Random Handwriting.ipynb

random-handwriting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random Handwriting.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "KT39N3UsblNu",
        "XVg-mxp3dTRb"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/NTT123/cfddd6acef2466d4db99434d62983423/random-handwriting.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "LkKyqlaFrUCc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generating Sequences With Recurrent Neural Networks\n",
        "Alex Graves\n",
        "\n",
        "https://arxiv.org/abs/1308.0850"
      ]
    },
    {
      "metadata": {
        "id": "KT39N3UsblNu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load libraries and data"
      ]
    },
    {
      "metadata": {
        "id": "VA6Igo0Fbk0S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!apt install libcairo2  > /dev/null\n",
        "!pip install drawSvg > /dev/null\n",
        "!rm -rf ./*\n",
        "\n",
        "from os import path\n",
        "\n",
        "# http://pytorch.org/\n",
        "from os.path import exists\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
        "accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision\n",
        "import torch\n",
        "\n",
        "# Install the PyDrive wrapper & import libraries.\n",
        "# This only needs to be done once per notebook.\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once per notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# Download a file based on its file ID.\n",
        "#\n",
        "# A file ID looks like: laggVyWshwcyP6kEI-y_W3P8D26sz\n",
        "file_id = '1_09aGHN_1JwuAli7uuUiz6IMM4Av5EEh'\n",
        "downloaded = drive.CreateFile({'id': file_id})\n",
        "!mkdir data\n",
        "downloaded.GetContentFile(\"./data/lineStrokes-all.tar.gz\")\n",
        "\n",
        "!cd data && tar -xzf lineStrokes-all.tar.gz > /dev/null"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vUGtFUgpdJXu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install svgwrite"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7J3_Jw1Nczqy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pickle\n",
        "import random\n",
        "import xml.etree.ElementTree as ET\n",
        "\n",
        "import numpy as np\n",
        "import svgwrite\n",
        "from IPython.display import SVG, display\n",
        "\n",
        "\n",
        "def get_bounds(data, factor):\n",
        "    min_x = 0\n",
        "    max_x = 0\n",
        "    min_y = 0\n",
        "    max_y = 0\n",
        "\n",
        "    abs_x = 0\n",
        "    abs_y = 0\n",
        "    for i in range(len(data)):\n",
        "        x = float(data[i, 0]) / factor\n",
        "        y = float(data[i, 1]) / factor\n",
        "        abs_x += x\n",
        "        abs_y += y\n",
        "        min_x = min(min_x, abs_x)\n",
        "        min_y = min(min_y, abs_y)\n",
        "        max_x = max(max_x, abs_x)\n",
        "        max_y = max(max_y, abs_y)\n",
        "\n",
        "    return (min_x, max_x, min_y, max_y)\n",
        "\n",
        "# old version, where each path is entire stroke (smaller svg size, but\n",
        "# have to keep same color)\n",
        "\n",
        "\n",
        "def draw_strokes(data, factor=10, svg_filename='sample.svg'):\n",
        "    min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
        "    dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
        "\n",
        "    dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
        "    dwg.add(dwg.rect(insert=(0, 0), size=dims, fill='white'))\n",
        "\n",
        "    lift_pen = 1\n",
        "\n",
        "    abs_x = 25 - min_x\n",
        "    abs_y = 25 - min_y\n",
        "    p = \"M%s,%s \" % (abs_x, abs_y)\n",
        "\n",
        "    command = \"m\"\n",
        "\n",
        "    for i in range(len(data)):\n",
        "        if (lift_pen == 1):\n",
        "            command = \"m\"\n",
        "        elif (command != \"l\"):\n",
        "            command = \"l\"\n",
        "        else:\n",
        "            command = \"\"\n",
        "        x = float(data[i, 0]) / factor\n",
        "        y = float(data[i, 1]) / factor\n",
        "        lift_pen = data[i, 2]\n",
        "        p += command + str(x) + \",\" + str(y) + \" \"\n",
