SPICEnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPICEnet: Classification of images of electrical components\n",
    "\n",
    "*Kristóf Kónya, Jakob Tschavoll*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version 2.11.0\n"
     ]
    }
   ],
   "source": [
    "# file related\n",
    "import os\n",
    "from os.path import join\n",
    "import shutil\n",
    "\n",
    "# image manipulation\n",
    "from PIL import Image, ImageChops, ImageDraw, ImageFont\n",
    "\n",
    "# machine learning\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.applications import VGG16\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import load_model\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# utils\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import random\n",
    "\n",
    "print(f\"TF version {tf.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if(gpus):\n",
    "    try:\n",
    "        for gpu in gpus:            \n",
    "            tf.config.experimental.set_memory_growth(gpu,True)\n",
    "            logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "            print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "gpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPICEnet parameters\n",
    "Data generation and training parameters are all accessible through the below listed dictionary `SPICENET_PARAMS`. The keys are used like this:\n",
    "\n",
    "- `src_path`: path to the base images relative to the project's root data_dir. Only few are needed, the rest will be created from the source image's augmentations.\n",
    "- `dataset_path`: path to the dataset relative to the project's root data_dir. This folder **does not need to exist yet**, but you can decide how it is named.\n",
    "- `resize`: amount of pixels to which input images will be resized (height and width are the same)\n",
    "- `offset_x`: distance and iterations with which the images will be shifted around for data augmentation. A python `range()` object is expected here. For example, a given `range(-150, 150, 30)` will offset a source image as many times as 30 pixels will fit into a range of 150 to the left and 150 to the right, meaning a total of 300 pixels. Since each step is 30 pixels, this range will produce 10 new images.\n",
    "- `offset_y`: same as `offset_x` but vertical.\n",
    "- `rotation`: rotational equivalent of `offset_x/y`. A given `range(0, 180, 2)` will rotate the image from 0 to 180 degrees in 2 degree steps, equating a total of 90 new images.\n",
    "- `zoom`: scaling equivalent of `offset_x/y`. A given `range(100, 300, 20)` will zoom into the image from 100% to 300% in 20% steps creating a total of 10 new images.\n",
    "- `letter_noise_n`: amount of artificially added letters for classes \"Corner\", \"Junction\" and \"Cross\" for network robustness.\n",
    "- `contrast_tresh`: `uint8_t` black and white pixel scaling at which point a pixel will be either shifted to 255 (black) or 0 (white).\n",
    "- `train_test_ratio`: ratio between the sizes of the train and test data splits, e.g. 0.2 (20%) means that 20% of the whole dataset are used for testing and 80% for training.\n",
    "- `only_train_test_split`: If this is set to `True`, the unsplit dataset will be temporary and removed after the splitting process is done. What will remain is only the train-test split and the source images.\n",
    "- `batches`: amount of batches for the all sets\n",
    "- `epochs`: number of training epochs (a low number is recommended, since the base model is already pretty competent (vgg16)).\n",
    "- `class_list`: python `list` with readable names of classes. In the dataset, their **intuitive** (not syntactical, e.g. first index is `1`, not `0`) indexes are used instead, e.g. `1` is `\"Resistor\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPICENET_PARAMS = dict({\n",
    "    \"src_path\": \"img_src\",\n",
    "    \"dataset_path\": \"dataset\",\n",
    "    \"resize\": 224,\n",
    "    \"offset_x\": range(-30, 30, 3),\n",
