diff --git a/Alternate_Dataset.py b/Alternate_Dataset.py
new file mode 100644
index 0000000..71ccc0d
--- /dev/null
+++ b/Alternate_Dataset.py
@@ -0,0 +1,136 @@
+import numpy as np
+import tensorflow as tf
+import pickle
+import matplotlib.pyplot as plt
+from matplotlib import image 
+import glob
+import os
+from PIL import Image
+from numpy import asarray
+import PIL
+import pathlib
+import tensorflow_datasets as tfds
+# import tensorflow.keras.datasets.cifar10 as cf
+
+from google.colab import drive
+drive.mount('/content/gdrive')
+
+# !unzip '/content/gdrive/MyDrive/In-shop Clothes Retrieval Benchmark/Img/img.zip'
+
+infile = open('/content/gdrive/MyDrive/poses_fashion3d.pkl','rb')
+poses = pickle.load(infile)
+
+poses['MEN']['Denim']
+
+poses['MEN']['Denim']['id_00007216']['01']['7_additional']
+
+#Function For Converting image into numpy array
+ 
+def img_to_tensor (path):
+  # load the image
+  image = Image.open(path)
+  # convert image to numpy array
+  data = asarray(image)
+  # print(type(data))
+  # # summarize shape
+  # print(data.shape)
+ 
+  # # create Pillow image
+  # image2 = Image.fromarray(data)
+  # print(type(image2))
+ 
+  # # summarize image details
+  # print(image2.mode)
+  # print(image2.size)
+  data.reshape(256,256,3)
+  return data
+
+img_to_tensor('img/MEN/Denim/id_00000080/01_1_front.jpg')
+
+data = {}
+for n in poses:
+  for i in poses[n]:
+  #data_men[i] = {} 
+    for j in poses[n][i]:
+    #data_men[i][j]={}
+      for k in poses[n][i][j]:
+      #data_men[i][j][k]={}
+        for l in poses[n][i][j][k]:
+          #data_men[i][j][k][l]={}
+          #for m in poses[n][i][j][k][l]:
+          #/img/MEN/Denim/id_00000182/01_1_front.jpg
+          path = 'img/'+n+'/'+i+'/'+j+'/'+k+'_'+l+'.jpg'
+          
+          x = img_to_tensor(path)
+          data.update({path : x})
+
+#x = img_to_tensor(path)
+#data_men[i][j][k][l]=x
+
+# print(data["img/MEN/Denim/id_00000080/01_7_additional.jpg"])
+
+# !mkdir Dataset
+
+# !cd Dataset
+
+tstImg2=np.round(np.array(Image.open('img/MEN/Denim/id_00000080/01_1_front.jpg')).convert('RGB').resize((224,224)),dtype=np.float32)
+
+tf.reshape(tstImg2, shape=[-1, 224, 224, 3])
+
+def my_func(arg):
+  arg = tf.convert_to_tensor(arg, dtype=tf.float32)
+  return arg
+
+tensor2=tf.io.decode_image(
+    '/content/img/MEN/Denim/id_00000080/01_1_front.jpg'
+)
+
+# img=Image.open('/content/img/MEN/Denim/id_00000080/01_1_front.jpg')
+# array = tf.keras.preprocessing.image.img_to_array(img)
+
+# print(array)
+
+data = image.imread('/content/img/MEN/Denim/id_00000080/01_1_front.jpg')
+plt.imshow(data)
+
+# len(data_pose)
+
+joint_order=['neck', 'nose', 'lsho', 'lelb', 'lwri', 'lhip', 'lkne', 'lank', 'rsho', 'relb', 'rwri', 'rhip', 'rkne', 'rank', 'leye', 'lear', 'reye', 'rear', 'pelv']
+
+def give_name_to_keypoints(array, joint_order):
+    #array = array.T
+    res = {}
+    for i, name in enumerate(joint_order):
+        res[name] = array[i]
+    return res
+
+for i, name in enumerate(joint_order):
+  print(i,name)
+
+path="img/MEN/Denim/id_00000080/01_7_additional.jpg"
+# print(data_pose.get(path))
+print(data.get(path))
+
+data_with_joints={}
+for path,image in data.items():
+  array=data.get(path)
+  data_with_joints[path]=give_name_to_keypoints(array, joint_order)
+
+data_with_joints["img/MEN/Denim/id_00000080/01_7_additional.jpg"]['lsho']
+
+img_men = tf.keras.preprocessing.image_dataset_from_directory(
+    'img/MEN',
+    image_size=(256, 256),
+    labels = 'inferred'
+)
+
+type(img_men)
+
+img_men_training = tf.keras.preprocessing.image_dataset_from_directory(
+    'img/MEN',
+    validation_split=0.2,
+    subset="training",
+    seed=123,
+    image_size=(256, 256),
+    labels = 'inferred'
+)
\ No newline at end of file
diff --git a/Task1/Data_Exploration.ipynb b/Task1/Data_Exploration.ipynb
new file mode 100644
index 0000000..5a0e6e3
--- /dev/null
+++ b/Task1/Data_Exploration.ipynb
@@ -0,0 +1,399 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Data_Exploration.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6MZ_9djs7wgf",
+        "outputId": "fb4b74ac-daa8-44a1-c5c6-33cc403b0497"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow.compat.v1 as tf\n",
+        "tf.disable_v2_behavior() \n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import os\n",
+        "import PIL\n",
+        "import PIL.Image\n",
+        "import tensorflow_datasets as tfds\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "non-resource variables are not supported in the long term\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_1RHs8xi-NAk",
+        "outputId": "a61a633d-abc1-475c-b445-724c13cb57f8"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/gdrive\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/gdrive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9jUH-KgEtb9u"
+      },
+      "source": [
+        "!unzip '/content/gdrive/My Drive/DeepFashion/In-shop Clothes Retrieval Benchmark/Img/img.zip'"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "IKVuqxPXORdG",
+        "outputId": "9526c3b9-a28a-43a4-ddb9-53b19700339f"
