project_1_facial_keypoints/1. Load and Visualize Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facial Keypoint Detection\n",
    "  \n",
    "This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces.  The first step in any challenge like this will be to load and visualize the data you'll be working with. \n",
    "\n",
    "Let's take a look at some examples of images and corresponding facial keypoints.\n",
    "\n",
    "<img src='images/key_pts_example.png' width=50% height=50%/>\n",
    "\n",
    "Facial keypoints (also called facial landmarks) are the small magenta dots shown on each of the faces in the image above. In each training and test image, there is a single face and **68 keypoints, with coordinates (x, y), for that face**.  These keypoints mark important areas of the face: the eyes, corners of the mouth, the nose, etc. These keypoints are relevant for a variety of tasks, such as face filters, emotion recognition, pose recognition, and so on. Here they are, numbered, and you can see that specific ranges of points match different portions of the face.\n",
    "\n",
    "<img src='images/landmarks_numbered.jpg' width=30% height=30%/>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Visualize Data\n",
    "\n",
    "The first step in working with any dataset is to become familiar with your data; you'll need to load in the images of faces and their keypoints and visualize them! This set of image data has been extracted from the [YouTube Faces Dataset](https://www.cs.tau.ac.il/~wolf/ytfaces/), which includes videos of people in YouTube videos. These videos have been fed through some processing steps and turned into sets of image frames containing one face and the associated keypoints.\n",
    "\n",
    "#### Training and Testing Data\n",
    "\n",
    "This facial keypoints dataset consists of 5770 color images. All of these images are separated into either a training or a test set of data.\n",
    "\n",
    "* 3462 of these images are training images, for you to use as you create a model to predict keypoints.\n",
    "* 2308 are test images, which will be used to test the accuracy of your model.\n",
    "\n",
    "The information about the images and keypoints in this dataset are summarized in CSV files, which we can read in using `pandas`. Let's read the training CSV and get the annotations in an (N, 2) array where N is the number of keypoints and 2 is the dimension of the keypoint coordinates (x, y).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the required libraries\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name:  Luis_Fonsi_21.jpg\n",
      "Landmarks shape:  (68, 2)\n",
      "First 4 key pts: [[  45.   98.]\n",
      " [  47.  106.]\n",
      " [  49.  110.]\n",
      " [  53.  119.]]\n"
     ]
    }
   ],
   "source": [
    "key_pts_frame = pd.read_csv('data/training_frames_keypoints.csv')\n",
    "\n",
    "n = 0\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].as_matrix()\n",
    "key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "\n",
    "print('Image name: ', image_name)\n",
    "print('Landmarks shape: ', key_pts.shape)\n",
    "print('First 4 key pts: {}'.format(key_pts[:4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# print out some stats about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n",
      "Number of keypoints per image:  68\n"
     ]
    }
   ],
   "source": [
    "print('Number of images: ', len(key_pts_frame))\n",
    "print('Number of keypoints per image: ', len(key_pts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at some images\n",
    "\n",
    "Below, is a function `show_keypoints` that takes in an image and keypoints and displays them.  As you look at this data, **note that these images are not all of the same size**, and neither are the faces! To eventually train a neural network on these images, we'll need to standardize their shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_keypoints(image, key_pts):\n",
    "    \"\"\"Show image with keypoints\"\"\"\n",
    "    plt.imshow(image)\n",
    "    plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f861985c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a few different types of images by changing the index n\n",
    "\n",
    "# select an image by index in our data frame\n",
    "n = 5\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].as_matrix()\n",
    "key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "show_keypoints(mpimg.imread(os.path.join('data/training/', image_name)), key_pts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset class and Transformations\n",
    "\n",
    "To prepare our data for training, we'll be using PyTorch's Dataset class. Much of this this code is a modified version of what can be found in the [PyTorch data loading tutorial](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html).\n",
    "\n",
    "#### Dataset class\n",
    "\n",
    "``torch.utils.data.Dataset`` is an abstract class representing a\n",
    "dataset. This class will allow us to load batches of image/keypoint data, and uniformly apply transformations to our data, such as rescaling and normalizing images for training a neural network.\n",
    "\n",
    "\n",
    "Your custom dataset should inherit ``Dataset`` and override the following\n",
    "methods:\n",
    "\n",
    "-  ``__len__`` so that ``len(dataset)`` returns the size of the dataset.\n",
    "-  ``__getitem__`` to support the indexing such that ``dataset[i]`` can\n",
    "   be used to get the i-th sample of image/keypoint data.\n",
    "\n",
    "Let's create a dataset class for our face keypoints dataset. We will\n",
    "read the CSV file in ``__init__`` but leave the reading of images to\n",
    "``__getitem__``. This is memory efficient because all the images are not\n",
    "stored in the memory at once but read as required.\n",
    "\n",
    "A sample of our dataset will be a dictionary\n",
    "``{'image': image, 'keypoints': key_pts}``. Our dataset will take an\n",
    "optional argument ``transform`` so that any required processing can be\n",
    "applied on the sample. We will see the usefulness of ``transform`` in the\n",
    "next section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class FacialKeypointsDataset(Dataset):\n",
    "    \"\"\"Face Landmarks dataset.\"\"\"\n",
    "\n",
    "    def __init__(self, csv_file, root_dir, transform=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            csv_file (string): Path to the csv file with annotations.\n",
    "            root_dir (string): Directory with all the images.\n",
    "            transform (callable, optional): Optional transform to be applied\n",
    "                on a sample.\n",
    "        \"\"\"\n",
    "        self.key_pts_frame = pd.read_csv(csv_file)\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.key_pts_frame)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image_name = os.path.join(self.root_dir,\n",
    "                                self.key_pts_frame.iloc[idx, 0])\n",
    "        \n",
    "        image = mpimg.imread(image_name)\n",
    "        \n",
    "        # if image has an alpha color channel, get rid of it\n",
    "        if(image.shape[2] == 4):\n",
    "            image = image[:,:,0:3]\n",
    "        \n",
    "        key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n",
    "        key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "        sample = {'image': image, 'keypoints': key_pts}\n",
    "\n",
    "        if self.transform:\n",
    "            sample = self.transform(sample)\n",
    "\n",
    "        return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've defined this class, let's instantiate the dataset and display some images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of dataset:  3462\n"
     ]
    }
   ],
   "source": [
    "# Construct the dataset\n",
    "face_dataset = FacialKeypointsDataset(csv_file='data/training_frames_keypoints.csv',\n",
    "                                      root_dir='data/training/')\n",
    "\n",
    "# print some stats about the dataset\n",
    "print('Length of dataset: ', len(face_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (270, 298, 3) (68, 2)\n",
      "1 (268, 323, 3) (68, 2)\n",
      "2 (228, 186, 3) (68, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f8841a898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f884602b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f8849e470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a few of the images from the dataset\n",
    "num_to_display = 3\n",
    "\n",
    "for i in range(num_to_display):\n",
    "    \n",
    "    # define the size of images\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "    \n",
    "    # randomly select a sample\n",
    "    rand_i = np.random.randint(0, len(face_dataset))\n",
    "    sample = face_dataset[rand_i]\n",
    "\n",
    "    # print the shape of the image and keypoints\n",
    "    print(i, sample['image'].shape, sample['keypoints'].shape)\n",
    "\n",
    "    ax = plt.subplot(1, num_to_display, i + 1)\n",
    "    ax.set_title('Sample #{}'.format(i))\n",
    "    \n",
    "    # Using the same display function, defined earlier\n",
    "    show_keypoints(sample['image'], sample['keypoints'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforms\n",
    "\n",
    "Now, the images above are not of the same size, and neural networks often expect images that are standardized; a fixed size, with a normalized range for color ranges and coordinates, and (for PyTorch) converted from numpy lists and arrays to Tensors.\n",
    "\n",
    "Therefore, we will need to write some pre-processing code.\n",
    "Let's create four transforms:\n",
    "\n",
    "-  ``Normalize``: to convert a color image to grayscale values with a range of [0,1] and normalize the keypoints to be in a range of about [-1, 1]\n",
    "-  ``Rescale``: to rescale an image to a desired size.\n",
    "-  ``RandomCrop``: to crop an image randomly.\n",
    "-  ``ToTensor``: to convert numpy images to torch images.\n",
    "\n",
    "\n",
    "We will write them as callable classes instead of simple functions so\n",
    "that parameters of the transform need not be passed everytime it's\n",
    "called. For this, we just need to implement ``__call__`` method and \n",
    "(if we require parameters to be passed in), the ``__init__`` method. \n",
    "We can then use a transform like this:\n",
    "\n",
    "    tx = Transform(params)\n",
    "    transformed_sample = tx(sample)\n",
    "\n",
    "Observe below how these transforms are generally applied to both the image and its keypoints.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import transforms, utils\n",
    "# tranforms\n",
    "\n",
    "class Normalize(object):\n",
    "    \"\"\"Convert a color image to grayscale and normalize the color range to [0,1].\"\"\"        \n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "        \n",
    "        image_copy = np.copy(image)\n",
    "        key_pts_copy = np.copy(key_pts)\n",
    "\n",
    "        # convert image to grayscale\n",
    "        image_copy = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
    "        \n",
    "        # scale color range from [0, 255] to [0, 1]\n",
    "        image_copy=  image_copy/255.0\n",
    "        \n",
    "        # scale keypoints to be centered around 0 with a range of [-1, 1]\n",
    "        # mean = 100, sqrt = 50, so, pts should be (pts - 100)/50\n",
    "        key_pts_copy = (key_pts_copy - 122)/122.0\n",
    "\n",
    "\n",
    "        return {'image': image_copy, 'keypoints': key_pts_copy}\n",
    "\n",
    "\n",
    "class Rescale(object):\n",
    "    \"\"\"Rescale the image in a sample to a given size.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If tuple, output is\n",
    "            matched to output_size. If int, smaller of image edges is matched\n",
    "            to output_size keeping aspect ratio the same.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        self.output_size = output_size\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "\n",
    "        h, w = image.shape[:2]\n",
    "        if isinstance(self.output_size, int):\n",
    "            if h > w:\n",
    "                new_h, new_w = self.output_size * h / w, self.output_size\n",
    "            else:\n",
    "                new_h, new_w = self.output_size, self.output_size * w / h\n",
    "        else:\n",
    "            new_h, new_w = self.output_size\n",
    "\n",
    "        new_h, new_w = int(new_h), int(new_w)\n",
    "\n",
    "        img = cv2.resize(image, (new_w, new_h))\n",
    "        \n",
    "        # scale the pts, too\n",
    "        key_pts = key_pts * [new_w / w, new_h / h]\n",
    "\n",
    "        return {'image': img, 'keypoints': key_pts}\n",
    "\n",
    "\n",
    "class RandomCrop(object):\n",
    "    \"\"\"Crop randomly the image in a sample.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If int, square crop\n",
    "            is made.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        if isinstance(output_size, int):\n",
    "            self.output_size = (output_size, output_size)\n",
    "        else:\n",
    "            assert len(output_size) == 2\n",
    "            self.output_size = output_size\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "\n",
    "        h, w = image.shape[:2]\n",
    "        new_h, new_w = self.output_size\n",
    "\n",
    "        top = np.random.randint(0, h - new_h)\n",
    "        left = np.random.randint(0, w - new_w)\n",
    "\n",
    "        image = image[top: top + new_h,\n",
    "                      left: left + new_w]\n",
    "\n",
    "        key_pts = key_pts - [left, top]\n",
    "\n",
    "        return {'image': image, 'keypoints': key_pts}\n",
    "\n",
    "\n",
    "class ToTensor(object):\n",
    "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "         \n",
    "        # if image has no grayscale color channel, add one\n",
    "        if(len(image.shape) == 2):\n",
    "            # add that third color dim\n",
    "            image = image.reshape(image.shape[0], image.shape[1], 1)\n",
    "            \n",
    "        # swap color axis because\n",
    "        # numpy image: H x W x C\n",
    "        # torch image: C X H X W\n",
    "        image = image.transpose((2, 0, 1))\n",
    "        \n",
    "        return {'image': torch.from_numpy(image),\n",
    "                'keypoints': torch.from_numpy(key_pts)}\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test out the transforms\n",
    "\n",
    "Let's test these transforms out to make sure they behave as expected. As you look at each transform, note that, in this case, **order does matter**. For example, you cannot crop a image using a value smaller than the original image (and the orginal images vary in size!), but, if you first rescale the original image, you can then crop it to any size smaller than the rescaled size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f884fcd30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_load  import Rescale, RandomCrop, FaceCrop, Normalize, ToTensor, RandomHorizontalFlip, Random90DegFlip\n",
    "\n",
    "# test out some of these transforms\n",
    "face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')\n",
    "face_cascade\n",
    "rescale = Rescale(50)\n",
    "crop = FaceCrop()\n",
    "composed = transforms.Compose([Rescale(250),\n",
    "                               FaceCrop(),Rescale(128)])\n",
    "\n",
    "\n",
    "# apply the transforms to a sample image\n",
    "test_num = 200\n",
    "sample = face_dataset[test_num]\n",
    "\n",
    "fig = plt.figure()\n",
    "for i, tx in enumerate([rescale, crop, composed]):\n",
    "    transformed_sample = tx(sample)\n",
    "\n",
    "    ax = plt.subplot(1, 3, i + 1)\n",
    "    plt.tight_layout()\n",
    "    ax.set_title(type(tx).__name__)\n",
    "    show_keypoints(transformed_sample['image'], transformed_sample['keypoints'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the transformed dataset\n",
    "\n",
    "Apply the transforms in order to get grayscale images of the same shape. Verify that your transform works by printing out the shape of the resulting data (printing out a few examples should show you a consistent tensor size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the data tranform\n",
    "# order matters! i.e. rescaling should come before a smaller crop\n",
    "data_transform = transforms.Compose([Rescale(250),\n",
    "                                     RandomCrop(224),\n",
    "                                     Normalize(),\n",
    "                                     ToTensor()])\n",
    "\n",
    "# create the transformed dataset\n",
    "transformed_dataset = FacialKeypointsDataset(csv_file='data/training_frames_keypoints.csv',\n",
    "                                             root_dir='data/training/',\n",
    "                                             transform=data_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n",
      "0 torch.Size([1, 224, 224]) tensor([[-1.1800,  0.2292],\n",
      "        [-1.1293,  0.4315],\n",
      "        [-1.0785,  0.5327],\n",
      "        [-0.9771,  0.7604],\n",
      "        [-0.9010,  0.9122],\n",
      "        [-0.7488,  1.0387],\n",
      "        [-0.6473,  1.1399],\n",
      "        [-0.4698,  1.1905],\n",
      "        [-0.2668,  1.2664],\n",
      "        [ 0.0122,  1.1399],\n",
      "        [ 0.1136,  1.0387],\n",
      "        [ 0.1644,  0.9375],\n",
      "        [ 0.2658,  0.7857],\n",
      "        [ 0.2658,  0.5580],\n",
      "        [ 0.3166,  0.4062],\n",
      "        [ 0.3166,  0.2545],\n",
      "        [ 0.2658,  0.0268],\n",
      "        [-1.0278,  0.0015],\n",
      "        [-0.9010, -0.0491],\n",
      "        [-0.7995, -0.0491],\n",
      "        [-0.6981, -0.0491],\n",
      "        [-0.5966, -0.0491],\n",
      "        [-0.2161, -0.0997],\n",
      "        [-0.1147, -0.1503],\n",
      "        [ 0.0122, -0.2009],\n",
      "        [ 0.1136, -0.2009],\n",
      "        [ 0.2151, -0.1250],\n",
      "        [-0.3683,  0.0774],\n",
      "        [-0.3176,  0.2292],\n",
      "        [-0.3176,  0.3304],\n",
      "        [-0.3176,  0.4568],\n",
      "        [-0.4698,  0.5074],\n",
      "        [-0.4190,  0.5074],\n",
      "        [-0.3176,  0.5580],\n",
      "        [-0.2668,  0.5074],\n",
      "        [-0.2161,  0.5074],\n",
      "        [-0.8502,  0.1280],\n",
      "        [-0.7488,  0.0774],\n",
      "        [-0.6981,  0.0774],\n",
      "        [-0.5966,  0.0774],\n",
      "        [-0.6473,  0.1280],\n",
      "        [-0.7488,  0.1280],\n",
      "        [-0.1654,  0.0774],\n",
      "        [-0.1147,  0.0268],\n",
      "        [ 0.0122,  0.0268],\n",
      "        [ 0.0629,  0.0268],\n",
      "        [ 0.0122,  0.0774],\n",
      "        [-0.1147,  0.0774],\n",
      "        [-0.5966,  0.7604],\n",
      "        [-0.4698,  0.7098],\n",
      "        [-0.3683,  0.6592],\n",
      "        [-0.3176,  0.7098],\n",
      "        [-0.2668,  0.6592],\n",
      "        [-0.1654,  0.7098],\n",
      "        [-0.0386,  0.7098],\n",
      "        [-0.1147,  0.8363],\n",
      "        [-0.1654,  0.8869],\n",
      "        [-0.2668,  0.8869],\n",
      "        [-0.3683,  0.8869],\n",
      "        [-0.4698,  0.8110],\n",
      "        [-0.5459,  0.7604],\n",
      "        [-0.3683,  0.7604],\n",
      "        [-0.3176,  0.7604],\n",
      "        [-0.2161,  0.7604],\n",
      "        [-0.0386,  0.7098],\n",
      "        [-0.2161,  0.7604],\n",
      "        [-0.2668,  0.8363],\n",
      "        [-0.3683,  0.8363]], dtype=torch.float64)\n",
      "1 torch.Size([1, 224, 224]) tensor([[-0.8699,  0.0915],\n",
      "        [-0.8130,  0.3189],\n",
      "        [-0.7561,  0.5747],\n",
      "        [-0.6993,  0.8021],\n",
      "        [-0.5855,  1.1148],\n",
      "        [-0.3865,  1.4275],\n",
      "        [-0.2159,  1.5980],\n",
      "        [ 0.0400,  1.7402],\n",
      "        [ 0.3813,  1.7970],\n",
      "        [ 0.6940,  1.6265],\n",
      "        [ 0.8646,  1.4559],\n",
      "        [ 1.0068,  1.2569],\n",
      "        [ 1.0637,  0.9727],\n",
      "        [ 1.1206,  0.7168],\n",
      "        [ 1.1206,  0.4894],\n",
      "        [ 1.1206,  0.2336],\n",
      "        [ 1.1206, -0.0507],\n",
      "        [-0.5855,  0.0915],\n",
      "        [-0.4434,  0.0346],\n",
      "        [-0.2727,  0.0346],\n",
      "        [-0.1590,  0.0346],\n",
      "        [-0.0453,  0.0915],\n",
      "        [ 0.4381,  0.0346],\n",
      "        [ 0.5803, -0.0222],\n",
      "        [ 0.6940, -0.0222],\n",
      "        [ 0.8646, -0.0791],\n",
      "        [ 0.9215, -0.0791],\n",
      "        [ 0.2675,  0.3473],\n",
      "        [ 0.3244,  0.5179],\n",
      "        [ 0.3244,  0.7737],\n",
      "        [ 0.3244,  0.8874],\n",
      "        [ 0.1538,  0.9442],\n",
      "        [ 0.2106,  0.9442],\n",
      "        [ 0.3244,  1.0011],\n",
      "        [ 0.3813,  0.9442],\n",
      "        [ 0.4381,  0.8874],\n",
      "        [-0.3865,  0.2620],\n",
      "        [-0.2727,  0.2905],\n",
      "        [-0.1590,  0.2905],\n",
      "        [-0.0453,  0.2905],\n",
      "        [-0.1590,  0.3473],\n",
      "        [-0.2727,  0.3473],\n",
      "        [ 0.4381,  0.2336],\n",
      "        [ 0.5803,  0.1767],\n",
      "        [ 0.6940,  0.1767],\n",
      "        [ 0.8078,  0.1767],\n",
      "        [ 0.6940,  0.2336],\n",
      "        [ 0.5803,  0.2336],\n",
      "        [-0.0453,  1.2001],\n",
      "        [ 0.0969,  1.2001],\n",
      "        [ 0.2675,  1.1432],\n",
      "        [ 0.3244,  1.1432],\n",
      "        [ 0.3813,  1.1432],\n",
      "        [ 0.5234,  1.1432],\n",
      "        [ 0.6372,  1.1432],\n",
      "        [ 0.5234,  1.2569],\n",
      "        [ 0.4381,  1.3138],\n",
      "        [ 0.3244,  1.3138],\n",
      "        [ 0.2106,  1.3138],\n",
      "        [ 0.0969,  1.2569],\n",
      "        [-0.0453,  1.2001],\n",
      "        [ 0.2106,  1.2001],\n",
      "        [ 0.3244,  1.2001],\n",
      "        [ 0.3813,  1.2001],\n",
      "        [ 0.6372,  1.1432],\n",
      "        [ 0.3813,  1.2001],\n",
      "        [ 0.3244,  1.2001],\n",
      "        [ 0.2106,  1.2001]], dtype=torch.float64)\n",
      "2 torch.Size([1, 224, 224]) tensor([[-0.6901, -0.3693],\n",
      "        [-0.6901, -0.1595],\n",
      "        [-0.6901,  0.0765],\n",
      "        [-0.6901,  0.2863],\n",
      "        [-0.6376,  0.5223],\n",
      "        [-0.5851,  0.7320],\n",
      "        [-0.5326,  0.8632],\n",
      "        [-0.4013,  1.0205],\n",
      "        [-0.1912,  1.1516],\n",
      "        [ 0.0977,  1.1516],\n",
      "        [ 0.3603,  1.0467],\n",
      "        [ 0.5966,  0.9943],\n",
      "        [ 0.8067,  0.8107],\n",
      "        [ 0.9380,  0.6009],\n",
      "        [ 1.0431,  0.4436],\n",
      "        [ 1.0956,  0.2076],\n",
      "        [ 1.2006, -0.0546],\n",
      "        [-0.6376, -0.7102],\n",
      "        [-0.5326, -0.7626],\n",
      "        [-0.4013, -0.7626],\n",
      "        [-0.2962, -0.7102],\n",
      "        [-0.2437, -0.6315],\n",
      "        [ 0.2027, -0.4742],\n",
      "        [ 0.3078, -0.4742],\n",
      "        [ 0.4916, -0.4742],\n",
      "        [ 0.6492, -0.3955],\n",
      "        [ 0.7542, -0.2906],\n",
      "        [-0.0861, -0.2382],\n",
      "        [-0.1387, -0.1333],\n",
      "        [-0.1912,  0.0241],\n",
      "        [-0.1912,  0.1289],\n",
      "        [-0.2962,  0.2601],\n",
      "        [-0.2437,  0.2601],\n",
      "        [-0.1912,  0.3125],\n",
      "        [-0.0861,  0.3125],\n",
      "        [-0.0074,  0.3125],\n",
      "        [-0.5326, -0.4217],\n",
      "        [-0.4538, -0.4217],\n",
      "        [-0.3487, -0.4217],\n",
      "        [-0.2437, -0.3168],\n",
      "        [-0.3487, -0.3168],\n",
      "        [-0.4538, -0.3693],\n",
      "        [ 0.2553, -0.1857],\n",
      "        [ 0.3603, -0.2382],\n",
      "        [ 0.4916, -0.2382],\n",
      "        [ 0.5966, -0.1857],\n",
      "        [ 0.4391, -0.1333],\n",
      "        [ 0.3078, -0.1333],\n",
      "        [-0.4013,  0.5223],\n",
      "        [-0.3487,  0.4698],\n",
      "        [-0.2437,  0.4698],\n",
      "        [-0.1912,  0.4698],\n",
      "        [-0.1387,  0.4698],\n",
      "        [ 0.0452,  0.5223],\n",
      "        [ 0.1502,  0.6534],\n",
      "        [-0.0074,  0.7058],\n",
      "        [-0.1387,  0.7058],\n",
      "        [-0.1912,  0.7058],\n",
      "        [-0.2962,  0.7058],\n",
      "        [-0.3487,  0.6272],\n",
      "        [-0.4013,  0.5223],\n",
      "        [-0.2437,  0.5223],\n",
      "        [-0.1912,  0.5747],\n",
      "        [-0.0861,  0.5747],\n",
      "        [ 0.0977,  0.6534],\n",
      "        [-0.1387,  0.5747],\n",
      "        [-0.1912,  0.5747],\n",
      "        [-0.2437,  0.5747]], dtype=torch.float64)\n",
      "3 torch.Size([1, 224, 224]) tensor([[-0.9894, -1.0417],\n",
      "        [-1.0522, -0.7281],\n",
      "        [-1.0522, -0.4562],\n",
      "        [-1.0522, -0.2054],\n",
      "        [-1.0522,  0.0455],\n",
      "        [-0.9894,  0.2337],\n",
      "        [-0.9265,  0.3801],\n",
      "        [-0.7378,  0.5682],\n",
      "        [-0.4863,  0.6937],\n",
      "        [-0.1510,  0.6937],\n",
      "        [ 0.1634,  0.7146],\n",
      "        [ 0.4149,  0.6519],\n",
      "        [ 0.6035,  0.5264],\n",
      "        [ 0.7503,  0.2755],\n",
      "        [ 0.8760,  0.0874],\n",
      "        [ 1.0018, -0.1844],\n",
      "        [ 1.1275, -0.4353],\n",
      "        [-0.8007, -1.2926],\n",
      "        [-0.6750, -1.3553],\n",
      "        [-0.4863, -1.2926],\n",
      "        [-0.4235, -1.2298],\n",
      "        [-0.2767, -1.1462],\n",
      "        [ 0.2263, -1.0208],\n",
      "        [ 0.3520, -1.0208],\n",
      "        [ 0.5407, -1.0208],\n",
      "        [ 0.7503, -0.9371],\n",
      "        [ 0.8131, -0.8117],\n",
      "        [-0.0881, -0.7699],\n",
      "        [-0.1510, -0.6444],\n",
      "        [-0.2139, -0.4562],\n",
      "        [-0.2767, -0.3308],\n",
      "        [-0.4235, -0.2681],\n",
      "        [-0.3606, -0.2054],\n",
      "        [-0.2767, -0.1844],\n",
      "        [-0.1510, -0.1844],\n",
      "        [-0.0881, -0.1844],\n",
      "        [-0.6750, -0.9789],\n",
      "        [-0.5492, -0.9580],\n",
      "        [-0.4235, -0.9580],\n",
      "        [-0.3606, -0.8953],\n",
      "        [-0.4863, -0.8326],\n",
      "        [-0.6121, -0.8953],\n",
      "        [ 0.1634, -0.7071],\n",
      "        [ 0.3520, -0.7699],\n",
      "        [ 0.4149, -0.6862],\n",
      "        [ 0.5407, -0.6235],\n",
      "        [ 0.4149, -0.6235],\n",
      "        [ 0.2892, -0.6235],\n",
      "        [-0.6750, -0.0172],\n",
      "        [-0.5492,  0.0037],\n",
      "        [-0.4235, -0.0590],\n",
      "        [-0.3606,  0.0037],\n",
      "        [-0.2139,  0.0037],\n",
      "        [-0.0881,  0.0664],\n",
      "        [ 0.0376,  0.1919],\n",
      "        [-0.1510,  0.2546],\n",
      "        [-0.2767,  0.3173],\n",
      "        [-0.4235,  0.2546],\n",
      "        [-0.5492,  0.2546],\n",
      "        [-0.6121,  0.1919],\n",
      "        [-0.6750, -0.0172],\n",
      "        [-0.4863,  0.0664],\n",
      "        [-0.3606,  0.0664],\n",
      "        [-0.2139,  0.1292],\n",
      "        [-0.0252,  0.1919],\n",
      "        [-0.2767,  0.1919],\n",
      "        [-0.3606,  0.1919],\n",
      "        [-0.4863,  0.1292]], dtype=torch.float64)\n",
      "4 torch.Size([1, 224, 224]) tensor([[-1.0214, -0.2060],\n",
      "        [-1.0214,  0.0362],\n",
      "        [-0.9728,  0.2541],\n",
      "        [-0.9243,  0.4478],\n",
      "        [-0.8030,  0.6658],\n",
      "        [-0.7059,  0.8111],\n",
      "        [-0.6089,  0.8595],\n",
      "        [-0.4633,  0.9321],\n",
      "        [-0.1964,  0.9806],\n",
      "        [ 0.1190,  0.8837],\n",
      "        [ 0.3131,  0.7868],\n",
      "        [ 0.5314,  0.6415],\n",
      "        [ 0.6285,  0.4720],\n",
      "        [ 0.6770,  0.2299],\n",
      "        [ 0.7255,  0.0120],\n",
      "        [ 0.7255, -0.1818],\n",
      "        [ 0.7255, -0.3997],\n",
      "        [-1.0214, -0.5208],\n",
      "        [-0.9728, -0.6903],\n",
      "        [-0.8758, -0.7387],\n",
      "        [-0.7545, -0.7387],\n",
      "        [-0.6574, -0.7145],\n",
      "        [-0.2450, -0.8113],\n",
      "        [-0.0994, -0.8598],\n",
      "        [ 0.0219, -0.8598],\n",
      "        [ 0.1675, -0.8598],\n",
      "        [ 0.3131, -0.7145],\n",
      "        [-0.3905, -0.4723],\n",
      "        [-0.4633, -0.3028],\n",
      "        [-0.4633, -0.2060],\n",
      "        [-0.4633, -0.1091],\n",
      "        [-0.5118,  0.1088],\n",
      "        [-0.4633,  0.1088],\n",
      "        [-0.3420,  0.1088],\n",
      "        [-0.2935,  0.0604],\n",
      "        [-0.1964,  0.0604],\n",
      "        [-0.8758, -0.3755],\n",
      "        [-0.8030, -0.4239],\n",
      "        [-0.7059, -0.4239],\n",
      "        [-0.6089, -0.3755],\n",
      "        [-0.6574, -0.3028],\n",
      "        [-0.8030, -0.3270],\n",
      "        [-0.1479, -0.4723],\n",
      "        [-0.0509, -0.5208],\n",
      "        [ 0.0705, -0.5692],\n",
      "        [ 0.1675, -0.4965],\n",
      "        [ 0.0705, -0.4481],\n",
      "        [-0.0509, -0.4481],\n",
      "        [-0.6089,  0.4478],\n",
      "        [-0.5118,  0.3510],\n",
      "        [-0.3905,  0.3025],\n",
      "        [-0.3420,  0.3025],\n",
      "        [-0.2450,  0.2541],\n",
      "        [-0.0994,  0.3025],\n",
      "        [ 0.0705,  0.3752],\n",
      "        [-0.0509,  0.4720],\n",
      "        [-0.1964,  0.5205],\n",
      "        [-0.2935,  0.5205],\n",
      "        [-0.3905,  0.5689],\n",
      "        [-0.5118,  0.5205],\n",
      "        [-0.5604,  0.4478],\n",
      "        [-0.3905,  0.3510],\n",
      "        [-0.2935,  0.3510],\n",
      "        [-0.1964,  0.3510],\n",
      "        [ 0.0705,  0.3752],\n",
      "        [-0.1964,  0.4236],\n",
      "        [-0.2935,  0.4720],\n",
      "        [-0.3905,  0.4720]], dtype=torch.float64)\n",
      "5 torch.Size([1, 224, 224]) tensor([[-1.0510, -0.3234],\n",
      "        [-1.0120, -0.1290],\n",
      "        [-0.9535,  0.0655],\n",
      "        [-0.8561,  0.2793],\n",
      "        [-0.8171,  0.4737],\n",
      "        [-0.6611,  0.6681],\n",
      "        [-0.5051,  0.8237],\n",
      "        [-0.3102,  0.9792],\n",
      "        [-0.0178,  0.9792],\n",
      "        [ 0.2357,  0.8820],\n",
      "        [ 0.3331,  0.7459],\n",
      "        [ 0.4306,  0.5904],\n",
      "        [ 0.5476,  0.3376],\n",
      "        [ 0.5866,  0.1432],\n",
      "        [ 0.5866, -0.0512],\n",
      "        [ 0.5866, -0.2456],\n",
      "        [ 0.5476, -0.4595],\n",
      "        [-0.8171, -0.3817],\n",
      "        [-0.7196, -0.4206],\n",
      "        [-0.5636, -0.4206],\n",
      "        [-0.4662, -0.4206],\n",
      "        [-0.3102, -0.4206],\n",
      "        [ 0.0407, -0.4595],\n",
      "        [ 0.1382, -0.4595],\n",
      "        [ 0.2357, -0.4984],\n",
      "        [ 0.3331, -0.4984],\n",
      "        [ 0.4306, -0.4984],\n",
      "        [-0.1153, -0.2067],\n",
      "        [-0.1153, -0.0706],\n",
      "        [-0.0568,  0.0849],\n",
      "        [-0.0568,  0.1821],\n",
      "        [-0.2127,  0.2793],\n",
      "        [-0.1542,  0.2793],\n",
      "        [-0.0568,  0.2793],\n",
      "        [-0.0178,  0.2793],\n",
      "        [ 0.0407,  0.2404],\n",
      "        [-0.6611, -0.2651],\n",
      "        [-0.5636, -0.2651],\n",
      "        [-0.4662, -0.2651],\n",
      "        [-0.3687, -0.2651],\n",
      "        [-0.4662, -0.2262],\n",
      "        [-0.5636, -0.2262],\n",
      "        [ 0.0407, -0.3039],\n",
      "        [ 0.1382, -0.3623],\n",
      "        [ 0.2357, -0.3623],\n",
      "        [ 0.3331, -0.3623],\n",
      "        [ 0.2357, -0.3039],\n",
      "        [ 0.1382, -0.2651],\n",
      "        [-0.3687,  0.5321],\n",
      "        [-0.2517,  0.4737],\n",
      "        [-0.1153,  0.4348],\n",
      "        [-0.0568,  0.4348],\n",
      "        [ 0.0407,  0.4348],\n",
      "        [ 0.1382,  0.4348],\n",
      "        [ 0.2357,  0.4932],\n",
      "        [ 0.1382,  0.5904],\n",
      "        [ 0.0797,  0.6293],\n",
      "        [-0.0178,  0.6876],\n",
      "        [-0.1153,  0.6876],\n",
      "        [-0.2127,  0.5904],\n",
      "        [-0.3102,  0.4737],\n",
      "        [-0.1153,  0.4737],\n",
      "        [-0.0568,  0.4932],\n",
      "        [ 0.0407,  0.4932],\n",
      "        [ 0.1967,  0.4932],\n",
      "        [ 0.0407,  0.5321],\n",
      "        [-0.0178,  0.5904],\n",
      "        [-0.1153,  0.5904]], dtype=torch.float64)\n",
      "6 torch.Size([1, 224, 224]) tensor([[-0.6315,  0.1378],\n",
      "        [-0.6315,  0.3583],\n",
      "        [-0.6315,  0.5787],\n",
      "        [-0.6315,  0.7991],\n",
      "        [-0.6315,  1.0195],\n",
      "        [-0.4989,  1.2400],\n",
      "        [-0.4547,  1.4604],\n",
      "        [-0.2779,  1.6367],\n",
      "        [-0.0127,  1.7249],\n",
      "        [ 0.2525,  1.6808],\n",
      "        [ 0.5177,  1.5926],\n",
      "        [ 0.6945,  1.4163],\n",
      "        [ 0.8713,  1.2840],\n",
      "        [ 0.9155,  1.0195],\n",
      "        [ 0.9597,  0.8432],\n",
      "        [ 1.0923,  0.6228],\n",
      "        [ 1.0923,  0.3583],\n",
      "        [-0.4989,  0.0497],\n",
      "        [-0.4105,  0.0056],\n",
      "        [-0.2779,  0.0056],\n",
      "        [-0.1895,  0.0056],\n",
      "        [-0.0569,  0.0497],\n",
      "        [ 0.3851,  0.1378],\n",
      "        [ 0.4735,  0.1378],\n",
      "        [ 0.6061,  0.1378],\n",
      "        [ 0.6945,  0.1819],\n",
      "        [ 0.8271,  0.2260],\n",
      "        [ 0.0757,  0.4023],\n",
      "        [ 0.0757,  0.6228],\n",
      "        [ 0.0757,  0.7550],\n",
      "        [ 0.0315,  0.8873],\n",
      "        [-0.0569,  0.8873],\n",
      "        [-0.0127,  0.9314],\n",
      "        [ 0.0315,  0.9754],\n",
      "        [ 0.1641,  0.9314],\n",
      "        [ 0.2083,  0.9314],\n",
      "        [-0.3221,  0.2701],\n",
      "        [-0.2779,  0.2701],\n",
      "        [-0.1895,  0.2701],\n",
      "        [-0.0569,  0.3142],\n",
      "        [-0.1895,  0.3142],\n",
      "        [-0.2779,  0.3142],\n",
      "        [ 0.3851,  0.4023],\n",
      "        [ 0.4293,  0.4023],\n",
      "        [ 0.5177,  0.4023],\n",
      "        [ 0.6503,  0.4023],\n",
      "        [ 0.5177,  0.4464],\n",
      "        [ 0.4293,  0.4464],\n",
      "        [-0.2337,  1.1077],\n",
      "        [-0.1895,  1.0195],\n",
      "        [-0.0127,  1.0195],\n",
      "        [ 0.0315,  1.1077],\n",
      "        [ 0.0757,  1.1077],\n",
      "        [ 0.2525,  1.1518],\n",
      "        [ 0.2967,  1.1959],\n",
      "        [ 0.2083,  1.2400],\n",
      "        [ 0.0757,  1.3281],\n",
      "        [ 0.0315,  1.3281],\n",
      "        [-0.0569,  1.3281],\n",
      "        [-0.1895,  1.2400],\n",
      "        [-0.2337,  1.1077],\n",
      "        [-0.0569,  1.1077],\n",
      "        [ 0.0315,  1.1518],\n",
      "        [ 0.1641,  1.1518],\n",
      "        [ 0.2967,  1.1959],\n",
      "        [ 0.0757,  1.1959],\n",
      "        [ 0.0315,  1.1959],\n",
      "        [-0.0569,  1.1959]], dtype=torch.float64)\n",
      "7 torch.Size([1, 224, 224]) tensor([[ 5.2484e-01, -2.2449e-01],\n",
      "        [ 4.6119e-01, -3.3163e-02],\n",
      "        [ 5.2484e-01,  1.2628e-01],\n",
      "        [ 5.2484e-01,  3.4949e-01],\n",
      "        [ 5.5666e-01,  5.4082e-01],\n",
      "        [ 6.2031e-01,  7.3214e-01],\n",
      "        [ 6.5213e-01,  8.2781e-01],\n",
      "        [ 8.1125e-01,  9.2347e-01],\n",
      "        [ 1.0022e+00,  9.8724e-01],\n",
      "        [ 1.2568e+00,  9.2347e-01],\n",
      "        [ 1.5114e+00,  8.9158e-01],\n",
      "        [ 1.7023e+00,  7.9592e-01],\n",
      "        [ 1.8296e+00,  6.0459e-01],\n",
      "        [ 1.9251e+00,  3.8138e-01],\n",
      "        [ 1.9887e+00,  1.9005e-01],\n",
      "        [ 2.0206e+00, -1.2755e-03],\n",
      "        [ 2.0206e+00, -1.9260e-01],\n",
