diff --git a/.gitignore b/.gitignore index 0f0e3031340..836dc6ca842 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,4 @@ src/HowBadIsMyBatch.nbconvert.html src/__pycache__/ src/intensivstationen/__pycache__/ google-chrome-stable_current_amd64* +src/captcha/__pycache__ \ No newline at end of file diff --git a/environment.yml b/environment.yml index d3de996c1fa..59c3150f39c 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,7 @@ channels: - defaults # - conda-forge dependencies: - - python=3 + - python=3.9 - ipykernel - numpy - pandas @@ -12,7 +12,7 @@ dependencies: - bs4 - lxml - jupyter - - tensorflow + - tensorflow=2.11 - nb_conda_kernels - pillow - openpyxl diff --git a/src/CaptchaReader.py b/src/CaptchaReader.py deleted file mode 100644 index fe3df56eb7d..00000000000 --- a/src/CaptchaReader.py +++ /dev/null @@ -1,74 +0,0 @@ -import numpy as np -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers -from PIL import Image -import numpy as np -import io - -# copied from value of characters variable in captcha_ocr.ipynb or captcha_ocr_trainAndSaveModel.ipynb -characters = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'a', 'b', 'c', 'd', 'e', 'f'] - -img_width = 241 -img_height = 62 - -downsample_factor = 4 - -# copied from value of max_length variable in captcha_ocr.ipynb or captcha_ocr_trainAndSaveModel.ipynb -max_length = 6 - -char_to_num = layers.StringLookup( - vocabulary=list(characters), - mask_token=None) - -num_to_char = layers.StringLookup( - vocabulary=char_to_num.get_vocabulary(), - mask_token=None, invert=True) - -def encode_single_sample(img_path): - # 1. Read image - img = tf.io.read_file(img_path) - # 2. Decode and convert to grayscale - img = tf.io.decode_png(img, channels=1) - # 3. Convert to float32 in [0, 1] range - img = tf.image.convert_image_dtype(img, tf.float32) - # 4. Resize to the desired size - img = tf.image.resize(img, [img_height, img_width]) - # 5. Transpose the image because we want the time - # dimension to correspond to the width of the image. - img = tf.transpose(img, perm=[1, 0, 2]) - # 7. Return a dict as our model is expecting two inputs - return asSingleSampleBatch(img) - -def asSingleSampleBatch(img): - array = keras.utils.img_to_array(img) - array = np.expand_dims(array, axis=0) - return array - -def decode_batch_predictions(pred): - input_len = np.ones(pred.shape[0]) * pred.shape[1] - # Use greedy search. For complex tasks, you can use beam search - results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][:, :max_length] - # Iterate over the results and get back the text - output_text = [] - for res in results: - res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8") - output_text.append(res) - return output_text - -def _getModel(): - print("loading model...") - model = load_model() - model.summary() - return model - -def load_model(): - model = keras.models.load_model('model') - return keras.models.Model( - model.get_layer(name="image").input, - model.get_layer(name="dense2").output) - -def getTextInCaptchaImage(captchaImageFile): - batchImages = encode_single_sample(captchaImageFile) - preds = _getModel().predict(batchImages) - return decode_batch_predictions(preds)[0] diff --git a/src/HowBadIsMyBatch.ipynb b/src/HowBadIsMyBatch.ipynb index a1ef9093c6d..d3d29ca3be4 100644 --- a/src/HowBadIsMyBatch.ipynb +++ b/src/HowBadIsMyBatch.ipynb @@ -329,7 +329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.15" }, "vscode": { "interpreter": { diff --git a/src/VAERSFileDownloader.py b/src/VAERSFileDownloader.py index cd6cf4b3717..7d59a83996b 100644 --- a/src/VAERSFileDownloader.py +++ b/src/VAERSFileDownloader.py @@ -2,8 +2,9 @@ import os import time from WebDriver import getWebDriver, isCaptchaSolved, saveCaptchaImageAs from selenium.webdriver.common.by import By -from CaptchaReader import getTextInCaptchaImage +from captcha.CaptchaReader import CaptchaReader from zipUtils import unzipAndRemove +from captcha.CaptchaShape import CaptchaShape #def getTextInCaptchaImage(captchaImageFile): @@ -15,11 +16,16 @@ from zipUtils import unzipAndRemove def solveCaptchaAndStartFileDownload(driver, captchaImageFile): saveCaptchaImageAs(driver, captchaImageFile) - textInCaptchaImage = getTextInCaptchaImage(captchaImageFile) + textInCaptchaImage = _createCaptchaReader().getTextInCaptchaImage(captchaImageFile) print('textInCaptchaImage:', textInCaptchaImage) driver.find_element(By.ID, "verificationCode").send_keys(textInCaptchaImage) driver.find_element(By.CSS_SELECTOR, '[name="downloadbut"]').click() +def _createCaptchaReader(): + working_directory = os.path.dirname(__file__) + return CaptchaReader(modelFilepath = f'{working_directory}/captcha/MobileNetV3Small', + captchaShape = CaptchaShape()) + def downloadFile(absoluteFile, driver, maxTries): def _downloadFile(): driver.get('https://vaers.hhs.gov/eSubDownload/index.jsp?fn=' + os.path.basename(absoluteFile)) @@ -42,7 +48,7 @@ def _waitUntilDownloadHasFinished(file): time.sleep(2) def downloadVAERSFile(file, downloadDir): - driver = getWebDriver(downloadDir, isHeadless = True) + driver = getWebDriver(downloadDir, isHeadless = False) downloadedFile = downloadFile( absoluteFile = downloadDir + "/" + file, driver = driver, diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/src/captcha.ipynb b/src/captcha.ipynb new file mode 100644 index 00000000000..3fffbb00435 --- /dev/null +++ b/src/captcha.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "UNKC5YSEIS_d" + }, + "source": [ + "# Captchas\n", + "\n", + "**see:** https://keras.io/examples/vision/captcha_ocr/
\n", + "**original:** https://colab.research.google.com/drive/1Olw2KMHfPlnGaYuzffl2zb6D1etlBGZf?usp=sharing
\n", + "**View Github version in Colab:** \"Open
