{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1eab7469",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/pranjul/.cache/torch/hub/pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model_dg1 = deepgaze_pytorch.DeepGazeI(pretrained=True).to(DEVICE)\n",
    "\n",
    "image = face()\n",
    "\n",
    "# load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "# you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "# alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "# rescale to match image size\n",
    "centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "# renormalize log density\n",
    "centerbias -= logsumexp(centerbias)\n",
    "\n",
    "image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "log_density_prediction = model_dg1(image_tensor, centerbias_tensor)\n",
    "\n",
    "f, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
    "axs[0].imshow(image)\n",
    "#axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "#axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "axs[0].set_axis_off()\n",
    "axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0])  # first image in batch, first (and only) channel\n",
    "#axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "#axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "axs[1].set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0a46c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pranjul/.local/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n",
      "/home/pranjul/.local/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.3.12, the latest is 0.5.3.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import helper_consol as helper\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "import rsatoolbox\n",
    "import torchvision\n",
    "import rsatoolbox.data as rsd # abbreviation to deal with dataset\n",
    "import rsatoolbox.rdm as rsr\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11c42ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa7ba50d5e0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pareidolia hypothesis - faces and pareidolia are same\n",
    "# all distances are 0 between and within faces and pareidolia, but 1 between faces/pareidolia and objects\n",
    "a = np.zeros((32,32))\n",
    "b = np.zeros((32,32))\n",
    "c = np.ones((32,32))\n",
    "d = np.zeros((32,32))\n",
    "e = np.zeros((32,32))\n",
    "f = np.ones((32,32))\n",
    "g = np.ones((32,32))\n",
    "h = np.ones((32,32))\n",
    "i = np.zeros((32,32))\n",
    "hypo_1 = np.bmat([[a, b, c], [d, e, f], [g, h, i]])\n",
    "plt.imshow(hypo_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0858982f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa7ba469a00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# face hypothesis - objects and pareidolia are same\n",
    "# all distances are 0 between and within objects and pareidolia, but 1 between objects/pareidolia and faces\n",
    "a = np.zeros((32,32))\n",
    "b = np.ones((32,32))\n",
    "c = np.ones((32,32))\n",
    "d = np.ones((32,32))\n",
    "e = np.zeros((32,32))\n",
    "f = np.zeros((32,32))\n",
    "g = np.ones((32,32))\n",
    "h = np.zeros((32,32))\n",
    "i = np.zeros((32,32))\n",
    "hypo_2 = np.bmat([[a, b, c], [d, e, f], [g, h, i]])\n",
    "plt.imshow(hypo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "900bdbc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restored from: ../../../raid/katha/BranchingNets/checkpoints/vgg/face_inanimate_400k_facesconsolidated/epoch_110.pth.tar\n"
     ]
    }
   ],
   "source": [
    "network_name = 'face_inanimate_400k_facesconsolidated'\n",
    "config = helper.Config(config_file='./configs/vgg/' + network_name + '.yaml')\n",
    "model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=False, epoch=110, consol=True)\n",
    "# model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=True, epoch=-1)\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56cc01a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#network_name = 'face_inanimate_400k_facesconsolidated'\n",
    "#config = helper.Config(config_file='./configs/vgg/' + network_name + '.yaml')\n",
    "#model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=False, epoch=110, consol=True)\n",
    "# model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=True, epoch=-1)\n",
    "model_dg1 = model_dg1.cuda()\n",
    "\n",
    "'''\n",
    "# Define the layer(s) you want to extract activations from\n",
    "layers = [model.module.features[1], model.module.features[3], model.module.features[6],\n",
    "          model.module.features[8], model.module.features[11], model.module.features[13],\n",
    "          model.module.features[15], model.module.features[18], model.module.features[20],\n",
    "          model.module.features[22], model.module.features[25], model.module.features[27],\n",
    "          model.module.features[29], model.module.classifier[1], model.module.classifier[4]]\n",
    "\n",
    "\n",
    "layers = [model.features[1], model.features[3], model.features[6],\n",
    "          model.features[8], model.features[11], model.features[13],\n",
    "          model.features[15], model.features[18], model.features[20],\n",
    "          model.features[22], model.features[25], model.features[27],\n",
    "          model.features[29], model.classifier[1], model.classifier[4]]\n",
    "'''\n",
    "\n",
    "layers = [model_dg1.features.features[1].features[1], model_dg1.features.features[1].features[4],\n",
    "         model_dg1.features.features[1].features[7], model_dg1.features.features[1].features[9],\n",
    "         model_dg1.features.features[1].features[11], model_dg1.features.features[1].classifier[2],\n",
    "         model_dg1.features.features[1].classifier[5], model_dg1.readout_network.conv0,\n",
    "         model_dg1.finalizer.gauss]\n",
    "\n",
    "\n",
    "# Register forward hooks to the selected layers\n",
    "activation = {}\n",
    "\n",
    "def get_activation(name):\n",
    "    def hook(model_dg1, input, output):\n",
    "        activation[name] = output.detach()\n",
    "    return hook\n",
    "\n",
    "for i, layer in enumerate(layers):\n",
    "    layer.register_forward_hook(get_activation(f'layer_{i+1}'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35566e1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "read_seed: 0\n",
      "\n",
      "Frequency of classes:\n",
      "[[ 0  1  2]\n",
      " [32 32 32]]\n",
      "\n",
      "Layer 1 activation shape for face01.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face01.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face01.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face01.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face01.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face01.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face01.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face01.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face01.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face02.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face02.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face02.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face02.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face02.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face02.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face02.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face02.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face02.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face03.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face03.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face03.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face03.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face03.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face03.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face03.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face03.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face03.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face04.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face04.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face04.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face04.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face04.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face04.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face04.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face04.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face04.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face05.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face05.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face05.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face05.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face05.