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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Yash-10/CAMELS-CNN-Interpretability-using-CKA/blob/main/Camels_CKA_Nbody.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Unchanged code"
      ],
      "metadata": {
        "id": "XMJdnrlUznJr"
      }
    },
    {
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      "execution_count": null,
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        "outputId": "b7726c05-5cbb-4f66-d0c3-dd39d6a1423a"
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          "name": "stdout",
          "text": [
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: pyperclip\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-py3-none-any.whl size=11124 sha256=4ee08e21a487cf70338acf828b8e1fcc702bf1572537cb81a75e43b6741f2eb9\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/24/fe/140a94a7f1036003ede94579e6b4227fe96c840c6f4dcbe307\n",
            "Successfully built pyperclip\n",
            "Installing collected packages: pyperclip, pbr, Mako, importlib-metadata, colorlog, cmd2, cmaes, autopage, stevedore, alembic, cliff, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.10.4 autopage-0.5.1 cliff-4.3.0 cmaes-0.9.1 cmd2-2.4.3 colorlog-6.7.0 importlib-metadata-6.6.0 optuna-2.4.0 pbr-5.11.1 pyperclip-1.8.2 stevedore-5.0.0\n",
            "CUDA Not Available\n"
          ]
        }
      ],
      "source": [
        "# Unchanged code\n",
        "!pip install optuna==2.4.0\n",
        "\n",
        "import tqdm\n",
        "import numpy as np\n",
        "\n",
        "import numpy as np\n",
        "import optuna\n",
        "from google.colab import drive\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import sys,os\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "import matplotlib as mpl\n",
        "mpl.rcParams['figure.dpi'] = 200\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    print(\"CUDA Available\")\n",
        "    device = torch.device('cuda')\n",
        "else:\n",
        "    print('CUDA Not Available')\n",
        "    device = torch.device('cpu')\n",
        "\n",
        "\n",
        "def unbiased_HSIC(K, L):\n",
        "  '''Computes an unbiased estimator of HISC. This is equation (2) from the paper'''\n",
        "\n",
        "  #create the unit **vector** filled with ones\n",
        "  n = K.shape[0]\n",
        "  ones = np.ones(shape=(n))\n",
        "\n",
        "  #fill the diagonal entries with zeros\n",
        "  np.fill_diagonal(K, val=0) #this is now K_tilde\n",
        "  np.fill_diagonal(L, val=0) #this is now L_tilde\n",
        "\n",
        "  #first part in the square brackets\n",
        "  trace = np.trace(np.dot(K, L))\n",
        "\n",
        "  #middle part in the square brackets\n",
        "  nominator1 = np.dot(np.dot(ones.T, K), ones)\n",
        "  nominator2 = np.dot(np.dot(ones.T, L), ones)\n",
        "  denominator = (n-1)*(n-2)\n",
        "  middle = np.dot(nominator1, nominator2) / denominator\n",
        "\n",
        "\n",
        "  #third part in the square brackets\n",
        "  multiplier1 = 2/(n-2)\n",
        "  multiplier2 = np.dot(np.dot(ones.T, K), np.dot(L, ones))\n",
        "  last = multiplier1 * multiplier2\n",
        "\n",
        "  #complete equation\n",
        "  unbiased_hsic = 1/(n*(n-3)) * (trace + middle - last)\n",
        "\n",
        "  return unbiased_hsic\n",
        "\n",
        "def CKA(X, Y):\n",
        "  '''Computes the CKA of two matrices. This is equation (1) from the paper'''\n",
        "\n",
        "  nominator = unbiased_HSIC(np.dot(X, X.T), np.dot(Y, Y.T))\n",
        "  denominator1 = unbiased_HSIC(np.dot(X, X.T), np.dot(X, X.T))\n",
        "  denominator2 = unbiased_HSIC(np.dot(Y, Y.T), np.dot(Y, Y.T))\n",
        "\n",
        "  cka = nominator/np.sqrt(denominator1*denominator2)\n",
        "\n",
        "  return cka\n",
        "\n",
        "def calculate_CKA_for_two_matrices(activationA, activationB):\n",
        "  '''Takes two activations A and B and computes the linear CKA to measure their similarity'''\n",
        "\n",
        "  #unfold the activations, that is make a (n, h*w*c) representation\n",
        "  shape = activationA.shape\n",
        "  activationA = np.reshape(activationA, newshape=(shape[0], np.prod(shape[1:])))\n",
        "\n",
        "  shape = activationB.shape\n",
        "  activationB = np.reshape(activationB, newshape=(shape[0], np.prod(shape[1:])))\n",
        "\n",
        "  #calculate the CKA score\n",
        "  cka_score = CKA(activationA, activationB)\n",
        "\n",
        "  del activationA\n",
        "  del activationB\n",
        "\n",
        "  return cka_score\n",
        "\n",
        "\n",
        "# def get_all_layer_outputs_fn(model):\n",
        "#   '''Builds and returns function that returns the output of every (intermediate) layer'''\n",
        "\n",
        "#   return tf.keras.backend.function([model.layers[0].input],\n",
        "#                                   [l.output for l in model.layers[1:]])\n",
        "\n",
        "def compare_activations(modelA, modelB, data_batch):\n",
        "  '''\n",
        "  Calculate a pairwise comparison of hidden representations and return a matrix\n",
        "  '''\n",
        "\n",
        "  _, intermediate_outputs_A = modelA(data_batch)\n",
        "  _, intermediate_outputs_B = modelB(data_batch)\n",
        "\n",
        "  #create a placeholder array\n",
        "  result_array = np.zeros(shape=(len(intermediate_outputs_A), len(intermediate_outputs_B)))\n",
        "\n",
        "  i = 0\n",
        "  for outputA in tqdm.tqdm_notebook(intermediate_outputs_A):\n",
        "    j = 0\n",
        "    for outputB in tqdm.tqdm_notebook(intermediate_outputs_B):\n",
        "      print(outputA.shape, outputB.shape)\n",
        "      cka_score = calculate_CKA_for_two_matrices(outputA, outputB)\n",
        "      result_array[i, j] = cka_score\n",
        "      j+=1\n",
        "    i+= 1\n",
        "\n",
        "  return result_array\n",
        "\n",
        "# This routine returns the data loader need to train the network\n",
        "def create_dataset_multifield(mode, seed, fmaps, fparams, batch_size, splits, fmaps_norm,\n",
        "                              rot_flip_in_mem=True, shuffle=True, verbose=False):\n",
        "\n",
        "    # whether rotations and flippings are kept in memory\n",
        "    if rot_flip_in_mem:\n",
        "        data_set = make_dataset_multifield(mode, seed, fmaps, fparams, splits, fmaps_norm, verbose)\n",
        "    else:\n",
        "        data_set = make_dataset_multifield2(mode, seed, fmaps, fparams, splits, fmaps_norm, verbose)\n",
        "\n",
        "    data_loader = DataLoader(dataset=data_set, batch_size=batch_size, shuffle=shuffle)\n",