        "\n",
        "    the_color = \"black\"\n",
        "    stroke_width = 1\n",
        "\n",
        "    dwg.add(dwg.path(p).stroke(the_color, stroke_width).fill(\"none\"))\n",
        "\n",
        "    dwg.save()\n",
        "    display(SVG(dwg.tostring()))\n",
        "\n",
        "\n",
        "def draw_strokes_eos_weighted(\n",
        "        stroke,\n",
        "        param,\n",
        "        factor=10,\n",
        "        svg_filename='sample_eos.svg'):\n",
        "    c_data_eos = np.zeros((len(stroke), 3))\n",
        "    for i in range(len(param)):\n",
        "        # make color gray scale, darker = more likely to eos\n",
        "        c_data_eos[i, :] = (1 - param[i][6][0]) * 225\n",
        "    draw_strokes_custom_color(\n",
        "        stroke,\n",
        "        factor=factor,\n",
        "        svg_filename=svg_filename,\n",
        "        color_data=c_data_eos,\n",
        "        stroke_width=3)\n",
        "\n",
        "\n",
        "def draw_strokes_random_color(\n",
        "        stroke,\n",
        "        factor=10,\n",
        "        svg_filename='sample_random_color.svg',\n",
        "        per_stroke_mode=True):\n",
        "    c_data = np.array(np.random.rand(len(stroke), 3) * 240, dtype=np.uint8)\n",
        "    if per_stroke_mode:\n",
        "        switch_color = False\n",
        "        for i in range(len(stroke)):\n",
        "            if switch_color == False and i > 0:\n",
        "                c_data[i] = c_data[i - 1]\n",
        "            if stroke[i, 2] < 1:  # same strike\n",
        "                switch_color = False\n",
        "            else:\n",
        "                switch_color = True\n",
        "    draw_strokes_custom_color(\n",
        "        stroke,\n",
        "        factor=factor,\n",
        "        svg_filename=svg_filename,\n",
        "        color_data=c_data,\n",
        "        stroke_width=2)\n",
        "\n",
        "\n",
        "def draw_strokes_custom_color(\n",
        "        data,\n",
        "        factor=10,\n",
        "        svg_filename='test.svg',\n",
        "        color_data=None,\n",
        "        stroke_width=1):\n",
        "    min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
        "    dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
        "\n",
        "    dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
        "    dwg.add(dwg.rect(insert=(0, 0), size=dims, fill='white'))\n",
        "\n",
        "    lift_pen = 1\n",
        "    abs_x = 25 - min_x\n",
        "    abs_y = 25 - min_y\n",
        "\n",
        "    for i in range(len(data)):\n",
        "\n",
        "        x = float(data[i, 0]) / factor\n",
        "        y = float(data[i, 1]) / factor\n",
        "\n",
        "        prev_x = abs_x\n",
        "        prev_y = abs_y\n",
        "\n",
        "        abs_x += x\n",
        "        abs_y += y\n",
        "\n",
        "        if (lift_pen == 1):\n",
        "            p = \"M \" + str(abs_x) + \",\" + str(abs_y) + \" \"\n",
        "        else:\n",
        "            p = \"M +\" + str(prev_x) + \",\" + str(prev_y) + \\\n",
        "                \" L \" + str(abs_x) + \",\" + str(abs_y) + \" \"\n",
        "\n",
        "        lift_pen = data[i, 2]\n",
        "\n",
        "        the_color = \"black\"\n",
        "\n",
        "        if (color_data is not None):\n",
        "            the_color = \"rgb(\" + str(int(color_data[i, 0])) + \",\" + str(\n",
        "                int(color_data[i, 1])) + \",\" + str(int(color_data[i, 2])) + \")\"\n",
        "\n",
        "        dwg.add(dwg.path(p).stroke(the_color, stroke_width).fill(the_color))\n",
        "    dwg.save()\n",
        "    display(SVG(dwg.tostring()))\n",
        "\n",
        "\n",
        "def draw_strokes_pdf(data, param, factor=10, svg_filename='sample_pdf.svg'):\n",
        "    min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
        "    dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
        "\n",
        "    dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
        "    dwg.add(dwg.rect(insert=(0, 0), size=dims, fill='white'))\n",
        "\n",
        "    abs_x = 25 - min_x\n",
        "    abs_y = 25 - min_y\n",
        "\n",
