth, hidden_size, out_dim, name='SimpleNet'):\n",
        "    super(MLP, self).__init__(name=name)\n",
        "    self._depth = depth\n",
        "    self._hidden_size = hidden_size\n",
        "    self._out_dim = out_dim\n",
        "    layers = []\n",
        "    for i in range(self._depth):\n",
        "      layers.append(hk.Linear(self._hidden_size, name='linear_%d'%(i)))\n",
        "    self._layers = layers\n",
        "    self._final_layer = hk.Linear(self._out_dim, name='final_layer')\n",
        "\n",
        "  def __call__(self, input):\n",
        "    h = input\n",
        "    for i in range(self._depth):\n",
        "      h = jax.nn.relu(self._layers[i](h))\n",
        "    return self._final_layer(h)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "KBgWwKKyv6VI"
      },
      "outputs": [],
      "source": [
        "#@title Real Data\n",
        "def real_data(batch_size):\n",
        "  mog_mean = np.array([\n",
        "        [ 1.50,  1.50],\n",
        "        [ 1.50,  0.50],\n",
        "        [ 1.50, -0.50],\n",
        "        [ 1.50, -1.50],\n",
        "        [ 0.50,  1.50],\n",
        "        [ 0.50,  0.50],\n",
        "        [ 0.50, -0.50],\n",
        "        [ 0.50, -1.50],\n",
        "        [-1.50,  1.50],\n",
        "        [-1.50,  0.50],\n",
        "        [-1.50, -0.50],\n",
        "        [-1.50, -1.50],\n",
        "        [-0.50,  1.50],\n",
        "        [-0.50,  0.50],\n",
        "        [-0.50, -0.50],\n",
        "        [-0.50, -1.50],\n",
        "    ])\n",
        "  temp = np.tile(mog_mean, (batch_size // 16 + 1,1))\n",
        "  mus = temp[0:batch_size,:]\n",
        "  return mus + 0.02 * np.random.normal(size=(batch_size, 2))\n",
        "                                 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "E6uViIllRDlL"
      },
      "outputs": [],
      "source": [
        "#@title ODE-integrators\n",
        "def euler_step(func, y0, f0, t0, dt):\n",
        "  # Euler update\n",
        "  y1 = jax.tree_multimap(lambda u, v: dt * v + u, y0, f0)\n",
        "  return y1\n",
        "\n",
        "def euler_heun_step(func, y0, f0, t0, dt):\n",
        "  # RK2 Butcher tableaux\n",
        "  alpha = jnp.array([1. / 2., 0.])\n",
        "  beta = jnp.array([\n",
        "      [1 / 2, 0,],\n",
        "  ])\n",
        "  c_sol = jnp.array([1 / 2, 1 / 2])\n",
        "\n",
        "  def body_fun(i, k):\n",
        "    ti = t0 + dt * alpha[i-1]\n",
        "    yi = jax.tree_multimap(lambda u, v: u + dt * jnp.tensordot(beta[i-1, :], v, axes=1), y0, k)\n",
        "    ft = func(yi, ti)\n",
        "    return jax.tree_multimap(lambda x, y: x.at[i, :].set(y), k, ft)\n",
        "  k = jax.tree_map(lambda f: jnp.zeros((2,) + f.shape,\n",
        "                                       f.dtype).at[0, :].set(f), f0)\n",
        "  k = lax.fori_loop(1, 2, body_fun, k)\n",
        "\n",
        "  y1 = jax.tree_multimap(lambda u, v: dt * jnp.tensordot(c_sol, v, axes=1) + u, y0, k)\n",
        "  return y1\n",
        "\n",
        "def runge_kutta_step(func, y0, f0, t0, dt):\n",
        "  # RK4 Butcher tableaux\n",
        "  alpha = jnp.array([1. / 2., 1. / 2.,  1., 0])\n",
        "  beta = jnp.array([\n",
        "      [1. / 2., 0, 0, 0],\n",
        "      [0, 1. / 2., 0, 0],\n",
        "      [0, 0, 1., 0],\n",
        "  ])\n",
        "  c_sol = jnp.array([1. / 6., 1. / 3., 1. / 3., 1. / 6.])\n",
        "\n",
        "  def body_fun(i, k):\n",
        "    ti = t0 + dt * alpha[i-1]\n",
        "    yi = jax.tree_multimap(lambda u, v: u + dt * jnp.tensordot(beta[i-1, :], v, axes=1), y0, k)\n",
        "    ft = func(yi, ti)\n",
        "    return jax.tree_multimap(lambda x, y: x.at[i, :].set(y), k, ft)\n",
        "  k = jax.tree_map(lambda f: jnp.zeros((4,) + f.shape,\n",
        "                                       f.dtype).at[0, :].set(f), f0)\n",