        "    return data_loader\n",
        "\n",
        "\n",
        "# This class creates the dataset. Rotations and flippings are precompued and stored in memory\n",
        "class make_dataset_multifield():\n",
        "\n",
        "    def __init__(self, mode, seed, fmaps, fparams, splits, fmaps_norm, verbose):\n",
        "\n",
        "        # get the total number of sims and maps\n",
        "        params_sims = np.loadtxt(fparams) #simulations parameters, NOT maps parameters\n",
        "        total_sims, total_maps, num_params = \\\n",
        "                params_sims.shape[0], params_sims.shape[0]*splits, params_sims.shape[1]\n",
        "        params_maps = np.zeros((total_maps, num_params), dtype=np.float32)\n",
        "        for i in range(total_sims):\n",
        "            for j in range(splits):\n",
        "                params_maps[i*splits + j] = params_sims[i]\n",
        "\n",
        "        # normalize the value of the cosmological & astrophysical parameters\n",
        "        minimum     = np.array([0.1, 0.6, 0.25, 0.25, 0.5, 0.5])\n",
        "        maximum     = np.array([0.5, 1.0, 4.00, 4.00, 2.0, 2.0])\n",
        "        params_maps = (params_maps - minimum)/(maximum - minimum)\n",
        "\n",
        "        # get the size and offset depending on the type of dataset\n",
        "        if   mode=='train':  offset, size_sims = int(0.00*total_sims), int(0.90*total_sims)\n",
        "        elif mode=='valid':  offset, size_sims = int(0.90*total_sims), int(0.05*total_sims)\n",
        "        elif mode=='test':   offset, size_sims = int(0.95*total_sims), int(0.05*total_sims)\n",
        "        elif mode=='all':    offset, size_sims = int(0.00*total_sims), int(1.00*total_sims)\n",
        "        else:                raise Exception('Wrong name!')\n",
        "        size_maps = size_sims*splits\n",
        "\n",
        "        # randomly shuffle the simulations (not maps). Instead of 0 1 2 3...999 have a\n",
        "        # random permutation. E.g. 5 9 0 29...342\n",
        "        np.random.seed(seed)\n",
        "        sim_numbers = np.arange(total_sims) #shuffle sims not maps\n",
        "        np.random.shuffle(sim_numbers)\n",
        "        sim_numbers = sim_numbers[offset:offset+size_sims] #select indexes of mode\n",
        "\n",
        "        # get the corresponding indexes of the maps associated to the sims\n",
        "        indexes = np.zeros(size_maps, dtype=np.int32)\n",
        "        count = 0\n",
        "        for i in sim_numbers:\n",
        "            for j in range(splits):\n",
        "                indexes[count] = i*splits + j\n",
        "                count += 1\n",
        "\n",
        "        # keep only the value of the parameters of the considered maps\n",
        "        params_maps = params_maps[indexes]\n",
        "\n",
        "        # define the matrix containing the maps with rotations and flipings\n",
        "        channels = len(fmaps)\n",
        "        dumb     = np.load(fmaps[0])    #[number of maps, height, width]\n",
        "        height, width = dumb.shape[1], dumb.shape[2];  del dumb\n",
        "        data     = np.zeros((size_maps*8, channels, height, width), dtype=np.float32)\n",
        "        params   = np.zeros((size_maps*8, num_params),              dtype=np.float32)\n",
        "\n",
        "        # read the data\n",
        "        print('Found %d channels\\nReading data...'%channels)\n",
        "        for channel, (fim, fnorm) in enumerate(zip(fmaps, fmaps_norm)):\n",
        "\n",
        "            # read maps in the considered channel\n",
        "            data_c = np.load(fim)\n",
        "            print(data_c.shape, total_maps)\n",
        "            if data_c.shape[0]!=total_maps:  raise Exception('sizes do not match')\n",
        "            if verbose:  print('%.3e < F(all|orig) < %.3e'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # rescale maps\n",
        "            if fim.find('Mstar')!=-1:  data_c = np.log10(data_c + 1.0)\n",
        "            else:                      data_c = np.log10(data_c)\n",
        "            if verbose:  print('%.3f < F(all|resc)  < %.3f'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # normalize maps\n",
        "            if fnorm is None:\n",
        "                mean,    std     = np.mean(data_c), np.std(data_c)\n",
        "            else:\n",
        "                # read data\n",
        "                data_norm = np.load(fnorm)\n",
        "\n",
        "                # rescale\n",
        "                if fnorm.find('Mstar')!=-1:  data_norm = np.log10(data_norm + 1.0)\n",
        "                else:                        data_norm = np.log10(data_norm)\n",
        "\n",
        "                # compute mean and std\n",
        "                mean,    std     = np.mean(data_norm), np.std(data_norm)\n",
        "                del data_norm\n",
        "\n",
        "            data_c = (data_c - mean)/std\n",
        "            if verbose:  print('%.3f < F(all|norm) < %.3f'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # keep only the data of the chosen set\n",
        "            data_c = data_c[indexes]\n",
        "\n",
        "            # do a loop over all rotations (each is 90 deg)\n",
        "            counted_maps = 0\n",
        "            for rot in [0,1,2,3]:\n",
        "                data_rot = np.rot90(data_c, k=rot, axes=(1,2))\n",
        "\n",
        "                data[counted_maps:counted_maps+size_maps,channel,:,:] = data_rot\n",
        "                params[counted_maps:counted_maps+size_maps]           = params_maps\n",
        "                counted_maps += size_maps\n",
        "\n",
        "                data[counted_maps:counted_maps+size_maps,channel,:,:] = \\\n",
        "                                                    np.flip(data_rot, axis=1)\n",
        "                params[counted_maps:counted_maps+size_maps]           = params_maps\n",
        "                counted_maps += size_maps\n",
        "\n",
        "            if verbose:\n",
        "                print('Channel %d contains %d maps'%(channel,counted_maps))\n",
        "                print('%.3f < F < %.3f\\n'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "        self.size = data.shape[0]\n",
        "        self.x    = torch.tensor(data,   dtype=torch.float32)\n",
        "        self.y    = torch.tensor(params, dtype=torch.float32)\n",
        "        del data, data_c\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.size\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.x[idx], self.y[idx]\n",
        "\n",