+      },
+      "source": [
+        "image = tf.keras.preprocessing.image.load_img('img/MEN/Shorts/id_00000056/01_1_front.jpg')\n",
+        "image"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x7F20B889F550>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tc0lcuH20TLX",
+        "outputId": "f32cf274-3eb5-4063-b69e-c647b0263d1b"
+      },
+      "source": [
+        "img_path = os.path.join(\"img\")\n",
+        "classes = os.listdir(img_path)\n",
+        "count = 0\n",
+        "for c in classes:\n",
+        "    count = count+len(os.listdir(img_path+\"/\"+c))\n",
+        "print(count)\n",
+        "print(classes)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "23\n",
+            "['MEN', 'WOMEN']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UAc9sIY51ePc",
+        "outputId": "ee6548a1-c408-4a05-803a-efcf2ecde208"
+      },
+      "source": [
+        "img_path_men = os.path.join(\"img/MEN\")\n",
+        "classes = os.listdir(img_path_men)\n",
+        "count = 0\n",
+        "for c in classes:\n",
+        "    count = count+len(os.listdir(img_path_men+\"/\"+c))\n",
+        "print(count)\n",
+        "print(classes)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "951\n",
+            "['Pants', 'Suiting', 'Sweaters', 'Tees_Tanks', 'Sweatshirts_Hoodies', 'Shirts_Polos', 'Jackets_Vests', 'Shorts', 'Denim']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kxH61Jbla6DX",
+        "outputId": "96ae63c3-265c-4dd9-ff54-eb7f3757e1ee"
+      },
+      "source": [
+        "img_path_women = os.path.join(\"img/WOMEN\")\n",
+        "classes = os.listdir(img_path_women)\n",
+        "count = 0\n",
+        "for c in classes:\n",
+        "    count = count+len(os.listdir(img_path_women+\"/\"+c))\n",
+        "print(count)\n",
+        "print(classes)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "7131\n",
+            "['Graphic_Tees', 'Pants', 'Skirts', 'Jackets_Coats', 'Dresses', 'Cardigans', 'Sweaters', 'Tees_Tanks', 'Rompers_Jumpsuits', 'Sweatshirts_Hoodies', 'Shorts', 'Blouses_Shirts', 'Denim', 'Leggings']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0mcIt2z9bopx"
+      },
+      "source": [
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D2-ysPZpbo1A",
+        "outputId": "4b640921-9142-42ad-c1ef-f9428e74ca5e"
+      },
+      "source": [
+        "train_gen = ImageDataGenerator(\n",
+        "    rescale = 1./255.,\n",
+        "    horizontal_flip = True,\n",
+        "    rotation_range = 20,\n",
+        "    width_shift_range = 0.2,\n",
+        "    height_shift_range = 0.2\n",
+        ")\n",
+        "\n",
+        "train_data = train_gen.flow_from_directory(\n",
+        "    img_path, \n",
+        "    target_size=(150, 150),\n",
+        "    batch_size = 64, \n",
+        "    class_mode = \"categorical\" ,\n",
+        "    classes  = ['MEN','WOMEN'],\n",
+        "    shuffle = True,\n",
+        ")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Found 52712 images belonging to 2 classes.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "PHbaAg8qbXYi",
+        "outputId": "ab86f209-c499-4f1e-83b6-2d1e307575b4"
+      },
+      "source": [
+        "x, y = next(train_data)\n",
+        "print(x.shape,y.shape)\n",
+        "plt.imshow(x[0])\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(64, 150, 150, 3) (64, 2)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f20b755d410>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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MaB0YgkopFhbmWVqquAGPdRaC/ebAhBtwHji1EPDe/zFHi5ufP+1xZ/h84bxEzbPQBh7j7k9N0MnpBcPhpG/Ap598yocffsQPvv8Dbt36ONQNKA2tVqtK/Y3RWlKW5aHn2x+BmfYH0FCFpZTMz8+xsDDfdBeaHh/CVzLg8XDjUQShp3ERzBiDLw1mK/+TEFbeydd9eoLW3n0QOOf5zre/y9bWNsYa3nvvQ+7fv8/tT28zHmU4G1b7PC+I45h2u9U4BvM8pyzzRgvYV0HIT7oL1ZGBKI5Jq0ajly9f5uq1q7Ta7UYTaCoKB/1/iiJ0vs96JgReKjxbQfBiip1pfv0Eh1GS6/ecdezs7rG+vsGff+/P+dEP32V7Z4fRaIy1nn6/T54VzX41F8CY0Gy0bjMWTi+qLELfkIuEEKFwyJSaH1XEoCiO6XTaLC0tsri0QJomB4SAn/qpTnGKYqJPwkwIvIyY+QMew2HsvXqi5kXB+qN1Pv30Nt/5zne5e/ce/f6A8Sin1+tRFCXG2CqvP+QI4H3Vatw3JKFQ4UcCtlLHp87ZmAJVdyEdhbLiUUS316Pb69Jut/dpK5MxTwhC4lg37NM/+5kQeCkgHvvvObLt99nBh/HmLzYX4PGE2ulYf/1/7bSz1rK+vsn3v/8j3n//fd577wMG/QHeQxTFVTchA0AcJ8CkpZgxFiE8SRIcg9bUpKEIIYJz0Tkb6jp5gYhVFRkIRUN0FJEkCTdvXGdurrevmOi+5iL7CELH04SfFjMh8NLggDPpmZztcExPtvMaz8mEyFHx8sfj9UIIjDHs7Ozy+7/3h7z33rusrq5hSkOW5UgpSdOU8XjU2PeDQR/vLHiH1BotNFIp4jjGOVc1HbGNgElEEBpaRyRVFKD2PWxublKWoShpXmToSFdlyQ6EBcMV7KMJN8VED7kPp5GvMyEww4XhMC3g4P8nJQcdv389QY7XPJzz7GzvsLW9zerqGu+9+z63b99hd2+3qQsAwWGY5xkgmm5CzlqcsyilmyhdbeMLISjLCUW4NhOUCrkBsiYhVULJmJLhaMi9e/e4desWxhpef/11er1udQz2JwtN/T5PzITA5xjTawOcTQs4zGa+iH0OO0aN0xQk3Zddt//Ij21T72dKw4cf3uIv//L7fPjBR3zve99nY2Mday0rKyvIqjBIlmWMx+OQ4adbaB0xLkuyLCOKEnxl+ztniaIIpSTGlI1db62tQoce5zxSBZ4AUtJudxBCsLe3wx//8Te5f/8e73zly/wHv/zLvPX2m1WnoYqJWP0O3YafeDeP+fxwzITA5xxnKzb99Pi8uhyFkHgXJm1RlHzrW9/ihz98l/v3HlAUOUmSYK1lMBhgTInzjjzPAnPPQ55lzM3P026H6r9ZNiZJEgSenZ1RVQswZm5urkkqcs6RpoECHMcJw+EwOBNrgSBp+AL9wYB7d+/xzW/+MdYZrl+/zpUrK0F7OFF3odM/mZkQeIlw1ijywRXz4PvVh0d/1mxyuBlw0vM/DfYTcqbTa6dfT/j/xliGwyEffPABG+sb5HmBEEzVBZhwCuq2X2VZkmcl49GosdettVUxEUleFESxbioLFUXRhAmFmJQbK4oCIQRK1e3Kg39BSElRFOzt7XH79m2++MW36fV6XLlyufEGTDsEj9K+TquUzYTA5xAHn/V0yuvT4rD9nnisE5znWecC7M/eO0IUeihLy3g8Zmtrmx/84IdIERh/UkjKoiAvCrQOFX+TJKHTaYc248ZQFDllUdBqtUjTBOcsWWbCJPWOLAumAEBZlJWfIDj6agdprT0kVTchW5RYZ1BoiqKk3x9w584dHj58yOUrK5X7wO9/4Bdwa2dC4HONKpNsxgvg8dkR7o33NB7/Dz/8iNu3b3Pro1tIqfDOk2cZ/X6fbq/DnOgxzjIirbHGsLW1SZqmeDxKS5aWLmFMSVHkKCXRutKcKuHjvW9KhltjKcoCURqcG1WaRnAqlmWBdWXla9BoFVEXHY2imMWlRXq9XriMJrX54p7xTAi8RDhKnT/380y9PuxcFy2UmuM/tkLur7YjhGA4GLK6uspHH93i1q2PWVtbY3X1IXiBMWXTRLSsVHtnLaaKzjlncdY2JkdR5JRliTGGbqdT0YRDVaG8LFGqziicWPF19eEQcXAYW+K8bWjEQkisdQjpcNZhrWNjfYOdazuVZiNOUEPobPd7JgQ+tzjbg/+86w5B+NTrYx1Tn/ZdBDv/0aNHvPfe+/zJH/8Jd+7eZTAYMR6Nq7h+WJWNMeR53pCIvA9xfk+VCQhopcjzvOICONJWi6Issc5WfoMCa2XlV5CNiRJ8CFV2oQx+AGsNSimcD5PeWouQsnm9sbHJ7s4ujab32HVP4+xPciYEPtd4+i/A533yP479NNpaHAwGA+7evcc//sf/C3fv3mN3e5fheEyn3aHVajMaDtBakyQJeZ6jtaoIQUNarXblFAyc/7SdsDC/QFn5BoqyZDwe471v+AFputAkB0EQQFmWkWVZ47DrddtkeUae5yRJm9CMVAGiSTMuS8PVq1dZXl6ZuraKNnwBAgBmQuBzhwk35nFuwJPMgJffb7B/xez3B7z37nusP1pn0O9TlgWCwBLMshHOWaSMiSJNt9tpNIMQFQipvsaU5Lkgz8c474mjGEfIGzBl2YQCa0FQC4E6PAg0pcKdc+R5TpHnzX5JHBFFMUmSTrErPVevXWV5JVTbCs7Bo4jg+yMgjSPxKTETAi8hTjrh96mZJz32Kc914fCVg86HoiD9fp+PPvqIQb+PKYO3XlU2f25NU0Mg1PyPK3MAokgj5aQoSFDzQ7dh0QkrNoKQF1C9H9iEkzs4nUY8qUDsgz/B2hA+rKjFdblx7z1CBubh8qVLzM/PNcerSIZPIGfNfAKvHELc+Mndec6Cg0d9nslIR2ESDqyr7IhmoLu7u9y5c4dv/X/fYjQaoXVEu50wGIwq514O0HQKqlOChRDEcTyVIiyrVuMJ3W4XKWVVW9DgXUkSx8Tekxc5pnIA1tvUHYdqrcC5kFwURxFpEgqLBlpyjnPBrGh3Wrz55hdYXFokTdOqiOnB654WBMdlFJ4MMyHwucSTw4LHeewP2/NiuGgXiSlbuXrH4zHGsLq6yubGJkmS0Gq1KgdgSV7kZOMRRZExPz+Pc46iKCoacNSs2mVZNhP6YAZivc9gMCCukoKAUG+QEgRIVdn5UpJlGdYavA9lyOIoRimNB6w1lTZh0LpLmibcfO0mcRxRpxkcfe3nh1lD0s8J9peVOBznW3X3xURdB7AOn00zBD1BAG5tbrK1tYkpS7zzEw6/nxxDTa3EddivThGetulDoo9pIgNl5Quot/feo6QE77HWhHNaB95ViUJuXyGS6qAh9FhFIqwzIEBX/gkhBO6Umt5pvgMzTeDzBjFpY7X/7Zdv8h99TdX7oibSTMptKKV48OABtz/5lNUHDygKgw/LM2krJUnjSjX3jLOM4SBECWr1PaQPjwGI45jRaESWZezs7NDtdht68SQtGNI0DczC6jPnLLggTJIowiqFsYYkSXDWkWU53hPajscx1ll0pFBKYr0nLw2lscSROu4Onf0mcw5CQAihgO8AD7z3vyiEeBP4HeAS8F3g73nvi7OeZ4Yn46IJQi8S/KH/hBdSwO3bt7lz5w5FWaLjFBBYD3t7/YavjwirdJwkVY1/j3WOLMuaxh8QSogJKWilrcAJUJJEJxR50eQETCcM1aZDnX9QFyTRKjAU65yCJAlVhanMjuWVZb7w5hf4+te/zsLCAnGkKyEnHlMBJxGD88F5mAP/gNCHsMb/APxP3vsvAtvAr53DOWYADqMJH6X+1e8f/PzzpC/Uk+xxATdlHE07CCt73hiDsRbrXAj5aR3i8E17b/DOoaQiiWPSJAnvO4dpWorJxmMvpUTJsCrXpoSOdEMuslOswmn/gda6aUmmpEKKcBytQ0UhKUOkQWvN/Nw8S0uXWFxcbPwTNQ5/vud3n8/agegm8HeA/7X6XwD/LvDPqk1+m1lD0nPFY577QyfJy4fDBUKVOAQI73HW0O/3aXc7tHtdjPdESUwURwgp6HQDUSiOYyIV0YoTummLuU4XJSS2qgqECFRdU3EAlFZhMkvR/LRaLZBQmjLkCFTCoQ4X1pEGPEghm/LitbOy0+mgtAYhSNOUS8uXWF5ZDu/LupLgNPdBHPLz2F1q7tVh9+4onNUc+J+B/waosh24BOx47031/31Cp+IZzoiGHivElCvswDYvoV/gUDQVdyYQCJTSzC8s8I2f/il0FNHv9xn2R4yGI0bDAdYapFJopenNzaF1WN37e32KPGQLRq2IdquF847xeExRFs2kbrfbQAgtZjYLjkQPSiusD3UCtNLESYx3nsFgAND0J2i1QlZi3cU4yzKMNSwtLTEejRkPR7TihEgHtuLxN+Hx16dZEM7ShuwXgUfe+++ecv9ZQ9JTQJzg9auAfVX4fUjEkVKhtOLmazf50jtf5Ctf/hJaeGyRYcscJQWRkkRaIaTEI3A+EH+EVCgdobRGyKrah5jU+zs4uWpHYFD3K7PA2SofwGKsqXICJkxA52wVQpR4qNqQhWYjtRkTx3F1zqlrbSoOH1/L4TQ4axuyf18I8e8BKTAH/CawIITQlTZwE3hw2M7e+98CfgvgG9/4xsuvz54Z4lg78EkMwJdJSHg8DpDUSURBCHgB1jtuvnYTJWF3/RHvfve72GKMKws6nRZKaaTSOO+xxoSkHe9RUYzCo6O4CjVCXTuwJgjVzyBkFBZNhKAuMgI0RUi8C8IhEpXTsSpCIqRskoeSNGkmcuhrWBLH0TFawMHCKfvfOyyT9DhhcWpNwHv/33rvb3rvvwD8XeBfe+//E+APgP+o2uxXmTUkPUfsf5hnrST0ecBjjs/KHA5GkWwq7jgfvPvWleTjAa7I6EaKbHcLUeYsLsxjS0NRFOR5znA4Ynt7m83NDcbjEVGkSVstlI4QUqOjmHa7WzUJiYnjmLrpqDEWKQMh6GCXIQjqv9Yq0JSdZTzO2N3dpTQVfVlrimJiZuR5XhGQhty9d5eiKkpyNE7CGjk5LoIn8A+B3xFC/PfAnxM6F89wZjzdVD8q5eQ8cBSH/eBkOEly05PKkz12zikTYDpxxiOw3uG8pSwzpCvQrkDbjMVuG+c8LooRSlNah3GehnEkwBlDp5WGOoCjUePVt/iqEEioGKyUbkqKa62aG1wzDAWhm5HzIf24NhOoTAaPxzoLhqkuRYGfsLm5yebGBraiLJ/MtD+4DJxuOTgXIeC9/0PgD6vXnwB//TyOO0ON4+yAw+vOXISGcNQKdZhguJhag/UqOPGSey9wBC2gLMZokyPLMSIbsDTXxTgYWolXApuXOFs2oT8pJR5PK01IkoThsI+SwSRwFeEnUIBD05B69VZKN2xAa0zQDKpJH/gCkwxChEBWUYM6r2AiMAOfYGtrk/WNdfI8288u5GTC9CxPe8YYfOFxyMOt0srqL8d5T/ajKhQ91+iDEOAFyluk9zjpcXXxTUGg4jqDKca43Q0Gq3dYe/8vWe5EmDIm2x6SZxZT7ZMXZegYhKDXmyMbjxkPh4wGA2xZIpTEEVh9demvoigp8pCDEEUq+BcaUpBt/ADZOMPjiaPQbyAUCzENEUlrjZSSKIqC1uEco9GQ1dUH/NE3/4jrN67Tbrfw3jcZhgcFw9SNOfOtnQmBzwWOEAQXhKcJM504bfms4/WTMKmn1jpcqPtvLaUpgsc+y9h+cI/N+3fIdjZpK8tc7OhHhs3dAR6NkBrval6/CyZBtaojAj8AF6oAh0iBb7xnUslqYjqcA6RARzFCGLwHrTSlMRhTUlpD3G7R6iTEUcSg30dXEYg8z9FKN1WLWmlKu9WumITTK7+bMg0ed/fWt/XgIzs67fhxzBKIPud4lmvzQY/zcRP7fBOa/IEyWzSCwNoCU5aYosCMR+yur7G78ZBiPCRRjpb2tKRBmDHC5khn8LbEOwM+NBJ13leCIGQKOj9pL46nWeWlrAlBk2us/QX1j9a6amASMgrjJKHT6YRKRVKiVWAwVhcRSEY68At6vV7ldJyeyP4xXsTUXeawb8HTCPKZJvA5wStDBDoUNRMuRAS8FL/MlDcAACAASURBVKEOIKE4xyjLMdZisjHDh/fZWb3LYPsRTjlirYikQ9jgLFRKIKRikPWDmi8VzmgMAus849LS6ybIKuynVSgcap0jrluJBY8BURRXeQYyhB2tJcuDKaB1VJUz1+RZQZEVVYhQEwvJ0twl8myEKwyqKxn3Q7myd975Emk6aUwagqGTe3ARYn8mBF5oHCLlXylhcBjbIazMQtSlhBRJFKNFjrUFm6v3WLt/h/7WBkJ4ksiTRJBozcriMpkxZKVB+hIpg2oeKY91Bc56lAQlRUXkidA6oihKsiyvSpLJUGbMGmRFUNJa46zHSYVSAllYnHXB6SfCFHPeYr2vGIjgIh98D8JTGsvCwiKtVptbtz7mrbfepN1u8TgfgKAR+KNow6fDTAh8DvB50QIutOS5F/tEQkOiA7QSCOvRwhFJsHmGKwoipVHeISt7v5OmyCzDlzlt4ZAapBaAxbgQDQj2eJWT0