CSE515_MWDB_Project/1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global imports\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Caltech101 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.datasets as datasets\n",
    "\n",
    "dataset_path = \"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\"\n",
    "\n",
    "dataset = datasets.Caltech101(\n",
    "    root=\"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\",\n",
    "    download=False,  # True if you wish to download for first time\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select image by ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_image_id = int(input(\"Enter image ID: \"))\n",
    "sample_image, sample_label = dataset[selected_image_id]\n",
    "plt.imshow(sample_image)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Class transform to partition image into rows x cols grid\n",
    "\n",
    "\n",
    "class GridPartition:\n",
    "    def __init__(self, rows, cols):\n",
    "        self.rows = rows\n",
    "        self.cols = cols\n",
    "\n",
    "    def __call__(self, img):\n",
    "        img_width, img_height = img.size\n",
    "        cell_width = img_width // self.cols\n",
    "        cell_height = img_height // self.rows\n",
    "\n",
    "        grids = []\n",
    "        for i in range(self.rows):\n",
    "            for j in range(self.cols):\n",
    "                left = j * cell_width\n",
    "                top = i * cell_height\n",
    "                right = left + cell_width\n",
    "                bottom = top + cell_height\n",
    "                grid = img.crop((left, top, right, bottom))\n",
    "                grids.append(grid)\n",
    "\n",
    "        return grids\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature model 1: Color moments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import skew\n",
    "\n",
    "\n",
    "def compute_color_moments(image):\n",
    "    image = np.array(image)  # Convert PIL Image to NumPy array\n",
    "    moments = []\n",
    "\n",
    "    for channel in range(3):  # Iterate over RGB channels\n",
    "        channel_data = image[:, :, channel]\n",
    "        mean = np.mean(channel_data)\n",
    "        std_dev = np.std(channel_data)\n",
    "        skewness = skew(channel_data, axis=None)\n",
    "        moments.append([mean, std_dev, skewness])\n",
    "\n",
    "    return moments\n",
    "\n",
    "\n",
    "# Iterate over grid cells and return as 1-d array for easier resizing by torch\n",
    "def compute_color_moments_for_grid(grid):\n",
    "    color_moments = [compute_color_moments(grid_cell) for grid_cell in grid]\n",
    "    return np.array(color_moments).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_color_moments(grid_color_moments):\n",
    "    return torch.Tensor(grid_color_moments).view(\n",
    "        10, 10, 3, 3\n",
    "    )  # resize as needed: 10x10 grid, 3 channels per cell, 3 moments per channel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Composed color moment transform (CM10x10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 8.98966675e+01  3.48893332e+00 -3.28439921e-01]\n",
      "   [ 1.36396667e+02  2.87390375e+00 -1.31204709e-01]\n",
      "   [ 1.86630005e+02  4.21502495e+00 -4.33173418e-01]]\n",
      "\n",
      "  [[ 9.76766663e+01  1.95417213e+00 -4.62716408e-02]\n",
      "   [ 1.44503326e+02  2.09045196e+00 -3.11197430e-01]\n",
      "   [ 1.97606674e+02  2.93574905e+00 -2.41185650e-01]]\n",
      "\n",
      "  [[ 1.02809998e+02  1.21129405e+00 -3.41436297e-01]\n",
      "   [ 1.49716660e+02  1.22870755e+00 -3.97477776e-01]\n",
      "   [ 2.03763336e+02  1.20857584e+00 -3.77465785e-01]]\n",
      "\n",
      "  [[ 1.04209999e+02  9.01424825e-01 -1.24480568e-01]\n",
      "   [ 1.51210007e+02  9.01424825e-01 -1.24480568e-01]\n",
      "   [ 2.05210007e+02  9.01424825e-01 -1.24480568e-01]]\n",
      "\n",
      "  [[ 1.03846664e+02  6.90281749e-01 -3.96895140e-01]\n",
      "   [ 1.50733337e+02  7.27247477e-01 -6.87909067e-01]\n",
      "   [ 2.04793335e+02  6.66299880e-01 -5.49638629e-01]]\n",
      "\n",
      "  [[ 1.03613335e+02  9.07646537e-01  1.37317646e+00]\n",
      "   [ 1.50093338e+02  5.92696309e-01 -4.12283629e-01]\n",
      "   [ 2.04360001e+02  6.45806968e-01  1.27313507e+00]]\n",
      "\n",
      "  [[ 1.02626663e+02  7.44281888e-01 -2.44649440e-01]\n",
      "   [ 1.49626663e+02  7.44281888e-01 -2.44649440e-01]\n",
      "   [ 2.03626663e+02  7.44281888e-01 -2.44649440e-01]]\n",
      "\n",
      "  [[ 9.92666702e+01  1.51510906e+00 -1.00269988e-01]\n",
      "   [ 1.45490005e+02  2.20073462e+00 -1.88316330e-01]\n",
      "   [ 1.99833328e+02  1.88826787e+00 -1.98917896e-01]]\n",
      "\n",
      "  [[ 9.24933319e+01  2.79104924e+00  5.72221614e-02]\n",
      "   [ 1.38423340e+02  2.71491957e+00  2.93120015e-02]\n",
      "   [ 1.90083328e+02  3.65555143e+00 -1.87065169e-01]]\n",
      "\n",
      "  [[ 8.24000015e+01  2.61023617e+00  3.80108207e-01]\n",
      "   [ 1.28656662e+02  2.51372004e+00  2.37066835e-01]\n",
      "   [ 1.75543335e+02  5.71851873e+00 -9.46527794e-02]]]\n",
      "\n",
      "\n",
      " [[[ 9.22033310e+01  2.95104313e+00 -2.01307759e-02]\n",
      "   [ 1.38203339e+02  2.95104313e+00 -2.01307759e-02]\n",
      "   [ 1.88953339e+02  3.74403811e+00  1.53495222e-01]]\n",
      "\n",
      "  [[ 9.97566681e+01  2.36871028e+00 -2.65643746e-02]\n",
      "   [ 1.46210007e+02  2.44797206e+00 -8.00686553e-02]\n",
      "   [ 2.00039993e+02  2.87490296e+00 -6.63764000e-01]]\n",
      "\n",
      "  [[ 1.08033333e+02  1.81937408e+01  4.62725830e+00]\n",
      "   [ 1.53633331e+02  1.35269690e+01  4.97076178e+00]\n",
      "   [ 2.04756668e+02  7.28039312e+00  3.49281192e+00]]\n",
      "\n",
      "  [[ 1.03816666e+02  8.18365574e-01  2.03911383e-02]\n",
      "   [ 1.50793335e+02  8.10733557e-01  5.56955859e-02]\n",
      "   [ 2.04806671e+02  8.09910834e-01  2.70058177e-02]]\n",
      "\n",
      "  [[ 1.03186668e+02  5.87499380e-01  1.41472101e+00]\n",
      "   [ 1.50186661e+02  5.87499380e-01  1.41472101e+00]\n",
      "   [ 2.04186661e+02  5.87499380e-01  1.41472101e+00]]\n",
      "\n",
      "  [[ 1.03000000e+02  1.63299322e-01  0.00000000e+00]\n",
      "   [ 1.50000000e+02  1.63299322e-01  0.00000000e+00]\n",
      "   [ 2.04000000e+02  1.63299322e-01  0.00000000e+00]]\n",
      "\n",
      "  [[ 1.02893333e+02  5.79041362e-01  9.47434455e-03]\n",
      "   [ 1.49893326e+02  5.79041362e-01  9.47434455e-03]\n",
      "   [ 2.03893326e+02  5.79041362e-01  9.47434455e-03]]\n",
      "\n",
      "  [[ 1.00556664e+02  1.53409767e+00 -4.67008770e-01]\n",
      "   [ 1.46756668e+02  2.12386179e+00 -4.40075248e-01]\n",
      "   [ 2.01156662e+02  1.78665113e+00 -4.60701376e-01]]\n",
      "\n",
      "  [[ 9.40699997e+01  2.79137373e+00  1.75330251e-01]\n",
      "   [ 1.39703339e+02  2.41977739e+00  7.48614445e-02]\n",
      "   [ 1.92476669e+02  4.03932190e+00  5.27464002e-02]]\n",
      "\n",
      "  [[ 8.47466660e+01  2.85233617e+00  1.10580832e-01]\n",
      "   [ 1.30616669e+02  2.85009742e+00  2.44239435e-01]\n",
      "   [ 1.78130005e+02  5.69793224e+00 -2.32627422e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.41833344e+01  2.82896733e+00 -9.03036594e-02]\n",
      "   [ 1.40183334e+02  2.82896733e+00 -9.03036594e-02]\n",
      "   [ 1.91416672e+02  3.66556811e+00 -2.28034556e-01]]\n",
      "\n",
      "  [[ 1.02526665e+02  5.34564829e+00  5.68530750e+00]\n",
      "   [ 1.47973328e+02  3.85909605e+00  4.85009193e+00]\n",
      "   [ 1.99820007e+02  2.84621382e+00  8.12783301e-01]]\n",
      "\n",
      "  [[ 1.56453339e+02  5.36964417e+01  1.25754148e-01]\n",
      "   [ 1.82190002e+02  4.36562386e+01 -6.70144260e-02]\n",
      "   [ 2.09803329e+02  3.51215706e+01 -1.00516641e+00]]\n",
      "\n",
      "  [[ 1.04403336e+02  8.16897929e-01 -9.71760690e-01]\n",
      "   [ 1.50453339e+02  6.38609588e-01 -2.85939783e-01]\n",
      "   [ 2.04960007e+02  5.33916354e-01 -1.48308408e+00]]\n",
      "\n",
      "  [[ 1.03760002e+02  1.31240237e+00  3.06992948e-01]\n",
      "   [ 1.49793335e+02  8.46929073e-01  6.69768274e-01]\n",
      "   [ 2.04046661e+02  1.26668417e+00 -8.35504591e-01]]\n",
      "\n",
      "  [[ 1.03779999e+02  1.12765241e+00  1.63879395e+00]\n",
      "   [ 1.49529999e+02  8.88312995e-01  2.94527084e-01]\n",