\n", + "**paper:** Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wRUsVuIiIS_s" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zZSwQragIS_v" + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import tensorflow as tf\n", + "from captcha.GoogleDriveManager import GoogleDriveManager\n", + "from captcha.CaptchaGenerator import CaptchaGenerator\n", + "from captcha.CharNumConverter import CharNumConverter\n", + "from captcha.DataSplitter import DataSplitter\n", + "from captcha.DatasetFactory import DatasetFactory\n", + "from captcha.ModelFactory import ModelFactory\n", + "from captcha.PredictionsDecoder import PredictionsDecoder\n", + "from captcha.ModelDAO import ModelDAO\n", + "from captcha.CaptchaShape import CaptchaShape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0DZfMrbe3MGN" + }, + "outputs": [], + "source": [ + "def getImagesAndLabels(dataDir):\n", + " fileSuffix = \".jpeg\"\n", + " images = sorted(list(map(str, list(dataDir.glob(\"*\" + fileSuffix)))))\n", + " labels = [image.split(os.path.sep)[-1].split(fileSuffix)[0] for image in images]\n", + " return images, labels\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kdL9_t03Mf3t" + }, + "outputs": [], + "source": [ + "def getTrainValidationTestDatasets(dataDir, datasetFactory):\n", + " images, labels = getImagesAndLabels(dataDir)\n", + " print(\"Number of images found:\", len(images))\n", + " print(\"Characters:\", CaptchaGenerator.characters)\n", + "\n", + " dataSplitter = DataSplitter(images, labels)\n", + " \n", + " return (\n", + " datasetFactory.createDataset(*dataSplitter.getTrain()),\n", + " datasetFactory.createDataset(*dataSplitter.getValid()),\n", + " datasetFactory.createDataset(*dataSplitter.getTest())\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqVSEuZp3MGT" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + "\n", + "def displayImagesInGrid(numGridCols, images, titles, titleColors):\n", + " assert len(images) == len(titles) == len(titleColors)\n", + " images = [image.numpy().astype(np.uint8) for image in images]\n", + " numGridRows = math.ceil(len(images) / numGridCols)\n", + " _, axs = plt.subplots(numGridRows, numGridCols, figsize=(15, 5))\n", + " for row in range(numGridRows):\n", + " for col in range(numGridCols):\n", + " ax = axs[row, col]\n", + " ax.axis(\"off\")\n", + " i = row * numGridCols + col\n", + " if(i < len(images)):\n", + " ax.imshow(images[i])\n", + " ax.set_title(titles[i], color=titleColors[i])\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "apkeCHhP3MGU" + }, + "outputs": [], + "source": [ + "def display16Predictions(model, dataset, predictionsDecoder):\n", + " for batch in dataset.take(1):\n", + " numPredictions2Display = 16\n", + " batch_images = batch[\"image\"][:numPredictions2Display]\n", + " batch_labels = batch[\"label\"][:numPredictions2Display]\n", + "\n", + " preds = model.predict(batch_images)\n", + " pred_texts = predictionsDecoder.decode_batch_predictions(preds)\n", + " orig_texts = predictionsDecoder.asStrings(batch_labels)\n", + "\n", + " displayImagesInGrid(\n", + " 4,\n", + " batch_images,\n", + " [f\"Prediction/Truth: {pred_text}/{orig_text}\" for (pred_text, orig_text) in zip(pred_texts, orig_texts)],\n", + " ['green' if pred_text == orig_text else 'red' for (pred_text, orig_text) in zip(pred_texts, orig_texts)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def printLayers(model):\n", + " for i, layer in enumerate(model.layers):\n", + " print(i, layer.name)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S3X_SslH3MGY" + }, + "outputs": [], + "source": [ + "# 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", + "def getAccuracy(dataset, prediction_model, ctc_decode):\n", + " accuracy = tf.keras.metrics.Accuracy()\n", + "\n", + " for batch in dataset:\n", + " accuracy.update_state(batch[\"label\"], ctc_decode(prediction_model.predict(batch[\"image\"], verbose=0)))\n", + "\n", + " return accuracy.result().numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "94755hrNMf3w" + }, + "source": [ + "## Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NZrKXF6P3MGY" + }, + "outputs": [], + "source": [ + "inColab = 'google.colab' in str(get_ipython())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7EsmTaF03MGZ" + }, + "outputs": [], + "source": [ + "if inColab:\n", + " GoogleDriveManager.mount()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S_4hl4S4BmZK" + }, + "outputs": [], + "source": [ + "if inColab:\n", + " !cp {GoogleDriveManager._baseFolder}/captchas.zip .\n", + " !unzip captchas.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WmUghcQaMf3y" + }, + "outputs": [], + "source": [ + "modelDAO = ModelDAO(inColab)\n", + "charNumConverter = CharNumConverter(CaptchaGenerator.characters)\n", + "predictionsDecoder = PredictionsDecoder(CaptchaGenerator.captchaLength, charNumConverter.num_to_char)\n", + "captchaShape = CaptchaShape()\n", + "datasetFactory = DatasetFactory(captchaShape, charNumConverter.char_to_num, batch_size = 64)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lsLuSi7h3MGZ" + }, + "source": [ + "## Create And Train Base Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oRcemcbG3MGa" + }, + "outputs": [], + "source": [ + "if inColab:\n", + " !sudo apt install ttf-mscorefonts-installer\n", + " !sudo fc-cache -f\n", + " !fc-match Arial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P7myCt7e2h6A" + }, + "outputs": [], + "source": [ + "# \"We generate 200,000 images for base model pre-training\"\n", + "captchaGenerator = CaptchaGenerator(\n", + " 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": "zDoFYKM2hdEW" + }, + "outputs": [], + "source": [ + "modelFactory = ModelFactory(captchaShape, charNumConverter.char_to_num)\n", + "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 +} diff --git a/src/captcha/CTCLayer.py b/src/captcha/CTCLayer.py new file mode 100644 index 00000000000..09c80461277 --- /dev/null +++ b/src/captcha/CTCLayer.py @@ -0,0 +1,27 @@ +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + + +# see https://keras.io/guides/making_new_layers_and_models_via_subclassing/ +class CTCLayer(layers.Layer): + + def __init__(self, name=None): + super().