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face05.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face05.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face05.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face05.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face06.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face06.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face06.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face06.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face06.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face06.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face06.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face06.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face06.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face07.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face07.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face07.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face07.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face07.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face07.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face07.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face07.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face07.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face08.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face08.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face08.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face08.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face08.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face08.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face08.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face08.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face08.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face09.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face09.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face09.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face09.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face09.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face09.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face09.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face09.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face09.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face10.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face10.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face10.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face10.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face10.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face10.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face10.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face10.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face10.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face11.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face11.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face11.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face11.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face11.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face11.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face11.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face11.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face11.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face12.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face12.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face12.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face12.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face12.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face12.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face12.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face12.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face12.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face13.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face13.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face13.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face13.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face13.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face13.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face13.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face13.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face13.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face14.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face14.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face14.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face14.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face14.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face14.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face14.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face14.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face14.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for face15.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face15.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face15.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face15.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face15.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face15.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face15.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face15.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face15.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face16.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face16.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face16.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face16.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face16.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face16.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face16.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face16.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face16.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face17.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face17.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face17.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face17.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face17.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face17.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face17.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face17.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face17.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face18.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face18.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face18.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face18.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face18.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face18.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face18.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face18.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face18.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face19.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face19.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face19.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face19.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face19.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face19.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face19.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face19.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face19.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face20.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face20.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face20.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face20.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face20.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face20.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face20.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face20.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face20.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face21.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face21.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face21.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face21.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face21.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face21.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face21.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face21.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face21.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face22.