    "    \"offset_y\": range(-30, 30, 3),\n",
    "    \"rotation\": range(0, 360, 90),\n",
    "    \"zoom\": range(100, 110, 1),\n",
    "    \"letter_noise_n\": 1,\n",
    "    \"contrast_thresh\": 170,\n",
    "    \"train_test_ratio\": 0.2,\n",
    "    \"only_train_test_split\": True,\n",
    "    \"batches\": 20,\n",
    "    \"epochs\": 2,\n",
    "    \"class_list\": [\"Resistor\",\n",
    "                   \"Capacitor\",\n",
    "                   \"Inductor\",\n",
    "                   \"Diode\",\n",
    "                   \"Corner\",\n",
    "                   \"Junction\",\n",
    "                   \"Cross\",\n",
    "                   \"GND\"]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_from_original(reference_image_path: str, output_path: str):\n",
    "    reference_image = Image.open(reference_image_path)\n",
    "    i = 2\n",
    "    reference_image = add_letter_noise(reference_image, amount=SPICENET_PARAMS[\"letter_noise_n\"])\n",
    "    for offset_value_x in SPICENET_PARAMS[\"offset_x\"]:\n",
    "        for offset_value_y in SPICENET_PARAMS[\"offset_y\"]:\n",
    "            offset_img = ImageChops.offset(reference_image, offset_value_x, offset_value_y)\n",
    "            fn = lambda x : 255 if x > SPICENET_PARAMS[\"contrast_thresh\"] else 0\n",
    "            offset_img = offset_img.convert('L').point(fn, mode='1')\n",
    "            for rot_value in SPICENET_PARAMS[\"rotation\"]:\n",
    "                new_img = offset_img.rotate(-rot_value)\n",
    "                for zoom_value in SPICENET_PARAMS[\"zoom\"]:\n",
    "                    new_img = scale_image(new_img, (0.5, 0.5), zoom_value/100)\n",
    "                    new_img.save(output_path + str(i) + \".png\", \"PNG\")\n",
    "                    i += 1\n",
    "\n",
    "def add_letter_noise(img: Image, amount=1):\n",
    "    components = ['R', 'R1', 'R2', 'R3', 'C', 'C1', 'C2', 'C3', 'L', 'L1', 'L2', 'L3']\n",
    "    font_size = 30\n",
    "    font = ImageFont.truetype(\"arial.ttf\", font_size)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "\n",
    "    for _ in range(amount):\n",
    "        component = random.choice(components)\n",
    "        x = random.randint(0, img.width - font_size)\n",
    "        y = random.randint(0, img.height - font_size)\n",
    "        color = (0, 0, 0)\n",
    "        draw.text((x, y), component, font=font, fill=color)\n",
    "\n",
    "    return img\n",
    "\n",
    "def scale_image(img: Image, xy, scale_factor):\n",
    "    center = (img.size[0] * xy[0], img.size[1] * xy[1])\n",
    "    new_size = (img.size[0] / scale_factor, img.size[1] / scale_factor)\n",
    "    left = max (0, (int) (center[0] - new_size[0] / 2))\n",
    "    right = min (img.size[0], (int) (center[0] + new_size[0] / 2))\n",
    "    upper = max (0, (int) (center[1] - new_size[1] / 2))\n",
    "    lower = min (img.size[1], (int) (center[1] + new_size[1] / 2))\n",
    "    cropped_img = img.crop((left, upper, right, lower))\n",
    "    return cropped_img\n",
    "\n",
    "def train_valid_dataset(dataset_path, train_path, test_path):\n",
    "    symbols = os.listdir(dataset_path)\n",
    "    symbols.sort()\n",
    "    splits = train_test_split(symbols, test_size=SPICENET_PARAMS[\"train_test_ratio\"])\n",
    "    dest_paths = [train_path, test_path]\n",
    "\n",
    "    for split, dest_path in zip(splits, dest_paths):\n",
    "        src_data = [dataset_path+'/{}'.format(image) for image in split]\n",
    "        dest_data = [dest_path+'/{}'.format(image) for image in split]\n",
    "        for i in range(len(src_data)):\n",