      "        [ 5.5666e-01, -4.4770e-01],\n",
      "        [ 6.2031e-01, -5.4337e-01],\n",
      "        [ 7.1578e-01, -5.4337e-01],\n",
      "        [ 8.1125e-01, -5.4337e-01],\n",
      "        [ 8.4308e-01, -5.1148e-01],\n",
      "        [ 1.2250e+00, -4.7959e-01],\n",
      "        [ 1.3204e+00, -4.7959e-01],\n",
      "        [ 1.4477e+00, -4.7959e-01],\n",
      "        [ 1.5432e+00, -4.4770e-01],\n",
      "        [ 1.7023e+00, -3.8393e-01],\n",
      "        [ 1.0340e+00, -2.5638e-01],\n",
      "        [ 1.0022e+00, -1.2883e-01],\n",
      "        [ 9.3855e-01, -3.3163e-02],\n",
      "        [ 9.3855e-01,  6.2500e-02],\n",
      "        [ 8.4308e-01,  1.5816e-01],\n",
      "        [ 9.0672e-01,  1.5816e-01],\n",
      "        [ 9.3855e-01,  2.2194e-01],\n",
      "        [ 1.0340e+00,  1.5816e-01],\n",
      "        [ 1.0658e+00,  1.5816e-01],\n",
      "        [ 6.5213e-01, -3.2015e-01],\n",
      "        [ 7.1578e-01, -3.2015e-01],\n",
      "        [ 8.1125e-01, -3.2015e-01],\n",
      "        [ 9.0672e-01, -2.5638e-01],\n",
      "        [ 8.1125e-01, -2.5638e-01],\n",
      "        [ 7.1578e-01, -2.5638e-01],\n",
      "        [ 1.2568e+00, -2.5638e-01],\n",
      "        [ 1.3523e+00, -2.5638e-01],\n",
      "        [ 1.4159e+00, -2.5638e-01],\n",
      "        [ 1.5114e+00, -1.9260e-01],\n",
      "        [ 1.4159e+00, -1.9260e-01],\n",
      "        [ 1.3204e+00, -1.9260e-01],\n",
      "        [ 7.1578e-01,  4.4515e-01],\n",
      "        [ 8.1125e-01,  3.4949e-01],\n",
      "        [ 9.0672e-01,  3.1760e-01],\n",
      "        [ 9.3855e-01,  3.1760e-01],\n",
      "        [ 1.0340e+00,  3.1760e-01],\n",
      "        [ 1.1613e+00,  4.1327e-01],\n",
      "        [ 1.2568e+00,  5.0893e-01],\n",
      "        [ 1.1613e+00,  6.0459e-01],\n",
      "        [ 1.0340e+00,  6.0459e-01],\n",
      "        [ 9.3855e-01,  6.3648e-01],\n",
      "        [ 8.4308e-01,  6.3648e-01],\n",
      "        [ 8.1125e-01,  5.4082e-01],\n",
      "        [ 7.4761e-01,  4.4515e-01],\n",
      "        [ 9.0672e-01,  4.1327e-01],\n",
      "        [ 9.3855e-01,  3.4949e-01],\n",
      "        [ 1.0340e+00,  4.1327e-01],\n",
      "        [ 1.2568e+00,  5.0893e-01],\n",
      "        [ 1.0340e+00,  5.4082e-01],\n",
      "        [ 9.3855e-01,  5.4082e-01],\n",
      "        [ 9.0672e-01,  5.0893e-01]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 torch.Size([1, 224, 224]) tensor([[-1.0770e+00, -1.4938e-01],\n",
      "        [-1.0770e+00,  1.8905e-01],\n",
      "        [-1.0135e+00,  5.0633e-01],\n",
      "        [-1.0135e+00,  7.6015e-01],\n",
      "        [-8.6544e-01,  1.0986e+00],\n",
      "        [-7.3849e-01,  1.4159e+00],\n",
      "        [-5.4807e-01,  1.6274e+00],\n",
      "        [-2.9418e-01,  1.8177e+00],\n",
      "        [ 4.4347e-02,  1.9658e+00],\n",
      "        [ 4.2519e-01,  1.8389e+00],\n",
      "        [ 6.1561e-01,  1.7120e+00],\n",
      "        [ 8.2718e-01,  1.5216e+00],\n",
      "        [ 1.0176e+00,  1.2466e+00],\n",
      "        [ 1.0811e+00,  9.9282e-01],\n",
      "        [ 1.2080e+00,  7.3900e-01],\n",
      "        [ 1.2715e+00,  4.2172e-01],\n",
      "        [ 1.2715e+00,  8.3289e-02],\n",
      "        [-6.7502e-01, -6.4773e-02],\n",
      "        [-5.4807e-01, -6.4773e-02],\n",
      "        [-3.5765e-01, -6.4773e-02],\n",
      "        [-1.6723e-01, -1.3179e-03],\n",
      "        [-1.9127e-02, -1.3179e-03],\n",
      "        [ 4.8866e-01,  8.3289e-02],\n",
      "        [ 6.1561e-01,  1.9834e-02],\n",
      "        [ 7.6371e-01,  1.9834e-02],\n",
      "        [ 9.5413e-01,  1.9834e-02],\n",
      "        [ 1.0811e+00,  1.9834e-02],\n",
      "        [ 2.3477e-01,  3.3711e-01],\n",
      "        [ 1.7129e-01,  5.2748e-01],\n",
      "        [ 2.3477e-01,  7.8130e-01],\n",
      "        [ 2.3477e-01,  9.0821e-01],\n",
      "        [-1.9127e-02,  9.7167e-01],\n",
      "        [ 4.4347e-02,  9.7167e-01],\n",
      "        [ 1.7129e-01,  1.0563e+00],\n",
      "        [ 2.9824e-01,  9.9282e-01],\n",
      "        [ 3.6171e-01,  9.9282e-01],\n",
      "        [-4.8460e-01,  1.8905e-01],\n",
      "        [-3.5765e-01,  1.8905e-01],\n",
      "        [-2.3070e-01,  1.8905e-01],\n",
      "        [-8.2600e-02,  1.8905e-01],\n",
      "        [-2.3070e-01,  2.5250e-01],\n",
      "        [-3.5765e-01,  2.5250e-01],\n",
      "        [ 4.8866e-01,  2.7366e-01],\n",
      "        [ 6.1561e-01,  2.1020e-01],\n",
      "        [ 7.6371e-01,  2.1020e-01],\n",
      "        [ 8.9066e-01,  2.1020e-01],\n",
      "        [ 7.6371e-01,  2.7366e-01],\n",
      "        [ 6.1561e-01,  2.7366e-01],\n",
      "        [-2.9418e-01,  1.2255e+00],\n",
      "        [-8.2600e-02,  1.2466e+00],\n",
      "        [ 4.4347e-02,  1.2466e+00],\n",
      "        [ 1.7129e-01,  1.2466e+00],\n",
      "        [ 2.3477e-01,  1.2466e+00],\n",
      "        [ 3.6171e-01,  1.2466e+00],\n",
      "        [ 4.8866e-01,  1.3101e+00],\n",
      "        [ 3.6171e-01,  1.3736e+00],\n",
      "        [ 2.3477e-01,  1.4370e+00],\n",
      "        [ 1.7129e-01,  1.4370e+00],\n",
      "        [ 4.4347e-02,  1.4370e+00],\n",
      "        [-8.2600e-02,  1.3736e+00],\n",
      "        [-2.3070e-01,  1.2466e+00],\n",
      "        [ 4.4347e-02,  1.3101e+00],\n",
      "        [ 1.7129e-01,  1.3101e+00],\n",
      "        [ 2.3477e-01,  1.3101e+00],\n",
      "        [ 4.2519e-01,  1.3101e+00],\n",
      "        [ 2.3477e-01,  1.3101e+00],\n",
      "        [ 1.7129e-01,  1.3101e+00],\n",
      "        [ 4.4347e-02,  1.3101e+00]], dtype=torch.float64)\n",
      "9 torch.Size([1, 224, 224]) tensor([[-0.1221, -0.1525],\n",
      "        [-0.1221,  0.1265],\n",
      "        [-0.0477,  0.3311],\n",
      "        [ 0.0266,  0.6101],\n",
      "        [ 0.1568,  0.8891],\n",
      "        [ 0.2311,  1.0938],\n",
      "        [ 0.3612,  1.2240],\n",
      "        [ 0.4356,  1.3728],\n",
      "        [ 0.7144,  1.4472],\n",
      "        [ 1.0490,  1.3728],\n",
      "        [ 1.3279,  1.1868],\n",
      "        [ 1.6067,  0.9821],\n",
      "        [ 1.8112,  0.7031],\n",
      "        [ 1.8669,  0.4241],\n",
      "        [ 1.8669,  0.1637],\n",
      "        [ 1.8669, -0.1153],\n",
      "        [ 1.8669, -0.3943],\n",
      "        [-0.1778, -0.4315],\n",
      "        [-0.1778, -0.4874],\n",
      "        [-0.1221, -0.4874],\n",
      "        [ 0.0266, -0.4874],\n",
      "        [ 0.1568, -0.4874],\n",
      "        [ 0.5657, -0.4874],\n",
      "        [ 0.7144, -0.5432],\n",
      "        [ 0.8446, -0.5432],\n",
      "        [ 1.0490, -0.5432],\n",
      "        [ 1.2535, -0.4688],\n",
      "        [ 0.3612, -0.2083],\n",
      "        [ 0.3612, -0.0037],\n",
      "        [ 0.2869,  0.2009],\n",
      "        [ 0.2869,  0.3311],\n",
      "        [ 0.3612,  0.4055],\n",
      "        [ 0.3612,  0.4799],\n",
      "        [ 0.4356,  0.4799],\n",
      "        [ 0.5657,  0.4055],\n",
      "        [ 0.6401,  0.4055],\n",
      "        [-0.0477, -0.2083],\n",
      "        [ 0.0266, -0.2827],\n",
      "        [ 0.0824, -0.2827],\n",
      "        [ 0.2311, -0.2083],\n",
      "        [ 0.1568, -0.2083],\n",
      "        [ 0.0266, -0.2083],\n",
      "        [ 0.7144, -0.2641],\n",
      "        [ 0.7702, -0.3385],\n",
      "        [ 0.9189, -0.3385],\n",
      "        [ 1.0490, -0.2641],\n",
      "        [ 0.9747, -0.1897],\n",
      "        [ 0.8446, -0.2083],\n",
      "        [ 0.2869,  0.7589],\n",
      "        [ 0.2869,  0.6845],\n",
      "        [ 0.3612,  0.6101],\n",
      "        [ 0.4356,  0.6101],\n",
      "        [ 0.5100,  0.6101],\n",
      "        [ 0.7144,  0.6845],\n",
      "        [ 0.9189,  0.7589],\n",
      "        [ 0.7702,  0.8891],\n",
      "        [ 0.6401,  0.9635],\n",
      "        [ 0.5657,  0.9635],\n",
      "        [ 0.4356,  0.9635],\n",
      "        [ 0.3612,  0.8891],\n",
      "        [ 0.2869,  0.7589],\n",
      "        [ 0.4356,  0.7589],\n",
      "        [ 0.5100,  0.7589],\n",
      "        [ 0.5657,  0.6845],\n",
      "        [ 0.8446,  0.7589],\n",
      "        [ 0.6401,  0.8333],\n",
      "        [ 0.5100,  0.8147],\n",
      "        [ 0.4356,  0.8147]], dtype=torch.float64)\n",
      "10 torch.Size([1, 224, 224]) tensor([[-1.0255,  0.2637],\n",
      "        [-1.0255,  0.4914],\n",
      "        [-0.9604,  0.7515],\n",
      "        [-0.9278,  1.0117],\n",
      "        [-0.7974,  1.2394],\n",
      "        [-0.6345,  1.4020],\n",
      "        [-0.4716,  1.5646],\n",
      "        [-0.1783,  1.6947],\n",
      "        [ 0.1150,  1.7597],\n",
      "        [ 0.4082,  1.6621],\n",
      "        [ 0.5712,  1.5320],\n",
      "        [ 0.7015,  1.3694],\n",
      "        [ 0.7993,  1.1418],\n",
      "        [ 0.8644,  0.9141],\n",
      "        [ 0.8644,  0.6865],\n",
      "        [ 0.9296,  0.4588],\n",
      "        [ 0.8644,  0.2312],\n",
      "        [-0.6997, -0.0940],\n",
      "        [-0.5042, -0.1916],\n",
      "        [-0.4064, -0.2566],\n",
      "        [-0.2435, -0.2566],\n",
      "        [-0.1131, -0.1916],\n",
      "        [ 0.3431, -0.2566],\n",
      "        [ 0.4734, -0.2891],\n",
      "        [ 0.5712, -0.2891],\n",
      "        [ 0.7015, -0.2566],\n",
      "        [ 0.7993, -0.1265],\n",
      "        [ 0.1150,  0.1011],\n",
      "        [ 0.1801,  0.2637],\n",
      "        [ 0.1801,  0.3938],\n",
      "        [ 0.2453,  0.4914],\n",
      "        [ 0.0172,  0.6540],\n",
      "        [ 0.0498,  0.6540],\n",
      "        [ 0.1801,  0.7190],\n",
      "        [ 0.2453,  0.6540],\n",
      "        [ 0.2779,  0.6865],\n",
      "        [-0.4716,  0.0686],\n",
      "        [-0.3412,  0.0361],\n",
      "        [-0.2435,  0.0361],\n",
      "        [-0.1783,  0.1011],\n",
      "        [-0.2435,  0.1336],\n",
      "        [-0.3412,  0.1336],\n",
      "        [ 0.3431,  0.1011],\n",
      "        [ 0.4734,  0.0361],\n",
      "        [ 0.5712, -0.0290],\n",
      "        [ 0.6363,  0.0361],\n",
      "        [ 0.5712,  0.1011],\n",
      "        [ 0.4734,  0.1011],\n",
      "        [-0.2435,  1.0768],\n",
      "        [-0.0480,  1.0117],\n",
      "        [ 0.0498,  0.9467],\n",
      "        [ 0.1801,  0.9467],\n",
      "        [ 0.2453,  0.9467],\n",
      "        [ 0.3431,  1.0117],\n",
      "        [ 0.4082,  1.0768],\n",
      "        [ 0.3431,  1.1743],\n",
      "        [ 0.2453,  1.3044],\n",
      "        [ 0.1150,  1.3044],\n",
      "        [ 0.0172,  1.3044],\n",
      "        [-0.0480,  1.2394],\n",
      "        [-0.1783,  1.0768],\n",
      "        [ 0.0498,  1.0117],\n",
      "        [ 0.1150,  1.0117],\n",
      "        [ 0.2453,  1.0117],\n",
      "        [ 0.4082,  1.0768],\n",
      "        [ 0.2453,  1.1743],\n",
      "        [ 0.1150,  1.1743],\n",
      "        [ 0.0498,  1.1743]], dtype=torch.float64)\n",
      "11 torch.Size([1, 224, 224]) tensor([[-1.0944, -0.4838],\n",
      "        [-1.0944, -0.2269],\n",
      "        [-1.0300,  0.0301],\n",
      "        [-0.9657,  0.2442],\n",
      "        [-0.9013,  0.5012],\n",
      "        [-0.7081,  0.6939],\n",
      "        [-0.5793,  0.7582],\n",
      "        [-0.3862,  0.8438],\n",
      "        [ 0.0216,  0.9080],\n",
      "        [ 0.2792,  0.8438],\n",
      "        [ 0.4724,  0.7153],\n",
      "        [ 0.6011,  0.5868],\n",
      "        [ 0.7514,  0.3941],\n",
      "        [ 0.8158,  0.1372],\n",
      "        [ 0.8158, -0.1412],\n",
      "        [ 0.8158, -0.3340],\n",
      "        [ 0.8158, -0.5909],\n",
      "        [-0.9657, -0.8907],\n",
      "        [-0.8369, -1.0834],\n",
      "        [-0.7081, -1.1477],\n",
      "        [-0.5149, -1.1263],\n",
      "        [-0.3862, -1.0620],\n",
      "        [ 0.1504, -1.1263],\n",
      "        [ 0.2792, -1.1905],\n",
      "        [ 0.4080, -1.1905],\n",
      "        [ 0.5367, -1.1905],\n",
      "        [ 0.6655, -0.9764],\n",
      "        [-0.1071, -0.7408],\n",
      "        [-0.1071, -0.6123],\n",
      "        [-0.0428, -0.4624],\n",
      "        [-0.0428, -0.3340],\n",
      "        [-0.2359, -0.1412],\n",
      "        [-0.1715, -0.1412],\n",
      "        [-0.0428, -0.1412],\n",
      "        [ 0.0216, -0.1412],\n",
      "        [ 0.1504, -0.2055],\n",
      "        [-0.7081, -0.6766],\n",
      "        [-0.6437, -0.7408],\n",
      "        [-0.5149, -0.7408],\n",
      "        [-0.3862, -0.6766],\n",
      "        [-0.5149, -0.6123],\n",
      "        [-0.6437, -0.6123],\n",
      "        [ 0.1504, -0.7408],\n",
      "        [ 0.2792, -0.8051],\n",
      "        [ 0.4080, -0.7836],\n",
      "        [ 0.5367, -0.7194],\n",
      "        [ 0.4080, -0.7194],\n",
      "        [ 0.2792, -0.6552],\n",
      "        [-0.4505,  0.2442],\n",
      "        [-0.3003,  0.0515],\n",
      "        [-0.1071, -0.0127],\n",
      "        [-0.0428, -0.0127],\n",
      "        [ 0.0216, -0.0127],\n",
      "        [ 0.2148,  0.0515],\n",
      "        [ 0.2792,  0.1800],\n",
      "        [ 0.2148,  0.3085],\n",
      "        [ 0.0860,  0.3727],\n",
      "        [-0.0428,  0.4369],\n",
      "        [-0.1715,  0.4369],\n",
      "        [-0.3003,  0.3727],\n",
      "        [-0.3862,  0.2442],\n",
      "        [-0.1715,  0.1157],\n",
      "        [-0.0428,  0.1157],\n",
      "        [ 0.0860,  0.1157],\n",
      "        [ 0.2792,  0.1800],\n",
      "        [ 0.0860,  0.2442],\n",
      "        [-0.0428,  0.3085],\n",
      "        [-0.1715,  0.3085]], dtype=torch.float64)\n",
      "12 torch.Size([1, 224, 224]) tensor([[-1.7429, -0.2378],\n",
      "        [-1.6619,  0.0260],\n",
      "        [-1.6012,  0.2695],\n",
      "        [-1.5405,  0.5130],\n",
      "        [-1.4190,  0.7768],\n",
      "        [-1.1762,  0.8985],\n",
      "        [-0.9333,  1.0203],\n",
      "        [-0.6095,  1.1015],\n",
      "        [-0.3060,  1.1015],\n",
      "        [-0.0024,  1.0406],\n",
      "        [ 0.1798,  0.9188],\n",
      "        [ 0.3214,  0.7971],\n",
      "        [ 0.3821,  0.5536],\n",
      "        [ 0.4429,  0.3101],\n",
      "        [ 0.4429,  0.0463],\n",
      "        [ 0.4429, -0.1364],\n",
      "        [ 0.3821, -0.3799],\n",
      "        [-1.3583, -0.6031],\n",
      "        [-1.1762, -0.7248],\n",
      "        [-1.0548, -0.7857],\n",
      "        [-0.8726, -0.7045],\n",
      "        [-0.7512, -0.7045],\n",
      "        [-0.1845, -0.7045],\n",
      "        [-0.1238, -0.7654],\n",
      "        [ 0.0583, -0.7654],\n",
      "        [ 0.1798, -0.7654],\n",
      "        [ 0.2405, -0.7045],\n",
      "        [-0.4274, -0.4610],\n",
      "        [-0.3667, -0.2784],\n",
      "        [-0.3060, -0.0755],\n",
      "        [-0.3060,  0.0463],\n",
      "        [-0.5488,  0.1680],\n",
      "        [-0.4274,  0.1680],\n",
      "        [-0.3667,  0.1680],\n",
      "        [-0.2452,  0.1680],\n",
      "        [-0.1845,  0.1071],\n",
      "        [-1.1155, -0.4002],\n",
      "        [-0.9940, -0.4610],\n",
      "        [-0.8726, -0.4610],\n",
      "        [-0.7512, -0.4002],\n",
      "        [-0.8726, -0.4002],\n",
      "        [-0.9940, -0.3393],\n",
      "        [-0.1845, -0.4002],\n",
      "        [-0.1238, -0.4610],\n",
      "        [ 0.0583, -0.5219],\n",
      "        [ 0.1190, -0.4407],\n",
      "        [-0.0024, -0.3799],\n",
      "        [-0.1238, -0.4002],\n",
      "        [-0.8726,  0.4724],\n",
      "        [-0.6702,  0.3506],\n",
      "        [-0.4274,  0.2898],\n",
      "        [-0.3060,  0.2898],\n",
      "        [-0.2452,  0.2898],\n",
      "        [-0.0631,  0.2898],\n",
      "        [ 0.0583,  0.4115],\n",
      "        [-0.0631,  0.5333],\n",
      "        [-0.1845,  0.5942],\n",
      "        [-0.3060,  0.5942],\n",
      "        [-0.4274,  0.5942],\n",
      "        [-0.6095,  0.5942],\n",
      "        [-0.8119,  0.4724],\n",
      "        [-0.4881,  0.4115],\n",
      "        [-0.3667,  0.3506],\n",
      "        [-0.1845,  0.3506],\n",
      "        [-0.0024,  0.4115],\n",
      "        [-0.1845,  0.4724],\n",
      "        [-0.3060,  0.4724],\n",
      "        [-0.4881,  0.5333]], dtype=torch.float64)\n",
      "13 torch.Size([1, 224, 224]) tensor([[-1.0257, -1.1875],\n",
      "        [-1.0257, -0.9062],\n",
      "        [-1.0257, -0.6652],\n",
      "        [-0.9453, -0.4241],\n",
      "        [-0.9051, -0.1429],\n",
      "        [-0.7844,  0.1786],\n",
      "        [-0.5833,  0.3393],\n",
      "        [-0.3420,  0.5402],\n",
      "        [-0.0605,  0.7411],\n",
      "        [ 0.2210,  0.6205],\n",
      "        [ 0.4221,  0.5000],\n",
      "        [ 0.5428,  0.3795],\n",
      "        [ 0.6634,  0.1384],\n",
      "        [ 0.7841, -0.1027],\n",
      "        [ 0.8243, -0.2634],\n",
      "        [ 0.9048, -0.5045],\n",
      "        [ 0.9450, -0.8259],\n",
      "        [-0.5833, -1.3080],\n",
      "        [-0.4225, -1.3884],\n",
      "        [-0.2214, -1.3884],\n",
      "        [-0.1007, -1.3482],\n",
      "        [ 0.0602, -1.3080],\n",
      "        [ 0.5428, -1.2277],\n",
      "        [ 0.6634, -1.2277],\n",
      "        [ 0.7841, -1.2277],\n",
      "        [ 0.9048, -1.1875],\n",
      "        [ 0.9450, -1.1071],\n",
      "        [ 0.2210, -0.9464],\n",
      "        [ 0.2210, -0.7455],\n",
      "        [ 0.2210, -0.5848],\n",
      "        [ 0.2210, -0.4241],\n",
      "        [-0.0203, -0.3036],\n",
      "        [ 0.0602, -0.3036],\n",
      "        [ 0.1808, -0.2232],\n",
      "        [ 0.2210, -0.3036],\n",
      "        [ 0.3015, -0.3036],\n",
      "        [-0.4225, -1.1473],\n",
      "        [-0.3018, -1.1473],\n",
      "        [-0.1811, -1.1473],\n",
      "        [-0.0605, -1.0670],\n",
      "        [-0.1811, -1.0268],\n",
      "        [-0.3018, -1.0670],\n",
      "        [ 0.4624, -0.9464],\n",
      "        [ 0.5830, -0.9866],\n",
      "        [ 0.7037, -0.9866],\n",
      "        [ 0.7841, -0.9464],\n",
      "        [ 0.6634, -0.8661],\n",
      "        [ 0.5428, -0.8661],\n",
      "        [-0.3420, -0.0625],\n",
      "        [-0.1007, -0.0625],\n",
      "        [ 0.0602, -0.0625],\n",
      "        [ 0.1004, -0.0625],\n",
      "        [ 0.2210, -0.0625],\n",
      "        [ 0.3417,  0.0179],\n",
      "        [ 0.4221,  0.0580],\n",
      "        [ 0.3015,  0.1384],\n",
      "        [ 0.1808,  0.1786],\n",
      "        [ 0.0602,  0.1786],\n",
      "        [-0.0605,  0.1786],\n",
      "        [-0.1811,  0.0580],\n",
      "        [-0.3018, -0.0625],\n",
      "        [-0.0203,  0.0179],\n",
      "        [ 0.1004,  0.0179],\n",
      "        [ 0.2210,  0.0179],\n",
      "        [ 0.4221,  0.0179],\n",
      "        [ 0.1808,  0.0580],\n",
      "        [ 0.1004,  0.0580],\n",
      "        [-0.0203,  0.0580]], dtype=torch.float64)\n",
      "14 torch.Size([1, 224, 224]) tensor([[-0.8804,  0.1553],\n",
      "        [-0.8804,  0.4371],\n",
      "        [-0.7957,  0.7470],\n",
      "        [-0.7392,  0.9443],\n",
      "        [-0.5696,  1.2542],\n",
      "        [-0.4283,  1.4796],\n",
      "        [-0.2871,  1.6205],\n",
      "        [-0.1458,  1.8459],\n",
      "        [ 0.1368,  1.9023],\n",
      "        [ 0.5041,  1.8459],\n",
      "        [ 0.8714,  1.6205],\n",
      "        [ 1.0974,  1.4233],\n",
      "        [ 1.2952,  1.1415],\n",
      "        [ 1.3800,  0.8316],\n",
      "        [ 1.4365,  0.5498],\n",
      "        [ 1.4365,  0.2680],\n",
      "        [ 1.4365, -0.0137],\n",
      "        [-0.8804, -0.0701],\n",
      "        [-0.7957, -0.1264],\n",
      "        [-0.6544, -0.1264],\n",
      "        [-0.5696, -0.0419],\n",
      "        [-0.4283, -0.0419],\n",
      "        [ 0.1368, -0.0419],\n",
      "        [ 0.2780, -0.0419],\n",
      "        [ 0.4193, -0.0983],\n",
      "        [ 0.6453, -0.0983],\n",
      "        [ 0.8714, -0.0419],\n",
      "        [-0.1458,  0.3244],\n",
      "        [-0.1458,  0.5216],\n",
      "        [-0.2306,  0.8316],\n",
      "        [-0.1458,  0.9725],\n",
      "        [-0.2306,  1.0288],\n",
      "        [-0.1458,  1.0288],\n",
      "        [-0.0610,  1.0288],\n",
      "        [-0.0045,  1.0288],\n",
      "        [ 0.1368,  1.0288],\n",
      "        [-0.6544,  0.1553],\n",
      "        [-0.5696,  0.1553],\n",
      "        [-0.4283,  0.1553],\n",
      "        [-0.3718,  0.2399],\n",
      "        [-0.4283,  0.3244],\n",
      "        [-0.5696,  0.2962],\n",
      "        [ 0.2215,  0.2399],\n",
      "        [ 0.3628,  0.1835],\n",
      "        [ 0.5041,  0.1835],\n",
      "        [ 0.6453,  0.2680],\n",
      "        [ 0.5041,  0.3244],\n",
      "        [ 0.3628,  0.3244],\n",
      "        [-0.2871,  1.3387],\n",
      "        [-0.2871,  1.2542],\n",
      "        [-0.1458,  1.2542],\n",
      "        [-0.0610,  1.2542],\n",
      "        [-0.0045,  1.2542],\n",
      "        [ 0.2215,  1.3387],\n",
      "        [ 0.4193,  1.3387],\n",
      "        [ 0.2215,  1.3951],\n",
      "        [ 0.0802,  1.4796],\n",
      "        [-0.0045,  1.4796],\n",
      "        [-0.1458,  1.4796],\n",
      "        [-0.2306,  1.3951],\n",
      "        [-0.2871,  1.3387],\n",
      "        [-0.1458,  1.3387],\n",
      "        [-0.0610,  1.3387],\n",
      "        [ 0.0802,  1.3387],\n",
      "        [ 0.3628,  1.3387],\n",
      "        [ 0.0802,  1.3387],\n",
      "        [-0.0610,  1.3387],\n",
      "        [-0.1458,  1.3387]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 torch.Size([1, 224, 224]) tensor([[-1.1951, -0.1755],\n",
      "        [-1.1516,  0.0202],\n",
      "        [-1.1080,  0.1941],\n",
      "        [-0.9991,  0.3898],\n",
      "        [-0.9120,  0.5637],\n",
      "        [-0.7378,  0.7593],\n",
      "        [-0.5418,  0.8463],\n",
      "        [-0.3023,  0.9115],\n",
      "        [-0.0845,  0.9550],\n",
      "        [ 0.1115,  0.8245],\n",
      "        [ 0.1551,  0.6724],\n",
      "        [ 0.1986,  0.5419],\n",
      "        [ 0.2422,  0.4115],\n",
      "        [ 0.2422,  0.2158],\n",
      "        [ 0.2422,  0.0419],\n",
      "        [ 0.2422, -0.1537],\n",
      "        [ 0.1986, -0.3494],\n",
      "        [-0.8249, -0.3059],\n",
      "        [-0.6725, -0.3929],\n",
      "        [-0.5854, -0.4363],\n",
      "        [-0.4547, -0.4363],\n",
      "        [-0.3458, -0.4363],\n",
      "        [-0.0409, -0.4798],\n",
      "        [ 0.0244, -0.5233],\n",
      "        [ 0.1115, -0.5668],\n",
      "        [ 0.1551, -0.5668],\n",
      "        [ 0.1551, -0.5233],\n",
      "        [-0.1716, -0.2624],\n",
      "        [-0.1280, -0.1102],\n",
      "        [-0.0845,  0.0202],\n",
      "        [-0.0409,  0.1289],\n",
      "        [-0.2152,  0.2158],\n",
      "        [-0.1716,  0.2158],\n",
      "        [-0.0845,  0.2158],\n",
      "        [-0.0409,  0.2158],\n",
      "        [-0.0409,  0.1724],\n",
      "        [-0.6289, -0.2189],\n",
      "        [-0.5418, -0.2189],\n",
      "        [-0.4547, -0.2624],\n",
      "        [-0.4111, -0.2189],\n",
      "        [-0.4547, -0.1755],\n",
      "        [-0.5418, -0.1755],\n",
      "        [-0.0845, -0.3059],\n",
      "        [ 0.0244, -0.3494],\n",
      "        [ 0.0679, -0.3929],\n",
      "        [ 0.1115, -0.3494],\n",
      "        [ 0.0679, -0.3059],\n",
      "        [ 0.0244, -0.3059],\n",
      "        [-0.3458,  0.4332],\n",
      "        [-0.2587,  0.3898],\n",
      "        [-0.1280,  0.3463],\n",
      "        [-0.0845,  0.3463],\n",
      "        [-0.0409,  0.3463],\n",
      "        [ 0.0244,  0.3463],\n",
      "        [ 0.0679,  0.3463],\n",
      "        [ 0.0244,  0.3898],\n",
      "        [ 0.0244,  0.4550],\n",
      "        [-0.0845,  0.4984],\n",
      "        [-0.1280,  0.4984],\n",
      "        [-0.2152,  0.4984],\n",
      "        [-0.3458,  0.4332],\n",
      "        [-0.1716,  0.3898],\n",
      "        [-0.0845,  0.3898],\n",
      "        [-0.0409,  0.3898],\n",
      "        [ 0.0679,  0.3463],\n",
      "        [-0.0409,  0.3898],\n",
      "        [-0.0845,  0.3898],\n",
      "        [-0.1716,  0.3898]], dtype=torch.float64)\n",
      "16 torch.Size([1, 224, 224]) tensor([[-0.9515, -0.4948],\n",
      "        [-0.9005, -0.2399],\n",
      "        [-0.8240, -0.0360],\n",
      "        [-0.6964,  0.2188],\n",
      "        [-0.6454,  0.4992],\n",
      "        [-0.4413,  0.6776],\n",
      "        [-0.2372,  0.8305],\n",
      "        [ 0.0179,  0.9580],\n",
      "        [ 0.3495,  1.0344],\n",
      "        [ 0.6811,  0.9070],\n",
      "        [ 0.8597,  0.7031],\n",
      "        [ 1.0128,  0.5756],\n",
      "        [ 1.1403,  0.3208],\n",
      "        [ 1.1403,  0.0659],\n",
      "        [ 1.1913, -0.1890],\n",
      "        [ 1.1913, -0.4693],\n",
      "        [ 1.1403, -0.7242],\n",
      "        [-0.6964, -0.6987],\n",
      "        [-0.4923, -0.7497],\n",
      "        [-0.3648, -0.7497],\n",
      "        [-0.1862, -0.7497],\n",
      "        [-0.0332, -0.7497],\n",
      "        [ 0.4005, -0.7242],\n",
      "        [ 0.5536, -0.8006],\n",
      "        [ 0.6811, -0.8006],\n",
      "        [ 0.8597, -0.8006],\n",
      "        [ 1.0128, -0.8006],\n",
      "        [ 0.2219, -0.4693],\n",
      "        [ 0.2219, -0.2909],\n",
      "        [ 0.2730, -0.1380],\n",
      "        [ 0.2730, -0.0106],\n",
      "        [ 0.0944,  0.0404],\n",
      "        [ 0.1454,  0.1169],\n",
      "        [ 0.2730,  0.1169],\n",
      "        [ 0.3495,  0.0404],\n",
      "        [ 0.4005,  0.0404],\n",
      "        [-0.4413, -0.4948],\n",
      "        [-0.3138, -0.4948],\n",
      "        [-0.1862, -0.5458],\n",
      "        [-0.1097, -0.4948],\n",
      "        [-0.1862, -0.4948],\n",
      "        [-0.3138, -0.4183],\n",
      "        [ 0.4770, -0.5458],\n",
      "        [ 0.5536, -0.5458],\n",
      "        [ 0.6811, -0.5968],\n",
      "        [ 0.8087, -0.5968],\n",
      "        [ 0.6811, -0.5458],\n",
      "        [ 0.5536, -0.4693],\n",
      "        [-0.1097,  0.3718],\n",
      "        [ 0.0179,  0.2953],\n",
      "        [ 0.2219,  0.2953],\n",
      "        [ 0.2730,  0.2953],\n",
      "        [ 0.4005,  0.2443],\n",
      "        [ 0.5536,  0.2443],\n",
      "        [ 0.6811,  0.2443],\n",
      "        [ 0.5536,  0.4482],\n",
      "        [ 0.4770,  0.5756],\n",
      "        [ 0.3495,  0.5756],\n",
      "        [ 0.2219,  0.5756],\n",
      "        [ 0.0179,  0.4992],\n",
      "        [-0.1097,  0.3718],\n",
      "        [ 0.1454,  0.3718],\n",
      "        [ 0.2730,  0.3718],\n",
      "        [ 0.4005,  0.3208],\n",
      "        [ 0.6046,  0.2443],\n",
      "        [ 0.4005,  0.4482],\n",
      "        [ 0.2730,  0.4482],\n",
      "        [ 0.1454,  0.4227]], dtype=torch.float64)\n",
      "17 torch.Size([1, 224, 224]) tensor([[-1.1594,  0.0651],\n",
      "        [-1.1184,  0.3308],\n",
      "        [-1.0570,  0.5352],\n",
      "        [-0.9546,  0.7601],\n",
      "        [-0.8522,  0.9236],\n",
      "        [-0.6884,  1.0667],\n",
      "        [-0.5860,  1.1894],\n",
      "        [-0.3812,  1.2303],\n",
      "        [-0.1150,  1.2916],\n",
      "        [ 0.1512,  1.1894],\n",
      "        [ 0.2946,  1.0463],\n",
      "        [ 0.4584,  0.8828],\n",
      "        [ 0.5198,  0.6783],\n",
      "        [ 0.5608,  0.4739],\n",
      "        [ 0.5608,  0.2082],\n",
      "        [ 0.5608,  0.0038],\n",
      "        [ 0.5198, -0.2620],\n",
      "        [-1.0570, -0.1393],\n",
      "        [-0.9546, -0.1802],\n",
      "        [-0.8522, -0.1802],\n",
      "        [-0.7498, -0.2415],\n",
      "        [-0.5860, -0.1802],\n",
      "        [-0.1764, -0.2824],\n",
      "        [-0.0740, -0.3233],\n",
      "        [ 0.0488, -0.3846],\n",
      "        [ 0.1922, -0.3846],\n",
      "        [ 0.2946, -0.3846],\n",
      "        [-0.3812, -0.0780],\n",
      "        [-0.3198,  0.0242],\n",
      "        [-0.2788,  0.1877],\n",
      "        [-0.2788,  0.2899],\n",
      "        [-0.3812,  0.3922],\n",
      "        [-0.3198,  0.3922],\n",
      "        [-0.2174,  0.3922],\n",
      "        [-0.1764,  0.3922],\n",
      "        [-0.1150,  0.3513],\n",
      "        [-0.8522,  0.0242],\n",
      "        [-0.7908, -0.0371],\n",
      "        [-0.6884, -0.0780],\n",
      "        [-0.5860, -0.0167],\n",
      "        [-0.6474,  0.0242],\n",
      "        [-0.7908,  0.0242],\n",
      "        [-0.1150, -0.1189],\n",
      "        [-0.0740, -0.2211],\n",
      "        [ 0.0898, -0.2211],\n",
      "        [ 0.1512, -0.2211],\n",
      "        [ 0.0898, -0.1802],\n",
      "        [-0.0126, -0.1189],\n",
      "        [-0.4836,  0.7601],\n",
      "        [-0.3812,  0.6579],\n",
      "        [-0.2788,  0.6170],\n",
      "        [-0.2174,  0.6170],\n",
      "        [-0.1764,  0.5557],\n",
      "        [-0.0126,  0.6170],\n",
      "        [ 0.0898,  0.6579],\n",
      "        [-0.0126,  0.7805],\n",
      "        [-0.0740,  0.8214],\n",
      "        [-0.1764,  0.8828],\n",
      "        [-0.2788,  0.8623],\n",
      "        [-0.3812,  0.8214],\n",
      "        [-0.4222,  0.7601],\n",
      "        [-0.2788,  0.6579],\n",
      "        [-0.2174,  0.6579],\n",
      "        [-0.1150,  0.6579],\n",
      "        [ 0.0488,  0.6579],\n",
      "        [-0.1150,  0.7192],\n",
      "        [-0.1764,  0.7601],\n",
      "        [-0.2788,  0.7601]], dtype=torch.float64)\n",
      "18 torch.Size([1, 224, 224]) tensor([[-0.7881, -0.5566],\n",
      "        [-0.7881, -0.3179],\n",
      "        [-0.7881, -0.1058],\n",
      "        [-0.7349,  0.1329],\n",
      "        [-0.6818,  0.3980],\n",
      "        [-0.5755,  0.5837],\n",
      "        [-0.4426,  0.7428],\n",
      "        [-0.2832,  0.8488],\n",
      "        [ 0.0091,  0.9814],\n",
      "        [ 0.2749,  0.9284],\n",
      "        [ 0.5140,  0.8223],\n",
      "        [ 0.6735,  0.6632],\n",
      "        [ 0.7798,  0.4776],\n",
      "        [ 0.8595,  0.2124],\n",
      "        [ 0.9658, -0.0262],\n",
      "        [ 1.0189, -0.2384],\n",
      "        [ 1.0189, -0.4771],\n",
      "        [-0.6286, -0.9013],\n",
      "        [-0.4957, -1.0074],\n",
      "        [-0.3895, -1.0074],\n",
      "        [-0.2832, -1.0074],\n",
      "        [-0.1769, -0.9279],\n",
      "        [ 0.2749, -0.9279],\n",
      "        [ 0.4077, -0.9279],\n",
      "        [ 0.5140, -0.9809],\n",
      "        [ 0.6735, -0.9279],\n",
      "        [ 0.7798, -0.8218],\n",
      "        [ 0.0623, -0.6096],\n",
      "        [ 0.0623, -0.4240],\n",
      "        [ 0.0623, -0.3179],\n",
      "        [ 0.0623, -0.1588],\n",
      "        [-0.1237, -0.0262],\n",
      "        [-0.0440,  0.0268],\n",
      "        [ 0.0091,  0.0268],\n",
      "        [ 0.1154,  0.0268],\n",
      "        [ 0.1686,  0.0268],\n",
      "        [-0.4426, -0.6096],\n",
      "        [-0.3895, -0.6627],\n",
      "        [-0.2832, -0.6627],\n",
      "        [-0.1769, -0.6096],\n",
      "        [-0.2832, -0.5566],\n",
      "        [-0.3895, -0.6096],\n",
      "        [ 0.2749, -0.5831],\n",
      "        [ 0.4077, -0.6627],\n",
      "        [ 0.5140, -0.6362],\n",
      "        [ 0.5672, -0.5831],\n",
      "        [ 0.5140, -0.5301],\n",
      "        [ 0.4077, -0.5301],\n",
      "        [-0.2832,  0.3450],\n",
      "        [-0.1769,  0.2389],\n",
      "        [-0.0440,  0.1859],\n",
      "        [ 0.0091,  0.2389],\n",
      "        [ 0.0623,  0.2389],\n",
      "        [ 0.1686,  0.2920],\n",
      "        [ 0.2749,  0.3715],\n",
      "        [ 0.1686,  0.4776],\n",
      "        [ 0.1154,  0.5306],\n",
      "        [ 0.0091,  0.5306],\n",
      "        [-0.1237,  0.5306],\n",
      "        [-0.1769,  0.4776],\n",
      "        [-0.2300,  0.3450],\n",
      "        [-0.0440,  0.2920],\n",
      "        [ 0.0091,  0.2920],\n",
      "        [ 0.1154,  0.2920],\n",
      "        [ 0.2749,  0.3715],\n",
      "        [ 0.1154,  0.4246],\n",
      "        [ 0.0091,  0.4246],\n",
      "        [-0.0440,  0.4246]], dtype=torch.float64)\n",
      "19 torch.Size([1, 224, 224]) tensor([[-1.1041, -0.1308],\n",
      "        [-0.9952,  0.0213],\n",
      "        [-0.9299,  0.2602],\n",
      "        [-0.8210,  0.4123],\n",
      "        [-0.7121,  0.6512],\n",
      "        [-0.4943,  0.8250],\n",
      "        [-0.2548,  0.8684],\n",
      "        [ 0.0283,  0.9336],\n",
      "        [ 0.2896,  0.9988],\n",
      "        [ 0.5292,  0.8250],\n",
      "        [ 0.5727,  0.7164],\n",
      "        [ 0.6381,  0.5643],\n",
      "        [ 0.6816,  0.3905],\n",
      "        [ 0.6816,  0.1733],\n",
      "        [ 0.7470, -0.0439],\n",
      "        [ 0.6816, -0.2177],\n",
      "        [ 0.6381, -0.5001],\n",
      "        [-0.6468, -0.5218],\n",
      "        [-0.4943, -0.6304],\n",
      "        [-0.3637, -0.6739],\n",
      "        [-0.2112, -0.6739],\n",
      "        [-0.1024, -0.6304],\n",
      "        [ 0.2896, -0.7390],\n",
      "        [ 0.3985, -0.7825],\n",
      "        [ 0.4639, -0.8476],\n",
      "        [ 0.5292, -0.8476],\n",
      "        [ 0.5727, -0.7825],\n",
      "        [ 0.1807, -0.4566],\n",
      "        [ 0.2461, -0.2829],\n",
      "        [ 0.2896, -0.1091],\n",
      "        [ 0.3550,  0.0430],\n",
      "        [ 0.1372,  0.1516],\n",
      "        [ 0.1807,  0.1516],\n",
      "        [ 0.2896,  0.1516],\n",
      "        [ 0.3550,  0.1081],\n",
      "        [ 0.3550,  0.1081],\n",
      "        [-0.4290, -0.3480],\n",
      "        [-0.3201, -0.4132],\n",
      "        [-0.2112, -0.4132],\n",
      "        [-0.1459, -0.3915],\n",
      "        [-0.2112, -0.3480],\n",
      "        [-0.3201, -0.3480],\n",
      "        [ 0.2896, -0.4566],\n",
      "        [ 0.3985, -0.5001],\n",
      "        [ 0.4639, -0.5652],\n",
      "        [ 0.5292, -0.5652],\n",
      "        [ 0.4639, -0.5001],\n",
      "        [ 0.3985, -0.4566],\n",
      "        [-0.1024,  0.4340],\n",
      "        [ 0.0719,  0.3905],\n",
      "        [ 0.2461,  0.3254],\n",
      "        [ 0.2896,  0.3254],\n",
      "        [ 0.3550,  0.2819],\n",
      "        [ 0.4639,  0.3254],\n",
      "        [ 0.5292,  0.3254],\n",
      "        [ 0.4639,  0.4340],\n",
      "        [ 0.3985,  0.4991],\n",
      "        [ 0.2896,  0.5426],\n",
      "        [ 0.1807,  0.5426],\n",
      "        [ 0.0719,  0.5426],\n",
      "        [-0.1024,  0.4340],\n",
      "        [ 0.1807,  0.3905],\n",
      "        [ 0.2896,  0.3905],\n",
      "        [ 0.3550,  0.3905],\n",
      "        [ 0.4639,  0.3254],\n",
      "        [ 0.3550,  0.4340],\n",
      "        [ 0.2896,  0.4340],\n",
      "        [ 0.1807,  0.4340]], dtype=torch.float64)\n",
      "20 torch.Size([1, 224, 224]) tensor([[-0.9444, -0.4821],\n",
      "        [-0.9444, -0.2840],\n",
      "        [-0.8948, -0.1106],\n",
      "        [-0.8948,  0.0876],\n",
      "        [-0.8452,  0.3105],\n",
      "        [-0.7460,  0.4591],\n",
      "        [-0.5724,  0.5829],\n",
      "        [-0.4236,  0.6820],\n",
      "        [-0.1508,  0.7811],\n",
      "        [ 0.0972,  0.7316],\n",
      "        [ 0.2212,  0.6820],\n",
      "        [ 0.3204,  0.5829],\n",
      "        [ 0.4692,  0.4839],\n",
      "        [ 0.5685,  0.2857],\n",
      "        [ 0.6429,  0.1123],\n",
      "        [ 0.6925, -0.0858],\n",
      "        [ 0.7421, -0.2592],\n",
      "        [-0.6220, -0.7546],\n",
      "        [-0.4732, -0.8041],\n",
      "        [-0.3244, -0.7794],\n",
      "        [-0.2004, -0.7794],\n",
      "        [-0.1012, -0.7298],\n",
      "        [ 0.3204, -0.6803],\n",
      "        [ 0.4196, -0.6803],\n",
      "        [ 0.5188, -0.6803],\n",
      "        [ 0.6429, -0.6308],\n",
      "        [ 0.7421, -0.5069],\n",
      "        [ 0.0972, -0.5317],\n",
      "        [ 0.0476, -0.3583],\n",
      "        [ 0.0476, -0.2592],\n",
      "        [ 0.0476, -0.1601],\n",
      "        [-0.1508, -0.0611],\n",
      "        [-0.0516, -0.0611],\n",
      "        [-0.0020,  0.0132],\n",
      "        [ 0.0972,  0.0132],\n",
      "        [ 0.1468,  0.0132],\n",
      "        [-0.4732, -0.5812],\n",
      "        [-0.3740, -0.5812],\n",