NQHDKaBdFRkH9l0EaqTg4TwSCcw3lTkpdqVEMKQgZ0YSENCGIys2IZAaQztbpc4qVuVuanrPNm9fBofwEHMhMALjuMEwDR/7FngoF/gsAjCuQst36QJUU9QX9vp3qGweFvQjiVf/cqXefTD7yDHIwQxptyjHOUMhhlxO0UqSywLWgn4VGG1ZCPLwErwklgprCnx1gLBrnfeIYUPBCAlkIKQflzFEbXWCEIHotFwEEhBzqOERHqgSjrytsQLECICJcNfYDwec2nlEu12i16vV4UfK46BqJ5vc0sPJ4s/KZJznHCYCYEXFgdUvidMrIsSBMcReQ77cu3vDXge8FMCIPwv8HihcDbHlwXOjJHe4MuCrc/uYQsLVmDKglFukUKzPD9H6QzdpQ7t63PsbA/IraH0joWFNnc2B+yMSoY2RpahZqFHEMdpVRtAoqTAO0tpy4Y1KISgLAqEkFjnQEiMCWOUWlDaAiUUUocehJHWRDqi0+6G+gLWkJsxP/dzP8dP/uRP8rWv/VhVz1DgvWR/lgQNQ7K+F7MQ4auCE0yqixAEB8k+zzJl2Ve2f8DRXHmYYuQ5x2BnG7AoLbDGYRF4IdFKESnJ4nyPpcUuaZwyHA3JywLRbrMzzMkLQ2kE2Jrxp8DbIIQ8eBdSir13GGtQVdzSmBKlgmYglcZXGpJDEFWEJClF024spBcLdKwQQpOgefutt3nnnS+xsrLctDav6CAVRdrvuwPTd+qsmAmBFxhHrahHrbbTX4eTCoSTqI0nXd0vxncRVuVpGnT9V0oQSiBQSO+x3rO7tY7UhrgtcTi8jXGFw7uchV6HKyvLXLu2wvzcNlubW4xGGe2FRTZ2MsoCKCU2L9ACWpFi0xgK6zGuYg6qUAikLAucC3yDQihaqUJJRRxHMFa4QDUgigMhSEiI41agFRsL3tBKO7RaKZ25Ll/+8ju8/dZbSCWbIiVHP1EBHCQPnT5MOBMCLySe7P09yWQ7r4DS89ACqjNP/aZeEglLskB4i5ISEce40tHf2mFz/RH3P7vPfOyQPU1eAt0Y6T1xMebKyjz4gtW1eww2d9AyoRd1EYXi7ZVlVrpdNsclu4NRUPcFRBYGRcZelmPyPqgEqVIirYnjJKQfC4nLgy+gn40ZWYESghiHEsGf4IXCGRcqGaUJsfa8885bvPOVd/iZn/1ZXn/9ZqhKjKqoyhMt7PHnfdizOL0uOBMCLzD2rdKnPMZx2sGLXJXIC4GsCDXVlKh+1wk5IZnYeiiKHFPktNMULQy2tBjnKUpLaUOevxCeLBszzkfE3qBljJKBQtxNJHGcELcTnLcUpSES0NUJe2ONwkKsEULj0OzlAu8NmFB3UDqL9xa8RSJRCCIslCOsi3EiIsJzfWWRa9eu4KXgrbe/wFtvvcWNGzcCN6B6QKLyBD6df2WWO/CS4WJKh590rTjK63/oMS8kHBiSbjxMSTHfCAVZOwZFcNQZB2URMvmWlhYo9wrGriS3nlFWkueh84+1htFwSH+wy81LHSIVSD/5qKTT9XQSTVcm7IzH6FzQloL5uQV2hgnaOZJE45ynMA72DKMilCFXXhBJhZSgtSI2oIUjFRaf9XEyxoqYTiT40mtX+frXf4yBdXzlq+/w+ptv0euFlGVZhRzr+oKH39ujsjEfpw6fFDMh8MJhf/GQ855ih9Fvnrj9KTMBzwrhqRxvVTgwjCZ43b1lLBJc4AqhtWTpynW6sURuvMvaYB3vBUnaYfxwjfEox+WOwdhyaWmFt9+4ibJ9huOCYTaiEIaW7FWagcSUY4SzLHa73JhLeH2hx1ev38DqiDzbIx/tsTMc8ag/op8XaCzKg7GwPZaUeKSARHp2hzleGKJI8XM/9TX+2k9/ha/9+JcZp4ssXn2D7sLypJDIlOr/pNTr825MOhMCLyQ+P01ELmScje1fTf6pbEkP2KpKby0UtFKUUuGkxumYwhRYW6KjNs7kxAraS/OkrQ7WOUaDAb1UEMcJqBQrBEpIsJJEK75weRnnLC0tUdJhXY41BTpZQCcJLdlBexBO0Iti0tQRCUlWWMzDMWhIY8lCqtmII/LSYYFLXU0rcghv6M3N0Wq3KgFQr/xVUtJ53cYT+nNmQuAFwXRCyjPF1PmOpyUdv9W5Qux/GQSCxBHoeLISAkppSiQOiROa0hqsK1FSIDHESczypUXiOMUVA0b5kE7cQemYNIpAJiH911q0F7y2vIj1oRmpqFqFGWuJ6BBHEqXb6NIhnCCLS7o9iJRkODKsbxfoWNBraa7Ot2hFKbvDMcMsY76tiFUoLNJptYmqNmaBE0BIh/buid+BoyM4T7iNx3ynZkLgBUVdjOIi8bx0jePr3wWSTNi42q55L4iDuOIHADg0SkoiGQg5QmssUIz6XFlcoNubY+XKZT765AELvYTl+XnsaI98nGNdSZq2Ed5hypxRkZN0uqhIk8YxkY7paoHQkt1BTqQTkqhFr52S7PXJshFSG5yxxBKWezEjB0JrRKR56wvXePhonXv37nPt6hLL168xd+014rnLSJ0Ep2fl/GhuyzNeCGZC4IXCgYd/BkHwJFXw0GYWj23TDOKILU6PJ69MYuqn4vBX46jfEXU9gSkqrVUJLkqrPgMttM4ROBbmFoiSlMI4xmVJvpmxu7fLylyC91Aay4P790gjiZIeZ4aoNEJqiESELUq0D6m/mxvbzM3PEyUxLirJyhGD4RAZgTcl47ykyEZ4qfHGUZSS/u4m3hTM97rs7Q3Y2dkl2dtj5UY0qWQsNXh3orv8uE/gBOHimTnwYmPf4xGT96bj45MF4ukn4nnm81+E7nCoo2ta7kwLo4o956m84dU2HkAqhIqJ0oS01SWOc7wfNp2DitJQGEuRZ+w5w3w3IZIKpGG7v8PSXJckEhhbUpgCqSRRpHHGYXEgHIP+KJgQaUpEzjjPGI0zXOZpt0IvQSWhcGWY095R5hngSNO46n8wIh9nIf5fJxcJBT6YNvsTBZ5N+HYmBF4UiOobX6/gPD91/XCcryZwWJVeoKmd4akFWM2dr7Ui3wiCsG/4q6QkiSPmegtcvv4G1kpWP1tn/eEmut2he2kF4yROxCgdI6KEdjdGSs/l8YBExyH7j5SdvQFRVLCAIpah16DJcqyB9dUdNlZ3SaXFGU9RljxYX+dv/vxPsbQ4R+/Th/zlh7fx1rEwN4dEMxxm7PaHFIMBompSSjnGyjbeaaS1FeMhFB+1MkxL6R2yYQYedv/P55mcSQgIIRYILcj+CuG5/efAB8D/CXwBuA38ivd++0yjfIVx3FrwNNliUxsedqCjznCyY06NQexbzQ7fZrLtpO9f5fsPiTMiJPBM+AK+SulpuHSTcwiBkCB0DJ3LtK8YLsmINwpLPvwO1jsoCy7NabLMY0uLLS2XFjssX+rQbcc8XNujvzfGlCCFwnnYG46IJERaEilJKxKUgPCaqytXQltzU7JybYUoUmTZkEg72u15Sgdr2wXXF2PiKKLTSmmnKa20TZwGp6BHhLoEskCoUMfQiVCwVHhPcIEGX4HbJ/rOF2ctL/abwP/rvf8K8BOExqS/Afy+9/5LwO9X/89wLPY3EBVif9z4REeY3vccxrP/B540sSfnnHb67d9muifetBYwGTcIEZh9j41DTKZ/tSPCT/wF4bfAeIlXiihNmF+Yo9dJSbTAFQXSW6S3eGfC6rw7YG9vQBwHm9xZg7OOdjslTSLKIsdYT5XRS6wFSgbdxFgLQhDHEZcuLRBHEoEnjnWoEygiBsPQ2kxLaKcRUaQrUo8LtQaVRAoQ3lXXKEHIqbvehIxO8sBOjVNrAkKIeeDfBv5TgKr9eCGE+CXg36k2+21CKfJ/eJZBvqyYmi5P3O64kNFxK+/54BBn4r636sntQqZcBeeD+l536alX/nriT4QChMIhIfe+PqQn5O8LVCgpJiptwNVro8AJi/MSYwyD3Q1G2w8xw10SabiyvMTm1h4Pt0OqcGkd1jn2dvt8/MmnaOX58b/yRQZ7Q8q8wHvHzevLlKXhk9v3SNpzxJFGiDDBM1Mwyse8+/E6i715FuZ6KA3tNEZJjzeKbstRFCP6wz7ZSNFrx8x1E4RS5HnOYG8XraPGseGFwFWhz1qohYCnxwo18Q8/pghMjMbnxRh8E1gH/g8hxE8A3yW0Kb/ivV+ttlkDrpzhHK8MzrKCP4NoIkcbJtPB/IlHz3uBd0FI7fUHPHq4yb0HqxR5iXNw48YVut2UVisijkM9Ph1FtFsRStiQJ2d9xQSoJ0EJPjT/KPNRiLErCaqk3Msp97ZojR+SDTcQeU4cxczPpcEZOBzzWm+ewXjMbn/I5ihjrrdEu91hry9ptXrMz0t6acza2kNGowxTGoajMbvGUhQ53bmYufkWnfkWt7MhKlIY53n3/Y/56pdWmO+kYBxXL/VIIoWwY+ZbEb12TLcdEXUiut2UdqcDrkRKXQkAhauLjlaahqh+N75C2GcM7X8uQegGQfD0kuAsQkAD3wD+vvf+T4UQv8kB1d9774U4vEyqEOLXgV8HeO21184wjJcBL5YL8OkwReh1nrwo8dZQlpbRMGN7e5etrV0+++wRa+vrFIXBWc/6+g6dTkq7nbAw36XdbdPttrh+ZZEk1igB3oZa/kE3FuBNqAjsDK7MEE6ABSFKyDNUOSKwBjxCR0SdDnm3SzwYoZVHCkMkHWkkWOh1EQJMmVMUEQ5HaUFIR380piwtOo6RWkJFFqIahhKeJAlMhXFRsLHTZ28wT6Q0Ps+JpSCWFpyhlQbTQukQgkyShFang3cOLx2gghnga/XfTxEm938vphOLngYXSRa6D9z33v9p9f8/IwiBh0KIa977VSHENeDRYTvPGpLWeP4U4ZOd/5iEIg/GeLa3hhS5YdAf8dn9R3z/++/zaH2LjfXtqlVYcHRZ+2EItyUpb7xxmUuXe1y5PE8s3mZxvk2iJdKXYExlLkucB48L2XoudP11OIQt0aZAixwTxSRJCyE13UtXyPtbJIMhOoI8H6Ccp5coLq9c4869O2xtPuRy9AYbu0OML0lTT+QFaZzQ7s0TxQlCS0pbIITHlAXWW7rdLnnhGI3GbO