{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing FocusDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2450\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'image': array([[[211, 185,  62],\n",
       "         [216, 192,  68],\n",
       "         [223, 198,  79],\n",
       "         ...,\n",
       "         [214, 190,  64],\n",
       "         [222, 199,  71],\n",
       "         [224, 201,  73]],\n",
       " \n",
       "        [[218, 192,  69],\n",
       "         [223, 197,  74],\n",
       "         [229, 205,  83],\n",
       "         ...,\n",
       "         [216, 193,  65],\n",
       "         [225, 202,  74],\n",
       "         [226, 203,  75]],\n",
       " \n",
       "        [[223, 198,  72],\n",
       "         [228, 202,  79],\n",
       "         [234, 210,  88],\n",
       "         ...,\n",
       "         [220, 197,  69],\n",
       "         [228, 205,  77],\n",
       "         [226, 203,  73]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[157, 138,  17],\n",
       "         [163, 145,  21],\n",
       "         [178, 157,  32],\n",
       "         ...,\n",
       "         [166, 169,  40],\n",
       "         [170, 173,  42],\n",
       "         [176, 179,  46]],\n",
       " \n",
       "        [[145, 126,   5],\n",
       "         [155, 137,  13],\n",
       "         [177, 156,  31],\n",
       "         ...,\n",
       "         [156, 158,  31],\n",
       "         [166, 169,  40],\n",
       "         [175, 178,  47]],\n",
       " \n",
       "        [[147, 128,   7],\n",
       "         [159, 141,  17],\n",
       "         [181, 160,  35],\n",
       "         ...,\n",
       "         [149, 151,  24],\n",
       "         [162, 164,  37],\n",
       "         [172, 175,  46]]], dtype=uint8),\n",
       " 'focus_value': tensor(0.5450)}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib.machinery import SourceFileLoader\n",
    "\n",
    "focus_datamodule = SourceFileLoader(\"focus_datamodule\", \"../src/datamodules/focus_datamodule.py\").load_module()\n",
    "from focus_datamodule import FocusDataSet\n",
    "\n",
    "ds = FocusDataSet(\"../data/focus150/metadata.csv\", \"../data/focus150/\")\n",
    "\n",
    "counter = 0\n",
    "for d in ds:\n",
    "    counter += 1\n",
    "\n",
    "print(counter)\n",
    "\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from focus_datamodule import FocusDataModule\n",
    "\n",
    "datamodule = FocusDataModule(data_dir=\"../data/focus150\", csv_file=\"../data/focus150/metadata.csv\")\n",
    "datamodule.setup()\n",
    "\n",
    "for data in datamodule.test_dataloader():\n",
    "    break\n",
    "\n",
    "len(data[\"focus_value\"])\n",
    "\n",
    "# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        pool_size = 3\n",
    "        \n",
    "        conv1_size = 5\n",
    "        conv1_out = 6\n",
    "        conv2_size = 5\n",
    "        conv2_out = 16\n",
    "        size_img = 150\n",
    "\n",
    "        size_img -= conv1_size - 1\n",
    "        size_img = int( (size_img) / pool_size)\n",
    "        size_img -= conv2_size - 1\n",
    "        size_img = int(size_img / pool_size)\n",
    "\n",
    "        print(size_img)\n",
    "\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Conv2d(3, conv1_out, conv1_size),\n",
    "            nn.MaxPool2d(pool_size, pool_size),\n",
    "            nn.Conv2d(conv1_out, conv2_out, conv2_size),\n",
    "            nn.MaxPool2d(pool_size, pool_size),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(conv2_out * size_img * size_img, 120), # 16 * 34 * 34 or [64, 16, 15, 15]\n",
    "            nn.Linear(120, 84),\n",
    "            nn.Linear(84, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()\n",
    "\n",
    "net(data[\"image\"]).shape"
   ]
  }
 ],
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