        "  k = lax.fori_loop(1, 4, body_fun, k)\n",
        "\n",
        "  y1 = jax.tree_multimap(lambda u, v: dt * jnp.tensordot(c_sol, v, axes=1) + u, y0, k)\n",
        "  return y1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "NHCYH1tnwaTL"
      },
      "outputs": [],
      "source": [
        "#@title Utility Functions.\n",
        "def disc_loss(disc_params, gen_params, real_examples, latents):\n",
        "  fake_examples = gen_model.apply(gen_params, None, latents)\n",
        "  real_logits  = disc_model.apply(disc_params, None, real_examples)\n",
        "  fake_logits = disc_model.apply(disc_params, None, fake_examples)\n",
        "  disc_real = real_logits - jax.nn.log_sigmoid(real_logits)\n",
        "  disc_fake = - jax.nn.log_sigmoid(fake_logits)\n",
        "  return - jnp.mean(disc_real + disc_fake)\n",
        "\n",
        "def gen_loss(disc_params, gen_params, real_examples, latents):\n",
        "  fake_examples = gen_model.apply(gen_params, None, latents)\n",
        "  fake_logits = disc_model.apply(disc_params, None, fake_examples)\n",
        "  disc_fake = fake_logits - jax.nn.log_sigmoid(fake_logits)\n",
        "  return - jnp.mean(disc_fake)\n",
        "\n",
        "def gen_norm(disc_params, gen_params, real_examples, latents):\n",
        "    grad = jax.grad(gen_loss, argnums=1)(\n",
        "      disc_params, gen_params, real_examples, latents)\n",
        "    flat, _ = jax.tree_flatten(grad)\n",
        "    norm = 0.\n",
        "    for a in flat:\n",
        "      norm += jnp.sum(a * a)\n",
        "    return - norm\n",
        "\n",
        "def get_gen_grad(gen_params, t, disc_params, real_examples, latents):\n",
        "  return jax.grad(gen_loss, argnums=1)(\n",
        "      disc_params, gen_params, real_examples, latents)\n",
        "\n",
        "def get_disc_grad(disc_params, t, gen_params, real_examples, latents):\n",
        "  return jax.grad(disc_loss, argnums=0)(\n",
        "      disc_params, gen_params, real_examples, latents)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "xjTBhJuOh_wO"
      },
      "outputs": [],
      "source": [
        "#@title Visualising the data.\n",
        "\n",
        "def kde(mu, tau, contours=None, bbox=None, xlabel=\"\", ylabel=\"\", cmap='Blues', st=0):\n",
        "    values = np.vstack([mu, tau])\n",
        "    kernel = sp.stats.gaussian_kde(values)\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.axis(bbox)\n",
        "    ax.set_aspect(abs(bbox[1]-bbox[0])/abs(bbox[3]-bbox[2]))\n",
        "    ax.set_xlabel(xlabel)\n",
        "    ax.set_ylabel(ylabel)\n",
        "    ax.set_xticks([])\n",
        "    ax.set_yticks([])\n",
        "\n",
        "    xx, yy = np.mgrid[bbox[0]:bbox[1]:300j, bbox[2]:bbox[3]:300j]\n",
        "    positions = np.vstack([xx.ravel(), yy.ravel()])\n",
        "    \n",
        "    f = np.reshape(kernel(positions).T, xx.shape)\n",
        "    cfset = ax.contourf(xx, yy, f, cmap=cmap)\n",
        "    if contours is not None:\n",
        "      x = np.arange(-2., 2., 0.1)\n",
        "      y = np.arange(-2., 2., 0.1)\n",
        "      cx, cy = np.meshgrid(x, y)\n",
        "      new_set = ax.contour(cx, cy, contours.squeeze().reshape(cx.shape), levels=20, colors='k', linewidths=0.8, alpha=0.5)  \n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "G2G32j5N1psa"
      },
      "outputs": [],
      "source": [
        "#@title Integration\n",
        "n_itrs = 30001 #@param {type : 'integer'}\n",
        "n_save = 2000 #@param {type : 'integer'}\n",
        "latent_size = 32 #@param {type : 'integer'}\n",
        "bs = 512 #@param {type : 'integer'}\n",
        "odeint = 'runge_kutta_step' #@param ['euler_step', 'euler_heun_step', 'runge_kutta_step'] {type : 'string'}\n",
        "delta_t = 0.10 #@param {type : 'number'}\n",
        "reg_param = 0.07 #@param {type : 'number'}\n",
