Quantized_model_analysis.ipynb

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     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pfKe80WdIt8E"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import cv2\n",
    "from skimage import io\n",
    "from skimage import transform\n",
    "from skimage import color\n",
    "import pickle\n",
    "\n",
    "import pickle\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k4jXhtuxI8SR"
   },
   "source": [
    "## load the images and counts from folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "car5EHYbI3fN"
   },
   "outputs": [],
   "source": [
    "# Load the images and counts\n",
    "with open('Images.pkl', 'rb') as f:\n",
    "  images = pickle.load(f)\n",
    "\n",
    "with open('Counts.pkl', 'rb') as f:\n",
    "  counts = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "7a238273-8c00-48f0-a70c-fc67e3817bae"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points in folder A1: 128\n",
      "Data points in folder A2: 31\n",
      "Data points in folder A3: 27\n",
      "Data points in folder A4: 624\n"
     ]
    }
   ],
   "source": [
    "A1_image_paths, A1_counts = [],[]\n",
    "A2_image_paths, A2_counts = [],[]\n",
    "A3_image_paths, A3_counts = [],[]\n",
    "A4_image_paths, A4_counts = [],[]\n",
    "\n",
    "for image, count in zip(images, counts):\n",
    "    folder = image.split(\"/\")[1]\n",
    "\n",
    "    if folder == 'A1':\n",
    "        A1_image_paths.append(image)\n",
    "        A1_counts.append(count)\n",
    "    elif folder == 'A2':\n",
    "        A2_image_paths.append(image)\n",
    "        A2_counts.append(count)\n",
    "    elif folder == 'A3':\n",
    "        A3_image_paths.append(image)\n",
    "        A3_counts.append(count)\n",
    "    else:\n",
    "        A4_image_paths.append(image)\n",
    "        A4_counts.append(count)\n",
    "\n",
    "print(\"Data points in folder A1:\", len(A1_image_paths))\n",
    "print(\"Data points in folder A2:\", len(A2_image_paths))\n",
    "print(\"Data points in folder A3:\", len(A3_image_paths))\n",
    "print(\"Data points in folder A4:\", len(A4_image_paths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1YOS2tQJL1g"
   },
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ERWy8lz-JLq1"
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   "source": [
    "def get_images(image_path, size = [256, 256]):\n",
    "    # Read the image path\n",
    "    image = tf.io.decode_png(tf.io.read_file(image_path), channels = 3)\n",
    "    image = tf.image.convert_image_dtype(image, tf.float64)\n",
    "    # Resizing\n",
    "    #image = tf.image.resize(image, [448, 448])  # Based on the values from the paper\n",
    "    image = tf.image.resize(image, size)\n",
    "    return image\n",
    "\n",
    "def get_MSE(counts, predictions):\n",
    "    return mean_squared_error(counts, predictions)\n",
    "\n",
    "def get_avg_MSE(mse_A1, mse_A2, mse_A3, mse_A4):\n",
    "    return (mse_A1 + mse_A2 + mse_A3 + mse_A4) / 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5f8Eot9xJDxX"
   },
   "source": [
    "## Prepare the data for the models"
   ]
  },
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   "source": [
    "A1_images = np.zeros(shape=(128, 256, 256, 3))\n",
    "for i, (image, count) in enumerate(zip(A1_image_paths, A1_counts)):\n",
    "     image = get_images(image, size = [256,256])\n",
    "     A1_images[i] = image\n",
