updating

src/captcha.ipynb

@@ -25,8 +25,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "ioGwCR3Xl31V"
+      },
       "outputs": [],
       "source": [
         "import sys\n",
@@ -35,8 +37,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "l-coMy_2l31X"
+      },
       "outputs": [],
       "source": [
         "def isInColab():\n",
@@ -49,8 +53,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "goO0feQwl31Y"
+      },
       "outputs": [],
       "source": [
         "inColab = isInColab()"
@@ -59,7 +65,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "nsE9VWCel31Z"
+      },
       "outputs": [],
       "source": [
         "if inColab:\n",
@@ -71,13 +79,27 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "l9qhlDVNl31b"
+      },
       "outputs": [],
       "source": [
-        "if inColab:\n",
-        "    sys.path.insert(0, '/content/HowBadIsMyBatch/src')"
+        "import os\n",
+        "srcPath = '/content/HowBadIsMyBatch/src' if inColab else os.getcwd()"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "if inColab:\n",
+        "    sys.path.insert(0, srcPath)"
+      ],
+      "metadata": {
+        "id": "c-2fE6vZsD7a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -86,7 +108,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
         "import numpy as np\n",
         "from pathlib import Path\n",
         "import tensorflow as tf\n",
@@ -104,7 +125,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "BWqAvnVOl31d"
+      },
       "outputs": [],
       "source": [
         "from pathlib import Path\n",
@@ -223,7 +246,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "HEKh6eval31k"
+      },
       "outputs": [],
       "source": [
         "def printLayers(model):\n",
@@ -249,6 +274,21 @@
         "    return accuracy.result().numpy()"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "def saveModel(srcPath, modelDAO, model):\n",
+        "    modelFilepath = f'{srcPath}/captcha/{model.name}'\n",
+        "    modelDAO.saveModel(model, modelFilepath)\n",
+        "    if inColab:\n",
+        "        GoogleDriveManager.uploadFolderToGoogleDrive(model.name)"
+      ],
+      "metadata": {
+        "id": "WG2rSl9nxZQ0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -303,6 +343,7 @@
       "outputs": [],
       "source": [
         "if inColab:\n",
+        "    !apt-get update\n",
         "    !sudo apt install ttf-mscorefonts-installer\n",
         "    !sudo fc-cache -f\n",
         "    !fc-match Arial"
@@ -318,8 +359,8 @@
       "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/\"))"
+        "    numCaptchas = 200000, # 50, # 200000,\n",
+        "    dataDir = Path(srcPath + '/captchas/generated/VAERS/'))"
       ]
     },
     {
@@ -395,9 +436,7 @@
       },
       "outputs": [],
       "source": [
-        "modelDAO.saveModel(model)\n",
-        "if inColab:\n",
-        "    GoogleDriveManager.uploadFolderToGoogleDrive(model.name)"
+        "saveModel(srcPath, modelDAO, model)"
       ]
     },
     {
@@ -460,7 +499,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "qZvn1k2Ul31v"
+      },
       "outputs": [],
       "source": [
         "modelName, numTrainableLayers = 'MobileNetV3Small', 104\n",
@@ -476,7 +517,7 @@
       "outputs": [],
       "source": [
         "# FK-TODO: DRY with VAERSFileDownloader\n",
-        "modelFilepath = f'{os.getcwd()}/captcha/{modelName}'\n",
+        "modelFilepath = f'{srcPath}/captcha/{modelName}'\n",
         "model = modelDAO.loadModel(modelFilepath)\n",
         "model.summary(show_trainable=True)"
       ]
@@ -584,21 +625,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
       "source": [
-        "modelDAO.saveModel(model)\n",
-        "if inColab:\n",
-        "    GoogleDriveManager.uploadFolderToGoogleDrive(model.name)"
-      ]
+        "saveModel(srcPath, modelDAO, model)"
+      ],
+      "metadata": {
+        "id": "FpJTHU6dxOVy"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ],
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "collapsed_sections": [],
-      "name": "captcha.ipynb",
       "private_outputs": true,
       "provenance": []
     },
@@ -623,4 +662,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

src/captcha/ModelDAO.py

@@ -4,9 +4,9 @@ import shutil
 
 class ModelDAO:
 
-    def saveModel(self, model):
-        shutil.rmtree(model.name, ignore_errors = True)
-        model.save(model.name)
+    def saveModel(self, model, modelFilepath):
+        shutil.rmtree(modelFilepath, ignore_errors = True)
+        model.save(modelFilepath)
 
     def loadModel(self, modelFilepath):
         return keras.models.load_model(modelFilepath)