        "\n",
        "def forward_disc(batch):\n",
        "  disc_model = MLP(2, 25, 1)\n",
        "  return disc_model(batch)\n",
        "\n",
        "def forward_gen(batch):\n",
        "  gen_model = MLP(2, 25, 2)\n",
        "  return gen_model(batch)\n",
        "\n",
        "\n",
        "disc_model = hk.transform(forward_disc)\n",
        "gen_model = hk.transform(forward_gen)\n",
        "real_examples = real_data(bs)\n",
        "\n",
        "ODEINT = {'runge_kutta_step': runge_kutta_step, \n",
        "          'euler_heun_step': euler_heun_step,\n",
        "          'euler_step': euler_step}\n",
        "@jax.jit\n",
        "def ode_update(i, disc_params, gen_params, real_examples, latents):\n",
        "  dloss, disc_grad = jax.value_and_grad(disc_loss, argnums=0)(\n",
        "      disc_params, gen_params, real_examples, latents)\n",
        "  gloss, gen_grad = jax.value_and_grad(gen_loss, argnums=1)(\n",
        "      disc_params, gen_params, real_examples, latents)\n",
        "  disc_gen_grad = jax.grad(gen_norm, argnums=0)(\n",
        "      disc_params, gen_params, real_examples, latents)\n",
        "  grad_disc_fn = functools.partial(get_disc_grad,\n",
        "                                   **{'gen_params' : gen_params,\n",
        "                                      'real_examples' : real_examples,\n",
        "                                      'latents' : latents})\n",
        "  grad_gen_fn = functools.partial(get_gen_grad, \n",
        "                                  **{'disc_params' : disc_params,\n",
        "                                     'real_examples' : real_examples,\n",
        "                                     'latents' : latents})  \n",
        "  new_gen_params = ODEINT[odeint](\n",
        "      grad_gen_fn, gen_params, gen_grad, 0., delta_t)\n",
        "  new_disc_params = ODEINT[odeint](\n",
        "      grad_disc_fn, disc_params, disc_grad, 0., delta_t)\n",
        "  new_disc_params = jax.tree_multimap(\n",
        "      lambda x, y: x + delta_t * reg_param * y, new_disc_params, disc_gen_grad)\n",
        "  return new_disc_params, new_gen_params, -dloss, -gloss\n",
        "\n",
        "rng = jax.random.PRNGKey(np.random.randint(low=0, high=int(1e7)))\n",
        "test_latents = np.random.normal(size=(bs * 10, latent_size))\n",
        "latents = np.random.normal(size=(bs, latent_size))\n",
        "disc_params = disc_model.init(rng, real_examples)\n",
        "gen_params = gen_model.init(\n",
        "    jax.random.PRNGKey(np.random.randint(low=0, high=int(1e7))), latents)\n",
        "\n",
        "x = np.arange(-2., 2., 0.1)\n",
        "y = np.arange(-2., 2., 0.1)\n",
        "X, Y = np.meshgrid(x, y)\n",
        "pairs = np.stack((X, Y), axis=-1)\n",
        "pairs = np.reshape(pairs, (-1, 2))\n",
        "\n",
        "for e in range(n_itrs):\n",
        "  real_examples = real_data(bs)\n",
        "  latents = np.random.normal(size=(bs, latent_size))\n",
        "\n",
        "  (disc_params, gen_params,\n",
        "   dloss, gloss) = ode_update(e, disc_params, gen_params, real_examples, latents)\n",
        "   \n",
        "  if e % n_save == 0:\n",
        "    real_logits = disc_model.apply(disc_params, None, pairs)\n",
        "    disc_contour = - real_logits + jax.nn.log_sigmoid(real_logits)\n",
        "    print('i = %d, discriminant loss = %s, generator loss = %s' %\n",
        "          (e, dloss, gloss))\n",
        "    bbox = [-2, 2, -2, 2]\n",
        "    fake_examples = gen_model.apply(gen_params, None, test_latents)\n",
        "    kde(fake_examples[:, 0], fake_examples[:, 1], contours=disc_contour, bbox=bbox, st=e)\n",
        "    disc_error = 0\n",
        "    gen_error = 0"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "odegan_mog16.ipynb"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}