        "# This class creates the dataset. It will read the maps and store them in memory.\n",
        "# The rotations and flipings are done when calling the data\n",
        "class make_dataset_multifield2():\n",
        "\n",
        "    def __init__(self, mode, seed, fmaps, fparams, splits, fmaps_norm, verbose):\n",
        "\n",
        "        # get the total number of simulations and maps\n",
        "        params_sims = np.loadtxt(fparams) #simulations parameters, NOT maps parameters\n",
        "        total_sims, total_maps, num_params = \\\n",
        "                params_sims.shape[0], params_sims.shape[0]*splits, params_sims.shape[1]\n",
        "        params = np.zeros((total_maps, num_params), dtype=np.float32)\n",
        "        for i in range(total_sims):\n",
        "            for j in range(splits):\n",
        "                params[i*splits + j] = params_sims[i]\n",
        "\n",
        "        # normalize params\n",
        "        minimum = np.array([0.1, 0.6, 0.25, 0.25, 0.5, 0.5])\n",
        "        maximum = np.array([0.5, 1.0, 4.00, 4.00, 2.0, 2.0])\n",
        "        params  = (params - minimum)/(maximum - minimum)\n",
        "\n",
        "        # get the size and offset depending on the type of dataset\n",
        "        if   mode=='train':  offset, size_sims = int(0.00*total_sims), int(0.90*total_sims)\n",
        "        elif mode=='valid':  offset, size_sims = int(0.90*total_sims), int(0.05*total_sims)\n",
        "        elif mode=='test':   offset, size_sims = int(0.95*total_sims), int(0.05*total_sims)\n",
        "        elif mode=='all':    offset, size_sims = int(0.00*total_sims), int(1.00*total_sims)\n",
        "        else:                raise Exception('Wrong name!')\n",
        "        size_maps = size_sims*splits\n",
        "\n",
        "        # randomly shuffle the simulations (not maps). Instead of 0 1 2 3...999 have a\n",
        "        # random permutation. E.g. 5 9 0 29...342\n",
        "        np.random.seed(seed)\n",
        "        sim_numbers = np.arange(total_sims) #shuffle maps not rotations\n",
        "        np.random.shuffle(sim_numbers)\n",
        "        sim_numbers = sim_numbers[offset:offset+size_sims] #select indexes of mode\n",
        "\n",
        "        # get the corresponding indexes of the maps associated to the sims\n",
        "        indexes = np.zeros(size_maps, dtype=np.int32)\n",
        "        count = 0\n",
        "        for i in sim_numbers:\n",
        "            for j in range(splits):\n",
        "                indexes[count] = i*splits + j\n",
        "                count += 1\n",
        "\n",
        "        # keep only the value of the parameters of the considered maps\n",
        "        params = params[indexes]\n",
        "\n",
        "        # define the matrix containing the maps without rotations or flippings\n",
        "        channels = len(fmaps)\n",
        "        dumb     = np.load(fmaps[0])    #[number of maps, height, width]\n",
        "        height, width = dumb.shape[1], dumb.shape[2];  del dumb\n",
        "        data     = np.zeros((size_maps, channels, height, width), dtype=np.float32)\n",
        "\n",
        "        # read the data\n",
        "        print('Found %d channels\\nReading data...'%channels)\n",
        "        for channel, (fim, fnorm) in enumerate(zip(fmaps, fmaps_norm)):\n",
        "\n",
        "            # read maps in the considered channel\n",
        "            data_c = np.load(fim)\n",
        "            print(data_c.shape, total_maps)\n",
        "            if data_c.shape[0]!=total_maps:  raise Exception('sizes do not match')\n",
        "            if verbose:\n",
        "                print('%.3e < F(all|orig) < %.3e'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # rescale maps\n",
        "            if fim.find('Mstar')!=-1:  data_c = np.log10(data_c + 1.0)\n",
        "            else:                      data_c = np.log10(data_c)\n",
        "            if verbose:\n",
        "                print('%.3f < F(all|resc)  < %.3f'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # normalize maps\n",
        "            if fnorm is None:\n",
        "                mean,    std     = np.mean(data_c), np.std(data_c)\n",
        "                minimum, maximum = np.min(data_c),  np.max(data_c)\n",
        "            else:\n",
        "                # read data\n",
        "                data_norm     = np.load(fnorm)\n",
        "\n",
        "                # rescale data\n",
        "                if fnorm.find('Mstar')!=-1:  data_norm = np.log10(data_norm + 1.0)\n",
        "                else:                        data_norm = np.log10(data_norm)\n",
        "\n",
        "                # compute mean and std\n",
        "                mean,    std     = np.mean(data_norm), np.std(data_norm)\n",
        "                minimum, maximum = np.min(data_norm),  np.max(data_norm)\n",
        "                del data_norm\n",
        "\n",
        "            data_c = (data_c - mean)/std\n",
        "            if verbose:  print('%.3f < F(all|norm) < %.3f'%(np.min(data_c), np.max(data_c)))\n",
        "\n",
        "            # keep only the data of the chosen set\n",
        "            data[:,channel,:,:] = data_c[indexes]\n",
        "\n",
        "        self.size = data.shape[0]\n",
        "        self.x    = torch.tensor(data,   dtype=torch.float32)\n",
        "        self.y    = torch.tensor(params, dtype=torch.float32)\n",
        "        del data, data_c\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.size\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "\n",
        "        # choose a rotation angle (0-0, 1-90, 2-180, 3-270)\n",
        "        # and whether do flipping or not\n",
        "        rot  = np.random.randint(0,4)\n",
        "        flip = np.random.randint(0,1)\n",
        "\n",
        "        # rotate and flip the maps\n",
        "        maps = torch.rot90(self.x[idx], k=rot, dims=[1,2])\n",
        "        if flip==1:  maps = torch.flip(maps, dims=[1])\n",
        "\n",
        "        return maps, self.y[idx]\n",
        "\n",
        "class model_o3_err(nn.Module):\n",
        "    def __init__(self, hidden, dr, channels):\n",
        "        super(model_o3_err, self).__init__()\n",
        "\n",
        "        # input: 1x256x256 ---------------> output: 2*hiddenx128x128\n",
        "        self.C01 = nn.Conv2d(channels,  2*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C02 = nn.Conv2d(2*hidden,  2*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C03 = nn.Conv2d(2*hidden,  2*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B01 = nn.BatchNorm2d(2*hidden)\n",
        "        self.B02 = nn.BatchNorm2d(2*hidden)\n",