      "        [-0.2748, -0.5812],\n",
      "        [-0.1508, -0.5317],\n",
      "        [-0.2748, -0.4821],\n",
      "        [-0.3740, -0.5317],\n",
      "        [ 0.2708, -0.4078],\n",
      "        [ 0.3700, -0.4574],\n",
      "        [ 0.4692, -0.4574],\n",
      "        [ 0.5685, -0.4078],\n",
      "        [ 0.4692, -0.3583],\n",
      "        [ 0.3700, -0.4078],\n",
      "        [-0.3740,  0.2114],\n",
      "        [-0.2748,  0.1123],\n",
      "        [-0.0516,  0.1123],\n",
      "        [-0.0020,  0.1123],\n",
      "        [ 0.0476,  0.1123],\n",
      "        [ 0.2212,  0.2114],\n",
      "        [ 0.2212,  0.3105],\n",
      "        [ 0.1468,  0.3848],\n",
      "        [ 0.0476,  0.4343],\n",
      "        [-0.0516,  0.4343],\n",
      "        [-0.1508,  0.4096],\n",
      "        [-0.2748,  0.3105],\n",
      "        [-0.3740,  0.2114],\n",
      "        [-0.1012,  0.1619],\n",
      "        [-0.0020,  0.1619],\n",
      "        [ 0.0476,  0.2114],\n",
      "        [ 0.2212,  0.3105],\n",
      "        [ 0.0476,  0.3105],\n",
      "        [-0.0516,  0.3105],\n",
      "        [-0.1508,  0.3105]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 torch.Size([1, 224, 224]) tensor([[-0.9075,  0.5057],\n",
      "        [-0.9075,  0.8001],\n",
      "        [-0.9075,  1.1313],\n",
      "        [-0.8337,  1.4257],\n",
      "        [-0.7599,  1.6833],\n",
      "        [-0.5754,  1.9041],\n",
      "        [-0.4278,  1.9777],\n",
      "        [-0.2803,  2.0513],\n",
      "        [ 0.1256,  2.1617],\n",
      "        [ 0.4945,  2.0881],\n",
      "        [ 0.8266,  2.0145],\n",
      "        [ 1.0480,  1.8673],\n",
      "        [ 1.2324,  1.6097],\n",
      "        [ 1.3062,  1.3153],\n",
      "        [ 1.3800,  0.9841],\n",
      "        [ 1.3800,  0.6897],\n",
      "        [ 1.4538,  0.3585],\n",
      "        [-0.8337,  0.0273],\n",
      "        [-0.6492, -0.0463],\n",
      "        [-0.5016, -0.1199],\n",
      "        [-0.3540, -0.0463],\n",
      "        [-0.2065, -0.0463],\n",
      "        [ 0.3470, -0.0463],\n",
      "        [ 0.4945, -0.1199],\n",
      "        [ 0.6790, -0.1199],\n",
      "        [ 0.9004, -0.1199],\n",
      "        [ 1.0480,  0.0641],\n",
      "        [ 0.0518,  0.2849],\n",
      "        [ 0.0518,  0.4321],\n",
      "        [ 0.0518,  0.6529],\n",
      "        [ 0.0518,  0.8001],\n",
      "        [-0.1327,  0.9841],\n",
      "        [-0.0220,  0.9841],\n",
      "        [ 0.0518,  1.0577],\n",
      "        [ 0.1994,  0.9841],\n",
      "        [ 0.2732,  0.9841],\n",
      "        [-0.5754,  0.2481],\n",
      "        [-0.5016,  0.2481],\n",
      "        [-0.3540,  0.2481],\n",
      "        [-0.2065,  0.3585],\n",
      "        [-0.3540,  0.3585],\n",
      "        [-0.5016,  0.3217],\n",
      "        [ 0.4207,  0.2849],\n",
      "        [ 0.4945,  0.2113],\n",
      "        [ 0.6790,  0.2113],\n",
      "        [ 0.8266,  0.2849],\n",
      "        [ 0.6790,  0.3585],\n",
      "        [ 0.4945,  0.3585],\n",
      "        [-0.2803,  1.3521],\n",
      "        [-0.1327,  1.2785],\n",
      "        [-0.0220,  1.2049],\n",
      "        [ 0.0518,  1.2049],\n",
      "        [ 0.1256,  1.2049],\n",
      "        [ 0.2732,  1.2785],\n",
      "        [ 0.4207,  1.3889],\n",
      "        [ 0.2732,  1.3521],\n",
      "        [ 0.1994,  1.4625],\n",
      "        [ 0.0518,  1.4257],\n",
      "        [-0.0220,  1.4257],\n",
      "        [-0.1327,  1.4257],\n",
      "        [-0.2803,  1.3521],\n",
      "        [-0.0220,  1.3521],\n",
      "        [ 0.0518,  1.3521],\n",
      "        [ 0.1994,  1.3521],\n",
      "        [ 0.4207,  1.3889],\n",
      "        [ 0.1994,  1.2785],\n",
      "        [ 0.0518,  1.2785],\n",
      "        [-0.0220,  1.2785]], dtype=torch.float64)\n",
      "22 torch.Size([1, 224, 224]) tensor([[-0.7272,  0.3695],\n",
      "        [-0.7272,  0.6260],\n",
      "        [-0.6759,  0.8312],\n",
      "        [-0.5220,  1.0877],\n",
      "        [-0.3937,  1.3698],\n",
      "        [-0.2654,  1.5494],\n",
      "        [-0.2141,  1.6263],\n",
      "        [-0.0601,  1.7032],\n",
      "        [ 0.2477,  1.7032],\n",
      "        [ 0.5813,  1.5750],\n",
      "        [ 0.8378,  1.4468],\n",
      "        [ 1.1201,  1.3185],\n",
      "        [ 1.2997,  1.0620],\n",
      "        [ 1.4280,  0.8055],\n",
      "        [ 1.4280,  0.5234],\n",
      "        [ 1.4280,  0.2669],\n",
      "        [ 1.4280, -0.0666],\n",
      "        [-0.8042,  0.0360],\n",
      "        [-0.7272, -0.0922],\n",
      "        [-0.6759, -0.1435],\n",
      "        [-0.5220, -0.1435],\n",
      "        [-0.3937, -0.1435],\n",
      "        [-0.0088, -0.2718],\n",
      "        [ 0.1195, -0.3487],\n",
      "        [ 0.3247, -0.4000],\n",
      "        [ 0.5300, -0.4000],\n",
      "        [ 0.7865, -0.3231],\n",
      "        [-0.1371,  0.0617],\n",
      "        [-0.2141,  0.2412],\n",
      "        [-0.2141,  0.3695],\n",
      "        [-0.2141,  0.4977],\n",
      "        [-0.2141,  0.7029],\n",
      "        [-0.1371,  0.7029],\n",
      "        [-0.0601,  0.7029],\n",
      "        [-0.0088,  0.6516],\n",
      "        [ 0.1195,  0.6516],\n",
      "        [-0.5989,  0.1643],\n",
      "        [-0.5220,  0.1130],\n",
      "        [-0.3937,  0.1130],\n",
      "        [-0.2654,  0.1130],\n",
      "        [-0.3937,  0.1899],\n",
      "        [-0.5220,  0.2412],\n",
      "        [ 0.1964, -0.0153],\n",
      "        [ 0.3247, -0.0666],\n",
      "        [ 0.4530, -0.0666],\n",
      "        [ 0.5813, -0.0666],\n",
      "        [ 0.4530, -0.0153],\n",
      "        [ 0.3247, -0.0153],\n",
      "        [-0.2654,  1.1133],\n",
      "        [-0.2141,  0.9594],\n",
      "        [-0.1371,  0.9081],\n",
      "        [-0.0601,  0.9081],\n",
      "        [-0.0088,  0.8312],\n",
      "        [ 0.1964,  0.9081],\n",
      "        [ 0.3760,  0.9081],\n",
      "        [ 0.1964,  1.0364],\n",
      "        [ 0.1195,  1.0364],\n",
      "        [-0.0088,  1.1133],\n",
      "        [-0.0601,  1.1133],\n",
      "        [-0.2141,  1.1133],\n",
      "        [-0.2654,  1.1133],\n",
      "        [-0.0601,  0.9851],\n",
      "        [-0.0088,  0.9851],\n",
      "        [ 0.0681,  0.9851],\n",
      "        [ 0.3247,  0.9081],\n",
      "        [ 0.0681,  0.9851],\n",
      "        [-0.0088,  0.9851],\n",
      "        [-0.0601,  0.9851]], dtype=torch.float64)\n",
      "23 torch.Size([1, 224, 224]) tensor([[-8.0495e-01, -1.2063e-01],\n",
      "        [-8.0495e-01,  1.2127e-01],\n",
      "        [-7.4434e-01,  3.0270e-01],\n",
      "        [-7.4434e-01,  5.6476e-01],\n",
      "        [-6.2314e-01,  8.0667e-01],\n",
      "        [-5.6254e-01,  9.8810e-01],\n",
      "        [-4.4134e-01,  1.0486e+00],\n",
      "        [-3.2014e-01,  1.1090e+00],\n",
      "        [ 3.0705e-03,  1.1897e+00],\n",
      "        [ 3.0608e-01,  1.1292e+00],\n",
      "        [ 5.4848e-01,  1.0687e+00],\n",
      "        [ 7.3028e-01,  1.0083e+00],\n",
      "        [ 8.7169e-01,  8.8730e-01],\n",
      "        [ 9.9289e-01,  6.4540e-01],\n",
      "        [ 1.0535e+00,  4.6397e-01],\n",
      "        [ 1.1141e+00,  2.2206e-01],\n",
      "        [ 1.1747e+00, -4.0000e-02],\n",
      "        [-6.8374e-01, -4.8349e-01],\n",
      "        [-5.6254e-01, -5.4397e-01],\n",
      "        [-4.4134e-01, -6.0444e-01],\n",
      "        [-3.2014e-01, -6.0444e-01],\n",
      "        [-1.9893e-01, -5.4397e-01],\n",
      "        [ 3.0608e-01, -6.0444e-01],\n",
      "        [ 4.2728e-01, -6.0444e-01],\n",
      "        [ 5.4848e-01, -6.0444e-01],\n",
      "        [ 7.3028e-01, -6.0444e-01],\n",
      "        [ 8.7169e-01, -4.6333e-01],\n",
      "        [ 3.0705e-03, -3.6254e-01],\n",
      "        [ 3.0705e-03, -2.4159e-01],\n",
      "        [ 3.0705e-03, -1.2063e-01],\n",
      "        [-5.7531e-02,  3.1746e-04],\n",
      "        [-1.9893e-01,  2.0190e-01],\n",
      "        [-1.1813e-01,  2.0190e-01],\n",
      "        [ 3.0705e-03,  2.0190e-01],\n",
      "        [ 1.2427e-01,  2.0190e-01],\n",
      "        [ 1.8487e-01,  2.0190e-01],\n",
      "        [-5.0194e-01, -3.0206e-01],\n",
      "        [-4.4134e-01, -3.6254e-01],\n",
      "        [-3.2014e-01, -3.6254e-01],\n",
      "        [-1.9893e-01, -3.0206e-01],\n",
      "        [-3.2014e-01, -2.4159e-01],\n",
      "        [-4.4134e-01, -2.4159e-01],\n",
      "        [ 3.0608e-01, -3.0206e-01],\n",
      "        [ 4.2728e-01, -3.4238e-01],\n",
      "        [ 5.4848e-01, -3.4238e-01],\n",
      "        [ 6.6968e-01, -2.8190e-01],\n",
      "        [ 5.4848e-01, -2.2143e-01],\n",
      "        [ 4.2728e-01, -2.8190e-01],\n",
      "        [-3.2014e-01,  5.6476e-01],\n",
      "        [-2.5953e-01,  4.4381e-01],\n",
      "        [-5.7531e-02,  3.8333e-01],\n",
      "        [ 3.0705e-03,  3.8333e-01],\n",
      "        [ 6.3672e-02,  3.8333e-01],\n",
      "        [ 2.4548e-01,  4.4381e-01],\n",
      "        [ 3.6668e-01,  5.6476e-01],\n",
      "        [ 1.8487e-01,  5.6476e-01],\n",
      "        [ 1.2427e-01,  6.2524e-01],\n",
      "        [ 3.0705e-03,  6.2524e-01],\n",
      "        [-1.1813e-01,  6.2524e-01],\n",
      "        [-2.5953e-01,  5.6476e-01],\n",
      "        [-3.2014e-01,  5.6476e-01],\n",
      "        [-1.1813e-01,  5.0429e-01],\n",
      "        [ 3.0705e-03,  4.4381e-01],\n",
      "        [ 1.2427e-01,  5.0429e-01],\n",
      "        [ 3.0608e-01,  5.6476e-01],\n",
      "        [ 1.2427e-01,  5.0429e-01],\n",
      "        [ 3.0705e-03,  5.0429e-01],\n",
      "        [-1.1813e-01,  5.0429e-01]], dtype=torch.float64)\n",
      "24 torch.Size([1, 224, 224]) tensor([[-1.0048,  0.5569],\n",
      "        [-1.0048,  0.8214],\n",
      "        [-1.0048,  0.9978],\n",
      "        [-0.9164,  1.2182],\n",
      "        [-0.8722,  1.4386],\n",
      "        [-0.7838,  1.6590],\n",
      "        [-0.6512,  1.7472],\n",
      "        [-0.4744,  1.8795],\n",
      "        [-0.2976,  1.9235],\n",
      "        [ 0.0560,  1.8795],\n",
      "        [ 0.2771,  1.7913],\n",
      "        [ 0.4539,  1.6590],\n",
      "        [ 0.5423,  1.5268],\n",
      "        [ 0.6307,  1.3064],\n",
      "        [ 0.7191,  1.0859],\n",
      "        [ 0.7633,  0.8655],\n",
      "        [ 0.7633,  0.6451],\n",
      "        [-0.9164,  0.3806],\n",
      "        [-0.8280,  0.3806],\n",
      "        [-0.6954,  0.3365],\n",
      "        [-0.6070,  0.4247],\n",
      "        [-0.4744,  0.4247],\n",
      "        [-0.0766,  0.4688],\n",
      "        [ 0.0560,  0.4688],\n",
      "        [ 0.1444,  0.4688],\n",
      "        [ 0.3213,  0.4688],\n",
      "        [ 0.4539,  0.5128],\n",
      "        [-0.2976,  0.6892],\n",
      "        [-0.2976,  0.8655],\n",
      "        [-0.2976,  0.9978],\n",
      "        [-0.2976,  1.1741],\n",
      "        [-0.4302,  1.1741],\n",
      "        [-0.3860,  1.2182],\n",
      "        [-0.2976,  1.2182],\n",
      "        [-0.2092,  1.2182],\n",
      "        [-0.1208,  1.1741],\n",
      "        [-0.7838,  0.6010],\n",
      "        [-0.6954,  0.6010],\n",
      "        [-0.6070,  0.6010],\n",
      "        [-0.4744,  0.6892],\n",
      "        [-0.6070,  0.6892],\n",
      "        [-0.6512,  0.6892],\n",
      "        [ 0.0118,  0.6892],\n",
      "        [ 0.0560,  0.6451],\n",
      "        [ 0.1444,  0.6892],\n",
      "        [ 0.2771,  0.6892],\n",
      "        [ 0.1444,  0.7333],\n",
      "        [ 0.0560,  0.7333],\n",
      "        [-0.5628,  1.3945],\n",
      "        [-0.4744,  1.3945],\n",
      "        [-0.3418,  1.3945],\n",
      "        [-0.2976,  1.3945],\n",
      "        [-0.2092,  1.3945],\n",
      "        [-0.0766,  1.3945],\n",
      "        [ 0.0560,  1.3945],\n",
      "        [-0.1208,  1.4386],\n",
      "        [-0.1650,  1.4827],\n",
      "        [-0.2976,  1.4827],\n",
      "        [-0.3860,  1.4827],\n",
      "        [-0.4302,  1.4386],\n",
      "        [-0.5628,  1.3945],\n",
      "        [-0.3860,  1.4386],\n",
      "        [-0.2976,  1.4386],\n",
      "        [-0.1650,  1.4386],\n",
      "        [ 0.0560,  1.3945],\n",
      "        [-0.2092,  1.3945],\n",
      "        [-0.2976,  1.3945],\n",
      "        [-0.3860,  1.3945]], dtype=torch.float64)\n",
      "25 torch.Size([1, 224, 224]) tensor([[-1.1786, -0.2679],\n",
      "        [-1.1071,  0.0536],\n",
      "        [-1.0357,  0.3036],\n",
      "        [-0.9643,  0.5536],\n",
      "        [-0.8571,  0.8036],\n",
      "        [-0.7143,  1.0179],\n",
      "        [-0.6071,  1.1250],\n",
      "        [-0.3571,  1.2679],\n",
      "        [-0.0357,  1.3393],\n",
      "        [ 0.2857,  1.1964],\n",
      "        [ 0.5357,  1.0893],\n",
      "        [ 0.6429,  0.9107],\n",
      "        [ 0.7857,  0.6607],\n",
      "        [ 0.8571,  0.3393],\n",
      "        [ 0.9286,  0.1607],\n",
      "        [ 0.9286, -0.1607],\n",
      "        [ 0.9286, -0.4107],\n",
      "        [-1.0357, -0.4464],\n",
      "        [-0.9643, -0.5893],\n",
      "        [-0.8571, -0.5893],\n",
      "        [-0.7143, -0.6250],\n",
      "        [-0.6071, -0.5536],\n",
      "        [-0.1071, -0.6250],\n",
      "        [ 0.0357, -0.6964],\n",
      "        [ 0.1429, -0.6964],\n",
      "        [ 0.3571, -0.6964],\n",
      "        [ 0.4643, -0.6250],\n",
      "        [-0.2857, -0.3036],\n",
      "        [-0.2857, -0.1250],\n",
      "        [-0.2857,  0.0536],\n",
      "        [-0.2857,  0.2679],\n",
      "        [-0.3571,  0.3750],\n",
      "        [-0.2857,  0.3750],\n",
      "        [-0.2143,  0.3750],\n",
      "        [-0.1071,  0.3036],\n",
      "        [-0.0357,  0.3036],\n",
      "        [-0.8571, -0.3393],\n",
      "        [-0.7857, -0.3750],\n",
      "        [-0.6786, -0.3750],\n",
      "        [-0.5357, -0.3036],\n",
      "        [-0.6786, -0.2679],\n",
      "        [-0.7857, -0.2679],\n",
      "        [-0.0357, -0.3750],\n",
      "        [ 0.1071, -0.4464],\n",
      "        [ 0.2143, -0.4464],\n",
      "        [ 0.3571, -0.4464],\n",
      "        [ 0.2143, -0.3750],\n",
      "        [ 0.1071, -0.3750],\n",
      "        [-0.5357,  0.6964],\n",
      "        [-0.4286,  0.6250],\n",
      "        [-0.2857,  0.5536],\n",
      "        [-0.1429,  0.5893],\n",
      "        [-0.1071,  0.5179],\n",
      "        [ 0.0357,  0.5893],\n",
      "        [ 0.2143,  0.6250],\n",
      "        [ 0.1071,  0.7679],\n",
      "        [-0.0357,  0.8393],\n",
      "        [-0.1429,  0.8393],\n",
      "        [-0.2143,  0.8750],\n",
      "        [-0.3571,  0.8036],\n",
      "        [-0.4643,  0.6964],\n",
      "        [-0.2857,  0.6250],\n",
      "        [-0.1429,  0.6250],\n",
      "        [-0.0357,  0.6250],\n",
      "        [ 0.1429,  0.6250],\n",
      "        [-0.0357,  0.6964],\n",
      "        [-0.1429,  0.6964],\n",
      "        [-0.2857,  0.7679]], dtype=torch.float64)\n",
      "26 torch.Size([1, 224, 224]) tensor([[-0.7018, -0.8589],\n",
      "        [-0.7018, -0.5446],\n",
      "        [-0.7018, -0.3089],\n",
      "        [-0.6428, -0.0732],\n",
      "        [-0.5248,  0.1821],\n",
      "        [-0.4461,  0.4179],\n",
      "        [-0.3281,  0.5357],\n",
      "        [-0.2101,  0.6732],\n",
      "        [ 0.0849,  0.7911],\n",
      "        [ 0.3995,  0.7321],\n",
      "        [ 0.6945,  0.5554],\n",
      "        [ 0.9305,  0.3786],\n",
      "        [ 1.1272,  0.1429],\n",
      "        [ 1.1862, -0.0929],\n",
      "        [ 1.2452, -0.3286],\n",
      "        [ 1.3042, -0.6429],\n",
      "        [ 1.3042, -0.9375],\n",
      "        [-0.7608, -1.0946],\n",
      "        [-0.7018, -1.1536],\n",
      "        [-0.5838, -1.1536],\n",
      "        [-0.4461, -1.1536],\n",
      "        [-0.3281, -1.1536],\n",
      "        [ 0.0849, -1.0750],\n",
      "        [ 0.2029, -1.1339],\n",
      "        [ 0.3995, -1.1339],\n",
      "        [ 0.5765, -1.0750],\n",
      "        [ 0.7535, -1.0161],\n",
      "        [-0.1511, -0.7804],\n",
      "        [-0.1511, -0.6036],\n",
      "        [-0.2101, -0.3679],\n",
      "        [-0.2101, -0.2304],\n",
      "        [-0.2101, -0.1714],\n",
      "        [-0.2101, -0.1125],\n",
      "        [-0.0921, -0.1125],\n",
      "        [-0.0331, -0.1125],\n",
      "        [ 0.0849, -0.1714],\n",
      "        [-0.5838, -0.9179],\n",
      "        [-0.5248, -0.9179],\n",
      "        [-0.3871, -0.8982],\n",
      "        [-0.2691, -0.8393],\n",
      "        [-0.3871, -0.8393],\n",
      "        [-0.5248, -0.8589],\n",
      "        [ 0.2029, -0.8393],\n",
      "        [ 0.2619, -0.8982],\n",
      "        [ 0.3995, -0.8982],\n",
      "        [ 0.5175, -0.8393],\n",
      "        [ 0.3995, -0.7804],\n",
      "        [ 0.2619, -0.7804],\n",
      "        [-0.2691,  0.1232],\n",
      "        [-0.2101,  0.1232],\n",
      "        [-0.1511,  0.1232],\n",
      "        [-0.0921,  0.1232],\n",
      "        [-0.0331,  0.1232],\n",
      "        [ 0.1439,  0.1232],\n",
      "        [ 0.2619,  0.1232],\n",
      "        [ 0.1439,  0.2411],\n",
      "        [ 0.0259,  0.2411],\n",
      "        [-0.0331,  0.3000],\n",
      "        [-0.1511,  0.2411],\n",
      "        [-0.2101,  0.2411],\n",
      "        [-0.2691,  0.1232],\n",
      "        [-0.1511,  0.1821],\n",
      "        [-0.0331,  0.1821],\n",
      "        [ 0.0259,  0.1821],\n",
      "        [ 0.2619,  0.1232],\n",
      "        [ 0.0259,  0.1821],\n",
      "        [-0.0331,  0.1821],\n",
      "        [-0.1511,  0.1821]], dtype=torch.float64)\n",
      "27 torch.Size([1, 224, 224]) tensor([[-0.5582, -0.5299],\n",
      "        [-0.5582, -0.2409],\n",
      "        [-0.5582,  0.0161],\n",
      "        [-0.5582,  0.2409],\n",
      "        [-0.5582,  0.4978],\n",
      "        [-0.4299,  0.7226],\n",
      "        [-0.3336,  0.7869],\n",
      "        [-0.2054,  0.9153],\n",
      "        [ 0.0512,  0.9796],\n",
      "        [ 0.4040,  0.9796],\n",
      "        [ 0.7568,  1.0117],\n",
      "        [ 1.0134,  0.8511],\n",
      "        [ 1.2058,  0.7226],\n",
      "        [ 1.3662,  0.4657],\n",
      "        [ 1.4303,  0.2730],\n",
      "        [ 1.4945, -0.0161],\n",
      "        [ 1.5586, -0.2730],\n",
      "        [-0.4940, -0.7869],\n",
      "        [-0.4299, -0.8511],\n",
      "        [-0.2695, -0.8511],\n",
      "        [-0.2054, -0.8511],\n",
      "        [-0.0771, -0.7869],\n",
      "        [ 0.4040, -0.7226],\n",
      "        [ 0.5323, -0.7869],\n",
      "        [ 0.7568, -0.7226],\n",
      "        [ 0.8851, -0.6905],\n",
      "        [ 1.0775, -0.5621],\n",
      "        [ 0.1154, -0.4978],\n",
      "        [ 0.0512, -0.3693],\n",
      "        [ 0.0512, -0.2409],\n",
      "        [ 0.0512, -0.0482],\n",
      "        [-0.0771,  0.0161],\n",
      "        [-0.0129,  0.1124],\n",
      "        [ 0.0512,  0.1124],\n",
      "        [ 0.2116,  0.1124],\n",
      "        [ 0.2757,  0.1124],\n",
      "        [-0.3336, -0.5942],\n",
      "        [-0.2695, -0.5942],\n",
      "        [-0.1412, -0.5942],\n",
      "        [-0.0771, -0.5299],\n",
      "        [-0.1412, -0.5299],\n",
      "        [-0.2695, -0.5299],\n",
      "        [ 0.4682, -0.4336],\n",
      "        [ 0.5965, -0.4978],\n",
      "        [ 0.6606, -0.4978],\n",
      "        [ 0.8210, -0.4336],\n",
      "        [ 0.6606, -0.3693],\n",
      "        [ 0.5965, -0.4336],\n",
      "        [-0.2054,  0.3693],\n",
      "        [-0.1412,  0.3051],\n",
      "        [-0.0129,  0.3051],\n",
      "        [ 0.0512,  0.3051],\n",
      "        [ 0.1154,  0.3051],\n",
      "        [ 0.3399,  0.3693],\n",
      "        [ 0.4682,  0.4336],\n",
      "        [ 0.2757,  0.4336],\n",
      "        [ 0.2116,  0.4978],\n",
      "        [ 0.0512,  0.4336],\n",
      "        [-0.0771,  0.4336],\n",
      "        [-0.1412,  0.4336],\n",
      "        [-0.2054,  0.3693],\n",
      "        [-0.0129,  0.3693],\n",
      "        [ 0.0512,  0.3693],\n",
      "        [ 0.2116,  0.3693],\n",
      "        [ 0.4682,  0.4336],\n",
      "        [ 0.2116,  0.3693],\n",
      "        [ 0.0512,  0.3693],\n",
      "        [-0.0129,  0.3693]], dtype=torch.float64)\n",
      "28 torch.Size([1, 224, 224]) tensor([[-0.8048, -0.1313],\n",
      "        [-0.7424,  0.0864],\n",
      "        [-0.7112,  0.2419],\n",
      "        [-0.6487,  0.4906],\n",
      "        [-0.5863,  0.7083],\n",
      "        [-0.4614,  0.8638],\n",
      "        [-0.3053,  0.9882],\n",
      "        [-0.1492,  1.0504],\n",
      "        [ 0.1317,  1.1126],\n",
      "        [ 0.4127,  1.0504],\n",
      "        [ 0.5688,  0.9260],\n",
      "        [ 0.7561,  0.8327],\n",
      "        [ 0.8498,  0.6772],\n",
      "        [ 0.9122,  0.4595],\n",
      "        [ 0.9122,  0.2108],\n",
      "        [ 0.9747, -0.0069],\n",
      "        [ 0.9747, -0.2246],\n",
      "        [-0.7112, -0.4734],\n",
      "        [-0.5863, -0.5045],\n",
      "        [-0.4614, -0.5667],\n",
      "        [-0.3678, -0.5667],\n",
      "        [-0.2429, -0.5045],\n",
      "        [ 0.1942, -0.5667],\n",
      "        [ 0.2878, -0.6289],\n",
      "        [ 0.4751, -0.5978],\n",
      "        [ 0.5688, -0.5978],\n",
      "        [ 0.6937, -0.5667],\n",
      "        [ 0.0381, -0.3490],\n",
      "        [ 0.0381, -0.1624],\n",
      "        [ 0.0381, -0.0691],\n",
      "        [ 0.0381,  0.0553],\n",
      "        [-0.0868,  0.2108],\n",
      "        [-0.0244,  0.2108],\n",
      "        [ 0.0381,  0.2108],\n",
      "        [ 0.1317,  0.2108],\n",
      "        [ 0.1942,  0.2108],\n",
      "        [-0.5239, -0.2868],\n",
      "        [-0.4302, -0.2868],\n",
      "        [-0.3053, -0.2868],\n",
      "        [-0.2117, -0.2868],\n",
      "        [-0.3053, -0.2246],\n",
      "        [-0.4302, -0.2246],\n",
      "        [ 0.2566, -0.2868],\n",
      "        [ 0.3503, -0.3490],\n",
      "        [ 0.4751, -0.3490],\n",
      "        [ 0.5688, -0.3490],\n",
      "        [ 0.4751, -0.2868],\n",
      "        [ 0.3503, -0.2868],\n",
      "        [-0.2429,  0.5528],\n",
      "        [-0.1492,  0.4284],\n",
      "        [-0.0244,  0.3352],\n",
      "        [ 0.0381,  0.3663],\n",
      "        [ 0.1317,  0.3352],\n",
      "        [ 0.2566,  0.3663],\n",
      "        [ 0.3503,  0.4906],\n",
      "        [ 0.2566,  0.6150],\n",
      "        [ 0.1942,  0.6461],\n",
      "        [ 0.0693,  0.6461],\n",
      "        [-0.0244,  0.6461],\n",
      "        [-0.1492,  0.5839],\n",
      "        [-0.2117,  0.5528],\n",
      "        [-0.0244,  0.4284],\n",
      "        [ 0.0381,  0.4284],\n",
      "        [ 0.1317,  0.4284],\n",
      "        [ 0.3503,  0.4906],\n",
      "        [ 0.1317,  0.5528],\n",
      "        [ 0.0693,  0.5528],\n",
      "        [-0.0244,  0.5528]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 torch.Size([1, 224, 224]) tensor([[-0.5989,  0.1534],\n",
      "        [-0.6291,  0.4246],\n",
      "        [-0.6291,  0.6356],\n",
      "        [-0.6291,  0.8466],\n",
      "        [-0.5989,  1.1179],\n",
      "        [-0.5386,  1.3289],\n",
      "        [-0.4783,  1.4796],\n",
      "        [-0.3878,  1.6604],\n",
      "        [-0.1163,  1.8111],\n",
      "        [ 0.1552,  1.7810],\n",
      "        [ 0.4266,  1.6604],\n",
      "        [ 0.6981,  1.5700],\n",
      "        [ 0.9093,  1.3590],\n",
      "        [ 0.9998,  1.0877],\n",
      "        [ 1.0601,  0.8768],\n",
      "        [ 1.1204,  0.6658],\n",
      "        [ 1.2109,  0.3945],\n",
      "        [-0.5386, -0.1179],\n",
      "        [-0.4783, -0.1480],\n",
      "        [-0.3878, -0.1480],\n",
      "        [-0.2671, -0.0877],\n",
      "        [-0.1766, -0.0576],\n",
      "        [ 0.2758,  0.0027],\n",
      "        [ 0.3663,  0.0027],\n",
      "        [ 0.4870,  0.0328],\n",
      "        [ 0.6378,  0.0630],\n",
      "        [ 0.7886,  0.1232],\n",
      "        [ 0.0043,  0.2739],\n",
      "        [-0.0560,  0.4246],\n",
      "        [-0.1766,  0.6055],\n",
      "        [-0.1766,  0.6959],\n",
      "        [-0.2068,  0.8165],\n",
      "        [-0.2068,  0.8466],\n",
      "        [-0.1163,  0.8466],\n",
      "        [-0.0560,  0.8466],\n",
      "        [ 0.0647,  0.8768],\n",
      "        [-0.4180,  0.1534],\n",
      "        [-0.3878,  0.1232],\n",
      "        [-0.2671,  0.1232],\n",
      "        [-0.1766,  0.2137],\n",
      "        [-0.2671,  0.2137],\n",
      "        [-0.3878,  0.2137],\n",
      "        [ 0.2758,  0.2739],\n",
      "        [ 0.3663,  0.2739],\n",
      "        [ 0.4870,  0.2739],\n",
      "        [ 0.5775,  0.3342],\n",
      "        [ 0.4870,  0.3342],\n",
      "        [ 0.3663,  0.3342],\n",
      "        [-0.3878,  1.1179],\n",
      "        [-0.3275,  1.0576],\n",
      "        [-0.2068,  1.0275],\n",
      "        [-0.1766,  1.0877],\n",
      "        [-0.0560,  1.0877],\n",
      "        [ 0.0647,  1.1179],\n",
      "        [ 0.1552,  1.2384],\n",
      "        [ 0.0647,  1.3289],\n",
      "        [-0.0560,  1.3289],\n",
      "        [-0.1766,  1.3289],\n",
      "        [-0.2671,  1.3289],\n",
      "        [-0.3275,  1.2686],\n",
      "        [-0.3878,  1.1179],\n",
      "        [-0.2068,  1.1179],\n",
      "        [-0.1766,  1.1179],\n",
      "        [-0.0560,  1.1179],\n",
      "        [ 0.1552,  1.2384],\n",
      "        [-0.0560,  1.2384],\n",
      "        [-0.1766,  1.2384],\n",
      "        [-0.2068,  1.2384]], dtype=torch.float64)\n",
      "30 torch.Size([1, 224, 224]) tensor([[-1.3253e+00, -5.1972e-02],\n",
      "        [-1.2740e+00,  2.2948e-01],\n",
      "        [-1.2227e+00,  4.5975e-01],\n",
      "        [-1.1714e+00,  6.6445e-01],\n",
      "        [-1.0431e+00,  9.4590e-01],\n",
      "        [-8.8916e-01,  1.1762e+00],\n",
      "        [-6.5825e-01,  1.2785e+00],\n",
      "        [-4.5300e-01,  1.4576e+00],\n",
      "        [-1.1946e-01,  1.5088e+00],\n",
      "        [ 1.6277e-01,  1.4064e+00],\n",
      "        [ 2.6539e-01,  1.2529e+00],\n",
      "        [ 3.6802e-01,  1.1250e+00],\n",
      "        [ 4.7065e-01,  9.2031e-01],\n",
      "        [ 5.4762e-01,  6.3886e-01],\n",
      "        [ 5.9893e-01,  4.3417e-01],\n",
      "        [ 5.9893e-01,  2.0389e-01],\n",
      "        [ 6.5025e-01, -7.9957e-04],\n",
      "        [-8.8916e-01, -2.0549e-01],\n",
      "        [-7.8654e-01, -2.5666e-01],\n",
      "        [-6.0694e-01, -2.5666e-01],\n",
      "        [-4.5300e-01, -2.5666e-01],\n",
      "        [-3.5037e-01, -1.7990e-01],\n",
      "        [ 8.5796e-02, -2.3108e-01],\n",
      "        [ 2.1408e-01, -2.8225e-01],\n",
      "        [ 3.1671e-01, -2.8225e-01],\n",
      "        [ 4.7065e-01, -2.8225e-01],\n",
      "        [ 5.4762e-01, -2.3108e-01],\n",
      "        [-6.8144e-02,  2.4787e-02],\n",
      "        [-6.8144e-02,  2.0389e-01],\n",
      "        [-1.6831e-02,  4.0858e-01],\n",
      "        [-1.6831e-02,  5.3651e-01],\n",
      "        [-2.2209e-01,  6.3886e-01],\n",
      "        [-1.7077e-01,  6.3886e-01],\n",
      "        [-6.8144e-02,  6.3886e-01],\n",
      "        [ 3.4483e-02,  6.3886e-01],\n",
      "        [ 8.5796e-02,  5.8769e-01],\n",
      "        [-7.3522e-01,  2.4787e-02],\n",
      "        [-6.0694e-01, -2.6386e-02],\n",
      "        [-5.0431e-01, -2.6386e-02],\n",
      "        [-4.0168e-01,  2.4787e-02],\n",
      "        [-5.0431e-01,  7.5959e-02],\n",
      "        [-6.0694e-01,  7.5959e-02],\n",
      "        [ 8.5796e-02, -2.6386e-02],\n",
      "        [ 2.1408e-01, -7.7559e-02],\n",
      "        [ 3.1671e-01, -7.7559e-02],\n",
      "        [ 4.1933e-01, -7.7559e-02],\n",
      "        [ 3.1671e-01, -2.6386e-02],\n",
      "        [ 2.1408e-01,  2.4787e-02],\n",
      "        [-5.0431e-01,  8.9472e-01],\n",
      "        [-3.5037e-01,  8.4355e-01],\n",
      "        [-1.1946e-01,  7.9238e-01],\n",
      "        [-6.8144e-02,  8.4355e-01],\n",
      "        [ 3.4483e-02,  7.9238e-01],\n",
      "        [ 1.6277e-01,  7.9238e-01],\n",
      "        [ 2.1408e-01,  8.6914e-01],\n",
      "        [ 1.6277e-01,  1.0227e+00],\n",
      "        [ 3.4483e-02,  1.0738e+00],\n",
      "        [-6.8144e-02,  1.0738e+00],\n",
      "        [-1.7077e-01,  1.0738e+00],\n",
      "        [-3.5037e-01,  1.0227e+00],\n",
      "        [-4.5300e-01,  8.9472e-01],\n",
      "        [-1.7077e-01,  8.9472e-01],\n",
      "        [-6.8144e-02,  8.9472e-01],\n",
      "        [ 3.4483e-02,  9.2031e-01],\n",
      "        [ 2.1408e-01,  8.6914e-01],\n",
      "        [ 3.4483e-02,  9.7148e-01],\n",
      "        [-6.8144e-02,  9.7148e-01],\n",
      "        [-1.7077e-01,  9.7148e-01]], dtype=torch.float64)\n",
      "31 torch.Size([1, 224, 224]) tensor([[-1.3290, -0.2159],\n",
      "        [-1.2478,  0.0270],\n",
      "        [-1.1396,  0.2429],\n",
      "        [-1.0855,  0.4317],\n",
      "        [-0.9773,  0.6476],\n",
      "        [-0.7879,  0.8905],\n",
      "        [-0.5714,  1.0524],\n",
      "        [-0.3279,  1.1873],\n",
      "        [-0.0574,  1.2413],\n",
      "        [ 0.2403,  1.1333],\n",
      "        [ 0.3485,  0.9714],\n",
      "        [ 0.4567,  0.7825],\n",
      "        [ 0.5920,  0.6206],\n",
      "        [ 0.6461,  0.4048],\n",
      "        [ 0.6461,  0.1619],\n",
      "        [ 0.7002, -0.0540],\n",
      "        [ 0.7002, -0.2968],\n",
      "        [-0.9232, -0.4317],\n",
      "        [-0.7338, -0.4857],\n",
      "        [-0.6255, -0.5397],\n",
      "        [-0.4632, -0.4857],\n",
      "        [-0.3279, -0.4857],\n",
      "        [ 0.1861, -0.5397],\n",
      "        [ 0.2944, -0.5937],\n",
      "        [ 0.4026, -0.5937],\n",
      "        [ 0.5108, -0.5937],\n",
      "        [ 0.5920, -0.5397],\n",
      "        [-0.0574, -0.1889],\n",
      "        [-0.0032, -0.0270],\n",
      "        [-0.0032,  0.1619],\n",
      "        [ 0.0509,  0.2698],\n",
      "        [-0.1656,  0.3778],\n",
      "        [-0.0574,  0.3778],\n",
      "        [-0.0032,  0.3778],\n",
      "        [ 0.1320,  0.3778],\n",
      "        [ 0.1320,  0.3238],\n",
      "        [-0.7338, -0.2159],\n",
      "        [-0.6255, -0.2698],\n",
      "        [-0.4632, -0.2429],\n",
      "        [-0.4091, -0.2429],\n",
      "        [-0.4632, -0.1889],\n",
      "        [-0.6255, -0.1889],\n",
      "        [ 0.1861, -0.2429],\n",
      "        [ 0.2944, -0.2968],\n",
      "        [ 0.4026, -0.3508],\n",
      "        [ 0.4567, -0.2968],\n",
      "        [ 0.4026, -0.2429],\n",
      "        [ 0.2944, -0.2429],\n",
      "        [-0.2738,  0.6746],\n",
      "        [-0.1656,  0.6206],\n",
      "        [-0.0574,  0.5397],\n",
      "        [ 0.0509,  0.5667],\n",
      "        [ 0.1320,  0.5667],\n",
      "        [ 0.1861,  0.6206],\n",
      "        [ 0.2403,  0.6746],\n",
      "        [ 0.1861,  0.7825],\n",
      "        [ 0.1320,  0.8365],\n",
      "        [-0.0032,  0.8365],\n",
      "        [-0.0574,  0.8365],\n",
      "        [-0.1656,  0.7825],\n",
      "        [-0.2738,  0.6746],\n",
      "        [-0.0574,  0.6746],\n",
      "        [-0.0032,  0.6746],\n",
      "        [ 0.1320,  0.6746],\n",
      "        [ 0.2403,  0.6746],\n",
      "        [ 0.1320,  0.7286],\n",
      "        [-0.0032,  0.7286],\n",
      "        [-0.0574,  0.7286]], dtype=torch.float64)\n",
      "32 torch.Size([1, 224, 224]) tensor([[-0.9130, -0.4581],\n",
      "        [-0.8565, -0.2039],\n",
      "        [-0.8000,  0.0220],\n",
      "        [-0.7434,  0.2197],\n",
      "        [-0.6304,  0.4456],\n",
      "        [-0.4326,  0.6433],\n",
      "        [-0.2066,  0.7562],\n",
      "        [ 0.0477,  0.8127],\n",
      "        [ 0.3302,  0.8127],\n",
      "        [ 0.6410,  0.7562],\n",
      "        [ 0.7541,  0.6433],\n",
      "        [ 0.8106,  0.5303],\n",
      "        [ 0.9236,  0.3609],\n",
      "        [ 0.9236,  0.1067],\n",
      "        [ 1.0084, -0.0627],\n",
      "        [ 1.0084, -0.3169],\n",
      "        [ 1.0084, -0.5428],\n",
      "        [-0.5174, -0.6840],\n",
      "        [-0.3196, -0.7404],\n",
      "        [-0.2066, -0.7404],\n",
      "        [-0.0371, -0.7404],\n",
      "        [ 0.1042, -0.7404],\n",
      "        [ 0.5845, -0.7969],\n",
      "        [ 0.6410, -0.8534],\n",
      "        [ 0.7541, -0.8534],\n",
      "        [ 0.8671, -0.8534],\n",
      "        [ 0.9236, -0.7969],\n",
      "        [ 0.3302, -0.4863],\n",
      "        [ 0.3868, -0.3733],\n",
      "        [ 0.4433, -0.2604],\n",
      "        [ 0.4433, -0.0627],\n",
      "        [ 0.2172, -0.0062],\n",
      "        [ 0.3302, -0.0062],\n",
      "        [ 0.3868,  0.0502],\n",
      "        [ 0.4433, -0.0062],\n",
      "        [ 0.5280, -0.0062],\n",
      "        [-0.2631, -0.5145],\n",
      "        [-0.1501, -0.5145],\n",
      "        [-0.0936, -0.5145],\n",
      "        [ 0.0477, -0.5145],\n",
      "        [-0.0371, -0.4298],\n",
      "        [-0.1501, -0.4581],\n",
      "        [ 0.5280, -0.5428],\n",
      "        [ 0.6410, -0.5428],\n",
      "        [ 0.7541, -0.5993],\n",
      "        [ 0.8106, -0.5993],\n",
      "        [ 0.7541, -0.5428],\n",
      "        [ 0.6410, -0.4863],\n",
      "        [ 0.0477,  0.2762],\n",
      "        [ 0.2172,  0.2197],\n",
      "        [ 0.3302,  0.1632],\n",
      "        [ 0.3868,  0.2197],\n",
      "        [ 0.4433,  0.1632],\n",
      "        [ 0.5845,  0.2197],\n",
      "        [ 0.6410,  0.2762],\n",
      "        [ 0.5845,  0.3326],\n",
      "        [ 0.5280,  0.3326],\n",
      "        [ 0.3868,  0.4174],\n",
      "        [ 0.3302,  0.3891],\n",
      "        [ 0.2172,  0.3326],\n",
      "        [ 0.1042,  0.2762],\n",
      "        [ 0.3302,  0.2762],\n",
      "        [ 0.3868,  0.2762],\n",
      "        [ 0.5280,  0.2762],\n",
      "        [ 0.6410,  0.2197],\n",
      "        [ 0.4433,  0.2762],\n",
      "        [ 0.3868,  0.2762],\n",
      "        [ 0.3302,  0.2762]], dtype=torch.float64)\n",
      "33 torch.Size([1, 224, 224]) tensor([[-1.2496, -0.5058],\n",
      "        [-1.1913, -0.3016],\n",
      "        [-1.1913, -0.0973],\n",
      "        [-1.1038,  0.1361],\n",
      "        [-0.9871,  0.3403],\n",
      "        [-0.7830,  0.4862],\n",
      "        [-0.5788,  0.5446],\n",
      "        [-0.3164,  0.6613],\n",
      "        [-0.0539,  0.7197],\n",
      "        [ 0.1503,  0.6613],\n",
      "        [ 0.2086,  0.5446],\n",
      "        [ 0.2086,  0.4570],\n",
      "        [ 0.2669,  0.3112],\n",
      "        [ 0.3252,  0.1069],\n",
      "        [ 0.3544, -0.1265],\n",
      "        [ 0.3544, -0.2724],\n",
      "        [ 0.3252, -0.4767],\n",
      "        [-0.7830, -0.7684],\n",
      "        [-0.6372, -0.8560],\n",
      "        [-0.4622, -0.9143],\n",
      "        [-0.3747, -0.9143],\n",
      "        [-0.2580, -0.8560],\n",
      "        [ 0.0919, -0.8560],\n",
      "        [ 0.2086, -0.8560],\n",
      "        [ 0.2669, -0.8560],\n",
      "        [ 0.3252, -0.8560],\n",
      "        [ 0.3544, -0.7393],\n",
      "        [-0.0539, -0.5934],\n",
      "        [ 0.0044, -0.4767],\n",
      "        [ 0.0628, -0.3891],\n",
      "        [ 0.0919, -0.2724],\n",
      "        [-0.1705, -0.1265],\n",
      "        [-0.0539, -0.1265],\n",
      "        [ 0.0044, -0.1265],\n",
      "        [ 0.0628, -0.1265],\n",
      "        [ 0.0919, -0.1265],\n",
      "        [-0.6372, -0.5934],\n",
      "        [-0.5205, -0.6517],\n",
      "        [-0.4038, -0.6517],\n",
      "        [-0.3164, -0.5934],\n",
      "        [-0.4038, -0.5934],\n",
      "        [-0.5205, -0.5350],\n",
      "        [ 0.0628, -0.5934],\n",
      "        [ 0.1503, -0.5934],\n",
      "        [ 0.2086, -0.6225],\n",
      "        [ 0.2669, -0.5934],\n",
      "        [ 0.2086, -0.5350],\n",
      "        [ 0.1503, -0.5350],\n",
      "        [-0.3747,  0.1944],\n",
      "        [-0.1997,  0.0777],\n",
      "        [-0.0539, -0.0098],\n",
      "        [ 0.0044,  0.0486],\n",
      "        [ 0.0628, -0.0098],\n",
      "        [ 0.1503,  0.0777],\n",
      "        [ 0.1503,  0.1944],\n",
      "        [ 0.1503,  0.2528],\n",