        "    num_mixture = len(param[0][0])\n",
        "\n",
        "    for i in range(len(data)):\n",
        "\n",
        "        x = float(data[i, 0]) / factor\n",
        "        y = float(data[i, 1]) / factor\n",
        "\n",
        "        for k in range(num_mixture):\n",
        "            pi = param[i][0][k]\n",
        "            if pi > 0.01:  # optimisation, ignore pi's less than 1% chance\n",
        "                mu1 = param[i][1][k]\n",
        "                mu2 = param[i][2][k]\n",
        "                s1 = param[i][3][k]\n",
        "                s2 = param[i][4][k]\n",
        "                sigma = np.sqrt(s1 * s2)\n",
        "                dwg.add(dwg.circle(center=(abs_x + mu1 * factor,\n",
        "                                           abs_y + mu2 * factor),\n",
        "                                   r=int(sigma * factor)).fill('red',\n",
        "                                                               opacity=pi / (sigma * sigma * factor)))\n",
        "\n",
        "        prev_x = abs_x\n",
        "        prev_y = abs_y\n",
        "\n",
        "        abs_x += x\n",
        "        abs_y += y\n",
        "\n",
        "    dwg.save()\n",
        "    display(SVG(dwg.tostring()))\n",
        "\n",
        "\n",
        "class DataLoader():\n",
        "    def __init__(\n",
        "            self,\n",
        "            batch_size=50,\n",
        "            seq_length=3,\n",
        "            scale_factor=10,\n",
        "            limit=500):\n",
        "        self.data_dir = \"./data\"\n",
        "        self.batch_size = batch_size\n",
        "        self.seq_length = seq_length\n",
        "        self.scale_factor = scale_factor  # divide data by this factor\n",
        "        self.limit = limit  # removes large noisy gaps in the data\n",
        "\n",
        "        data_file = os.path.join(self.data_dir, \"strokes_training_data.cpkl\")\n",
        "        raw_data_dir = self.data_dir + \"/lineStrokes\"\n",
        "\n",
        "        if not (os.path.exists(data_file)):\n",
        "            print(\"creating training data pkl file from raw source\")\n",
        "            self.preprocess(raw_data_dir, data_file)\n",
        "\n",
        "        self.load_preprocessed(data_file)\n",
        "        self.reset_batch_pointer()\n",
        "\n",
        "    def preprocess(self, data_dir, data_file):\n",
        "        # create data file from raw xml files from iam handwriting source.\n",
        "\n",
        "        # build the list of xml files\n",
        "        filelist = []\n",
        "        # Set the directory you want to start from\n",
        "        rootDir = data_dir\n",
        "        for dirName, subdirList, fileList in os.walk(rootDir):\n",
        "            #print('Found directory: %s' % dirName)\n",
        "            for fname in fileList:\n",
        "                #print('\\t%s' % fname)\n",
        "                filelist.append(dirName + \"/\" + fname)\n",
        "\n",
        "        # function to read each individual xml file\n",
        "        def getStrokes(filename):\n",
        "            tree = ET.parse(filename)\n",
        "            root = tree.getroot()\n",
        "\n",
        "            result = []\n",
        "\n",
        "            x_offset = 1e20\n",
        "            y_offset = 1e20\n",
        "            y_height = 0\n",
        "            for i in range(1, 4):\n",
        "                x_offset = min(x_offset, float(root[0][i].attrib['x']))\n",
        "                y_offset = min(y_offset, float(root[0][i].attrib['y']))\n",
        "                y_height = max(y_height, float(root[0][i].attrib['y']))\n",
        "            y_height -= y_offset\n",
        "            x_offset -= 100\n",
        "            y_offset -= 100\n",
        "\n",
        "            for stroke in root[1].findall('Stroke'):\n",
        "                points = []\n",
        "                for point in stroke.findall('Point'):\n",
        "                    points.append(\n",
        "                        [float(point.attrib['x']) - x_offset, float(point.attrib['y']) - y_offset])\n",
        "                result.append(points)\n",
        "\n",
        "            return result\n",