    "            shutil.copy(src_data[i], dest_data[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb Cell 10\u001b[0m line \u001b[0;36m7\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     data_dir \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m./raw_dataset\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39maccess(data_dir, os\u001b[39m.\u001b[39mF_OK) \u001b[39m==\u001b[39m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     shutil\u001b[39m.\u001b[39;49mrmtree(data_dir) \u001b[39m# doesnt work\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m os\u001b[39m.\u001b[39mmkdir(data_dir)\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m parts \u001b[39m=\u001b[39m [p \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mlistdir(SPICENET_PARAMS[\u001b[39m\"\u001b[39m\u001b[39msrc_path\u001b[39m\u001b[39m\"\u001b[39m]) \u001b[39mif\u001b[39;00m p\u001b[39m.\u001b[39mendswith((\u001b[39m\"\u001b[39m\u001b[39m.png\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m.PNG\u001b[39m\u001b[39m\"\u001b[39m, \n\u001b[1;32m     <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m                                                                            \u001b[39m\"\u001b[39m\u001b[39m.jpg\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m.jpeg\u001b[39m\u001b[39m\"\u001b[39m, \n\u001b[1;32m     <a href='vscode-notebook-cell:/media/j/USBSSD/TUB/Repos/SPICEnet/SPICEnet.ipynb#X12sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m                                                                            \u001b[39m\"\u001b[39m\u001b[39m.JPG\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m.JPEG\u001b[39m\u001b[39m\"\u001b[39m))]\n",
      "File \u001b[0;32m/usr/lib/python3.8/shutil.py:718\u001b[0m, in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m    716\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    717\u001b[0m     \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39msamestat(orig_st, os\u001b[39m.\u001b[39mfstat(fd)):\n\u001b[0;32m--> 718\u001b[0m         _rmtree_safe_fd(fd, path, onerror)\n\u001b[1;32m    719\u001b[0m         \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    720\u001b[0m             os\u001b[39m.\u001b[39mrmdir(path)\n",
      "File \u001b[0;32m/usr/lib/python3.8/shutil.py:655\u001b[0m, in \u001b[0;36m_rmtree_safe_fd\u001b[0;34m(topfd, path, onerror)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    654\u001b[0m     \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39msamestat(orig_st, os\u001b[39m.\u001b[39mfstat(dirfd)):\n\u001b[0;32m--> 655\u001b[0m         _rmtree_safe_fd(dirfd, fullname, onerror)\n\u001b[1;32m    656\u001b[0m         \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    657\u001b[0m             os\u001b[39m.\u001b[39mrmdir(entry\u001b[39m.\u001b[39mname, dir_fd\u001b[39m=\u001b[39mtopfd)\n",
      "File \u001b[0;32m/usr/lib/python3.8/shutil.py:673\u001b[0m, in \u001b[0;36m_rmtree_safe_fd\u001b[0;34m(topfd, path, onerror)\u001b[0m\n\u001b[1;32m    671\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    672\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 673\u001b[0m         os\u001b[39m.\u001b[39;49munlink(entry\u001b[39m.\u001b[39;49mname, dir_fd\u001b[39m=\u001b[39;49mtopfd)\n\u001b[1;32m    674\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m:\n\u001b[1;32m    675\u001b[0m         onerror(os\u001b[39m.\u001b[39munlink, fullname, sys\u001b[39m.\u001b[39mexc_info())\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if SPICENET_PARAMS[\"only_train_test_split\"]:\n",
    "    data_dir = \"./temp\"\n",
    "else:\n",
    "    data_dir = \"./raw_dataset\"\n",
    "\n",
    "if os.access(data_dir, os.F_OK) == True:\n",
    "    shutil.rmtree(data_dir) # doesnt work\n",
    "\n",
    "os.mkdir(data_dir)\n",
    "parts = [p for p in os.listdir(SPICENET_PARAMS[\"src_path\"]) if p.endswith((\".png\", \".PNG\", \n",
    "                                                                           \".jpg\", \".jpeg\", \n",
    "                                                                           \".JPG\", \".JPEG\"))]\n",
    "for part in parts:\n",
    "    part_path = join(data_dir, part[0])\n",
    "    if not os.path.exists(part_path):\n",
    "        os.mkdir(part_path)\n",
    "    output_path = join(part_path, os.path.splitext(part)[0] + \"_\")\n",
    "    reference_image_path = join(SPICENET_PARAMS[\"src_path\"], part)\n",