      "        [ 0.0919,  0.3112],\n",
      "        [ 0.0044,  0.2820],\n",
      "        [-0.1122,  0.2820],\n",
      "        [-0.1997,  0.2820],\n",
      "        [-0.3164,  0.1944],\n",
      "        [-0.1122,  0.0777],\n",
      "        [ 0.0044,  0.0777],\n",
      "        [ 0.0628,  0.0777],\n",
      "        [ 0.1503,  0.1944],\n",
      "        [ 0.0628,  0.1944],\n",
      "        [ 0.0044,  0.2528],\n",
      "        [-0.1122,  0.2528]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34 torch.Size([1, 224, 224]) tensor([[-0.9737,  0.0410],\n",
      "        [-0.8687,  0.3029],\n",
      "        [-0.7637,  0.5124],\n",
      "        [-0.7111,  0.7742],\n",
      "        [-0.5536,  1.0361],\n",
      "        [-0.4485,  1.2455],\n",
      "        [-0.2910,  1.3503],\n",
      "        [-0.1334,  1.4026],\n",
      "        [ 0.2342,  1.5074],\n",
      "        [ 0.5494,  1.3503],\n",
      "        [ 0.7595,  1.1932],\n",
      "        [ 0.8120,  0.9837],\n",
      "        [ 0.9170,  0.7742],\n",
      "        [ 0.9695,  0.4600],\n",
      "        [ 0.9695,  0.3029],\n",
      "        [ 0.9695,  0.0410],\n",
      "        [ 0.9695, -0.2208],\n",
      "        [-0.8687, -0.2208],\n",
      "        [-0.7637, -0.2732],\n",
      "        [-0.6061, -0.3255],\n",
      "        [-0.5011, -0.3255],\n",
      "        [-0.3435, -0.3255],\n",
      "        [ 0.0767, -0.4303],\n",
      "        [ 0.2342, -0.4303],\n",
      "        [ 0.3393, -0.4303],\n",
      "        [ 0.5494, -0.4303],\n",
      "        [ 0.6544, -0.3779],\n",
      "        [-0.1334, -0.1161],\n",
      "        [-0.0284,  0.0934],\n",
      "        [-0.0284,  0.2505],\n",
      "        [ 0.0242,  0.4076],\n",
      "        [-0.1334,  0.5124],\n",
      "        [-0.0284,  0.5124],\n",
      "        [ 0.0767,  0.5124],\n",
      "        [ 0.1292,  0.4600],\n",
      "        [ 0.2342,  0.4600],\n",
      "        [-0.7111, -0.0637],\n",
      "        [-0.5536, -0.0637],\n",
      "        [-0.5011, -0.1161],\n",
      "        [-0.3435, -0.0637],\n",
      "        [-0.4485, -0.0113],\n",
      "        [-0.5536, -0.0113],\n",
      "        [ 0.1292, -0.1161],\n",
      "        [ 0.2868, -0.1684],\n",
      "        [ 0.3918, -0.2208],\n",
      "        [ 0.5494, -0.1684],\n",
      "        [ 0.3918, -0.1161],\n",
      "        [ 0.2868, -0.1161],\n",
      "        [-0.2384,  0.8266],\n",
      "        [-0.1859,  0.7742],\n",
      "        [ 0.0242,  0.7218],\n",
      "        [ 0.0767,  0.7218],\n",
      "        [ 0.1292,  0.6695],\n",
      "        [ 0.3393,  0.7218],\n",
      "        [ 0.4968,  0.7742],\n",
      "        [ 0.3393,  0.9313],\n",
      "        [ 0.2868,  0.9837],\n",
      "        [ 0.1292,  0.9837],\n",
      "        [ 0.0242,  1.0361],\n",
      "        [-0.1334,  0.9837],\n",
      "        [-0.2384,  0.8266],\n",
      "        [-0.0284,  0.7742],\n",
      "        [ 0.0767,  0.7742],\n",
      "        [ 0.2342,  0.7742],\n",
      "        [ 0.4968,  0.7742],\n",
      "        [ 0.2342,  0.8266],\n",
      "        [ 0.0767,  0.8266],\n",
      "        [ 0.0242,  0.9313]], dtype=torch.float64)\n",
      "35 torch.Size([1, 224, 224]) tensor([[-1.1443,  0.1161],\n",
      "        [-1.0927,  0.3222],\n",
      "        [-0.9895,  0.5540],\n",
      "        [-0.8863,  0.7601],\n",
      "        [-0.7056,  0.9405],\n",
      "        [-0.4476,  1.0435],\n",
      "        [-0.1637,  1.1208],\n",
      "        [ 0.1718,  1.1723],\n",
      "        [ 0.4298,  1.1723],\n",
      "        [ 0.5330,  1.0693],\n",
      "        [ 0.6104,  0.9662],\n",
      "        [ 0.6104,  0.8374],\n",
      "        [ 0.6104,  0.6313],\n",
      "        [ 0.6104,  0.3995],\n",
      "        [ 0.6621,  0.1934],\n",
      "        [ 0.6104,  0.0388],\n",
      "        [ 0.4298, -0.1931],\n",
      "        [-0.3960, -0.2704],\n",
      "        [-0.2669, -0.3734],\n",
      "        [-0.1121, -0.4249],\n",
      "        [ 0.0427, -0.4249],\n",
      "        [ 0.0943, -0.4249],\n",
      "        [ 0.3782, -0.5280],\n",
      "        [ 0.3782, -0.5795],\n",
      "        [ 0.4298, -0.5795],\n",
      "        [ 0.4298, -0.5795],\n",
      "        [ 0.3782, -0.4765],\n",
      "        [ 0.3266, -0.2446],\n",
      "        [ 0.4298, -0.1416],\n",
      "        [ 0.5330, -0.0385],\n",
      "        [ 0.6104,  0.0903],\n",
      "        [ 0.3266,  0.2964],\n",
      "        [ 0.3782,  0.2964],\n",
      "        [ 0.4814,  0.2449],\n",
      "        [ 0.4814,  0.2449],\n",
      "        [ 0.4814,  0.2449],\n",
      "        [-0.2153, -0.0900],\n",
      "        [-0.0605, -0.1416],\n",
      "        [-0.0089, -0.1416],\n",
      "        [-0.0089, -0.1416],\n",
      "        [-0.0089, -0.0900],\n",
      "        [-0.1121, -0.0900],\n",
      "        [ 0.2750, -0.2446],\n",
      "        [ 0.3782, -0.2961],\n",
      "        [ 0.4298, -0.2961],\n",
      "        [ 0.4298, -0.2961],\n",
      "        [ 0.4298, -0.2446],\n",
      "        [ 0.3782, -0.2446],\n",
      "        [ 0.0427,  0.6313],\n",
      "        [ 0.2750,  0.5283],\n",
      "        [ 0.4298,  0.4767],\n",
      "        [ 0.4814,  0.4767],\n",
      "        [ 0.5330,  0.4767],\n",
      "        [ 0.5330,  0.4767],\n",
      "        [ 0.5330,  0.5283],\n",
      "        [ 0.5330,  0.5798],\n",
      "        [ 0.5330,  0.6313],\n",
      "        [ 0.4814,  0.6313],\n",
      "        [ 0.4298,  0.6828],\n",
      "        [ 0.2750,  0.6313],\n",
      "        [ 0.0943,  0.6313],\n",
      "        [ 0.3782,  0.5798],\n",
      "        [ 0.4298,  0.5283],\n",
      "        [ 0.4814,  0.5283],\n",
      "        [ 0.4814,  0.5283],\n",
      "        [ 0.4814,  0.5283],\n",
      "        [ 0.4814,  0.5798],\n",
      "        [ 0.3782,  0.5798]], dtype=torch.float64)\n",
      "36 torch.Size([1, 224, 224]) tensor([[-1.2544, -0.3868],\n",
      "        [-1.1890, -0.2125],\n",
      "        [-1.1019, -0.0166],\n",
      "        [-1.0148,  0.1577],\n",
      "        [-0.8841,  0.3537],\n",
      "        [-0.6882,  0.5061],\n",
      "        [-0.4486,  0.5932],\n",
      "        [-0.2308,  0.6368],\n",
      "        [ 0.0523,  0.7021],\n",
      "        [ 0.2265,  0.5497],\n",
      "        [ 0.2918,  0.4625],\n",
      "        [ 0.3354,  0.3319],\n",
      "        [ 0.3354,  0.1359],\n",
      "        [ 0.3354, -0.0383],\n",
      "        [ 0.3354, -0.2343],\n",
      "        [ 0.3354, -0.4085],\n",
      "        [ 0.2265, -0.6045],\n",
      "        [-0.7753, -0.7134],\n",
      "        [-0.6446, -0.8005],\n",
      "        [-0.5139, -0.8441],\n",
      "        [-0.4051, -0.8441],\n",
      "        [-0.3179, -0.8441],\n",
      "        [ 0.0087, -0.8876],\n",
      "        [ 0.0958, -0.9312],\n",
      "        [ 0.1394, -0.9312],\n",
      "        [ 0.1829, -0.9312],\n",
      "        [ 0.2265, -0.8876],\n",
      "        [-0.0784, -0.6481],\n",
      "        [ 0.0087, -0.5174],\n",
      "        [ 0.0958, -0.4085],\n",
      "        [ 0.0958, -0.2779],\n",
      "        [-0.0784, -0.1472],\n",
      "        [-0.0348, -0.1472],\n",
      "        [ 0.0523, -0.1472],\n",
      "        [ 0.0958, -0.1908],\n",
      "        [ 0.0958, -0.1908],\n",
      "        [-0.6010, -0.5610],\n",
      "        [-0.5139, -0.6045],\n",
      "        [-0.4051, -0.6045],\n",
      "        [-0.3615, -0.5610],\n",
      "        [-0.4051, -0.5610],\n",
      "        [-0.5139, -0.5174],\n",
      "        [ 0.0087, -0.6481],\n",
      "        [ 0.0523, -0.6916],\n",
      "        [ 0.1394, -0.6916],\n",
      "        [ 0.1829, -0.6916],\n",
      "        [ 0.1394, -0.6481],\n",
      "        [ 0.0958, -0.6481],\n",
      "        [-0.2744,  0.1794],\n",
      "        [-0.1873,  0.0488],\n",
      "        [ 0.0087, -0.0383],\n",
      "        [ 0.0523,  0.0052],\n",
      "        [ 0.0958, -0.0383],\n",
      "        [ 0.1829,  0.0052],\n",
      "        [ 0.1829,  0.0923],\n",
      "        [ 0.1829,  0.2230],\n",
      "        [ 0.1394,  0.3319],\n",
      "        [ 0.0958,  0.3319],\n",
      "        [ 0.0087,  0.3319],\n",
      "        [-0.1437,  0.2666],\n",
      "        [-0.2744,  0.1794],\n",
      "        [-0.0348,  0.0488],\n",
      "        [ 0.0523,  0.0488],\n",
      "        [ 0.1394,  0.0488],\n",
      "        [ 0.1829,  0.1359],\n",
      "        [ 0.1394,  0.2230],\n",
      "        [ 0.0523,  0.2230],\n",
      "        [-0.0348,  0.2230]], dtype=torch.float64)\n",
      "37 torch.Size([1, 224, 224]) tensor([[-1.1075,  0.1013],\n",
      "        [-1.0440,  0.3761],\n",
      "        [-0.9382,  0.5874],\n",
      "        [-0.8324,  0.7565],\n",
      "        [-0.6632,  0.9678],\n",
      "        [-0.4516,  1.1369],\n",
      "        [-0.2400,  1.2426],\n",
      "        [ 0.0350,  1.3060],\n",
      "        [ 0.3524,  1.3694],\n",
      "        [ 0.5851,  1.2003],\n",
      "        [ 0.6275,  1.0524],\n",
      "        [ 0.6275,  0.8833],\n",
      "        [ 0.6909,  0.6720],\n",
      "        [ 0.6909,  0.4395],\n",
      "        [ 0.6909,  0.2281],\n",
      "        [ 0.7332,  0.0168],\n",
      "        [ 0.6909, -0.2580],\n",
      "        [-0.7266, -0.2791],\n",
      "        [-0.6209, -0.3848],\n",
      "        [-0.4516, -0.4270],\n",
      "        [-0.2823, -0.4270],\n",
      "        [-0.1765, -0.3848],\n",
      "        [ 0.2466, -0.4693],\n",
      "        [ 0.3524, -0.5327],\n",
      "        [ 0.4582, -0.5327],\n",
      "        [ 0.5851, -0.5750],\n",
      "        [ 0.6275, -0.4693],\n",
      "        [ 0.0774, -0.1523],\n",
      "        [ 0.2043,  0.0168],\n",
      "        [ 0.2466,  0.1647],\n",
      "        [ 0.3101,  0.2704],\n",
      "        [ 0.0774,  0.4395],\n",
      "        [ 0.2043,  0.4395],\n",
      "        [ 0.2466,  0.4395],\n",
      "        [ 0.3524,  0.4395],\n",
      "        [ 0.3524,  0.3972],\n",
      "        [-0.5151, -0.0678],\n",
      "        [-0.4093, -0.1100],\n",
      "        [-0.2823, -0.1523],\n",
      "        [-0.1765, -0.1100],\n",
      "        [-0.2823, -0.0466],\n",
      "        [-0.4093, -0.0466],\n",
      "        [ 0.2466, -0.2157],\n",
      "        [ 0.3524, -0.2580],\n",
      "        [ 0.4582, -0.2580],\n",
      "        [ 0.5217, -0.2580],\n",
      "        [ 0.4582, -0.2157],\n",
      "        [ 0.3524, -0.2157],\n",
      "        [-0.0284,  0.8199],\n",
      "        [ 0.0774,  0.7142],\n",
      "        [ 0.2466,  0.6508],\n",
      "        [ 0.3101,  0.6508],\n",
      "        [ 0.3524,  0.6085],\n",
      "        [ 0.4582,  0.6508],\n",
      "        [ 0.5217,  0.6720],\n",
      "        [ 0.4582,  0.7776],\n",
      "        [ 0.4159,  0.8833],\n",
      "        [ 0.3524,  0.8833],\n",
      "        [ 0.2466,  0.9256],\n",
      "        [ 0.1408,  0.8833],\n",
      "        [-0.0284,  0.8199],\n",
      "        [ 0.2043,  0.7142],\n",
      "        [ 0.3101,  0.7142],\n",
      "        [ 0.3524,  0.7142],\n",
      "        [ 0.4582,  0.6508],\n",
      "        [ 0.4159,  0.7776],\n",
      "        [ 0.3101,  0.7776],\n",
      "        [ 0.2466,  0.8199]], dtype=torch.float64)\n",
      "38 torch.Size([1, 224, 224]) tensor([[-0.8802, -1.0203],\n",
      "        [-0.8158, -0.7422],\n",
      "        [-0.7514, -0.5496],\n",
      "        [-0.6870, -0.2929],\n",
      "        [-0.5582, -0.0147],\n",
      "        [-0.4080,  0.1778],\n",
      "        [-0.2148,  0.3062],\n",
      "        [-0.0216,  0.3918],\n",
      "        [ 0.3003,  0.4560],\n",
      "        [ 0.6437,  0.2634],\n",
      "        [ 0.8369,  0.1350],\n",
      "        [ 0.9657, -0.0575],\n",
      "        [ 1.0300, -0.2501],\n",
      "        [ 1.0300, -0.5924],\n",
      "        [ 1.0300, -0.7850],\n",
      "        [ 1.0300, -0.9775],\n",
      "        [ 1.0300, -1.2557],\n",
      "        [-0.6870, -1.1487],\n",
      "        [-0.6226, -1.2129],\n",
      "        [-0.4080, -1.2129],\n",
      "        [-0.2792, -1.1915],\n",
      "        [-0.1504, -1.1915],\n",
      "        [ 0.3647, -1.3199],\n",
      "        [ 0.5149, -1.3199],\n",
      "        [ 0.6437, -1.3841],\n",
      "        [ 0.7725, -1.3841],\n",
      "        [ 0.9013, -1.3841],\n",
      "        [ 0.1715, -1.0631],\n",
      "        [ 0.2359, -0.8706],\n",
      "        [ 0.2359, -0.7422],\n",
      "        [ 0.3003, -0.5924],\n",
      "        [ 0.0428, -0.4640],\n",
      "        [ 0.1715, -0.4640],\n",
      "        [ 0.2359, -0.4640],\n",
      "        [ 0.3647, -0.4640],\n",
      "        [ 0.4505, -0.5282],\n",
      "        [-0.4938, -0.9989],\n",
      "        [-0.3436, -0.9989],\n",
      "        [-0.2148, -1.0631],\n",
      "        [-0.1504, -0.9989],\n",
      "        [-0.2148, -0.9989],\n",
      "        [-0.3436, -0.9347],\n",
      "        [ 0.4505, -1.1273],\n",
      "        [ 0.5149, -1.1915],\n",
      "        [ 0.6437, -1.1915],\n",
      "        [ 0.7725, -1.1915],\n",
      "        [ 0.6437, -1.1273],\n",
      "        [ 0.5149, -1.1273],\n",
      "        [-0.0216, -0.1431],\n",
      "        [ 0.0428, -0.2073],\n",
      "        [ 0.2359, -0.2715],\n",
      "        [ 0.3003, -0.2715],\n",
      "        [ 0.3647, -0.2715],\n",
      "        [ 0.5149, -0.2715],\n",
      "        [ 0.6437, -0.2073],\n",
      "        [ 0.5149, -0.1431],\n",
      "        [ 0.4505, -0.0789],\n",
      "        [ 0.3003, -0.0789],\n",
      "        [ 0.1715, -0.0789],\n",
      "        [ 0.1071, -0.0789],\n",
      "        [-0.0216, -0.1431],\n",
      "        [ 0.1715, -0.2073],\n",
      "        [ 0.3003, -0.2073],\n",
      "        [ 0.3647, -0.2073],\n",
      "        [ 0.5793, -0.2073],\n",
      "        [ 0.3647, -0.2073],\n",
      "        [ 0.3003, -0.2073],\n",
      "        [ 0.1715, -0.2073]], dtype=torch.float64)\n",
      "39 torch.Size([1, 224, 224]) tensor([[-0.5853,  0.2563],\n",
      "        [-0.5853,  0.3876],\n",
      "        [-0.5525,  0.5846],\n",
      "        [-0.4870,  0.7815],\n",
      "        [-0.4543,  0.9785],\n",
      "        [-0.3560,  1.1426],\n",
      "        [-0.1922,  1.2411],\n",
      "        [-0.0612,  1.2739],\n",
      "        [ 0.2336,  1.2739],\n",
      "        [ 0.4629,  1.2411],\n",
      "        [ 0.6595,  1.1426],\n",
      "        [ 0.8232,  1.0441],\n",
      "        [ 0.9215,  0.9128],\n",
      "        [ 1.0198,  0.7159],\n",
      "        [ 1.0198,  0.5189],\n",
      "        [ 1.0525,  0.3220],\n",
      "        [ 1.0525,  0.0922],\n",
      "        [-0.5853, -0.0391],\n",
      "        [-0.5525, -0.1376],\n",
      "        [-0.4543, -0.1376],\n",
      "        [-0.3560, -0.1376],\n",
      "        [-0.2577, -0.1376],\n",
      "        [ 0.1354, -0.2361],\n",
      "        [ 0.2336, -0.2361],\n",
      "        [ 0.3319, -0.2689],\n",
      "        [ 0.5284, -0.2689],\n",
      "        [ 0.6267, -0.1704],\n",
      "        [-0.0612,  0.0265],\n",
      "        [-0.0612,  0.1578],\n",
      "        [-0.0612,  0.2563],\n",
      "        [-0.0612,  0.3548],\n",
      "        [-0.0939,  0.4861],\n",
      "        [-0.0612,  0.4861],\n",
      "        [-0.0284,  0.4861],\n",
      "        [ 0.0698,  0.4861],\n",
      "        [ 0.1354,  0.4861],\n",
      "        [-0.4543,  0.0922],\n",
      "        [-0.3888,  0.0593],\n",
      "        [-0.2905,  0.0593],\n",
      "        [-0.1922,  0.0593],\n",
      "        [-0.2905,  0.0922],\n",
      "        [-0.3888,  0.0922],\n",
      "        [ 0.2336,  0.0265],\n",
      "        [ 0.2664, -0.0391],\n",
      "        [ 0.3646, -0.0391],\n",
      "        [ 0.5284, -0.0391],\n",
      "        [ 0.4302,  0.0265],\n",
      "        [ 0.2664,  0.0265],\n",
      "        [-0.1595,  0.8472],\n",
      "        [-0.0939,  0.7487],\n",
      "        [-0.0612,  0.6830],\n",
      "        [ 0.0371,  0.6830],\n",
      "        [ 0.0698,  0.6830],\n",
      "        [ 0.1681,  0.6830],\n",
      "        [ 0.2664,  0.7815],\n",
      "        [ 0.2336,  0.8472],\n",
      "        [ 0.1354,  0.8800],\n",
      "        [ 0.0698,  0.8800],\n",
      "        [-0.0284,  0.9456],\n",
      "        [-0.0612,  0.8800],\n",
      "        [-0.1595,  0.8472],\n",
      "        [-0.0284,  0.7815],\n",
      "        [ 0.0371,  0.7487],\n",
      "        [ 0.0698,  0.7487],\n",
      "        [ 0.2664,  0.7487],\n",
      "        [ 0.1354,  0.7815],\n",
      "        [ 0.0371,  0.8472],\n",
      "        [-0.0284,  0.8472]], dtype=torch.float64)\n",
      "40 torch.Size([1, 224, 224]) tensor([[-1.0436, -0.5908],\n",
      "        [-0.9628, -0.2883],\n",
      "        [-0.8213, -0.0665],\n",
      "        [-0.7405,  0.1553],\n",
      "        [-0.5991,  0.3771],\n",
      "        [-0.4375,  0.5989],\n",
      "        [-0.2153,  0.8207],\n",
      "        [ 0.0069,  1.0626],\n",
      "        [ 0.3705,  1.0626],\n",
      "        [ 0.6735,  0.9215],\n",
      "        [ 0.8957,  0.6997],\n",
      "        [ 1.0573,  0.4779],\n",
      "        [ 1.1179,  0.1754],\n",
      "        [ 1.1987, -0.1068],\n",
      "        [ 1.1987, -0.4093],\n",
      "        [ 1.2795, -0.6311],\n",
      "        [ 1.2795, -0.9336],\n",
      "        [-0.8819, -0.8126],\n",
      "        [-0.8213, -0.8932],\n",
      "        [-0.6597, -0.8932],\n",
      "        [-0.4375, -0.8932],\n",
      "        [-0.2961, -0.8126],\n",
      "        [ 0.3099, -0.9537],\n",
      "        [ 0.4513, -1.0142],\n",
      "        [ 0.5927, -1.0949],\n",
      "        [ 0.7543, -1.0949],\n",
      "        [ 0.9765, -1.0949],\n",
      "        [ 0.0877, -0.5101],\n",
      "        [ 0.0877, -0.3488],\n",
      "        [ 0.0877, -0.1270],\n",
      "        [ 0.1483,  0.0141],\n",
      "        [-0.0739,  0.0948],\n",
      "        [ 0.0069,  0.0948],\n",
      "        [ 0.1483,  0.0948],\n",
      "        [ 0.2291,  0.0948],\n",
      "        [ 0.3705,  0.0141],\n",
      "        [-0.6597, -0.5101],\n",
      "        [-0.5183, -0.5908],\n",
      "        [-0.3769, -0.5908],\n",
      "        [-0.2153, -0.5101],\n",
      "        [-0.3769, -0.5101],\n",
      "        [-0.5183, -0.4295],\n",
      "        [ 0.3705, -0.6513],\n",
      "        [ 0.5321, -0.7118],\n",
      "        [ 0.6735, -0.7924],\n",
      "        [ 0.7543, -0.7118],\n",
      "        [ 0.6735, -0.6513],\n",
      "        [ 0.5321, -0.6513],\n",
      "        [-0.2153,  0.3771],\n",
      "        [-0.1345,  0.3166],\n",
      "        [ 0.0877,  0.2359],\n",
      "        [ 0.1483,  0.2359],\n",
      "        [ 0.3099,  0.2359],\n",
      "        [ 0.4513,  0.2359],\n",
      "        [ 0.6735,  0.2561],\n",
      "        [ 0.5321,  0.5384],\n",
      "        [ 0.3705,  0.6190],\n",
      "        [ 0.2291,  0.6795],\n",
      "        [ 0.0877,  0.6795],\n",
      "        [-0.0739,  0.5989],\n",
      "        [-0.2153,  0.3771],\n",
      "        [ 0.0877,  0.3166],\n",
      "        [ 0.1483,  0.3166],\n",
      "        [ 0.3099,  0.3166],\n",
      "        [ 0.5927,  0.2561],\n",
      "        [ 0.3705,  0.5384],\n",
      "        [ 0.2291,  0.5384],\n",
      "        [ 0.0877,  0.5384]], dtype=torch.float64)\n",
      "41 torch.Size([1, 224, 224]) tensor([[-1.3571, -0.2404],\n",
      "        [-1.2982, -0.0437],\n",
      "        [-1.2000,  0.1726],\n",
      "        [-1.1018,  0.3103],\n",
      "        [-1.0036,  0.5659],\n",
      "        [-0.8464,  0.7823],\n",
      "        [-0.6893,  0.9789],\n",
      "        [-0.4929,  1.1362],\n",
      "        [-0.1982,  1.1952],\n",
      "        [ 0.0571,  1.0379],\n",
      "        [ 0.2536,  0.8019],\n",
      "        [ 0.3714,  0.5856],\n",
      "        [ 0.4696,  0.2906],\n",
      "        [ 0.4696,  0.0939],\n",
      "        [ 0.4696, -0.1027],\n",
      "        [ 0.4696, -0.3191],\n",
      "        [ 0.4107, -0.5157],\n",
      "        [-1.1018, -0.1421],\n",
      "        [-1.0036, -0.1421],\n",
      "        [-0.8464, -0.1224],\n",
      "        [-0.7482, -0.1224],\n",
      "        [-0.5911, -0.0831],\n",
      "        [-0.1982, -0.1617],\n",
      "        [-0.1000, -0.2207],\n",
      "        [ 0.0179, -0.2601],\n",
      "        [ 0.1554, -0.3191],\n",
      "        [ 0.2536, -0.3584],\n",
      "        [-0.3946,  0.0349],\n",
      "        [-0.3357,  0.2316],\n",
      "        [-0.2964,  0.3889],\n",
      "        [-0.2964,  0.4873],\n",
      "        [-0.4339,  0.4873],\n",
      "        [-0.3946,  0.5266],\n",
      "        [-0.2964,  0.5266],\n",
      "        [-0.1982,  0.4873],\n",
      "        [-0.1393,  0.4283],\n",
      "        [-0.9446,  0.0153],\n",
      "        [-0.8464,  0.0153],\n",
      "        [-0.7482,  0.0153],\n",
      "        [-0.6500,  0.0153],\n",
      "        [-0.6893,  0.0743],\n",
      "        [-0.8464,  0.0743],\n",
      "        [-0.1393, -0.0634],\n",
      "        [-0.1000, -0.1224],\n",
      "        [ 0.0571, -0.1617],\n",
      "        [ 0.1161, -0.1617],\n",
      "        [ 0.0571, -0.1224],\n",
      "        [-0.0411, -0.0634],\n",
      "        [-0.5911,  0.6839],\n",
      "        [-0.4929,  0.6839],\n",
      "        [-0.3357,  0.6839],\n",
      "        [-0.2964,  0.6839],\n",
      "        [-0.1982,  0.6446],\n",
      "        [-0.0411,  0.6446],\n",
      "        [ 0.0571,  0.6446],\n",
      "        [-0.0411,  0.7429],\n",
      "        [-0.1393,  0.8413],\n",
      "        [-0.2375,  0.8413],\n",
      "        [-0.3357,  0.8413],\n",
      "        [-0.4929,  0.7823],\n",
      "        [-0.5911,  0.6839],\n",
      "        [-0.3946,  0.7429],\n",
      "        [-0.2964,  0.7429],\n",
      "        [-0.1982,  0.6839],\n",
      "        [ 0.0179,  0.6446],\n",
      "        [-0.1982,  0.7429],\n",
      "        [-0.2375,  0.7429],\n",
      "        [-0.3357,  0.7429]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 torch.Size([1, 224, 224]) tensor([[-0.8793, -0.1937],\n",
      "        [-0.7982,  0.0225],\n",
      "        [-0.7982,  0.2657],\n",
      "        [-0.7440,  0.4279],\n",
      "        [-0.6899,  0.6711],\n",
      "        [-0.5817,  0.8332],\n",
      "        [-0.4735,  0.9684],\n",
      "        [-0.2841,  1.0765],\n",
      "        [-0.0676,  1.2116],\n",
      "        [ 0.2300,  1.0765],\n",
      "        [ 0.4735,  0.9684],\n",
      "        [ 0.6358,  0.8603],\n",
      "        [ 0.7982,  0.6981],\n",
      "        [ 0.8793,  0.4549],\n",
      "        [ 0.9334,  0.2387],\n",
      "        [ 0.9876, -0.0045],\n",
      "        [ 1.0417, -0.2207],\n",
      "        [-0.7440, -0.4369],\n",
      "        [-0.6358, -0.4910],\n",
      "        [-0.5276, -0.4910],\n",
      "        [-0.3923, -0.4910],\n",
      "        [-0.2841, -0.4099],\n",
      "        [ 0.1759, -0.4099],\n",
      "        [ 0.2841, -0.4640],\n",
      "        [ 0.4735, -0.4640],\n",
      "        [ 0.5817, -0.4640],\n",
      "        [ 0.7440, -0.4099],\n",
      "        [-0.0676, -0.1937],\n",
      "        [-0.0676, -0.0045],\n",
      "        [-0.0676,  0.1576],\n",
      "        [-0.0676,  0.2657],\n",
      "        [-0.2300,  0.4008],\n",
      "        [-0.1218,  0.4008],\n",
      "        [-0.0676,  0.4008],\n",
      "        [ 0.0676,  0.4008],\n",
      "        [ 0.1218,  0.4008],\n",
      "        [-0.5817, -0.1937],\n",
      "        [-0.5276, -0.2478],\n",
      "        [-0.3923, -0.2478],\n",
      "        [-0.2841, -0.1937],\n",
      "        [-0.3923, -0.1937],\n",
      "        [-0.5276, -0.1937],\n",
      "        [ 0.2300, -0.1937],\n",
      "        [ 0.3382, -0.2478],\n",
      "        [ 0.4735, -0.2478],\n",
      "        [ 0.5817, -0.1667],\n",
      "        [ 0.4735, -0.1126],\n",
      "        [ 0.3382, -0.1126],\n",
      "        [-0.3382,  0.6711],\n",
      "        [-0.2841,  0.6170],\n",
      "        [-0.1218,  0.5630],\n",
      "        [-0.0676,  0.5630],\n",
      "        [ 0.0135,  0.5630],\n",
      "        [ 0.1218,  0.6170],\n",
      "        [ 0.2300,  0.6711],\n",
      "        [ 0.1218,  0.7522],\n",
      "        [ 0.0676,  0.8062],\n",
      "        [-0.0676,  0.8062],\n",
      "        [-0.1759,  0.8062],\n",
      "        [-0.2841,  0.7251],\n",
      "        [-0.3382,  0.6711],\n",
      "        [-0.1759,  0.6170],\n",
      "        [-0.0676,  0.6170],\n",
      "        [ 0.0676,  0.6711],\n",
      "        [ 0.2300,  0.6711],\n",
      "        [ 0.0135,  0.6711],\n",
      "        [-0.0676,  0.6711],\n",
      "        [-0.1759,  0.6711]], dtype=torch.float64)\n",
      "43 torch.Size([1, 224, 224]) tensor([[-1.5473e+00, -3.1960e-01],\n",
      "        [-1.5016e+00, -1.5934e-01],\n",
      "        [-1.5016e+00,  9.1575e-04],\n",
      "        [-1.4559e+00,  1.6117e-01],\n",
      "        [-1.4102e+00,  3.2143e-01],\n",
      "        [-1.3416e+00,  4.5879e-01],\n",
      "        [-1.2502e+00,  5.2747e-01],\n",
      "        [-1.1817e+00,  6.6484e-01],\n",
      "        [-9.7597e-01,  7.1062e-01],\n",
      "        [-7.7028e-01,  6.6484e-01],\n",
      "        [-6.1030e-01,  5.5037e-01],\n",
      "        [-4.7318e-01,  4.3590e-01],\n",
      "        [-3.5891e-01,  2.7564e-01],\n",
      "        [-3.1320e-01,  1.1538e-01],\n",
      "        [-2.9034e-01, -6.7766e-02],\n",
      "        [-2.9034e-01, -2.2802e-01],\n",
      "        [-2.9034e-01, -4.3407e-01],\n",
      "        [-1.5473e+00, -4.3407e-01],\n",
      "        [-1.4559e+00, -4.7985e-01],\n",
      "        [-1.3873e+00, -4.7985e-01],\n",
      "        [-1.2959e+00, -4.7985e-01],\n",
      "        [-1.2274e+00, -4.3407e-01],\n",
      "        [-8.8455e-01, -4.7985e-01],\n",
      "        [-8.1599e-01, -5.2564e-01],\n",
      "        [-7.2457e-01, -5.0275e-01],\n",
      "        [-6.1030e-01, -5.0275e-01],\n",
      "        [-5.1889e-01, -4.7985e-01],\n",
      "        [-1.0445e+00, -2.7381e-01],\n",
      "        [-1.0445e+00, -1.5934e-01],\n",
      "        [-1.0445e+00, -2.1978e-02],\n",
      "        [-1.0445e+00,  9.2491e-02],\n",
      "        [-1.1359e+00,  1.3828e-01],\n",
      "        [-1.0902e+00,  1.3828e-01],\n",
      "        [-1.0445e+00,  1.8407e-01],\n",
      "        [-9.7597e-01,  1.3828e-01],\n",
      "        [-9.3026e-01,  1.3828e-01],\n",
      "        [-1.4102e+00, -3.1960e-01],\n",
      "        [-1.3873e+00, -3.1960e-01],\n",
      "        [-1.2959e+00, -3.1960e-01],\n",
      "        [-1.2274e+00, -3.1960e-01],\n",
      "        [-1.2502e+00, -2.7381e-01],\n",
      "        [-1.3416e+00, -2.7381e-01],\n",
      "        [-8.8455e-01, -3.1960e-01],\n",
      "        [-8.1599e-01, -3.1960e-01],\n",
      "        [-7.2457e-01, -3.1960e-01],\n",
      "        [-6.5601e-01, -3.1960e-01],\n",
      "        [-7.2457e-01, -2.7381e-01],\n",
      "        [-8.1599e-01, -2.7381e-01],\n",
      "        [-1.2274e+00,  3.4432e-01],\n",
      "        [-1.1817e+00,  2.9853e-01],\n",
      "        [-1.0902e+00,  2.5275e-01],\n",
      "        [-1.0445e+00,  2.9853e-01],\n",
      "        [-9.7597e-01,  2.5275e-01],\n",
      "        [-8.8455e-01,  2.9853e-01],\n",
      "        [-8.1599e-01,  3.4432e-01],\n",
      "        [-8.8455e-01,  3.9011e-01],\n",
      "        [-9.3026e-01,  4.1300e-01],\n",
      "        [-1.0217e+00,  4.1300e-01],\n",
      "        [-1.0902e+00,  4.1300e-01],\n",
      "        [-1.1817e+00,  3.6722e-01],\n",
      "        [-1.2274e+00,  3.4432e-01],\n",
      "        [-1.0902e+00,  3.4432e-01],\n",
      "        [-1.0217e+00,  3.4432e-01],\n",
      "        [-9.7597e-01,  3.4432e-01],\n",
      "        [-8.1599e-01,  3.4432e-01],\n",
      "        [-9.7597e-01,  3.4432e-01],\n",
      "        [-1.0217e+00,  3.4432e-01],\n",
      "        [-1.0902e+00,  3.4432e-01]], dtype=torch.float64)\n",
      "44 torch.Size([1, 224, 224]) tensor([[-1.4259e+00, -6.5541e-01],\n",
      "        [-1.4259e+00, -3.9286e-01],\n",
      "        [-1.4259e+00, -9.7490e-02],\n",
      "        [-1.3275e+00,  9.9421e-02],\n",
      "        [-1.2618e+00,  3.9479e-01],\n",
      "        [-1.0649e+00,  5.9170e-01],\n",
      "        [-8.6791e-01,  7.5579e-01],\n",
      "        [-5.7248e-01,  8.8707e-01],\n",
      "        [-2.1140e-01,  9.5270e-01],\n",
      "        [ 1.1686e-01,  8.8707e-01],\n",
      "        [ 1.8251e-01,  8.2143e-01],\n",
      "        [ 3.4664e-01,  7.5579e-01],\n",
      "        [ 4.7794e-01,  5.5888e-01],\n",
      "        [ 5.4359e-01,  2.6351e-01],\n",
      "        [ 6.0924e-01,  6.6602e-02],\n",
      "        [ 6.7489e-01, -1.3031e-01],\n",
      "        [ 6.0924e-01, -4.2568e-01],\n",
      "        [-9.3356e-01, -8.5232e-01],\n",
      "        [-7.0378e-01, -9.1795e-01],\n",
      "        [-5.0683e-01, -9.8359e-01],\n",
      "        [-3.7553e-01, -9.1795e-01],\n",
      "        [-2.1140e-01, -8.5232e-01],\n",
      "        [ 2.4816e-01, -7.8668e-01],\n",
      "        [ 4.1229e-01, -8.5232e-01],\n",
      "        [ 5.4359e-01, -8.5232e-01],\n",
      "        [ 6.0924e-01, -7.5386e-01],\n",
      "        [ 6.7489e-01, -6.8822e-01],\n",
      "        [ 5.1208e-02, -5.5695e-01],\n",
      "        [ 5.1208e-02, -3.6004e-01],\n",
      "        [ 5.1208e-02, -2.2876e-01],\n",
      "        [ 5.1208e-02, -6.4672e-02],\n",
      "        [-2.1140e-01,  9.6525e-04],\n",
      "        [-1.4575e-01,  6.6602e-02],\n",
      "        [-1.4443e-02,  6.6602e-02],\n",
      "        [ 5.1208e-02,  6.6602e-02],\n",
      "        [ 1.1686e-01,  6.6602e-02],\n",
      "        [-7.0378e-01, -6.5541e-01],\n",
      "        [-5.7248e-01, -6.5541e-01],\n",
      "        [-4.4118e-01, -6.5541e-01],\n",
      "        [-3.7553e-01, -5.5695e-01],\n",
      "        [-4.4118e-01, -5.8977e-01],\n",
      "        [-5.7248e-01, -5.8977e-01],\n",
      "        [ 1.8251e-01, -4.9131e-01],\n",
      "        [ 3.4664e-01, -5.5695e-01],\n",
      "        [ 4.1229e-01, -5.5695e-01],\n",
      "        [ 4.7794e-01, -4.9131e-01],\n",
      "        [ 4.1229e-01, -4.9131e-01],\n",
      "        [ 2.4816e-01, -4.9131e-01],\n",
      "        [-5.0683e-01,  3.2915e-01],\n",
      "        [-3.7553e-01,  2.6351e-01],\n",
      "        [-1.4575e-01,  1.9788e-01],\n",
      "        [-8.0095e-02,  2.6351e-01],\n",
      "        [ 5.1208e-02,  2.6351e-01],\n",
      "        [ 1.1686e-01,  2.6351e-01],\n",
      "        [ 1.8251e-01,  3.2915e-01],\n",
      "        [ 1.1686e-01,  3.9479e-01],\n",
      "        [ 5.1208e-02,  4.9324e-01],\n",
      "        [-8.0095e-02,  4.9324e-01],\n",
      "        [-2.1140e-01,  4.6042e-01],\n",
      "        [-3.7553e-01,  3.9479e-01],\n",
      "        [-5.0683e-01,  3.2915e-01],\n",
      "        [-2.1140e-01,  3.2915e-01],\n",
      "        [-8.0095e-02,  3.2915e-01],\n",
      "        [ 5.1208e-02,  3.2915e-01],\n",
      "        [ 1.8251e-01,  3.2915e-01],\n",
      "        [ 5.1208e-02,  3.2915e-01],\n",
      "        [-8.0095e-02,  3.2915e-01],\n",
      "        [-2.1140e-01,  3.2915e-01]], dtype=torch.float64)\n",
      "45 torch.Size([1, 224, 224]) tensor([[-0.6193, -0.6693],\n",
      "        [-0.6193, -0.3882],\n",
      "        [-0.5367, -0.0906],\n",
      "        [-0.4706,  0.2070],\n",
      "        [-0.4046,  0.5046],\n",
      "        [-0.2559,  0.8022],\n",
      "        [-0.1072,  0.9511],\n",
      "        [ 0.0415,  1.1660],\n",
      "        [ 0.4049,  1.3148],\n",
      "        [ 0.7684,  1.2487],\n",
      "        [ 1.0658,  1.0337],\n",
      "        [ 1.3631,  0.8188],\n",
      "        [ 1.5779,  0.5377],\n",
      "        [ 1.7266,  0.1739],\n",
      "        [ 1.7266, -0.1237],\n",
      "        [ 1.8092, -0.4213],\n",
      "        [ 1.8092, -0.7024],\n",
      "        [-0.6193, -0.9008],\n",
      "        [-0.5367, -0.9008],\n",
      "        [-0.4046, -0.8843],\n",
      "        [-0.2559, -0.8843],\n",
      "        [-0.1072, -0.8181],\n",
      "        [ 0.4049, -0.8016],\n",
      "        [ 0.5536, -0.8016],\n",
      "        [ 0.7684, -0.8016],\n",
      "        [ 0.9171, -0.8016],\n",
      "        [ 1.1484, -0.8016],\n",
      "        [ 0.1902, -0.5205],\n",
      "        [ 0.1241, -0.2890],\n",
      "        [ 0.1241, -0.0741],\n",
      "        [ 0.1241,  0.0747],\n",
      "        [ 0.0415,  0.1409],\n",
      "        [ 0.0415,  0.1409],\n",
      "        [ 0.1902,  0.2235],\n",
      "        [ 0.2563,  0.1409],\n",
      "        [ 0.3389,  0.1409],\n",
      "        [-0.4046, -0.6693],\n",
      "        [-0.3220, -0.6693],\n",
      "        [-0.1733, -0.6693],\n",
      "        [-0.0246, -0.5866],\n",
      "        [-0.1733, -0.5205],\n",
      "        [-0.3220, -0.6032],\n",
      "        [ 0.5536, -0.5040],\n",
      "        [ 0.6362, -0.5866],\n",
      "        [ 0.7684, -0.5866],\n",
      "        [ 0.9171, -0.5040],\n",
      "        [ 0.7684, -0.5040],\n",
      "        [ 0.6362, -0.5040],\n",
      "        [-0.1072,  0.5046],\n",
      "        [-0.0246,  0.5046],\n",
      "        [ 0.1241,  0.4385],\n",
      "        [ 0.1902,  0.4385],\n",
      "        [ 0.2563,  0.4385],\n",
      "        [ 0.4049,  0.5212],\n",
      "        [ 0.6362,  0.5873],\n",
      "        [ 0.4875,  0.6700],\n",
      "        [ 0.3389,  0.7361],\n",
      "        [ 0.1902,  0.7361],\n",
      "        [ 0.1241,  0.7361],\n",
      "        [ 0.0415,  0.6534],\n",
      "        [-0.0246,  0.5046],\n",
      "        [ 0.1241,  0.5046],\n",
      "        [ 0.1902,  0.5046],\n",
      "        [ 0.3389,  0.5212],\n",
      "        [ 0.5536,  0.5212],\n",
      "        [ 0.3389,  0.5873],\n",
      "        [ 0.1902,  0.5873],\n",
      "        [ 0.1241,  0.5873]], dtype=torch.float64)\n",
      "46 torch.Size([1, 224, 224]) tensor([[-0.6874, -0.4036],\n",
      "        [-0.6874, -0.2779],\n",
      "        [-0.6559, -0.1207],\n",
      "        [-0.6245,  0.0364],\n",
      "        [-0.5302,  0.1936],\n",
      "        [-0.4359,  0.3193],\n",
      "        [-0.3101,  0.3507],\n",
      "        [-0.1844,  0.4136],\n",
      "        [ 0.0357,  0.4136],\n",
      "        [ 0.1929,  0.3193],\n",
      "        [ 0.3187,  0.2879],\n",
      "        [ 0.3815,  0.1936],\n",
      "        [ 0.4759,  0.0679],\n",
      "        [ 0.5073, -0.0893],\n",
      "        [ 0.5073, -0.2464],\n",
      "        [ 0.5073, -0.4036],\n",
      "        [ 0.5073, -0.5607],\n",
      "        [-0.5931, -0.6864],\n",
      "        [-0.5302, -0.7807],\n",
      "        [-0.4673, -0.7493],\n",
      "        [-0.3415, -0.8121],\n",
      "        [-0.2787, -0.7493],\n",
      "        [ 0.0043, -0.7807],\n",
      "        [ 0.0672, -0.8436],\n",
      "        [ 0.1929, -0.8436],\n",
      "        [ 0.2872, -0.8436],\n",
      "        [ 0.3501, -0.7807],\n",
      "        [-0.1215, -0.5921],\n",
      "        [-0.0900, -0.5293],\n",
      "        [-0.0900, -0.4350],\n",
      "        [-0.0900, -0.3721],\n",
      "        [-0.1529, -0.2464],\n",
      "        [-0.1215, -0.2464],\n",
      "        [-0.0272, -0.2464],\n",
      "        [ 0.0043, -0.2779],\n",
      "        [ 0.0357, -0.2779],\n",
      "        [-0.4673, -0.5607],\n",
      "        [-0.4359, -0.6236],\n",
      "        [-0.3415, -0.5921],\n",
      "        [-0.2787, -0.5921],\n",
      "        [-0.3101, -0.5607],\n",
      "        [-0.4359, -0.5607],\n",
      "        [ 0.0357, -0.5921],\n",
      "        [ 0.1300, -0.6236],\n",
      "        [ 0.1929, -0.6864],\n",
      "        [ 0.2872, -0.6236],\n",
      "        [ 0.1929, -0.5921],\n",
      "        [ 0.1300, -0.5921],\n",
      "        [-0.2787,  0.0050],\n",
      "        [-0.1844, -0.0893],\n",
      "        [-0.0900, -0.1521],\n",
      "        [-0.0272, -0.1521],\n",
      "        [ 0.0043, -0.1836],\n",
      "        [ 0.1300, -0.1207],\n",
      "        [ 0.1615, -0.0579],\n",
      "        [ 0.1300,  0.0050],\n",
      "        [ 0.0357,  0.0679],\n",
      "        [ 0.0043,  0.0679],\n",
      "        [-0.0900,  0.0679],\n",
      "        [-0.1529,  0.0679],\n",
      "        [-0.2787,  0.0050],\n",
      "        [-0.1215, -0.0893],\n",
      "        [-0.0272, -0.0893],\n",
      "        [ 0.0357, -0.0893],\n",
      "        [ 0.1615, -0.0579],\n",
      "        [ 0.0357, -0.0264],\n",
      "        [-0.0272, -0.0264],\n",
      "        [-0.0900, -0.0264]], dtype=torch.float64)\n",
      "47 torch.Size([1, 224, 224]) tensor([[-0.8823,  0.4804],\n",
      "        [-0.8497,  0.6429],\n",
      "        [-0.7520,  0.7728],\n",
      "        [-0.7194,  0.8703],\n",