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face22.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face22.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face22.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face22.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face22.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face22.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face22.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face22.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face23.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face23.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face23.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face23.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face23.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face23.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face23.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face23.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face23.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face24.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face24.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face24.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face24.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face24.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face24.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face24.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face24.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face24.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face25.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face25.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face25.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face25.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face25.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face25.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face25.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face25.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face25.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face26.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face26.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face26.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face26.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face26.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face26.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face26.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face26.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face26.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face27.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face27.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face27.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face27.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face27.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face27.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face27.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face27.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face27.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face28.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face28.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face28.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face28.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face28.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face28.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face28.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face28.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face28.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for face29.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face29.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face29.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face29.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face29.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face29.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face29.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face29.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face29.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face30.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face30.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face30.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face30.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face30.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face30.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face30.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face30.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face30.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face31.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face31.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face31.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face31.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face31.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face31.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face31.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face31.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face31.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for face32.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for face32.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for face32.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for face32.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for face32.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for face32.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for face32.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for face32.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for face32.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 04.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 04.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 04.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 04.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 04.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 04.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 04.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 04.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 04.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 06.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 06.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 06.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 06.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 06.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 06.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 06.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 06.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 06.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 08.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 08.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 08.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 08.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 08.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 08.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 08.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 08.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 08.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 10.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 10.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 10.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 10.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 10.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 10.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 10.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 10.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 10.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 12.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 12.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 12.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 12.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 12.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 12.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 12.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 12.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 12.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 13.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 13.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 13.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 13.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 13.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 13.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 13.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 13.