        "\n",
        "        # converts a list of arrays into a 2d numpy int16 array\n",
        "        def convert_stroke_to_array(stroke):\n",
        "\n",
        "            n_point = 0\n",
        "            for i in range(len(stroke)):\n",
        "                n_point += len(stroke[i])\n",
        "            stroke_data = np.zeros((n_point, 3), dtype=np.int16)\n",
        "\n",
        "            prev_x = 0\n",
        "            prev_y = 0\n",
        "            counter = 0\n",
        "\n",
        "            for j in range(len(stroke)):\n",
        "                for k in range(len(stroke[j])):\n",
        "                    stroke_data[counter, 0] = int(stroke[j][k][0]) - prev_x\n",
        "                    stroke_data[counter, 1] = int(stroke[j][k][1]) - prev_y\n",
        "                    prev_x = int(stroke[j][k][0])\n",
        "                    prev_y = int(stroke[j][k][1])\n",
        "                    stroke_data[counter, 2] = 0\n",
        "                    if (k == (len(stroke[j]) - 1)):  # end of stroke\n",
        "                        stroke_data[counter, 2] = 1\n",
        "                    counter += 1\n",
        "            return stroke_data\n",
        "\n",
        "        # build stroke database of every xml file inside iam database\n",
        "        strokes = []\n",
        "        for i in range(len(filelist)):\n",
        "            if (filelist[i][-3:] == 'xml'):\n",
        "                print('processing ' + filelist[i])\n",
        "                strokes.append(\n",
        "                    convert_stroke_to_array(\n",
        "                        getStrokes(\n",
        "                            filelist[i])))\n",
        "\n",
        "        f = open(data_file, \"wb\")\n",
        "        pickle.dump(strokes, f, protocol=2)\n",
        "        f.close()\n",
        "\n",
        "    def load_preprocessed(self, data_file):\n",
        "        f = open(data_file, \"rb\")\n",
        "        self.raw_data = pickle.load(f)\n",
        "        f.close()\n",
        "\n",
        "        # goes thru the list, and only keeps the text entries that have more\n",
        "        # than seq_length points\n",
        "        self.data = []\n",
        "        self.valid_data = []\n",
        "        counter = 0\n",
        "\n",
        "        # every 1 in 20 (5%) will be used for validation data\n",
        "        cur_data_counter = 0\n",
        "        self.mean = np.zeros((1,2))\n",
        "        self.std  = np.zeros((1,2))\n",
        "        self.counter = 0\n",
        "        for data in self.raw_data:\n",
        "            if len(data) > (self.seq_length + 2):\n",
        "                # removes large gaps from the data\n",
        "                data = np.minimum(data, self.limit)\n",
        "                data = np.maximum(data, -self.limit)\n",
        "                data = np.array(data, dtype=np.float32)\n",
        "                data[0, 0:2] = 0.0  # set the first entry to zeros\n",
        "                data[:, 0:2] /= self.scale_factor\n",
        "\n",
        "                self.mean += np.sum(data[:, 0:2], axis=0,   keepdims=True)\n",
        "                self.std  += np.sum(np.power(data[:, 0:2],2), axis=0, keepdims=True) \n",
        "                self.counter += data.shape[0]\n",
        "                cur_data_counter = cur_data_counter + 1\n",
        "                if cur_data_counter % 20 == 0:\n",
        "                    self.valid_data.append(data)\n",
        "                else:\n",
        "                    self.data.append(data)\n",
        "                    # number of equiv batches this datapoint is worth\n",
        "                    counter += int(len(data) / ((self.seq_length + 2)))\n",
        "\n",
        "        self.mean /= self.counter\n",
        "        self.std  /= self.counter\n",
        "        self.std  = np.sqrt(self.std - np.power(self.mean, 2))\n",
        "\n",
        "        for idx in range(len(self.data)):\n",
        "            self.data[idx][:, 0:2] -= self.mean \n",
        "            self.data[idx][:, 0:2] /= self.std\n",
        "\n",
        "        for idx in range(len(self.valid_data)):\n",
        "            self.valid_data[idx][:, 0:2] -= self.mean \n",