      "        [-0.6542,  1.0327],\n",
      "        [-0.5239,  1.1951],\n",
      "        [-0.4261,  1.2926],\n",
      "        [-0.2632,  1.3576],\n",
      "        [-0.0351,  1.3901],\n",
      "        [ 0.1605,  1.2926],\n",
      "        [ 0.2908,  1.1951],\n",
      "        [ 0.3560,  1.0652],\n",
      "        [ 0.4537,  0.9352],\n",
      "        [ 0.4863,  0.7728],\n",
      "        [ 0.5189,  0.6104],\n",
      "        [ 0.5515,  0.4479],\n",
      "        [ 0.5515,  0.3180],\n",
      "        [-0.7520,  0.3505],\n",
      "        [-0.6868,  0.3505],\n",
      "        [-0.5564,  0.3180],\n",
      "        [-0.4913,  0.3180],\n",
      "        [-0.3609,  0.3830],\n",
      "        [-0.0351,  0.3180],\n",
      "        [ 0.0301,  0.2855],\n",
      "        [ 0.1605,  0.2855],\n",
      "        [ 0.2582,  0.2530],\n",
      "        [ 0.3560,  0.2530],\n",
      "        [-0.1980,  0.5454],\n",
      "        [-0.1654,  0.6429],\n",
      "        [-0.1654,  0.7728],\n",
      "        [-0.1328,  0.8378],\n",
      "        [-0.2306,  0.9352],\n",
      "        [-0.1980,  0.9352],\n",
      "        [-0.1328,  0.9352],\n",
      "        [-0.0676,  0.9352],\n",
      "        [-0.0025,  0.9028],\n",
      "        [-0.5890,  0.4804],\n",
      "        [-0.5239,  0.4804],\n",
      "        [-0.4261,  0.4479],\n",
      "        [-0.3609,  0.4804],\n",
      "        [-0.4261,  0.5129],\n",
      "        [-0.5239,  0.5129],\n",
      "        [-0.0025,  0.4479],\n",
      "        [ 0.0953,  0.4154],\n",
      "        [ 0.1605,  0.4154],\n",
      "        [ 0.2582,  0.4154],\n",
      "        [ 0.1605,  0.4479],\n",
      "        [ 0.0953,  0.4479],\n",
      "        [-0.3283,  1.0977],\n",
      "        [-0.2306,  1.0977],\n",
      "        [-0.1654,  1.0652],\n",
      "        [-0.1328,  1.0652],\n",
      "        [-0.0676,  1.0652],\n",
      "        [ 0.0301,  1.0652],\n",
      "        [ 0.0953,  1.0652],\n",
      "        [ 0.0301,  1.1302],\n",
      "        [-0.0025,  1.1627],\n",
      "        [-0.0676,  1.2276],\n",
      "        [-0.1654,  1.2276],\n",
      "        [-0.2306,  1.1627],\n",
      "        [-0.2632,  1.0977],\n",
      "        [-0.1654,  1.0977],\n",
      "        [-0.0676,  1.0977],\n",
      "        [-0.0351,  1.0977],\n",
      "        [ 0.0953,  1.0652],\n",
      "        [-0.0351,  1.0977],\n",
      "        [-0.1328,  1.1302],\n",
      "        [-0.1654,  1.1302]], dtype=torch.float64)\n",
      "48 torch.Size([1, 224, 224]) tensor([[-0.4452, -0.6056],\n",
      "        [-0.4452, -0.3725],\n",
      "        [-0.3867, -0.1200],\n",
      "        [-0.3867,  0.0548],\n",
      "        [-0.3282,  0.2880],\n",
      "        [-0.2112,  0.5405],\n",
      "        [-0.0943,  0.6571],\n",
      "        [ 0.0812,  0.7736],\n",
      "        [ 0.3931,  0.8319],\n",
      "        [ 0.6855,  0.7930],\n",
      "        [ 0.9194,  0.7348],\n",
      "        [ 1.0559,  0.6182],\n",
      "        [ 1.2314,  0.4434],\n",
      "        [ 1.2898,  0.1908],\n",
      "        [ 1.3483, -0.0423],\n",
      "        [ 1.4068, -0.2171],\n",
      "        [ 1.4068, -0.5279],\n",
      "        [-0.2697, -0.8388],\n",
      "        [-0.1528, -0.8970],\n",
      "        [-0.0358, -0.9553],\n",
      "        [ 0.0812, -0.9553],\n",
      "        [ 0.2176, -0.8970],\n",
      "        [ 0.6855, -0.8970],\n",
      "        [ 0.8025, -0.9553],\n",
      "        [ 0.9194, -0.9553],\n",
      "        [ 1.1144, -0.8776],\n",
      "        [ 1.2314, -0.7611],\n",
      "        [ 0.3931, -0.6639],\n",
      "        [ 0.3931, -0.5279],\n",
      "        [ 0.3931, -0.3531],\n",
      "        [ 0.3931, -0.2366],\n",
      "        [ 0.2176, -0.1200],\n",
      "        [ 0.2761, -0.1200],\n",
      "        [ 0.3931, -0.0617],\n",
      "        [ 0.5101, -0.0617],\n",
      "        [ 0.5685, -0.1200],\n",
      "        [-0.0943, -0.6639],\n",
      "        [-0.0358, -0.7222],\n",
      "        [ 0.0812, -0.7222],\n",
      "        [ 0.2176, -0.6639],\n",
      "        [ 0.0812, -0.6639],\n",
      "        [-0.0358, -0.6639],\n",
      "        [ 0.6855, -0.6445],\n",
      "        [ 0.8025, -0.6445],\n",
      "        [ 0.9194, -0.6445],\n",
      "        [ 0.9974, -0.5862],\n",
      "        [ 0.9194, -0.5862],\n",
      "        [ 0.8025, -0.5862],\n",
      "        [ 0.0227,  0.2297],\n",
      "        [ 0.1397,  0.1131],\n",
      "        [ 0.2761,  0.0548],\n",
      "        [ 0.3931,  0.0548],\n",
      "        [ 0.4516,  0.0548],\n",
      "        [ 0.5685,  0.1908],\n",
      "        [ 0.6855,  0.3074],\n",
      "        [ 0.5685,  0.3657],\n",
      "        [ 0.4516,  0.4239],\n",
      "        [ 0.3346,  0.4239],\n",
      "        [ 0.2176,  0.4239],\n",
      "        [ 0.1397,  0.3657],\n",
      "        [ 0.0812,  0.2297],\n",
      "        [ 0.2761,  0.1714],\n",
      "        [ 0.3931,  0.1908],\n",
      "        [ 0.4516,  0.1908],\n",
      "        [ 0.6270,  0.3074],\n",
      "        [ 0.4516,  0.3074],\n",
      "        [ 0.3346,  0.3074],\n",
      "        [ 0.2761,  0.3074]], dtype=torch.float64)\n",
      "49 torch.Size([1, 224, 224]) tensor([[-1.1121, -0.3812],\n",
      "        [-1.1121, -0.1387],\n",
      "        [-1.0409,  0.0609],\n",
      "        [-1.0409,  0.3034],\n",
      "        [-0.9129,  0.5031],\n",
      "        [-0.7990,  0.6885],\n",
      "        [-0.6709,  0.8169],\n",
      "        [-0.4148,  0.8882],\n",
      "        [-0.1017,  0.8882],\n",
      "        [ 0.2114,  0.8882],\n",
      "        [ 0.4675,  0.8311],\n",
      "        [ 0.5956,  0.7170],\n",
      "        [ 0.7095,  0.5887],\n",
      "        [ 0.8375,  0.3319],\n",
      "        [ 0.8375,  0.1465],\n",
      "        [ 0.9087, -0.1102],\n",
      "        [ 0.9087, -0.2956],\n",
      "        [-0.8559, -0.7663],\n",
      "        [-0.6709, -0.9517],\n",
      "        [-0.5429, -1.0088],\n",
      "        [-0.4148, -1.0088],\n",
      "        [-0.2867, -0.9375],\n",
      "        [ 0.2114, -0.9375],\n",
      "        [ 0.3395, -0.9375],\n",
      "        [ 0.5245, -0.9232],\n",
      "        [ 0.6525, -0.7948],\n",
      "        [ 0.7806, -0.6807],\n",
      "        [-0.0448, -0.6807],\n",
      "        [-0.0448, -0.5666],\n",
      "        [ 0.0264, -0.4383],\n",
      "        [ 0.0264, -0.3099],\n",
      "        [-0.2298, -0.1815],\n",
      "        [-0.1017, -0.1245],\n",
      "        [-0.0448, -0.1245],\n",
      "        [ 0.0833, -0.1102],\n",
      "        [ 0.1545, -0.1102],\n",
      "        [-0.5998, -0.5666],\n",
      "        [-0.5429, -0.6380],\n",
      "        [-0.4148, -0.6237],\n",
      "        [-0.2867, -0.5666],\n",
      "        [-0.4148, -0.5666],\n",
      "        [-0.5429, -0.5666],\n",
      "        [ 0.2114, -0.5524],\n",
      "        [ 0.3395, -0.6237],\n",
      "        [ 0.4675, -0.5524],\n",
      "        [ 0.5245, -0.5524],\n",
      "        [ 0.4675, -0.4953],\n",
      "        [ 0.3395, -0.5524],\n",
      "        [-0.4859,  0.1893],\n",
      "        [-0.2867,  0.0609],\n",
      "        [-0.1017,  0.0039],\n",
      "        [-0.0448,  0.0752],\n",
      "        [ 0.0264,  0.0752],\n",
      "        [ 0.2114,  0.0752],\n",
      "        [ 0.3395,  0.2036],\n",
      "        [ 0.1545,  0.2606],\n",
      "        [ 0.0833,  0.3177],\n",
      "        [-0.0448,  0.3177],\n",
      "        [-0.1586,  0.3177],\n",
      "        [-0.2867,  0.2606],\n",
      "        [-0.4148,  0.1893],\n",
      "        [-0.1586,  0.1322],\n",
      "        [-0.0448,  0.1322],\n",
      "        [ 0.0833,  0.1322],\n",
      "        [ 0.2825,  0.2036],\n",
      "        [ 0.0264,  0.2036],\n",
      "        [-0.0448,  0.2036],\n",
      "        [-0.1586,  0.1893]], dtype=torch.float64)\n",
      "50 torch.Size([1, 224, 224]) tensor([[-1.0650e+00, -2.1806e-01],\n",
      "        [-1.0287e+00, -2.9036e-04],\n",
      "        [-9.5631e-01,  2.1748e-01],\n",
      "        [-9.2009e-01,  4.3525e-01],\n",
      "        [-8.4764e-01,  5.8043e-01],\n",
      "        [-7.0276e-01,  7.6190e-01],\n",
      "        [-4.8544e-01,  8.7079e-01],\n",
      "        [-1.9567e-01,  1.0523e+00],\n",
      "        [ 5.7877e-02,  1.0886e+00],\n",
      "        [ 2.7520e-01,  9.7967e-01],\n",
      "        [ 3.8387e-01,  9.4338e-01],\n",
      "        [ 3.8387e-01,  7.6190e-01],\n",
      "        [ 4.9253e-01,  6.1672e-01],\n",
      "        [ 5.2875e-01,  3.9895e-01],\n",
      "        [ 6.0119e-01,  2.5377e-01],\n",
      "        [ 6.3741e-01,  7.2300e-02],\n",
      "        [ 6.3741e-01, -1.4547e-01],\n",
      "        [-4.8544e-01, -4.3583e-01],\n",
      "        [-3.0433e-01, -4.3583e-01],\n",
      "        [-1.5945e-01, -4.7213e-01],\n",
      "        [-5.0785e-02, -3.9954e-01],\n",
      "        [ 5.7877e-02, -3.9954e-01],\n",
      "        [ 4.2009e-01, -3.9954e-01],\n",
      "        [ 5.2875e-01, -4.7213e-01],\n",
      "        [ 6.0119e-01, -4.7213e-01],\n",
      "        [ 7.0985e-01, -3.9954e-01],\n",
      "        [ 7.0985e-01, -3.6324e-01],\n",
      "        [ 2.7520e-01, -1.8177e-01],\n",
      "        [ 3.1142e-01, -3.6585e-02],\n",
      "        [ 3.8387e-01,  1.4489e-01],\n",
      "        [ 3.1142e-01,  2.5377e-01],\n",
      "        [ 5.7877e-02,  2.9007e-01],\n",
      "        [ 1.6654e-01,  2.9007e-01],\n",
      "        [ 2.0276e-01,  3.2636e-01],\n",
      "        [ 2.7520e-01,  2.9007e-01],\n",
      "        [ 3.1142e-01,  2.9007e-01],\n",
      "        [-3.0433e-01, -2.5436e-01],\n",
      "        [-1.9567e-01, -2.5436e-01],\n",
      "        [-1.2323e-01, -2.5436e-01],\n",
      "        [-5.0785e-02, -2.5436e-01],\n",
      "        [-1.2323e-01, -2.1806e-01],\n",
      "        [-1.9567e-01, -2.1806e-01],\n",
      "        [ 3.8387e-01, -1.8177e-01],\n",
      "        [ 4.9253e-01, -2.5436e-01],\n",
      "        [ 5.2875e-01, -2.5436e-01],\n",
      "        [ 5.2875e-01, -1.8177e-01],\n",
      "        [ 5.2875e-01, -1.8177e-01],\n",
      "        [ 4.2009e-01, -1.8177e-01],\n",
      "        [-3.0433e-01,  4.3525e-01],\n",
      "        [-1.2323e-01,  4.3525e-01],\n",
      "        [ 9.4098e-02,  4.3525e-01],\n",
      "        [ 1.6654e-01,  4.3525e-01],\n",
      "        [ 2.7520e-01,  4.3525e-01],\n",
      "        [ 3.1142e-01,  4.3525e-01],\n",
      "        [ 3.8387e-01,  4.7154e-01],\n",
      "        [ 3.1142e-01,  5.4413e-01],\n",
      "        [ 2.7520e-01,  6.1672e-01],\n",
      "        [ 1.6654e-01,  6.1672e-01],\n",
      "        [ 5.7877e-02,  6.1672e-01],\n",
      "        [-1.2323e-01,  5.4413e-01],\n",
      "        [-2.6811e-01,  4.3525e-01],\n",
      "        [ 5.7877e-02,  5.0784e-01],\n",
      "        [ 1.6654e-01,  5.0784e-01],\n",
      "        [ 2.0276e-01,  5.0784e-01],\n",
      "        [ 3.8387e-01,  4.7154e-01],\n",
      "        [ 2.0276e-01,  5.0784e-01],\n",
      "        [ 1.6654e-01,  5.0784e-01],\n",
      "        [ 5.7877e-02,  5.0784e-01]], dtype=torch.float64)\n",
      "51 torch.Size([1, 224, 224]) tensor([[-9.8962e-01, -8.5733e-01],\n",
      "        [-9.8962e-01, -5.9055e-01],\n",
      "        [-9.1331e-01, -3.6188e-01],\n",
      "        [-8.3700e-01, -9.5091e-02],\n",
      "        [-7.7976e-01,  1.9075e-01],\n",
      "        [-6.2714e-01,  4.0037e-01],\n",
      "        [-4.9359e-01,  5.5281e-01],\n",
      "        [-2.8373e-01,  7.6243e-01],\n",
      "        [ 7.8755e-02,  8.3865e-01],\n",
      "        [ 4.2216e-01,  7.8149e-01],\n",
      "        [ 6.3202e-01,  6.2904e-01],\n",
      "        [ 8.4188e-01,  4.9565e-01],\n",
      "        [ 9.9451e-01,  2.8603e-01],\n",
      "        [ 1.0517e+00,  1.9245e-02],\n",
      "        [ 1.1281e+00, -1.9037e-01],\n",
      "        [ 1.2044e+00, -4.7621e-01],\n",
      "        [ 1.2616e+00, -7.6205e-01],\n",
      "        [-7.7976e-01, -1.0669e+00],\n",
      "        [-6.2714e-01, -1.1432e+00],\n",
      "        [-4.9359e-01, -1.1432e+00],\n",
      "        [-2.8373e-01, -1.1241e+00],\n",
      "        [-1.3111e-01, -1.0669e+00],\n",
      "        [ 4.2216e-01, -1.0479e+00],\n",
      "        [ 5.5571e-01, -1.0479e+00],\n",
      "        [ 7.0833e-01, -1.0479e+00],\n",
      "        [ 9.1819e-01, -9.7167e-01],\n",
      "        [ 1.0517e+00, -8.9544e-01],\n",
      "        [ 7.8755e-02, -7.0488e-01],\n",
      "        [ 7.8755e-02, -4.9527e-01],\n",
      "        [ 7.8755e-02, -3.6188e-01],\n",
      "        [ 7.8755e-02, -1.5226e-01],\n",
      "        [-1.3111e-01, -7.6035e-02],\n",
      "        [-7.3871e-02, -7.6035e-02],\n",
      "        [ 7.8755e-02,  1.8930e-04],\n",
      "        [ 1.3599e-01, -7.6035e-02],\n",
      "        [ 2.1230e-01, -7.6035e-02],\n",
      "        [-5.6990e-01, -7.8111e-01],\n",
      "        [-4.9359e-01, -8.5733e-01],\n",
      "        [-3.6004e-01, -7.8111e-01],\n",
      "        [-2.0742e-01, -7.8111e-01],\n",
      "        [-3.6004e-01, -7.0488e-01],\n",
      "        [-4.9359e-01, -7.0488e-01],\n",
      "        [ 4.2216e-01, -7.0488e-01],\n",
      "        [ 5.5571e-01, -7.6205e-01],\n",
      "        [ 7.0833e-01, -7.6205e-01],\n",
      "        [ 7.6557e-01, -6.8583e-01],\n",
      "        [ 7.0833e-01, -7.0488e-01],\n",
      "        [ 5.5571e-01, -7.0488e-01],\n",
      "        [-3.6004e-01,  2.0981e-01],\n",
      "        [-2.0742e-01,  2.0981e-01],\n",
      "        [-7.3871e-02,  1.3358e-01],\n",
      "        [ 7.8755e-02,  2.0981e-01],\n",
      "        [ 1.3599e-01,  1.3358e-01],\n",
      "        [ 2.8861e-01,  2.0981e-01],\n",
      "        [ 4.2216e-01,  2.8603e-01],\n",
      "        [ 2.8861e-01,  2.8603e-01],\n",
      "        [ 1.3599e-01,  3.4320e-01],\n",
      "        [ 2.4420e-03,  3.4320e-01],\n",
      "        [-7.3871e-02,  3.4320e-01],\n",
      "        [-2.0742e-01,  2.6697e-01],\n",
      "        [-2.8373e-01,  2.0981e-01],\n",
      "        [-7.3871e-02,  2.0981e-01],\n",
      "        [ 7.8755e-02,  2.0981e-01],\n",
      "        [ 1.3599e-01,  2.8603e-01],\n",
      "        [ 4.2216e-01,  2.0981e-01],\n",
      "        [ 1.3599e-01,  2.0981e-01],\n",
      "        [ 7.8755e-02,  2.0981e-01],\n",
      "        [-7.3871e-02,  2.0981e-01]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 torch.Size([1, 224, 224]) tensor([[-7.8116e-01, -1.8076e-01],\n",
      "        [-7.8116e-01,  1.1505e-01],\n",
      "        [-7.8116e-01,  3.1985e-01],\n",
      "        [-7.1283e-01,  5.2464e-01],\n",
      "        [-6.4450e-01,  7.9769e-01],\n",
      "        [-5.0784e-01,  1.0252e+00],\n",
      "        [-3.7117e-01,  1.2300e+00],\n",
      "        [-1.6618e-01,  1.3666e+00],\n",
      "        [ 1.7547e-01,  1.5258e+00],\n",
      "        [ 5.3991e-01,  1.4576e+00],\n",
      "        [ 8.1323e-01,  1.3211e+00],\n",
      "        [ 1.0182e+00,  1.1845e+00],\n",
      "        [ 1.2232e+00,  9.7973e-01],\n",
      "        [ 1.2915e+00,  7.0668e-01],\n",
      "        [ 1.3827e+00,  4.1086e-01],\n",
      "        [ 1.4510e+00,  1.3781e-01],\n",
      "        [ 1.5193e+00, -1.3525e-01],\n",
      "        [-6.4450e-01, -3.8555e-01],\n",
      "        [-5.7617e-01, -3.8555e-01],\n",
      "        [-3.7117e-01, -3.8555e-01],\n",
      "        [-2.3451e-01, -3.6279e-01],\n",
      "        [-9.7850e-02, -3.6279e-01],\n",
      "        [ 4.7157e-01, -2.9453e-01],\n",
      "        [ 6.0824e-01, -3.6279e-01],\n",
      "        [ 8.1323e-01, -3.6279e-01],\n",
      "        [ 1.0182e+00, -2.9453e-01],\n",
      "        [ 1.1549e+00, -2.0351e-01],\n",
      "        [ 1.7547e-01, -8.9738e-02],\n",
      "        [ 1.7547e-01,  1.1505e-01],\n",
      "        [ 1.0714e-01,  2.5158e-01],\n",
      "        [ 1.0714e-01,  3.8811e-01],\n",
      "        [-2.9519e-02,  4.5637e-01],\n",
      "        [ 3.8812e-02,  4.5637e-01],\n",
      "        [ 1.0714e-01,  5.4739e-01],\n",
      "        [ 2.6658e-01,  4.7913e-01],\n",
      "        [ 4.0324e-01,  4.7913e-01],\n",
      "        [-4.3950e-01, -1.5800e-01],\n",
      "        [-3.7117e-01, -1.5800e-01],\n",
      "        [-2.3451e-01, -1.5800e-01],\n",
      "        [-9.7850e-02, -8.9738e-02],\n",
      "        [-2.3451e-01, -2.1474e-02],\n",
      "        [-3.7117e-01, -8.9738e-02],\n",
      "        [ 5.3991e-01, -8.9738e-02],\n",
      "        [ 6.7657e-01, -8.9738e-02],\n",
      "        [ 8.1323e-01, -6.6983e-02],\n",
      "        [ 9.4989e-01, -6.6983e-02],\n",
      "        [ 8.1323e-01,  1.2809e-03],\n",
      "        [ 6.7657e-01, -2.1474e-02],\n",
      "        [-3.0284e-01,  6.6117e-01],\n",
      "        [-1.6618e-01,  6.8392e-01],\n",
      "        [ 3.8812e-02,  6.1566e-01],\n",
      "        [ 1.0714e-01,  6.1566e-01],\n",
      "        [ 2.6658e-01,  6.1566e-01],\n",
      "        [ 4.7157e-01,  6.8392e-01],\n",
      "        [ 6.7657e-01,  7.5219e-01],\n",
      "        [ 4.7157e-01,  9.5698e-01],\n",
      "        [ 3.3491e-01,  1.0252e+00],\n",
      "        [ 1.0714e-01,  1.0252e+00],\n",
      "        [-2.9519e-02,  1.0252e+00],\n",
      "        [-1.6618e-01,  8.8871e-01],\n",
      "        [-3.0284e-01,  6.6117e-01],\n",
      "        [-2.9519e-02,  6.8392e-01],\n",
      "        [ 1.0714e-01,  6.8392e-01],\n",
      "        [ 3.3491e-01,  6.8392e-01],\n",
      "        [ 6.7657e-01,  7.5219e-01],\n",
      "        [ 3.3491e-01,  8.8871e-01],\n",
      "        [ 1.0714e-01,  8.8871e-01],\n",
      "        [-2.9519e-02,  8.8871e-01]], dtype=torch.float64)\n",
      "53 torch.Size([1, 224, 224]) tensor([[-0.2358, -0.0705],\n",
      "        [-0.3460,  0.1680],\n",
      "        [-0.3460,  0.3880],\n",
      "        [-0.2909,  0.6081],\n",
      "        [-0.2909,  0.8282],\n",
      "        [-0.2909,  1.0666],\n",
      "        [-0.2358,  1.1766],\n",
      "        [-0.1807,  1.3417],\n",
      "        [ 0.0398,  1.4517],\n",
      "        [ 0.3888,  1.4701],\n",
      "        [ 0.6644,  1.4151],\n",
      "        [ 0.8848,  1.3600],\n",
      "        [ 1.1053,  1.1950],\n",
      "        [ 1.2890,  0.9749],\n",
      "        [ 1.3993,  0.7548],\n",
      "        [ 1.5095,  0.5347],\n",
      "        [ 1.5646,  0.2963],\n",
      "        [-0.2358, -0.3456],\n",
      "        [-0.1807, -0.4006],\n",
      "        [-0.0705, -0.4556],\n",
      "        [ 0.0398, -0.4006],\n",
      "        [ 0.0949, -0.3456],\n",
      "        [ 0.5542, -0.2172],\n",
      "        [ 0.6644, -0.2722],\n",
      "        [ 0.8297, -0.2722],\n",
      "        [ 0.9951, -0.2172],\n",
      "        [ 1.1604, -0.0338],\n",
      "        [ 0.2602,  0.0029],\n",
      "        [ 0.2051,  0.1680],\n",
      "        [ 0.1500,  0.2780],\n",
      "        [ 0.0949,  0.3880],\n",
      "        [ 0.0398,  0.4981],\n",
      "        [ 0.0398,  0.5531],\n",
      "        [ 0.1500,  0.5531],\n",
      "        [ 0.2051,  0.5714],\n",
      "        [ 0.3337,  0.5714],\n",
      "        [-0.1256, -0.1071],\n",
      "        [-0.0705, -0.1071],\n",
      "        [ 0.0398, -0.1071],\n",
      "        [ 0.1500, -0.0521],\n",
      "        [ 0.0398,  0.0029],\n",
      "        [-0.0705, -0.0521],\n",
      "        [ 0.5542,  0.0579],\n",
      "        [ 0.6644,  0.0029],\n",
      "        [ 0.7746,  0.0579],\n",
      "        [ 0.9400,  0.1313],\n",
      "        [ 0.7746,  0.1313],\n",
      "        [ 0.6644,  0.1129],\n",
      "        [-0.1256,  0.8465],\n",
      "        [-0.0705,  0.7181],\n",
      "        [ 0.0398,  0.6815],\n",
      "        [ 0.0949,  0.7365],\n",
      "        [ 0.1500,  0.7365],\n",
      "        [ 0.3337,  0.7915],\n",
      "        [ 0.4439,  0.9566],\n",
      "        [ 0.2602,  1.0116],\n",
      "        [ 0.1500,  1.0666],\n",
      "        [ 0.0398,  1.0666],\n",
      "        [-0.0705,  1.0116],\n",
      "        [-0.1256,  0.9566],\n",
      "        [-0.1256,  0.8465],\n",
      "        [-0.0154,  0.7915],\n",
      "        [ 0.0949,  0.7915],\n",
      "        [ 0.1500,  0.7915],\n",
      "        [ 0.3888,  0.9566],\n",
      "        [ 0.1500,  0.9566],\n",
      "        [ 0.0398,  0.9566],\n",
      "        [-0.0154,  0.9015]], dtype=torch.float64)\n",
      "54 torch.Size([1, 224, 224]) tensor([[-0.5233, -0.0598],\n",
      "        [-0.5233,  0.1907],\n",
      "        [-0.5233,  0.4412],\n",
      "        [-0.5233,  0.6917],\n",
      "        [-0.5233,  0.8922],\n",
      "        [-0.4731,  1.1427],\n",
      "        [-0.3728,  1.2930],\n",
      "        [-0.2223,  1.4934],\n",
      "        [ 0.0787,  1.5936],\n",
      "        [ 0.3796,  1.5936],\n",
      "        [ 0.6806,  1.4934],\n",
      "        [ 0.9314,  1.3932],\n",
      "        [ 1.1320,  1.1928],\n",
      "        [ 1.2323,  0.9423],\n",
      "        [ 1.3327,  0.6917],\n",
      "        [ 1.4330,  0.4412],\n",
      "        [ 1.4831,  0.1907],\n",
      "        [-0.4230, -0.2602],\n",
      "        [-0.3728, -0.3103],\n",
      "        [-0.2223, -0.2602],\n",
      "        [-0.1220, -0.2101],\n",
      "        [ 0.0285, -0.1099],\n",
      "        [ 0.5301, -0.0097],\n",
      "        [ 0.6304, -0.0097],\n",
      "        [ 0.7809, -0.0097],\n",
      "        [ 0.9314,  0.0905],\n",
      "        [ 1.1320,  0.1406],\n",
      "        [ 0.1790,  0.2408],\n",
      "        [ 0.1288,  0.4412],\n",
      "        [ 0.0787,  0.5915],\n",
      "        [ 0.0787,  0.7920],\n",
      "        [-0.0217,  0.7920],\n",
      "        [ 0.0285,  0.7920],\n",
      "        [ 0.1288,  0.8421],\n",
      "        [ 0.1790,  0.8421],\n",
      "        [ 0.3295,  0.8421],\n",
      "        [-0.2725, -0.0097],\n",
      "        [-0.2223,  0.0404],\n",
      "        [-0.1220,  0.0404],\n",
      "        [ 0.0285,  0.1406],\n",
      "        [-0.1220,  0.1406],\n",
      "        [-0.2223,  0.1406],\n",
      "        [ 0.5301,  0.2408],\n",
      "        [ 0.6304,  0.2408],\n",
      "        [ 0.7809,  0.3410],\n",
      "        [ 0.8812,  0.3410],\n",
      "        [ 0.7809,  0.3911],\n",
      "        [ 0.6304,  0.2909],\n",
      "        [-0.1722,  0.9924],\n",
      "        [-0.1220,  0.9924],\n",
      "        [ 0.0285,  0.9924],\n",
      "        [ 0.0787,  1.0425],\n",
      "        [ 0.1288,  1.0425],\n",
      "        [ 0.3295,  1.0425],\n",
      "        [ 0.4298,  1.0926],\n",
      "        [ 0.2793,  1.1928],\n",
      "        [ 0.1790,  1.2429],\n",
      "        [ 0.0787,  1.2429],\n",
      "        [-0.0217,  1.1928],\n",
      "        [-0.1220,  1.0926],\n",
      "        [-0.1722,  0.9924],\n",
      "        [ 0.0285,  1.0425],\n",
      "        [ 0.0787,  1.0926],\n",
      "        [ 0.1790,  1.0926],\n",
      "        [ 0.4298,  1.0926],\n",
      "        [ 0.1790,  1.0926],\n",
      "        [ 0.0787,  1.0926],\n",
      "        [ 0.0285,  1.0425]], dtype=torch.float64)\n",
      "55 torch.Size([1, 224, 224]) tensor([[-6.5651e-01, -6.3602e-01],\n",
      "        [-7.0903e-01, -4.5251e-01],\n",
      "        [-7.0903e-01, -2.4278e-01],\n",
      "        [-6.5651e-01, -5.9271e-02],\n",
      "        [-6.5651e-01,  9.8024e-02],\n",
      "        [-5.5147e-01,  2.5532e-01],\n",
      "        [-4.9895e-01,  3.8640e-01],\n",
      "        [-4.4643e-01,  4.9126e-01],\n",
      "        [-2.6261e-01,  5.9612e-01],\n",
      "        [ 2.5377e-16,  5.4369e-01],\n",
      "        [ 2.3634e-01,  4.3883e-01],\n",
      "        [ 3.9391e-01,  3.3397e-01],\n",
      "        [ 5.5147e-01,  2.2910e-01],\n",
      "        [ 6.8277e-01,  1.9377e-02],\n",
      "        [ 7.3529e-01, -1.6413e-01],\n",
      "        [ 7.8782e-01, -3.7386e-01],\n",
      "        [ 8.4034e-01, -5.5737e-01],\n",
      "        [-7.0903e-01, -8.9818e-01],\n",
      "        [-6.5651e-01, -9.5061e-01],\n",
      "        [-6.0399e-01, -9.5061e-01],\n",
      "        [-5.5147e-01, -9.5061e-01],\n",
      "        [-4.9895e-01, -8.9818e-01],\n",
      "        [-1.0504e-01, -8.9818e-01],\n",
      "        [ 2.5377e-16, -8.9818e-01],\n",
      "        [ 1.0504e-01, -8.9818e-01],\n",
      "        [ 2.8887e-01, -8.4574e-01],\n",
      "        [ 3.9391e-01, -7.1467e-01],\n",
      "        [-3.4139e-01, -6.0980e-01],\n",
      "        [-3.4139e-01, -4.5251e-01],\n",
      "        [-3.9391e-01, -3.4764e-01],\n",
      "        [-3.9391e-01, -2.4278e-01],\n",
      "        [-4.4643e-01, -1.9035e-01],\n",
      "        [-3.9391e-01, -1.1170e-01],\n",
      "        [-3.4139e-01, -1.1170e-01],\n",
      "        [-2.6261e-01, -1.1170e-01],\n",
      "        [-2.1008e-01, -1.1170e-01],\n",
      "        [-6.0399e-01, -6.8845e-01],\n",
      "        [-6.0399e-01, -7.4088e-01],\n",
      "        [-4.9895e-01, -6.8845e-01],\n",
      "        [-3.9391e-01, -6.0980e-01],\n",
      "        [-4.9895e-01, -6.3602e-01],\n",
      "        [-5.5147e-01, -6.3602e-01],\n",
      "        [-5.2521e-02, -6.0980e-01],\n",
      "        [ 2.5377e-16, -6.0980e-01],\n",
      "        [ 1.0504e-01, -6.0980e-01],\n",
      "        [ 2.3634e-01, -5.5737e-01],\n",
      "        [ 1.0504e-01, -5.5737e-01],\n",
      "        [ 2.5377e-16, -5.5737e-01],\n",
      "        [-4.9895e-01,  9.8024e-02],\n",
      "        [-4.9895e-01,  4.5593e-02],\n",
      "        [-3.9391e-01, -6.8389e-03],\n",
      "        [-3.4139e-01, -6.8389e-03],\n",
      "        [-2.6261e-01, -6.8389e-03],\n",
      "        [-1.5756e-01,  4.5593e-02],\n",
      "        [-5.2521e-02,  1.5046e-01],\n",
      "        [-1.5756e-01,  2.2910e-01],\n",
      "        [-2.6261e-01,  2.0289e-01],\n",
      "        [-3.4139e-01,  2.0289e-01],\n",
      "        [-4.4643e-01,  2.0289e-01],\n",
      "        [-4.9895e-01,  1.5046e-01],\n",
      "        [-4.9895e-01,  9.8024e-02],\n",
      "        [-3.9391e-01,  9.8024e-02],\n",
      "        [-3.4139e-01,  9.8024e-02],\n",
      "        [-2.6261e-01,  9.8024e-02],\n",
      "        [-1.0504e-01,  1.5046e-01],\n",
      "        [-2.6261e-01,  9.8024e-02],\n",
      "        [-3.4139e-01,  9.8024e-02],\n",
      "        [-3.9391e-01,  9.8024e-02]], dtype=torch.float64)\n",
      "56 torch.Size([1, 224, 224]) tensor([[-1.0536,  0.1429],\n",
      "        [-1.0179,  0.3571],\n",
      "        [-0.9464,  0.5357],\n",
      "        [-0.9107,  0.7500],\n",
      "        [-0.8393,  1.0357],\n",
      "        [-0.7321,  1.1786],\n",
      "        [-0.6250,  1.3571],\n",
      "        [-0.4821,  1.5000],\n",
      "        [-0.2321,  1.5714],\n",
      "        [ 0.0536,  1.4643],\n",
      "        [ 0.1964,  1.3571],\n",
      "        [ 0.3036,  1.2143],\n",
      "        [ 0.4107,  1.0000],\n",
      "        [ 0.5179,  0.7857],\n",
      "        [ 0.5179,  0.5714],\n",
      "        [ 0.5893,  0.3571],\n",
      "        [ 0.5893,  0.1071],\n",
      "        [-0.9107, -0.0714],\n",
      "        [-0.8036, -0.1786],\n",
      "        [-0.6250, -0.1786],\n",
      "        [-0.5179, -0.1786],\n",
      "        [-0.4107, -0.1071],\n",
      "        [-0.0179, -0.1071],\n",
      "        [ 0.0893, -0.1786],\n",
      "        [ 0.1964, -0.1786],\n",
      "        [ 0.3750, -0.1786],\n",
      "        [ 0.4107, -0.1071],\n",
      "        [-0.2321,  0.1429],\n",
      "        [-0.2321,  0.2857],\n",
      "        [-0.2321,  0.4643],\n",
      "        [-0.2321,  0.5714],\n",
      "        [-0.3036,  0.6786],\n",
      "        [-0.2679,  0.7143],\n",
      "        [-0.2321,  0.7143],\n",
      "        [-0.1250,  0.6786],\n",
      "        [-0.0536,  0.6786],\n",
      "        [-0.6964,  0.1429],\n",
      "        [-0.6250,  0.1429],\n",
      "        [-0.5179,  0.1429],\n",
      "        [-0.4107,  0.1429],\n",
      "        [-0.5179,  0.1786],\n",
      "        [-0.6250,  0.1786],\n",
      "        [-0.0179,  0.1429],\n",
      "        [ 0.0893,  0.1071],\n",
      "        [ 0.1964,  0.1071],\n",
      "        [ 0.2679,  0.1429],\n",
      "        [ 0.1964,  0.1429],\n",
      "        [ 0.0893,  0.1429],\n",
      "        [-0.4821,  1.0357],\n",
      "        [-0.3750,  0.9286],\n",
      "        [-0.2679,  0.8929],\n",
      "        [-0.2321,  0.8929],\n",
      "        [-0.1607,  0.8929],\n",
      "        [-0.0536,  0.9286],\n",
      "        [-0.0179,  1.0357],\n",
      "        [-0.0536,  1.1429],\n",
      "        [-0.1250,  1.1786],\n",
      "        [-0.2321,  1.1786],\n",
      "        [-0.3036,  1.1786],\n",
      "        [-0.3750,  1.1429],\n",
      "        [-0.4821,  1.0357],\n",
      "        [-0.2679,  0.9286],\n",
      "        [-0.2321,  0.9286],\n",
      "        [-0.1607,  0.9286],\n",
      "        [-0.0179,  1.0357],\n",
      "        [-0.1250,  1.0714],\n",
      "        [-0.2321,  1.0714],\n",
      "        [-0.3036,  1.0714]], dtype=torch.float64)\n",
      "57 torch.Size([1, 224, 224]) tensor([[-0.7217,  0.1322],\n",
      "        [-0.6447,  0.4136],\n",
      "        [-0.5934,  0.6695],\n",
      "        [-0.5164,  0.9253],\n",
      "        [-0.4651,  1.1300],\n",
      "        [-0.3368,  1.3346],\n",
      "        [-0.1316,  1.4626],\n",
      "        [ 0.0737,  1.5393],\n",
      "        [ 0.4072,  1.6161],\n",
      "        [ 0.7151,  1.5393],\n",
      "        [ 0.9204,  1.4114],\n",
      "        [ 1.1256,  1.2835],\n",
      "        [ 1.2539,  1.1044],\n",
      "        [ 1.3309,  0.8230],\n",
      "        [ 1.3822,  0.6439],\n",
      "        [ 1.3822,  0.3625],\n",
      "        [ 1.4592,  0.1066],\n",
      "        [-0.5164, -0.1748],\n",
      "        [-0.3881, -0.3027],\n",
      "        [-0.2599, -0.3795],\n",
      "        [-0.1316, -0.3027],\n",
      "        [ 0.0737, -0.3027],\n",
      "        [ 0.5868, -0.3027],\n",
      "        [ 0.7151, -0.3539],\n",
      "        [ 0.8690, -0.3539],\n",
      "        [ 1.0486, -0.2771],\n",
      "        [ 1.1769, -0.2260],\n",
      "        [ 0.3303, -0.0469],\n",
      "        [ 0.3303,  0.1066],\n",
      "        [ 0.3303,  0.2857],\n",
      "        [ 0.3303,  0.4136],\n",
      "        [ 0.1250,  0.5416],\n",
      "        [ 0.2789,  0.6183],\n",
      "        [ 0.3303,  0.6183],\n",
      "        [ 0.4585,  0.5671],\n",
      "        [ 0.5355,  0.5671],\n",
      "        [-0.3368,  0.0299],\n",
      "        [-0.1829, -0.0469],\n",
      "        [-0.0546, -0.0469],\n",
      "        [ 0.0737,  0.0299],\n",
      "        [-0.0546,  0.0299],\n",
      "        [-0.1829,  0.0810],\n",
      "        [ 0.5868, -0.0213],\n",
      "        [ 0.7151, -0.0980],\n",
      "        [ 0.8690, -0.0980],\n",
      "        [ 0.9973, -0.0213],\n",
      "        [ 0.8690,  0.0299],\n",
      "        [ 0.7151,  0.0299],\n",
      "        [-0.0033,  1.0021],\n",
      "        [ 0.1250,  0.8741],\n",
      "        [ 0.2789,  0.7462],\n",
      "        [ 0.3303,  0.8230],\n",
      "        [ 0.4072,  0.7462],\n",
      "        [ 0.5868,  0.8230],\n",
      "        [ 0.6638,  0.9509],\n",
      "        [ 0.5355,  1.0788],\n",
      "        [ 0.4585,  1.1556],\n",
      "        [ 0.3303,  1.1556],\n",
      "        [ 0.2020,  1.1300],\n",
      "        [ 0.1250,  1.0788],\n",
      "        [-0.0033,  0.9509],\n",
      "        [ 0.2789,  0.8741],\n",
      "        [ 0.3303,  0.8741],\n",
      "        [ 0.4585,  0.8741],\n",
      "        [ 0.6638,  0.9509],\n",
      "        [ 0.4585,  1.0021],\n",
      "        [ 0.3303,  1.0021],\n",
      "        [ 0.2789,  1.0021]], dtype=torch.float64)\n",
      "58 torch.Size([1, 224, 224]) tensor([[-0.3654,  0.3184],\n",
      "        [-0.3654,  0.5460],\n",
      "        [-0.3002,  0.7410],\n",
      "        [-0.2350,  0.9686],\n",
      "        [-0.2024,  1.1961],\n",
      "        [-0.0721,  1.3911],\n",
      "        [-0.0069,  1.4887],\n",
      "        [ 0.0908,  1.5537],\n",
      "        [ 0.3841,  1.5537],\n",
      "        [ 0.6774,  1.4562],\n",
      "        [ 0.9707,  1.3261],\n",
      "        [ 1.1336,  1.1636],\n",
      "        [ 1.3291,  1.0011],\n",
      "        [ 1.4269,  0.7085],\n",
      "        [ 1.4269,  0.5460],\n",
      "        [ 1.4269,  0.2534],\n",
      "        [ 1.4269,  0.0259],\n",
      "        [-0.3654,  0.0259],\n",
      "        [-0.3654, -0.0716],\n",
      "        [-0.2350, -0.1367],\n",
      "        [-0.2024, -0.1367],\n",
      "        [-0.0721, -0.1367],\n",
      "        [ 0.3190, -0.1692],\n",
      "        [ 0.4493, -0.2342],\n",
      "        [ 0.5471, -0.2342],\n",
      "        [ 0.7426, -0.2342],\n",
      "        [ 0.9055, -0.2342],\n",
      "        [ 0.1560,  0.0909],\n",
      "        [ 0.0908,  0.2209],\n",
      "        [ 0.0908,  0.3834],\n",
      "        [ 0.0908,  0.5135],\n",
      "        [ 0.0908,  0.6110],\n",
      "        [ 0.0908,  0.6760],\n",
      "        [ 0.1560,  0.6760],\n",
      "        [ 0.2864,  0.6435],\n",
      "        [ 0.3190,  0.6435],\n",
      "        [-0.2350,  0.1559],\n",
      "        [-0.2024,  0.0909],\n",
      "        [-0.0721,  0.0909],\n",
      "        [-0.0069,  0.1559],\n",
      "        [-0.0721,  0.1559],\n",
      "        [-0.2024,  0.2209],\n",
      "        [ 0.4493,  0.0584],\n",
      "        [ 0.5145, -0.0066],\n",
      "        [ 0.6122, -0.0066],\n",
      "        [ 0.7426, -0.0066],\n",
      "        [ 0.6122,  0.0584],\n",
      "        [ 0.5145,  0.0584],\n",
      "        [-0.0069,  0.9686],\n",
      "        [ 0.0257,  0.9036],\n",
      "        [ 0.1560,  0.8710],\n",
      "        [ 0.2212,  0.8710],\n",
      "        [ 0.2864,  0.8060],\n",
      "        [ 0.3841,  0.8710],\n",
      "        [ 0.5471,  0.8710],\n",
      "        [ 0.3841,  0.9361],\n",
      "        [ 0.3190,  0.9686],\n",
      "        [ 0.2212,  0.9686],\n",
      "        [ 0.1560,  1.0336],\n",
      "        [ 0.0908,  0.9686],\n",
      "        [ 0.0257,  0.9686],\n",
      "        [ 0.1560,  0.9036],\n",
      "        [ 0.2212,  0.9036],\n",
      "        [ 0.3190,  0.9036],\n",
      "        [ 0.5145,  0.8710],\n",
      "        [ 0.2864,  0.8710],\n",
      "        [ 0.2212,  0.9036],\n",
      "        [ 0.1560,  0.9036]], dtype=torch.float64)\n",
      "59 torch.Size([1, 224, 224]) tensor([[-1.4286, -0.4474],\n",
      "        [-1.4286, -0.1506],\n",
      "        [-1.4286,  0.0613],\n",
      "        [-1.3435,  0.3157],\n",
      "        [-1.2160,  0.6125],\n",
      "        [-0.9609,  0.9092],\n",
      "        [-0.7483,  1.0788],\n",
      "        [-0.3656,  1.2908],\n",
      "        [ 0.0170,  1.3755],\n",
      "        [ 0.3146,  1.3332],\n",
      "        [ 0.3997,  1.1636],\n",
      "        [ 0.4422,  0.9516],\n",
      "        [ 0.5272,  0.6972],\n",
      "        [ 0.6973,  0.4853],\n",
      "        [ 0.7823,  0.2733],\n",
      "        [ 0.7823, -0.0234],\n",
      "        [ 0.7823, -0.3202],\n",
      "        [-0.7483, -0.5746],\n",
      "        [-0.5357, -0.6594],\n",
      "        [-0.2806, -0.6594],\n",
      "        [-0.1531, -0.6594],\n",
      "        [ 0.0170, -0.5746],\n",
      "        [ 0.5272, -0.5746],\n",
      "        [ 0.6122, -0.5746],\n",
      "        [ 0.7823, -0.5746],\n",
      "        [ 0.8248, -0.5746],\n",
      "        [ 0.8248, -0.5746],\n",
      "        [ 0.2296, -0.2778],\n",
      "        [ 0.3146, -0.0234],\n",
      "        [ 0.3997,  0.1885],\n",
      "        [ 0.3997,  0.3157],\n",
      "        [ 0.0170,  0.3157],\n",
      "        [ 0.0595,  0.4005],\n",
      "        [ 0.2296,  0.4005],\n",
      "        [ 0.3146,  0.4005],\n",
      "        [ 0.3146,  0.3157],\n",
      "        [-0.5357, -0.3626],\n",
      "        [-0.3656, -0.4474],\n",
      "        [-0.2381, -0.4474],\n",
      "        [-0.1531, -0.3626],\n",
      "        [-0.2381, -0.2778],\n",
      "        [-0.3656, -0.2778],\n",
      "        [ 0.3997, -0.3626],\n",
      "        [ 0.5272, -0.3626],\n",
      "        [ 0.6122, -0.3202],\n",
      "        [ 0.6973, -0.3202],\n",
      "        [ 0.6122, -0.2778],\n",
      "        [ 0.5272, -0.2778],\n",
      "        [-0.3656,  0.6125],\n",
      "        [-0.1531,  0.6125],\n",
      "        [ 0.0595,  0.5701],\n",
      "        [ 0.1446,  0.5701],\n",
      "        [ 0.2296,  0.5701],\n",
      "        [ 0.3997,  0.6549],\n",
      "        [ 0.3997,  0.6549],\n",
      "        [ 0.3146,  0.7820],\n",
      "        [ 0.2296,  0.7820],\n",
      "        [ 0.1446,  0.8668],\n",
      "        [ 0.0170,  0.8668],\n",
      "        [-0.1531,  0.7820],\n",
      "        [-0.3656,  0.6125],\n",
      "        [ 0.0170,  0.6125],\n",
      "        [ 0.1446,  0.6549],\n",
