refactoring
This commit is contained in:
frankknoll
717
src/captcha.ipynb
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717
src/captcha.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "UNKC5YSEIS_d"
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},
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"source": [
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"# Captchas\n",
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"\n",
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"**see:** https://keras.io/examples/vision/captcha_ocr/<br>\n",
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"**original:** https://colab.research.google.com/drive/1Olw2KMHfPlnGaYuzffl2zb6D1etlBGZf?usp=sharing<br>\n",
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"**View Github version in Colab:** <a href=\"https://colab.research.google.com/github/KnollFrank/2captcha-worker-assistant-server/blob/master/captcha_ocr_trainAndSaveModel_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a><br>\n",
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"**paper:** Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wRUsVuIiIS_s"
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},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "zZSwQragIS_v"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "QB8QZJPg3MGI"
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},
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"outputs": [],
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"source": [
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"from captcha.GoogleDriveManager import GoogleDriveManager"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "C3bxU1US2blM"
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},
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"outputs": [],
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"source": [
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"from captcha.CaptchaGenerator import CaptchaGenerator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "0DZfMrbe3MGN"
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},
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"outputs": [],
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"source": [
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"def getImagesAndLabels(dataDir):\n",
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" fileSuffix = \".jpeg\"\n",
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" images = sorted(list(map(str, list(dataDir.glob(\"*\" + fileSuffix)))))\n",
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" labels = [image.split(os.path.sep)[-1].split(fileSuffix)[0] for image in images]\n",
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" return images, labels\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "sNJjugG83MGO"
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},
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"outputs": [],
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"source": [
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"from captcha.CharNumConverter import CharNumConverter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "qxs04OTR3MGP"
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},
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"outputs": [],
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"source": [
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"class DataSplitter:\n",
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"\n",
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" def __init__(self, x, y):\n",
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" (self.x_train, self.y_train), (x_valid_test, y_valid_test) = DataSplitter._splitData(np.array(x), np.array(y), train_size=0.7)\n",
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" (self.x_valid, self.y_valid), (self.x_test, self.y_test) = DataSplitter._splitData(x_valid_test, y_valid_test, train_size=0.5)\n",
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"\n",
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" def getTrain(self):\n",
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" return (self.x_train, self.y_train)\n",
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"\n",
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" def getValid(self):\n",
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" return (self.x_valid, self.y_valid)\n",
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"\n",
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" def getTest(self):\n",
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" return (self.x_test, self.y_test)\n",
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"\n",
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" @staticmethod\n",
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" def _splitData(x, y, train_size=0.9, shuffle=True):\n",
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" size = len(x)\n",
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" indices = np.arange(size)\n",
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" if shuffle:\n",
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" np.random.shuffle(indices)\n",
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" train_samples = int(size * train_size)\n",
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" x_train, y_train = x[indices[:train_samples]], y[indices[:train_samples]]\n",
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" x_test, y_test = x[indices[train_samples:]], y[indices[train_samples:]]\n",
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" return (x_train, y_train), (x_test, y_test)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "dAAACymS3MGR"
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},
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"outputs": [],
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"source": [
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"from captcha.DatasetFactory import DatasetFactory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "kdL9_t03Mf3t"
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},
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"outputs": [],
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"source": [
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"def getTrainValidationTestDatasets(dataDir, datasetFactory):\n",
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" images, labels = getImagesAndLabels(dataDir)\n",
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" print(\"Number of images found:\", len(images))\n",
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" print(\"Characters:\", CaptchaGenerator.characters)\n",
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"\n",
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" dataSplitter = DataSplitter(images, labels)\n",
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" \n",
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" return (\n",
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" datasetFactory.createDataset(*dataSplitter.getTrain()),\n",
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" datasetFactory.createDataset(*dataSplitter.getValid()),\n",
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" datasetFactory.createDataset(*dataSplitter.getTest())\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "FqVSEuZp3MGT"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import math\n",
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"\n",
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"def displayImagesInGrid(numGridCols, images, titles, titleColors):\n",
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" assert len(images) == len(titles) == len(titleColors)\n",
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" images = [image.numpy().astype(np.uint8) for image in images]\n",
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" numGridRows = math.ceil(len(images) / numGridCols)\n",