        "        self.B03 = nn.BatchNorm2d(2*hidden)\n",
        "\n",
        "        # input: 2*hiddenx128x128 ----------> output: 4*hiddenx64x64\n",
        "        self.C11 = nn.Conv2d(2*hidden, 4*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C12 = nn.Conv2d(4*hidden, 4*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C13 = nn.Conv2d(4*hidden, 4*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B11 = nn.BatchNorm2d(4*hidden)\n",
        "        self.B12 = nn.BatchNorm2d(4*hidden)\n",
        "        self.B13 = nn.BatchNorm2d(4*hidden)\n",
        "\n",
        "        # input: 4*hiddenx64x64 --------> output: 8*hiddenx32x32\n",
        "        self.C21 = nn.Conv2d(4*hidden, 8*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C22 = nn.Conv2d(8*hidden, 8*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C23 = nn.Conv2d(8*hidden, 8*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B21 = nn.BatchNorm2d(8*hidden)\n",
        "        self.B22 = nn.BatchNorm2d(8*hidden)\n",
        "        self.B23 = nn.BatchNorm2d(8*hidden)\n",
        "\n",
        "        # input: 8*hiddenx32x32 ----------> output: 16*hiddenx16x16\n",
        "        self.C31 = nn.Conv2d(8*hidden,  16*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C32 = nn.Conv2d(16*hidden, 16*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C33 = nn.Conv2d(16*hidden, 16*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B31 = nn.BatchNorm2d(16*hidden)\n",
        "        self.B32 = nn.BatchNorm2d(16*hidden)\n",
        "        self.B33 = nn.BatchNorm2d(16*hidden)\n",
        "\n",
        "        # input: 16*hiddenx16x16 ----------> output: 32*hiddenx8x8\n",
        "        self.C41 = nn.Conv2d(16*hidden, 32*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C42 = nn.Conv2d(32*hidden, 32*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C43 = nn.Conv2d(32*hidden, 32*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B41 = nn.BatchNorm2d(32*hidden)\n",
        "        self.B42 = nn.BatchNorm2d(32*hidden)\n",
        "        self.B43 = nn.BatchNorm2d(32*hidden)\n",
        "\n",
        "        # input: 32*hiddenx8x8 ----------> output:64*hiddenx4x4\n",
        "        self.C51 = nn.Conv2d(32*hidden, 64*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C52 = nn.Conv2d(64*hidden, 64*hidden, kernel_size=3, stride=1, padding=1,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.C53 = nn.Conv2d(64*hidden, 64*hidden, kernel_size=2, stride=2, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B51 = nn.BatchNorm2d(64*hidden)\n",
        "        self.B52 = nn.BatchNorm2d(64*hidden)\n",
        "        self.B53 = nn.BatchNorm2d(64*hidden)\n",
        "\n",
        "        # input: 64*hiddenx4x4 ----------> output: 128*hiddenx1x1\n",
        "        self.C61 = nn.Conv2d(64*hidden, 128*hidden, kernel_size=4, stride=1, padding=0,\n",
        "                            padding_mode='circular', bias=True)\n",
        "        self.B61 = nn.BatchNorm2d(128*hidden)\n",
        "\n",
        "        self.P0  = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
        "\n",
        "        self.FC1  = nn.Linear(128*hidden, 64*hidden)\n",
        "        self.FC2  = nn.Linear(64*hidden,  12)\n",
        "\n",
        "        self.dropout   = nn.Dropout(p=dr)\n",
        "        self.ReLU      = nn.ReLU()\n",
        "        self.LeakyReLU = nn.LeakyReLU(0.2)\n",
        "        self.tanh      = nn.Tanh()\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):\n",
        "                nn.init.constant_(m.weight, 1)\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "            elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear):\n",
        "                nn.init.kaiming_normal_(m.weight)\n",
        "\n",
        "\n",
        "    def forward(self, image):\n",
        "        x01 = self.C01(image)\n",
        "        x01_ = self.LeakyReLU(x01)\n",
        "        x02 = self.C02(x01_)\n",
        "        x02_ = self.LeakyReLU(self.B02(x02))\n",
        "        x03 = self.C03(x02_)\n",
        "        x03_ = self.LeakyReLU(self.B03(x03))\n",
        "\n",
        "        x11 = self.C11(x03_)\n",
        "        x11_ = self.LeakyReLU(self.B11(x11))\n",
        "        x12 = self.C12(x11_)\n",
        "        x12_ = self.LeakyReLU(self.B12(x12))\n",
        "        x13 = self.C13(x12_)\n",
        "        x13_ = self.LeakyReLU(self.B13(x13))\n",
        "\n",
        "        x21 = self.C21(x13_)\n",
        "        x21_ = self.LeakyReLU(self.B21(x21))\n",
        "        x22 = self.C22(x21_)\n",
        "        x22_ = self.LeakyReLU(self.B22(x22))\n",
        "        x23 = self.C23(x22_)\n",
        "        x23_ = self.LeakyReLU(self.B23(x23))\n",
        "\n",
        "        x31 = self.C31(x23_)\n",
        "        x31_ = self.LeakyReLU(self.B31(x31))\n",
        "        x32 = self.C32(x31_)\n",
        "        x32_ = self.LeakyReLU(self.B32(x32))\n",
        "        x33 = self.C33(x32_)\n",
        "        x33_ = self.LeakyReLU(self.B33(x33))\n",
        "\n",
        "        x41 = self.C41(x33_)\n",
        "        x41_ = self.LeakyReLU(self.B41(x41))\n",
        "        x42 = self.C42(x41_)\n",
        "        x42_ = self.LeakyReLU(self.B42(x42))\n",
        "        x43 = self.C43(x42_)\n",
        "        x43_ = self.LeakyReLU(self.B43(x43))\n",
        "\n",
        "        x51 = self.C51(x43_)\n",
        "        x51_ = self.LeakyReLU(self.B51(x51))\n",
        "        x52 = self.C52(x51_)\n",
        "        x52_ = self.LeakyReLU(self.B52(x52))\n",
        "        x53 = self.C53(x52_)\n",
        "        x53_ = self.LeakyReLU(self.B53(x53))\n",
        "\n",
        "        x61 = self.C61(x53_)\n",
        "        x61_ = self.LeakyReLU(self.B61(x61))\n",
        "\n",
        "        x61__ = x61_.view(image.shape[0],-1)\n",
        "        x61__ = self.dropout(x61__)\n",
        "\n",
        "        x71 = self.FC1(x61__)\n",
        "        x71_ = self.LeakyReLU(x71)\n",
        "        x71__ = self.dropout(x71_)\n",
        "        x81 = self.FC2(x71__)\n",
        "\n",
        "        # enforce the errors to be positive\n",
        "        y = torch.clone(x81)\n",
        "        y[:,6:12] = torch.square(x81[:,6:12])\n",
        "\n",
        "        return y, [x01_, x02_, x03_, x11_, x12_, x13_, x21_, x22_, x23_, x31_, x32_, x33_, x41_, x42_, x43_, x51_, x52_, x53_, x61_, x71_, x81]\n",
        "\n",
        "def load_two_models_for_similarity_measure(fweights_1, fweights_2, fdatabase1, study_name, trial_number_1, trial_number_2, fdatabase2=None):\n",
        "  if fdatabase2 is None:\n",
        "    print('Assuming same database for the two models...')\n",
        "\n",
        "  if torch.cuda.is_available():\n",