__init__(name=name) + self.loss_fn = keras.backend.ctc_batch_cost + + def call(self, y_true, y_pred): + # Compute the training-time loss value and add it + # to the layer using `self.add_loss()`. + batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64") + input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64") + label_length = tf.cast(tf.shape(y_true)[1], dtype="int64") + + input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64") + label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64") + + loss = self.loss_fn(y_true, y_pred, input_length, label_length) + self.add_loss(loss) + + # At test time, just return the computed predictions + return y_pred diff --git a/src/captcha/CaptchaGenerator.py b/src/captcha/CaptchaGenerator.py new file mode 100644 index 00000000000..affc3f6ebcf --- /dev/null +++ b/src/captcha/CaptchaGenerator.py @@ -0,0 +1,43 @@ +from PIL import Image, ImageDraw, ImageFont +import random +import string +import shutil + + +class CaptchaGenerator: + + characters = sorted(set(list(string.ascii_letters + string.digits))) + captchaLength = 6 + + def __init__(self, numCaptchas, dataDir): + self.numCaptchas = numCaptchas + self.dataDir = dataDir + + def createAndSaveCaptchas(self): + self._prepareDataDir() + for _ in range(self.numCaptchas): + self._createAndSaveCaptcha() + + def _prepareDataDir(self): + shutil.rmtree(self.dataDir, ignore_errors = True) + self.dataDir.mkdir(parents=True, exist_ok=True) + + def _createAndSaveCaptcha(self): + captchaString = self._createCaptchaString() + captcha = self._createCaptcha(captchaString) + captcha.save(f"{str(self.dataDir)}/{captchaString}.jpeg") + + def _createCaptchaString(self): + return ''.join(random.choice(CaptchaGenerator.characters) for _ in range(CaptchaGenerator.captchaLength)) + + def _createCaptcha(self, word): + image = Image.new("RGB", (360, 96), "#373737") + draw = ImageDraw.Draw(image) + font = ImageFont.truetype("ariali.ttf", size=40) + draw.text((30, 10), word[0], font=font) + draw.text((80, 30), word[1], font=font) + draw.text((135, 10), word[2], font=font) + draw.text((190, 30), word[3], font=font) + draw.text((250, 10), word[4], font=font) + draw.text((295, 30), word[5], font=font) + return image diff --git a/src/captcha/CaptchaReader.py b/src/captcha/CaptchaReader.py new file mode 100644 index 00000000000..460083f7f87 --- /dev/null +++ b/src/captcha/CaptchaReader.py @@ -0,0 +1,31 @@ +from captcha.ModelDAO import ModelDAO +from captcha.ModelFactory import ModelFactory +from captcha.PredictionsDecoder import PredictionsDecoder +from captcha.CaptchaGenerator import CaptchaGenerator +from captcha.CharNumConverter import CharNumConverter +from captcha.DatasetFactory import DatasetFactory +import numpy as np +from tensorflow import keras + + +class CaptchaReader: + + def __init__(self, modelFilepath, captchaShape): + self.modelFilepath = modelFilepath + self.captchaShape = captchaShape + + def getTextInCaptchaImage(self, captchaImageFile): + return self._getTextsInCaptchaImage(self._getCaptchaImage(captchaImageFile))[0] + + def _getCaptchaImage(self, captchaImageFile): + return self._asSingleSampleBatch(DatasetFactory.encodeImage(captchaImageFile, self.captchaShape)) + + def _asSingleSampleBatch(self, img): + return np.expand_dims(keras.utils.img_to_array(img), axis=0) + + def _getTextsInCaptchaImage(self, captchaImage): + preds = self._createPredictionModel().predict(captchaImage) + return PredictionsDecoder(CaptchaGenerator.captchaLength, CharNumConverter(CaptchaGenerator.characters).num_to_char).decode_batch_predictions(preds) + + def _createPredictionModel(self): + return ModelFactory.createPredictionModel(ModelDAO(inColab=False).loadModel(self.modelFilepath)) diff --git a/src/captcha/CaptchaReaderTest.py b/src/captcha/CaptchaReaderTest.py new file mode 100644 index 00000000000..3a9112f5b47 --- /dev/null +++ b/src/captcha/CaptchaReaderTest.py @@ -0,0 +1,22 @@ +import unittest +from captcha.CaptchaReader import CaptchaReader +from captcha.CaptchaShape import CaptchaShape +import os + +class CaptchaReaderTest(unittest.TestCase): + + def setUp(self): + self.working_directory = os.path.dirname(__file__) + + def test_getTextInCaptchaImage(self): + # Given + textInCaptchaImage = '1Ad47a' + captchaReader = CaptchaReader( + modelFilepath = f'{self.working_directory}/MobileNetV3Small', + captchaShape = CaptchaShape()) + + # When + textInCaptchaImageActual = captchaReader.getTextInCaptchaImage(f'{self.working_directory}/captchas/VAERS/{textInCaptchaImage}.jpeg') + + # Then + self.assertEqual(textInCaptchaImageActual, textInCaptchaImage) diff --git a/src/captcha/CaptchaShape.py b/src/captcha/CaptchaShape.py new file mode 100644 index 00000000000..ff46a10f315 --- /dev/null +++ b/src/captcha/CaptchaShape.py @@ -0,0 +1,5 @@ +class CaptchaShape: + + def __init__(self): + self.width = 241 + self.height = 62 diff --git a/src/captcha/CharNumConverter.py b/src/captcha/CharNumConverter.py new file mode 100644 index 00000000000..6187b087a8b --- /dev/null +++ b/src/captcha/CharNumConverter.py @@ -0,0 +1,10 @@ +from tensorflow.keras import layers + +class CharNumConverter: + + def __init__(self, characters): + self.char_to_num = layers.StringLookup(vocabulary=list(characters), mask_token=None) + self.num_to_char = layers.StringLookup( + vocabulary=self.char_to_num.get_vocabulary(), + mask_token=None, + invert=True) \ No newline at end of file diff --git a/src/captcha/DataSplitter.py b/src/captcha/DataSplitter.py new file mode 100644 index 00000000000..d4f619ea982 --- /dev/null +++ b/src/captcha/DataSplitter.py @@ -0,0 +1,28 @@ +import numpy as np + + +class DataSplitter: + + def __init__(self, x, y): + (self.x_train, self.y_train), (x_valid_test, y_valid_test) = DataSplitter._splitData(np.array(x), np.array(y), train_size=0.7) + (self.x_valid, self.y_valid), (self.x_test, self.y_test) = DataSplitter._splitData(x_valid_test, y_valid_test, train_size=0.5) + + def getTrain(self): + return (self.x_train, self.y_train) + + def getValid(self): + return (self.x_valid, self.y_valid) + + def getTest(self): + return (self.x_test, self.y_test) + + @staticmethod + def _splitData(x, y, train_size=0.9, shuffle=True): + size = len(x) + indices = np.arange(size) + if shuffle: + np.random.shuffle(indices) + train_samples = int(size * train_size) + x_train, y_train = x[indices[:train_samples]], y[indices[:train_samples]] + x_test, y_test = x[indices[train_samples:]], y[indices[train_samples:]] + return (x_train, y_train), (x_test, y_test) diff --git a/src/captcha/DatasetFactory.py b/src/captcha/DatasetFactory.py new file mode 100644 index 00000000000..a45aa3ddaf9 --- /dev/null +++ b/src/captcha/DatasetFactory.py @@ -0,0 +1,27 @@ +import tensorflow as tf + + +class DatasetFactory: + + def __init__(self, captchaShape, char_to_num, batch_size): + self.captchaShape = captchaShape + self.char_to_num = char_to_num + self.batch_size = batch_size + + def createDataset(self, x, y): + dataset = tf.data.Dataset.from_tensor_slices((x, y)) + dataset = dataset.map(self._encodeImageAndLabel, num_parallel_calls=tf.data.AUTOTUNE) + dataset = dataset.batch(self.batch_size).prefetch(buffer_size=tf.data.AUTOTUNE) + return dataset + + def _encodeImageAndLabel(self, imageFilename, label): + return { + "image": DatasetFactory.encodeImage(imageFilename, self.captchaShape), + "label": self.char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))} + + @staticmethod + def encodeImage(imageFilename, captchaShape): + img = tf.io.read_file(imageFilename) + img = tf.io.decode_jpeg(img, channels=3) + img = tf.image.resize(img, [captchaShape.height, captchaShape.width]) + return img diff --git a/src/captcha/GoogleDriveManager.py b/src/captcha/GoogleDriveManager.py new file mode 100644 index 00000000000..3b622677801 --- /dev/null +++ b/src/captcha/GoogleDriveManager.py @@ -0,0 +1,27 @@ +from pathlib import Path + + +class GoogleDriveManager: + + _googleDriveFolder = Path('/content/gdrive') + _baseFolder = _googleDriveFolder / 'MyDrive/CAPTCHA/models/' + + @staticmethod + def mount(): + from google.colab import drive + drive.mount(str(GoogleDriveManager._googleDriveFolder)) + + @staticmethod + def uploadFolderToGoogleDrive(folder): + pass + # FK-FIXME: + # !zip -r {folder}.zip {folder}/ + # !cp {folder}.zip {GoogleDriveManager._baseFolder} + + @staticmethod + def downloadFolderFromGoogleDrive(folder): + pass + # FK-FIXME: + # !cp {GoogleDriveManager._baseFolder}/{folder}.zip . + # !rm -rf {folder} + # !unzip {folder}.zip diff --git a/src/captcha/MobileNetV3Small/fingerprint.pb b/src/captcha/MobileNetV3Small/fingerprint.pb new file mode 100644 index 00000000000..6cf754ced3d Binary files /dev/null and b/src/captcha/MobileNetV3Small/fingerprint.pb differ diff --git a/src/captcha/MobileNetV3Small/keras_metadata.pb b/src/captcha/MobileNetV3Small/keras_metadata.pb new file mode 100644 index 00000000000..4a613237688 --- /dev/null +++ b/src/captcha/MobileNetV3Small/keras_metadata.pb @@ -0,0 +1,220 @@ + +¡«root"_tf_keras_network*þª{"name": "MobileNetV3Small", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Functional", "config": {"name": "MobileNetV3Small", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": 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"input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 16}}, "shared_object_id": 346}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 121, 31, 16]}}2 +ô root.layer-5"_tf_keras_layer*Ê{"name": "re_lu", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["Conv/BatchNorm", 0, 0, {}]]], "shared_object_id": 11}2 +¼ root.layer-6"_tf_keras_layer*’{"name": "expanded_conv/depthwise/pad", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ZeroPadding2D", "config": {"name": "expanded_conv/depthwise/pad", "trainable": false, "dtype": "float32", "padding": {"class_name": "__tuple__", "items": [{"class_name": "__tuple__", "items": [1, 1]}, {"class_name": "__tuple__", "items": [1, 1]}]}, "data_format": "channels_last"}, "inbound_nodes": [[["re_lu", 0, 0, {}]]], "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 347}}2 +‰ root.layer_with_weights-2"_tf_keras_layer*Ò +{"name": "expanded_conv/depthwise", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "DepthwiseConv2D", "config": {"name": "expanded_conv/depthwise", "trainable": false, "dtype": "float32", "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 15}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["expanded_conv/depthwise/pad", 0, 0, {}]]], "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 16}}, "shared_object_id": 348}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 123, 33, 16]}}2 +ª + root.layer_with_weights-3"_tf_keras_layer*ó {"name": "expanded_conv/depthwise/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv/depthwise/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 17}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 18}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 20}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv/depthwise", 0, 0, {}]]], "shared_object_id": 21, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 