6N2R5YtDLofA8dG7zJyfOMbrdLK5V4nwOWpJXSnV+sKMIeVNAEavZj0OmP98FMPYnJI3hCPYgn4dRCwHu/JoS4J4T4svf+A+DngXern18F/hGzhqQvCSZforpPntbqQOELQX9vxO/+7h/x8UefsrW1y97uiGxsqwIbAueqSYxDKFA6Jy9GvPveDuYHGUrBv/mDRX7lP/zbvHFzmbbKmE8NSgIovA9qs2viA2HSWK8QUiOiBDpLzLUXQCpUnLLy5pe5dPUmX/uJrzMY5jxcfcjq/VXuP3gEZUaCI7IjenGLnDYjl7Ez2KWTeLqdOZbme+x6x+5mhrc99oY5/fGIS5cuI5TAC42K2+z2x8RacGPlEnujDJKYazevMTKOdtJhZXmF3FocguFwRLyigw/EeoSk8gEwfWUnEAEvQIgQ+PvAPxFCxMAnwH9GMFD+qRDi14A7wK+c8RwvHc5T7TlRxZ9zPWft1Q+va8rrcDBifX2D99/7kIcPNxkORgwHOXiJIIS/rHXU1CfpJc4ZyjJ8YYoyR+CxxvLpp2vgPCsLMXJBksaSJApTRNBYwBWvRmJk1axUWqyMQEqE0ogoJmrNg06g1UZ3PBYNQpK2UhbmO/R3+5jCEElF7AWmkAyNJ/Mle/0Ri/MZUgnmF3pIIbHWUIxzEq2ItcQ7zzjLMLaDEJC2W2yPCwzB45/lBaPxiPFI4OOU0npy4ygdVcViWbk3J3zISQj08af2pFX9tBWHzyQEvPd/Afz0IR/9/FmO++pgHyXuVHhSqq844vXTeREfp6ju80R7QVEaNja2uXv3Pu+/9xEgsdZTFCGJJzg9Q1itbiCiiXCVk08Kgatos8P+mI9u3SPPDcWbV5Gqy3xHI7UiqhyGVDyCZrqoMCCHpUQjlUZpDTpGJl2cjHAyImnHLEpF2kp44/VrbDzaZnN9m08+vIXzgtw6Cgt9ITClYWu7z3wvpd1OWV5ZYndvF+8NrixJIoWSGm8d42xc9SGAKI5xCEoLtvqs3y/RYkxn5XqofeAgN55ECrSQIURYFUk55Gmd8Bk9OXz7JOEwYwy+IHjefoEAf+Dv/th/TXUN5b6DMMiynH/zh9/mhz/8gDt37lPkHiUFzoZtrLMgHGBxLpTxFkJjy6AZuIoVp3Tl6feSTz6+y97ugCwrSfVbOKvwKHothRYOJeqYQRiIcg6MRzhB2poDrZqF1Mtg02sh2RsOaXcXWFi5wmi4Td6ex84v8HbaYe39H8HGNkt5xtU3rtHPDJ/cfcSHH9xl5fICN1+/wv3PVtE64dobr5G0O6GKcJTykz/+ZUbDHT59sMruxgM6cwtoGYOOMSbHliXeOpJ2l97lmyzc+CKD3CAINRNEklYCQEyui5Nob4eFbZ9eG5gJgeeGoAWcNDR4XG74PurtZKdTjGl6VZFQqbXTY54+fFkavvPtH3Lv7gN2dnZRKq7Uf4cINYCaQ/pq0uItDkudDowP5b9wIQKwu7tJlg/YG2yzufmAL3/xDX76G19DKkirjkLKldV1i4qnHyaPkgITCPngHFJG4eQCdOKQcYLTCSIpiTqGtle00yW0g925VXYefkZrvkUS5Wx1Nf1+jnSCWMb0Oh2sCw1QH6xu0EsjIq3oxIq9PngrkWmX+cVLWAdbO0Na3Q7dhRZzl7rE7Q5xq0Pc7lIWEKkqJCgU+Jr1EO7z8WLg8Wf7ZFPh6CPNhMBzxEmn6On5A0ebCk88X/P7oLmy/zjWOsajjHff/Yi9nT2MsbQ77TDPa47/VLZPEDFVN2BsxRAMLbrwvnIcwnA0Zm/gWH30GZ+t3qUoC958+028UPQ6ChkphCsJ66hs/BKCUNEn+AsDo1BJVcXiBVEqQEdYqRG6jU4MLRHRWVlAWU+r3UNaQ5oYtLRcuZTirSeNkyAE2m2yrGCcl6yt72LnE3rtGCXiwCz0Epl0SdtdirLAuB3iVkqrN0d7YZG41SVKW+g4IcETSYMSAium0qS9Y18q9cFnIwSHzfUnr/4XFB2Y4fSoJ1lDDa7ef3HipLVtOsnZh9BF1+OhWuXv31/jg/c/YXNjE2ctSim0koyLEWVZVqYAjQNBiPoYFiHy6gyiIRl5PL4q2ClxRBUD7969+/yr3/sjXnvjBm++foU3X7vM5QSEtAjh8Wh8w1IsUd6HQ3qQMsZ6MCJCK43znrz0yCImlV3iOEVpzdyVq8RJgrBjRhsfc7kT8/ZX3+LhtmQ4kowGiqQoSISjowUfbu0QmYhyFNH3mnEeKNFrD+6T7W3iEYxLw8J8h3Zbk+dtLq9co91pE4sS2UqRXk25Ag+yAk6Ck8UQjsNMCDxnHPYIz/JYT71v3dlGTKUuN/ZppRGIsLo7B/fu3ueD9z/mvXdvURYjpArdeIwtAI+UAqXCMZ0nxMTxTYhxul6BtS6QZ6gSikSgFSsBrnRsrW/xo+w98nFJMS7Jc8t4JWK+o+ikEi3LyuEYogtKiMoUqXoPCFCAdB5F8CvaBGSpkA50pBHzPWQkmM9vIMUAl+9i8jGLiz268ymjPGanv44vC7Q1vPP2taBVCInsj0l8gcDT6yRoKTHG4YwILdPLAleWtOeXiZIOzoebIkTwAgjvGhHgj7HpJx+dnw9pJgSeCw55gFXm2KE4IS94ny/g1FWAp8gnwKT77+RdZz23b9/j1kef8skntylNTiziMOmcIU40EDrylKbEmJKiMCRJgnMudPcVEqU1QgjGo5wiz7HO7csrEDL04xsNRgz7I1pxB+cgtw4truJ8ghQRrchWTkvwPhyj0S6q4iNC1PpN+N8pgfAK4QRaK0SkQQray1dw2R7lIMZkG3S6LVAd2nR4tHUdMx5BniEizV7h6ecO78fByacEUaRRzmOp3B8uCDjnPTrtIHSEd0EbQqlKtgYTwNcZk83dPx4ndwLOogMvHE6bK3BszP9U/gNBnbIbYu+1BuCbzyBMSGcdw2HGv/qXf8THtz5lbe0RRREKZyAccaJ4/eYXmJubI4lbrK6usbOzw/a25a2338QYw3g8QghYXFyk1Wpx7+4DHj56xHA4DEw6L/Fe4OyEQot33P7oFndXH9D6eAH03yI3KxQ2YrlraakyUIO1wlRR9+CWnCrfVTPyPEghQYcsPkUIWsoowV3pkKQL+FEf198kL+4St9q056/wha98ncHmNjtrD/nLb32TvZ0HjDa32N4rKYUm1pqRdHSjINBj5dESpIogalMYT2Q9yjkKkyGTYIrUFOFJbsBxpsG0c/b03YhrzITAc8EpJuqkEulk76mqQ/Vnk5cnP8eTvkKurniDI89KNjd2uH9/ld2dPUajEXmekRc5cRLTm+vxUz/1DRYW5kmSlDiOeOvt19ne3ubu3Tt89atfxjrLeJxV7bzDGK9cucL21jb9wYDtrR12d/cYDofs7e3hbHAaCg+J0ghT4IcDdjc2uOMMe7sd3rjS5fWVhLmWQBZjUBEIiZMSqHoDCgFCIWQ1wSoHmwGsqGr9C4lEw6VFfK+D7PWQ/RjhC4wpGO8+Qre6LLz+JuJHP+La64L2fId7f/YuI59gU0WUJPR6oYlpWjqSKMY5QX9sGIwyxHiMT3KEjOsnNTXlj/cLPJ4kdFyZ8uMFxEwIPHMcEtqB8EU/5dEOuoear1TtNT+FdjAJUU264j56tMlnDx5y5/Z9RqMxWmvm5+dotVp0u12Wlha5cuUycRyhlCaKNItLi7TaCVJ5lpcvYZ0lz3O8o2oIYisHnqDVSkmShLSVMBi0iRLNaDjCGoM1ljiKSNKEdiulv7tNNhqwsR6juclCZ5k0jkm8Q4hQAMDVEQoRmpK6iucQKpZPipUE56RESIlEgEqCqi4Vypdghng3QuQlKoqI2h2WX/sCorjEwu4Wlz99SLlb4j3kRUGazCOEZFSOMEVBlhfoLIQ0hZBIIZFKhXOFN/c/uyes7o+/fZDTcTie9B2YCYHniKNk9BOLhO7fMITCnvoM+z9vVO5G9Z+YA42u4T1/9mff4969VdZW19nb22N5eZnFxYWqdr4iTVM6nQ55nmFtjnMGZw0L8/MsLy8xHo8xxiAE9PvDJvdgPB5jTUkUa27euMaN61ex1pIVGXfu3mV3b4+9vT5pknBpYYGluTnW1h6wtr5OXpaU5c8w122ho4TlJA5ZhMLiLVhpkJWz0QiNUgqEQlXXFNyRkqriHwqP8iEnwEcSMbeC8AsInxONdkFp0Iq/9jd/AT/aY7i1QWYTvvndH7L28CEPV1f58TdfwzhY31rDZpAaRxl1aHe6dLs9Wt0e3ouq7sLEDDiZqD7smR625xRv5JgDz4TAM8WEIDSNurrOYyv6Odh708c/fDyHBZ2Ddz3IBE9ZWAb9ER99eJv19U0GgzGD4R4rK5dYWAiagJQSpTTWlqGxpg98AAClFUmSYIwhiiK8T0NGYJVDn6ZpcKBV/5uixDlH17dCHN07hJRk4zGLi4ssLV3ixuY2a6ur9Pf2SJKEj+5tcH99l1jmfOnmIou9hPl2hNbghcQLj5C2YjGC13HwGwiP9oHR553Ei1D9RwpAycCM9ApEiu5qpLcI77ASbHeBdtzmr/7M3yBzcOeTNg8+tTxYW8c4hykLknSRtN0lTttIFYPQOC+rugGyiQaEb8bTPmtx4G/zAJ/qKDMh8LzhJystUxrASQXAudGNK0IP1KpyPS7BeJSx+tkjHj3aYG+3j3WWdiul023T6bTRUURdPagsQogQwDlHnucoHSrxeu9x1WSPoii8dg6lVLO/FII8yyjLkrIomev1EFKio4jBsM/c/8/em8Valmb5Xb/v29OZ7jzFkJkRmVFZ1ZlVWVPbxm7zQNNIGAvZIFnIICEGI4RkhIQEGIsH88CDJZCQEOMDCIMs2gaB1AIkbFkgoEXbrp5cXXNl5RAZ0x3PPdOevoGHbzj73LgxZMaQWdWxUjfvjXPPOXvfs/e3vrX+67/+a2OD0doIawXawmC0Rr/fRxuYzGuqcsZoMKA1CcZK1/+fQJYI8tzPJbAJxrhjCcCqFqVdmTHLocgT39wjXIciAolEpilYjUs0pIsoioLtvT1u3Hwdq1rOTw4plcYYS68o0BYQCWneQ1vh26HpbM8hCni6hbuqKfl0l3XlcJfYKyfwUu3xV2SlEPccIoBPP9FWQPcmtYAVnJ/P+MH3f8K9O/dpVUu/n7N/sM/u7jbrG2s0TesZf5aqWjiNPSlQWjObTVHalQmFECilaJViOBiglEJbS5qmZGlGlqYURcF8PqMsSyZqwmg4ciSkNI24QZKl5KMhW1nBWqtJjKauFsznC45PK+6MGqo2o24FlRJkKQx6kq11SZqnyDSlaq1LDRBUVUK5KBFWszGy7Kz1yXPhn5cihSZPDCQJMimQUmJU4xIJKRhtbXDry2+TZzl3oFC9kgAAIABJREFU7tzh5GSMlIad0Yi6KVFWkPUGtBqU8dRp0uXnHgMyTx36VNf+UZWCp8MK4JUTeOn20MJ8ppr+8zBPBOrchMEsGmzG8dEZf/e3foe79+6yt7fN9etXufnm6wwGA7IsI0kSrLEujzeaPM/Ii4Je32EExhiqqmJ9fZ3FYoFqW9I0pWka6rpGSklVVajEpQ1t21LXNU1Ts721Rb8/oN/vsdaMCM3I/SxhWPSQIkErg6r6bAz7rA9SGi0Yz2rG85Y8zykK6NUJv//Dn3Ln7l0ODw/ZP9hnc3ub0fom61s7WKtRTc347A47O9skwlIvpmityIuM0XDAzsaAzfUBm2sDdrdGCKvAapRM2Xq9R397j+HOLp989AmT40MmD26zqGsObt7i1nvfYn33Cr3BkEQGBUH3WS9lxT/LtXvaZ73iCXwBbIkFXMzEBavO4XlhAU97XoFAgxUdlNAdvyprzk7Puf3JXcpyTlFc4eBgn6IoSFMHtGml8JwcF9r781fKq/8YEx0BEElD4XchTdBAXdcsFguapqEoCoqiIMtSpJTkWYYyGqU0bV2TZSBTh1uIBJIsoT/okbQWg0QmOUImKK05PTvnhz/4McfHh5yfnzObTukPHlAMBozWN+n3eyituXv3HpubW0gJulnQNg1ZnjIYDLh2sM/e7hZ7O9vUZh+rW9A1CQ1b6z2yYo396zcRouAwz5menzIYbbJ97Q2u37xFf7RBmuX+M5bLxRnUhgOh/DNe/4ejgKezV07gJdtljuDTdBE+57NZLnorWAqFOoDOWMNkMuXk5JQH9w8xRjMcDTi4sk/btkgpkVLSNm2crJumKRbQWqErHZ2CMYbFYkGe5/T7fZRS0QFYa7HGoIyhLEsWiwXWWNY31smLgiRJnO6AlEhrAU29KDG5xuQ5MklcE5LE6wJaZJKSF46gM53MOBuP+dn7P6Mu5xjdsphO0Z6kk+cFW7s7GAQf3v6E4XCNNJFIq6nq0jmgvODmzZscHFzh4KChNCN0O8e2C1JRcvP6NlvrA9ZGu+xdTWmVQd6+TX9zm53X3+TqzS+h+2u4iotb9KLzWdt4LR6fClq7Gq099upGQOAJz3t5O86j7dvf/rb9zd/8zc/7NF6gOS8fFkqwR2Vtl+0EL3QE+CoaAUKwmJd8+MFtvv8HP3Xfv/8TDg522NvbZX9/j36/R1VWlGXJ+fmYXq8gz3OX32cZ1lqqpmZ9fT3+3XEh+68QDai2JZEJSeLKjKFKYKyhlxcxY6rqml6/R6/X586dO45vYC3rmxtR2dgY7Sf7OgJQmhVUdcN0uqCqHdhYVxW3f/ZTzk6OmE0mlHWDMpYg354mruQpZEaSJWijUaolz3ukaU6WZfT7A4q8B0JwcnpKWZUMBwXfevdNvv3OTa7sb3DlyhqiV7C+vcv2/jU0uYcApSM0WYPAgFGunTi4hSc6gXDxHq4ydaO4QKUOzn04GPy2tfYhEaBXkcBLsofKgk94zssRGXlUrwKMz875zd/8exiVMBiO+MY330MIQ68o3PkhMNbGWQLBcTVN4/j/UpAkCf1+H2stZVlhO2O3tdakaUqe5yTDkecKKFdNSNz8QGM0k+k0phPgGIzGuOpJiBCqqiJJ0limxGjvSFzvf5pI1kYDhKz9KHDJ/rXX0DiVn0VzisXp/wkh0T4ySaxG+2NaI1FKY0zt0hylKJMFAklTVphWU5qGH3/wgOmkZGN9wNb2iGuvH3DtdcVN3WP/YB8pHSDoTs96RxBYCr5S8BzSwYv3z+PupldO4HOwJzkAeHg3eBFO4REsAZRSnJ2d87u/+12+9NZX2N7eZmt7g+n5eSzrGZ/Hh909DBzRngWYkJBIh+q73F8DgbEnYzpR5AWDfh/VtrRNg1Ku0cgNKzHM5nO/+AxFkSNk0NgXrgHJmPheQiTIxLXnOj0B36yUJAwGPZpWx/Rja3eX+WLBoqo5G4/dNKPAHrTgflLQWkc5BqyxXvLc0FiDoHGcCgN54kaX3T+acHh0Rpok9Po9vvLOgi/NDWR9dvf2kXLJCDD+w19SlR53VR6mC1/4bafodCGye4K9cgIv3FYJQhcvycvY759sPiXwFYJ7dx/w0Ycfc+/Ofb759V8GLO+//1PeeO112qZhNp+j/eILiypJJFmWO6DQOwIhJYeHh/5vFwwHI9exJyVSOgfiGIRLhSWlVCQfaa1pW69DKAWp7zp0x3MLHqAQRXREroU5wxqDVoqqrklSQ5okrI8KlEpp2pzpdM7e7jaJFJweHzopca1QTY1NXdjeGoW0ypOnhOf7S4yVaGsc/VgI0sQ5NulbmPFiprWSfPd7n3D3wYyP75zxy9/6hiNUGYs1kCcpUiQo3frrEKIBvYrVhN8+QlDkUfa099azDiT9N4F/BXf7fBenNnwV+HVgBzeV6J/3I8r+UFmn32c1zOfTYrcvyjqU4QhRuY672x9/wr17h+zv7WO0RpCxsb5Olrnbpa+1bwP2pFcBvV6fJEmp6wqwCCnIi9y/uwuzHUBowbhBHtJHD+PxmLquMcaQZRlVVWF9qpFlmcvt09QN/PDh/3Q6ZbQ2oj8YRHwhsBS1Mq5d1+mOoZWmLMtY0ZBSotoc1kckieDmzRvMZlMW8zknx0dou5T5sKF4Yg3SGj//MIxaEyDBSOmO5Ru6hPAcAAuClHKhuPvJGX/9r/8f3Lh5jddeO+DGzevu/aUFof0lEQRVIREBRH99rD/eY++eC797OlzwmWYRXgf+DeBda20phPibwJ8H/jTwH1trf10I8V8CfwH4Lz7rcX7+7dGX4IsRBQTzLUMW7t17wPHRCZubWxFsS2SC0W5KrisNSrR0Xw74Ez4c9bt6J8cF5wi7AGGXKTmfz9HaheppmlLXtWsswpUcQzNSkiQx8lBKYaxFCucgQjnSOQ+3+KV3vsYajDL0+31/Lq7cKIdOS+Dq1atMJgNm0ynWWurW0LaKumn8exqfYnhU3wLSgvEjQ8JUZQtgQDqac2gKaxvNZLzgO3//+8znFRbB9etXSKVECgvS+OqM8I6gw9iMeg4+osRjCZ+CDPQke9Z0IAX6QogWGAD3gH8U+Of87/8a8O/zh9oJPNk+D6XhsGBXzS2wH//4fT764BNu3foKSeI4+7PZhLapyfOcPM8xxqK1om0bFosFWiuyzCHnQqQYY5hNZmR5RpImZKmg1+thjKFpGqq6ctRciPyBICgS+AOhymCMomk04NKAwWBAnrt6uzaaQT6IlQatteddJGRZRt24+YVCiIhPKKUpipzBoA+sMxgMaDweYa3l9OSM45MTPvjgI06OT9EChBRocLu9tUhtkcIijQBtwvAzkE4vwAiFlGCswGqnw3B4NKb30X2SvOCdr34ZkTolZHdBTCcSEDiVg0AkE52v4CJaVpfvBQzpksceZc8ygeiOEOI/Aj4GSuBv4cL/sbVW+ad9Alz/rMf4+TfBxfX9ECbwKArxC3QMlzsApxx8984DJuMZde1y9fPzMQKX82utUMrvRdZSVVUM48MCAiKLMM9zpF/oSrUcnxyRpRlpmvrjtahWxQWapim9Xi+G90II72BcVGCtjeVFhIsoHCdBI6UTHDXao+uel9A2jXv/LGU8HkcK8mA4otfrkUhJkkhapWjblsV8xsGVffIiYzqdMB6fopsg/rm8fgbrBVBACA2iBSGxNgcaRwXSEkuBSFKMcAzJBw8sxtbc/uRrpMk+eTb0isgd1qDtLnqfKgjjU4IQBSxnFHRJQp/lvnmWdGAL+LPAm8AY+B+BP/UpXv8LPZD0IlL7xQAEIxvlkhMQKKW5d+8BrdIx9NZakaXJyuJs2xbly3mtpwAv258NSi0XbSKl1yj1i8Zo18VnLUr5XVv6VEEImhiCL0tlXVZhWMQikbFC0T0Ho10VIjzfeC0/YwzaLMuZaZqQpglSJmRZSpql5FmGMcqDdYr9/T0mkwlN47QCzs8nqFbH93HL0SKtZqnG5MasWV9hsNrJtgsMLYb51CIl/PhHP8PqhnK+xfbOiF4vjaXR5cUJ3y/rB7jIE3gYD3gZwOA/BnxgrT0CEEL8z8CfBDaFEKmPBl4D7lz24j8cA0l9+CY+fZPo87eHySXOPAQmJHXd8IPv/wQpE0ajNYzRFIUT8djYWKfy3X11XVOWZawOrK2tgfQogBAsFnOMcaF7f9DzCxSKXi++PixsgDzLQEDbNJydncXdPi5sX4rUWjtnlLjFa7Svs3ecRePbkAOPAJwDaBtDXuQxEgmVCCE0Qgr6PacGlOUp89mC9bU1vvKVr7Czs0fbtjRNw3e+8ztMJlOUcoGuEMvPz5X7BEI4B+BGIRisUGDc2CEjW0yraJuW//U3/ha33nyNmzeu8Sv/8Ld448YBo5EHFEXk92CFicdYpgshZeheww7+Eh97OnsWJ/Ax8MeFEANcOvBrwHeA/xP4c7gKwb/AH9qBpE8/WOTFW+ccAkP44lMstI3iww9vs7G+RVEUrK2N2NvbJk1kDMnDomx8mJ1lrllIawfUZVlOupG5RSgTVNu65qC2ZX1j3b22bZnP5vQHfXq9Hlmeu90fS1bkjqjjRUqCowkYgZQSow3WusilbRVNo1zpUQjato1RTF07DCOMQXONkcFZ1BhfQVBKx7+nqioHv/noIUkkSVIwGAx4/fXX+OSTO1RV5ev97rM1NiUuTrNcwdaCsi46EMIgNWirsVoxt5IPP7DMpnM2t9ZY3xhSFDlJohFeem2pOWg6azo4ArO8cCvfL/78ZHsWTODvCiH+J+B3