        "    print(\"GPUs available\")\n",
        "    device = torch.device('cuda')\n",
        "  else:\n",
        "    print('GPUs not available')\n",
        "    device = torch.device('cpu')\n",
        "\n",
        "  fweights = [fweights_1, fweights_2]\n",
        "  fdatabases = [fdatabase1, fdatabase2]\n",
        "  trial_numbers = [trial_number_1, trial_number_2]\n",
        "  models = []\n",
        "\n",
        "  for i in range(2):\n",
        "    if fdatabase2 is None:\n",
        "      study = optuna.load_study(study_name=study_name, storage=fdatabase1)\n",
        "    else:\n",
        "      study = optuna.load_study(study_name=study_name, storage=fdatabases[i])\n",
        "\n",
        "    print(f\"\\nTrial number: {trial_numbers[i]}\")\n",
        "    trial_number = trial_numbers[i]\n",
        "    trial = study.trials[trial_number]\n",
        "    print(\"Trial number:  number {}\".format(trial.number))\n",
        "    print(\"Loss:          %.5e\"%trial.value)\n",
        "    print(\"Params: \")\n",
        "    for key, value in trial.params.items():\n",
        "      print(\"    {}: {}\".format(key, value))\n",
        "\n",
        "    model = model_o3_err(trial.params['hidden'], trial.params['dr'], 1)\n",
        "    model = nn.DataParallel(model)\n",
        "    model.to(device=device)\n",
        "    network_total_params = sum(p.numel() for p in model.parameters())\n",
        "    print('total number of parameters in the model = %d'%network_total_params)\n",
        "\n",
        "    if os.path.exists(fweights[i]):\n",
        "      model.load_state_dict(torch.load(fweights[i], map_location=torch.device(device)))\n",
        "      print('Weights loaded')\n",
        "      models.append(model)\n",
        "    else:\n",
        "      raise Exception('file with weights not found!!!')\n",
        "\n",
        "  if len(models) != 2:\n",
        "    print(\"Warning! Two models were not loaded...\")\n",
        "  return models\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_context(\"paper\", font_scale = 2)\n",
        "sns.set_style('whitegrid')\n",
        "sns.set(style='ticks')\n",
        "def plot_cka(sim):\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "\n",
        "  divider = make_axes_locatable(ax)\n",
        "  cax = divider.append_axes('right', size='5%', pad=0.05)\n",
        "\n",
        "  im = ax.imshow(sim, cmap='magma', vmin=0.0,vmax=1.0)\n",
        "  layers = np.arange(1, 21, 2)\n",
        "  ax.set_xticks(layers); ax.set_yticks(layers)\n",
        "  ax.set_xticklabels(layers); ax.set_yticklabels(layers)\n",
        "  ax.axes.invert_yaxis()\n",
        "  fig.colorbar(im, cax=cax, orientation='vertical')\n",
        "  return ax\n",
        "\n",
        "\n",
        "def get_test_acc(model, test_loader):\n",
        "  # get the number of maps in the test set\n",
        "  num_maps = 0\n",
        "  for x,y in test_loader:\n",
        "        num_maps += x.shape[0]\n",
        "  print('\\nNumber of maps in the test set: %d'%num_maps)\n",
        "\n",
        "  # define the arrays containing the value of the parameters\n",
        "  params_true = np.zeros((num_maps,6), dtype=np.float32)\n",
        "  params_NN   = np.zeros((num_maps,6), dtype=np.float32)\n",
        "  errors_NN   = np.zeros((num_maps,6), dtype=np.float32)\n",
        "\n",
        "  # get test loss\n",
        "  test_loss1, test_loss2 = torch.zeros(len(g)).to(device), torch.zeros(len(g)).to(device)\n",
        "  test_loss, points = 0.0, 0\n",
        "  model.eval()\n",
        "  for x, y in test_loader:\n",
        "      with torch.no_grad():\n",
        "          bs    = x.shape[0]    #batch size\n",
        "          x     = x.to(device)  #send data to device\n",
        "          y     = y.to(device)  #send data to device\n",
        "          p     = model(x)[0]      #prediction for mean and variance  # EDIT: Take 0th element because we modify the architecture to return the features as well.\n",
        "          y_NN  = p[:,:6]       #prediction for mean\n",
        "          e_NN  = p[:,6:]       #prediction for error\n",
        "          loss1 = torch.mean((y_NN[:,g] - y[:,g])**2,                     axis=0)\n",
        "          loss2 = torch.mean(((y_NN[:,g] - y[:,g])**2 - e_NN[:,g]**2)**2, axis=0)\n",
        "          test_loss1 += loss1*bs\n",
        "          test_loss2 += loss2*bs\n",
        "\n",
        "          # save results to their corresponding arrays\n",
        "          params_true[points:points+x.shape[0]] = y.cpu().numpy()\n",
        "          params_NN[points:points+x.shape[0]]   = y_NN.cpu().numpy()\n",
        "          errors_NN[points:points+x.shape[0]]   = e_NN.cpu().numpy()\n",
        "          points    += x.shape[0]\n",
        "  test_loss = torch.log(test_loss1/points) + torch.log(test_loss2/points)\n",
        "  test_loss = torch.mean(test_loss).item()\n",
        "  print('Test loss = %.3e\\n'%test_loss)\n",
        "  return params_true, params_NN, errors_NN\n",
        "\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "\n",
        "def get_r2_score(params_true, params_NN):\n",
        "  r2_scores_params = []\n",
        "  for i in range(params_true.shape[1]):\n",
        "    r2_scores_params.append(\n",
        "      r2_score(params_true[:, i], params_NN[:, i])\n",
        "    )\n",
        "  return r2_scores_params\n",
        "\n",
        "def get_rmse(params_true, params_NN):\n",
        "  rmse_scores_params = []\n",
        "  for i in range(params_true.shape[1]):\n",
        "    rmse_scores_params.append(\n",
        "      mean_squared_error(params_true[:, i], params_NN[:, i])\n",
        "    )\n",
        "  return rmse_scores_params"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data download"
      ],
      "metadata": {
        "id": "bg1PNfz2z35V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Database by default needed for both experiments.\n",
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/inference/databases/Nbody_o3_Mtot_all_steps_500_500_o3.db\n",
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/data/params_Nbody_IllustrisTNG.txt\n",
        "\n",
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/inference/weights/weights_Nbody_Mtot_44_all_steps_500_500_o3.pt\n",
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/inference/weights/weights_Nbody_Mtot_29_all_steps_500_500_o3.pt\n",
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/data/Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy"
      ],
      "metadata": {
        "id": "hjMiNH8-z3SO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ec70ea9-51d7-42f0-ef0d-0271f42764f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-12 13:49:03--  https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/data/params_Nbody_IllustrisTNG.txt\n",