16}}, "shared_object_id": 349}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 61, 16, 16]}}2 +‹ + root.layer-9"_tf_keras_layer*á{"name": "re_lu_1", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_1", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv/depthwise/BatchNorm", 0, 0, {}]]], "shared_object_id": 22}2 +Õ + root.layer_with_weights-4"_tf_keras_layer*ž +{"name": "expanded_conv/project", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv/project", "trainable": false, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 23}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["re_lu_1", 0, 0, {}]]], "shared_object_id": 25, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 16}}, "shared_object_id": 350}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 61, 16, 16]}}2 +¤ + root.layer_with_weights-5"_tf_keras_layer*í {"name": "expanded_conv/project/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv/project/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 26}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 27}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 28}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 29}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv/project", 0, 0, {}]]], "shared_object_id": 30, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 16}}, "shared_object_id": 351}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 61, 16, 16]}}2 +ï + root.layer_with_weights-6"_tf_keras_layer*¸ +{"name": "expanded_conv_1/expand", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_1/expand", "trainable": false, "dtype": "float32", "filters": 72, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 31}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 32}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["expanded_conv/project/BatchNorm", 0, 0, {}]]], "shared_object_id": 33, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 16}}, "shared_object_id": 352}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 61, 16, 16]}}2 +§ +root.layer_with_weights-7"_tf_keras_layer*ð {"name": "expanded_conv_1/expand/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_1/expand/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 34}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 35}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 36}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 37}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_1/expand", 0, 0, {}]]], "shared_object_id": 38, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 72}}, "shared_object_id": 353}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 61, 16, 72]}}2 +‹ root.layer-14"_tf_keras_layer*à{"name": "re_lu_2", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_2", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_1/expand/BatchNorm", 0, 0, {}]]], "shared_object_id": 39}2 +à root.layer-15"_tf_keras_layer*˜{"name": "expanded_conv_1/depthwise/pad", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ZeroPadding2D", "config": {"name": "expanded_conv_1/depthwise/pad", "trainable": false, "dtype": "float32", "padding": {"class_name": "__tuple__", "items": [{"class_name": "__tuple__", "items": [1, 1]}, {"class_name": "__tuple__", "items": [0, 1]}]}, "data_format": "channels_last"}, "inbound_nodes": [[["re_lu_2", 0, 0, {}]]], "shared_object_id": 40, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 354}}2 +Ž root.layer_with_weights-8"_tf_keras_layer*× +{"name": "expanded_conv_1/depthwise", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "DepthwiseConv2D", "config": {"name": "expanded_conv_1/depthwise", "trainable": false, "dtype": "float32", "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 42}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 43}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["expanded_conv_1/depthwise/pad", 0, 0, {}]]], "shared_object_id": 44, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 72}}, "shared_object_id": 355}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 63, 17, 72]}}2 +¯ +root.layer_with_weights-9"_tf_keras_layer*ø {"name": "expanded_conv_1/depthwise/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_1/depthwise/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 46}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 47}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 48}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_1/depthwise", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 72}}, "shared_object_id": 356}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 72]}}2 +Ž root.layer-18"_tf_keras_layer*ã{"name": "re_lu_3", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_3", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_1/depthwise/BatchNorm", 0, 0, {}]]], "shared_object_id": 50}2 +Ù +root.layer_with_weights-10"_tf_keras_layer*¡ +{"name": "expanded_conv_1/project", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_1/project", "trainable": false, "dtype": "float32", "filters": 24, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 51}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["re_lu_3", 0, 0, {}]]], "shared_object_id": 53, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 72}}, "shared_object_id": 357}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 72]}}2 +ª +root.layer_with_weights-11"_tf_keras_layer*ò {"name": "expanded_conv_1/project/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_1/project/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 54}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 55}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 56}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 57}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_1/project", 