      "        [ 0.2296,  0.6549],\n",
      "        [ 0.3997,  0.6549],\n",
      "        [ 0.2296,  0.6972],\n",
      "        [ 0.1446,  0.6972],\n",
      "        [ 0.0170,  0.6972]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60 torch.Size([1, 224, 224]) tensor([[-9.1470e-01, -6.5940e-01],\n",
      "        [-8.1400e-01, -4.5860e-01],\n",
      "        [-7.5806e-01, -2.5780e-01],\n",
      "        [-6.5736e-01, -5.7007e-02],\n",
      "        [-5.6785e-01,  8.8013e-02],\n",
      "        [-3.6645e-01,  2.4419e-01],\n",
      "        [-1.6506e-01,  3.4459e-01],\n",
      "        [ 8.1096e-02,  4.0036e-01],\n",
      "        [ 3.7200e-01,  4.4499e-01],\n",
      "        [ 6.2934e-01,  4.0036e-01],\n",
      "        [ 7.7479e-01,  3.1112e-01],\n",
      "        [ 8.1955e-01,  2.1072e-01],\n",
      "        [ 9.2025e-01,  9.9253e-03],\n",
      "        [ 9.7619e-01, -2.3549e-01],\n",
      "        [ 9.7619e-01, -4.2514e-01],\n",
      "        [ 9.7619e-01, -6.7055e-01],\n",
      "        [ 9.7619e-01, -8.7135e-01],\n",
      "        [-5.1190e-01, -1.1391e+00],\n",
      "        [-3.6645e-01, -1.2395e+00],\n",
      "        [-2.2100e-01, -1.2395e+00],\n",
      "        [-6.4357e-02, -1.2395e+00],\n",
      "        [ 3.6341e-02, -1.2283e+00],\n",
      "        [ 4.7270e-01, -1.2729e+00],\n",
      "        [ 5.7340e-01, -1.2729e+00],\n",
      "        [ 6.7410e-01, -1.2729e+00],\n",
      "        [ 8.1955e-01, -1.2729e+00],\n",
      "        [ 8.7549e-01, -1.1725e+00],\n",
      "        [ 2.8249e-01, -1.0275e+00],\n",
      "        [ 2.8249e-01, -9.3828e-01],\n",
      "        [ 3.2725e-01, -8.3789e-01],\n",
      "        [ 3.7200e-01, -6.8171e-01],\n",
      "        [ 1.8179e-01, -5.3669e-01],\n",
      "        [ 2.2655e-01, -5.3669e-01],\n",
      "        [ 3.2725e-01, -5.3669e-01],\n",
      "        [ 4.2794e-01, -5.3669e-01],\n",
      "        [ 4.7270e-01, -5.3669e-01],\n",
      "        [-3.1051e-01, -9.9406e-01],\n",
      "        [-2.2100e-01, -1.0387e+00],\n",
      "        [-1.2030e-01, -1.0387e+00],\n",
      "        [-1.9603e-02, -9.8291e-01],\n",
      "        [-6.4357e-02, -9.8291e-01],\n",
      "        [-2.2100e-01, -9.3828e-01],\n",
      "        [ 4.7270e-01, -1.0275e+00],\n",
      "        [ 5.7340e-01, -1.0721e+00],\n",
      "        [ 6.7410e-01, -1.0721e+00],\n",
      "        [ 7.7479e-01, -1.0275e+00],\n",
      "        [ 6.7410e-01, -1.0275e+00],\n",
      "        [ 5.7340e-01, -9.8291e-01],\n",
      "        [-6.4357e-02, -1.0163e-01],\n",
      "        [ 8.1096e-02, -2.4665e-01],\n",
      "        [ 2.8249e-01, -3.3589e-01],\n",
      "        [ 3.2725e-01, -3.3589e-01],\n",
      "        [ 4.2794e-01, -3.3589e-01],\n",
      "        [ 5.2864e-01, -2.9127e-01],\n",
      "        [ 6.2934e-01, -1.9087e-01],\n",
      "        [ 5.2864e-01, -4.5852e-02],\n",
      "        [ 4.7270e-01,  9.9253e-03],\n",
      "        [ 3.7200e-01,  9.9253e-03],\n",
      "        [ 2.2655e-01, -1.2301e-03],\n",
      "        [ 1.2585e-01, -4.5852e-02],\n",
      "        [-1.9603e-02, -1.4625e-01],\n",
      "        [ 2.2655e-01, -2.4665e-01],\n",
      "        [ 3.2725e-01, -2.9127e-01],\n",
      "        [ 4.2794e-01, -2.9127e-01],\n",
      "        [ 6.2934e-01, -1.9087e-01],\n",
      "        [ 4.2794e-01, -9.0473e-02],\n",
      "        [ 3.2725e-01, -9.0473e-02],\n",
      "        [ 2.2655e-01, -9.0473e-02]], dtype=torch.float64)\n",
      "61 torch.Size([1, 224, 224]) tensor([[-0.8526, -0.0885],\n",
      "        [-0.8070,  0.1393],\n",
      "        [-0.7387,  0.3670],\n",
      "        [-0.6931,  0.5491],\n",
      "        [-0.5565,  0.7769],\n",
      "        [-0.4426,  0.9590],\n",
      "        [-0.3287,  1.0729],\n",
      "        [-0.1465,  1.1412],\n",
      "        [ 0.1268,  1.2095],\n",
      "        [ 0.4229,  1.0957],\n",
      "        [ 0.6051,  0.9818],\n",
      "        [ 0.7874,  0.8680],\n",
      "        [ 0.9012,  0.6858],\n",
      "        [ 0.9468,  0.4581],\n",
      "        [ 1.0151,  0.2303],\n",
      "        [ 1.0151,  0.0026],\n",
      "        [ 1.0151, -0.2479],\n",
      "        [-0.8070, -0.4528],\n",
      "        [-0.7387, -0.6122],\n",
      "        [-0.5565, -0.6122],\n",
      "        [-0.4426, -0.6122],\n",
      "        [-0.3287, -0.5439],\n",
      "        [ 0.1268, -0.6122],\n",
      "        [ 0.2407, -0.6578],\n",
      "        [ 0.4229, -0.6578],\n",
      "        [ 0.5368, -0.6578],\n",
      "        [ 0.7190, -0.5439],\n",
      "        [-0.0326, -0.2023],\n",
      "        [-0.0326, -0.0202],\n",
      "        [-0.0326,  0.0937],\n",
      "        [-0.0326,  0.2759],\n",
      "        [-0.1465,  0.3897],\n",
      "        [-0.1009,  0.3897],\n",
      "        [-0.0326,  0.3897],\n",
      "        [ 0.0813,  0.3897],\n",
      "        [ 0.1268,  0.3214],\n",
      "        [-0.6248, -0.2023],\n",
      "        [-0.5109, -0.2706],\n",
      "        [-0.3970, -0.2706],\n",
      "        [-0.2832, -0.2023],\n",
      "        [-0.3970, -0.1340],\n",
      "        [-0.5109, -0.1340],\n",
      "        [ 0.1952, -0.2479],\n",
      "        [ 0.3090, -0.3162],\n",
      "        [ 0.4229, -0.3162],\n",
      "        [ 0.5368, -0.2934],\n",
      "        [ 0.4229, -0.2479],\n",
      "        [ 0.3090, -0.1796],\n",
      "        [-0.2148,  0.7313],\n",
      "        [-0.2148,  0.6175],\n",
      "        [-0.1009,  0.5719],\n",
      "        [-0.0326,  0.5719],\n",
      "        [ 0.0129,  0.5719],\n",
      "        [ 0.1952,  0.6175],\n",
      "        [ 0.2407,  0.6858],\n",
      "        [ 0.1952,  0.7996],\n",
      "        [ 0.1268,  0.8680],\n",
      "        [ 0.0129,  0.8452],\n",
      "        [-0.1009,  0.8452],\n",
      "        [-0.1465,  0.7996],\n",
      "        [-0.2148,  0.6858],\n",
      "        [-0.1009,  0.6858],\n",
      "        [ 0.0129,  0.6858],\n",
      "        [ 0.0813,  0.6858],\n",
      "        [ 0.2407,  0.6858],\n",
      "        [ 0.0813,  0.7313],\n",
      "        [ 0.0129,  0.7313],\n",
      "        [-0.1009,  0.7313]], dtype=torch.float64)\n",
      "62 torch.Size([1, 224, 224]) tensor([[-1.1439,  0.1772],\n",
      "        [-1.1439,  0.4421],\n",
      "        [-1.1439,  0.7335],\n",
      "        [-1.0642,  0.9984],\n",
      "        [-1.0111,  1.3428],\n",
      "        [-0.8782,  1.6077],\n",
      "        [-0.6656,  1.8196],\n",
      "        [-0.3999,  1.9520],\n",
      "        [ 0.0253,  2.0845],\n",
      "        [ 0.2910,  1.9785],\n",
      "        [ 0.5036,  1.8990],\n",
      "        [ 0.5568,  1.7666],\n",
      "        [ 0.7693,  1.5017],\n",
      "        [ 0.8491,  1.2368],\n",
      "        [ 0.9022,  1.0249],\n",
      "        [ 0.9819,  0.7600],\n",
      "        [ 0.9819,  0.4951],\n",
      "        [-0.6656,  0.1242],\n",
      "        [-0.5327,  0.0448],\n",
      "        [-0.3202,  0.0448],\n",
      "        [-0.1873,  0.0448],\n",
      "        [-0.0544,  0.1242],\n",
      "        [ 0.5036,  0.2037],\n",
      "        [ 0.6365,  0.2037],\n",
      "        [ 0.7693,  0.2037],\n",
      "        [ 0.8491,  0.2832],\n",
      "        [ 0.9022,  0.2832],\n",
      "        [ 0.2113,  0.4686],\n",
      "        [ 0.2113,  0.6805],\n",
      "        [ 0.2113,  0.8659],\n",
      "        [ 0.2113,  1.0249],\n",
      "        [-0.0544,  1.0779],\n",
      "        [ 0.0253,  1.0779],\n",
      "        [ 0.1582,  1.1573],\n",
      "        [ 0.2113,  1.1573],\n",
      "        [ 0.2910,  1.0779],\n",
      "        [-0.4530,  0.3362],\n",
      "        [-0.3999,  0.3362],\n",
      "        [-0.2404,  0.3362],\n",
      "        [-0.1076,  0.3891],\n",
      "        [-0.2404,  0.3891],\n",
      "        [-0.3999,  0.3891],\n",
      "        [ 0.4239,  0.4686],\n",
      "        [ 0.5568,  0.4156],\n",
      "        [ 0.6365,  0.4156],\n",
      "        [ 0.7693,  0.4686],\n",
      "        [ 0.6365,  0.4686],\n",
      "        [ 0.5036,  0.4686],\n",
      "        [-0.3202,  1.3428],\n",
      "        [-0.1076,  1.3428],\n",
      "        [ 0.0253,  1.3428],\n",
      "        [ 0.1582,  1.3428],\n",
      "        [ 0.2113,  1.3428],\n",
      "        [ 0.3707,  1.3428],\n",
      "        [ 0.4239,  1.4222],\n",
      "        [ 0.2910,  1.5017],\n",
      "        [ 0.2113,  1.5547],\n",
      "        [ 0.0784,  1.5547],\n",
      "        [-0.0544,  1.5547],\n",
      "        [-0.1076,  1.4752],\n",
      "        [-0.3202,  1.3428],\n",
      "        [ 0.0253,  1.4222],\n",
      "        [ 0.0784,  1.4222],\n",
      "        [ 0.2113,  1.4222],\n",
      "        [ 0.4239,  1.4222],\n",
      "        [ 0.2113,  1.4222],\n",
      "        [ 0.0784,  1.4222],\n",
      "        [ 0.0253,  1.4222]], dtype=torch.float64)\n",
      "63 torch.Size([1, 224, 224]) tensor([[-1.2684, -0.8512],\n",
      "        [-1.3148, -0.6419],\n",
      "        [-1.3148, -0.4559],\n",
      "        [-1.3148, -0.3164],\n",
      "        [-1.3148, -0.1071],\n",
      "        [-1.2220,  0.0789],\n",
      "        [-1.1756,  0.1951],\n",
      "        [-1.0132,  0.3346],\n",
      "        [-0.7811,  0.4509],\n",
      "        [-0.5258,  0.4509],\n",
      "        [-0.3866,  0.4044],\n",
      "        [-0.2242,  0.3579],\n",
      "        [-0.0849,  0.2649],\n",
      "        [ 0.0543,  0.1021],\n",
      "        [ 0.1239, -0.0839],\n",
      "        [ 0.2168, -0.2234],\n",
      "        [ 0.3096, -0.4327],\n",
      "        [-1.0596, -1.0372],\n",
      "        [-0.9203, -1.0837],\n",
      "        [-0.8275, -1.0837],\n",
      "        [-0.7347, -1.0837],\n",
      "        [-0.6187, -1.0372],\n",
      "        [-0.2242, -0.9442],\n",
      "        [-0.1313, -0.9209],\n",
      "        [-0.0385, -0.8744],\n",
      "        [ 0.0543, -0.8279],\n",
      "        [ 0.1703, -0.7349],\n",
      "        [-0.5258, -0.7349],\n",
      "        [-0.5723, -0.6419],\n",
      "        [-0.5723, -0.5024],\n",
      "        [-0.6187, -0.3862],\n",
      "        [-0.7811, -0.3397],\n",
      "        [-0.7347, -0.2932],\n",
      "        [-0.6187, -0.2932],\n",
      "        [-0.5723, -0.2932],\n",
      "        [-0.5258, -0.2932],\n",
      "        [-0.9203, -0.8512],\n",
      "        [-0.8739, -0.8512],\n",
      "        [-0.7811, -0.8512],\n",
      "        [-0.7347, -0.7814],\n",
      "        [-0.7811, -0.7814],\n",
      "        [-0.8739, -0.8047],\n",
      "        [-0.3402, -0.6884],\n",
      "        [-0.2242, -0.6884],\n",
      "        [-0.1313, -0.6884],\n",
      "        [-0.0385, -0.5722],\n",
      "        [-0.1313, -0.5954],\n",
      "        [-0.2242, -0.6419],\n",
      "        [-0.9668, -0.1071],\n",
      "        [-0.8739, -0.1536],\n",
      "        [-0.7347, -0.1536],\n",
      "        [-0.6651, -0.1536],\n",
      "        [-0.6187, -0.1536],\n",
      "        [-0.5258, -0.1071],\n",
      "        [-0.4330,  0.0091],\n",
      "        [-0.5723,  0.0556],\n",
      "        [-0.6187,  0.0556],\n",
      "        [-0.7347,  0.0556],\n",
      "        [-0.8275, -0.0141],\n",
      "        [-0.8739, -0.0606],\n",
      "        [-0.9203, -0.1071],\n",
      "        [-0.7811, -0.1071],\n",
      "        [-0.6651, -0.1071],\n",
      "        [-0.6187, -0.0606],\n",
      "        [-0.4794,  0.0091],\n",
      "        [-0.6187, -0.0606],\n",
      "        [-0.7347, -0.0606],\n",
      "        [-0.7811, -0.0606]], dtype=torch.float64)\n",
      "64 torch.Size([1, 224, 224]) tensor([[-0.7366, -0.3422],\n",
      "        [-0.7366, -0.0831],\n",
      "        [-0.7366,  0.1362],\n",
      "        [-0.6967,  0.2757],\n",
      "        [-0.6370,  0.5348],\n",
      "        [-0.5971,  0.7341],\n",
      "        [-0.5373,  0.8935],\n",
      "        [-0.3779,  1.0529],\n",
      "        [-0.1188,  1.1526],\n",
      "        [ 0.1204,  1.1127],\n",
      "        [ 0.3396,  1.0131],\n",
      "        [ 0.5389,  0.8536],\n",
      "        [ 0.6983,  0.7141],\n",
      "        [ 0.7382,  0.4550],\n",
      "        [ 0.8379,  0.2557],\n",
      "        [ 0.8976,  0.0963],\n",
      "        [ 0.9375, -0.1628],\n",
      "        [-0.6967, -0.5415],\n",
      "        [-0.5971, -0.6411],\n",
      "        [-0.4775, -0.6411],\n",
      "        [-0.3779, -0.5813],\n",
      "        [-0.2782, -0.5215],\n",
      "        [ 0.1204, -0.4817],\n",
      "        [ 0.2200, -0.5215],\n",
      "        [ 0.3795, -0.5215],\n",
      "        [ 0.5389, -0.5215],\n",
      "        [ 0.6386, -0.4219],\n",
      "        [-0.1188, -0.2226],\n",
      "        [-0.1188, -0.0631],\n",
      "        [-0.1786,  0.0365],\n",
      "        [-0.1786,  0.1960],\n",
      "        [-0.2782,  0.2358],\n",
      "        [-0.2384,  0.2956],\n",
      "        [-0.1188,  0.2956],\n",
      "        [-0.0789,  0.2956],\n",
      "        [ 0.0207,  0.2956],\n",
      "        [-0.5373, -0.3222],\n",
      "        [-0.4775, -0.3222],\n",
      "        [-0.3779, -0.3222],\n",
      "        [-0.2782, -0.2824],\n",
      "        [-0.3779, -0.2226],\n",
      "        [-0.4775, -0.2824],\n",
      "        [ 0.1802, -0.2226],\n",
      "        [ 0.2200, -0.2226],\n",
      "        [ 0.3396, -0.2226],\n",
      "        [ 0.4393, -0.2026],\n",
      "        [ 0.3396, -0.1628],\n",
      "        [ 0.2200, -0.1628],\n",
      "        [-0.3779,  0.5348],\n",
      "        [-0.3380,  0.4949],\n",
      "        [-0.2384,  0.4351],\n",
      "        [-0.1786,  0.4351],\n",
      "        [-0.0789,  0.4351],\n",
      "        [ 0.0207,  0.5547],\n",
      "        [ 0.1204,  0.6543],\n",
      "        [ 0.0207,  0.7540],\n",
      "        [-0.0789,  0.7938],\n",
      "        [-0.1786,  0.7938],\n",
      "        [-0.2782,  0.7938],\n",
      "        [-0.3380,  0.6942],\n",
      "        [-0.3779,  0.5348],\n",
      "        [-0.2384,  0.4949],\n",
      "        [-0.1786,  0.4949],\n",
      "        [-0.0789,  0.5547],\n",
      "        [ 0.1204,  0.6543],\n",
      "        [-0.0789,  0.6942],\n",
      "        [-0.1786,  0.6942],\n",
      "        [-0.2384,  0.6942]], dtype=torch.float64)\n",
      "65 torch.Size([1, 224, 224]) tensor([[-0.8120, -0.0812],\n",
      "        [-0.8120,  0.1296],\n",
      "        [-0.7650,  0.3871],\n",
      "        [-0.7650,  0.5744],\n",
      "        [-0.7180,  0.8320],\n",
      "        [-0.6241,  0.9959],\n",
      "        [-0.5066,  1.1364],\n",
      "        [-0.3186,  1.3003],\n",
      "        [-0.0602,  1.3471],\n",
      "        [ 0.1983,  1.3003],\n",
      "        [ 0.3863,  1.1598],\n",
      "        [ 0.5508,  1.0661],\n",
      "        [ 0.6917,  0.8554],\n",
      "        [ 0.8092,  0.6681],\n",
      "        [ 0.9032,  0.4574],\n",
      "        [ 0.9502,  0.2466],\n",
      "        [ 0.9502,  0.0125],\n",
      "        [-0.6241, -0.2685],\n",
      "        [-0.5066, -0.3153],\n",
      "        [-0.3656, -0.3153],\n",
      "        [-0.2716, -0.3153],\n",
      "        [-0.1541, -0.3153],\n",
      "        [ 0.2453, -0.2685],\n",
      "        [ 0.3393, -0.2685],\n",
      "        [ 0.4568, -0.2451],\n",
      "        [ 0.5977, -0.2451],\n",
      "        [ 0.6917, -0.1983],\n",
      "        [-0.0132, -0.0578],\n",
      "        [-0.0132,  0.0827],\n",
      "        [-0.0132,  0.2466],\n",
      "        [-0.0602,  0.3403],\n",
      "        [-0.1541,  0.4339],\n",
      "        [-0.1071,  0.4339],\n",
      "        [-0.0132,  0.5042],\n",
      "        [ 0.0338,  0.5042],\n",
      "        [ 0.1513,  0.4339],\n",
      "        [-0.4596, -0.1280],\n",
      "        [-0.3656, -0.1280],\n",
      "        [-0.2716, -0.1046],\n",
      "        [-0.2011, -0.0578],\n",
      "        [-0.2716, -0.0578],\n",
      "        [-0.3656, -0.0578],\n",
      "        [ 0.2453, -0.0578],\n",
      "        [ 0.3393, -0.1046],\n",
      "        [ 0.4568, -0.1046],\n",
      "        [ 0.5508, -0.0578],\n",
      "        [ 0.4568, -0.0109],\n",
      "        [ 0.3393, -0.0109],\n",
      "        [-0.3656,  0.7383],\n",
      "        [-0.2716,  0.6915],\n",
      "        [-0.1071,  0.6447],\n",
      "        [-0.0602,  0.6447],\n",
      "        [ 0.0338,  0.6447],\n",
      "        [ 0.1513,  0.6915],\n",
      "        [ 0.2453,  0.8086],\n",
      "        [ 0.1513,  0.9491],\n",
      "        [ 0.0338,  0.9959],\n",
      "        [-0.0602,  0.9959],\n",
      "        [-0.1541,  0.9959],\n",
      "        [-0.2716,  0.9022],\n",
      "        [-0.3656,  0.7383],\n",
      "        [-0.1541,  0.7383],\n",
      "        [-0.0602,  0.7383],\n",
      "        [ 0.0338,  0.7383],\n",
      "        [ 0.2453,  0.8086],\n",
      "        [ 0.0338,  0.9022],\n",
      "        [-0.0602,  0.9022],\n",
      "        [-0.1541,  0.9022]], dtype=torch.float64)\n",
      "66 torch.Size([1, 224, 224]) tensor([[-1.3637, -0.7814],\n",
      "        [-1.3309, -0.5521],\n",
      "        [-1.3309, -0.3229],\n",
      "        [-1.2652, -0.0936],\n",
      "        [-1.1339,  0.1357],\n",
      "        [-0.9698,  0.3650],\n",
      "        [-0.7400,  0.4961],\n",
      "        [-0.4446,  0.6271],\n",
      "        [-0.1492,  0.7254],\n",
      "        [ 0.0150,  0.6271],\n",
      "        [ 0.0806,  0.5616],\n",
      "        [ 0.1134,  0.3978],\n",
      "        [ 0.1791,  0.2340],\n",
      "        [ 0.3104, -0.0608],\n",
      "        [ 0.4089, -0.2901],\n",
      "        [ 0.4089, -0.4866],\n",
      "        [ 0.3761, -0.7159],\n",
      "        [-0.6744, -1.0107],\n",
      "        [-0.5102, -1.1090],\n",
      "        [-0.3461, -1.1745],\n",
      "        [-0.2148, -1.1745],\n",
      "        [-0.1163, -1.1090],\n",
      "        [ 0.2447, -1.1090],\n",
      "        [ 0.3104, -1.1090],\n",
      "        [ 0.3761, -1.1090],\n",
      "        [ 0.4089, -1.1090],\n",
      "        [ 0.4089, -0.9780],\n",
      "        [ 0.1134, -0.8142],\n",
      "        [ 0.1791, -0.6504],\n",
      "        [ 0.3104, -0.4866],\n",
      "        [ 0.3104, -0.3556],\n",
      "        [-0.0507, -0.2901],\n",
      "        [ 0.0150, -0.2573],\n",
      "        [ 0.1134, -0.2573],\n",
      "        [ 0.1791, -0.2573],\n",
      "        [ 0.1791, -0.2573],\n",
      "        [-0.5102, -0.8470],\n",
      "        [-0.3789, -0.8797],\n",
      "        [-0.2805, -0.8797],\n",
      "        [-0.2148, -0.8142],\n",
      "        [-0.2805, -0.8142],\n",
      "        [-0.3789, -0.7814],\n",
      "        [ 0.1134, -0.8142],\n",
      "        [ 0.2447, -0.8797],\n",
      "        [ 0.3104, -0.8797],\n",
      "        [ 0.3104, -0.8142],\n",
      "        [ 0.3104, -0.7487],\n",
      "        [ 0.2447, -0.7487],\n",
      "        [-0.3789, -0.0280],\n",
      "        [-0.1492, -0.0608],\n",
      "        [ 0.0150, -0.1263],\n",
      "        [ 0.0806, -0.0608],\n",
      "        [ 0.1134, -0.0608],\n",
      "        [ 0.1791, -0.0280],\n",
      "        [ 0.1134,  0.0375],\n",
      "        [ 0.1134,  0.1685],\n",
      "        [ 0.1134,  0.2340],\n",
      "        [ 0.0150,  0.2012],\n",
      "        [-0.0507,  0.2012],\n",
      "        [-0.2148,  0.1030],\n",
      "        [-0.3461, -0.0280],\n",
      "        [-0.0507, -0.0280],\n",
      "        [ 0.0806, -0.0280],\n",
      "        [ 0.1134, -0.0280],\n",
      "        [ 0.1134,  0.0375],\n",
      "        [ 0.0806,  0.1030],\n",
      "        [ 0.0150,  0.1030],\n",
      "        [-0.0507,  0.1030]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 torch.Size([1, 224, 224]) tensor([[-0.8295, -0.7208],\n",
      "        [-0.7734, -0.4673],\n",
      "        [-0.6470, -0.2138],\n",
      "        [-0.5207,  0.0397],\n",
      "        [-0.3943,  0.2228],\n",
      "        [-0.2680,  0.4200],\n",
      "        [-0.1416,  0.5467],\n",
      "        [-0.0293,  0.6734],\n",
      "        [ 0.2936,  0.6734],\n",
      "        [ 0.6024,  0.4904],\n",
      "        [ 0.8551,  0.2510],\n",
      "        [ 1.0376,  0.0679],\n",
      "        [ 1.1640, -0.1856],\n",
      "        [ 1.1640, -0.4954],\n",
      "        [ 1.1640, -0.7489],\n",
      "        [ 1.0938, -1.0588],\n",
      "        [ 1.0376, -1.3122],\n",
      "        [-0.8997, -1.0306],\n",
      "        [-0.8997, -1.1573],\n",
      "        [-0.7734, -1.2137],\n",
      "        [-0.6470, -1.2137],\n",
      "        [-0.5909, -1.2700],\n",
      "        [-0.0855, -1.3967],\n",
      "        [ 0.0409, -1.4531],\n",
      "        [ 0.1672, -1.5094],\n",
      "        [ 0.3497, -1.5657],\n",
      "        [ 0.5322, -1.5094],\n",
      "        [-0.2680, -1.0165],\n",
      "        [-0.2118, -0.8334],\n",
      "        [-0.2118, -0.6503],\n",
      "        [-0.2118, -0.4532],\n",
      "        [-0.2118, -0.3264],\n",
      "        [-0.2118, -0.3264],\n",
      "        [-0.0855, -0.3264],\n",
      "        [-0.0293, -0.3264],\n",
      "        [ 0.0409, -0.3828],\n",
      "        [-0.7032, -0.9038],\n",
      "        [-0.7032, -0.9602],\n",
      "        [-0.5909, -0.9602],\n",
      "        [-0.3943, -0.9602],\n",
      "        [-0.5207, -0.9038],\n",
      "        [-0.6470, -0.8334],\n",
      "        [ 0.0970, -1.0728],\n",
      "        [ 0.1672, -1.1996],\n",
      "        [ 0.2936, -1.2559],\n",
      "        [ 0.4059, -1.2559],\n",
      "        [ 0.2936, -1.1292],\n",
      "        [ 0.1672, -1.1433],\n",
      "        [-0.2118,  0.1101],\n",
      "        [-0.2118, -0.0166],\n",
      "        [-0.1416, -0.1434],\n",
      "        [-0.0855, -0.1434],\n",
      "        [-0.0293, -0.1434],\n",
      "        [ 0.1672, -0.1434],\n",
      "        [ 0.3497, -0.0729],\n",
      "        [ 0.2234,  0.0538],\n",
      "        [ 0.1672,  0.1101],\n",
      "        [ 0.0409,  0.1665],\n",
      "        [-0.0293,  0.2369],\n",
      "        [-0.1416,  0.1665],\n",
      "        [-0.2118,  0.1101],\n",
      "        [-0.0855, -0.0166],\n",
      "        [-0.0293, -0.0166],\n",
      "        [ 0.0970, -0.0729],\n",
      "        [ 0.3497, -0.0729],\n",
      "        [ 0.0970,  0.0538],\n",
      "        [ 0.0409,  0.0538],\n",
      "        [-0.0855,  0.1101]], dtype=torch.float64)\n",
      "68 torch.Size([1, 224, 224]) tensor([[-1.5219, -0.1959],\n",
      "        [-1.4613,  0.0463],\n",
      "        [-1.4007,  0.3086],\n",
      "        [-1.3401,  0.5507],\n",
      "        [-1.2795,  0.7928],\n",
      "        [-1.1583,  1.1156],\n",
      "        [-1.0371,  1.4183],\n",
      "        [-0.7745,  1.6201],\n",
      "        [-0.4715,  1.7411],\n",
      "        [-0.1483,  1.6201],\n",
      "        [ 0.0941,  1.3780],\n",
      "        [ 0.2759,  1.1964],\n",
      "        [ 0.4577,  0.8937],\n",
      "        [ 0.5789,  0.6516],\n",
      "        [ 0.6597,  0.3893],\n",
      "        [ 0.7203,  0.1471],\n",
      "        [ 0.7809, -0.0950],\n",
      "        [-1.2189, -0.1959],\n",
      "        [-1.0977, -0.2564],\n",
      "        [-0.8957, -0.1757],\n",
      "        [-0.7139, -0.1152],\n",
      "        [-0.5927, -0.0546],\n",
      "        [-0.0271, -0.0546],\n",
      "        [ 0.1547, -0.1152],\n",
      "        [ 0.2759, -0.0950],\n",
      "        [ 0.4577, -0.1555],\n",
      "        [ 0.5789, -0.0950],\n",
      "        [-0.3503,  0.2480],\n",
      "        [-0.3503,  0.5103],\n",
      "        [-0.3503,  0.6919],\n",
      "        [-0.3503,  0.8130],\n",
      "        [-0.5927,  0.8735],\n",
      "        [-0.4715,  0.8735],\n",
      "        [-0.4109,  0.9341],\n",
      "        [-0.2897,  0.9341],\n",
      "        [-0.2291,  0.8735],\n",
      "        [-1.0371,  0.1270],\n",
      "        [-0.8957,  0.1270],\n",
      "        [-0.7745,  0.1875],\n",
      "        [-0.6533,  0.1875],\n",
      "        [-0.7745,  0.1875],\n",
      "        [-0.8957,  0.1875],\n",
      "        [ 0.0335,  0.2077],\n",
      "        [ 0.1547,  0.2077],\n",
      "        [ 0.2759,  0.2077],\n",
      "        [ 0.3971,  0.2077],\n",
      "        [ 0.2759,  0.2682],\n",
      "        [ 0.0941,  0.2682],\n",
      "        [-0.8351,  1.1762],\n",
      "        [-0.6533,  1.1156],\n",
      "        [-0.4715,  1.0551],\n",
      "        [-0.4109,  1.1358],\n",
      "        [-0.3503,  1.1358],\n",
      "        [-0.1483,  1.1964],\n",
      "        [-0.0877,  1.1964],\n",
      "        [-0.2291,  1.3780],\n",
      "        [-0.3503,  1.4385],\n",
      "        [-0.4715,  1.4385],\n",
      "        [-0.5927,  1.4385],\n",
      "        [-0.7139,  1.3174],\n",
      "        [-0.7745,  1.1762],\n",
      "        [-0.5321,  1.1964],\n",
      "        [-0.4109,  1.1964],\n",
      "        [-0.2897,  1.1964],\n",
      "        [-0.0877,  1.1964],\n",
      "        [-0.3503,  1.3174],\n",
      "        [-0.4109,  1.3174],\n",
      "        [-0.5321,  1.3174]], dtype=torch.float64)\n",
      "69 torch.Size([1, 224, 224]) tensor([[-1.2724, -0.2664],\n",
      "        [-1.2167, -0.0432],\n",
      "        [-1.1611,  0.1801],\n",
      "        [-1.1054,  0.4033],\n",
      "        [-0.9940,  0.6451],\n",
      "        [-0.8270,  0.8125],\n",
      "        [-0.5487,  0.9241],\n",
      "        [-0.3074,  1.0543],\n",
      "        [ 0.0266,  1.1101],\n",
      "        [ 0.1936,  0.9985],\n",
      "        [ 0.2493,  0.8869],\n",
      "        [ 0.3050,  0.7195],\n",
      "        [ 0.3606,  0.4963],\n",
      "        [ 0.4163,  0.2731],\n",
      "        [ 0.4163,  0.0313],\n",
      "        [ 0.4163, -0.1362],\n",
      "        [ 0.3606, -0.3594],\n",
      "        [-0.8270, -0.6012],\n",
      "        [-0.6600, -0.7128],\n",
      "        [-0.5487, -0.7128],\n",
      "        [-0.3631, -0.7128],\n",
      "        [-0.2518, -0.7128],\n",
      "        [ 0.1380, -0.7128],\n",
      "        [ 0.1936, -0.7686],\n",
      "        [ 0.2493, -0.8244],\n",
      "        [ 0.3050, -0.7686],\n",
      "        [ 0.3606, -0.6570],\n",
      "        [-0.0291, -0.4338],\n",
      "        [ 0.0823, -0.2478],\n",
      "        [ 0.1380, -0.1362],\n",
      "        [ 0.1936, -0.0246],\n",
      "        [-0.0847,  0.0871],\n",
      "        [-0.0291,  0.0871],\n",
      "        [ 0.0823,  0.0871],\n",
      "        [ 0.1380,  0.0871],\n",
      "        [ 0.1936,  0.0871],\n",
      "        [-0.6043, -0.3780],\n",
      "        [-0.4930, -0.4338],\n",
      "        [-0.3631, -0.4338],\n",
      "        [-0.3074, -0.3780],\n",
      "        [-0.3631, -0.3780],\n",
      "        [-0.4930, -0.3222],\n",
      "        [ 0.0823, -0.4152],\n",
      "        [ 0.1936, -0.4710],\n",
      "        [ 0.2493, -0.4710],\n",
      "        [ 0.3050, -0.4710],\n",
      "        [ 0.2493, -0.4152],\n",
      "        [ 0.1936, -0.4152],\n",
      "        [-0.3631,  0.4777],\n",
      "        [-0.1961,  0.3103],\n",
      "        [ 0.0266,  0.2545],\n",
      "        [ 0.0823,  0.2545],\n",
      "        [ 0.1380,  0.1987],\n",
      "        [ 0.2493,  0.2545],\n",
      "        [ 0.2493,  0.4405],\n",
      "        [ 0.1936,  0.6079],\n",
      "        [ 0.1380,  0.6637],\n",
      "        [ 0.0823,  0.7195],\n",
      "        [-0.0291,  0.7195],\n",
      "        [-0.1961,  0.5893],\n",
      "        [-0.3631,  0.4777],\n",
      "        [-0.0291,  0.3103],\n",
      "        [ 0.0823,  0.3103],\n",
      "        [ 0.1380,  0.3103],\n",
      "        [ 0.1936,  0.4405],\n",
      "        [ 0.1380,  0.5521],\n",
      "        [ 0.0823,  0.6079],\n",
      "        [-0.0291,  0.6079]], dtype=torch.float64)\n",
      "70 torch.Size([1, 224, 224]) tensor([[-1.1702,  0.3020],\n",
      "        [-1.1702,  0.5411],\n",
      "        [-1.1017,  0.7461],\n",
      "        [-1.0333,  0.9852],\n",
      "        [-0.9820,  1.2415],\n",
      "        [-0.8452,  1.4293],\n",
      "        [-0.6570,  1.5660],\n",
      "        [-0.4689,  1.6856],\n",
      "        [-0.1610,  1.7539],\n",
      "        [ 0.1640,  1.7026],\n",
      "        [ 0.3521,  1.5831],\n",
      "        [ 0.4719,  1.4635],\n",
      "        [ 0.6087,  1.2073],\n",
      "        [ 0.6600,  0.9511],\n",
      "        [ 0.7284,  0.7632],\n",
      "        [ 0.7284,  0.5070],\n",
      "        [ 0.7969,  0.2679],\n",
      "        [-0.9820,  0.1141],\n",
      "        [-0.8452,  0.0458],\n",
      "        [-0.7254,  0.0458],\n",
      "        [-0.5373,  0.0629],\n",
      "        [-0.4005,  0.0629],\n",
      "        [ 0.0272,  0.0629],\n",
      "        [ 0.1640,  0.0629],\n",
      "        [ 0.2837,  0.0629],\n",
      "        [ 0.4035,  0.1312],\n",
      "        [ 0.5403,  0.1312],\n",
      "        [-0.2123,  0.3191],\n",
      "        [-0.2123,  0.4387],\n",
      "        [-0.2123,  0.6266],\n",
      "        [-0.2123,  0.7461],\n",
      "        [-0.3491,  0.8144],\n",
      "        [-0.2807,  0.8144],\n",
      "        [-0.2123,  0.8144],\n",
      "        [-0.0926,  0.8144],\n",
      "        [-0.0242,  0.8144],\n",
      "        [-0.7939,  0.3020],\n",
      "        [-0.6570,  0.2337],\n",
      "        [-0.5373,  0.2508],\n",
      "        [-0.4689,  0.3020],\n",
      "        [-0.5373,  0.3020],\n",
      "        [-0.6570,  0.3020],\n",
      "        [ 0.0272,  0.3191],\n",
      "        [ 0.1640,  0.2508],\n",
      "        [ 0.2837,  0.2508],\n",
      "        [ 0.3521,  0.3191],\n",
      "        [ 0.2837,  0.3874],\n",
      "        [ 0.1640,  0.3191],\n",
      "        [-0.5373,  1.1219],\n",
      "        [-0.4005,  1.0023],\n",
      "        [-0.2807,  1.0023],\n",
      "        [-0.2123,  1.0023],\n",
      "        [-0.0926,  1.0023],\n",
      "        [ 0.0272,  1.0707],\n",
      "        [ 0.0956,  1.1390],\n",
      "        [ 0.0272,  1.1902],\n",
      "        [-0.0926,  1.2585],\n",
      "        [-0.2123,  1.2585],\n",
      "        [-0.2807,  1.2585],\n",
      "        [-0.4005,  1.1902],\n",
      "        [-0.5373,  1.1219],\n",
      "        [-0.2807,  1.0707],\n",
      "        [-0.2123,  1.0707],\n",
      "        [-0.0926,  1.0707],\n",
      "        [ 0.0956,  1.1390],\n",
      "        [-0.0926,  1.1390],\n",
      "        [-0.2123,  1.1219],\n",
      "        [-0.2807,  1.1219]], dtype=torch.float64)\n",
      "71 torch.Size([1, 224, 224]) tensor([[-1.2283, -0.0160],\n",
      "        [-1.1735,  0.2303],\n",
      "        [-1.0640,  0.4767],\n",
      "        [-0.9544,  0.6957],\n",
      "        [-0.8175,  0.8873],\n",
      "        [-0.6532,  1.0515],\n",
      "        [-0.4888,  1.1610],\n",
      "        [-0.2423,  1.2432],\n",
      "        [ 0.1137,  1.2979],\n",
      "        [ 0.3328,  1.1337],\n",
      "        [ 0.4972,  1.0242],\n",
      "        [ 0.6341,  0.8325],\n",
      "        [ 0.7436,  0.6136],\n",
      "        [ 0.7436,  0.3672],\n",
      "        [ 0.7436,  0.1482],\n",
      "        [ 0.7436, -0.0982],\n",
      "        [ 0.7436, -0.3172],\n",
      "        [-0.9544, -0.2350],\n",
      "        [-0.8175, -0.2898],\n",
      "        [-0.7079, -0.3445],\n",
      "        [-0.5984, -0.3993],\n",
      "        [-0.4888, -0.3993],\n",
      "        [-0.0232, -0.4540],\n",
      "        [ 0.1137, -0.5088],\n",
      "        [ 0.2233, -0.5088],\n",
      "        [ 0.3328, -0.5635],\n",
      "        [ 0.4424, -0.5088],\n",
      "        [-0.1876, -0.1529],\n",
      "        [-0.1876, -0.0434],\n",
      "        [-0.1328,  0.1208],\n",
      "        [-0.0780,  0.2577],\n",
      "        [-0.2423,  0.3672],\n",
      "        [-0.1876,  0.3672],\n",
      "        [-0.0780,  0.3672],\n",
      "        [-0.0232,  0.3672],\n",
      "        [ 0.0316,  0.3124],\n",
      "        [-0.7079, -0.1255],\n",
      "        [-0.6532, -0.0982],\n",
      "        [-0.5436, -0.1803],\n",
      "        [-0.4340, -0.0982],\n",
      "        [-0.5436, -0.0982],\n",
      "        [-0.6532, -0.0434],\n",
      "        [ 0.0316, -0.2077],\n",
      "        [ 0.1137, -0.2624],\n",
      "        [ 0.2233, -0.3172],\n",
      "        [ 0.3328, -0.2624],\n",
      "        [ 0.2233, -0.2077],\n",
      "        [ 0.1137, -0.2077],\n",
      "        [-0.3519,  0.6957],\n",
      "        [-0.2423,  0.5862],\n",
      "        [-0.1328,  0.5314],\n",
      "        [-0.0780,  0.5314],\n",
      "        [-0.0232,  0.4767],\n",
      "        [ 0.1685,  0.5314],\n",
      "        [ 0.2780,  0.6136],\n",
      "        [ 0.2233,  0.7231],\n",
      "        [ 0.1137,  0.7778],\n",
      "        [-0.0232,  0.8325],\n",
      "        [-0.1328,  0.8325],\n",
      "        [-0.1876,  0.7778],\n",
      "        [-0.2971,  0.6957],\n",
      "        [-0.1328,  0.5862],\n",
      "        [-0.0232,  0.5862],\n",
      "        [ 0.0316,  0.5862],\n",
      "        [ 0.2233,  0.6136],\n",
      "        [ 0.0316,  0.6683],\n",
      "        [-0.0232,  0.7231],\n",
      "        [-0.1328,  0.7231]], dtype=torch.float64)\n",
      "72 torch.Size([1, 224, 224]) tensor([[-2.2113, -0.7134],\n",
      "        [-2.1425, -0.3414],\n",
      "        [-2.0599, -0.0520],\n",
      "        [-1.9222,  0.2511],\n",
      "        [-1.7708,  0.5405],\n",
      "        [-1.5506,  0.7609],\n",
      "        [-1.3304,  0.9125],\n",
      "        [-1.0413,  0.9951],\n",
      "        [-0.6834,  1.0640],\n",
      "        [-0.3118,  0.9263],\n",
      "        [-0.0915,  0.7058],\n",
      "        [ 0.1287,  0.5680],\n",
      "        [ 0.2801,  0.3476],\n",
      "        [ 0.3490, -0.0107],\n",
      "        [ 0.3490, -0.3000],\n",
      "        [ 0.4178, -0.6032],\n",
      "        [ 0.4178, -0.8925],\n",
      "        [-1.9222, -1.1405],\n",
      "        [-1.7708, -1.2921],\n",
      "        [-1.6332, -1.2921],\n",
      "        [-1.4129, -1.2783],\n",
      "        [-1.2615, -1.2094],\n",
      "        [-0.6008, -1.2783],\n",
      "        [-0.4632, -1.3472],\n",
      "        [-0.3118, -1.4161],\n",
      "        [-0.1603, -1.4161],\n",
      "        [ 0.0599, -1.2646],\n",
      "        [-0.8899, -0.9890],\n",
      "        [-0.8899, -0.7685],\n",
      "        [-0.8211, -0.6170],\n",
      "        [-0.8211, -0.3965],\n",
      "        [-1.0413, -0.2587],\n",
      "        [-0.9725, -0.1760],\n",
      "        [-0.8211, -0.1760],\n",
      "        [-0.7522, -0.2449],\n",
      "        [-0.6008, -0.2449],\n",
      "        [-1.6332, -0.8512],\n",
      "        [-1.5506, -0.9890],\n",
      "        [-1.4129, -0.9890],\n",
      "        [-1.2615, -0.9201],\n",
      "        [-1.3304, -0.8512],\n",
      "        [-1.5506, -0.8512],\n",
      "        [-0.6008, -0.9752],\n",
      "        [-0.4632, -1.0441],\n",
      "        [-0.3118, -1.0441],\n",
      "        [-0.1603, -1.0441],\n",
      "        [-0.3118, -0.9752],\n",
      "        [-0.4632, -0.9752],\n",
      "        [-1.2615,  0.3338],\n",
      "        [-1.0413,  0.1822],\n",
      "        [-0.8899,  0.1133],\n",
      "        [-0.8211,  0.1133],\n",
      "        [-0.6834,  0.1133],\n",
      "        [-0.5320,  0.1133],\n",
      "        [-0.3806,  0.1960],\n",
      "        [-0.5320,  0.3338],\n",
      "        [-0.6008,  0.4853],\n",
      "        [-0.7522,  0.4853],\n",
      "        [-0.8899,  0.4853],\n",
      "        [-1.0413,  0.4027],\n",
      "        [-1.1927,  0.3338],\n",
      "        [-0.8899,  0.1822],\n",
      "        [-0.8211,  0.1822],\n",
      "        [-0.6834,  0.1960],\n",
      "        [-0.3806,  0.1960],\n",
      "        [-0.6834,  0.3338],\n",
      "        [-0.7522,  0.3338],\n",
      "        [-0.8899,  0.3338]], dtype=torch.float64)\n",
      "73 torch.Size([1, 224, 224]) tensor([[-1.1651, -0.1099],\n",
      "        [-1.2269,  0.1071],\n",
      "        [-1.2269,  0.3242],\n",
      "        [-1.1651,  0.4792],\n",
      "        [-1.1032,  0.6962],\n",
      "        [-1.0105,  0.8822],\n",
      "        [-1.0105,  0.9752],\n",
      "        [-0.8868,  1.0372],\n",
      "        [-0.7323,  1.1302],\n",
      "        [-0.4540,  1.0992],\n",
      "        [-0.1758,  1.0372],\n",
      "        [ 0.1025,  0.9442],\n",
      "        [ 0.3189,  0.7272],\n",
      "        [ 0.4116,  0.5102],\n",
      "        [ 0.4734,  0.2932],\n",
      "        [ 0.4734,  0.0761],\n",
      "        [ 0.4734, -0.1409],\n",
      "        [-1.2269, -0.3269],\n",
      "        [-1.2269, -0.3889],\n",
      "        [-1.1032, -0.4509],\n",
      "        [-1.0414, -0.4509],\n",
      "        [-1.0105, -0.3889],\n",
      "        [-0.6704, -0.3269],\n",
      "        [-0.5468, -0.3269],\n",
      "        [-0.4540, -0.3269],\n",
      "        [-0.2994, -0.3269],\n",