        "            self.valid_data[idx][:, 0:2] /= self.std\n",
        "\n",
        "        print(\"train data: {}, valid data: {}\".format(\n",
        "            len(self.data), len(self.valid_data)))\n",
        "        # minus 1, since we want the ydata to be a shifted version of x data\n",
        "        self.num_batches = len(self.data) // self.batch_size\n",
        "\n",
        "    def validation_data(self):\n",
        "        # returns validation data\n",
        "        x_batch = []\n",
        "        y_batch = []\n",
        "        for i in range(self.batch_size):\n",
        "            data = self.valid_data[i % len(self.valid_data)]\n",
        "            idx = 0\n",
        "            x_batch.append(np.copy(data[idx:idx + self.seq_length]))\n",
        "            y_batch.append(np.copy(data[idx + 1:idx + self.seq_length + 1]))\n",
        "        return x_batch, y_batch\n",
        "\n",
        "    def next_batch(self):\n",
        "        # returns a randomised, seq_length sized portion of the training data\n",
        "        x_batch = []\n",
        "        y_batch = []\n",
        "        end = self.pointer + self.batch_size\n",
        "        if end > len(self.data):\n",
        "            end = len(self.data)\n",
        "            \n",
        "        x_batch = self.data[self.pointer:end]\n",
        "        self.pointer = self.pointer + batch_size \n",
        "        if self.pointer > len(self.data):\n",
        "            self.reset_batch_pointer()\n",
        "            \n",
        "        return x_batch\n",
        "\n",
        "    def tick_batch_pointer(self):\n",
        "        self.pointer += 1\n",
        "        if (self.pointer >= len(self.data)):\n",
        "            self.pointer = 0\n",
        "\n",
        "    def reset_batch_pointer(self):\n",
        "        self.pointer  = 0\n",
        "        random.shuffle(self.data)\n",
        "\n",
        "\n",
        "\n",
        "import torch\n",
        "import math\n",
        "device = torch.device(\"cuda\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "et4j_BMIdO8o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XVg-mxp3dTRb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model"
      ]
    },
    {
      "metadata": {
        "id": "Gqd7YUTwdUJA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import math\n",
        "import torch.nn as nn\n",
        "\n",
        "from torch.nn import functional as F\n",
        "\n",
        "\n",
        "class MDN(nn.Module):\n",
        "    def __init__(self, input_size, m):\n",
        "        super().__init__()\n",
        "        self.input_size = input_size\n",
        "        self.m = m\n",
        "        \n",
        "        s = 6 * m + 1 # weight, meanx, meany, varx, vary, rho, e \n",
        "        self.fc1 = nn.Linear(self.input_size, s)\n",
        "        #self.selu1 = nn.SELU()\n",
        "        #self.fc2 = nn.Linear(256, s)\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.fc1(x)\n",
        "        return x\n",
        "        #out = self.fc2(x)\n",
        "        \n",
        "        #return out\n",
        "\n",
        "    def myloss(self, output, target):\n",
        "        w, mx, my, logvarx, logvary, rho, en = output.split(self.m, dim=1)\n",
        "        w = w.log_softmax(dim=1)\n",
        "        varx = logvarx.exp()\n",
        "        vary = logvary.exp()\n",
        "        rho = rho.tanh() \n",
        "        en = torch.sigmoid(en)\n",
        "\n",
        "        tx, ty, te = target.chunk(3, dim=1)\n",
        "        dx = mx - tx\n",
        "        dy = my - ty\n",
        "        z  = torch.pow(dx,2) / varx + torch.pow(dy,2) / vary - 2.0 * rho * dx *dy / torch.sqrt( varx*vary)\n",
        "\n",
        "\n",
        "        ro2 = torch.ones(1).to(device) - rho * rho # torch.pow(rho, 2)\n",
        "        llh = -z / 2.0 / ro2 - math.log(2*math.pi) - 0.5 * (logvarx + logvary + ro2.log())\n",
        "        \n",
        "        #bb = F.binary_cross_entropy(en, te , reduction=\"none\")\n",
        "        \n",
        "        llh = torch.logsumexp(llh + w, dim=1, keepdim=True)\n",
        "\n",
        "        out = -llh + F.binary_cross_entropy(en, te, reduction=\"none\")\n",