      "   [ 2.04013336e+02  1.43288362e+00 -2.27331907e-01]]\n",
      "\n",
      "  [[ 1.03500000e+02  5.00000000e-01  0.00000000e+00]\n",
      "   [ 1.49500000e+02  5.00000000e-01  0.00000000e+00]\n",
      "   [ 2.04000000e+02  0.00000000e+00             nan]]\n",
      "\n",
      "  [[ 1.01949997e+02  1.15794361e+00 -6.11068070e-01]\n",
      "   [ 1.47553329e+02  1.37616217e+00 -7.75007665e-01]\n",
      "   [ 2.02220001e+02  1.24026883e+00 -1.05138719e+00]]\n",
      "\n",
      "  [[ 9.64666672e+01  2.37112260e+00 -2.22494856e-01]\n",
      "   [ 1.41320007e+02  2.72475076e+00  1.30500764e-01]\n",
      "   [ 1.93889999e+02  3.40361094e+00  6.44793687e-03]]\n",
      "\n",
      "  [[ 8.75899963e+01  3.18881893e+00  4.17145967e-01]\n",
      "   [ 1.32016663e+02  3.05555058e+00  1.79819033e-01]\n",
      "   [ 1.80149994e+02  5.82129717e+00 -8.20338354e-02]]]\n",
      "\n",
      "\n",
      " [[[ 9.52699966e+01  2.50275183e+00 -1.68615967e-01]\n",
      "   [ 1.41270004e+02  2.50275183e+00 -1.68615967e-01]\n",
      "   [ 1.91983337e+02  3.19004536e+00  6.25989139e-02]]\n",
      "\n",
      "  [[ 1.13410004e+02  2.98071899e+01  2.93903017e+00]\n",
      "   [ 1.55896667e+02  2.26944771e+01  3.08493042e+00]\n",
      "   [ 2.03820007e+02  1.31273603e+01  2.94030738e+00]]\n",
      "\n",
      "  [[ 1.72190002e+02  5.90429840e+01 -3.80760342e-01]\n",
      "   [ 1.89363327e+02  5.12488518e+01 -6.16875410e-01]\n",
      "   [ 2.07526672e+02  4.64884491e+01 -1.24065042e+00]]\n",
      "\n",
      "  [[ 1.05849998e+02  1.34691000e+00  1.22354257e+00]\n",
      "   [ 1.51410004e+02  8.99203360e-01 -4.60178405e-02]\n",
      "   [ 2.04610001e+02  1.35569167e+00  2.36779618e+00]]\n",
      "\n",
      "  [[ 1.06029999e+02  6.29939938e+00  2.98012638e+00]\n",
      "   [ 1.45380005e+02  1.48813839e+01 -2.39619374e+00]\n",
      "   [ 1.90259995e+02  3.17843628e+01 -2.39913392e+00]]\n",
      "\n",
      "  [[ 1.04206665e+02  1.53106918e+01 -1.27013803e+00]\n",
      "   [ 1.38986664e+02  2.52974281e+01 -1.68959332e+00]\n",
      "   [ 1.77460007e+02  4.73520317e+01 -1.41168928e+00]]\n",
      "\n",
      "  [[ 1.05879997e+02  1.17427993e+00 -3.71687263e-01]\n",
      "   [ 1.49643326e+02  7.27636993e-01  6.62483573e-01]\n",
      "   [ 2.03233337e+02  1.06718743e+00  6.77474141e-01]]\n",
      "\n",
      "  [[ 1.03486664e+02  1.41297162e+00  1.76012665e-01]\n",
      "   [ 1.48686661e+02  1.05916107e+00 -9.66223955e-01]\n",
      "   [ 2.01806671e+02  1.32008421e+00  1.84582010e-01]]\n",
      "\n",
      "  [[ 9.87766647e+01  2.52720976e+00  1.09471619e-01]\n",
      "   [ 1.42113327e+02  2.84964705e+00  1.50857031e-01]\n",
      "   [ 1.94493332e+02  3.34314561e+00 -2.44295880e-01]]\n",
      "\n",
      "  [[ 8.98666687e+01  3.23144269e+00  1.21680707e-01]\n",
      "   [ 1.34020004e+02  2.63051319e+00 -3.59949693e-02]\n",
      "   [ 1.80020004e+02  6.08053732e+00  2.96428293e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.78733368e+01  2.29578352e+00 -5.27412236e-01]\n",
      "   [ 1.41896667e+02  2.89124918e+00  1.81603745e-01]\n",
      "   [ 1.93630005e+02  3.44960380e+00 -9.81889665e-02]]\n",
      "\n",
      "  [[ 1.26980003e+02  4.41824188e+01  1.59510815e+00]\n",
      "   [ 1.64406662e+02  3.09624710e+01  1.68056011e+00]\n",
      "   [ 2.08183334e+02  1.85189018e+01  1.48030818e+00]]\n",
      "\n",
      "  [[ 1.67656662e+02  5.96662827e+01  1.04937091e-01]\n",
      "   [ 1.71616669e+02  6.24662819e+01  6.34714589e-02]\n",
      "   [ 1.72536667e+02  6.64541626e+01 -1.82714202e-02]]\n",
      "\n",
      "  [[ 1.72723328e+02  5.84519768e+01  5.03296889e-02]\n",
      "   [ 1.96376663e+02  4.40061531e+01  1.00495160e-01]\n",
      "   [ 2.22846664e+02  2.25047359e+01  1.28726661e-01]]\n",
      "\n",
      "  [[ 1.16730003e+02  2.92252827e+01  1.92545891e+00]\n",
      "   [ 1.46770004e+02  3.23179588e+01 -2.43335426e-01]\n",
      "   [ 1.82293335e+02  4.58156509e+01 -1.61399293e+00]]\n",
      "\n",
      "  [[ 1.00489998e+02  2.89910889e+01  7.72838235e-01]\n",
      "   [ 1.18353333e+02  4.02959290e+01  5.95054701e-02]\n",
      "   [ 1.35149994e+02  6.49438248e+01 -6.20669639e-03]]\n",
      "\n",
      "  [[ 1.07286667e+02  8.25617886e+00 -1.12381458e+00]\n",
      "   [ 1.49270004e+02  7.62957621e+00 -4.85430861e+00]\n",
      "   [ 1.99926666e+02  1.06852522e+01 -5.08314133e+00]]\n",
      "\n",
      "  [[ 1.06043335e+02  1.26284957e+00 -9.55358148e-01]\n",
      "   [ 1.49373337e+02  1.11383224e+00 -4.83554900e-01]\n",
      "   [ 2.02256668e+02  1.15936279e+00 -3.30621630e-01]]\n",
      "\n",
      "  [[ 1.00646667e+02  2.21099281e+00  2.70693377e-02]\n",
      "   [ 1.43199997e+02  2.70431757e+00 -1.59574673e-01]\n",
      "   [ 1.95923340e+02  3.09151340e+00 -3.70189875e-01]]\n",
      "\n",
      "  [[ 9.10199966e+01  3.45344281e+00  1.44814208e-01]\n",
      "   [ 1.34523331e+02  2.86637783e+00 -1.82556883e-01]\n",
      "   [ 1.81919998e+02  6.35507154e+00 -2.09640130e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.87733307e+01  2.53152561e+00  2.48306338e-02]\n",
      "   [ 1.41936661e+02  2.30781031e+00  2.12251201e-01]\n",
      "   [ 1.92229996e+02  4.35780144e+00 -3.86985332e-01]]\n",
      "\n",
      "  [[ 1.20336670e+02  3.49577370e+01  2.35125852e+00]\n",
      "   [ 1.57683334e+02  2.64850216e+01  2.46620750e+00]\n",
      "   [ 2.02936661e+02  1.50676031e+01  1.11123455e+00]]\n",
      "\n",
      "  [[ 1.55786667e+02  5.02684250e+01  8.25997412e-01]\n",
      "   [ 1.69179993e+02  4.65390244e+01  8.21900487e-01]\n",
      "   [ 1.84070007e+02  4.27766876e+01  5.02548516e-01]]\n",
      "\n",
      "  [[ 2.19563339e+02  3.42698975e+01 -1.04674172e+00]\n",
      "   [ 2.26336670e+02  3.29651833e+01 -1.15026438e+00]\n",
      "   [ 2.32426666e+02  2.96285553e+01 -1.66973448e+00]]\n",
      "\n",
      "  [[ 1.36449997e+02  8.17804031e+01  2.04478838e-02]\n",
      "   [ 1.37893326e+02  8.40404358e+01  5.57339890e-03]\n",
      "   [ 1.36246674e+02  8.96980438e+01 -3.30493860e-02]]\n",
      "\n",
      "  [[ 1.14779999e+02  7.27228394e+01  6.32253766e-01]\n",
      "   [ 1.14483330e+02  7.56272659e+01  6.32408261e-01]\n",
      "   [ 1.10093330e+02  8.30251236e+01  6.03123188e-01]]\n",
      "\n",
      "  [[ 1.75686661e+02  5.72215729e+01 -6.04357757e-02]\n",
      "   [ 1.90946671e+02  4.92270622e+01 -1.45042792e-01]\n",
      "   [ 2.06720001e+02  4.21222229e+01 -1.00701237e+00]]\n",
      "\n",
      "  [[ 1.35523331e+02  5.09584427e+01  1.48007786e+00]\n",
      "   [ 1.68830002e+02  3.76666222e+01  1.54083514e+00]\n",
      "   [ 2.08770004e+02  1.96288166e+01  1.40394819e+00]]\n",
      "\n",
      "  [[ 1.01260002e+02  2.30052161e+00 -1.87728569e-01]\n",
      "   [ 1.42913330e+02  3.14841056e+00  1.22121252e-01]\n",
      "   [ 1.95886673e+02  3.56283903e+00 -2.74973810e-01]]\n",
      "\n",
      "  [[ 9.21333313e+01  3.25917506e+00  5.99102568e-05]\n",
      "   [ 1.34389999e+02  2.93676114e+00 -1.46873772e-01]\n",
      "   [ 1.79693329e+02  5.40116882e+00  5.14075756e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.97433319e+01  2.32754278e+00  1.75400078e-01]\n",
      "   [ 1.42693329e+02  2.32793665e+00  2.79643804e-01]\n",
      "   [ 1.92283340e+02  4.16769981e+00 -5.63374341e-01]]\n",
      "\n",
      "  [[ 1.06616669e+02  1.73485506e+00 -2.92223305e-01]\n",
      "   [ 1.49716660e+02  1.84834766e+00 -1.48330286e-01]\n",
      "   [ 2.01383331e+02  2.46503329e+00  3.73306647e-02]]\n",
      "\n",
      "  [[ 1.08970001e+02  3.36091757e+00 -3.35983777e+00]\n",
      "   [ 1.49956665e+02  7.70031118e+00 -3.89067101e+00]\n",
      "   [ 1.99236664e+02  1.77326622e+01 -3.38130093e+00]]\n",
      "\n",
      "  [[ 1.14839996e+02  1.17226734e+01  1.58053231e+00]\n",
      "   [ 1.37220001e+02  1.72330570e+01 -2.90629506e-01]\n",
      "   [ 1.60509995e+02  3.84233856e+01 -1.25245407e-01]]\n",
      "\n",
      "  [[ 1.24896667e+02  2.38746586e+01  1.74737918e+00]\n",
      "   [ 1.31723328e+02  2.35146790e+01  1.13733780e+00]\n",
      "   [ 1.39876663e+02  3.47092896e+01  6.05512738e-01]]\n",
      "\n",
      "  [[ 1.22309998e+02  4.04512939e+01  7.59080648e-01]\n",
      "   [ 1.23190002e+02  3.97253151e+01  8.79103720e-01]\n",
      "   [ 1.22983330e+02  4.38521347e+01  9.11588788e-01]]\n",
      "\n",