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 13.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 15.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 15.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 15.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 15.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 15.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 15.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 15.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 15.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 15.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 16.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 16.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 16.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 16.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 16.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 16.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 16.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 16.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 16.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 17.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 17.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 17.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 17.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 17.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 17.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 17.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 17.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 17.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 20.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 20.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 20.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 20.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 20.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 20.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 20.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 20.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 20.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 22.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 22.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 22.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 22.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 22.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 22.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 22.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 22.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 22.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for 26.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 26.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 26.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 26.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 26.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 26.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 26.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 26.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 26.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 34.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 34.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 34.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 34.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 34.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 34.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 34.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 34.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 34.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 37.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 37.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 37.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 37.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 37.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 37.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 37.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 37.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 37.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 39.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 39.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 39.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 39.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 39.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 39.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 39.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 39.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 39.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 42.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 42.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 42.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 42.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 42.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 42.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 42.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 42.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 42.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 43.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 43.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 43.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 43.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 43.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 43.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 43.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 43.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 43.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 44.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 44.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 44.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 44.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 44.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 44.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 44.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 44.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 44.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 46.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 46.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 46.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 46.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 46.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 46.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 46.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 46.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 46.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 48.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 48.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 48.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 48.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 48.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 48.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 48.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 48.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 48.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 53.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 53.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 53.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 53.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 53.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 53.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 53.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 53.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 53.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 56.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 56.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 56.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 56.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 56.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 56.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 56.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 56.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 56.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 57.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 57.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 57.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 57.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 57.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 57.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 57.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 57.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 57.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 59.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 59.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 59.