    "    print(f\"Creating '{(SPICENET_PARAMS['class_list'])[int(part[0])-1]}' symbols...\")\n",
    "    create_dataset_from_original(reference_image_path, output_path)\n",
    "\n",
    "dataset_path = SPICENET_PARAMS[\"dataset_path\"]\n",
    "if os.access(dataset_path, os.F_OK) == True:\n",
    "    shutil.rmtree(dataset_path)\n",
    "\n",
    "os.mkdir(dataset_path)\n",
    "os.mkdir(join(dataset_path, \"train\"))\n",
    "os.mkdir(join(dataset_path, \"test\"))\n",
    "\n",
    "print(\"Creating dataset directory...\")\n",
    "for part in parts:\n",
    "    part_train_path = join(dataset_path, \"train\", part[0])\n",
    "    part_test_path = join(dataset_path, \"test\", part[0])\n",
    "    if not os.path.exists(part_train_path):\n",
    "        os.mkdir(part_train_path)\n",
    "    if not os.path.exists(part_test_path):\n",
    "        os.mkdir(part_test_path)\n",
    "    part_path = join(data_dir, part[0])\n",
    "    train_valid_dataset(dataset_path=part_path,\n",
    "                        train_path=part_train_path,\n",
    "                        test_path=part_test_path)\n",
    "\n",
    "shutil.copytree('./validation', dataset_path+'/valid')\n",
    "if SPICENET_PARAMS[\"only_train_test_split\"]:\n",
    "    shutil.rmtree(data_dir)\n",
    "print(\"Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 5000x5000 with 43 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parts = os.listdir(SPICENET_PARAMS[\"src_path\"])\n",
    "SPICENET_PARAMS[\"num_classes\"] = len(SPICENET_PARAMS[\"class_list\"])\n",
    "fig, axs = plt.subplots(nrows=1, ncols=len(parts), figsize=(50, 50))\n",
    "\n",
    "for i, part in enumerate(parts):\n",
    "    part_symbol = plt.imread(join(SPICENET_PARAMS[\"src_path\"], part))\n",
    "    axs[i].imshow(part_symbol)\n",
    "    axs[i].title.set_text(os.path.splitext(part)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5000x5000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = join(SPICENET_PARAMS[\"dataset_path\"], \"train\")\n",
    "parts = [p for p in os.listdir(path) if os.path.isdir(join(path, p))]\n",
    "fig, axs = plt.subplots(nrows=1, ncols=len(parts), figsize=(50, 50))\n",
    "\n",
    "for i, part in enumerate(parts):\n",
    "    part_path = join(path, part, os.listdir(join(path, part))[0])\n",
    "    part_img = plt.imread(part_path)\n",
    "    axs[i].imshow(part_img)\n",
    "    axs[i].title.set_text(part)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_generator = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_input,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 687749 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = data_generator.flow_from_directory(\n",
    "    join(SPICENET_PARAMS[\"dataset_path\"], \"train\"),\n",
    "    target_size=(SPICENET_PARAMS[\"resize\"], SPICENET_PARAMS[\"resize\"]),\n",
    "    batch_size=SPICENET_PARAMS[\"batches\"],\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 482948 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = data_generator.flow_from_directory(\n",
    "    join(SPICENET_PARAMS[\"dataset_path\"], \"test\"),\n",
    "    target_size=(SPICENET_PARAMS[\"resize\"], SPICENET_PARAMS[\"resize\"]),\n",
    "    batch_size=SPICENET_PARAMS[\"batches\"],\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spicenet = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spicenet.add(VGG16(\n",
    "    include_top=False,\n",
    "    pooling='avg',\n",
    "    weights='imagenet',\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " vgg16 (Functional)          (None, 512)               14714688  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 8)                 4104      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,718,792\n",
      "Trainable params: 4,104\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "spicenet.add(Dense(SPICENET_PARAMS[\"num_classes\"], activation='softmax'))\n",
    "spicenet.layers[0].trainable = False\n",