      "  [[ 1.74220001e+02  4.92392616e+01  1.14831798e-01]\n",
      "   [ 1.79250000e+02  4.99169388e+01  7.21178427e-02]\n",
      "   [ 1.86076660e+02  5.27390823e+01 -7.41457418e-02]]\n",
      "\n",
      "  [[ 1.99233337e+02  4.36449928e+01 -6.75240099e-01]\n",
      "   [ 2.09000000e+02  4.06227341e+01 -6.88499629e-01]\n",
      "   [ 2.19949997e+02  3.65390320e+01 -9.80908096e-01]]\n",
      "\n",
      "  [[ 1.24483330e+02  4.45726700e+01  1.67995286e+00]\n",
      "   [ 1.58346664e+02  3.35572128e+01  1.79459977e+00]\n",
      "   [ 2.00493332e+02  1.92451706e+01  1.80683017e+00]]\n",
      "\n",
      "  [[ 9.29333344e+01  3.12125325e+00 -5.78114055e-02]\n",
      "   [ 1.34043335e+02  2.86032438e+00 -2.22221285e-01]\n",
      "   [ 1.78326660e+02  4.36347961e+00  7.53879428e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.86600037e+01  2.26224089e+00 -3.74916106e-01]\n",
      "   [ 1.41639999e+02  2.08895516e+00 -2.15071172e-01]\n",
      "   [ 1.89776672e+02  5.05636120e+00 -4.46663946e-01]]\n",
      "\n",
      "  [[ 1.05666664e+02  1.86607134e+00 -6.02343619e-01]\n",
      "   [ 1.48666672e+02  1.86607134e+00 -6.02343619e-01]\n",
      "   [ 1.99869995e+02  2.13379931e+00 -1.59756020e-01]]\n",
      "\n",
      "  [[ 1.08186668e+02  7.77917445e-01 -5.07103860e-01]\n",
      "   [ 1.51186661e+02  7.77917445e-01 -5.07103860e-01]\n",
      "   [ 2.04186661e+02  7.77917445e-01 -5.07103860e-01]]\n",
      "\n",
      "  [[ 1.08516670e+02  1.12681949e+00 -1.74261376e-01]\n",
      "   [ 1.51259995e+02  1.30603731e+00  3.28560919e-01]\n",
      "   [ 2.03203339e+02  1.44982374e+00  4.04498100e-01]]\n",
      "\n",
      "  [[ 1.08266670e+02  1.42672896e+00  2.01063538e+00]\n",
      "   [ 1.50710007e+02  9.65694904e-01 -2.17003393e+00]\n",
      "   [ 2.03059998e+02  2.46638727e+00 -1.09411120e+00]]\n",
      "\n",
      "  [[ 9.85500031e+01  2.43967381e+01 -1.84110558e+00]\n",
      "   [ 1.32710007e+02  3.28915291e+01 -1.79126072e+00]\n",
      "   [ 1.71360001e+02  4.70838661e+01 -1.56818557e+00]]\n",
      "\n",
      "  [[ 1.10543335e+02  4.65848207e+00  1.05157208e+00]\n",
      "   [ 1.42550003e+02  1.30993958e+01 -1.38763893e+00]\n",
      "   [ 1.83100006e+02  3.14113045e+01 -1.23489392e+00]]\n",
      "\n",
      "  [[ 1.10103333e+02  1.73416443e+01 -2.87093353e-02]\n",
      "   [ 1.35396667e+02  1.97064953e+01 -7.12515473e-01]\n",
      "   [ 1.66826660e+02  3.56275635e+01 -4.23399240e-01]]\n",
      "\n",
      "  [[ 1.04053337e+02  8.81648922e+00  3.97378731e+00]\n",
      "   [ 1.40449997e+02  5.90938520e+00  1.94472992e+00]\n",
      "   [ 1.87283340e+02  1.43013887e+01 -2.34256101e+00]]\n",
      "\n",
      "  [[ 9.22033310e+01  3.35787463e+00  1.78806148e-02]\n",
      "   [ 1.34076660e+02  2.80073595e+00  1.47117376e-01]\n",
      "   [ 1.77336670e+02  4.08533812e+00  4.74913478e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.88366699e+01  2.64385867e+00 -1.37110770e-01]\n",
      "   [ 1.41446671e+02  2.33962011e+00 -1.77826956e-01]\n",
      "   [ 1.87823334e+02  5.39618301e+00  2.40830719e-01]]\n",
      "\n",
      "  [[ 1.05416664e+02  1.70969462e+00 -8.59405622e-02]\n",
      "   [ 1.48263336e+02  1.90805018e+00 -2.60711342e-01]\n",
      "   [ 1.99396667e+02  2.03125310e+00 -3.07325572e-01]]\n",
      "\n",
      "  [[ 1.09046669e+02  7.98637748e-01  1.12428688e-01]\n",
      "   [ 1.52046661e+02  7.98637748e-01  1.12428688e-01]\n",
      "   [ 2.03600006e+02  1.03279555e+00  2.03331620e-01]]\n",
      "\n",
      "  [[ 1.09083336e+02  6.08047903e-01  1.20246208e+00]\n",
      "   [ 1.51963333e+02  4.64028269e-01  2.70909399e-01]\n",
      "   [ 2.03043335e+02  5.24203122e-01  7.46497750e-01]]\n",
      "\n",
      "  [[ 1.09089996e+02  7.27025926e-01  5.89562356e-01]\n",
      "   [ 1.51873337e+02  6.30308568e-01 -2.94170529e-01]\n",
      "   [ 2.03443329e+02  8.79463196e-01  1.09890401e+00]]\n",
      "\n",
      "  [[ 1.09603333e+02  1.66112077e+00  8.87810647e-01]\n",
      "   [ 1.50199997e+02  1.95277584e+00 -6.00221443e+00]\n",
      "   [ 2.02013336e+02  4.67473602e+00 -6.05241299e+00]]\n",
      "\n",
      "  [[ 1.09040001e+02  1.95959181e-01  4.69485521e+00]\n",
      "   [ 1.50039993e+02  1.95959181e-01  4.69485521e+00]\n",
      "   [ 2.02623337e+02  9.49450135e-01  9.29350078e-01]]\n",
      "\n",
      "  [[ 1.07160004e+02  2.68968391e+00 -7.04979181e+00]\n",
      "   [ 1.47946671e+02  3.46657681e+00 -7.83300543e+00]\n",
      "   [ 1.99113327e+02  6.31034756e+00 -6.27204704e+00]]\n",
      "\n",
      "  [[ 1.01513336e+02  2.81244063e+00  7.53259420e-01]\n",
      "   [ 1.41946671e+02  1.96396422e+00 -6.81208223e-02]\n",
      "   [ 1.90690002e+02  4.53731585e+00 -4.56629127e-01]]\n",
      "\n",
      "  [[ 9.28466644e+01  2.94785047e+00 -7.01600671e-01]\n",
      "   [ 1.33083328e+02  3.17538285e+00  1.05004586e-01]\n",
      "   [ 1.76630005e+02  3.59069633e+00  1.79543301e-01]]]\n",
      "\n",
      "\n",
      " [[[ 9.74033356e+01  2.58469653e+00 -3.43181401e-01]\n",
      "   [ 1.40313339e+02  2.47827005e+00 -4.48768139e-01]\n",
      "   [ 1.83546661e+02  3.73199987e+00  3.86729509e-01]]\n",
      "\n",
      "  [[ 1.04416664e+02  2.00907660e+00 -1.65382773e-01]\n",
      "   [ 1.45929993e+02  1.74502146e+00  1.67454526e-01]\n",
      "   [ 1.97036667e+02  3.13719440e+00 -1.03399920e+00]]\n",
      "\n",
      "  [[ 1.08986664e+02  1.11646271e+00 -5.77348173e-01]\n",
      "   [ 1.50206665e+02  1.10632527e+00 -6.21089399e-01]\n",
      "   [ 2.02086670e+02  1.06731236e+00 -6.33445263e-01]]\n",
      "\n",
      "  [[ 1.09830002e+02  5.72509110e-01 -7.35019684e-01]\n",
      "   [ 1.51029999e+02  5.85178018e-01 -6.03070676e-01]\n",
      "   [ 2.02929993e+02  4.95075762e-01 -1.13880420e+00]]\n",
      "\n",
      "  [[ 1.09816666e+02  4.72287565e-01 -1.84620261e+00]\n",
      "   [ 1.51003326e+02  5.06611824e-01 -1.07082129e+00]\n",
      "   [ 2.02903336e+02  3.83825779e-01 -3.41385245e+00]]\n",
      "\n",
      "  [[ 1.09893333e+02  7.08488226e-01 -2.38527000e-01]\n",
      "   [ 1.50889999e+02  7.05620289e-01 -2.40122959e-01]\n",
      "   [ 2.02830002e+02  6.74116731e-01 -3.69934022e-01]]\n",
      "\n",
      "  [[ 1.10323334e+02  1.88293803e+00  9.00322497e-01]\n",
      "   [ 1.50746674e+02  6.70190692e-01  2.79992342e-01]\n",
      "   [ 2.02433334e+02  1.28019094e+00 -1.08603144e+00]]\n",
      "\n",
      "  [[ 1.10843330e+02  3.54383826e+00 -5.41539006e-02]\n",
      "   [ 1.47793335e+02  1.41796410e+00 -1.16189077e-01]\n",
      "   [ 1.97683334e+02  3.11818147e+00 -1.72437698e-01]]\n",
      "\n",
      "  [[ 1.04559998e+02  2.66953182e+00 -1.37087226e-01]\n",
      "   [ 1.40373337e+02  2.69949794e+00  4.67433110e-02]\n",
      "   [ 1.87186661e+02  3.96213198e+00  5.14297009e-01]]\n",
      "\n",
      "  [[ 9.56966629e+01  3.43966889e+00  3.56567204e-02]\n",
      "   [ 1.30183334e+02  3.79513574e+00 -3.59395072e-02]\n",
      "   [ 1.75119995e+02  3.86336637e+00 -1.23986481e-02]]]]\n"
     ]
    }
   ],
   "source": [
    "CM_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((100, 300)),  # resize to H:W=100:300\n",
    "        GridPartition(\n",
    "            rows=10, cols=10\n",
    "        ),  # partition into grid of 10 rows, 10 columns as a list\n",
    "        compute_color_moments_for_grid,\n",
    "        combine_color_moments,\n",
    "    ]\n",
    ")\n",
    "\n",
    "if np.array(sample_image).shape[2] >= 3:\n",
    "    # no color moments for grayscale images\n",
    "    cm_fd = CM_transform(sample_image)\n",
    "else:\n",
    "    cm_fd = torch.zeros((10, 10, 3, 3))\n",
    "\n",
    "print(np.array(cm_fd))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(note that skewness is NaN when all elements are equal i.e. std. dev. = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing color moments - stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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e6sXRY/RyM+gFvagqLzXD671wOgc0AxXxUi+OHqOXm0Ev6EVVeakZXu+F0zmgGaiIl3px9Bi93Ax6QS+qykvN8HovnM4BzUBFvNSLo8fo5WbQC3pRVV5qhtd74XQOaEbFfP+ZIAAAAAAAAAAAAAAAAAAAAMSAuEgfAAAAAAAAAAAAAAAAAAAAAJxj8ScAAAAAAAAAAAAAAAAAAEAMYfEnAAAAAAAAAAAAAAAAAABADGHxJwAAAAAAAAAAAAAAAAAAQAxh8ScAAAAAAAAAAAAAAAAAAEAMYfEnAAAAAAAAAAAAAAAAAABADGHxJwAAAAAAAAAAAAAAAAAAQAxh8ScAAAAAAAAAAAAAAAAAAEAMYfEnAAAAAAAAAAAAAAAAAABADGHxJwAAAAAAAAAAAAAAAAAAQAxh8ScAAAAAAAAAAAAAAAAAAEAM+f/I7+7SWb2eJgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2700x1800 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cm_fd should be a NumPy array of shape (M, N, 3, 3), where (M, N) is the grid size\n",