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 59.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 59.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 59.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 59.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 59.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 59.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 64.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 64.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 64.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 64.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 64.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 64.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 64.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 64.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 64.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 72.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 72.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 72.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 72.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 72.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 72.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 72.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 72.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 72.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for 74.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 74.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 74.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 74.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 74.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 74.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 74.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 74.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 74.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 75.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 75.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 75.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 75.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 75.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 75.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 75.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 75.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 75.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 78.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 78.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 78.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 78.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 78.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 78.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 78.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 78.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 78.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 80.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 80.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 80.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 80.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 80.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 80.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 80.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 80.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 80.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 81.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 81.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 81.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 81.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 81.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 81.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 81.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 81.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 81.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 83.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 83.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 83.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 83.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 83.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 83.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 83.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 83.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 83.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 04_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 04_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 04_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 04_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 04_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 04_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 04_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 04_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 04_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 06_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 06_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 06_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 06_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 06_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 06_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 06_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 06_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 06_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 08_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 08_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 08_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 08_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 08_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 08_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 08_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 08_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 08_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 10_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 10_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 10_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 10_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 10_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 10_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 10_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 10_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 10_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 12_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 12_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 12_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 12_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 12_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 12_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 12_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 12_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 12_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 13_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 13_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 13_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 13_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 13_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 13_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 13_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 13_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 13_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 15_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 15_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 15_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 15_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 15_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 15_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 15_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 15_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 15_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 16_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 16_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 16_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 16_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 16_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 16_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 16_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 16_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 16_match.