        "        return out.sum()\n",
        "\n",
        "class Strokes(nn.Module):\n",
        "    def __init__(self, M=20):\n",
        "        super().__init__()\n",
        "        self.M = M\n",
        "\n",
        "        self.rnn = nn.LSTM(input_size=3, hidden_size=512, num_layers=3, dropout=0.2)\n",
        "        self.mdn = MDN(self.rnn.hidden_size, M)\n",
        "\n",
        "    def forward(self, x, hidden):\n",
        "        x, hidden = self.rnn(x, hidden)\n",
        "        #print(x)\n",
        "        out = self.mdn(x)\n",
        "        return out, hidden\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P3V5lj4nwpDP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def generateSample(model, seq=200):\n",
        "    model.eval()\n",
        "    h0 = torch.zeros(model.rnn.num_layers, 1, model.rnn.hidden_size).to(device).detach()\n",
        "    c0 = torch.zeros(model.rnn.num_layers, 1, model.rnn.hidden_size).to(device).detach()\n",
        "    hidden = (h0, c0)\n",
        "    inp = torch.zeros(3).to(device)\n",
        "    inp[2] = 0.0\n",
        "    inp[0] = 0.0#bat[0][0][0][0]\n",
        "    inp[1] = 0.0#bat[0][0][0][1]\n",
        "    lst = [ inp.tolist() ]\n",
        "    inp = inp.view(1, 1, 3)\n",
        "    \n",
        "    M = model.M\n",
        "    for i in range(seq-1):\n",
        "        x, hidden = model.rnn(inp, hidden)\n",
        "        hidden = (hidden[0].data, hidden[1].data)\n",
        "        output = model.mdn(x).cpu()\n",
        "        #print(output.size())\n",
        "        w, mx, my, logvarx, logvary, rho, en = output.split(model.mdn.m, dim=2)\n",
        "        w = (w).softmax(dim=2).detach().view(-1).numpy()\n",
        "        idx = np.random.choice(range(M), size=2, p=w)[0]\n",
        "        \n",
        "        varx = 0.2*(logvarx.exp().view(-1)[idx].item())\n",
        "        vary = 0.2*(logvary.exp().view(-1)[idx].item())\n",
        "        mx = mx.view(-1)[idx].item()\n",
        "        my = my.view(-1)[idx].item()\n",
        "        rho = (rho.tanh().view(-1)[idx].item())\n",
        "        en = torch.sigmoid(en).view(-1).item()\n",
        "        mean   = torch.empty(2)\n",
        "        mean[0] = mx\n",
        "        mean[1] = my\n",
        "        cov    = torch.Tensor( [ [varx , rho*math.sqrt(varx*vary) ], \n",
        "                                   [rho*math.sqrt(varx*vary), vary ] ]  ) \n",
        "                                  \n",
        "        \n",
        "        z = torch.distributions.MultivariateNormal(loc=mean, \n",
        "                       covariance_matrix=cov)\n",
        "        t = z.sample()\n",
        "\n",
        "        e = 0\n",
        "        if torch.rand(1).item() < en:\n",
        "            e = 1\n",
        "\n",
        "        lst.append( [ t[0].item(), t[1].item(), e])\n",
        "        inp[0,0,0]= t[0]\n",
        "        inp[0,0,1] = t[1]\n",
        "        inp[0,0,2] = e\n",
        "        \n",
        "    return lst"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9tkjQMU1fI5I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training"
      ]
    },
    {
      "metadata": {
        "id": "_VozqWYPdWQ4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch \n",
        "device = torch.device(\"cuda\")\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "batch_size = 16\n",
        "dataloader = DataLoader(batch_size=batch_size)\n",
        "\n",
        "\n",
        "from torch.nn.utils.rnn import *\n",
        "\n",
        "model = Strokes(20).to(device)\n",
        "\n",
        "h0 = torch.zeros(model.rnn.num_layers, batch_size, model.rnn.hidden_size).to(device).detach()\n",
        "c0 = torch.zeros(model.rnn.num_layers, batch_size, model.rnn.hidden_size).to(device).detach()\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "lr = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.95)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w1vypZQYu7cv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "lr = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.95)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "38ta5IbHhatr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "h0_ = torch.zeros(model.rnn.num_layers, 1, model.rnn.hidden_size).to(device).detach()\n",