    "\n",
    "# Define the number of grid cells (M and N)\n",
    "M, N = cm_fd.shape[:2]\n",
    "\n",
    "# Create subplots for each color moment (mean, standard deviation, skewness)\n",
    "fig, axes = plt.subplots(M, N, figsize=(27, 18))\n",
    "\n",
    "# Loop through each grid cell and visualize the color moments as stacked bar charts\n",
    "for i in range(M):\n",
    "    for j in range(N):\n",
    "        moments = cm_fd[i, j]\n",
    "        colors = [\"red\", \"green\", \"blue\"]\n",
    "\n",
    "        for c in range(3):\n",
    "            ax = axes[i, j]\n",
    "            ax.bar(\n",
    "                [\"Mean\", \"Std Dev\", \"Skewness\"],\n",
    "                moments[c],\n",
    "                color=colors[c],\n",
    "                bottom=np.sum(np.array(moments[:c])),\n",
    "            ) # stack channels\n",
    "            ax.set_ylim(\n",
    "                0, 3 * 255\n",
    "            )  # since max possible meanset can be [255, 255, 255] for entirely white grid cell\n",
    "            ax.set_title(f\"Grid Cell ({i}, {j})\")\n",
    "\n",
    "# Adjust spacing and show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature model 2: Histogram Oriented Gradients (HOG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gradient_histogram(grid_cell):\n",
    "    histograms = []\n",
    "\n",
    "    # Convert grid cell to NumPy array\n",
    "    grid_array = np.array(grid_cell, dtype=np.uint8)\n",
    "\n",
    "    # Compute the gradient using first-order central differences\n",
    "    dx = cv2.Sobel(\n",
    "        grid_array, cv2.CV_32F, dx=1, dy=0, ksize=1\n",
    "    )  # first order x derivative = [-1, 0, 1]\n",
    "    dy = cv2.Sobel(\n",
    "        grid_array, cv2.CV_32F, dx=0, dy=1, ksize=1\n",
    "    )  # first order y derivative = [-1, 0, 1]^T\n",
    "\n",
    "    # Compute magnitude and direction of gradients\n",
    "    magnitude = np.sqrt(dx**2 + dy**2)\n",
    "    direction = np.arctan2(dy, dx) * 180 / np.pi  # in degrees\n",
    "\n",
    "    # Compute HOG - 9 bins, counted across the range of -180 to 180 degrees, weighted by gradient magnitude\n",
    "    histogram, _ = np.histogram(direction, bins=9, range=(-180, 180), weights=magnitude)\n",
    "\n",
    "    histograms.append(histogram)\n",
    "\n",
    "    return histograms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_histograms_for_grid(grid):\n",
    "    histograms = [compute_gradient_histogram(grid_cell) for grid_cell in grid]\n",
    "    return np.array(histograms).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_histograms(grid_histograms):\n",
    "    return torch.Tensor(grid_histograms).view(10, 10, 9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
      " [[0.00000000e+00 1.32810621e+01 2.58313103e+01 2.50929794e+01\n",
      "   1.72324554e+02 1.97989902e+01 2.39442711e+01 5.65685415e+00\n",
      "   2.50000000e+01]\n",
      "  [2.23606801e+00 7.30056286e+00 3.94721375e+01 3.27564087e+01\n",
      "   8.50000000e+01 1.88437672e+01 2.49442711e+01 0.00000000e+00\n",
      "   7.00000000e+00]\n",
      "  [2.23606801e+00 4.24264050e+00 5.60000000e+01 1.77924175e+01\n",
      "   3.70000000e+01 1.07213497e+01 6.34721375e+01 1.29574175e+01\n",
      "   1.57082043e+01]\n",
      "  [0.00000000e+00 4.24264050e+00 3.90000000e+01 5.06449509e+00\n",
      "   9.00000000e+00 9.30713558e+00 3.50000000e+01 6.47870874e+00\n",
      "   1.60000000e+01]\n",
      "  [2.23606801e+00 4.24264050e+00 5.60000000e+01 6.47870874e+00\n",
      "   8.00000000e+00 9.30713558e+00 6.10000000e+01 8.48528099e+00\n",
      "   1.72360687e+01]\n",
      "  [9.47213554e+00 5.65685415e+00 2.98572998e+01 1.64984741e+01\n",
      "   6.60000000e+01 7.89292240e+00 1.03637924e+02 3.60067291e+01\n",
      "   7.80776901e+01]\n",
      "  [0.00000000e+00 1.41421354e+00 8.00000000e+00 1.41421354e+00\n",
      "   2.00000000e+00 1.41421354e+00 5.00000000e+00 1.41421354e+00\n",
      "   4.00000000e+00]\n",
      "  [2.23606801e+00 1.16475592e+01 2.12360687e+01 2.82842708e+00\n",
      "   6.60000000e+01 4.24264050e+00 1.00115036e+02 4.14030685e+01\n",
      "   1.54000000e+02]\n",
      "  [4.47213602e+00 1.77924175e+01 2.32360687e+01 2.82842708e+00\n",
      "   3.40000000e+01 2.82842708e+00 1.62360687e+01 2.16721935e+01\n",
      "   1.67166168e+02]\n",
      "  [0.00000000e+00 9.89949512e+00 1.00000000e+01 0.00000000e+00\n",
      "   3.00000000e+00 1.41421354e+00 1.10000000e+01 9.89949512e+00\n",
      "   1.78708206e+02]]\n",
      "\n",
      " [[2.23606801e+00 7.89292240e+00 7.88147507e+01 4.61280861e+01\n",
      "   1.24324554e+02 1.43716307e+01 2.22360687e+01 0.00000000e+00\n",
      "   1.30000000e+01]\n",
      "  [0.00000000e+00 1.01289902e+01 7.74164047e+01 5.29696579e+01\n",
      "   9.11622772e+01 9.89949512e+00 2.24721355e+01 5.99070454e+00\n",
      "   2.02360687e+01]\n",
      "  [0.00000000e+00 3.65028143e+00 5.75786858e+01 7.89292240e+00\n",
      "   4.80000000e+01 1.86142712e+01 3.00000000e+01 2.82842708e+00\n",
      "   1.50000000e+01]\n",
      "  [2.23606801e+00 8.48528099e+00 2.70000000e+01 1.07213497e+01\n",
      "   1.20000000e+01 1.29574175e+01 2.60000000e+01 8.48528099e+00\n",
      "   1.52360678e+01]\n",
      "  [0.00000000e+00 3.65028143e+00 1.90000000e+01 0.00000000e+00\n",
      "   3.00000000e+00 1.41421354e+00 2.00000000e+01 6.47870874e+00\n",
      "   4.00000000e+00]\n",
      "  [0.00000000e+00 7.07106781e+00 4.42360687e+01 7.89292240e+00\n",
      "   5.20000000e+01 1.49639902e+01 3.00000000e+01 1.27279215e+01\n",
      "   4.62360687e+01]\n",
      "  [0.00000000e+00 2.82842708e+00 9.00000000e+00 0.00000000e+00\n",
      "   1.30000000e+01 1.51934853e+01 7.62360687e+01 1.15432034e+01\n",
      "   3.19442711e+01]\n",
      "  [0.00000000e+00 9.30713558e+00 1.90000000e+01 1.41421354e+00\n",
      "   4.00000000e+00 4.24264050e+00 9.44721375e+01 4.95936012e+01\n",
      "   8.31803436e+01]\n",
      "  [4.47213602e+00 3.45798149e+01 2.01622772e+01 7.89292240e+00\n",
      "   3.20000000e+01 8.48528099e+00 3.67082024e+01 5.97481346e+01\n",
      "   1.27708206e+02]\n",
      "  [8.94427204e+00 8.75942001e+01 4.20000000e+01 2.82842708e+00\n",
      "   1.00000000e+01 2.82842708e+00 3.00000000e+01 3.83685303e+01\n",
      "   1.56106552e+02]]]\n"
     ]
    }
   ],
   "source": [
    "HOG_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Grayscale(num_output_channels=1),  # grayscale transform\n",
    "        transforms.Resize((100, 300)),  # resize to H:W=100:300\n",
    "        GridPartition(\n",
    "            rows=10, cols=10\n",
    "        ),  # partition into grid of 10 rows, 10 columns as a list\n",
    "        compute_histograms_for_grid,\n",
    "        combine_histograms,\n",
    "    ]\n",
    ")\n",
    "\n",
    "hog_fd = HOG_transform(sample_image)\n",
    "print(np.array(hog_fd))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing HOG as histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o3r17RETMnTu3wxfQiGh/e+7cudk3f/jhhzF27Ng4//zzY9CgQSs8ZmWbFy5cGIsWLcoxs9Oblt63qmNy7n7ppZciIuLMM8+M0047Le64445oamqK//u//4s333xzlTuX3lcE//73v+M3v/lNfO9732u/rUi7W1tb4/jjj4+RI0fGsGHD2jesu+660bdv3+U21vN5femll+Lyyy+PbbfdNu65556YOHFiHHvssXHddddFxCfP3Yqe26KcD53ZOHfu3OjXr1+H+7t37x4bbrhhXR/Hr371q+jevXsce+yxK7y/qLtZtTL2N6KcDdbfvPS3evRXf6k+/c2rDA3W3zz0Ny/9pWj0Ny/9zUd/q0d/9Zfq09+89Dcf/a0e/dVfqk9/89LffPS3evRXf6k+/c1Lf/PR3+rRX/2l+vQ3L/3NR3+rR3/1l+rT37z0N5+i9zeiPA3WX/2l+vQ3L/3NR3+rpyv0N6K8DV7b+luXC1v99Kc/jW7duq3y1wsvvNCp32vRokUxYcKEGDlyZDz22GPxyCOPxLBhw2L06NFV/SJezc0nn3xyDB06NA4//PCq7VuZau6ut84+ltbW1oiIOPXUU+Oggw6K4cOHxzXXXBPdunWLm266qbC7P23OnDmx//77x8EHHxxHHnlk9s2dccwxx8T06dPjxhtvrPeU1WptbY3ddtstfvnLX8auu+4aRx11VBx55JFxxRVX1Htalzd16tS45JJL4tprr41u3brVe85ar4z9rfbuXA3WX/2tFf2lM/S3WPTXa+BK6G+x6C+dob/For/6Wwn9LRb9pTP0t1j0V38rob/For90hv4Wi/7qbyX0t1j0l87Q32LRX/2thP4Wi/7SGfpbLPqrv5XQ32LRXzpDf4tFf/W3EvpbPGVpsP7Wj/4Wi/7qbyX0t3j0l9VZG/vbvR4f9MQTT4xvf/vbqzxmq6226tTvdcMNN8SsWbOipaUlGhoa2m9ramqK2267Lb7xjW/EgAEDYt68eR3eb+nbAwYMyL753nvvjX/+85/x17/+NSIiUkoREbHxxhvHqaeeGmedddZKN/fu3Tt69uzZqY9T7d0DBgyIxx9/fLlNS+9b+s9q7F6Rzj6W119/PSIidthhh/bbGxsbY6uttorZs2d3+rFUy5p+Dl577bXYZ599Ys8994yrrrqqw3HVOJer4Qc/+EHccccd8eCDD8bmm2/eYd/ixYtjwYIFHa4YOW/evKz7lrXpppt2OB8iIoYOHRo333xzRHzy3M2bNy823XTT9mPmzZsXu+yyS7adq9KZjQMGDIj58+d3eL+PPvoo3nzzzbo9/w899FDMnz+/w5VxP/744zjxxBPj4osvjlmzZhVyd1dVxv5We3euButvG/2tLv3NT3+pBv31GrgS+qu/ldJf/aWN/upvJfRXfyulv/pLG/3V30ror/5WSn/1lzb6q7+V0F/9rZT+6i9t9Fd/K6G/+lsp/dVf2uiv/lZCf/W3Uvqrv7TRX/2thP4Wp78R5Wqw/uovbfRXfyuhv/pbqa7Q34hyNnht7G9dLmy1ySabxCabbFKV3+v999+PhoaGDlciW/r20isHjhgxIk499dRYsmRJ9OjRIyIiJk2aFNttt100NTVl33zzzTd3uJrlE088Ed/5znfioYceiq233rp985133tnh/SZNmhQjRoxYo49Vzd0jRoyIs88+O+bPnx/9+vVr39S7d+/2L1rV2r0inX0sw4cPj8bGxpg5c2bstddeERGxZMmSmDVrVgwePLjTj6Va1uRzMGfOnNhnn33ar3K59JvFpapxLn8WKaX44Q9/GLfcckvcf//9MWTIkA73Dx8+PHr06BGTJ0+Ogw46KCIiZs6cGbNnz67KOVCpkSNHxsyZMzvc9q9//av9fBgyZEgMGDAgJk+e3B6ohQsXxpQpU2LixIm5565QZzaOGDEiFixYEFOnTo3hw4dHRNs38a2trbHHHnvUZfe4ceNi1KhRHW5rbm6OcePGxfjx4yOimLu7qjL2t9q7czVYf/W3mvS3fvSXatBfr4Erob/6Wyn91V/a6K/+VkJ/9bdS+qu/tNFf/a2E/upvpfRXf2mjv/pbCf3V30rpr/7SRn/1txL6q7+V0l/9pY3+6m8l9Fd/K6W/+ksb/dXfSuhv/fsbUc4G66/+0kZ/9bcS+qu/leoK/Y0oZ4PXyv6mgnvllVfStGnT0llnnZXWX3/9NG3atDRt2rT0zjvvpJRSev7551NjY2OaOHFimjFjRpo+fXo6/PDDU58+fdJrr72WUkppwYIFqX///mncuHFp+vTp6cYbb0y9evVKV155ZV02L+u+++5LEZHeeuut9tteeuml1KtXr3TSSSel559/Pl166aVpnXXWSXfffXdNNndm90cffZSGDRuW9ttvv/T000+nu+++O22yySbp5JNPruvuFTnuuOPSwIED0z333JNeeOGFNGHChNSvX7/05ptvdvqx5Pbqq6+mbbbZJu27777p1VdfTa+//nr7r6Vyn8vLmjhxYurTp0+6//77O+x7//332485+uij06BBg9K9996bnnzyyTRixIg0YsSILPtW5vHHH0/du3dPZ599dnrxxRfTn/70p9SrV690/fXXtx9z7rnnpr59+6bbbrstPfvss+mrX/1qGjJkSFq0aFG2ne+88077n7uISBdeeGGaNm1aeuWVVzq9cf/990+77rprmjJlSnr44YfTtttum8aOHVvX3csaPHhwuuiiizrcVo/drFoZ+9uZ3csqQoP1V39XR39rS3/1t0j012vgSuhvbehvbemv/haJ/upvJfS3NvS3tvRXf4tEf/W3EvpbG/pbW/qrv0Wiv/pbCf2tDf2tLf3V3yLRX/2thP7Whv7Wlv7qb5Hor/5WQn9rQ39rS3/1t0j0V38rob+1U8YG66/+sub0V38rob+1o7+1VcYG629Hhb+w1RFHHJEiYrlf9913X/sx//jHP9LIkSNTnz59UlNTU/rKV76SWlpaOvw+zzzzTNprr71SY2NjGjhwYDr33HPruvnTVhTVpbfvsssuad11101bbbVVuuaaa2q2ubO7Z82alQ444IDUs2fPtPHGG6cTTzwxLVmypK67V2Tx4sXpxBNPTP369UsbbLBBGjVqVJo+fXqHYzrzWHK65pprVvj8L3v9uZzn8rJWtu/Tn+NFixal73//+6mpqSn16tUrfe1rX+vwzUG93H777WnYsGGpsbExbb/99umqq67qcH9ra2v62c9+lvr3758aGxvTvvvum2bOnJl149KvBcv+OuKIIzq98Y033khjx45N66+/furdu3caP378Sr+hz7V7WSsKaz12s2pl7G9nd39aERqsv/q7OvpbW/qrv0Wiv14DV0J/a0N/a0t/9bdI9Fd/K6G/taG/taW/+lsk+qu/ldDf2tDf2tJf/S0S/dXfSuhvbehvbemv/haJ/upvJfS3NvS3tvRXf4tEf/W3EvpbG/pbW/qrv0Wiv/pbCf2tnbI2WH/rt3tZ+lsO+qu/ldDf2tHf2ipjg/W3o24ppRQAAAAAAAAAAAAAAAAAAAAAAADwGTXUewAAAAAAAAAAAAAAAAAAAAAAAABdgwtbAQAAAAAAAAAAAAAAAAAAAAAAUBUubAUAAAAAAAAAAAAAAAAAAAAAAEBVuLAVAAAAAAAAAAAAAAAAAAAAAAAAVeHCVgAAAAAAAAAAAAAAAAAAAAAAAFSFC1sBAAAAAAAAAAAAAAAAAAAAAABQFS5sBQAAAAAAAAAAAAAAAAAAAAAAQFW4sBUAAAAAAAAAAAAAAAAAAAAAAABV4cJWAAAAAAAAAAAAAAAAAAAAAAAAVIULWwEAAAAAAAAAAAAAAAAAAAAAAFAVLmwFAAAAAAAAAAAAAAAAAAAAAABAVbiwFQAAAAAAAAAAAAAAAAAAAAAAAFXx/9on27y04TVAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 4800x4800 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # The 'hog' should now contain the 10x10x9 feature descriptor\n",
    "# # Reshape it to a 10x10 grid of histograms, each of shape (9,)\n",
    "histograms = np.array(hog_fd.reshape(10, 10, 9))\n",
    "\n",
    "# # Visualize the histograms\n",
    "fig, axes = plt.subplots(10, 10, figsize=(48, 48))\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        histogram = histograms[i, j]\n",
    "        angles = np.linspace(-180, 180, 9, endpoint=False)\n",
    "\n",
    "        axes[i, j].bar(angles, histogram, width=40, align=\"edge\", color=\"b\", alpha=0.7)\n",
    "        axes[i, j].set_xlim(-180, 180)\n",
    "        axes[i, j].set_ylim(0, np.max(histogram) * 1.1)\n",
    "        axes[i, j].set_xticks([-180, -140, -100, -60, -20, 20, 60, 100, 140])\n",
    "        axes[i, j].set_title(f\"Grid Cell ({i}, {j})\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing HOG gradient directions as an arrow overlay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a blank canvas to visualize the HOG features\n",
    "block_size = 30\n",
    "canvas = np.zeros((10 * block_size, 10 * block_size, 3), dtype=np.uint8)\n",
    "\n",
    "# Define the arrow properties\n",
    "base_arrow_length = block_size * 3 / 4  # Adjust as needed\n",
    "arrow_color = (0, 255, 0)  # Green arrows for gradient direction\n",
    "\n",
    "# Loop through each block in the HOG feature descriptor\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        block = hog_fd[i, j]\n",
    "        x, y = j * block_size, i * block_size  # Top-left corner of the block\n",
    "\n",
    "        # Compute the center position of the block\n",
    "        center_x = x + block_size // 2\n",
    "        center_y = y + block_size // 2\n",
    "\n",
    "        # Find n most dominant directions (pairs of index and gradient magnitude in that direction)\n",
    "        num_dir = 1\n",
    "        dominant_directions = [pair for pair in sorted(\n",
    "                list(enumerate(block)), key=lambda x: x[1], reverse=True\n",
    "            )[:num_dir]\n",
    "        ]\n",
    "\n",
    "        for idx, (direction, magnitude) in enumerate(dominant_directions):\n",
    "            arrow_thickness = num_dir - idx # decreasing thickness\n",
    "            \n",
    "            # Compute end points\n",
    "            # arrow_start_x = int(\n",
    "            #     center_x - ((base_arrow_length * np.cos(np.deg2rad(direction * 40))) / 2)\n",
    "            # )\n",
    "            # arrow_start_y = int(\n",
    "            #     center_y - ((base_arrow_length * np.sin(np.deg2rad(direction * 40))) / 2)\n",
    "            # )\n",
    "            arrow_end_x = int(\n",
    "                center_x\n",
    "                + ((base_arrow_length * np.cos(np.deg2rad(direction * 40))) / 2)\n",
    "            )\n",
    "            arrow_end_y = int(\n",
    "                center_y\n",
    "                + ((base_arrow_length * np.sin(np.deg2rad(direction * 40))) / 2 )\n",
    "            )\n",
    "\n",
    "            # Draw the arrow on the canvas\n",
    "            cv2.arrowedLine(\n",
    "                canvas,\n",
    "                (center_x, center_y),\n",
    "                # (arrow_start_x, arrow_start_y),\n",
    "                (arrow_end_x, arrow_end_y),\n",
    "                arrow_color,\n",
    "                thickness=arrow_thickness,\n",
    "                tipLength=0.2,\n",
    "            )\n",
    "\n",
    "# Display the canvas with HOG arrows visualization alongside HOG overlay over resized image\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), sharex=True, sharey=False)\n",
    "\n",
    "ax1.imshow(canvas)\n",
    "ax1.set_title('Histogram of Oriented Gradients (HOG)')\n",
    "\n",
    "ax2.imshow(transforms.Resize((300,300))(sample_image))\n",
    "ax2.imshow(canvas, alpha=0.5) # overlay HOG on image\n",
    "# ax.imshow(transforms.Resize((300, 300))(sample_image))\n",
    "ax2.set_title('HOG Overlay (original resized to 300x300)')\n",
    "\n",
    "ax1.set_xticks(range(0, 300, 30))\n",
    "ax1.set_yticks(range(0, 300, 30))\n",
    "ax2.set_xticks(range(0, 300, 30))\n",
    "ax2.set_yticks(range(0, 300, 30))\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      |    865 MiB |    865 MiB |   1253 MiB | 397887 KiB |\n",
      "|       from large pool |    671 MiB |    671 MiB |    920 MiB | 254816 KiB |\n",
      "|       from small pool |    193 MiB |    212 MiB |    333 MiB | 143071 KiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         |    865 MiB |    865 MiB |   1253 MiB | 397887 KiB |\n",
      "|       from large pool |    671 MiB |    671 MiB |    920 MiB | 254816 KiB |\n",
      "|       from small pool |    193 MiB |    212 MiB |    333 MiB | 143071 KiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Requested memory      |    855 MiB |    855 MiB |   1235 MiB | 388820 KiB |\n",
      "|       from large pool |    662 MiB |    662 MiB |    906 MiB | 250016 KiB |\n",
      "|       from small pool |    193 MiB |    212 MiB |    329 MiB | 138804 KiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |    896 MiB |    896 MiB |   1034 MiB | 141312 KiB |\n",
      "|       from large pool |    700 MiB |    700 MiB |    760 MiB |  61440 KiB |\n",
      "|       from small pool |    196 MiB |    216 MiB |    274 MiB |  79872 KiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory |  31369 KiB |  90900 KiB |   1156 MiB |   1126 MiB |\n",