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for 17_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 17_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 17_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 17_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 17_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 17_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 17_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 17_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 17_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 20_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 20_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 20_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 20_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 20_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 20_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 20_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 20_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 20_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 22_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 22_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 22_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 22_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 22_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 22_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 22_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 22_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 22_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 26_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 26_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 26_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 26_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 26_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 26_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 26_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 26_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 26_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 34_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 34_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 34_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 34_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 34_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 34_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 34_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 34_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 34_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 37_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 37_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 37_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 37_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 37_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 37_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 37_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 37_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 37_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 39_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 39_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 39_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 39_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 39_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 39_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 39_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 39_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 39_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 42_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 42_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 42_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 42_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 42_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 42_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 42_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 42_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 42_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 43_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 43_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 43_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 43_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 43_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 43_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 43_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 43_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 43_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 44_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 44_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 44_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 44_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 44_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 44_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 44_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 44_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 44_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 46_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 46_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 46_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 46_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 46_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 46_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 46_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 46_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 46_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 48_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 48_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 48_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 48_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 48_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 48_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 48_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 48_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 48_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 53_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 53_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 53_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 53_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 53_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 53_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 53_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 53_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 53_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 56_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 56_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 56_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 56_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 56_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 56_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 56_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 56_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 56_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 57_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 57_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 57_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 57_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 57_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 57_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 57_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 57_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 57_match.png: torch.Size([1, 1, 56, 56])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 activation shape for 59_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 59_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 59_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 59_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 59_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 59_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 59_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 59_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 59_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 64_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 64_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 64_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 64_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 64_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 64_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 64_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 64_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 64_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 72_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 72_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 72_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 72_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 72_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 72_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 72_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 72_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 72_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 