    "A1_counts = np.array(A1_counts)\n",
    "\n",
    "A2_images = np.zeros(shape=(31, 256, 256, 3))\n",
    "for i, (image, count) in enumerate(zip(A2_image_paths, A2_counts)):\n",
    "     image = get_images(image, size = [256,256])\n",
    "     A2_images[i] = image\n",
    "A2_counts = np.array(A2_counts)    \n",
    "\n",
    "A3_images = np.zeros(shape=(27, 256, 256, 3))\n",
    "for i, (image, count) in enumerate(zip(A3_image_paths, A3_counts)):\n",
    "     image = get_images(image, size = [256,256])\n",
    "     A3_images[i] = image\n",
    "A3_counts = np.array(A3_counts)\n",
    "\n",
    "A4_images = np.zeros(shape=(624, 256, 256, 3))\n",
    "for i, (image, count) in enumerate(zip(A4_image_paths, A4_counts)):\n",
    "     image = get_images(image, size = [256,256])\n",
    "     A4_images[i] = image\n",
    "A4_counts = np.array(A4_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dO3Pu0FCJ6W_"
   },
   "source": [
    "## Unquantized model results:"
   ]
  },
  {
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   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/"
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     "text": [
      "MSE for A1:  0.7734375\n",
      "MSE for A2:  2.870967741935484\n",
      "MSE for A3:  2.0\n",
      "MSE for A4:  1.2307692307692308\n",
      "Average MSE:  1.7187936181761787\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "model_path = \"Transfer_learning_models/DenseNet121/\"\n",
    "model = tf.keras.models.load_model(model_path)\n",
    "\n",
    "A1_preds = model.predict(A1_images)\n",
    "A1_preds = A1_preds.reshape(A1_preds.shape[0])\n",
    "A2_preds = model.predict(A2_images)\n",
    "A2_preds = A2_preds.reshape(A2_preds.shape[0])\n",
    "A3_preds = model.predict(A3_images)\n",
    "A3_preds = A3_preds.reshape(A3_preds.shape[0])\n",
    "A4_preds = model.predict(A4_images)\n",
    "A4_preds = A4_preds.reshape(A4_preds.shape[0])\n",
    "\n",
    "for i in range(len(A1_preds)):\n",
    "    A1_preds[i] = round(A1_preds[i])\n",
    "A1_preds = A1_preds.astype('int32')\n",
    "\n",
    "for i in range(len(A2_preds)):\n",
    "    A2_preds[i] = round(A2_preds[i])\n",
    "A2_preds = A2_preds.astype('int32')\n",
    "\n",
    "for i in range(len(A3_preds)):\n",
    "    A3_preds[i] = round(A3_preds[i])\n",
    "A3_preds = A3_preds.astype('int32')\n",
    "\n",
    "for i in range(len(A4_preds)):\n",
    "    A4_preds[i] = round(A4_preds[i])\n",
    "A4_preds = A4_preds.astype('int32')\n",
    "\n",
    "\n",
    "mse_1 = get_MSE(A1_preds, A1_counts)\n",
    "\n",
    "mse_2 = get_MSE(A2_preds, A2_counts)\n",
    "\n",
    "mse_3 = get_MSE(A3_preds, A3_counts)\n",
    "\n",
    "mse_4 = get_MSE(A4_preds, A4_counts)\n",
    "\n",
    "avg_MSE = get_avg_MSE(mse_1, mse_2, mse_3, mse_4)\n",
    "\n",
    "print(\"MSE for A1: \", mse_1)\n",
    "print(\"MSE for A2: \", mse_2)\n",
    "print(\"MSE for A3: \", mse_3)\n",
    "print(\"MSE for A4: \", mse_4)\n",
    "print(\"Average MSE: \", avg_MSE)"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "bh5fnYqHKFDc"
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   "source": [
    "## Quantized model results:"
   ]
  },
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      "\n",
      "MSE for A1:  0.859375\n",
      "MSE for A2:  2.6774193548387095\n",
      "MSE for A3:  2.1481481481481484\n",
      "MSE for A4:  1.2163461538461537\n",