      "|       from large pool |  29248 KiB |  81760 KiB |    849 MiB |    821 MiB |\n",
      "|       from small pool |   2121 KiB |  10298 KiB |    307 MiB |    304 MiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |    2462    |    2474    |   12422    |    9960    |\n",
      "|       from large pool |     169    |     173    |     261    |      92    |\n",
      "|       from small pool |    2293    |    2305    |   12161    |    9868    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |    2462    |    2474    |   12422    |    9960    |\n",
      "|       from large pool |     169    |     173    |     261    |      92    |\n",
      "|       from small pool |    2293    |    2305    |   12161    |    9868    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |     133    |     141    |     175    |      42    |\n",
      "|       from large pool |      35    |      35    |      38    |       3    |\n",
      "|       from small pool |      98    |     108    |     137    |      39    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      32    |      58    |    3971    |    3939    |\n",
      "|       from large pool |      14    |      16    |      67    |      53    |\n",
      "|       from small pool |      18    |      46    |    3904    |    3886    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize allocations  |       0    |       0    |       0    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize GPU segments |       0    |       0    |       0    |       0    |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.memory_summary(device=None, abbreviated=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchvision.models as models\n",
    "\n",
    "# Load model\n",
    "model = models.resnet50(models.ResNet50_Weights.DEFAULT)\n",
    "\n",
    "# use GPU (Nvidia)\n",
    "if torch.cuda.is_available():\n",
    "    dev = torch.device(\"cuda\")\n",
    "    torch.cuda.empty_cache()\n",
    "else:\n",
    "    dev = torch.device(\"cpu\")\n",
    "\n",
    "print(f\"Using {dev}\")\n",
    "model = model.to(dev)\n",
    "\n",
    "# ResNet architecture in brief\n",
    "model.eval()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ResNet50 - Feature extractor module (for ease of use - attach hooks at multiple layers at once)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generalized feature extractor module for any PyTorch model\n",
    "\n",
    "class FeatureExtractor(torch.nn.Module):\n",
    "    def __init__(self, model, layers):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.layers = layers\n",
    "        self._features = {\n",
    "            layer: torch.empty(0) for layer in layers\n",
    "        }  # store layer outputs here\n",
    "\n",
    "        # Create hooks for all specified layers at once\n",
    "        for layer_id in layers:\n",
    "            layer = dict([*self.model.named_modules()])[layer_id]  # get actual layer in the model\n",
    "            layer.register_forward_hook(\n",
    "                self.save_outputs_hook(layer_id)\n",
    "            )  # register feature extractor hook on layer\n",
    "\n",
    "    # Hook to save output of layer\n",
    "    def save_outputs_hook(self, layer_id):\n",
    "        def fn(_module, _input, output):\n",
    "            self._features[layer_id] = output\n",
    "\n",
    "        return fn\n",
    "\n",
    "    # Forward pass returns extracted features\n",
    "    def forward(self, x):\n",
    "        _ = self.model(x)\n",
    "        return self._features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resize image to 224x224 with 3 channels for ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "resized_image = (\n",
    "    torch.Tensor(np.array(transforms.Resize((224, 224))(sample_image)).flatten())\n",
    "    .view(1, 3, 224, 224)\n",
    "    .to(dev)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'avgpool': torch.Size([1, 2048, 1, 1]), 'layer3': torch.Size([1, 1024, 14, 14]), 'fc': torch.Size([1, 1000])}\n"
     ]
    }
   ],
   "source": [
    "# Attach all hooks on model and extract features\n",
    "\n",
    "resnet_features = FeatureExtractor(model=model, layers=[\"avgpool\", \"layer3\", \"fc\"])\n",
    "\n",
    "features = resnet_features(resized_image)\n",
    "\n",
    "print({name: output.shape for name, output in features.items()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ResNet-AvgPool-1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.8399, 8.7920, 0.0692,  ..., 0.0000, 0.0000, 0.4238], device='cuda:0',\n",
      "       grad_fn=<ReshapeAliasBackward0>)\n",
      "tensor([5.3159, 0.0346, 0.5737,  ..., 0.0000, 0.0000, 0.2119], device='cuda:0',\n",
      "       grad_fn=<MeanBackward1>)\n"
     ]
    }
   ],
   "source": [
    "avgpool_2048 = features[\"avgpool\"]\n",
    "\n",
    "# Reshape the vector into row pairs of elements and average across rows\n",
    "avgpool_1024 = torch.mean(avgpool_2048.view(-1, 2), axis=1)\n",
    "\n",
    "print(avgpool_2048.flatten())\n",
    "print(avgpool_1024)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ResNet-Layer3-1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          ...,\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000]],\n",
      "\n",
      "         [[ 5.0407,  8.4676,  8.9004,  ..., 11.0762,  9.5168, 11.4591],\n",
      "          [ 4.8538,  9.3801, 10.1979,  ..., 18.1353, 10.1105, 13.3719],\n",
      "          [ 6.2345, 13.2864, 13.7493,  ..., 19.6763, 15.1503, 13.7038],\n",
      "          ...,\n",
      "          [12.7917, 28.0994, 30.1812,  ..., 23.6287, 21.4789, 20.7834],\n",
      "          [ 9.0382, 19.9966, 26.6109,  ..., 23.3203, 19.5199, 18.7572],\n",
      "          [ 6.4684, 16.9173, 14.7637,  ..., 13.9358,  8.7940, 11.2343]],\n",
      "\n",
      "         [[ 2.3567, 10.6136, 42.1683,  ..., 10.7147, 11.1157,  2.5210],\n",
      "          [ 8.7863,  9.3639, 30.8021,  ...,  5.7335, 10.8095,  0.6209],\n",
      "          [10.8371, 12.9079, 33.7315,  ...,  2.0164, 11.2772,  5.3151],\n",
      "          ...,\n",
      "          [ 0.0000,  0.0000,  0.0000,  ..., 12.1474,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  7.4394,  1.3967,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  6.0191,  7.2032,  0.0000]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  4.8448],\n",
      "          ...,\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000]],\n",
      "\n",
      "         [[ 3.5423,  2.7825,  4.9552,  ...,  3.5812,  3.1950, 26.2831],\n",
      "          [ 8.1167, 39.0223, 46.6680,  ...,  0.9467, 31.2026, 43.7220],\n",
      "          [ 3.3023,  3.1652,  9.1928,  ...,  0.5595,  4.1617,  0.0000],\n",
      "          ...,\n",
      "          [12.3539,  0.0000,  2.1233,  ..., 16.0220,  1.7682,  0.0000],\n",
      "          [ 5.8694,  0.0000, 11.8233,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 3.0764,  0.0000,  6.1394,  ...,  0.0000,  2.0365,  0.0000]],\n",
      "\n",
      "         [[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          ...,\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "          [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000]]]],\n",
      "       device='cuda:0', grad_fn=<ReluBackward0>)\n",
      "tensor([ 0.0000, 21.4303,  7.7714,  ...,  0.0570,  5.2553,  0.1071],\n",
      "       device='cuda:0', grad_fn=<MeanBackward1>)\n"
     ]
    }
   ],
   "source": [
    "layer3_1024_14_14 = features[\"layer3\"]\n",
    "\n",
    "# Reshape the vector into 1024 rows of 196 elements and average across rows\n",
    "layer3_1024 = torch.mean(layer3_1024_14_14.view(1024, -1), axis=1)\n",
    "\n",
    "print(layer3_1024_14_14)\n",
    "print(layer3_1024)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ResNet-FC-1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.9137e+01,  2.6573e+01,  1.2918e+01,  2.3530e+01,  2.4001e+01,\n",
      "          3.2967e+01,  3.8136e+00,  5.1861e+00, -1.3473e+01,  4.0824e+01,\n",
      "          2.2985e+00, -2.8794e+01, -7.9621e+00,  9.5298e+00, -1.1334e+01,\n",
      "         -3.7700e+00, -2.8737e+01,  5.3688e+00, -2.2668e-01,  3.1083e+01,\n",
      "          1.2332e+01,  6.0961e+00,  1.2618e+01,  1.2539e+01,  4.4857e-01,\n",
      "         -6.1363e+00, -3.6046e+01,  1.5961e+01,  2.1800e+01,  3.8819e+01,\n",