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" _, axs = plt.subplots(numGridRows, numGridCols, figsize=(15, 5))\n",
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" for row in range(numGridRows):\n",
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" for col in range(numGridCols):\n",
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" ax = axs[row, col]\n",
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" ax.axis(\"off\")\n",
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" i = row * numGridCols + col\n",
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" if(i < len(images)):\n",
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" ax.imshow(images[i])\n",
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" ax.set_title(titles[i], color=titleColors[i])\n",
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" plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "apkeCHhP3MGU"
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},
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"outputs": [],
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"source": [
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"def display16Predictions(model, dataset, predictionsDecoder):\n",
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" for batch in dataset.take(1):\n",
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" numPredictions2Display = 16\n",
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" batch_images = batch[\"image\"][:numPredictions2Display]\n",
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" batch_labels = batch[\"label\"][:numPredictions2Display]\n",
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"\n",
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" preds = model.predict(batch_images)\n",
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" pred_texts = predictionsDecoder.decode_batch_predictions(preds)\n",
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" orig_texts = predictionsDecoder.asStrings(batch_labels)\n",
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"\n",
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" displayImagesInGrid(\n",
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" 4,\n",
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" batch_images,\n",
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" [f\"Prediction/Truth: {pred_text}/{orig_text}\" for (pred_text, orig_text) in zip(pred_texts, orig_texts)],\n",
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" ['green' if pred_text == orig_text else 'red' for (pred_text, orig_text) in zip(pred_texts, orig_texts)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "st13jAjL3MGV"
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},
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"outputs": [],
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"source": [
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"from captcha.ModelFactory import ModelFactory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def printLayers(model):\n",
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" for i, layer in enumerate(model.layers):\n",
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" print(i, layer.name)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "B7GZlk2_3MGX"
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},
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"outputs": [],
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"source": [
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"from captcha.PredictionsDecoder import PredictionsDecoder"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8Oa7avYt3MGX"
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},
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"outputs": [],
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"source": [
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"from captcha.ModelDAO import ModelDAO"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "S3X_SslH3MGY"
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},
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"outputs": [],
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"source": [
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"# FK-TODO: entferne die getAccuracy()-Methode. Implementiere stattdessen https://stackoverflow.com/questions/37657260/how-to-implement-custom-metric-in-keras oder https://keras.io/api/metrics/#custom-metrics\n",
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"def getAccuracy(dataset, prediction_model, ctc_decode):\n",
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" accuracy = tf.keras.metrics.Accuracy()\n",
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"\n",
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" for batch in dataset:\n",
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" accuracy.update_state(batch[\"label\"], ctc_decode(prediction_model.predict(batch[\"image\"], verbose=0)))\n",
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"\n",
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" return accuracy.result().numpy()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "94755hrNMf3w"
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},
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"source": [
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"## Preparation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "NZrKXF6P3MGY"
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},
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"outputs": [],
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"source": [
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"inColab = 'google.colab' in str(get_ipython())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "7EsmTaF03MGZ"
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},
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"outputs": [],
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"source": [
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"if inColab:\n",
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" GoogleDriveManager.mount()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "S_4hl4S4BmZK"
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},
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"outputs": [],
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"source": [
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"if inColab:\n",
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" !cp {GoogleDriveManager._baseFolder}/captchas.zip .\n",
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" !unzip captchas.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "WmUghcQaMf3y"
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},
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"outputs": [],
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"source": [
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"modelDAO = ModelDAO(inColab)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "cpxO7yGAMf3z"
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},
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"outputs": [],
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"source": [
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"charNumConverter = CharNumConverter(CaptchaGenerator.characters)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "tVb5nDFTMf3z"
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},
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"outputs": [],
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"source": [
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"predictionsDecoder = PredictionsDecoder(CaptchaGenerator.captchaLength, charNumConverter.num_to_char)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from captcha.CaptchaShape import CaptchaShape\n",
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"captchaShape = CaptchaShape()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "s35OUslsMf30"
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},