0, 0, {}]]], "shared_object_id": 58, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 24}}, "shared_object_id": 358}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 24]}}2 +ñ +root.layer_with_weights-12"_tf_keras_layer*¹ +{"name": "expanded_conv_2/expand", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_2/expand", "trainable": false, "dtype": "float32", "filters": 88, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 59}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["expanded_conv_1/project/BatchNorm", 0, 0, {}]]], "shared_object_id": 61, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 24}}, "shared_object_id": 359}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 24]}}2 +§ +root.layer_with_weights-13"_tf_keras_layer*ï {"name": "expanded_conv_2/expand/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_2/expand/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 62}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 63}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 64}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 65}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_2/expand", 0, 0, {}]]], "shared_object_id": 66, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 88}}, "shared_object_id": 360}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 88]}}2 +‹ root.layer-23"_tf_keras_layer*à{"name": "re_lu_4", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_4", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_2/expand/BatchNorm", 0, 0, {}]]], "shared_object_id": 67}2 +÷ +root.layer_with_weights-14"_tf_keras_layer*¿ +{"name": "expanded_conv_2/depthwise", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "DepthwiseConv2D", "config": {"name": "expanded_conv_2/depthwise", "trainable": false, "dtype": "float32", "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 69}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 70}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["re_lu_4", 0, 0, {}]]], "shared_object_id": 71, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 88}}, "shared_object_id": 361}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 88]}}2 +° +root.layer_with_weights-15"_tf_keras_layer*ø {"name": "expanded_conv_2/depthwise/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_2/depthwise/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 72}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 73}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 74}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 75}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_2/depthwise", 0, 0, {}]]], "shared_object_id": 76, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 88}}, "shared_object_id": 362}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 88]}}2 +Ž root.layer-26"_tf_keras_layer*ã{"name": "re_lu_5", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_5", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_2/depthwise/BatchNorm", 0, 0, {}]]], "shared_object_id": 77}2 +Ù +root.layer_with_weights-16"_tf_keras_layer*¡ +{"name": "expanded_conv_2/project", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_2/project", "trainable": false, "dtype": "float32", "filters": 24, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 78}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 79}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["re_lu_5", 0, 0, {}]]], "shared_object_id": 80, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 88}}, "shared_object_id": 363}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 88]}}2 +ª +root.layer_with_weights-17"_tf_keras_layer*ò {"name": "expanded_conv_2/project/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_2/project/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 81}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 82}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 83}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 84}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_2/project", 0, 0, {}]]], "shared_object_id": 85, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 24}}, "shared_object_id": 364}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 24]}}2 +¥ root.layer-29"_tf_keras_layer*ú{"name": "expanded_conv_2/Add", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Add", "config": {"name": "expanded_conv_2/Add", "trainable": false, "dtype": "float32"}, "inbound_nodes": [[["expanded_conv_1/project/BatchNorm", 0, 0, {}], ["expanded_conv_2/project/BatchNorm", 0, 0, {}]]], "shared_object_id": 86, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 31, 8, 24]}, {"class_name": "TensorShape", "items": [null, 31, 8, 24]}]}2 +ã +root.layer_with_weights-18"_tf_keras_layer*« +{"name": "expanded_conv_3/expand", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_3/expand", "trainable": false, "dtype": "float32", "filters": 96, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 87}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 88}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["expanded_conv_2/Add", 0, 0, {}]]], "shared_object_id": 89, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 24}}, "shared_object_id": 365}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 24]}}2 +§ + root.layer_with_weights-19"_tf_keras_layer*ï {"name": "expanded_conv_3/expand/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_3/expand/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 90}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 91}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 92}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 93}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_3/expand", 0, 0, {}]]], "shared_object_id": 94, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 96}}, "shared_object_id": 366}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 31, 8, 96]}}2 +‹! root.layer-32"_tf_keras_layer*à{"name": "re_lu_6", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_6", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_3/expand/BatchNorm", 0, 0, {}]]], "shared_object_id": 95}2 +Ã" root.layer-33"_tf_keras_layer*˜{"name": "expanded_conv_3/depthwise/pad", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ZeroPadding2D", "config": {"name": "expanded_conv_3/depthwise/pad", "trainable": false, "dtype": "float32", "padding": {"class_name": "__tuple__", "items": [{"class_name": "__tuple__", "items": [1, 1]}, {"class_name": "__tuple__", "items": [0, 1]}]}, "data_format": "channels_last"}, "inbound_nodes": [[["re_lu_6", 0, 0, {}]]], "shared_object_id": 96, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 367}}2 + #root.layer_with_weights-20"_tf_keras_layer*× +{"name": "expanded_conv_3/depthwise", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "DepthwiseConv2D", "config": {"name": "expanded_conv_3/depthwise", "trainable": false, "dtype": "float32", "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 98}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 99}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["expanded_conv_3/depthwise/pad", 0, 0, {}]]], "shared_object_id": 100, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 96}}, "shared_object_id": 368}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 33, 9, 96]}}2 +µ +$root.layer_with_weights-21"_tf_keras_layer*ý {"name": "expanded_conv_3/depthwise/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_3/depthwise/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 101}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 102}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 103}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 104}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_3/depthwise", 0, 0, {}]]], "shared_object_id": 105, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 96}}, "shared_object_id": 369}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 4, 96]}}2 +% root.layer-36"_tf_keras_layer*ä{"name": "re_lu_7", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "ReLU", "config": {"name": "re_lu_7", "trainable": false, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["expanded_conv_3/depthwise/BatchNorm", 0, 0, {}]]], "shared_object_id": 106}2 +Ü +&root.layer_with_weights-22"_tf_keras_layer*¤ +{"name": "expanded_conv_3/project", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_3/project", "trainable": false, "dtype": "float32", "filters": 40, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 107}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 108}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["re_lu_7", 0, 0, {}]]], "shared_object_id": 109, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 96}}, "shared_object_id": 370}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 4, 96]}}2 +¯ +'root.layer_with_weights-23"_tf_keras_layer*÷ {"name": "expanded_conv_3/project/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_3/project/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 110}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 111}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 112}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 113}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["expanded_conv_3/project", 0, 0, {}]]], "shared_object_id": 114, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 40}}, "shared_object_id": 371}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 4, 40]}}2 +õ +(root.layer_with_weights-24"_tf_keras_layer*½ +{"name": "expanded_conv_4/expand", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "expanded_conv_4/expand", "trainable": false, "dtype": "float32", "filters": 240, "kernel_size": {"class_name": "__tuple__", "items": [1, 1]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 115}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 116}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["expanded_conv_3/project/BatchNorm", 0, 0, {}]]], "shared_object_id": 117, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 40}}, "shared_object_id": 372}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 4, 40]}}2 +® +)root.layer_with_weights-25"_tf_keras_layer*ö {"name": "expanded_conv_4/expand/BatchNorm", "trainable": false, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "BatchNormalization", "config": {"name": "expanded_conv_4/expand/BatchNorm", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 118}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 119}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 120}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 121}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": 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b/src/captcha/MobileNetV3Small/variables/variables.data-00000-of-00001 differ diff --git a/src/captcha/MobileNetV3Small/variables/variables.index b/src/captcha/MobileNetV3Small/variables/variables.index new file mode 100644 index 00000000000..178a0dc09ee Binary files /dev/null and b/src/captcha/MobileNetV3Small/variables/variables.index differ diff --git a/src/captcha/ModelDAO.py b/src/captcha/ModelDAO.py new file mode 100644 index 00000000000..168d9c37ee8 --- /dev/null +++ b/src/captcha/ModelDAO.py @@ -0,0 +1,20 @@ +from tensorflow import keras +from captcha.GoogleDriveManager import GoogleDriveManager +import shutil + + +class ModelDAO: + + def __init__(self, inColab): + self.inColab = inColab + + def saveModel(self, model): + shutil.rmtree(model.name, ignore_errors = True) + model.save(model.name) + if