      "         -1.5190e+01,  1.5157e+01,  1.2280e+01,  9.7240e+00,  2.5503e+01,\n",
      "          1.7647e+00,  1.3362e+01, -2.4889e+01,  7.3737e+01,  1.0355e+01,\n",
      "         -2.1421e+01,  4.3789e+01,  4.3061e+01,  6.2216e+01, -1.0180e+01,\n",
      "         -1.6713e+01, -1.6305e+01, -7.5167e+00, -1.7158e+00,  1.4992e+01,\n",
      "         -1.0222e+00,  6.6237e+00,  4.1793e+01,  4.2032e+01, -5.3093e+01,\n",
      "          9.4202e+00, -1.1857e+01, -4.3591e+01,  1.4767e+01,  1.9185e+01,\n",
      "          2.0064e+02,  2.3772e+01,  1.1698e+01, -5.8784e+00,  3.5569e+01,\n",
      "          4.8605e+01,  9.0204e+00, -1.0869e+01, -5.8653e+00,  2.3622e+01,\n",
      "         -3.8949e+01,  1.8964e+01, -5.2852e+01, -2.3524e+01, -4.8324e+01,\n",
      "         -5.4496e+01,  1.7265e+01, -5.6380e+00, -3.5020e+01, -2.5843e+01,\n",
      "          1.4726e+01, -9.7950e+00, -1.5128e+01, -4.6746e+01, -1.2438e+01,\n",
      "          2.7828e+01,  3.1110e+01,  2.4905e+01,  3.4063e+00,  5.3606e+00,\n",
      "         -3.8463e+01, -2.0978e+01, -1.2148e+01, -7.5806e+00, -3.5966e+01,\n",
      "          1.0656e-01,  1.9615e+01, -8.3334e+00, -4.5493e+00,  1.2688e+01,\n",
      "          2.0768e+01, -1.8333e+01, -3.1807e+01,  5.6364e+01, -9.3830e+00,\n",
      "         -5.6871e+01, -1.9824e+01, -4.6257e+00,  2.6429e+01, -2.2983e+00,\n",
      "          3.8949e+01,  1.9928e+01, -3.1417e+00, -3.5863e+00, -3.0098e+00,\n",
      "         -4.7480e+00,  4.3366e+00,  1.4842e+01, -2.3426e+01, -3.2668e+01,\n",
      "         -4.1153e+01,  2.7980e+01, -1.5139e+01,  2.4511e+01, -8.8192e+01,\n",
      "         -8.9311e+00,  8.0358e+01,  6.9539e+00,  2.0210e+01,  1.1859e+01,\n",
      "          3.3418e+01, -2.5281e+00,  2.2073e+01, -1.0326e+01, -1.7064e+01,\n",
      "         -1.8120e+01, -6.8070e-01,  7.7647e+00, -2.7914e+01, -2.0909e+01,\n",
      "         -2.3790e+01, -2.8975e+01, -3.6210e+01, -1.2425e+01,  4.0598e+01,\n",
      "          2.4731e+01, -2.0666e+00, -6.2624e+01,  4.3363e+01,  3.0750e+01,\n",
      "          4.1587e+01,  2.3907e+01,  9.9128e+00,  1.6809e+01,  2.3355e+01,\n",
      "          5.1597e+01,  2.1279e+01, -1.3519e+01,  2.3115e+00, -1.6876e+01,\n",
      "          7.1189e+00,  3.2410e+01,  3.8460e+01,  3.7881e+01,  2.8153e+01,\n",
      "          9.5478e+01,  6.0410e+01,  2.3276e+01,  6.6044e+01,  6.8400e+00,\n",
      "          5.5431e+01,  2.2693e+01,  1.3626e+01, -1.3745e+00,  3.1962e+01,\n",
      "          3.9741e+01,  2.1039e+01,  3.6592e+01,  2.2208e+01,  5.2005e+01,\n",
      "          5.1948e+01, -1.0310e+01,  2.9719e+01,  5.6508e+01,  7.1071e+01,\n",
      "         -4.8667e+00,  2.3205e+01,  7.4669e+01, -9.2749e+00,  2.0102e+01,\n",
      "          1.5380e+01,  6.0694e+01,  7.4842e+01,  9.0952e+01, -1.0194e+01,\n",
      "          1.2167e+02,  1.6317e+01, -2.8178e+01,  8.8402e+01,  6.8242e+01,\n",
      "          3.0583e+01,  2.4264e+01,  3.8485e+01,  1.0682e+02,  1.0269e+00,\n",
      "          7.1469e+01,  4.2180e+01,  1.8774e+01,  2.6515e+01,  8.5264e+01,\n",
      "          4.3061e+01, -1.2876e+01,  2.0593e+01,  3.0470e+01,  2.9683e+01,\n",
      "          3.4667e+01,  3.6230e+01,  6.5101e+01,  2.4114e+01, -2.9179e+01,\n",
      "         -1.1210e+01,  6.6902e+01, -2.1017e+01,  6.0887e+01,  3.6309e+01,\n",
      "          3.6140e+00, -2.9488e+01,  7.7537e+01,  7.8150e+00,  5.6825e+01,\n",
      "          9.2847e+01, -3.9856e+01,  2.1794e+01,  2.5136e+01,  8.0551e+01,\n",
      "          6.4929e+01,  2.9171e+01,  8.1020e+01,  4.0289e+01,  2.3340e+01,\n",
      "         -5.0174e+01,  5.4576e+01,  1.1611e+02,  5.4578e+01,  1.3173e+01,\n",
      "          4.0046e+01,  5.8507e+01,  8.2102e+00, -4.9891e+00,  7.0474e+00,\n",
      "          5.9474e+01,  5.0094e+01,  2.4758e+01,  1.1338e+01,  2.8775e+01,\n",
      "          1.5780e+01,  4.0971e+01,  6.6056e+01,  1.9702e+01, -4.1424e-01,\n",
      "          1.9668e+01,  2.6641e+01,  9.5265e+00,  5.7025e+01,  4.8952e+01,\n",
      "          1.1065e+01,  3.9324e+01, -1.7641e+01, -3.3407e+00,  1.1485e+01,\n",
      "          5.0039e+01,  3.8337e+01, -1.6570e+01,  1.3714e+01,  3.3709e+01,\n",
      "          1.9855e+01, -1.0354e+00, -3.0161e+01,  1.1551e+01,  1.4794e+01,\n",
      "         -1.8569e+01, -4.7907e+01,  4.2187e+01, -4.5514e+01,  3.2515e+01,\n",
      "         -2.0470e+01,  3.6095e+00, -6.6406e+00,  1.5356e+01, -1.8384e+01,\n",
      "         -4.0868e+01,  1.2609e+01,  4.5202e+01, -1.3498e+01,  5.6710e+00,\n",
      "         -2.5848e+01,  5.5118e+01,  5.7272e+01, -1.9021e+01,  2.4003e+01,\n",
      "         -4.4167e+01, -5.6767e+01, -2.2670e+01, -4.7065e+00, -1.1088e+01,\n",
      "         -5.5830e+01, -2.1061e+01, -1.0460e+01, -1.8849e+01, -4.3281e+01,\n",
      "         -1.8867e+01, -6.4717e+01, -7.9229e+00, -4.1887e+01, -8.6040e+00,\n",
      "         -1.4378e+01, -3.2788e+01, -2.4396e+01, -1.0177e+01,  1.5284e+01,\n",
      "          4.7197e+00, -3.5546e+01, -2.2263e+01, -4.9641e+01, -7.0908e-01,\n",
      "         -1.5311e+01, -2.0079e+01,  1.9611e+00, -3.6088e+00, -7.3762e+00,\n",
      "          1.8117e+01, -1.2670e+01, -1.5833e+01, -5.6403e+01, -1.4460e+01,\n",
      "          3.3584e+01, -1.4680e+01,  1.8473e+01,  3.3481e+00,  3.1159e+01,\n",
      "          2.4525e+01, -2.9938e-01,  8.4721e+00,  1.7866e-01,  4.6863e+01,\n",
      "          3.2673e+01, -3.0175e+01,  2.8326e+01, -1.2070e+01,  2.8631e+01,\n",
      "          3.0556e+01,  9.8744e+00, -1.6508e+01,  3.1292e+01,  6.1547e+01,\n",
      "          7.5226e+00,  3.4126e+01,  1.2628e+01, -4.3422e+01,  6.7448e+01,\n",
      "          3.3953e+01,  3.3366e+00,  4.8471e+00, -2.7114e+01,  1.4546e+01,\n",
      "         -1.1629e+01, -3.0626e+01,  7.0284e+00, -1.1486e+01,  1.3993e+01,\n",
      "          9.6301e+00,  7.3167e+01, -5.7450e+00,  1.8842e+01,  2.2013e+01,\n",
      "          5.7907e+00,  2.8085e+00, -1.3821e+01, -2.6377e+00, -1.2604e+01,\n",
      "          8.4316e+01, -1.4051e+01,  4.2690e+00, -2.8252e+00,  6.2757e+01,\n",
      "          1.8336e+01, -8.1506e-01,  1.0152e+00,  4.0688e+00,  1.4054e+01,\n",
      "          1.5931e+01,  2.8655e+00,  2.8298e+01,  3.5059e+00,  3.5241e+01,\n",
      "          4.7932e+01, -7.4239e+00,  2.8017e+01, -3.7022e+01,  3.6968e+01,\n",
      "         -1.3616e+01, -3.5988e+01,  6.0927e+01, -5.5721e+01, -3.0496e+01,\n",
      "         -3.5095e+01,  3.7705e+01, -1.0972e+01,  1.3424e+00, -7.1245e+00,\n",
      "         -3.6964e+01, -7.7948e+01, -5.3895e+01,  3.1151e+01, -1.2711e+01,\n",
      "         -7.0327e+01,  5.3746e+01, -7.9876e+01,  1.0760e+01, -5.7234e+00,\n",
      "          3.6784e+01,  3.4122e+01,  2.3690e+01,  1.0308e+01, -7.3465e+01,\n",
      "         -2.0185e+01, -1.9334e+01, -2.2627e+01, -4.3994e+01,  1.0145e+01,\n",
      "          1.6689e+01,  4.2458e+01,  5.7363e+00, -2.9536e+01, -4.7392e+01,\n",
      "         -2.9725e+01,  2.6052e+01, -1.4535e+01, -5.8372e+01, -3.0552e+01,\n",
      "         -2.4599e+01, -6.9166e+01,  5.4890e+00, -1.8398e+01,  2.8679e+01,\n",
      "          1.2638e+01, -6.5351e+01,  3.3826e+01,  1.1317e+01, -8.9846e+00,\n",
      "         -1.4411e+01,  2.8822e+02, -4.7291e+01,  6.3492e+01, -6.9248e+01,\n",
      "          3.7973e+00,  2.4367e+01,  3.5558e+01, -4.8132e+01,  1.4458e+01,\n",
      "         -3.1017e+01,  6.3870e+01,  7.0341e+01,  4.0150e+01,  1.1628e+02,\n",
      "         -2.8542e+00, -7.2545e+01, -6.4737e+01, -2.3359e-01, -1.9641e+01,\n",
      "          7.0322e+00, -8.2951e+00, -6.1010e+00, -1.8290e+01, -6.0170e+01,\n",
      "          2.1431e+00, -3.8345e+01, -8.3541e+01, -6.5383e+01, -1.4218e+01,\n",
      "         -5.7455e+01,  4.7379e+01, -3.8950e+01, -3.4892e+01, -2.8443e+01,\n",
      "          1.1886e+01, -1.2424e+01, -3.8868e+01,  5.2372e+01, -4.9960e+01,\n",
      "          1.7689e+01,  2.0902e+00,  4.1918e+00, -1.5068e+01,  4.9196e+01,\n",
      "          1.7268e+01, -3.2239e+00,  1.7989e+01, -5.0241e+01,  2.8635e+01,\n",
      "         -3.5370e+01,  3.6481e+01, -4.6790e+01,  4.1956e+01, -1.1656e+01,\n",
      "         -3.5140e+01,  4.3526e+01, -3.6326e+01,  1.9510e+01, -5.5316e+01,\n",
      "         -4.1406e+01,  1.4158e+00,  9.9397e+00, -4.2710e+01,  4.5476e+01,\n",
      "         -2.4606e+01, -4.9811e+01,  2.4419e+01,  1.8858e+00, -2.7279e+01,\n",