74_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 74_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 74_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 74_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 74_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 74_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 74_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 74_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 74_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 75_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 75_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 75_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 75_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 75_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 75_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 75_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 75_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 75_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 78_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 78_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 78_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 78_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 78_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 78_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 78_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 78_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 78_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 80_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 80_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 80_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 80_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 80_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 80_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 80_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 80_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 80_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 81_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 81_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 81_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 81_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 81_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 81_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 81_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 81_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 81_match.png: torch.Size([1, 1, 56, 56])\n",
      "Layer 1 activation shape for 83_match.png: torch.Size([1, 64, 27, 27])\n",
      "Layer 2 activation shape for 83_match.png: torch.Size([1, 192, 13, 13])\n",
      "Layer 3 activation shape for 83_match.png: torch.Size([1, 384, 6, 6])\n",
      "Layer 4 activation shape for 83_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 5 activation shape for 83_match.png: torch.Size([1, 256, 6, 6])\n",
      "Layer 6 activation shape for 83_match.png: torch.Size([1, 4096])\n",
      "Layer 7 activation shape for 83_match.png: torch.Size([1, 4096])\n",
      "Layer 8 activation shape for 83_match.png: torch.Size([1, 1, 28, 28])\n",
      "Layer 9 activation shape for 83_match.png: torch.Size([1, 1, 56, 56])\n",
      "boot_itr:  1\n",
      "boot_itr:  2\n",
      "boot_itr:  3\n",
      "boot_itr:  4\n",
      "boot_itr:  5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n# Plotting\\nfig = plt.figure(figsize=(14, 8))\\nax = fig.add_subplot(111)\\nplt.plot(layer_no, spearmanr_values_hypo_1, '-o', label='Faces~Pareidolia', markersize=10)\\nplt.plot(layer_no, spearmanr_values_hypo_2, '-o', label='Pareidolia~Objects', markersize=10)\\nplt.plot(layer_no, spearmanr_values_hypo_3, '-o', label='Faces~Objects', markersize=10)\\n\\nplt.xticks(layer_no, fontsize=18)\\nplt.yticks(fontsize=18)\\nax.set_xticklabels(layer_no)\\nplt.ylabel('Partial spearmanr (rho)', fontsize=18)\\nplt.yticks(np.arange(-0.15, 0.65, 0.05))\\nleg = plt.legend(loc = 2, prop={'size': 15})\\nleg.get_frame().set_edgecolor('k')\\nfig.set_size_inches(14.,8.)\\n#plt.savefig('3_hypos_partial_spearman.png', dpi=600)\\n\\nplt.show()\\n\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bts_no = 5\n",
    "folder_dir = ['/home/pranjul/DeepGaze/all_images_net_input/']\n",
    "\n",
    "validator = helper.Validator(name='things_validator',\n",
    "                             model=model,\n",
    "                             batch_size=10,\n",
    "                             data_dir=folder_dir,\n",
    "                             ngpus=1,\n",
    "                             workers=4,\n",
    "                             task=None,\n",
    "                             max_samples={'all_images_net_input': 32},\n",
    "                             maxout=True,\n",
    "                             read_seed=0,\n",
    "                             shuffle=False,\n",
    "                             data_subdir='test',\n",
    "                             includePaths=True)\n",
    "\n",
    "num_imgs = len(validator.dataset)\n",
    "centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "\n",
    "img_act = []\n",
    "spearmanr_values_hypo_1_bts, spearmanr_values_hypo_2_bts = [], []\n",
    "\n",
    "for i, image in enumerate(validator.dataset):\n",
    "        \n",
    "    filename = image[-1].split('/')[-1]\n",
    "    image = image[0].unsqueeze(0).cuda()\n",
    "    \n",
    "    \n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[2]/centerbias_template.shape[0], image.shape[3]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "    # Forward pass the image through the model\n",
    "    output = model_dg1(image, centerbias_tensor)\n",
    "\n",
    "    # Retrieve the extracted activations\n",
    "    activations = [activation[f'layer_{i+1}'] for i in range(len(layers))]\n",
    "\n",
    "    # Print out the extracted activations\n",
    "    for j, act in enumerate(activations):\n",
    "        print(f\"Layer {j+1} activation shape for {filename}: {act.shape}\")\n",
    "        act = act.cpu().detach().numpy().squeeze()\n",
    "        act = np.reshape(act, (np.prod(act.shape[0:])))\n",
    "        img_act.append(np.array(act))\n",
    "\n",
    "\n",
    "for boot_itr in range(bts_no):\n",
    "    \n",
    "    print(\"boot_itr: \", boot_itr + 1)\n",
    "    \n",
    "    np.random.seed(boot_itr)\n",
    "\n",
    "    randomRows_0 = []\n",
    "    randomRows_1 = []\n",
    "    randomRows_2 = []\n",
    "\n",
    "    randomRows_0 = np.random.choice(np.arange(0, 32), size=np.arange(32).shape, replace=True)\n",
    "    randomRows_1 = np.random.choice(np.arange(32, 64), size=np.arange(32).shape, replace=True)\n",
    "    randomRows_2 = np.random.choice(np.arange(64, 96), size=np.arange(32).shape, replace=True)\n",
    "    #print(randomRows)\n",
    "\n",
    "    bootstrap_idx = [*randomRows_0, *randomRows_1, *randomRows_2]\n",
    "    # print(bootstrap_idx)\n",
    "\n",
    "    \n",
    "    img_act_layer = []\n",
    "    dist_metric_layers_dual_task_fc = []\n",
    "    \n",
    "    for i in range(len(layers)):\n",
    "        for j in bootstrap_idx:\n",
    "            img_act_layer.append(img_act[i + (j*len(layers))])\n",
    "\n",
    "    for i in range(len(layers)):\n",
    "        dist_metric = 1 - np.corrcoef(np.array(img_act_layer[i*96:(i+1)*96]))\n",
    "        dist_metric_layers_dual_task_fc.append(dist_metric[np.triu_indices(96, k = 1)])\n",
    "\n",
    "\n",
    "    data = np.vstack([dist_metric_layers_dual_task_fc, \n",
    "                      np.array(hypo_1[np.triu_indices(96, k = 1)]),\n",
    "                       np.array(hypo_2[np.triu_indices(96, k = 1)])\n",
    "                   ])\n",
    "\n",
    "    # print(np.shape(data))\n",
    "    df = pd.DataFrame(data=data.T)\n",
    "\n",
    "    col_names = []\n",
    "\n",
    "    for i in range(len(layers)):\n",
    "        col_names.append('layer_' + str(i + 1))\n",
    "\n",
    "    col_names.extend(('hypo_1', 'hypo_2'))\n",
    "    df.columns = col_names\n",
    "\n",
    "    spearmanr_values_hypo_1, spearmanr_values_hypo_2 = [], []\n",
    "    layer_no = np.arange(1, len(layers)+1)\n",
    "\n",
    "    for i in range(len(layers)):\n",
    "\n",
    "        zero_indices = df.loc[df['layer_'+ str(i + 1)] == 0].index \n",
    "        df_0_removed = df.drop(zero_indices)\n",
    "        \n",
    "        spearmanr_values_hypo_1.append(pg.partial_corr(data=df_0_removed, x='layer_' + str(i + 1), y='hypo_1', covar=['hypo_2'], method='spearman').round(3)['r'][0])\n",
    "        spearmanr_values_hypo_2.append(pg.partial_corr(data=df_0_removed, x='layer_' + str(i + 1), y='hypo_2', covar=['hypo_1'], method='spearman').round(3)['r'][0])\n",
    "        \n",
    "    spearmanr_values_hypo_1_bts.append(np.array(spearmanr_values_hypo_1))\n",
    "    spearmanr_values_hypo_2_bts.append(np.array(spearmanr_values_hypo_2))\n",