self.inColab: + GoogleDriveManager.uploadFolderToGoogleDrive(model.name) + + def loadModel(self, modelFilepath): + if self.inColab: + GoogleDriveManager.downloadFolderFromGoogleDrive(modelFilepath) + return keras.models.load_model(modelFilepath) diff --git a/src/captcha/ModelFactory.py b/src/captcha/ModelFactory.py new file mode 100644 index 00000000000..07deafabb05 --- /dev/null +++ b/src/captcha/ModelFactory.py @@ -0,0 +1,102 @@ +from captcha.CTCLayer import CTCLayer +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + + +class ModelFactory: + + predictionModelInputLayerName = "image" + predictionModelOutputLayerName = "dense2" + + def __init__(self, captchaShape, char_to_num): + self.captchaShape = captchaShape + self.char_to_num = char_to_num + + # see https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet101 + def createResNet101(self): + return self._createModel( + baseModelFactory = lambda input_tensor: tf.keras.applications.resnet.ResNet101( + input_tensor = input_tensor, + weights = 'imagenet', + include_top = False), + preprocess_input = tf.keras.applications.resnet.preprocess_input, + name = 'ResNet101') + + def createMobileNetV2(self): + return self._createModel( + baseModelFactory = lambda input_tensor: tf.keras.applications.MobileNetV2( + input_tensor = input_tensor, + weights = 'imagenet', + include_top = False), + preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input, + name = 'MobileNetV2') + + def createMobileNetV3Small(self): + return self._createModel( + baseModelFactory = lambda input_tensor: tf.keras.applications.MobileNetV3Small( + input_tensor = input_tensor, + minimalistic = True, + weights = 'imagenet', + include_top = False), + preprocess_input = tf.keras.applications.mobilenet_v3.preprocess_input, + name = 'MobileNetV3Small') + + @staticmethod + def createPredictionModel(model): + return keras.models.Model( + model.get_layer(name=ModelFactory.predictionModelInputLayerName).input, + model.get_layer(name=ModelFactory.predictionModelOutputLayerName).output) + + def _createModel(self, baseModelFactory, preprocess_input, name): + # Inputs to the model + input_image = layers.Input( + shape = (self.captchaShape.height, self.captchaShape.width, 3), + name = ModelFactory.predictionModelInputLayerName, + dtype = "float32") + labels = layers.Input(name="label", shape=(None,), dtype="float32") + + image = preprocess_input(input_image) + # Transpose the image because we want the time dimension to correspond to the width of the image. + image = tf.keras.layers.Permute(dims=[2, 1, 3])(image) + base_model = baseModelFactory(image) + x = layers.Reshape( + target_shape=(base_model.output_shape[1], base_model.output_shape[2] * base_model.output_shape[3]), + name="reshape")(base_model.output) + x = layers.Dense(64, activation="relu", name="dense1")(x) + x = layers.Dropout(0.2)(x) + + # RNNs + x = layers.Bidirectional( + layers.LSTM( + 128, + return_sequences=True, + dropout=0.25, + unroll=False, + name="LSTM1"))(x) + x = layers.Bidirectional( + layers.LSTM( + 64, + return_sequences=True, + dropout=0.25, + unroll=False, + name="LSTM2"))(x) + + # Output layer + x = layers.Dense( + len(self.char_to_num.get_vocabulary()) + 1, + activation="softmax", + name=ModelFactory.predictionModelOutputLayerName)(x) + + # Add CTC layer for calculating CTC loss at each step + output = CTCLayer(name="ctc_loss")(labels, x) + + model = keras.models.Model( + inputs=[input_image, labels], + outputs=output, + name=name) + # "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." + # from tensorflow.keras.optimizers import SGD + # model.compile(optimizer=SGD(learning_rate=0.004, "weight_decay=0.00004," momentum=0.9) + model.compile(optimizer=keras.optimizers.Adam()) + return model diff --git a/src/captcha/PredictionsDecoder.py b/src/captcha/PredictionsDecoder.py new file mode 100644 index 00000000000..15a1dfaa7ca --- /dev/null +++ b/src/captcha/PredictionsDecoder.py @@ -0,0 +1,24 @@ +import tensorflow as tf +from tensorflow import keras +import numpy as np + + +class PredictionsDecoder: + + def __init__(self, captchaLength, num_to_char): + self.captchaLength = captchaLength + self.num_to_char = num_to_char + + def decode_batch_predictions(self, pred): + return self.asStrings(self.ctc_decode(pred)) + + def ctc_decode(self, pred): + input_len = np.ones(pred.shape[0]) * pred.shape[1] + # Use greedy search. 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00000000000..4ef6af409c8 Binary files /dev/null and b/src/captcha/captchas/VAERS/fd9847.jpeg differ diff --git a/src/captcha/captchas/VAERS/fdb514.jpeg b/src/captcha/captchas/VAERS/fdb514.jpeg new file mode 100644 index 00000000000..d1a8b96d238 Binary files /dev/null and b/src/captcha/captchas/VAERS/fdb514.jpeg differ diff --git a/src/captcha/captchas/VAERS/ffcAb5.jpeg b/src/captcha/captchas/VAERS/ffcAb5.jpeg new file mode 100644 index 00000000000..5cba1ebc6b3 Binary files /dev/null and b/src/captcha/captchas/VAERS/ffcAb5.jpeg differ diff --git a/src/help.txt b/src/help.txt index 6389ef7c837..a2ba76d686e 100644 --- a/src/help.txt +++ b/src/help.txt @@ -7,13 +7,17 @@ FK-TODO: anacron job: sudo cp src/intensivstationen_howbadismybatch.sh /etc/cron.daily/intensivstationen_howbadismybatch -conda create --name howbadismybatch-venv python=3 +conda create --name howbadismybatch-venv python=3.9 conda activate howbadismybatch-venv ipython kernel install --user --name=howbadismybatch-venv-kernel jupyter kernelspec list conda env export --from-history > environment.yml conda env create -f environment.yml +update howbadismybatch-venv: +conda activate howbadismybatch-venv +conda env update --file environment.yml --prune + www.HowBadIsMyBatch.info /etc/apache2/sites-available/HowBadIsMyBatch.conf