      "        [-0.1140, -0.2649],\n",
      "        [-0.8250, -0.1719],\n",
      "        [-0.8868, -0.0169],\n",
      "        [-0.9487,  0.1691],\n",
      "        [-0.9487,  0.2622],\n",
      "        [-0.9487,  0.3242],\n",
      "        [-0.8868,  0.3862],\n",
      "        [-0.8250,  0.3862],\n",
      "        [-0.7632,  0.3862],\n",
      "        [-0.7323,  0.3242],\n",
      "        [-1.1032, -0.1719],\n",
      "        [-1.0414, -0.1719],\n",
      "        [-1.0105, -0.1719],\n",
      "        [-0.8868, -0.1719],\n",
      "        [-1.0105, -0.1099],\n",
      "        [-1.0414, -0.1099],\n",
      "        [-0.5468, -0.1099],\n",
      "        [-0.5158, -0.1719],\n",
      "        [-0.3922, -0.1099],\n",
      "        [-0.2994, -0.1099],\n",
      "        [-0.3922, -0.0479],\n",
      "        [-0.5158, -0.1099],\n",
      "        [-1.0105,  0.6032],\n",
      "        [-0.9487,  0.5412],\n",
      "        [-0.8868,  0.5412],\n",
      "        [-0.8250,  0.5412],\n",
      "        [-0.7632,  0.5412],\n",
      "        [-0.6704,  0.6032],\n",
      "        [-0.5468,  0.6652],\n",
      "        [-0.6704,  0.6652],\n",
      "        [-0.7632,  0.6962],\n",
      "        [-0.8250,  0.6962],\n",
      "        [-0.8868,  0.6962],\n",
      "        [-0.9487,  0.6342],\n",
      "        [-1.0105,  0.6032],\n",
      "        [-0.8868,  0.6032],\n",
      "        [-0.8250,  0.6032],\n",
      "        [-0.7632,  0.6032],\n",
      "        [-0.5468,  0.6652],\n",
      "        [-0.7632,  0.6032],\n",
      "        [-0.8250,  0.6032],\n",
      "        [-0.8868,  0.6032]], dtype=torch.float64)\n",
      "74 torch.Size([1, 224, 224]) tensor([[-1.0104, -1.0971],\n",
      "        [-1.0104, -0.8468],\n",
      "        [-1.0104, -0.5965],\n",
      "        [-0.9603, -0.4463],\n",
      "        [-0.9101, -0.1459],\n",
      "        [-0.7596,  0.1045],\n",
      "        [-0.6593,  0.2547],\n",
      "        [-0.4085,  0.4549],\n",
      "        [-0.0072,  0.5551],\n",
      "        [ 0.2937,  0.4549],\n",
      "        [ 0.4944,  0.3548],\n",
      "        [ 0.5947,  0.2046],\n",
      "        [ 0.7452, -0.0457],\n",
      "        [ 0.7953, -0.2961],\n",
      "        [ 0.8455, -0.5464],\n",
      "        [ 0.8957, -0.6966],\n",
      "        [ 0.8957, -0.9469],\n",
      "        [-0.7596, -1.1973],\n",
      "        [-0.5590, -1.1472],\n",
      "        [-0.4587, -1.1472],\n",
      "        [-0.2580, -1.1472],\n",
      "        [-0.1577, -1.0971],\n",
      "        [ 0.3439, -1.0971],\n",
      "        [ 0.4944, -1.0971],\n",
      "        [ 0.5947, -1.0971],\n",
      "        [ 0.7452, -1.0971],\n",
      "        [ 0.8455, -1.0971],\n",
      "        [ 0.0931, -0.8468],\n",
      "        [ 0.0931, -0.6465],\n",
      "        [ 0.0931, -0.4963],\n",
      "        [ 0.0931, -0.3461],\n",
      "        [-0.1577, -0.3461],\n",
      "        [-0.0072, -0.2961],\n",
      "        [ 0.0429, -0.2460],\n",
      "        [ 0.1433, -0.2961],\n",
      "        [ 0.2436, -0.2961],\n",
      "        [-0.5590, -0.9469],\n",
      "        [-0.4587, -0.8969],\n",
      "        [-0.3082, -0.8969],\n",
      "        [-0.2079, -0.8969],\n",
      "        [-0.3082, -0.8468],\n",
      "        [-0.4587, -0.8468],\n",
      "        [ 0.2937, -0.8468],\n",
      "        [ 0.3941, -0.8468],\n",
      "        [ 0.5445, -0.8468],\n",
      "        [ 0.6449, -0.8969],\n",
      "        [ 0.5445, -0.8468],\n",
      "        [ 0.3941, -0.7967],\n",
      "        [-0.4085, -0.0958],\n",
      "        [-0.2079, -0.0958],\n",
      "        [-0.0072, -0.0958],\n",
      "        [ 0.0429, -0.0958],\n",
      "        [ 0.0931, -0.0958],\n",
      "        [ 0.2937, -0.0958],\n",
      "        [ 0.3439, -0.0457],\n",
      "        [ 0.2436,  0.0544],\n",
      "        [ 0.0931,  0.1545],\n",
      "        [ 0.0429,  0.1545],\n",
      "        [-0.1075,  0.0544],\n",
      "        [-0.2079,  0.0043],\n",
      "        [-0.4085, -0.0958],\n",
      "        [-0.1075, -0.0457],\n",
      "        [ 0.0429, -0.0457],\n",
      "        [ 0.0931, -0.0457],\n",
      "        [ 0.2937, -0.0457],\n",
      "        [ 0.0931,  0.0043],\n",
      "        [ 0.0429,  0.0043],\n",
      "        [-0.1075, -0.0457]], dtype=torch.float64)\n",
      "75 torch.Size([1, 224, 224]) tensor([[-0.9099, -0.6984],\n",
      "        [-0.8480, -0.4380],\n",
      "        [-0.7736, -0.1652],\n",
      "        [-0.7116,  0.0952],\n",
      "        [-0.6497,  0.3557],\n",
      "        [-0.4513,  0.6285],\n",
      "        [-0.3150,  0.7649],\n",
      "        [-0.1167,  0.9633],\n",
      "        [ 0.2056,  1.0377],\n",
      "        [ 0.6022,  0.9137],\n",
      "        [ 0.8005,  0.7153],\n",
      "        [ 0.9988,  0.5169],\n",
      "        [ 1.1351,  0.2564],\n",
      "        [ 1.1971, -0.0660],\n",
      "        [ 1.1971, -0.2644],\n",
      "        [ 1.2591, -0.5992],\n",
      "        [ 1.2591, -0.8596],\n",
      "        [-0.8480, -1.0952],\n",
      "        [-0.7116, -1.2192],\n",
      "        [-0.5753, -1.2812],\n",
      "        [-0.3770, -1.2192],\n",
      "        [-0.2530, -1.2192],\n",
      "        [ 0.3419, -1.2688],\n",
      "        [ 0.4782, -1.3309],\n",
      "        [ 0.6022, -1.3309],\n",
      "        [ 0.8005, -1.3309],\n",
      "        [ 0.9368, -1.1944],\n",
      "        [ 0.0816, -0.7480],\n",
      "        [ 0.0816, -0.4876],\n",
      "        [ 0.0196, -0.2892],\n",
      "        [ 0.0816, -0.1528],\n",
      "        [-0.0547, -0.0288],\n",
      "        [ 0.0196,  0.0456],\n",
      "        [ 0.0816,  0.0456],\n",
      "        [ 0.2056, -0.0164],\n",
      "        [ 0.2799, -0.0164],\n",
      "        [-0.5753, -0.7604],\n",
      "        [-0.5133, -0.8224],\n",
      "        [-0.3770, -0.8224],\n",
      "        [-0.1787, -0.7480],\n",
      "        [-0.3150, -0.6860],\n",
      "        [-0.5133, -0.6860],\n",
      "        [ 0.4039, -0.7480],\n",
      "        [ 0.4782, -0.8720],\n",
      "        [ 0.6766, -0.8720],\n",
      "        [ 0.8005, -0.7976],\n",
      "        [ 0.6766, -0.7356],\n",
      "        [ 0.5402, -0.7356],\n",
      "        [-0.2530,  0.3061],\n",
      "        [-0.1787,  0.2316],\n",
      "        [ 0.0196,  0.1696],\n",
      "        [ 0.1436,  0.2440],\n",
      "        [ 0.2056,  0.1820],\n",
      "        [ 0.4782,  0.1820],\n",
      "        [ 0.6766,  0.2564],\n",
      "        [ 0.4782,  0.4425],\n",
      "        [ 0.3419,  0.5789],\n",
      "        [ 0.1436,  0.6409],\n",
      "        [ 0.0196,  0.5665],\n",
      "        [-0.1167,  0.5045],\n",
      "        [-0.2530,  0.3061],\n",
      "        [ 0.0196,  0.3061],\n",
      "        [ 0.1436,  0.2440],\n",
      "        [ 0.2799,  0.2440],\n",
      "        [ 0.6022,  0.2440],\n",
      "        [ 0.3419,  0.4425],\n",
      "        [ 0.1436,  0.5045],\n",
      "        [ 0.0196,  0.5045]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76 torch.Size([1, 224, 224]) tensor([[-0.1647, -0.9155],\n",
      "        [-0.2639, -0.6676],\n",
      "        [-0.1647, -0.4196],\n",
      "        [-0.1151, -0.2213],\n",
      "        [-0.0655, -0.0230],\n",
      "        [-0.0159,  0.2250],\n",
      "        [ 0.1329,  0.2746],\n",
      "        [ 0.3313,  0.4233],\n",
      "        [ 0.6290,  0.5721],\n",
      "        [ 0.9266,  0.4233],\n",
      "        [ 1.1746,  0.3737],\n",
      "        [ 1.4226,  0.1754],\n",
      "        [ 1.5714,  0.0266],\n",
      "        [ 1.7202, -0.2213],\n",
      "        [ 1.7698, -0.4196],\n",
      "        [ 1.7698, -0.6676],\n",
      "        [ 1.8194, -0.9155],\n",
      "        [-0.1647, -1.1634],\n",
      "        [-0.1151, -1.1634],\n",
      "        [-0.0159, -1.1634],\n",
      "        [ 0.1329, -1.1634],\n",
      "        [ 0.1825, -1.1138],\n",
      "        [ 0.6290, -1.1138],\n",
      "        [ 0.7282, -1.1138],\n",
      "        [ 0.8770, -1.1138],\n",
      "        [ 1.0258, -1.1138],\n",
      "        [ 1.2242, -1.1138],\n",
      "        [ 0.4306, -0.8659],\n",
      "        [ 0.3810, -0.7172],\n",
      "        [ 0.3810, -0.5684],\n",
      "        [ 0.3810, -0.3701],\n",
      "        [ 0.3313, -0.3205],\n",
      "        [ 0.3810, -0.3205],\n",
      "        [ 0.4306, -0.3205],\n",
      "        [ 0.5298, -0.3205],\n",
      "        [ 0.6290, -0.3205],\n",
      "        [-0.0159, -1.0147],\n",
      "        [ 0.0833, -1.0147],\n",
      "        [ 0.1329, -0.9651],\n",
      "        [ 0.2321, -0.9155],\n",
      "        [ 0.1825, -0.9155],\n",
      "        [ 0.0833, -0.9155],\n",
      "        [ 0.7282, -0.9155],\n",
      "        [ 0.7778, -0.9651],\n",
      "        [ 0.9266, -0.9155],\n",
      "        [ 1.0258, -0.9155],\n",
      "        [ 0.9266, -0.8659],\n",
      "        [ 0.7778, -0.8659],\n",
      "        [ 0.1825, -0.0725],\n",
      "        [ 0.2321, -0.0725],\n",
      "        [ 0.3810, -0.1221],\n",
      "        [ 0.4306, -0.1221],\n",
      "        [ 0.4802, -0.1221],\n",
      "        [ 0.6786, -0.1221],\n",
      "        [ 0.7778, -0.0725],\n",
      "        [ 0.6786,  0.0762],\n",
      "        [ 0.6290,  0.1258],\n",
      "        [ 0.4802,  0.1258],\n",
      "        [ 0.3810,  0.1258],\n",
      "        [ 0.3313,  0.0762],\n",
      "        [ 0.2321, -0.0725],\n",
      "        [ 0.3810, -0.0725],\n",
      "        [ 0.4802, -0.0725],\n",
      "        [ 0.5298, -0.0725],\n",
      "        [ 0.7778, -0.0725],\n",
      "        [ 0.5298, -0.0230],\n",
      "        [ 0.4802, -0.0230],\n",
      "        [ 0.3810, -0.0230]], dtype=torch.float64)\n",
      "77 torch.Size([1, 224, 224]) tensor([[-1.4778, -1.3118],\n",
      "        [-1.4037, -0.9769],\n",
      "        [-1.4037, -0.6979],\n",
      "        [-1.3295, -0.4189],\n",
      "        [-1.1998, -0.0841],\n",
      "        [-0.9959,  0.1949],\n",
      "        [-0.7178,  0.3996],\n",
      "        [-0.4583,  0.6786],\n",
      "        [-0.1061,  0.8274],\n",
      "        [ 0.1719,  0.6972],\n",
      "        [ 0.3017,  0.5670],\n",
      "        [ 0.3758,  0.3624],\n",
      "        [ 0.5056,  0.1577],\n",
      "        [ 0.6353, -0.1957],\n",
      "        [ 0.7095, -0.4561],\n",
      "        [ 0.7095, -0.7351],\n",
      "        [ 0.7095, -1.0141],\n",
      "        [-0.6622, -1.6466],\n",
      "        [-0.4583, -1.7210],\n",
      "        [-0.2359, -1.7024],\n",
      "        [-0.0320, -1.7024],\n",
      "        [ 0.0978, -1.6280],\n",
      "        [ 0.5797, -1.5536],\n",
      "        [ 0.6353, -1.5536],\n",
      "        [ 0.7836, -1.5536],\n",
      "        [ 0.8578, -1.5536],\n",
      "        [ 0.8578, -1.4234],\n",
      "        [ 0.3017, -1.2932],\n",
      "        [ 0.3758, -1.0885],\n",
      "        [ 0.4314, -0.8839],\n",
      "        [ 0.4314, -0.7351],\n",
      "        [ 0.0978, -0.5305],\n",
      "        [ 0.1719, -0.5305],\n",
      "        [ 0.3017, -0.4747],\n",
      "        [ 0.3758, -0.4747],\n",
      "        [ 0.3758, -0.4747],\n",
      "        [-0.4583, -1.3676],\n",
      "        [-0.3100, -1.3676],\n",
      "        [-0.1803, -1.3676],\n",
      "        [-0.1061, -1.2932],\n",
      "        [-0.1803, -1.2932],\n",
      "        [-0.3100, -1.2932],\n",
      "        [ 0.4314, -1.2188],\n",
      "        [ 0.5797, -1.2188],\n",
      "        [ 0.6353, -1.2188],\n",
      "        [ 0.7095, -1.1443],\n",
      "        [ 0.6353, -1.1443],\n",
      "        [ 0.5056, -1.1443],\n",
      "        [-0.3842, -0.1399],\n",
      "        [-0.1061, -0.1957],\n",
      "        [ 0.1719, -0.1957],\n",
      "        [ 0.2275, -0.1957],\n",
      "        [ 0.3017, -0.1957],\n",
      "        [ 0.3758, -0.1213],\n",
      "        [ 0.3758,  0.0089],\n",
      "        [ 0.3017,  0.1577],\n",
      "        [ 0.2275,  0.2135],\n",
      "        [ 0.0978,  0.2135],\n",
      "        [-0.0320,  0.1391],\n",
      "        [-0.1803,  0.0647],\n",
      "        [-0.3100, -0.1399],\n",
      "        [ 0.0236, -0.1213],\n",
      "        [ 0.1719, -0.1213],\n",
      "        [ 0.3017, -0.0469],\n",
      "        [ 0.3758,  0.0089],\n",
      "        [ 0.2275,  0.0833],\n",
      "        [ 0.0978,  0.0833],\n",
      "        [-0.0320,  0.0833]], dtype=torch.float64)\n",
      "78 torch.Size([1, 224, 224]) tensor([[-1.0800, -0.1394],\n",
      "        [-1.0371,  0.1181],\n",
      "        [-1.0371,  0.3328],\n",
      "        [-0.9512,  0.5474],\n",
      "        [-0.8225,  0.8049],\n",
      "        [-0.6078,  0.9766],\n",
      "        [-0.3503,  1.0625],\n",
      "        [-0.0927,  1.1913],\n",
      "        [ 0.1648,  1.1913],\n",
      "        [ 0.4224,  1.1484],\n",
      "        [ 0.5082,  1.0196],\n",
      "        [ 0.5512,  0.8908],\n",
      "        [ 0.5941,  0.7191],\n",
      "        [ 0.6370,  0.5045],\n",
      "        [ 0.6370,  0.2898],\n",
      "        [ 0.6370,  0.0752],\n",
      "        [ 0.6370, -0.1394],\n",
      "        [-0.4791, -0.3541],\n",
      "        [-0.3073, -0.3970],\n",
      "        [-0.1356, -0.4399],\n",
      "        [-0.0498, -0.4399],\n",
      "        [ 0.0790, -0.4399],\n",
      "        [ 0.4224, -0.4399],\n",
      "        [ 0.5082, -0.4828],\n",
      "        [ 0.5941, -0.4828],\n",
      "        [ 0.6370, -0.4399],\n",
      "        [ 0.6370, -0.3970],\n",
      "        [ 0.2936, -0.2253],\n",
      "        [ 0.3365, -0.0965],\n",
      "        [ 0.3795,  0.0752],\n",
      "        [ 0.4224,  0.2040],\n",
      "        [ 0.1648,  0.3328],\n",
      "        [ 0.2078,  0.3328],\n",
      "        [ 0.3365,  0.3328],\n",
      "        [ 0.3795,  0.3328],\n",
      "        [ 0.3795,  0.2898],\n",
      "        [-0.3073, -0.1823],\n",
      "        [-0.1786, -0.2253],\n",
      "        [-0.0927, -0.2253],\n",
      "        [-0.0498, -0.1823],\n",
      "        [-0.0927, -0.1823],\n",
      "        [-0.1786, -0.1823],\n",
      "        [ 0.3365, -0.2253],\n",
      "        [ 0.4224, -0.2682],\n",
      "        [ 0.5512, -0.2682],\n",
      "        [ 0.5512, -0.2253],\n",
      "        [ 0.5512, -0.2253],\n",
      "        [ 0.4224, -0.1823],\n",
      "        [-0.0927,  0.6762],\n",
      "        [ 0.1219,  0.5474],\n",
      "        [ 0.2936,  0.5045],\n",
      "        [ 0.3365,  0.5474],\n",
      "        [ 0.3795,  0.5045],\n",
      "        [ 0.4224,  0.5474],\n",
      "        [ 0.4224,  0.6332],\n",
      "        [ 0.4224,  0.6762],\n",
      "        [ 0.3795,  0.7191],\n",
      "        [ 0.2936,  0.7620],\n",
      "        [ 0.2078,  0.7620],\n",
      "        [ 0.0790,  0.7191],\n",
      "        [-0.0498,  0.5903],\n",
      "        [ 0.2078,  0.5903],\n",
      "        [ 0.2936,  0.5903],\n",
      "        [ 0.3365,  0.6332],\n",
      "        [ 0.4224,  0.6332],\n",
      "        [ 0.3365,  0.6332],\n",
      "        [ 0.2936,  0.6762],\n",
      "        [ 0.2078,  0.6762]], dtype=torch.float64)\n",
      "79 torch.Size([1, 224, 224]) tensor([[-1.0352, -0.1627],\n",
      "        [-0.9836,  0.0694],\n",
      "        [-0.8804,  0.3016],\n",
      "        [-0.8288,  0.4563],\n",
      "        [-0.6997,  0.6885],\n",
      "        [-0.5449,  0.8948],\n",
      "        [-0.3127,  1.0238],\n",
      "        [-0.0546,  1.1270],\n",
      "        [ 0.2293,  1.1786],\n",
      "        [ 0.4615,  1.0754],\n",
      "        [ 0.5647,  0.9206],\n",
      "        [ 0.6163,  0.8175],\n",
      "        [ 0.6679,  0.5853],\n",
      "        [ 0.7195,  0.3790],\n",
      "        [ 0.7195,  0.1984],\n",
      "        [ 0.7195, -0.0079],\n",
      "        [ 0.7195, -0.2401],\n",
      "        [-0.6481, -0.4206],\n",
      "        [-0.4933, -0.5754],\n",
      "        [-0.3127, -0.5754],\n",
      "        [-0.2094, -0.5754],\n",
      "        [-0.1062, -0.5754],\n",
      "        [ 0.3325, -0.6270],\n",
      "        [ 0.4615, -0.6786],\n",
      "        [ 0.5647, -0.7302],\n",
      "        [ 0.6679, -0.6786],\n",
      "        [ 0.7195, -0.5754],\n",
      "        [ 0.1776, -0.1885],\n",
      "        [ 0.2293, -0.0337],\n",
      "        [ 0.2809,  0.1468],\n",
      "        [ 0.2809,  0.2500],\n",
      "        [ 0.1260,  0.3532],\n",
      "        [ 0.1776,  0.4048],\n",
      "        [ 0.2809,  0.4306],\n",
      "        [ 0.3325,  0.3532],\n",
      "        [ 0.3325,  0.3532],\n",
      "        [-0.4417, -0.1885],\n",
      "        [-0.3127, -0.1885],\n",
      "        [-0.2094, -0.1885],\n",
      "        [-0.1062, -0.1885],\n",
      "        [-0.1578, -0.1369],\n",
      "        [-0.3127, -0.1369],\n",
      "        [ 0.3325, -0.2401],\n",
      "        [ 0.4615, -0.2401],\n",
      "        [ 0.5647, -0.2917],\n",
      "        [ 0.6163, -0.2917],\n",
      "        [ 0.5131, -0.1885],\n",
      "        [ 0.4615, -0.1885],\n",
      "        [-0.0546,  0.6885],\n",
      "        [ 0.1260,  0.5853],\n",
      "        [ 0.2293,  0.5337],\n",
      "        [ 0.2809,  0.5337],\n",
      "        [ 0.3325,  0.5337],\n",
      "        [ 0.4615,  0.5853],\n",
      "        [ 0.4615,  0.6369],\n",
      "        [ 0.3841,  0.6885],\n",
      "        [ 0.3325,  0.7401],\n",
      "        [ 0.2809,  0.8175],\n",
      "        [ 0.1776,  0.7917],\n",
      "        [ 0.0744,  0.7401],\n",
      "        [ 0.0228,  0.6885],\n",
      "        [ 0.1776,  0.6369],\n",
      "        [ 0.2809,  0.6369],\n",
      "        [ 0.3325,  0.6369],\n",
      "        [ 0.4615,  0.6369],\n",
      "        [ 0.3325,  0.6369],\n",
      "        [ 0.2809,  0.6885],\n",
      "        [ 0.1776,  0.6885]], dtype=torch.float64)\n",
      "80 torch.Size([1, 224, 224]) tensor([[-0.6621, -1.0521],\n",
      "        [-0.6621, -0.7421],\n",
      "        [-0.6621, -0.4196],\n",
      "        [-0.6621, -0.1096],\n",
      "        [-0.6621,  0.1260],\n",
      "        [-0.5879,  0.4484],\n",
      "        [-0.4272,  0.6096],\n",
      "        [-0.1923,  0.7708],\n",
      "        [ 0.1168,  1.0064],\n",
      "        [ 0.5124,  0.9320],\n",
      "        [ 0.8214,  0.8576],\n",
      "        [ 1.0563,  0.7832],\n",
      "        [ 1.2170,  0.6344],\n",
      "        [ 1.3654,  0.3120],\n",
      "        [ 1.5261,  0.0888],\n",
      "        [ 1.6126, -0.1468],\n",
      "        [ 1.6868, -0.4692],\n",
      "        [-0.3530, -1.3621],\n",
      "        [-0.1181, -1.5109],\n",
      "        [ 0.0426, -1.5109],\n",
      "        [ 0.1909, -1.5109],\n",
      "        [ 0.3516, -1.4241],\n",
      "        [ 0.9821, -1.2629],\n",
      "        [ 1.1305, -1.2629],\n",
      "        [ 1.2912, -1.1761],\n",
      "        [ 1.4519, -1.1017],\n",
      "        [ 1.5261, -0.9405],\n",
      "        [ 0.5865, -0.8785],\n",
      "        [ 0.5124, -0.7173],\n",
      "        [ 0.5124, -0.4816],\n",
      "        [ 0.4258, -0.3204],\n",
      "        [ 0.1909, -0.2460],\n",
      "        [ 0.2775, -0.1716],\n",
      "        [ 0.4258, -0.1716],\n",
      "        [ 0.5124, -0.1716],\n",
      "        [ 0.5865, -0.1716],\n",
      "        [-0.1181, -1.0397],\n",
      "        [-0.0440, -1.0397],\n",
      "        [ 0.1168, -1.0397],\n",
      "        [ 0.2775, -0.9529],\n",
      "        [ 0.1168, -0.9653],\n",
      "        [-0.0440, -0.9653],\n",
      "        [ 0.8214, -0.7917],\n",
      "        [ 0.9821, -0.8661],\n",
      "        [ 1.1305, -0.7917],\n",
      "        [ 1.2912, -0.7049],\n",
      "        [ 1.1305, -0.7049],\n",
      "        [ 0.9821, -0.7173],\n",
      "        [-0.0440,  0.2128],\n",
      "        [ 0.0426,  0.0640],\n",
      "        [ 0.2775, -0.0104],\n",
      "        [ 0.3516,  0.0640],\n",
      "        [ 0.4258,  0.0640],\n",
      "        [ 0.5865,  0.2252],\n",
      "        [ 0.6607,  0.3864],\n",
      "        [ 0.5124,  0.4608],\n",
      "        [ 0.3516,  0.4608],\n",
      "        [ 0.2775,  0.4608],\n",
      "        [ 0.1168,  0.3740],\n",
      "        [ 0.0426,  0.2996],\n",
      "        [-0.0440,  0.2128],\n",
      "        [ 0.1909,  0.1384],\n",
      "        [ 0.2775,  0.1384],\n",
      "        [ 0.4258,  0.2252],\n",
      "        [ 0.5865,  0.3864],\n",
      "        [ 0.3516,  0.2996],\n",
      "        [ 0.2775,  0.2996],\n",
      "        [ 0.1909,  0.2996]], dtype=torch.float64)\n",
      "81 torch.Size([1, 224, 224]) tensor([[-0.9397, -0.7436],\n",
      "        [-0.8281, -0.4926],\n",
      "        [-0.7723, -0.2417],\n",
      "        [-0.7165, -0.0186],\n",
      "        [-0.5212,  0.1766],\n",
      "        [-0.2980,  0.2881],\n",
      "        [-0.0469,  0.3996],\n",
      "        [ 0.2321,  0.4833],\n",
      "        [ 0.5391,  0.5391],\n",
      "        [ 0.7623,  0.3718],\n",
      "        [ 0.8460,  0.2323],\n",
      "        [ 0.9018,  0.1208],\n",
      "        [ 0.9576, -0.0465],\n",
      "        [ 0.9576, -0.2975],\n",
      "        [ 1.0134, -0.5205],\n",
      "        [ 0.9576, -0.6878],\n",
      "        [ 0.9018, -0.8830],\n",
      "        [-0.4096, -1.0224],\n",
      "        [-0.2422, -1.1340],\n",
      "        [-0.1027, -1.1897],\n",
      "        [ 0.0647, -1.1897],\n",
      "        [ 0.1763, -1.1897],\n",
      "        [ 0.5949, -1.2455],\n",
      "        [ 0.6507, -1.3013],\n",
      "        [ 0.7623, -1.3013],\n",
      "        [ 0.8460, -1.3013],\n",
      "        [ 0.9018, -1.1619],\n",
      "        [ 0.4275, -0.9946],\n",
      "        [ 0.5391, -0.8830],\n",
      "        [ 0.5949, -0.7715],\n",
      "        [ 0.5949, -0.6600],\n",
      "        [ 0.3717, -0.4648],\n",
      "        [ 0.4275, -0.4648],\n",
      "        [ 0.5391, -0.4648],\n",
      "        [ 0.5949, -0.4648],\n",
      "        [ 0.6507, -0.5205],\n",
      "        [-0.1864, -0.8830],\n",
      "        [-0.0469, -0.9667],\n",
      "        [ 0.0647, -0.9388],\n",
      "        [ 0.1205, -0.9388],\n",
      "        [ 0.0647, -0.8830],\n",
      "        [-0.0469, -0.8273],\n",
      "        [ 0.5391, -0.9388],\n",
      "        [ 0.6507, -0.9946],\n",
      "        [ 0.7623, -0.9946],\n",
      "        [ 0.7623, -0.9946],\n",
      "        [ 0.7065, -0.9388],\n",
      "        [ 0.6507, -0.9388],\n",
      "        [ 0.0647, -0.1302],\n",
      "        [ 0.2321, -0.2417],\n",
      "        [ 0.4275, -0.3532],\n",
      "        [ 0.5391, -0.2975],\n",
      "        [ 0.5949, -0.3532],\n",
      "        [ 0.7065, -0.2975],\n",
      "        [ 0.7623, -0.1580],\n",
      "        [ 0.7065, -0.0465],\n",
      "        [ 0.6507,  0.0093],\n",
      "        [ 0.5391,  0.0650],\n",
      "        [ 0.4275,  0.0650],\n",
      "        [ 0.2321,  0.0093],\n",
      "        [ 0.0647, -0.1302],\n",
      "        [ 0.4275, -0.2417],\n",
      "        [ 0.5391, -0.2417],\n",
      "        [ 0.5949, -0.2417],\n",
      "        [ 0.7065, -0.1580],\n",
      "        [ 0.5949, -0.1023],\n",
      "        [ 0.5391, -0.0465],\n",
      "        [ 0.4275, -0.0465]], dtype=torch.float64)\n",
      "82 torch.Size([1, 224, 224]) tensor([[-0.9048,  0.4093],\n",
      "        [-0.8236,  0.6520],\n",
      "        [-0.7695,  0.8677],\n",
      "        [-0.7154,  1.1104],\n",
      "        [-0.6071,  1.4071],\n",
      "        [-0.4989,  1.5689],\n",
      "        [-0.3095,  1.6767],\n",
      "        [-0.0931,  1.7577],\n",
      "        [ 0.2045,  1.8116],\n",
      "        [ 0.5022,  1.7577],\n",
      "        [ 0.7186,  1.6498],\n",
      "        [ 0.8539,  1.4880],\n",
      "        [ 0.9080,  1.2992],\n",
      "        [ 0.9621,  1.0295],\n",
      "        [ 0.9621,  0.7868],\n",
      "        [ 1.0162,  0.5441],\n",
      "        [ 0.9621,  0.2744],\n",
      "        [-0.6613,  0.1126],\n",
      "        [-0.4989,  0.0587],\n",
      "        [-0.3636,  0.0047],\n",
      "        [-0.2013,  0.0047],\n",
      "        [-0.0931,  0.0317],\n",
      "        [ 0.3669,  0.0317],\n",
      "        [ 0.5022, -0.0222],\n",
      "        [ 0.6104, -0.0222],\n",
      "        [ 0.7727, -0.0222],\n",
      "        [ 0.8539,  0.0856],\n",
      "        [ 0.1504,  0.2474],\n",
      "        [ 0.2045,  0.3823],\n",
      "        [ 0.2045,  0.5441],\n",
      "        [ 0.2045,  0.6520],\n",
      "        [ 0.0422,  0.8407],\n",
      "        [ 0.0963,  0.8407],\n",
      "        [ 0.2045,  0.8407],\n",
      "        [ 0.2587,  0.8407],\n",
      "        [ 0.3128,  0.7868],\n",
      "        [-0.4177,  0.3014],\n",
      "        [-0.3095,  0.2474],\n",
      "        [-0.2013,  0.2474],\n",
      "        [-0.0931,  0.3014],\n",
      "        [-0.2013,  0.3014],\n",
      "        [-0.3095,  0.3553],\n",
      "        [ 0.3669,  0.2474],\n",
      "        [ 0.5022,  0.2474],\n",
      "        [ 0.6104,  0.1935],\n",
      "        [ 0.7186,  0.2744],\n",
      "        [ 0.6104,  0.3284],\n",
      "        [ 0.5022,  0.3284],\n",
      "        [-0.1472,  1.1644],\n",
      "        [-0.0119,  1.1104],\n",
      "        [ 0.0963,  1.0565],\n",
      "        [ 0.2045,  1.0565],\n",
      "        [ 0.2587,  1.0026],\n",
      "        [ 0.3669,  1.0565],\n",
      "        [ 0.5022,  1.1913],\n",
      "        [ 0.3669,  1.2453],\n",
      "        [ 0.2587,  1.2992],\n",
      "        [ 0.2045,  1.2992],\n",
      "        [ 0.0963,  1.2992],\n",
      "        [-0.0119,  1.2183],\n",
      "        [-0.1472,  1.1644],\n",
      "        [ 0.0963,  1.1104],\n",
      "        [ 0.2045,  1.1104],\n",
      "        [ 0.2587,  1.1104],\n",
      "        [ 0.5022,  1.1913],\n",
      "        [ 0.2587,  1.1913],\n",
      "        [ 0.1504,  1.1644],\n",
      "        [ 0.0963,  1.1644]], dtype=torch.float64)\n",
      "83 torch.Size([1, 224, 224]) tensor([[-0.8196, -0.3458],\n",
      "        [-0.7521, -0.1090],\n",
      "        [-0.6845,  0.1615],\n",
      "        [-0.6508,  0.3983],\n",
      "        [-0.5157,  0.6350],\n",
      "        [-0.2793,  0.8041],\n",
      "        [ 0.0247,  0.8718],\n",
      "        [ 0.3286,  0.9394],\n",
      "        [ 0.6325,  1.0070],\n",
      "        [ 0.8689,  0.9056],\n",
      "        [ 0.9702,  0.7703],\n",
      "        [ 1.0378,  0.6688],\n",
      "        [ 1.1053,  0.4659],\n",
      "        [ 1.1053,  0.2292],\n",
      "        [ 1.1729, -0.0076],\n",
      "        [ 1.1729, -0.1767],\n",
      "        [ 1.1053, -0.4134],\n",
      "        [-0.2793, -0.6163],\n",
      "        [-0.1104, -0.6840],\n",
      "        [ 0.0922, -0.7516],\n",
      "        [ 0.1935, -0.7516],\n",
      "        [ 0.3286, -0.6840],\n",
      "        [ 0.7338, -0.7516],\n",
      "        [ 0.8689, -0.7854],\n",
      "        [ 0.9702, -0.7854],\n",
      "        [ 1.0378, -0.7854],\n",
      "        [ 1.1053, -0.7178],\n",
      "        [ 0.6325, -0.4811],\n",
      "        [ 0.6663, -0.3796],\n",
      "        [ 0.7338, -0.2443],\n",
      "        [ 0.7338, -0.0752],\n",
      "        [ 0.4974,  0.0262],\n",
      "        [ 0.5650,  0.0601],\n",
      "        [ 0.6663,  0.0601],\n",
      "        [ 0.7338,  0.0601],\n",
      "        [ 0.8014,  0.0601],\n",
      "        [-0.0429, -0.4472],\n",
      "        [ 0.0922, -0.5149],\n",
      "        [ 0.1935, -0.5149],\n",
      "        [ 0.2611, -0.4472],\n",
      "        [ 0.1935, -0.4472],\n",
      "        [ 0.0922, -0.4472],\n",
      "        [ 0.7338, -0.4811],\n",
      "        [ 0.8689, -0.5487],\n",
      "        [ 0.9365, -0.5487],\n",
      "        [ 0.9702, -0.5487],\n",
      "        [ 0.9365, -0.4811],\n",
      "        [ 0.8689, -0.4811],\n",
      "        [ 0.3286,  0.3983],\n",
      "        [ 0.4974,  0.2968],\n",
      "        [ 0.6325,  0.2292],\n",
      "        [ 0.6663,  0.2968],\n",
      "        [ 0.7338,  0.2292],\n",
      "        [ 0.8689,  0.2968],\n",
      "        [ 0.8689,  0.3644],\n",
      "        [ 0.8689,  0.4659],\n",
      "        [ 0.8014,  0.5335],\n",
      "        [ 0.6663,  0.6012],\n",
      "        [ 0.5650,  0.5335],\n",
      "        [ 0.4974,  0.5335],\n",
      "        [ 0.3961,  0.3983],\n",
      "        [ 0.5650,  0.3306],\n",
      "        [ 0.6663,  0.3306],\n",
      "        [ 0.7338,  0.3644],\n",
      "        [ 0.8689,  0.3644],\n",
      "        [ 0.7338,  0.3983],\n",
      "        [ 0.6663,  0.3983],\n",
      "        [ 0.5650,  0.3983]], dtype=torch.float64)\n",
      "84 torch.Size([1, 224, 224]) tensor([[-1.3948, -0.4232],\n",
      "        [-1.3287, -0.0439],\n",
      "        [-1.1799,  0.2530],\n",
      "        [-1.1138,  0.4674],\n",
      "        [-0.8823,  0.7642],\n",
      "        [-0.6012,  0.9951],\n",
      "        [-0.3036,  1.2095],\n",
      "        [ 0.0602,  1.3579],\n",
      "        [ 0.4405,  1.4404],\n",
      "        [ 0.7216,  1.3085],\n",
      "        [ 0.8042,  1.0776],\n",
      "        [ 0.8042,  0.8632],\n",
      "        [ 0.8704,  0.5663],\n",
      "        [ 0.9530,  0.2859],\n",
      "        [ 0.9530, -0.0109],\n",
      "        [ 0.9530, -0.3078],\n",
      "        [ 0.8704, -0.6046],\n",
      "        [-0.7335, -0.7696],\n",
      "        [-0.5185, -0.8355],\n",
      "        [-0.3036, -0.9180],\n",
      "        [-0.0721, -0.9015],\n",
      "        [ 0.0602, -0.9015],\n",
      "        [ 0.5728, -0.9675],\n",
      "        [ 0.6554, -1.0499],\n",
      "        [ 0.8042, -1.0334],\n",
      "        [ 0.8704, -1.0334],\n",
      "        [ 0.8704, -0.9675],\n",
      "        [ 0.3578, -0.6046],\n",
      "        [ 0.5066, -0.3902],\n",
      "        [ 0.5728, -0.1593],\n",
      "        [ 0.6554,  0.0551],\n",
      "        [ 0.2917,  0.2035],\n",
      "        [ 0.3578,  0.2035],\n",
      "        [ 0.5066,  0.2035],\n",
      "        [ 0.5728,  0.2035],\n",
      "        [ 0.6554,  0.1210],\n",
      "        [-0.4524, -0.5552],\n",
      "        [-0.3036, -0.6211],\n",
      "        [-0.1548, -0.6211],\n",
      "        [-0.0721, -0.5387],\n",
      "        [-0.1548, -0.5387],\n",
      "        [-0.3036, -0.4727],\n",
      "        [ 0.5066, -0.6046],\n",
      "        [ 0.5728, -0.7531],\n",
      "        [ 0.7216, -0.7531],\n",
      "        [ 0.7216, -0.6706],\n",
      "        [ 0.7216, -0.6046],\n",
      "        [ 0.5728, -0.6046],\n",
      "        [-0.0721,  0.6323],\n",
      "        [ 0.1429,  0.4838],\n",
      "        [ 0.4405,  0.4179],\n",
      "        [ 0.5066,  0.4179],\n",
      "        [ 0.5728,  0.4179],\n",
      "        [ 0.7216,  0.4179],\n",
      "        [ 0.7216,  0.5003],\n",
      "        [ 0.7216,  0.7147],\n",
      "        [ 0.6554,  0.7807],\n",
      "        [ 0.5066,  0.8632],\n",
      "        [ 0.3578,  0.8632],\n",
      "        [ 0.2090,  0.7807],\n",
      "        [-0.0060,  0.6323],\n",
      "        [ 0.3578,  0.5663],\n",
      "        [ 0.5066,  0.4838],\n",
      "        [ 0.5728,  0.5003],\n",
      "        [ 0.7216,  0.5003],\n",
      "        [ 0.5728,  0.6323],\n",
      "        [ 0.5066,  0.7147],\n",
      "        [ 0.3578,  0.7147]], dtype=torch.float64)\n",
      "85 torch.Size([1, 224, 224]) tensor([[-0.9856, -0.0053],\n",
      "        [-0.9856,  0.1990],\n",
      "        [-0.9446,  0.4033],\n",
      "        [-0.8627,  0.6076],\n",
      "        [-0.8218,  0.8119],\n",
      "        [-0.7398,  0.9754],\n",
      "        [-0.6170,  1.0571],\n",
      "        [-0.5351,  1.1797],\n",
      "        [-0.2893,  1.2206],\n",
      "        [ 0.0383,  1.1797],\n",
      "        [ 0.2431,  1.0980],\n",
      "        [ 0.4889,  0.9345],\n",
      "        [ 0.6527,  0.7711],\n",
      "        [ 0.7346,  0.5259],\n",
      "        [ 0.7346,  0.3216],\n",
      "        [ 0.7346,  0.0764],\n",
      "        [ 0.7346, -0.1279],\n",
      "        [-0.9446, -0.2096],\n",
      "        [-0.8627, -0.2505],\n",
      "        [-0.7808, -0.2913],\n",
      "        [-0.7398, -0.2913],\n",
      "        [-0.6170, -0.2505],\n",
      "        [-0.2074, -0.2913],\n",
      "        [-0.1255, -0.2913],\n",
      "        [ 0.0383, -0.2913],\n",
      "        [ 0.2022, -0.2913],\n",
      "        [ 0.2841, -0.2913],\n",
      "        [-0.3712, -0.0462],\n",
      "        [-0.4122,  0.0764],\n",
      "        [-0.4122,  0.1990],\n",
      "        [-0.4122,  0.3216],\n",
      "        [-0.4531,  0.4033],\n",
      "        [-0.4122,  0.4033],\n",
      "        [-0.3712,  0.4033],\n",
      "        [-0.2893,  0.4033],\n",
      "        [-0.2074,  0.4033],\n",
      "        [-0.7808, -0.0462],\n",
      "        [-0.7398, -0.0462],\n",
      "        [-0.6170, -0.0462],\n",
      "        [-0.5760, -0.0462],\n",
      "        [-0.6170, -0.0053],\n",
      "        [-0.7398, -0.0053],\n",
      "        [-0.1255, -0.0870],\n",
      "        [-0.0845, -0.0870],\n",
      "        [ 0.0383, -0.0870],\n",
      "        [ 0.1202, -0.0870],\n",
      "        [ 0.0383, -0.0462],\n",
      "        [-0.0845, -0.0462],\n",
      "        [-0.5760,  0.7302],\n",
      "        [-0.5351,  0.6076],\n",
      "        [-0.4122,  0.5668],\n",
      "        [-0.3712,  0.5668],\n",
      "        [-0.3303,  0.5668],\n",
      "        [-0.1664,  0.6076],\n",
      "        [-0.0845,  0.6894],\n",
      "        [-0.1664,  0.7711],\n",
      "        [-0.2893,  0.8937],\n",
      "        [-0.3712,  0.8937],\n",
      "        [-0.4122,  0.8937],\n",
      "        [-0.5351,  0.8119],\n",
      "        [-0.5760,  0.7302],\n",
      "        [-0.4122,  0.6894],\n",
      "        [-0.3712,  0.6076],\n",
      "        [-0.2893,  0.6076],\n",
      "        [-0.0845,  0.6894],\n",
      "        [-0.2893,  0.7711],\n",
      "        [-0.3712,  0.7711],\n",
      "        [-0.4122,  0.7711]], dtype=torch.float64)\n",
      "86 torch.Size([1, 224, 224]) tensor([[-0.9745,  0.1549],\n",
      "        [-0.9745,  0.4362],\n",
      "        [-0.8432,  0.6237],\n",
      "        [-0.7869,  0.8488],\n",
      "        [-0.6744,  1.0738],\n",
      "        [-0.5618,  1.2613],\n",
      "        [-0.3930,  1.3738],\n",
      "        [-0.2054,  1.4864],\n",
      "        [ 0.0759,  1.5614],\n",
      "        [ 0.3573,  1.4489],\n",
      "        [ 0.6011,  1.2801],\n",
      "        [ 0.7137,  1.0925],\n",
      "        [ 0.8262,  0.8675],\n",
      "        [ 0.8825,  0.6425],\n",
      "        [ 0.9388,  0.4174],\n",
      "        [ 0.9388,  0.1736],\n",
      "        [ 0.9388, -0.0514],\n",
      "        [-0.9745, -0.0701],\n",
      "        [-0.8432, -0.1264],\n",
      "        [-0.7306, -0.1827],\n",
      "        [-0.6181, -0.1264],\n",
      "        [-0.5056, -0.1264],\n",
      "        [ 0.0759, -0.1827],\n",
      "        [ 0.1885, -0.2389],\n",
      "        [ 0.3010, -0.2952],\n",
      "        [ 0.4698, -0.2952],\n",
      "        [ 0.6011, -0.2202],\n",
      "        [-0.1492,  0.1736],\n",
      "        [-0.1492,  0.3424],\n",
      "        [-0.1492,  0.4549],\n",
      "        [-0.1492,  0.6237],\n",
      "        [-0.2805,  0.6800],\n",
      "        [-0.2054,  0.6800],\n",
      "        [-0.0929,  0.6800],\n",
      "        [ 0.0197,  0.6800],\n",
      "        [ 0.0759,  0.6237],\n",
      "        [-0.7306,  0.1549],\n",
      "        [-0.6744,  0.1549],\n",
      "        [-0.5618,  0.1549],\n",
      "        [-0.3930,  0.1549],\n",
      "        [-0.5056,  0.2111],\n",
      "        [-0.6181,  0.2111],\n",
      "        [ 0.1322,  0.1174],\n",
      "        [ 0.2447,  0.0611],\n",
      "        [ 0.3573,  0.0611],\n",
      "        [ 0.4698,  0.0611],\n",
      "        [ 0.3573,  0.1174],\n",
      "        [ 0.2447,  0.1174],\n",
      "        [-0.3367,  0.9613],\n",
      "        [-0.2805,  0.9050],\n",
      "        [-0.1492,  0.8488],\n",
      "        [-0.0366,  0.8675],\n",
      "        [ 0.0197,  0.8675],\n",
      "        [ 0.1885,  0.8675],\n",
      "        [ 0.3573,  0.9238],\n",
      "        [ 0.2447,  1.0925],\n",
      "        [ 0.1322,  1.1488],\n",
      "        [ 0.0197,  1.2051],\n",
      "        [-0.0929,  1.2051],\n",
      "        [-0.2054,  1.1488],\n",
      "        [-0.3367,  0.9613],\n",
      "        [-0.1492,  0.9238],\n",
      "        [-0.0366,  0.9238],\n",
      "        [ 0.0759,  0.9238],\n",
      "        [ 0.3010,  0.9238],\n",
      "        [ 0.0759,  1.0363],\n",
      "        [ 0.0197,  1.0925],\n",
      "        [-0.0929,  1.0925]], dtype=torch.float64)\n",
      "87 torch.Size([1, 224, 224]) tensor([[-1.1454e+00, -6.5498e-01],\n",
      "        [-1.0573e+00, -3.3251e-01],\n",