    "    \n",
    "#np.save('spearmanr_values_hypo_1_FC_bootstrap_new_2hypos.npy', np.array(spearmanr_values_hypo_1_bts))\n",
    "#np.save('spearmanr_values_hypo_2_FC_bootstrap_new_2hypos.npy', np.array(spearmanr_values_hypo_2_bts))\n",
    "\n",
    "'''\n",
    "# Plotting\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(layer_no, spearmanr_values_hypo_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
    "plt.plot(layer_no, spearmanr_values_hypo_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
    "plt.plot(layer_no, spearmanr_values_hypo_3, '-o', label='Faces~Objects', markersize=10)\n",
    "\n",
    "plt.xticks(layer_no, fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "ax.set_xticklabels(layer_no)\n",
    "plt.ylabel('Partial spearmanr (rho)', fontsize=18)\n",
    "plt.yticks(np.arange(-0.15, 0.65, 0.05))\n",
    "leg = plt.legend(loc = 2, prop={'size': 15})\n",
    "leg.get_frame().set_edgecolor('k')\n",
    "fig.set_size_inches(14.,8.)\n",
    "#plt.savefig('3_hypos_partial_spearman.png', dpi=600)\n",
    "\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78040ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_imgs = len(validator.dataset)\n",
    "\n",
    "img_act = []\n",
    "\n",
    "for i, image in enumerate(validator.dataset):\n",
    "        \n",
    "    filename = image[-1].split('/')[-1]\n",
    "    image = image[0].unsqueeze(0).cuda()\n",
    "\n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[2]/centerbias_template.shape[0], image.shape[3]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "    # Forward pass the image through the model\n",
    "    output = model_dg1(image, centerbias_tensor)\n",
    "\n",
    "    # Retrieve the extracted activations\n",
    "    activations = [activation[f'layer_{i+1}'] for i in range(len(layers))]\n",
    "\n",
    "    # Print out the extracted activations\n",
    "    for j, act in enumerate(activations):\n",
    "        # print(f\"Layer {j+1} activation shape for {filename}: {act.shape}\")\n",
    "        act = act.cpu().detach().numpy().squeeze()\n",
    "        act = np.reshape(act, (np.prod(act.shape[0:])))\n",
    "        img_act.append(np.array(act))\n",
    "\n",
    "img_act_layer = []\n",
    "dist_metric_layers_dual_task_fc = []\n",
    "\n",
    "\n",
    "for i in range(len(layers)):\n",
    "    for j in range(96):\n",
    "        img_act_layer.append(img_act[i + (j*len(layers))])\n",
    "\n",
    "for i in range(len(layers)):\n",
    "    dist_metric = 1 - np.corrcoef(np.array(img_act_layer[i*96:(i+1)*96]))\n",
    "    dist_metric_layers_dual_task_fc.append(dist_metric[np.triu_indices(96, k = 1)])\n",
    "\n",
    "#np.save('dist_metric_layers_dual_task_fc_2_hypos.npy', np.array(dist_metric_layers_dual_task_fc))\n",
    "\n",
    "data = np.vstack([dist_metric_layers_dual_task_fc, \n",
    "                np.array(hypo_1[np.triu_indices(96, k = 1)]),\n",
    "               np.array(hypo_2[np.triu_indices(96, k = 1)]),\n",
    "               ])\n",
    "\n",
    "# print(np.shape(data))\n",
    "df = pd.DataFrame(data=data.T)\n",
    "\n",
    "col_names = []\n",
    "\n",
    "for i in range(len(layers)):\n",
    "    col_names.append('layer_' + str(i + 1))\n",
    "\n",
    "col_names.extend(('hypo_1', 'hypo_2'))\n",
    "df.columns = col_names\n",
    "\n",
    "spearmanr_values_hypo_1, spearmanr_values_hypo_2 = [], []\n",
    "layer_no = np.arange(1, len(layers)+1)\n",
    "\n",
    "for i in range(len(layers)):\n",
    "\n",
    "    spearmanr_values_hypo_1.append(pg.partial_corr(data=df, x='layer_' + str(i + 1), y='hypo_1', covar=['hypo_2'], method='spearman').round(3)['r'][0])\n",
    "    spearmanr_values_hypo_2.append(pg.partial_corr(data=df, x='layer_' + str(i + 1), y='hypo_2', covar=['hypo_1'], method='spearman').round(3)['r'][0])\n",
    "\n",
    "#np.save('inanimate_vgg_large_spearmanr_values_hypo_1_2hypos.npy', np.array(spearmanr_values_hypo_1))\n",
    "#np.save('inanimate_vgg_large_spearmanr_values_hypo_2_2hypos.npy', np.array(spearmanr_values_hypo_2))\n",
    "\n",
    "#face_inanimate_400k_seed_spearmanr_values_hypo_1_bootstrap_new_2hypos\n",
    "\n",
    "# Plotting\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(layer_no, spearmanr_values_hypo_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
    "plt.plot(layer_no, spearmanr_values_hypo_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
    "\n",
    "plt.xticks(layer_no, fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "ax.set_xticklabels(layer_no)\n",
    "plt.ylabel('Partial spearmanr (rho)', fontsize=18)\n",
    "plt.yticks(np.arange(-0.15, 0.65, 0.05))\n",
    "leg = plt.legend(loc = 2, prop={'size': 15})\n",
    "leg.get_frame().set_edgecolor('k')\n",
    "fig.set_size_inches(14.,8.)\n",
    "#plt.savefig('3_hypos_partial_spearman.png', dpi=600)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "705971ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "face_vgg_large_spearmanr_values_hypo_1 = spearmanr_values_hypo_1_bts\n",
    "face_vgg_large_spearmanr_values_hypo_1 = np.reshape(face_vgg_large_spearmanr_values_hypo_1, (bts_no, len(layers)))\n",
    "face_vgg_large_spearmanr_values_hypo_2 = spearmanr_values_hypo_2_bts\n",
    "face_vgg_large_spearmanr_values_hypo_2 = np.reshape(face_vgg_large_spearmanr_values_hypo_2, (bts_no, len(layers)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6aa9c33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_1 = spearmanr_values_hypo_1\n",
    "mean_2 = spearmanr_values_hypo_2\n",
    "\n",
    "std_1 = []\n",
    "std_2 = []\n",
    "\n",
    "\n",
    "for i in range(len(layers)):\n",
    "    std_1.append(np.std(face_vgg_large_spearmanr_values_hypo_1, axis = 0)[i])\n",
    "    std_2.append(np.std(face_vgg_large_spearmanr_values_hypo_2, axis = 0)[i])\n",
    "\n",
    "std_1 = np.array(std_1)\n",
    "std_2 = np.array(std_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b813dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "#plt.axis('on')\n",
    "#ax = plt.gca()\n",
    "#plt.plot(layer_no, mean_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
    "#plt.plot(layer_no, mean_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
    "#plt.errorbar(layer_no, mean_1, yerr=[confidence_interval_1_0, confidence_interval_1_1], fmt='-o', color = 'tab:blue', capsize=8, label='Faces~Pareidolia', markersize=2)\n",
    "#plt.errorbar(layer_no, mean_2, yerr=[confidence_interval_2_0, confidence_interval_2_1], fmt='-o', color = 'tab:orange', capsize=8, label='Pareidolia~Objects', markersize=3)\n",
    "#plt.errorbar(layer_no, mean_3, yerr=[confidence_interval_3_0, confidence_interval_3_1], fmt='-o', color = 'tab:green', capsize=8, label='Objects~Faces', markersize=3)\n",
    "\n",
    "plt.plot(layer_no, mean_1, color = 'tab:blue', label='Faces~Pareidolia')\n",
    "plt.fill_between(layer_no, mean_1-std_1, mean_1+std_1, color = 'lightblue', alpha=.3)\n",
    "\n",
    "plt.plot(layer_no, mean_2, color = 'tab:orange', label='Pareidolia~Objects')\n",
    "plt.fill_between(layer_no, mean_2-std_2, mean_2+std_2, color = 'peachpuff', alpha=.3)\n",
    "\n",
    "# plt.plot(layer_no, mean_3, color = 'tab:green', label='Objects~Faces')\n",
    "# plt.fill_between(layer_no, mean_3-std_3, mean_3+std_3, color = 'lightgreen', alpha=.3)\n",
    "\n",
    "#plt.plot(layer_no, spearmanr_values_hypo_3, '-o', label='Faces~Objects', markersize=10)\n",
    "#plt.axhline(y=0.5, color='r', linestyle='--', label='Random chance')\n",
    "#plt.axvline(x=4, color='k', linestyle='--')\n",
    "#plt.axvline(x=13,color='k', linestyle='--')\n",
    "\n",
    "plt.xticks(layer_no, fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "#ax.tick_params(axis='both', which='major')\n",
    "ax.set_xticklabels(layer_no)\n",
    "plt.axis([0, len(layers)+1, -0.2, 0.3])\n",
    "plt.xlabel('CNN layer', fontsize=30)\n",
    "plt.ylabel(\"CNN idealized model correlation \\n [Partial Spearman's r]\", fontsize=30)\n",
    "plt.title('DG1_RSA', fontsize=30)\n",
    "#ax.grid(which='both')\n",
    "#ax.grid(which='minor', alpha=0.2)\n",
    "#ax.grid(which='major', alpha=0.5)\n",
    "#ax.grid(color='k', alpha=1, linestyle='--')\n",
    "#plt.legend(loc = 4, prop={'size': 20})\n",
    "leg = plt.legend(loc = 2, prop={'size': 20})\n",
    "leg.get_frame().set_edgecolor('k')\n",
    "#fig.set_size_inches(14.,8.)\n",
    "plt.tight_layout()\n",
    "plt.savefig('DG1_RSA.png', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0eac54e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f71f0c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f5607f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da34189",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82e25b3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7817cbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b73b99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}