            "Resolving users.flatironinstitute.org (users.flatironinstitute.org)... 144.121.86.9\n",
            "Connecting to users.flatironinstitute.org (users.flatironinstitute.org)|144.121.86.9|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 48000 (47K) [text/plain]\n",
            "Saving to: ‘params_Nbody_IllustrisTNG.txt’\n",
            "\n",
            "\r          params_Nb   0%[                    ]       0  --.-KB/s               \rparams_Nbody_Illust 100%[===================>]  46.88K  --.-KB/s    in 0.007s  \n",
            "\n",
            "2023-05-12 13:49:03 (6.51 MB/s) - ‘params_Nbody_IllustrisTNG.txt’ saved [48000/48000]\n",
            "\n",
            "--2023-05-12 13:49:03--  https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/data/Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy\n",
            "Resolving users.flatironinstitute.org (users.flatironinstitute.org)... 144.121.86.9\n",
            "Connecting to users.flatironinstitute.org (users.flatironinstitute.org)|144.121.86.9|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3932160128 (3.7G) [application/octet-stream]\n",
            "Saving to: ‘Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy’\n",
            "\n",
            "Maps_Mtot_Nbody_Ill 100%[===================>]   3.66G   135MB/s    in 31s     \n",
            "\n",
            "2023-05-12 13:49:34 (120 MB/s) - ‘Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy’ saved [3932160128/3932160128]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Main code"
      ],
      "metadata": {
        "id": "FfKCjrGJ0P11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fweights_1   = '/content/weights_Nbody_Mtot_29_all_steps_500_500_o3.pt'\n",
        "fweights_2   = '/content/weights_Nbody_Mtot_44_all_steps_500_500_o3.pt'\n",
        "fdatabase1  = 'sqlite://///content/Nbody_o3_Mtot_all_steps_500_500_o3.db'\n",
        "# fdatabase2  = 'sqlite://///content/Nbody_o3_Mtot_all_steps_500_500_o3_smoothing_2.db'\n",
        "study_name = 'wd_dr_hidden_lr_o3'\n",
        "\n",
        "study = optuna.load_study(study_name=study_name, storage=fdatabase1)\n",
        "for i, t in enumerate(study.trials):\n",
        "  print(i, t.value)\n",
        "\n",
        "trial_number_1 = int(fweights_1.split('/')[2].split('_')[3])\n",
        "trial_number_2 = int(fweights_2.split('/')[2].split('_')[3])\n",
        "\n",
        "models = load_two_models_for_similarity_measure(\n",
        "    fweights_1, fweights_2, fdatabase1, study_name, trial_number_1, trial_number_2, fdatabase2=None\n",
        ")"
      ],
      "metadata": {
        "id": "wMGQtO4f0O3X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c912c0d2-7d16-455a-ba9c-8e04023d2c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 -17.11434555053711\n",
            "1 -20.204898834228516\n",
            "2 -13.66412353515625\n",
            "3 -13.498547554016113\n",
            "4 -19.727401733398438\n",
            "5 -17.831928253173828\n",
            "6 -12.714550018310547\n",
            "7 -20.95783233642578\n",
            "8 -19.872589111328125\n",
            "9 -18.439697265625\n",
            "10 -20.236310958862305\n",
            "11 -12.596137046813965\n",
            "12 -19.608352661132812\n",
            "13 -18.258684158325195\n",
            "14 -18.580381393432617\n",
            "15 -13.801666259765625\n",
            "16 -21.24994468688965\n",
            "17 -18.8332462310791\n",
            "18 -6.121070384979248\n",
            "19 -12.968928337097168\n",
            "20 -5.718955993652344\n",
            "21 -21.55377197265625\n",
            "22 -21.185760498046875\n",
            "23 -21.183767318725586\n",
            "24 -21.51211166381836\n",
            "25 -20.595727920532227\n",
            "26 -20.561124801635742\n",
            "27 -20.71259307861328\n",
            "28 -20.954097747802734\n",
            "29 -21.624774932861328\n",
            "30 -21.238500595092773\n",
            "31 -20.570392608642578\n",
            "32 -21.50737762451172\n",
            "33 -21.183792114257812\n",
            "34 -20.93977165222168\n",
            "35 -21.103559494018555\n",
            "36 -20.19617462158203\n",
            "37 -21.141979217529297\n",
            "38 -20.59976577758789\n",
            "39 -20.39032554626465\n",
            "40 -20.888219833374023\n",
            "41 -19.769338607788086\n",
            "42 -21.45238494873047\n",
            "43 -21.229965209960938\n",
            "44 -21.510690689086914\n",
            "45 -21.46448516845703\n",
            "46 -20.741519927978516\n",
            "47 -20.699420928955078\n",
            "48 -20.64196014404297\n",
            "49 -21.23257064819336\n",
            "50 -21.019798278808594\n",
            "51 None\n",
            "52 None\n",
            "Assuming same database for the two models...\n",
            "GPUs not available\n",
            "\n",
            "Trial number: 29\n",
            "Trial number:  number 29\n",
            "Loss:          -2.16248e+01\n",
            "Params: \n",
            "    dr: 0.004468805436150754\n",
            "    hidden: 11\n",
            "    lr: 0.002648072745767048\n",
            "    wd: 6.181939363914276e-05\n",
            "total number of parameters in the model = 28436376\n",
            "Weights loaded\n",
            "\n",
            "Trial number: 44\n",
            "Trial number:  number 44\n",
            "Loss:          -2.15107e+01\n",
            "Params: \n",
            "    dr: 0.008532125458895722\n",
            "    hidden: 12\n",
            "    lr: 0.004522825230895613\n",
            "    wd: 0.0004250180264031109\n",
            "total number of parameters in the model = 33839052\n",
            "Weights loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data parameters\n",
        "fmaps      = ['/content/maps_T.npy'] #tuple containing the maps with the different fields to consider\n",
        "fmaps_norm = [None] #if you want to normalize the maps according to the properties of some data set, put that data set here (This is mostly used when training on IllustrisTNG and testing on SIMBA, or vicerversa)\n",
        "fparams    = '/content/params_Nbody_IllustrisTNG.txt'\n",
        "seed       = 1   #random seed to split maps among training, validation and testing\n",
        "splits     = 5   #number of maps per simulation\n",
        "\n",
        "channels        = 1                #we only consider here 1 field\n",
        "params          = [0,1,2,3,4,5]    #0(Omega_m) 1(sigma_8) 2(A_SN1) 3 (A_AGN1) 4(A_SN2) 5(A_AGN2). The code will be trained to predict all these parameters.\n",