      "        [-9.9859e-01, -9.7976e-02],\n",
      "        [-8.8111e-01,  1.0724e-01],\n",
      "        [-7.6363e-01,  3.4177e-01],\n",
      "        [-4.9930e-01,  5.4698e-01],\n",
      "        [-2.6433e-01,  6.6425e-01],\n",
      "        [-2.5377e-16,  7.8151e-01],\n",
      "        [ 3.5244e-01,  8.1083e-01],\n",
      "        [ 6.1678e-01,  6.9356e-01],\n",
      "        [ 6.7552e-01,  4.8835e-01],\n",
      "        [ 7.9300e-01,  3.7108e-01],\n",
      "        [ 8.5174e-01,  1.3655e-01],\n",
      "        [ 8.5174e-01, -1.2729e-01],\n",
      "        [ 8.5174e-01, -3.6182e-01],\n",
      "        [ 9.3985e-01, -6.2567e-01],\n",
      "        [ 8.5174e-01, -8.6020e-01],\n",
      "        [-7.0489e-01, -9.4815e-01],\n",
      "        [-5.5804e-01, -1.0947e+00],\n",
      "        [-3.8181e-01, -1.1534e+00],\n",
      "        [-2.0559e-01, -1.1240e+00],\n",
      "        [-5.8741e-02, -1.0654e+00],\n",
      "        [ 3.5244e-01, -1.1240e+00],\n",
      "        [ 4.9930e-01, -1.1827e+00],\n",
      "        [ 6.1678e-01, -1.1827e+00],\n",
      "        [ 7.3426e-01, -1.1827e+00],\n",
      "        [ 7.9300e-01, -1.1240e+00],\n",
      "        [ 2.3496e-01, -8.8951e-01],\n",
      "        [ 2.3496e-01, -6.8430e-01],\n",
      "        [ 3.5244e-01, -5.0840e-01],\n",
      "        [ 3.5244e-01, -3.9114e-01],\n",
      "        [ 1.1748e-01, -1.8592e-01],\n",
      "        [ 1.7622e-01, -1.8592e-01],\n",
      "        [ 2.9370e-01, -1.8592e-01],\n",
      "        [ 3.5244e-01, -2.4456e-01],\n",
      "        [ 4.4055e-01, -2.4456e-01],\n",
      "        [-4.4055e-01, -7.7225e-01],\n",
      "        [-3.2307e-01, -8.3088e-01],\n",
      "        [-2.0559e-01, -8.3088e-01],\n",
      "        [-1.4685e-01, -8.3088e-01],\n",
      "        [-2.0559e-01, -7.7225e-01],\n",
      "        [-3.2307e-01, -7.7225e-01],\n",
      "        [ 3.5244e-01, -8.8951e-01],\n",
      "        [ 4.9930e-01, -9.4815e-01],\n",
      "        [ 6.1678e-01, -9.4815e-01],\n",
      "        [ 6.7552e-01, -9.4815e-01],\n",
      "        [ 6.1678e-01, -8.8951e-01],\n",
      "        [ 4.9930e-01, -8.3088e-01],\n",
      "        [-5.8741e-02,  1.6587e-01],\n",
      "        [ 1.1748e-01,  1.0724e-01],\n",
      "        [ 2.3496e-01,  4.8606e-02],\n",
      "        [ 3.5244e-01,  4.8606e-02],\n",
      "        [ 4.4055e-01, -1.0027e-02],\n",
      "        [ 4.9930e-01,  4.8606e-02],\n",
      "        [ 6.1678e-01,  1.0724e-01],\n",
      "        [ 5.5804e-01,  1.9519e-01],\n",
      "        [ 4.4055e-01,  3.1245e-01],\n",
      "        [ 3.5244e-01,  3.1245e-01],\n",
      "        [ 2.3496e-01,  3.1245e-01],\n",
      "        [ 1.1748e-01,  3.1245e-01],\n",
      "        [-5.8741e-02,  1.6587e-01],\n",
      "        [ 2.3496e-01,  1.0724e-01],\n",
      "        [ 2.9370e-01,  1.0724e-01],\n",
      "        [ 4.4055e-01,  1.0724e-01],\n",
      "        [ 5.5804e-01,  1.0724e-01],\n",
      "        [ 4.4055e-01,  1.6587e-01],\n",
      "        [ 2.9370e-01,  1.6587e-01],\n",
      "        [ 2.3496e-01,  1.6587e-01]], dtype=torch.float64)\n",
      "88 torch.Size([1, 224, 224]) tensor([[-1.2917, -0.2450],\n",
      "        [-1.2119, -0.0325],\n",
      "        [-1.0791,  0.1533],\n",
      "        [-1.0259,  0.3657],\n",
      "        [-0.8665,  0.6313],\n",
      "        [-0.6008,  0.7906],\n",
      "        [-0.3882,  0.9233],\n",
      "        [-0.0693,  1.0030],\n",
      "        [ 0.2230,  1.0030],\n",
      "        [ 0.4887,  0.8702],\n",
      "        [ 0.5419,  0.6578],\n",
      "        [ 0.6216,  0.5251],\n",
      "        [ 0.6216,  0.2595],\n",
      "        [ 0.7013,  0.0471],\n",
      "        [ 0.7013, -0.1387],\n",
      "        [ 0.6216, -0.3512],\n",
      "        [ 0.5419, -0.6432],\n",
      "        [-0.8665, -0.5105],\n",
      "        [-0.7336, -0.6432],\n",
      "        [-0.5476, -0.7229],\n",
      "        [-0.3882, -0.7760],\n",
      "        [-0.2553, -0.7229],\n",
      "        [ 0.1433, -0.9088],\n",
      "        [ 0.2761, -0.9088],\n",
      "        [ 0.3559, -0.9619],\n",
      "        [ 0.4887, -0.9619],\n",
      "        [ 0.4887, -0.9088],\n",
      "        [ 0.0104, -0.4308],\n",
      "        [ 0.1433, -0.2981],\n",
      "        [ 0.2230, -0.0856],\n",
      "        [ 0.2230,  0.0471],\n",
      "        [-0.0693,  0.1002],\n",
      "        [ 0.0636,  0.1002],\n",
      "        [ 0.1433,  0.1002],\n",
      "        [ 0.2230,  0.1002],\n",
      "        [ 0.2761,  0.0471],\n",
      "        [-0.6008, -0.3777],\n",
      "        [-0.4679, -0.3777],\n",
      "        [-0.3882, -0.4308],\n",
      "        [-0.2553, -0.4308],\n",
      "        [-0.3350, -0.3777],\n",
      "        [-0.4679, -0.2981],\n",
      "        [ 0.2230, -0.5105],\n",
      "        [ 0.2761, -0.5636],\n",
      "        [ 0.4090, -0.6432],\n",
      "        [ 0.4090, -0.6432],\n",
      "        [ 0.4090, -0.5636],\n",
      "        [ 0.2761, -0.5105],\n",
      "        [-0.2022,  0.4454],\n",
      "        [-0.0693,  0.3923],\n",
      "        [ 0.0636,  0.2595],\n",
      "        [ 0.2230,  0.2595],\n",
      "        [ 0.2761,  0.2595],\n",
      "        [ 0.3559,  0.2595],\n",
      "        [ 0.4090,  0.3126],\n",
      "        [ 0.4090,  0.4454],\n",
      "        [ 0.3559,  0.5251],\n",
      "        [ 0.2230,  0.5782],\n",
      "        [ 0.1433,  0.5782],\n",
      "        [ 0.0104,  0.5782],\n",
      "        [-0.2022,  0.4454],\n",
      "        [ 0.0636,  0.3923],\n",
      "        [ 0.2230,  0.3126],\n",
      "        [ 0.2761,  0.3126],\n",
      "        [ 0.4090,  0.3126],\n",
      "        [ 0.2761,  0.3923],\n",
      "        [ 0.2230,  0.4454],\n",
      "        [ 0.0636,  0.4454]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89 torch.Size([1, 224, 224]) tensor([[-0.9724,  0.4738],\n",
      "        [-0.9003,  0.6417],\n",
      "        [-0.7803,  0.9057],\n",
      "        [-0.7083,  1.0977],\n",
      "        [-0.5883,  1.3376],\n",
      "        [-0.4203,  1.5296],\n",
      "        [-0.2763,  1.6496],\n",
      "        [-0.0363,  1.7215],\n",
      "        [ 0.2757,  1.7695],\n",
      "        [ 0.5637,  1.6016],\n",
      "        [ 0.7558,  1.4096],\n",
      "        [ 0.8758,  1.2416],\n",
      "        [ 0.9478,  0.9777],\n",
      "        [ 0.9478,  0.7377],\n",
      "        [ 0.9478,  0.4978],\n",
      "        [ 0.9478,  0.2578],\n",
      "        [ 0.9478, -0.0061],\n",
      "        [-0.9003,  0.0898],\n",
      "        [-0.8523, -0.0301],\n",
      "        [-0.6603, -0.0781],\n",
      "        [-0.5403, -0.0781],\n",
      "        [-0.4203, -0.0061],\n",
      "        [ 0.0837, -0.1981],\n",
      "        [ 0.2037, -0.2461],\n",
      "        [ 0.3237, -0.3181],\n",
      "        [ 0.4437, -0.3181],\n",
      "        [ 0.6358, -0.2461],\n",
      "        [-0.1083,  0.2338],\n",
      "        [-0.0363,  0.4258],\n",
      "        [-0.0363,  0.5938],\n",
      "        [ 0.0117,  0.7857],\n",
      "        [-0.1083,  0.9057],\n",
      "        [-0.0363,  0.9057],\n",
      "        [ 0.0837,  0.9057],\n",
      "        [ 0.1317,  0.8577],\n",
      "        [ 0.2037,  0.7857],\n",
      "        [-0.6603,  0.2818],\n",
      "        [-0.5883,  0.2818],\n",
      "        [-0.4683,  0.2338],\n",
      "        [-0.3483,  0.2818],\n",
      "        [-0.4203,  0.3538],\n",
      "        [-0.5403,  0.3538],\n",
      "        [ 0.2037,  0.1858],\n",
      "        [ 0.2757,  0.1138],\n",
      "        [ 0.3957,  0.0658],\n",
      "        [ 0.5157,  0.0658],\n",
      "        [ 0.3957,  0.1138],\n",
      "        [ 0.2757,  0.1858],\n",
      "        [-0.2283,  1.1456],\n",
      "        [-0.1083,  1.1456],\n",
      "        [ 0.0117,  1.0977],\n",
      "        [ 0.0837,  1.0977],\n",
      "        [ 0.1317,  1.0497],\n",
      "        [ 0.3237,  1.0497],\n",
      "        [ 0.4437,  1.0497],\n",
      "        [ 0.3237,  1.1696],\n",
      "        [ 0.2757,  1.2176],\n",
      "        [ 0.1317,  1.2176],\n",
      "        [ 0.0117,  1.2896],\n",
      "        [-0.1083,  1.2176],\n",
      "        [-0.2283,  1.1456],\n",
      "        [ 0.0117,  1.1696],\n",
      "        [ 0.0837,  1.1696],\n",
      "        [ 0.2037,  1.0977],\n",
      "        [ 0.4437,  1.0497],\n",
      "        [ 0.2037,  1.0977],\n",
      "        [ 0.0837,  1.1696],\n",
      "        [ 0.0117,  1.1696]], dtype=torch.float64)\n",
      "90 torch.Size([1, 224, 224]) tensor([[-0.8184, -0.0792],\n",
      "        [-0.8184,  0.1765],\n",
      "        [-0.8184,  0.4039],\n",
      "        [-0.7615,  0.6028],\n",
      "        [-0.6762,  0.8301],\n",
      "        [-0.5624,  1.0290],\n",
      "        [-0.3918,  1.1426],\n",
      "        [-0.1928,  1.1995],\n",
      "        [ 0.0916,  1.2847],\n",
      "        [ 0.4043,  1.2279],\n",
      "        [ 0.5750,  1.1142],\n",
      "        [ 0.6887,  1.0574],\n",
      "        [ 0.8877,  0.8585],\n",
      "        [ 0.9446,  0.6312],\n",
      "        [ 1.0015,  0.4323],\n",
      "        [ 1.0015,  0.2050],\n",
      "        [ 1.0583, -0.0223],\n",
      "        [-0.6762, -0.3065],\n",
      "        [-0.5624, -0.3633],\n",
      "        [-0.4487, -0.4201],\n",
      "        [-0.3350, -0.4201],\n",
      "        [-0.1928, -0.3633],\n",
      "        [ 0.2906, -0.4201],\n",
      "        [ 0.4043, -0.4201],\n",
      "        [ 0.5181, -0.4201],\n",
      "        [ 0.6318, -0.3917],\n",
      "        [ 0.8309, -0.2781],\n",
      "        [ 0.0347, -0.1076],\n",
      "        [-0.0222,  0.0061],\n",
      "        [-0.0222,  0.1765],\n",
      "        [-0.0222,  0.3186],\n",
      "        [-0.1359,  0.4323],\n",
      "        [-0.0790,  0.4323],\n",
      "        [ 0.0347,  0.4323],\n",
      "        [ 0.0916,  0.4323],\n",
      "        [ 0.1484,  0.4323],\n",
      "        [-0.5056, -0.1076],\n",
      "        [-0.4487, -0.1928],\n",
      "        [-0.3350, -0.1644],\n",
      "        [-0.1928, -0.1076],\n",
      "        [-0.3350, -0.0508],\n",
      "        [-0.4487, -0.0508],\n",
      "        [ 0.2906, -0.1076],\n",
      "        [ 0.4043, -0.1644],\n",
      "        [ 0.5181, -0.1644],\n",
      "        [ 0.5750, -0.1076],\n",
      "        [ 0.5181, -0.0508],\n",
      "        [ 0.4043, -0.0508],\n",
      "        [-0.2781,  0.7164],\n",
      "        [-0.1928,  0.6596],\n",
      "        [-0.0222,  0.6028],\n",
      "        [ 0.0347,  0.6028],\n",
      "        [ 0.0916,  0.6028],\n",
      "        [ 0.2053,  0.6880],\n",
      "        [ 0.3475,  0.7448],\n",
      "        [ 0.2053,  0.8017],\n",
      "        [ 0.1484,  0.8017],\n",
      "        [ 0.0347,  0.8017],\n",
      "        [-0.0790,  0.8017],\n",
      "        [-0.1359,  0.8017],\n",
      "        [-0.2781,  0.7164],\n",
      "        [-0.0790,  0.7448],\n",
      "        [ 0.0347,  0.6596],\n",
      "        [ 0.1484,  0.7448],\n",
      "        [ 0.3475,  0.7448],\n",
      "        [ 0.0916,  0.7448],\n",
      "        [ 0.0347,  0.7448],\n",
      "        [-0.0790,  0.7448]], dtype=torch.float64)\n",
      "91 torch.Size([1, 224, 224]) tensor([[-1.3586, -0.5506],\n",
      "        [-1.3586, -0.3213],\n",
      "        [-1.3127, -0.0920],\n",
      "        [-1.2516,  0.1221],\n",
      "        [-1.1905,  0.4125],\n",
      "        [-1.0224,  0.6419],\n",
      "        [-0.9154,  0.8101],\n",
      "        [-0.6862,  0.9324],\n",
      "        [-0.4112,  1.0394],\n",
      "        [-0.1209,  0.9324],\n",
      "        [-0.0139,  0.8253],\n",
      "        [ 0.0931,  0.7183],\n",
      "        [ 0.2153,  0.4890],\n",
      "        [ 0.3223,  0.2597],\n",
      "        [ 0.3834,  0.0915],\n",
      "        [ 0.4445, -0.1226],\n",
      "        [ 0.4904, -0.3519],\n",
      "        [-1.0224, -0.7188],\n",
      "        [-0.8543, -0.7647],\n",
      "        [-0.6862, -0.7647],\n",
      "        [-0.5793, -0.7035],\n",
      "        [-0.4570, -0.6424],\n",
      "        [ 0.0472, -0.6424],\n",
      "        [ 0.1542, -0.6424],\n",
      "        [ 0.2611, -0.6882],\n",
      "        [ 0.3834, -0.6882],\n",
      "        [ 0.4445, -0.6271],\n",
      "        [-0.2431, -0.3672],\n",
      "        [-0.2431, -0.1990],\n",
      "        [-0.2431, -0.0308],\n",
      "        [-0.2431,  0.1526],\n",
      "        [-0.4112,  0.1985],\n",
      "        [-0.3501,  0.2597],\n",
      "        [-0.2890,  0.2597],\n",
      "        [-0.1820,  0.2597],\n",
      "        [-0.1209,  0.1985],\n",
      "        [-0.8543, -0.4895],\n",
      "        [-0.7474, -0.5353],\n",
      "        [-0.5793, -0.5353],\n",
      "        [-0.5182, -0.4283],\n",
      "        [-0.5793, -0.4283],\n",
      "        [-0.7474, -0.4283],\n",
      "        [-0.0139, -0.4130],\n",
      "        [ 0.1542, -0.4742],\n",
      "        [ 0.2611, -0.4742],\n",
      "        [ 0.3223, -0.4130],\n",
      "        [ 0.2153, -0.3519],\n",
      "        [ 0.0931, -0.3519],\n",
      "        [-0.6404,  0.4737],\n",
      "        [-0.5182,  0.4737],\n",
      "        [-0.3501,  0.4278],\n",
      "        [-0.2890,  0.4278],\n",
      "        [-0.2431,  0.4278],\n",
      "        [-0.1209,  0.4890],\n",
      "        [-0.0750,  0.4890],\n",
      "        [-0.1209,  0.5960],\n",
      "        [-0.2431,  0.6572],\n",
      "        [-0.2890,  0.6572],\n",
      "        [-0.4112,  0.6572],\n",
      "        [-0.5182,  0.5960],\n",
      "        [-0.6404,  0.4737],\n",
      "        [-0.4112,  0.5349],\n",
      "        [-0.2890,  0.5349],\n",
      "        [-0.2431,  0.4890],\n",
      "        [-0.0750,  0.4890],\n",
      "        [-0.2431,  0.5349],\n",
      "        [-0.2890,  0.5349],\n",
      "        [-0.4112,  0.5349]], dtype=torch.float64)\n",
      "92 torch.Size([1, 224, 224]) tensor([[-1.1696, -0.4769],\n",
      "        [-1.0952, -0.1979],\n",
      "        [-1.0394,  0.0067],\n",
      "        [-0.9650,  0.2113],\n",
      "        [-0.8348,  0.4159],\n",
      "        [-0.6302,  0.6391],\n",
      "        [-0.4256,  0.6949],\n",
      "        [-0.1466,  0.8438],\n",
      "        [ 0.2068,  0.8438],\n",
      "        [ 0.5417,  0.7879],\n",
      "        [ 0.6719,  0.6577],\n",
      "        [ 0.8207,  0.5089],\n",
      "        [ 0.9509,  0.3043],\n",
      "        [ 1.0253,  0.0997],\n",
      "        [ 1.0253, -0.1049],\n",
      "        [ 1.0253, -0.3095],\n",
      "        [ 1.0253, -0.5699],\n",
      "        [-0.7604, -0.5327],\n",
      "        [-0.6302, -0.6071],\n",
      "        [-0.4256, -0.6071],\n",
      "        [-0.2768, -0.5885],\n",
      "        [-0.1466, -0.5885],\n",
      "        [ 0.4115, -0.6629],\n",
      "        [ 0.5417, -0.6443],\n",
      "        [ 0.6719, -0.6443],\n",
      "        [ 0.8207, -0.6443],\n",
      "        [ 0.9509, -0.6443],\n",
      "        [ 0.2068, -0.3839],\n",
      "        [ 0.2068, -0.1793],\n",
      "        [ 0.2068,  0.0253],\n",
      "        [ 0.2068,  0.0997],\n",
      "        [ 0.0022,  0.0997],\n",
      "        [ 0.0580,  0.1555],\n",
      "        [ 0.2068,  0.1555],\n",
      "        [ 0.2626,  0.1741],\n",
      "        [ 0.3371,  0.0997],\n",
      "        [-0.5558, -0.4025],\n",
      "        [-0.4256, -0.4025],\n",
      "        [-0.2768, -0.3839],\n",
      "        [-0.1466, -0.3839],\n",
      "        [-0.2768, -0.3281],\n",
      "        [-0.4256, -0.3281],\n",
      "        [ 0.4115, -0.3839],\n",
      "        [ 0.5417, -0.4397],\n",
      "        [ 0.6719, -0.4397],\n",
      "        [ 0.7463, -0.4397],\n",
      "        [ 0.6719, -0.3839],\n",
      "        [ 0.5417, -0.3839],\n",
      "        [-0.2768,  0.2857],\n",
      "        [-0.1466,  0.3043],\n",
      "        [ 0.0580,  0.2299],\n",
      "        [ 0.2068,  0.3043],\n",
      "        [ 0.2626,  0.2299],\n",
      "        [ 0.4673,  0.2299],\n",
      "        [ 0.5417,  0.2299],\n",
      "        [ 0.4115,  0.3787],\n",
      "        [ 0.3371,  0.4345],\n",
      "        [ 0.2068,  0.4345],\n",
      "        [ 0.0580,  0.4345],\n",
      "        [-0.0722,  0.3601],\n",
      "        [-0.2768,  0.2857],\n",
      "        [ 0.0580,  0.3043],\n",
      "        [ 0.2068,  0.3043],\n",
      "        [ 0.3371,  0.3043],\n",
      "        [ 0.5417,  0.2299],\n",
      "        [ 0.2626,  0.3043],\n",
      "        [ 0.2068,  0.3043],\n",
      "        [ 0.0580,  0.3043]], dtype=torch.float64)\n",
      "93 torch.Size([1, 224, 224]) tensor([[-0.1137, -0.5743],\n",
      "        [-0.1137, -0.3583],\n",
      "        [-0.0778, -0.1783],\n",
      "        [-0.0778, -0.0343],\n",
      "        [-0.0060,  0.1817],\n",
      "        [ 0.1018,  0.3618],\n",
      "        [ 0.2455,  0.4338],\n",
      "        [ 0.4252,  0.5778],\n",
      "        [ 0.6407,  0.6498],\n",
      "        [ 0.8563,  0.5418],\n",
      "        [ 1.0000,  0.4698],\n",
      "        [ 1.1078,  0.3257],\n",
      "        [ 1.2156,  0.1457],\n",
      "        [ 1.2515, -0.0343],\n",
      "        [ 1.2874, -0.1423],\n",
      "        [ 1.3233, -0.3223],\n",
      "        [ 1.3233, -0.5383],\n",
      "        [ 0.1018, -0.6103],\n",
      "        [ 0.1737, -0.6463],\n",
      "        [ 0.2815, -0.6463],\n",
      "        [ 0.4252, -0.6463],\n",
      "        [ 0.4970, -0.6463],\n",
      "        [ 0.8563, -0.6103],\n",
      "        [ 0.9281, -0.6463],\n",
      "        [ 1.0359, -0.6463],\n",
      "        [ 1.1796, -0.6463],\n",
      "        [ 1.2515, -0.6103],\n",
      "        [ 0.6767, -0.4303],\n",
      "        [ 0.6767, -0.2863],\n",
      "        [ 0.6767, -0.1783],\n",
      "        [ 0.6767, -0.0703],\n",
      "        [ 0.4970,  0.0017],\n",
      "        [ 0.5689,  0.0017],\n",
      "        [ 0.6407,  0.0017],\n",
      "        [ 0.7126,  0.0017],\n",
      "        [ 0.7485,  0.0017],\n",
      "        [ 0.2455, -0.5023],\n",
      "        [ 0.3174, -0.4663],\n",
      "        [ 0.4252, -0.4663],\n",
      "        [ 0.4611, -0.4303],\n",
      "        [ 0.4252, -0.4303],\n",
      "        [ 0.3174, -0.4303],\n",
      "        [ 0.8563, -0.4663],\n",
      "        [ 0.9281, -0.4663],\n",
      "        [ 1.0359, -0.4663],\n",
      "        [ 1.1078, -0.4663],\n",
      "        [ 1.0359, -0.4303],\n",
      "        [ 0.9281, -0.4303],\n",
      "        [ 0.3533,  0.1817],\n",
      "        [ 0.4970,  0.1817],\n",
      "        [ 0.5689,  0.1457],\n",
      "        [ 0.6767,  0.1817],\n",
      "        [ 0.7126,  0.1457],\n",
      "        [ 0.8204,  0.1817],\n",
      "        [ 0.8922,  0.1817],\n",
      "        [ 0.8204,  0.2897],\n",
      "        [ 0.7126,  0.3257],\n",
      "        [ 0.6407,  0.3257],\n",
      "        [ 0.5689,  0.3257],\n",
      "        [ 0.4970,  0.2897],\n",
      "        [ 0.4252,  0.1817],\n",
      "        [ 0.5689,  0.2177],\n",
      "        [ 0.6407,  0.2177],\n",
      "        [ 0.7126,  0.2177],\n",
      "        [ 0.8563,  0.1817],\n",
      "        [ 0.7126,  0.2177],\n",
      "        [ 0.6407,  0.2177],\n",
      "        [ 0.5689,  0.2177]], dtype=torch.float64)\n",
      "94 torch.Size([1, 224, 224]) tensor([[-0.8684, -0.2141],\n",
      "        [-0.8199,  0.0519],\n",
      "        [-0.7714,  0.2454],\n",
      "        [-0.7228,  0.4146],\n",
      "        [-0.6743,  0.6807],\n",
      "        [-0.5045,  0.8258],\n",
      "        [-0.4074,  0.9950],\n",
      "        [-0.1891,  1.0918],\n",
      "        [ 0.0536,  1.1885],\n",
      "        [ 0.3205,  1.0918],\n",
      "        [ 0.5388,  0.9950],\n",
      "        [ 0.6359,  0.8499],\n",
      "        [ 0.7329,  0.6323],\n",
      "        [ 0.7814,  0.4388],\n",
      "        [ 0.8300,  0.2212],\n",
      "        [ 0.8300,  0.0277],\n",
      "        [ 0.8300, -0.2383],\n",
      "        [-0.6743, -0.4801],\n",
      "        [-0.5530, -0.5285],\n",
      "        [-0.4074, -0.5769],\n",
      "        [-0.3104, -0.5769],\n",
      "        [-0.1891, -0.5043],\n",
      "        [ 0.2719, -0.5043],\n",
      "        [ 0.3690, -0.5527],\n",
      "        [ 0.4660, -0.5527],\n",
      "        [ 0.5873, -0.5527],\n",
      "        [ 0.6844, -0.4560],\n",
      "        [ 0.0536, -0.2625],\n",
      "        [ 0.0536, -0.0932],\n",
      "        [ 0.0536,  0.0519],\n",
      "        [ 0.0536,  0.1728],\n",
      "        [-0.0920,  0.2695],\n",
      "        [-0.0435,  0.2695],\n",
      "        [ 0.0536,  0.2695],\n",
      "        [ 0.1021,  0.2695],\n",
      "        [ 0.2234,  0.2695],\n",
      "        [-0.5045, -0.2625],\n",
      "        [-0.4074, -0.3109],\n",
      "        [-0.3104, -0.3109],\n",
      "        [-0.1891, -0.2625],\n",
      "        [-0.3104, -0.2141],\n",
      "        [-0.4074, -0.2141],\n",
      "        [ 0.2719, -0.2383],\n",
      "        [ 0.3690, -0.2867],\n",
      "        [ 0.4660, -0.2867],\n",
      "        [ 0.5388, -0.2383],\n",
      "        [ 0.4175, -0.2383],\n",
      "        [ 0.3205, -0.2383],\n",
      "        [-0.3104,  0.5114],\n",
      "        [-0.1891,  0.4630],\n",
      "        [-0.0435,  0.4146],\n",
      "        [ 0.0536,  0.4146],\n",
      "        [ 0.1021,  0.4146],\n",
      "        [ 0.2719,  0.4872],\n",
      "        [ 0.3690,  0.5839],\n",
      "        [ 0.2719,  0.6807],\n",
      "        [ 0.1749,  0.7290],\n",
      "        [ 0.0536,  0.7290],\n",
      "        [-0.0920,  0.7290],\n",
      "        [-0.1891,  0.6807],\n",
      "        [-0.3104,  0.5114],\n",
      "        [-0.0435,  0.4630],\n",
      "        [ 0.0536,  0.4630],\n",
      "        [ 0.1749,  0.5356],\n",
      "        [ 0.3205,  0.5839],\n",
      "        [ 0.1021,  0.6323],\n",
      "        [ 0.0536,  0.6323],\n",
      "        [-0.0435,  0.6323]], dtype=torch.float64)\n",
      "95 torch.Size([1, 224, 224]) tensor([[-0.4487, -0.8079],\n",
      "        [-0.4487, -0.6316],\n",
      "        [-0.3603, -0.4332],\n",
      "        [-0.3161, -0.2568],\n",
      "        [-0.2498, -0.0584],\n",
      "        [-0.1614,  0.1179],\n",
      "        [-0.0288,  0.2281],\n",
      "        [ 0.1259,  0.3163],\n",
      "        [ 0.3469,  0.3604],\n",
      "        [ 0.6342,  0.3163],\n",
      "        [ 0.8110,  0.1840],\n",
      "        [ 0.9657,  0.0959],\n",
      "        [ 1.0983, -0.0364],\n",
      "        [ 1.1425, -0.2789],\n",
      "        [ 1.1867, -0.4111],\n",
      "        [ 1.1867, -0.6095],\n",
      "        [ 1.2530, -0.8300],\n",
      "        [-0.3603, -1.0945],\n",
      "        [-0.2498, -1.1827],\n",
      "        [-0.1172, -1.1827],\n",
      "        [-0.0288, -1.1827],\n",
      "        [ 0.0596, -1.1827],\n",
      "        [ 0.5016, -1.1827],\n",
      "        [ 0.5900, -1.2268],\n",
      "        [ 0.7226, -1.2268],\n",
      "        [ 0.8773, -1.1827],\n",
      "        [ 0.9657, -1.0725],\n",
      "        [ 0.2585, -0.9402],\n",
      "        [ 0.2585, -0.8079],\n",
      "        [ 0.2585, -0.7198],\n",
      "        [ 0.2585, -0.5654],\n",
      "        [ 0.1701, -0.4773],\n",
      "        [ 0.2143, -0.4773],\n",
      "        [ 0.2585, -0.4332],\n",
      "        [ 0.3469, -0.4773],\n",
      "        [ 0.4353, -0.4773],\n",
      "        [-0.2056, -0.9181],\n",
      "        [-0.1172, -0.9622],\n",
      "        [-0.0288, -0.9622],\n",
      "        [ 0.0596, -0.8961],\n",
      "        [-0.0288, -0.8520],\n",
      "        [-0.1172, -0.8520],\n",
      "        [ 0.5458, -0.8961],\n",
      "        [ 0.6342, -0.9402],\n",
      "        [ 0.7226, -0.9402],\n",
      "        [ 0.8110, -0.9402],\n",
      "        [ 0.7226, -0.8961],\n",
      "        [ 0.5900, -0.8961],\n",
      "        [ 0.0154, -0.1466],\n",
      "        [ 0.0596, -0.2568],\n",
      "        [ 0.2143, -0.3450],\n",
      "        [ 0.3027, -0.3009],\n",
      "        [ 0.3469, -0.3450],\n",
      "        [ 0.5458, -0.2789],\n",
      "        [ 0.6784, -0.1907],\n",
      "        [ 0.5458, -0.1025],\n",
      "        [ 0.3911, -0.0144],\n",
      "        [ 0.3027, -0.0144],\n",
      "        [ 0.2143, -0.0144],\n",
      "        [ 0.1259, -0.0584],\n",
      "        [ 0.0154, -0.1466],\n",
      "        [ 0.2143, -0.2348],\n",
      "        [ 0.3027, -0.2348],\n",
      "        [ 0.3911, -0.2348],\n",
      "        [ 0.6342, -0.1907],\n",
      "        [ 0.3911, -0.1025],\n",
      "        [ 0.3027, -0.1025],\n",
      "        [ 0.2143, -0.1025]], dtype=torch.float64)\n",
      "96 torch.Size([1, 224, 224]) tensor([[-0.5298, -0.6238],\n",
      "        [-0.5298, -0.4536],\n",
      "        [-0.4678, -0.2834],\n",
      "        [-0.4213, -0.1133],\n",
      "        [-0.2972,  0.1188],\n",
      "        [-0.1887,  0.2425],\n",
      "        [-0.1267,  0.3508],\n",
      "        [ 0.0438,  0.3508],\n",
      "        [ 0.2763,  0.3508],\n",
      "        [ 0.5553,  0.2425],\n",
      "        [ 0.7258,  0.1342],\n",
      "        [ 0.8963,  0.0260],\n",
      "        [ 1.0668, -0.1442],\n",
      "        [ 1.1288, -0.3144],\n",
      "        [ 1.1288, -0.5464],\n",
      "        [ 1.1288, -0.7166],\n",
      "        [ 1.1288, -0.9486],\n",
      "        [-0.5918, -0.9177],\n",
      "        [-0.5298, -1.0260],\n",
      "        [-0.4213, -1.0878],\n",
      "        [-0.3593, -1.0724],\n",
      "        [-0.2507, -1.0724],\n",
      "        [ 0.1523, -1.1342],\n",
      "        [ 0.2763, -1.1806],\n",
      "        [ 0.3848, -1.2425],\n",
      "        [ 0.5553, -1.2425],\n",
      "        [ 0.7258, -1.1806],\n",
      "        [-0.0182, -0.9022],\n",
      "        [-0.0182, -0.7939],\n",
      "        [-0.0802, -0.7320],\n",
      "        [-0.0802, -0.6238],\n",
      "        [-0.1267, -0.5000],\n",
      "        [-0.0802, -0.5000],\n",
      "        [ 0.0438, -0.5000],\n",
      "        [ 0.1058, -0.5000],\n",
      "        [ 0.1523, -0.5619],\n",
      "        [-0.4213, -0.7320],\n",
      "        [-0.3593, -0.7939],\n",
      "        [-0.2507, -0.8558],\n",
      "        [-0.1887, -0.7939],\n",
      "        [-0.2507, -0.7939],\n",
      "        [-0.3593, -0.7320],\n",
      "        [ 0.2763, -0.9022],\n",
      "        [ 0.3228, -0.9486],\n",
      "        [ 0.4468, -1.0105],\n",
      "        [ 0.5553, -0.9486],\n",
      "        [ 0.4933, -0.9022],\n",
      "        [ 0.3848, -0.9022],\n",
      "        [-0.1887, -0.1133],\n",
      "        [-0.1267, -0.2834],\n",
      "        [-0.0802, -0.3917],\n",
      "        [ 0.0438, -0.3917],\n",
      "        [ 0.1058, -0.4381],\n",
      "        [ 0.2143, -0.3298],\n",
      "        [ 0.3228, -0.2061],\n",
      "        [ 0.2763, -0.0978],\n",
      "        [ 0.2143, -0.0514],\n",
      "        [ 0.1058,  0.0105],\n",
      "        [-0.0182,  0.0105],\n",
      "        [-0.0802, -0.0514],\n",
      "        [-0.1267, -0.1133],\n",
      "        [-0.0182, -0.2680],\n",
      "        [ 0.0438, -0.3298],\n",
      "        [ 0.1058, -0.3298],\n",
      "        [ 0.3228, -0.2061],\n",
      "        [ 0.1523, -0.1597],\n",
      "        [ 0.1058, -0.0978],\n",
      "        [-0.0182, -0.0978]], dtype=torch.float64)\n",
      "97 torch.Size([1, 224, 224]) tensor([[-0.9366, -0.5174],\n",
      "        [-0.9366, -0.2435],\n",
      "        [-0.8544,  0.0578],\n",
      "        [-0.8544,  0.2495],\n",
      "        [-0.7996,  0.5508],\n",
      "        [-0.6353,  0.7425],\n",
      "        [-0.4984,  0.9616],\n",
      "        [-0.3614,  1.0985],\n",
      "        [-0.0054,  1.2628],\n",
      "        [ 0.3233,  1.1807],\n",
      "        [ 0.6246,  1.0437],\n",
      "        [ 0.7615,  0.9068],\n",
      "        [ 0.9806,  0.7151],\n",
      "        [ 1.0354,  0.3590],\n",
      "        [ 1.1176,  0.1399],\n",
      "        [ 1.1723, -0.1339],\n",
      "        [ 1.2545, -0.4078],\n",
      "        [-0.7996, -0.7912],\n",
      "        [-0.7175, -0.8734],\n",
      "        [-0.5805, -0.8734],\n",
      "        [-0.4436, -0.8460],\n",
      "        [-0.3066, -0.8460],\n",
      "        [ 0.2685, -0.7912],\n",
      "        [ 0.4055, -0.8460],\n",
      "        [ 0.5424, -0.8460],\n",
      "        [ 0.7615, -0.8460],\n",
      "        [ 0.8984, -0.7091],\n",
      "        [-0.0054, -0.4900],\n",
      "        [-0.0875, -0.3530],\n",
      "        [-0.0875, -0.2161],\n",
      "        [-0.0875, -0.0792],\n",
      "        [-0.2245,  0.0578],\n",
      "        [-0.1423,  0.0578],\n",
      "        [-0.0875,  0.1399],\n",
      "        [ 0.0494,  0.0578],\n",
      "        [ 0.1316,  0.0578],\n",
      "        [-0.6353, -0.5721],\n",
      "        [-0.5805, -0.5721],\n",
      "        [-0.4436, -0.5721],\n",
      "        [-0.3066, -0.5174],\n",
      "        [-0.4436, -0.5174],\n",
      "        [-0.4984, -0.5174],\n",
      "        [ 0.2685, -0.4900],\n",
      "        [ 0.4055, -0.5721],\n",
      "        [ 0.5424, -0.5721],\n",
      "        [ 0.6793, -0.4900],\n",
      "        [ 0.5424, -0.4900],\n",
      "        [ 0.4055, -0.4900],\n",
      "        [-0.4436,  0.4686],\n",
      "        [-0.3614,  0.3317],\n",
      "        [-0.1423,  0.2769],\n",
      "        [-0.0875,  0.2769],\n",
      "        [ 0.0494,  0.2769],\n",
      "        [ 0.1863,  0.3317],\n",
      "        [ 0.3233,  0.4960],\n",
      "        [ 0.1863,  0.6877],\n",
      "        [ 0.0494,  0.7699],\n",
      "        [-0.0875,  0.7699],\n",
      "        [-0.2245,  0.7699],\n",
      "        [-0.3614,  0.6877],\n",
      "        [-0.4436,  0.4686],\n",
      "        [-0.2245,  0.3317],\n",
      "        [-0.0875,  0.3317],\n",
      "        [ 0.0494,  0.3317],\n",
      "        [ 0.3233,  0.4960],\n",
      "        [ 0.0494,  0.6329],\n",
      "        [-0.0875,  0.6329],\n",
      "        [-0.2245,  0.6055]], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98 torch.Size([1, 224, 224]) tensor([[-0.6933, -0.1808],\n",
      "        [-0.5446,  0.0982],\n",
      "        [-0.4145,  0.3028],\n",
      "        [-0.2844,  0.5074],\n",
      "        [-0.0800,  0.6562],\n",
      "        [ 0.2731,  0.7865],\n",
      "        [ 0.5519,  0.8051],\n",
      "        [ 0.8121,  0.7307],\n",
      "        [ 1.1652,  0.7307],\n",
      "        [ 1.4254,  0.5446],\n",
      "        [ 1.5741,  0.4702],\n",
      "        [ 1.6484,  0.3400],\n",
      "        [ 1.7042,  0.0610],\n",
      "        [ 1.7042, -0.1436],\n",
      "        [ 1.6484, -0.4226],\n",
      "        [ 1.5741, -0.6272],\n",
      "        [ 1.4254, -0.9062],\n",
      "        [-0.3401, -0.6644],\n",
      "        [-0.2101, -0.8690],\n",
      "        [-0.0056, -0.9249],\n",
      "        [ 0.1245, -0.9993],\n",
      "        [ 0.2731, -0.9993],\n",
      "        [ 0.8121, -1.1109],\n",
      "        [ 0.8864, -1.1853],\n",
      "        [ 1.0165, -1.2597],\n",
      "        [ 1.1652, -1.2597],\n",
      "        [ 1.2953, -1.1853],\n",
      "        [ 0.6077, -0.8504],\n",
      "        [ 0.7563, -0.7016],\n",
      "        [ 0.8121, -0.6458],\n",
      "        [ 0.8864, -0.4970],\n",
      "        [ 0.6820, -0.2366],\n",
      "        [ 0.8121, -0.2366],\n",
      "        [ 0.8864, -0.2924],\n",
      "        [ 0.9608, -0.2924],\n",
      "        [ 1.0165, -0.3668],\n",
      "        [-0.0056, -0.5900],\n",
      "        [ 0.0687, -0.6458],\n",
      "        [ 0.1988, -0.6458],\n",
      "        [ 0.3289, -0.6458],\n",
      "        [ 0.1988, -0.5714],\n",
      "        [ 0.1245, -0.5900],\n",
      "        [ 0.8864, -0.8504],\n",
      "        [ 0.9608, -0.9062],\n",
      "        [ 1.0909, -0.9807],\n",
      "        [ 1.1652, -0.9062],\n",
      "        [ 1.0909, -0.8318],\n",
      "        [ 1.0165, -0.8504],\n",
      "        [ 0.5519,  0.1726],\n",
      "        [ 0.6820,  0.0424],\n",
      "        [ 0.8864, -0.0134],\n",
      "        [ 0.9608, -0.0134],\n",
      "        [ 1.0165, -0.0878],\n",
      "        [ 1.1652, -0.0878],\n",
      "        [ 1.2953, -0.0134],\n",
      "        [ 1.2210,  0.0610],\n",
      "        [ 1.1652,  0.1168],\n",
      "        [ 1.0165,  0.1912],\n",
      "        [ 0.8864,  0.1912],\n",
      "        [ 0.7563,  0.1912],\n",
      "        [ 0.5519,  0.1726],\n",
      "        [ 0.8864,  0.0424],\n",
      "        [ 0.9608,  0.0424],\n",
      "        [ 1.0909, -0.0134],\n",
      "        [ 1.2953, -0.0134],\n",
      "        [ 1.0909, -0.0134],\n",
      "        [ 1.0165,  0.0424],\n",
      "        [ 0.8864,  0.0424]], dtype=torch.float64)\n",
      "99 torch.Size([1, 224, 224]) tensor([[-0.9705, -1.2133],\n",
      "        [-1.0149, -0.9915],\n",
      "        [-1.0149, -0.8140],\n",
      "        [-1.0816, -0.5922],\n",
      "        [-1.0816, -0.3704],\n",
      "        [-1.0149, -0.1929],\n",
      "        [-0.9039, -0.0820],\n",
      "        [-0.7928, -0.0154],\n",
      "        [-0.5041,  0.0955],\n",
      "        [-0.2154,  0.0955],\n",
      "        [ 0.0512,  0.2064],\n",
      "        [ 0.2955,  0.1621],\n",
      "        [ 0.5176,  0.0511],\n",
      "        [ 0.6953, -0.1263],\n",
      "        [ 0.8063, -0.2816],\n",
      "        [ 0.9174, -0.5035],\n",
      "        [ 0.9618, -0.7475],\n",
      "        [-0.7928, -1.5017],\n",
      "        [-0.7262, -1.6126],\n",
      "        [-0.5707, -1.6126],\n",
      "        [-0.4597, -1.6126],\n",
      "        [-0.3930, -1.5461],\n",
      "        [ 0.0512, -1.5461],\n",
      "        [ 0.2289, -1.5461],\n",
      "        [ 0.3399, -1.5461],\n",
      "        [ 0.5176, -1.4795],\n",
      "        [ 0.6286, -1.3021],\n",
      "        [-0.2154, -1.3243],\n",
      "        [-0.2820, -1.1468],\n",
      "        [-0.2820, -1.0359],\n",
      "        [-0.3264, -0.9250],\n",
      "        [-0.5041, -0.7697],\n",
      "        [-0.4597, -0.7697],\n",
      "        [-0.3264, -0.7475],\n",
      "        [-0.2820, -0.7475],\n",
      "        [-0.1709, -0.7475],\n",
      "        [-0.6818, -1.2799],\n",
      "        [-0.6151, -1.3243],\n",
      "        [-0.5041, -1.3243],\n",
      "        [-0.3930, -1.2799],\n",
      "        [-0.5041, -1.2799],\n",
      "        [-0.6151, -1.2799],\n",
      "        [ 0.1178, -1.2133],\n",
      "        [ 0.2289, -1.2577],\n",
      "        [ 0.3399, -1.2577],\n",
      "        [ 0.4510, -1.1912],\n",
      "        [ 0.3399, -1.1468],\n",
      "        [ 0.1844, -1.1468],\n",
      "        [-0.7928, -0.5478],\n",
      "        [-0.6151, -0.5922],\n",
      "        [-0.5041, -0.6366],\n",
      "        [-0.3930, -0.5922],\n",
      "        [-0.3264, -0.5922],\n",
      "        [-0.1709, -0.4813],\n",
      "        [-0.1043, -0.3482],\n",
      "        [-0.2154, -0.3038],\n",
      "        [-0.3930, -0.3038],\n",
      "        [-0.5041, -0.3704],\n",
      "        [-0.6151, -0.3704],\n",
      "        [-0.6818, -0.4147],\n",
      "        [-0.7928, -0.5478],\n",
      "        [-0.5041, -0.5256],\n",
      "        [-0.4597, -0.5256],\n",
      "        [-0.3264, -0.4813],\n",
      "        [-0.1043, -0.3482],\n",
      "        [-0.3264, -0.4147],\n",
      "        [-0.4597, -0.4147],\n",
      "        [-0.5707, -0.4813]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# print some stats about the transformed data\n",
    "print('Number of images: ', len(transformed_dataset))\n",
    "\n",
    "# make sure the sample tensors are the expected size\n",
    "for i in range(100):\n",
    "    sample = transformed_dataset[i]\n",
    "    print(i, sample['image'].size(), sample['keypoints'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Iteration and Batching\n",
    "\n",
    "Right now, we are iterating over this data using a ``for`` loop, but we are missing out on a lot of PyTorch's dataset capabilities, specifically the abilities to:\n",
    "\n",
    "-  Batch the data\n",
    "-  Shuffle the data\n",
    "-  Load the data in parallel using ``multiprocessing`` workers.\n",
    "\n",
    "``torch.utils.data.DataLoader`` is an iterator which provides all these\n",
    "features, and we'll see this in use in the *next* notebook, Notebook 2, when we load data in batches to train a neural network!\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ready to Train!\n",
    "\n",
    "Now that you've seen how to load and transform our data, you're ready to build a neural network to train on this data.\n",
    "\n",
    "In the next notebook, you'll be tasked with creating a CNN for facial keypoint detection."
   ]
  }
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