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"outputs": [],
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"source": [
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"datasetFactory = DatasetFactory(captchaShape, charNumConverter.char_to_num, batch_size = 64)"
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]
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||||
},
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{
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||||
"cell_type": "markdown",
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||||
"metadata": {
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||||
"id": "lsLuSi7h3MGZ"
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||||
},
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"source": [
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"## Create And Train Base Model"
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]
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||||
},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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||||
"metadata": {
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||||
"id": "oRcemcbG3MGa"
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||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"if inColab:\n",
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" !sudo apt install ttf-mscorefonts-installer\n",
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||||
" !sudo fc-cache -f\n",
|
||||
" !fc-match Arial"
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||||
]
|
||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "P7myCt7e2h6A"
|
||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"# \"We generate 200,000 images for base model pre-training\"\n",
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||||
"captchaGenerator = CaptchaGenerator(\n",
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||||
" numCaptchas = 50, # 50, # 200000,\n",
|
||||
" dataDir = Path(\"captchas/generated/VAERS/\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "j9apYsyI3MGb"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"captchaGenerator.createAndSaveCaptchas()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "AgN4skCkMf31"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_dataset, validation_dataset, test_dataset = getTrainValidationTestDatasets(captchaGenerator.dataDir, datasetFactory)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "RcgWHXVSNsa7"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for batch in train_dataset.take(1):\n",
|
||||
" numImages2Display = 16\n",
|
||||
" images = batch[\"image\"][:numImages2Display]\n",
|
||||
" labels = batch[\"label\"][:numImages2Display]\n",
|
||||
" displayImagesInGrid(4, images, predictionsDecoder.asStrings(labels), ['black'] * len(labels))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "V8ELN-qJ3MGe"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"modelFactory = ModelFactory(captchaShape, charNumConverter.char_to_num)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "zDoFYKM2hdEW"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = modelFactory.createMobileNetV3Small()\n",
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "ltXYrpjIITAb"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# \"the success rates became stable after the base-model training epochs exceeded 20\"\n",
|
||||
"history = model.fit(\n",
|
||||
" train_dataset,\n",
|
||||
" validation_data=validation_dataset,\n",
|
||||
" epochs=20)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "fPG-Yl1SJfF7"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"modelDAO.saveModel(model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "NnNHMtIGITAe"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction_model = ModelFactory.createPredictionModel(model)\n",
|
||||
"prediction_model.summary()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "YW651ztD8sKI"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"display16Predictions(prediction_model, test_dataset, predictionsDecoder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "V5gqMBIwBmZU"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"getAccuracy(test_dataset, prediction_model, predictionsDecoder.ctc_decode)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "UYxiYTH9BmZU"
|
||||
},
|
||||
"source": [
|
||||
"## Transfer learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "WV8IS4KrBmZU"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# \"we collected 1,500 real CAPTCHAs from the websites. Note that only 500 of them are used for fine-tuning, and another 1,000 are applied to calculate the test accuracy\"\n",
|
||||
"# FK-TODO: lade das pre-trainierte model und trainiere es mit 500 real-world-Daten aus dem Ordner captchas/VAERS/, die restlichen 540 (es sollten nach obigem Zitat aber 1,000 sein) sind dann die Test-Daten.\n",
|
||||
"# see https://keras.io/guides/transfer_learning/\n",
|
||||
"# see https://www.tensorflow.org/tutorials/images/transfer_learning\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"modelName, numTrainableLayers = 'MobileNetV3Small', 104\n",
|
||||
"# modelName, numTrainableLayers = 'ResNet101', 348"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "D7ogEQmB3MGj"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# FK-TODO: DRY with VAERSFileDownloader\n",
|
||||
"modelFilepath = f'{os.getcwd()}/captcha/{modelName}'\n",
|
||||
"model = modelDAO.loadModel(modelFilepath)\n",
|
||||
"model.summary(show_trainable=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "gbPigogKNFrD"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# printLayers(model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "59quw8o3Mf34"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.trainable = True\n",
|
||||
"for layer in model.layers[:numTrainableLayers]:\n",
|
||||
" layer.trainable = False"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "acGczax3Mf34"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.summary(show_trainable=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "q7_MjUO0BmZV"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_dataset, validation_dataset, test_dataset = getTrainValidationTestDatasets(Path(\"captcha/captchas/VAERS/\"), datasetFactory)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "dZsCpibkBmZX"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# \"The model is optimized by a stochastic gradient descent (SGD) strategy with an initial learning rate of 0.004, weight decay of 0.00004 and momentum of 0.9.\"\n",
|
||||
"from tensorflow.keras.optimizers import SGD\n",
|
||||
"# model.compile(optimizer=SGD(learning_rate=0.0001, momentum=0.9))\n",
|
||||
"model.compile(optimizer='adam')\n",
|
||||
"\n",
|
||||
"# \"Therefore, in our experiments, we chose 1 epoch for the fine-tuning stage.\"\n",
|
||||
"history = model.fit(\n",
|
||||
" train_dataset,\n",
|
||||
" validation_data=validation_dataset,\n",
|
||||
" epochs=20)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "TRbJigbH3MGl"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction_model = ModelFactory.createPredictionModel(model)\n",
|
||||
"prediction_model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "rPszfhJ4BmZX"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"getAccuracy(test_dataset, prediction_model, predictionsDecoder.ctc_decode)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"id": "hfmRY1qC7aVV"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"display16Predictions(prediction_model, test_dataset, predictionsDecoder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"modelDAO.saveModel(model)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"name": "captcha.ipynb",
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "howbadismybatch-venv-kernel",
|
||||
"language": "python",
|
||||
"name": "howbadismybatch-venv-kernel"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.15"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in New Issue
Block a user