      "Average MSE:  1.725322164208253\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "model_path = \"Tflite_models/optimized_densenet_model.tflite\"\n",
    "interpreter_quant = tf.lite.Interpreter(model_path=model_path)\n",
    "interpreter_quant.allocate_tensors()\n",
    "input_index = interpreter_quant.get_input_details()[0][\"index\"]\n",
    "output_index = interpreter_quant.get_output_details()[0][\"index\"]\n",
    "\n",
    "A1_preds = []\n",
    "for image in tqdm(A1_images):\n",
    "    image = image.reshape(1, 256, 256, 3)\n",
    "    interpreter_quant.set_tensor(input_index, np.array(image, dtype=np.float32))\n",
    "    interpreter_quant.invoke()\n",
    "    prediction = interpreter_quant.get_tensor(output_index)[0][0]\n",
    "    A1_preds.append(round(prediction))\n",
    "\n",
    "A2_preds = []\n",
    "for image in tqdm(A2_images):\n",
    "    image = image.reshape(1, 256, 256, 3)\n",
    "    interpreter_quant.set_tensor(input_index, np.array(image, dtype=np.float32))\n",
    "    interpreter_quant.invoke()\n",
    "    prediction = interpreter_quant.get_tensor(output_index)[0][0]\n",
    "    A2_preds.append(round(prediction))\n",
    "\n",
    "A3_preds = []\n",
    "for image in tqdm(A3_images):\n",
    "    image = image.reshape(1, 256, 256, 3)\n",
    "    interpreter_quant.set_tensor(input_index, np.array(image, dtype=np.float32))\n",
    "    interpreter_quant.invoke()\n",
    "    prediction = interpreter_quant.get_tensor(output_index)[0][0]\n",
    "    A3_preds.append(round(prediction))\n",
    "\n",
    "\n",
    "A4_preds = []\n",
    "for image in tqdm(A4_images):\n",
    "    image = image.reshape(1, 256, 256, 3)\n",
    "    interpreter_quant.set_tensor(input_index, np.array(image, dtype=np.float32))\n",
    "    interpreter_quant.invoke()\n",
    "    prediction = interpreter_quant.get_tensor(output_index)[0][0]\n",
    "    A4_preds.append(round(prediction))\n",
    "\n",
    "mse_1 = get_MSE(A1_preds, A1_counts)\n",
    "\n",
    "mse_2 = get_MSE(A2_preds, A2_counts)\n",
    "\n",
    "mse_3 = get_MSE(A3_preds, A3_counts)\n",
    "\n",
    "mse_4 = get_MSE(A4_preds, A4_counts)\n",
    "\n",
    "avg_MSE = get_avg_MSE(mse_1, mse_2, mse_3, mse_4)\n",
    "\n",
    "print(\"MSE for A1: \", mse_1)\n",
    "print(\"MSE for A2: \", mse_2)\n",
    "print(\"MSE for A3: \", mse_3)\n",
    "print(\"MSE for A4: \", mse_4)\n",
    "print(\"Average MSE: \", avg_MSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VLskpZYWORQ0"
   },
   "source": [
    "## Results:"
   ]
  },
  {
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-----------+-----------+-----------+-------------+\n",
      "|     Model_type    | MSE on A1 | MSE on A2 | MSE on A3 | MSE on A4 | Average MSE |\n",
      "+-------------------+-----------+-----------+-----------+-----------+-------------+\n",
      "| Unquantized_model |   0.7734  |   2.8709  |    2.0    |   1.2307  |    1.7187   |\n",
      "|  Quantized_model  |   0.8593  |   2.6774  |   2.1481  |   1.2163  |    1.7253   |\n",
      "+-------------------+-----------+-----------+-----------+-----------+-------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "\n",
    "x = PrettyTable(['Model_type', 'MSE on A1', 'MSE on A2', 'MSE on A3', 'MSE on A4', 'Average MSE'])\n",
    "x.add_row(['Unquantized_model', 0.7734, 2.8709, 2.0000, 1.2307, 1.7187])\n",
    "x.add_row(['Quantized_model', 0.8593, 2.6774, 2.1481, 1.2163, 1.7253])\n",
    "\n",
    "print(x)"
   ]
  }
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