{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from util import UIDataset, Vocabulary\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from model import *\n",
    "from torchvision import transforms\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = UIDataset('./dataset/training', 'voc.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Pix2Code().cuda()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr = 0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "Loss: 0.10032295435667038, Epoch: 8\n",
      "Loss: 0.0936238169670105, Epoch: 8\n",
      "Loss: 0.09704777598381042, Epoch: 8\n",
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      "Loss: 0.08044221997261047, Epoch: 9\n",
      "Loss: 0.09291483461856842, Epoch: 9\n",
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      "Loss: 0.07598816603422165, Epoch: 9\n",
      "Loss: 0.08768333494663239, Epoch: 9\n",
      "Loss: 0.08585868775844574, Epoch: 9\n",
      "Loss: 0.08838070183992386, Epoch: 9\n",
      "Loss: 0.08511821925640106, Epoch: 9\n",
      "Loss: 0.07946973294019699, Epoch: 9\n",
      "Loss: 0.09176114201545715, Epoch: 9\n",
      "Loss: 0.06730469316244125, Epoch: 9\n",
      "Loss: 0.0944196805357933, Epoch: 9\n",
      "Loss: 0.05201512947678566, Epoch: 9\n",
      "Loss: 0.07947589457035065, Epoch: 9\n",
      "Loss: 0.07191232591867447, Epoch: 9\n",
      "Loss: 0.09604261815547943, Epoch: 9\n",
      "Loss: 0.09151257574558258, Epoch: 9\n",
      "Loss: 0.08036286383867264, Epoch: 9\n",
      "Loss: 0.08668318390846252, Epoch: 9\n",
      "Loss: 0.08424527198076248, Epoch: 9\n",
      "Loss: 0.08812244981527328, Epoch: 9\n",
      "Loss: 0.08832524716854095, Epoch: 9\n",
      "Loss: 0.08474713563919067, Epoch: 9\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(10):\n",
    "    net.zero_grad()\n",
    "    for j, data in enumerate(dataset):\n",
    "        image, context, prediction = data\n",
    "        image = image.unsqueeze(0).cuda()\n",
    "        context = context.unsqueeze(0).cuda()\n",
    "        prediction = prediction.cuda()\n",
    "        output = net(image, context)\n",
    "        output = output.squeeze(0)\n",
    "        prediction = torch.argmax(prediction, 1)\n",
    "        loss = criterion(output, prediction)\n",
    "        loss.backward()\n",
    "        if j%10 == 0:\n",
    "            optimizer.step()\n",
    "            print('Loss: {}, Epoch: {}'.format(loss.data, epoch))\n",
    "            net.zero_grad()\n",
    "\n",
    "torch.save(net.state_dict(), './pix2code.weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pix2Code(\n",
       "  (image_encoder): ImageEncoder(\n",
       "    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (conv4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (conv5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (conv6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (fc1): Linear(in_features=100352, out_features=1024, bias=True)\n",
       "    (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  )\n",
       "  (context_encoder): ContextEncoder(\n",
       "    (rnn): RNN(19, 128, num_layers=2, batch_first=True)\n",
       "  )\n",
       "  (decoder): Decoder(\n",
       "    (rnn): RNN(1152, 512, num_layers=2, batch_first=True)\n",
       "    (l1): Linear(in_features=512, out_features=19, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Pix2Code()\n",
    "net.load_state_dict(torch.load('./pix2code.weights'))\n",
    "net.cuda().eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = UIDataset('./dataset/evaluation', 'voc.pkl')\n",
    "vocab = Vocabulary('voc.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x1523D723D60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image, *_ = test_data.__getitem__(np.random.randint(len(test_data)))\n",
    "t = transforms.ToPILImage()\n",
    "image = image.unsqueeze(0)\n",
    "t(image.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "header{\n",
      "btn-inactive,btn-active\n",
      "}\n",
      "row{\n",
      "single{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "}\n",
      "row{\n",
      "double{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "double{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "}\n",
      "row{\n",
      "quadruple{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "quadruple{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "quadruple{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "quadruple{\n",
      "small-title,text,btn-green\n",
      "}\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image = image.cuda()\n",
    "ct = []\n",
    "ct.append(vocab.to_vec(' '))\n",
    "ct.append(vocab.to_vec('<START>'))\n",
    "output = ''\n",
    "for i in range(200):\n",
    "    context = torch.tensor(ct).unsqueeze(0).float().cuda()\n",
    "    index = torch.argmax(net(image, context), 2).squeeze()[-1:].squeeze()\n",
    "    v = vocab.to_vocab(int(index))\n",
    "    if v == '<END>':\n",
    "        break\n",
    "    output += v\n",
    "    ct.append(vocab.to_vec(v))\n",
    "\n",
    "with open('./compiler/output.gui', 'w') as f:\n",
    "    f.write(output)\n",
    "\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now from the compiler directory in your terminal run\n",
    "`python web-compiler.py output.gui`.\n",
    "This will generate a `output.html` file that you can open in your browser."
   ]
  }
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