        "g               = params           #g will contain the mean of the posterior\n",
        "h               = [6+i for i in g] #h will contain the variance of the posterior\n",
        "rot_flip_in_mem = False            #whether rotations and flipings are kept in memory. True will make the code faster but consumes more RAM memory.\n",
        "\n",
        "fmaps2 = '/content/Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy'\n",
        "maps  = np.load(fmaps2)\n",
        "print('Shape of the maps:',maps.shape)\n",
        "\n",
        "# define the array that will contain the indexes of the maps\n",
        "indexes = np.zeros(1000*splits, dtype=np.int32)\n",
        "\n",
        "# do a loop over all maps and choose the ones we want\n",
        "count = 0\n",
        "for i in range(15000):\n",
        "    if i%15 in np.arange(splits):\n",
        "      indexes[count] = i\n",
        "      count += 1\n",
        "print('Selected %d maps out of 15000'%count)\n",
        "\n",
        "# save these maps to a new file\n",
        "maps = maps[indexes]\n",
        "np.save('maps_T.npy', maps)\n",
        "del maps\n",
        "\n",
        "# load test set\n",
        "test_loader  = create_dataset_multifield('test', seed, fmaps, fparams, 16, splits, fmaps_norm,  # 16 is batch_size. IMPORTANT: batch_size=1 gave error.\n",
        "                                         rot_flip_in_mem=False, verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rb_ygSD70dDG",
        "outputId": "8811f98f-a602-4816-f878-30cd1c2c7528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the maps: (15000, 256, 256)\n",
            "Selected 5000 maps out of 15000\n",
            "Found 1 channels\n",
            "Reading data...\n",
            "(5000, 256, 256) 5000\n",
            "4.957e+09 < F(all|orig) < 2.880e+15\n",
            "9.695 < F(all|resc)  < 15.459\n",
            "-2.795 < F(all|norm) < 8.860\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test the model and get accuracy"
      ],
      "metadata": {
        "id": "0OhsfyQK4uTt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Model 1')\n",
        "params_true, params_NN, errors_NN = get_test_acc(models[0], test_loader)\n",
        "r2 = get_r2_score(params_true, params_NN)\n",
        "rmse = get_rmse(params_true, params_NN)\n",
        "print(r2)\n",
        "print(rmse)\n",
        "print('Model 2')\n",
        "params_true, params_NN, errors_NN = get_test_acc(models[1], test_loader)\n",
        "r2 = get_r2_score(params_true, params_NN)\n",
        "rmse = get_rmse(params_true, params_NN)\n",
        "print(r2)\n",
        "print(rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9AzVJXk5J8d",
        "outputId": "2c28c72b-1511-4330-f2c8-ec837a888b76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 1\n",
            "\n",
            "Number of maps in the test set: 250\n",
            "Test loss = -9.752e+00\n",
            "\n",
            "[0.9976863927090424, 0.9863357235207331, -1.0295366622116302, -1.391402294536249, -3.224491237943994, -1.4130821049779043]\n",
            "[0.00020528723, 0.0012141687, 0.13715681, 0.23222223, 0.31397122, 0.21414581]\n",
            "Model 2\n",
            "\n",
            "Number of maps in the test set: 250\n",
            "Test loss = -9.724e+00\n",
            "\n",
            "[0.9972981984146236, 0.9883995493108773, -1.3989122280436557, -1.2994808572034717, -2.0445265117365006, -1.4524473497850168]\n",
            "[0.00023973187, 0.001030783, 0.16211933, 0.22329599, 0.2262743, 0.2176392]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run CKA"
      ],
      "metadata": {
        "id": "WW9F0C5201l1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_batch = []\n",
        "for i, (x, y) in enumerate(test_loader):\n",
        "  data_batch.append(x)\n",
        "  if i == 2:\n",
        "    break\n",
        "x = torch.vstack(data_batch)\n",
        "print(x.shape)\n",
        "\n",
        "with torch.no_grad():\n",
        "  sim = compare_activations(models[0], models[1], x)"
      ],
      "metadata": {
        "id": "jZ2Urgf701Hu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          ]
        },
        "outputId": "e60e4b8a-a593-4469-dbd6-9c367e28fb14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 1, 256, 256])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-4fed2b3fdbaa>:109: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for outputA in tqdm.tqdm_notebook(intermediate_outputs_A):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "305b06aac6314a478c36058d5f36a7c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-4fed2b3fdbaa>:111: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for outputB in tqdm.tqdm_notebook(intermediate_outputs_B):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5173d5e22f294ad88c116011d749aaed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adbd1486018e49aeb2b8ae4d12e706b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 768])\n",
            "torch.Size([48, 22, 256, 256]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abf04b390c30475889b481d2718f1af2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 768])\n",
            "torch.Size([48, 22, 128, 128]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "812cd820eac84858a592835495d2841d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 12])\n"
          ]
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            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 768])\n",
            "torch.Size([48, 44, 128, 128]) torch.Size([48, 12])\n"
          ]
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            "torch.Size([48, 44, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 768])\n",
            "torch.Size([48, 44, 64, 64]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 768])\n",
            "torch.Size([48, 88, 64, 64]) torch.Size([48, 12])\n"
          ]
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            "torch.Size([48, 88, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 768])\n",
            "torch.Size([48, 88, 32, 32]) torch.Size([48, 12])\n"
          ]
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            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 768])\n",
            "torch.Size([48, 176, 32, 32]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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          "text": [
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 768])\n",
            "torch.Size([48, 176, 16, 16]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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          "text": [
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 12])\n"
          ]
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              "  0%|          | 0/21 [00:00<?, ?it/s]"