    "spicenet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spicenet.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_per_epoch_training = len(train_generator)\n",
    "steps_per_epoch_test = len(test_generator) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All background processes were killed.\n",
      "./logs/fit/20240122_223502\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-21d341030e54d875\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-21d341030e54d875\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%killbgscripts\n",
    "%reload_ext tensorboard\n",
    "log_dir = join(os.curdir, \"logs\", \"fit\", f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}\") \n",
    "print(log_dir)\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "%tensorboard --logdir ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "Model fitting"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-22 22:35:16.245071: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8600\n",
      "2024-01-22 22:35:16.409256: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:115] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "You may not need to update to CUDA 11.1; cherry-picking the ptxas binary is often sufficient.\n",
      "2024-01-22 22:35:17.867561: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x1e73e620 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-01-22 22:35:17.867604: I tensorflow/compiler/xla/service/service.cc:181]   StreamExecutor device (0): NVIDIA GeForce GTX 1070, Compute Capability 6.1\n",
      "2024-01-22 22:35:17.885124: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-01-22 22:35:18.032335: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:115] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "You may not need to update to CUDA 11.1; cherry-picking the ptxas binary is often sufficient.\n",
      "2024-01-22 22:35:18.127319: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "2024-01-22 22:35:18.224096: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:115] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "You may not need to update to CUDA 11.1; cherry-picking the ptxas binary is often sufficient.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34388/34388 [==============================] - ETA: 0s - loss: 0.0358 - accuracy: 0.9889"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-22 23:59:01.212420: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 70778880 exceeds 10% of free system memory.\n",
      "2024-01-22 23:59:01.326921: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 141557760 exceeds 10% of free system memory.\n",
      "2024-01-22 23:59:01.434397: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 141557760 exceeds 10% of free system memory.\n",
      "2024-01-22 23:59:01.549167: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 283115520 exceeds 10% of free system memory.\n",
      "2024-01-22 23:59:02.059601: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 566231040 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34388/34388 [==============================] - 5029s 146ms/step - loss: 0.0358 - accuracy: 0.9889 - val_loss: 0.0144 - val_accuracy: 0.9949\n",
      "Epoch 2/2\n",
      "34388/34388 [==============================] - 5087s 148ms/step - loss: 0.0138 - accuracy: 0.9951 - val_loss: 0.0137 - val_accuracy: 0.9951\n"
     ]
    }
   ],
   "source": [
    "fit_history = spicenet.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch_training,\n",
    "    epochs=SPICENET_PARAMS[\"epochs\"],\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=steps_per_epoch_test,\n",
    "    validation_batch_size=SPICENET_PARAMS[\"batches\"],\n",
    "    verbose=1,\n",
    "    callbacks=[tensorboard_callback]\n",
    ")\n",
    "spicenet.save(f'SPICEnet.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[dev fd3db90] autopush SPICEnet: 01/23/2024, 01:25:00\n",
      " 1 file changed, 0 insertions(+), 0 deletions(-)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "remote: warning: See https://gh.io/lfs for more information.        \n",