      "          3.1655e+01,  2.4185e+01, -2.2954e+01,  8.1428e+01, -1.4325e+01,\n",
      "         -2.7662e+01, -4.2940e+00,  3.4567e+01, -5.7497e+01,  4.0834e+00,\n",
      "         -3.0168e+01,  6.3671e+00, -1.1505e+01, -8.5797e+01, -1.7238e+01,\n",
      "         -9.4269e+00,  1.5808e+00,  8.6950e-02,  1.1770e+01, -5.5255e+01,\n",
      "         -2.5803e+01, -1.0860e+01,  4.8454e+01,  3.8328e+01, -3.4036e+01,\n",
      "         -6.1584e+01, -2.0895e+00, -3.5642e+01, -2.3661e+01, -5.5576e+01,\n",
      "         -3.1315e+01, -6.5401e+00,  5.5797e+01, -2.3741e+00, -4.8415e+01,\n",
      "         -1.4946e+01, -5.7058e+01,  1.5379e+00,  2.3751e+01,  5.2096e+01,\n",
      "          5.9637e+01, -2.4779e+01,  1.3491e+01,  7.1367e-01, -5.0342e+01,\n",
      "         -4.1183e+01,  3.7905e+00,  2.6641e+01,  1.2941e+01, -2.1756e+00,\n",
      "          2.8191e+01, -6.0664e+01,  9.2960e+01,  2.0649e+01,  1.8499e+01,\n",
      "         -1.4260e+01, -4.8213e+01,  2.5566e+01, -4.5764e+01, -6.2208e+00,\n",
      "         -5.0164e+01,  3.9069e+00, -7.8955e+01, -2.5887e+01,  2.7402e+00,\n",
      "         -6.0652e+00, -2.1921e+01,  7.5494e+01, -3.2003e+00,  1.2448e+01,\n",
      "         -2.6378e+01, -1.0503e+02, -5.1537e+00,  5.9451e+01, -5.4255e+00,\n",
      "          1.3674e+01,  1.3901e+02, -1.4085e+01,  4.3661e+00, -3.7537e-01,\n",
      "          1.5341e+02, -1.8558e+01, -1.9617e+01, -1.2259e+01, -1.2904e+00,\n",
      "          2.9822e+01, -1.5183e+01, -2.2380e+01, -1.3114e+00, -1.9271e+01,\n",
      "         -2.0128e+01,  1.9436e+01, -2.9589e+01,  4.0330e+01, -4.4414e+01,\n",
      "          3.0084e+01,  5.5420e+01, -3.7801e+01,  1.5842e+02, -8.2126e+00,\n",
      "         -5.3494e+01, -1.0035e+01, -3.8463e+01,  2.8277e+02, -5.5625e+01,\n",
      "         -3.1926e+01,  2.9022e+01, -5.5759e+00, -2.1608e+01, -8.4087e+00,\n",
      "         -3.6824e+01, -3.9066e+01,  1.0311e+02, -1.0440e+01, -3.0347e+01,\n",
      "         -2.6532e+01, -2.0280e+01, -3.4263e+01,  2.3570e+01,  3.3103e+01,\n",
      "         -8.6941e+01,  8.2241e+01,  1.5924e+01,  2.7222e+00,  5.9304e+01,\n",
      "          4.5058e+01,  7.5515e+00, -3.1735e+01, -3.7293e+01, -9.0410e+00,\n",
      "          1.9139e+01, -5.4337e+01,  8.0273e-01, -1.8867e+01,  8.8235e+00,\n",
      "          2.1774e+01, -4.7140e+01,  6.7111e+00, -2.5560e+01, -1.8620e+01,\n",
      "         -2.1442e+01, -2.2015e+00, -1.4082e+01, -1.4561e+01,  2.8844e+00,\n",
      "          1.9553e+01, -6.2259e+01, -3.3990e+00, -3.7375e+01, -1.1860e+01,\n",
      "         -1.0388e+01, -2.1040e+01, -2.2966e-01,  4.3068e+01,  2.3263e+01,\n",
      "         -3.0580e+01, -6.0788e+00,  5.9072e+01, -1.4420e+01,  1.1896e+02,\n",
      "         -4.1425e+01, -1.2253e+01, -1.0495e+01,  1.9547e+01,  1.1522e+01,\n",
      "         -6.0899e+01, -4.5501e+01, -4.8784e+01, -6.7811e+01, -6.8417e+01,\n",
      "          3.5252e+00, -3.6040e+01,  3.3556e+00, -8.3960e-01, -6.9306e+01,\n",
      "         -1.7313e+01,  1.6539e+01,  5.2081e+01, -2.4058e+01,  1.7890e+01,\n",
      "          5.0073e+01, -4.0821e+01,  1.2417e+01, -4.2135e+01,  2.8563e+01,\n",
      "         -3.8689e+01, -5.7395e+01, -5.6364e+01, -4.8169e+01, -4.3573e+01,\n",
      "         -4.1638e+00, -4.2959e+01, -2.5426e+01, -4.8638e+01,  5.7608e+01,\n",
      "         -2.4898e+01, -3.0556e+01,  1.1909e+01, -1.0060e+01,  3.6817e+00,\n",
      "          2.4898e+00, -4.1356e+01,  8.2820e+01,  9.9215e+00, -4.2198e+01,\n",
      "          3.0077e+00,  1.8933e+01, -1.0460e+01,  2.4943e+01, -7.3883e+01,\n",
      "         -1.4280e+01,  2.1287e+02, -1.8647e+01,  7.7476e+01, -2.4999e+01,\n",
      "         -3.0370e+00,  3.3763e+01,  3.7551e+01, -5.0873e+01, -6.2179e+01,\n",
      "          4.4669e+01, -2.2824e+01,  1.4737e+01, -1.7245e+01,  3.7855e+01,\n",
      "          2.2968e+01,  2.2008e+01,  8.4642e+01,  5.6674e+01,  8.5621e+01,\n",
      "          1.1880e+02,  7.6051e+00, -2.3053e+01,  2.5563e+01, -1.9032e+01,\n",
      "         -5.5356e+01, -1.4677e+01, -3.3103e+01,  9.2400e+00, -2.3014e+01,\n",
      "         -3.1556e+01,  2.8067e+00, -5.2998e+01,  1.8515e+01, -8.7491e+00,\n",
      "         -9.1976e+00,  1.9762e+00,  1.2175e+01, -4.1488e+00,  7.5634e-01,\n",
      "         -4.1713e+01, -5.0994e+01, -5.3960e+00, -2.4181e+01, -4.8250e+01,\n",
      "          1.5827e+01,  7.4855e-01,  2.6394e+01, -3.9540e+01, -1.1770e+01,\n",
      "         -6.0993e+01, -8.5240e+00, -1.1489e+01, -7.3342e+00, -3.3686e+01,\n",
      "         -2.4242e+01,  4.5523e+00, -2.3993e+01, -7.6946e+01, -5.7153e+01,\n",
      "         -6.0288e+01, -1.2612e+01,  7.0545e+01,  3.5725e+01, -4.2106e+01,\n",
      "         -3.2127e+01,  4.5788e+01,  4.5440e+01, -1.8483e+01, -1.0414e+01,\n",
      "          1.5929e+01,  6.4309e+01,  2.1167e+01, -2.6050e+01, -2.0833e+01,\n",
      "          3.9158e+01, -4.3313e+01,  1.0207e+00, -3.6356e+00, -4.5458e+01,\n",
      "         -6.9104e+01,  2.3217e+01,  2.7410e+01,  8.8609e+01, -1.2790e+01,\n",
      "         -4.8168e+01,  3.9662e+01, -1.6752e+01,  5.4221e+01, -5.0725e+01,\n",
      "         -1.1266e+01, -3.0464e+00, -2.1626e+01, -9.4375e-01, -5.7354e+01,\n",
      "         -3.7752e+00, -3.0178e+01, -4.9578e+01,  6.8099e+01, -4.3223e+01,\n",
      "          4.7795e+00,  2.7946e+01, -6.0596e+01, -4.6932e+01,  5.3342e+01,\n",
      "         -2.5690e+01, -4.0409e+01, -1.8477e+01, -5.7708e+00, -4.5635e+01,\n",
      "         -4.1887e+01, -2.8802e+01,  2.7082e+01,  1.2724e+01, -1.9736e+00,\n",
      "          4.4626e+01,  6.2695e+01,  1.8641e+01, -5.5323e+01,  1.4127e+01,\n",
      "         -7.7912e+00, -1.4208e+01,  3.2146e+01, -1.6924e+01, -6.1842e+01,\n",
      "          1.4737e+01, -2.5401e+01, -4.0083e+01, -3.8090e+01, -5.1528e+00,\n",
      "          1.1721e+00,  5.6254e+00, -3.2128e+01,  4.6560e+00, -5.3339e+00,\n",
      "         -1.1470e+02, -2.3319e+01,  1.9491e-01, -3.8114e+01,  1.7095e+01,\n",
      "          2.6106e+00, -2.2992e+01,  3.3210e+01, -1.7601e+01, -8.7665e+00,\n",
      "          2.3063e+01,  4.0788e+01, -3.3634e+00, -4.6971e+00, -1.6054e+01,\n",
      "         -7.5381e-01, -2.4830e+01,  3.0246e+01, -1.3012e+00, -1.7947e+01,\n",
      "         -1.4875e+01, -6.0745e+01, -2.7892e+01, -4.1659e+01, -5.9600e+00,\n",
      "         -3.9064e+01, -1.1155e+01,  2.5594e+01, -7.2732e+01, -3.2140e+01,\n",
      "         -2.1684e+01, -5.4229e+01, -6.9800e+01,  4.5362e+01,  4.8975e+01,\n",
      "         -3.2218e+01, -5.0037e+01, -3.2632e+01,  1.9321e+01, -7.5254e+01,\n",
      "         -3.7978e+01, -1.0885e+01,  3.2710e+01, -3.4976e+01,  1.1790e+01,\n",
      "          6.1619e+01, -6.9152e+01,  1.6315e+01, -4.3740e+00, -5.8171e+01,\n",
      "         -5.2712e+01,  2.1681e-01,  2.1471e+00, -2.1898e+00, -8.4105e+00,\n",
      "         -1.0898e+01,  1.7609e+01, -6.8120e+01, -6.5330e+01, -4.7274e+00,\n",
      "         -1.9437e+01, -3.0543e+01, -3.8935e+00, -1.9276e+01, -2.1230e-01,\n",
      "          1.3115e+01, -4.1050e+01, -2.1992e+01, -3.9226e+01, -2.7945e+01,\n",
      "         -2.5003e+01, -4.0463e+01, -1.7846e+01, -1.2938e+01, -9.2372e-01,\n",
      "         -2.5728e+01, -4.8689e+01, -2.5524e+01, -5.3136e+01, -3.7633e+01,\n",
      "         -4.7456e+01, -3.3163e+01, -1.8113e+01,  2.0470e+00, -2.7294e+01,\n",
      "         -1.3399e+01, -9.7914e+00, -4.5892e+01, -8.3083e+00, -1.5716e+01,\n",
      "         -5.8087e+01, -4.0284e+01, -4.5363e+01, -5.3498e+01, -4.8949e+01,\n",
      "          6.5357e+01, -6.7774e+01, -1.8758e+01, -4.7766e+01, -6.2769e+01,\n",
      "         -7.6437e+00,  1.9680e+01, -8.5755e+00, -2.3984e+01,  1.9154e+01,\n",
      "         -4.0768e+01,  1.1264e+02, -2.7336e+01,  5.8752e+01, -2.0102e+01,\n",
      "         -3.0633e+01,  4.0709e+00, -3.5420e+01,  3.5555e+01, -1.7835e+01,\n",
      "         -1.2161e+01, -2.2525e+01, -8.9280e+00, -9.0564e+00, -2.1945e+01,\n",
      "         -1.3962e+01, -8.7918e+00, -2.8135e+01, -2.6216e+01, -6.0851e+01,\n",
      "          5.7569e+00, -1.7618e+01, -5.0559e+01, -1.8148e+01,  2.4784e+01,\n",
      "         -7.9058e+00, -3.6890e+01, -1.6334e+01, -2.5645e+01,  4.1136e+00]],\n",
      "       device='cuda:0', grad_fn=<AddmmBackward0>)\n"
     ]
    }
   ],
   "source": [
    "fc_1000 = features['fc']\n",
    "\n",
    "print(fc_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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