        "c0_ = torch.zeros(model.rnn.num_layers, 1, model.rnn.hidden_size).to(device).detach()\n",
        "\n",
        "def evalTestSet(model, data):\n",
        "    model.eval()\n",
        "\n",
        "    llh = 0.0\n",
        "        \n",
        "    with torch.no_grad():\n",
        "        for line in data:\n",
        "            inp = torch.Tensor(line)\n",
        "            #print(inp.size())\n",
        "            inp = inp.unsqueeze(1).to(device).data\n",
        "            out, _ = model.rnn(inp[0:-1], (h0_.data, c0_.data) )\n",
        "            out = model.mdn(out)\n",
        "            llh += - model.mdn.myloss(out.squeeze(1), inp[1:].squeeze(1).data )\n",
        "    print(\"Test llh:\", llh.item() / len(data))\n",
        "    \n",
        "    model.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2gxhMGjghZ0Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2096
        },
        "outputId": "128d6f25-d268-4993-85b4-fb3bed432d2e"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "model.train()\n",
        "les = 0.0\n",
        "cc = 0\n",
        "\n",
        "\n",
        "for epoch in range(100):\n",
        "    lr.step()\n",
        "    dataloader.reset_batch_pointer()\n",
        "\n",
        "    for b in range(dataloader.num_batches):\n",
        "        batch = dataloader.next_batch()\n",
        "\n",
        "        batch.sort(key=len, reverse=True)\n",
        "\n",
        "        batch = [(s[:-1], s[1:]) for s in batch]\n",
        "        inp, target = list(zip(*batch))\n",
        "\n",
        "        inp = [torch.Tensor(i).to(device).data for i in inp]\n",
        "        target = [torch.Tensor(i).to(device).data for i in target]\n",
        "        \n",
        "        for qq in range(len(inp)):\n",
        "            noise = 0.2* torch.rand(inp[qq].size(0)+1, 2, device=device) + 0.9\n",
        "            inp[qq][:,0:2] = inp[qq][:,0:2] * noise[:-1]\n",
        "            #noise = 0.02* torch.rand_like(target[qq][:,0:2]) + 0.99\n",
        "            target[qq][:,0:2] = target[qq][:,0:2] * noise[1:]\n",
        "\n",
        "        pp = pack_sequence(inp).to(device)\n",
        "        out, _ = model.rnn(pp, (h0.data, c0.data) )\n",
        "        out, lengths = pad_packed_sequence(out)\n",
        "        out = model.mdn(out)\n",
        "        loss = 0.0\n",
        "        for j in range(lengths.size(0)):\n",
        "            le = lengths[j]\n",
        "            loss += model.mdn.myloss( out[:le,j,:].contiguous(), target[j].data )\n",
        "\n",
        "        loss /= lengths.size(0)\n",
        "        \n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 10.0)\n",
        "\n",
        "        optimizer.step()\n",
        "        les += loss.detach().item()\n",
        "        \n",
        "        cc += 1\n",
        "\n",
        "        if cc % 500 == 499:\n",
        "            evalTestSet(model, dataloader.valid_data)\n",
        "            \n",
        "        if cc % 50 == 49:     \n",
        "            print(cc, epoch, b, les /50)\n",
        "            les = .0\n",
        "            with torch.no_grad():\n",
        "                path= np.asarray(generateSample(model, 1000))\n",
        "                path[:,0:2] *= dataloader.std\n",
        "                path[:,0:2] += dataloader.mean\n",
        "                draw_strokes( path , factor=1, svg_filename=\"sample_%d.svg\"%cc)\n",
        "\n",
        "            model.train()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49 0 48 -945.445654296875\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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        {
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          "text": [
            "Test llh: 980.9336001642037\n",
            "499 0 498 -941.8813830566406\n"
          ],
          "name": "stdout"
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    {
      "metadata": {
        "id": "jP35yYbtWDUv",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        "evalTestSet(model, dataloader.valid_data)"
      ],
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    },
    {
      "metadata": {
        "id": "ZhOJp0qYWHPQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}