      "remote: warning: File SPICEnet.h5 is 56.24 MB; this is larger than GitHub's recommended maximum file size of 50.00 MB        \n",
      "remote: warning: GH001: Large files detected. You may want to try Git Large File Storage - https://git-lfs.github.com.        \n",
      "To github.com:jake-is-ESD-protected/SPICEnet.git\n",
      "   2b89c39..fd3db90  dev -> dev\n"
     ]
    }
   ],
   "source": [
    "def autopush_model(commit_msg):\n",
    "    os.system('git add SPICEnet.h5')\n",
    "    os.system(f'git commit -m \"autopush SPICEnet: {commit_msg}\"')\n",
    "    os.system(\"git push origin\")\n",
    "\n",
    "autopush_model(datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8 images belonging to 8 classes.\n",
      "Found 43 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "data_generator = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_input,\n",
    ")\n",
    "\n",
    "evaluate_generator_basic = data_generator.flow_from_directory(\n",
    "    join(SPICENET_PARAMS[\"dataset_path\"], \"valid\"),\n",
    "    target_size=(SPICENET_PARAMS[\"resize\"], SPICENET_PARAMS[\"resize\"]),\n",
    "    shuffle=False)\n",
    "\n",
    "temp_path = \"temp\"\n",
    "fn = lambda x : 255 if x > SPICENET_PARAMS[\"contrast_thresh\"] else 0\n",
    "os.makedirs(temp_path)\n",
    "for i in range(len(SPICENET_PARAMS[\"class_list\"])):\n",
    "    cur_src_path = SPICENET_PARAMS[\"src_path\"]\n",
    "    cur_dest_path = join(temp_path, str(i+1))\n",
    "    files = [f for f in os.listdir(SPICENET_PARAMS[\"src_path\"]) if f.startswith(str(i+1))]\n",
    "    os.makedirs(join(temp_path, str(i+1)))\n",
    "    for f in files:\n",
    "        img = Image.open(join(cur_src_path, f))\n",
    "        img = img.convert('L').point(fn, mode='1')\n",
    "        img.save(join(cur_dest_path, f), \"PNG\")\n",
    "    \n",
    "evaluate_generator_source = data_generator.flow_from_directory( # this can be done because the source images are not part of the training\n",
    "    temp_path,\n",
    "    target_size=(SPICENET_PARAMS[\"resize\"], SPICENET_PARAMS[\"resize\"]),\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x7f798d7ea550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 366ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "SPICEnet Accuracy on basic eval: 1.0\n"
     ]
    }
   ],
   "source": [
    "spicenet = load_model(\"SPICEnet.h5\")\n",
    "spicenet_evaluate_history = spicenet.evaluate(\n",
    "    evaluate_generator_basic,\n",
    "    verbose=1,\n",
    ")\n",
    "print(\"SPICEnet Accuracy on basic eval:\", spicenet_evaluate_history[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 59ms/step - loss: 0.1367 - accuracy: 0.9767\n",
      "SPICEnet Accuracy on source eval: 0.9767441749572754\n"
     ]
    }
   ],
   "source": [
    "spicenet_evaluate_history = spicenet.evaluate(\n",
    "    evaluate_generator_source,\n",
    "    verbose=1,\n",
    ")\n",
    "print(\"SPICEnet Accuracy on source eval:\", spicenet_evaluate_history[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f798d7f5430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 224ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spicenet_predict = spicenet.predict(\n",
    "    evaluate_generator_basic\n",
    ")\n",
    "\n",
    "result_index = [SPICENET_PARAMS[\"class_list\"][s] for s in np.argmax(spicenet_predict,axis=1)]\n",
    "\n",
    "batch = evaluate_generator_basic.next()\n",
    "batch_images = evaluate_generator_basic.next()[0]\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=len(SPICENET_PARAMS[\"class_list\"]), figsize=(40, 5))\n",
    "ind = 0\n",
    "for ax1 in axs:\n",
    "    image_data = batch_images[ind]\n",
    "    ax1.imshow(image_data, vmin=0, vmax=255)\n",
    "    ax1.title.set_text(result_index[ind])\n",
    "    ind += 1\n",
    "\n",
    "fig.suptitle('SPICEnet Accuracy basic') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.rmtree(temp_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}