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          "text": [
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 768])\n",
            "torch.Size([48, 352, 16, 16]) torch.Size([48, 12])\n"
          ]
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            "torch.Size([48, 352, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 768])\n",
            "torch.Size([48, 352, 8, 8]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
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            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 768])\n",
            "torch.Size([48, 704, 8, 8]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 768])\n",
            "torch.Size([48, 704, 4, 4]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 768])\n",
            "torch.Size([48, 1408, 1, 1]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83d1c6c84803461799b07f8fd3678421"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 704]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 704]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 704]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 704]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 704]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 704]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 704]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 704]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 704]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 704]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 704]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 704]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 704]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 704]) torch.Size([48, 768])\n",
            "torch.Size([48, 704]) torch.Size([48, 12])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "166376ed0c684f2cb7e337a6537a8f01"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([48, 12]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 12]) torch.Size([48, 24, 256, 256])\n",
            "torch.Size([48, 12]) torch.Size([48, 24, 128, 128])\n",
            "torch.Size([48, 12]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 12]) torch.Size([48, 48, 128, 128])\n",
            "torch.Size([48, 12]) torch.Size([48, 48, 64, 64])\n",
            "torch.Size([48, 12]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 12]) torch.Size([48, 96, 64, 64])\n",
            "torch.Size([48, 12]) torch.Size([48, 96, 32, 32])\n",
            "torch.Size([48, 12]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 12]) torch.Size([48, 192, 32, 32])\n",
            "torch.Size([48, 12]) torch.Size([48, 192, 16, 16])\n",
            "torch.Size([48, 12]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 12]) torch.Size([48, 384, 16, 16])\n",
            "torch.Size([48, 12]) torch.Size([48, 384, 8, 8])\n",
            "torch.Size([48, 12]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 12]) torch.Size([48, 768, 8, 8])\n",
            "torch.Size([48, 12]) torch.Size([48, 768, 4, 4])\n",
            "torch.Size([48, 12]) torch.Size([48, 1536, 1, 1])\n",
            "torch.Size([48, 12]) torch.Size([48, 768])\n",
            "torch.Size([48, 12]) torch.Size([48, 12])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'cka_nbody_tng_64examples.npy'\n",
        "np.save(filename, sim)\n",
        "!cp cka_nbody_tng_64examples.npy drive/MyDrive/CAMELS/CKA_matrices_and_results"
      ],
      "metadata": {
        "id": "XO91vaeNjIY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = plot_cka(sim)\n",
        "ax.set_title('64 examples')\n",
        "ax.set_ylabel('Layers Model 2')\n",
        "ax.set_ylabel('Layers Model 1')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mrAUPcPB0xiG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "nbody_tng = np.load('/content/Maps_Mtot_Nbody_IllustrisTNG_LH_z=0.00.npy')\n",
        "nbody_tng.shape"
      ],
      "metadata": {
        "id": "iLErBO8pmTBB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7db0e30e-49a7-4f12-8f29-19da0a10273d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 256, 256)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fparams = '/content/params_Nbody_IllustrisTNG.txt'\n",
        "params = np.loadtxt(fparams)\n",
        "map_number = 101\n",
        "params_map = params[map_number//15]\n",
        "filename = 'Nbody_100_example_simParams.npy'\n",
        "np.save(filename, params_map)\n",
        "\n",
        "shutil.copy(filename, 'drive/MyDrive/CAMELS/CKA_matrices_and_results')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "UyP8HmJUPB8F",
        "outputId": "53141ea8-aa19-43b5-cd9a-900bccc3a629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drive/MyDrive/CAMELS/CKA_matrices_and_results/Nbody_100_example_simParams.npy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'Nbody_100_example.npy'\n",
        "np.save(filename, nbody_tng[100].squeeze())\n",
        "import shutil\n",
        "shutil.copy(filename, 'drive/MyDrive/CAMELS/CKA_matrices_and_results')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Gahvztx-O4-b",
        "outputId": "e14a3bf3-5c63-45a0-ef07-56379ac667e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drive/MyDrive/CAMELS/CKA_matrices_and_results/Nbody_100_example.npy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://users.flatironinstitute.org/~fvillaescusa/priv/DEPnzxoWlaTQ6CjrXqsm0vYi8L7Jy/CMD/2D_maps/data/Maps_Mtot_Nbody_SIMBA_LH_z=0.00.npy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2kqc3eUPSIq",
        "outputId": "072eb842-a3d0-43cc-b788-911d690346b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "HTTP request sent, awaiting response... 200 OK\n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "nbody_simba = np.load('/content/Maps_Mtot_Nbody_SIMBA_LH_z=0.00.npy')\n",
        "nbody_simba.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdoG3CwXDGOe",
        "outputId": "e983faf3-2f33-4c6f-f6d0-623847b5b472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 256, 256)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'Nbody_simba_100_example.npy'\n",
        "np.save(filename, nbody_simba[100].squeeze())\n",
        "import shutil\n",
        "shutil.copy(filename, 'drive/MyDrive/CAMELS/CKA_matrices_and_results')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "GxIoD82lHdbb",
        "outputId": "7ebb38b7-46d8-4a1d-f0ba-c75ee59aa1cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drive/MyDrive/CAMELS/CKA_matrices_and_results/Nbody_simba_100_example.npy'"
            ],
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          "metadata": {},
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      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    }
  ]
}