cFJtv4vb2f834NeFEP+Bf+y//qzH+EW3F+UgLjLO4h5xyX0SHpovFpyejjk6POH119cYDoYMh32kf6+QjwdacHAGSilU2xLkwoV0ix9w/QBKxW7BsIhdxECMHGJ5D0fICZLjWqt4usumJI2xOLEQ/zeGPoSABYTnXmQzik6akCQpxjQxrcnSFOHPLcszMnKKvPDpidvdDw72qeuauq6ZTKadYN1XDKwv60lfWgzt2J0d2hqDthralulshpSSO3fuM53cYmtrjf4gxRdrVy/UI/71PFj/zzqQ9K8Af+XCwz8D/tizvO/Pvz0q9F7+9oWfgVjd/S8eddVFwPHRGR9/dIf79x7w+ms36Q+cbmBVLdwCkiKW48Ah+U3TxMagLM9jt19Y2FVVof0u63ZPSb/fd92BeUGapAgLqm0jE7BVrQvNEZRl1dnJRUT2EYJeT/jzSFCqBqtAQOpTgLDYw+dgjJt3GB7v9QqM1zusqoqNtQ2KvEC3in6vT164KkjbtsznC6qq5rXXrsf3mk6nKx9m4O07LGMpktJN7wWO8qyNxeoWvXCDVj76+DanZ+fs7W8zGOQ8tMtHoFCuPn7J1Vy9qk9nrxiDL9C+GKlAx/yNuMwhfc3ZCt7/6cf84Pvvs7+/z3DUo9/PGA6HjnxjXHnOofktTd34fn8XQh8cHLgd01OIw86cpil5ljMYDNjZ2YnRRJIkUWdAa01VVRRFQSIko8EwPq/X65FlGa1PKYbDoUs30sxVDfzri6KHUg3W9xeE/gDA9Sb4TsaqrtDKleum05lnHjpAbjKZxNdlqkVpxWQywWIZDgesr6/RtorBoMfGxhpvv/0lzs/Pmc8XLOYLH83AxQUprfW1fofzGwxGWHSrSNIMpSWz2YKNtXV2trd9+U8tLxh2GVEIuxJZwPLnbnfqp7VXTuCF2OeLBcQZhJ1bYuVsBAQqnOvQExweHnP37gN293YoehlgaZo6vo/xbb5NXVNXdWwOStM0Hk8GHMCqJTEeIkjXze+bpok/t20byURd7CHk+BfD+lCONFq78WdSoL17CyBi+PzDMdM0RQqJxTmOxXzhpiADuec3SCEjuBgwgaBoJKVksZiTFxm7uzusr69TVftMJlO++93vxkrBaskuXBD3PwfxdZMISKRgbbSOEClaC5I0XiBiRGlX3mjFHir3ioef8yR75QSeuz28+O2F335+DsJ2bpIAYDmG3eGDYx48OOSb33yHosix1lCVJULIyMIry4qqLKmqKnYABgHRriMQncOF3Dbk6KHMF7oFw0IPu7/WqxoEbet49a48aTHGlevCYg99BeEj7ZYThS85hsfc+4PRLoVJs9RHK5lrWkL4CohnuQtHcIodkm1Dryjo7/dIkhQhEiaTKT/84Q8A7QDL4A4uWYs2fv7LvsEkcZUYpQVVrRkmSbe+TIzYVhzBxdJh95mf3l45gRdgjyQAveTz6FSN4KGb0yHObav54fc/4PR0jLWWwbDPoN8nTTOMtijVxsW2sbZOlqS+aSah1yvIMheaW0ssuwFxZw+U3qqq4s4aUPiAD4QqQIgOwq4f/p0kie8pqFf0CPLc9SYEXoFSCqWUIwH5bsLFfB7bm4ui8OeUub6ENCfNvMxZ5aKeyEHwDij0G6Rpyt7e3pJZaJxjHAz6/Oqv/iq3b9/m5OSUo6Njr21A/Fu6F0SKBCETjG2RAtq64Uc//Akff/whm5sFw5sHqxcwsIf9tfxs0ykeb6+cwHO3JTfgC2HdbTnWmn0UYAVaaT788DYC2NrcIM9yrLGoVnkOPr6bz9X3lWoR4EtxoXNPxgYh97M7sJSroXGeu7A7y7IY4mutI/gXIoXCaxYEh+IIQhlKufMJO32wEF10+QLCYw/BGUSmIV4foJsyhZ+Fa3QKUUS/33e7e2qj4ElwDv1Bn8WiRAjY2tpkOp1S1zWHDw6xVmBj919HVk6IyHEQxiKkFzqtG3784/cp+gmv3dxBRh7AihcHEeYtwqVRwKcgCHXtlRN4CeYu0It1Cg85nYD/df558WcBKG2588l9hBRsbKy53VxrsK4Gv+TbK9/d5xZf09Q+DJcURe5ubBF6/cOXWPkKDiDLsoi8BwIQsJIOhEUadlN3LLHyuO0s5lCVSJLEjU7zUUfSqWbEbsPYcXjhfTyab7SjSed57hiCOjAPl7LprtzpIpPRaMT6+hqz2axTFnSLNXxG4TOQ0oGPlsR3UTrdhHv3HrC7v+6Rg6AyJHB04XiVn3AXfLZ64Ssn8FztksEifA5pQMcu7CVLswZLgmo177//MVubIza2h0wnU/K8wBjLYrFgNBrF/L9tG5RqaduG8/NzyrKk3++zs7NLnqcIIZlOp2RZEl+TZRlN08SQPNTlQ2XgodPyJJ/QTxB27/l8gdYmIvjBYYRdO4ibDIfDmHoYs+wcDM7EtC3GL3ilWoxWtKr18xIE/f7AAYzagq19iqNZzEvm8zlJIkmzlKqqybKAJ/T99GXN2tpHlIvacwusj1CWoivCn4s1TqxEJJJBP2dra52NjXUSkbHaNmwfQfR46JP7TPcHvHICz90uSwNcdcc+8vfPwx5SIlohjy73/vhvITg7PeeTj++zmM/Z3hr5Hd0tpLCrdktsrqvPUNeWDz/8kI2NDXZ2dtje3o27XpalvuKwzNG7oX/4Crl2CLtDVJCmrgMxz3O2traYTCY+5yfKigXHEHbw8P4B/Au7evg5OIRwTuH4Urq0yNGFc4SQJGkSlYyapiFNs5ju9AeuZBreM7AcEQlrozVu3HiDwWDIH3z3e5yejZlNZx5AFGAl2rTeIUDbNi6V0glNXbO7t82Vg30CJXiVBLy8tqsAIfEZz2KvnMBzs8cThD5/W41JBILJ+ZQH9w/RWiGE9RRZt4NLmazccC4Ht3EBjMfjiJwLj1otm36WcuJhpw4LMnwFBxB4A91+AWttTBtCnu6OoVfwgG77cFz8EPkK3eN1qwnBWQQaMT51kR0H7f4G6z8H99llueMsaOOovRgnXlLXFXlesJ6v0x8MuX//AUppqrKKn7b7fByXQFiB8TqI1j++s73F7u72JRv640uE3ae9qg58AeyyVOCLYN0adnhECMnhg2N+8pMP6PcLrLW0rSLPC8BN1llbW2MymVCWJbPZDK1bjFfeTRLJzs4216+/5nNlByDWdfVQPp/nOb1eL+7YARMIDqRumpg6hOcbYzg+PvZORK70AwQnEboZQwRhgbqqyAtXtajrOjIcgZXGIid/tnw8lCJ7PdzfYzwwKQSqVdR1w9b2FlXtSoi7uzsYo2mamgcPDtnc3HRVB6O4ctUh/PP5nPPzqY+SHN/f4SvGjUiXkCSuavHWWzd4880bPAzkXFzaz/+ueuUEnos9HAVc5Aa8XBPLUHKlC82fmZPG4fjojA9/9gm7u5v0ekXMwxeLEmPmaG3iJKDFYkHb1j7Eb7lx8wZbW9uxU88tIruyAyul4mLvhuIh3I+7unCvD5x+pVSMEMJnG3N6v2CHw2EEFGezWXTAaZrG1CLPc4bDYWQlwhJAbNsW40HOLE3JsyICd0Y7Z9Hr90mzFJMbsjzDWBdhhLKocyY2SqplmeureOPGG+R5ztn4jLppXGej1rRqiRVkmdMyGAx6fP0bv8Tm5og0cYxC9/d2uQBPQpaezTG8cgLPyQIa/ujfv2RXcLF1oGPWwmQy5Xw8ZTabc/XqtpsZkKRkWU5ZTqjrJpJ4ug06SrUsFgs2NzbjIoQlc63bvgvEKUWhjBcsEIwsIKSTLzfWgtEIRZQP74b03a7McD7dVAOWVYCAZ3SrE0qp2ADVBSZ7RUhriESkkB6kSYoWTkMgVEaECH0NWUwtgjkmpWRtfcTOzjYPHhxhrUbpNn6ey7KqEzu5enWfosg6nEKB7VCEbbhoj7/Mn9leOYHnZi+XIHSxU/Chx+JEoe5O4sN0Y3n/px9zfHyC1prBoE+WpOR5xnA44PT0jMViEdF/gDSV5PmIRbng/HzM1atX42KSkkiv1VquLM5u7T68V2guCl2H0kuTh5xdGQ3GkBiL0SGEdiSggBmUZRn/7rD7uyahpULRxsYGs9lsRaVoMBhEHkJwAuHfxhjPhVDRmQgpQNv4WMA7qmoaz6ffH3b4BcY3KPW4cfMGP/jBj2ia2k9aVqRZRp4nsfU5TVMOrlzxlZOWNJOdgoC9kMZddiOsfPtM9soJPBf7fLCA2CLbiTLcY2J5IqLjFPwNpTV89x/8iNl0wfbWulPZyVKQCeeTCYVnAlrr+geUapHShd2npyccnxxz7dp16rqhLEusDVWB5eyBAPKFcl+SJJRlGZ1DlmWuddfX5kN+b4xhc3MTKSRlWSOEdPLmSpHH+r5dea/ABxCe1BSmGM3nc+q6jp/LcDgkSZIVmrPWmpOTk8gmjH0JdjnwxKkPZStgZ54XlGXlKw15HMjaNg1KG6aTGYcPDiOJCItvfkp901JGmuZIkbBYlFgcTuBansP+H2YUPuL6x4v8bPbKCTyzfT7iId0oYMURrIAR9pJbxMl+37l9j7quGY4GeHjcL0bl5brds0PY3LYtp6cn1HVNv9f37DnbqfkrhFhSeJ0DaVbCc9NZxOCLYUKgzXIwqXNi3l116v+9Xs+1G/tdXWvjw3AbG36EWHL9lVLMfL9+oP2GpiOtNXTSi1ju6/QcyABGajfSLPNTlEP4L+VSTyHPM8qqRNR4oVPJfD7j5OQU1ba+IisdWzBxQ1mEcPTlUHZdppOdaxaVhl+sySc/5ZU9q71MB2Ev+Wnl9xa0Mnz0oZvlt742WHEoYeBH27SUpRPQqKuKxWLB3bt3aZuW3b29uLDSLEMbTdO0USo8LKqyLCNpKOTiwRkYYyJ9NwB5gVyj2mX+HJD/MNPQaRF6qW//V5Ze9gwEGxsbSCmp65rxeByrEaFPQUjpRFD8jhuoxRfFUkO04hyG6zQMFQTnUEITk8MKptMpZ6ennJ2eMp/NmJxPeHD/fgQZg46im7mYIoUDI3tFDymJnYrWrpYEHSz6qPvn+cScryKBZ7ZHRwEvMzZYLuQuucSufEMIDu8f8/5PPkbplqLIGa4NEUCv1/NMQbAGmtrp/ZVl6foFhFMe3j844Pq16xFPcDRfr/6LnxHoh4sOfXceEMk3TdNEYdDE59kInCCpLw9a68RF6qqOcmFh1BhAliZsb29Gh+PQdLfbLhbO8WxsbHJwcEDbtpFzsFgs0F5VOHATRIdOLKVkMBjENKssS9f0lKX0egWDQZ+2VVRVHUlMrtLQ0O/3wBScTyY8ODzk3t0H3L79SYw4wn0SxqWBoyWP1oa8cfMqvV7hoi+buFgg0Ji5/D56nvfWKyfwTPakfO3zsA7dNJYF8T9LlDLUteLgyh4bG2sO4Tar6YT2uXle5DFcbRrl8vIsZ3NzywNzjsiT97Jwv8adM7TUunFhHmBjmV6E44XdPvHAIhbPCXDvFwG6ADBqjTaGXhEqE0sGYXiOtQGcdEw/rbUvcS5FSCOXwSwVjEOVYTkCzc849Pux0R05NeN2aelTh4DoCyGYzxYsFk5oRHZYPNbX/WNDkbTkheDq1V0/rUmwOiL+CXfRMxCEuvbKCTxni1iueHQQ96x2KTX5YmQowtl0JakE1oDWcP36FXq9jCxzxByBjO2xVVWjjSbPXCivdQDtNFmesba2xv37R66kmCeu8xAT0XWbWIRxeblMEnI8+6/TBNRl8rVtiwwaAtaSyYQ0XZb+wiJtvRy5UgrZ7yONwAhHuOkOLgnsRaV0JEG1rXut6VCLuw4hTFVyrL5lvt/v950TE66tWrUtWjkZdmPC7i6WqYGxzBcLqrLyDMRlFBAuTABuZWLJi4T9g2034s2GAqEh6A482p4f/PzKCXxmu3hxvyAMQRvIQA+DgtYIVCtoG1jfGJIkrkTVy3t+xzScn58ymUy8HDc0TcV0OuHuvbsxLz89PSPPi6jIk6ZZRN3H4wl5DnmerfQEKK3Z3t6madzEompRxnw8sP2wkKXZSr/BCuW38zMQh6LO53N2d3cpioKyLJEyASx1HZiODnRcX19z6YYxVHWN9hiA1posT91GbA2NB0eLXs5obYTR2guqOCGUtbU1NjY2ODs7Yz6f07YNR0fnTpCkUYzPzln46kUYL+YqJMsKitINGxsDDq5sdyorwvczAPblydQ/ERiWQAKCAAAgAElEQVQUQvw3QohDIcQfdB7bFkL8bSHET/z3Lf+4EEL8J0KInwoh/oEQ4tsv8uRf2WUmLv1ZCEFdNSitSBJBknQnJdsI9IUFGMaJj8/HHB0fcfjgPrduvcX+/v5K84xIZFxI1lp6RUGWZkjpNPSlSMBC27Rek2+OapXvT5CxLdca43ZZvZxHADAYDOLgEyDqBCilEBA5AQFgDD0O4Rxj7i+I2ECaZb6U5/6GJF1qEoaBKk3b0jQ10+mERblY0SwIPAf3vIaz0zNfQcio64bFoqSpG4LY6Cpvwi12YxS7e1u89tqViBm4r2XrcNAferg57Pna01QH/lseniz07wJ/x1r7NvB3/L8B/gngbf/1r/KHaAbh5xYFhPQfiEliIAlFmrD73WJReVBM+MXhRa46uepy3LhBKVcWPDk5ZjI958tf/hLXrl1FJkmn8YYYojuqbkGaZqR+7HYYD9YqxXQ6o1yUKKU6E450XFhK6SWT0H91ewoCyCaTBG0MeLJNICF1y3xh4biFn64oErmy5LKxKe0Ik2rl9APcnMWWxWJO47kGXSewxBg0dVOTJK5SopSOjsRxIERc5HGSMW4Owu7uFlevHSyvnQWLWSI6nXLqionlpX4e9sR0wFr7fwshbl54+M8C/4j/+a8B/xfwl/zj/511Z/5bQohNIcRVa+2953S+XyC7vCrweQGCgtXwMfYOYOKOcvfOEbPpnCSB0WjgWma1u+lmsylV5XoEQhg+Hp/xve/9AU1TsbW1wT/1T/8ZTk8n/N7vfY/x2SQuziqW6GA4HFL4Bp48z1n4nb+tXdkvL3J6RRF307BgwGmTzmazWOt3DUluAYaUIhCPAgVZCNeK3G1bDjJmPd+urLWOVYnwvLW1tUhpBmJUkqYZSSqjwrEQgjzL6ff6tM00OpGTkxPyPGNtNGJ3d4eqrL2CMZ6LYCKoGCKAILwipKTXL7j19g2++rUvx7TBYYKWx0mIPS+CUNc+KyZw0FnY94Hgzq4DtzvPCwNJf8GcwGqY/fhnPH+L0lgrx340kBRUdMpSMV+U1PXU0YCzBJtCuahpW+Wn9Ra0bUNdV9y/f5+yXLC5uc7Xv/Gek9deOLpunmeEFt+LqsPhmNqrEQGMRqNYmkuShKpqvHyZE+0I2ECeFyugXV3XsY/ASYxp2kaRJOkKgy/s5A7L8DMKfHkwlAIdPrAUOw3ly7C7hwYho0MLtCRLM0BEvkTuHYsDMwWJcQKoTjcQxuNztF6qHYcqhYsiEqdYVGT80js32N7ejJFSSAascINMHtEq8ELsmYFBa60V4tPrHItf8IGkL8NWQ8VLKga+3qyUpi5rTk/HlIsplopEOtRb2uWwkFDe01pRVSWnpyc0TcNwNORLX/oS80XpKK7WOrluF7MiA7jXIdpEUM+6oaShPTg83vr839jAHFzqCQRtQfAlRyHIZMilra8ALEHDumnIszyWKcMCjAveE5W6nYQhFQHiuYeGpsBgBOsmMBlD29Roo7A2W5YY/Wu0MrStoq4chmCMJlT4l5iA10xIEooi4+abr7O2Noqpgq/dEJqYHnPVP91N8hT2WZ3AgxDmCyGuAof+8TtAd0X/gg4kXaUKfzFOPqyAKPINuBv97GzKhz+7y+/8znfo9zOuXNmOYW6auF7/+WxBXTXMZgvmiwmnp6fcvXcPrVt2d3f5I3/0j3J2es54PHHhu3WUVoNFN06iOzTEFEURe/azLCPx7LtAsAnofpAD7/XWvRbgcucPC00my9FnxiwjICEFSZohZELTNmhrSYQgTTP6/SV1+OTkhCxNGQyHsSFqMBiQZZmbo9A08XxDx6TSCq2c0KoSoVIBaSJJhEu8iryIswyqquLk5JQHD444OjyO2IFgGQkEklCWpYzW+nzjm++yvb3JQ9wAGyKIi04+QjvPPcr8rE7gN3DDRv8qq0NHfwP414UQvw78Q8D5Lx4e8AUrCV6w0I4arK4V81nN+GzB3u6mq3e3imkzReBy1SwrYuvw+fmY+w/ucHxySFUt2Nzc5Nq1a9y8+Rb/7//zWywWJVJK2kbRbYkNYbdSbnIPLCMCdx513LkDap9lWWQJBufQ3aWryjHxhI8AnKqPwQpo2pY0cSPFszzzf7zF4HgOATvo95wmQJ7nbGxsAk5m3YGQOkYWXfISPiKwFrI8pywXjMdnnI8n9HoDBoOha1NuHb5QliVHR8ecnp4hhXT8gA4YGByAENLhCOsDfumdW4xGTsbNeuzG+r/B2ctDl57oBIQQ/wMOBNwVQnyCmz34V4G/KYT4C8BHwD/jn/6/A38a+CmwAP6lF3DOX1hbydAvdPe9eFulCruFpDg5GTM+m6IVrK+P0Lp1tfDafXchNl5A1DmBs7NTJudu194/2GdnZ8ct/DgNSJIm/tYRqxJhIcQOZJs0SRAdjYFlZ96y5Nfd+cPzHGvWLaJAIw5KP1JIqqaK6L5Mgp6AIEkFbRMkxKyX+PZ049gJaGMpcoXn0UkRpJBkaUqSSjeSbDrFGIvSrk+iKIJYiuMqlGVFXdV+pqKvunQqA84hwGDQY2trg9Fo6AhC7lO4cA2fdI2frz1NdeCffcSvfu2S51rgLz7rSX1x7WGCUPc3F+1yAO85nMVDgGAndLSAWE4M+tEP36dcaPrFJjs7mzRNTbmoaJvQpipompamaSnLBffu3+Po6Ij5YooQlnffeYdr165xdjr24bgT2+j3+45PYEyk3IZzGw6HKKU4Pz93rbb5crhnZAD6VuVgISR30uY1Qtj4mDGGtm6jIlGaptRVFddEAPQS3/Lr6MVLmXBj3O4frlKXunyRltw0DXnuyopF0SORgrpuGI/HvHnzFlIkWHxnYpZgrZttWFcNde3EQ0PVVUbqcqgKWLZ3Nrhx87ojBS3hwO7VveQ6v9iY8xVj8BfCAgqwDCVnswX/4Pe/x+/97veQpLz+2hvs7PYdgDZIybI+Rjtn0TQNWLc73793h7JexHr9t779Td544yZHR8fUdeNJOhKRyli2C2PHQ8dgWNxFUaxEBgGAS5KEnZ0dyrKMGIDyjT3WWvr9PtY6zKBVAUB0I77C4i2KHmmSObqzdvC6sc7xtX5KkFIqko0GgwFHR0e0rYpYBYTcm5V0RPm+gVaFaMly7dp1smw5XyHNEicWUteUVUW5KKN2gssGfCogUk/JBiEsV6/t8e67b/uyICv4zeeVX75yAj/X1kkBVoINQV233P74Hk2t6PUyZAKqDQvSYowLi7sNM3VTUzd1FAUZDEbs71+h3x/wyZ37NE2NtYI0LBS/0C5atwzX1RaIU3/jAlyqD3V1CMLuGHdrPzg0VBcQxOGhtrW+DVe4xXaBYBMqH90RZ105skDV7RKMsBZtNG3b+KGsgiIv4mtDn0GSpshW0dSOiekasYil09DAFL6KPGNnZ5Nrrx100oTudXyyvYgE85UT+FT28jkBDx3rQmqxEgN45p9FUC0afvazO2RJwWgwpN/PqZvGCYYYS1U3vnXVIfpltWA2m7qb2VqKImdvb5/dnT2ETDh8cERV1o4NmLs6eSDfBPnwJfNvOT4s/LtuGifoWRQUeR7FSLpYQldKPDQj9fp9pAAj3fsIP/rMQuT+J6n0TT4OkXcTfiyZcMBj0zaRdBSOF86xO8gkRgOJ27WFMMznc7I0YzAcYa3wgGZKkgh6ssAaF30Y37i06gAcN8DNH5QMR332D3Z5/fVrLHd+gRta3nUKl9mLCxNeOYGntjD59rJ6/OfHFCRWA5Y30Wwy5/79Y370gw+4eeN1Bv2CqpzSNK0PUV2zENYtuLPxKT/+8Q/5+OMP0VqxtbXJwcEe7733dU5OxrStoqlbgo5e01TUTRvD7KZpnAOwhnJeslgsAJaSXW3rBEbW1nx/gI76hGEi0VKBaKk1mOU5Sqsl0u/TBGsNUiYUwx5JIimrRYwkgoCI1pqqrlxfgBDOTTZgjcUYi5RLFea6rl1Pg1ZkWcrm9gZ7+7u89eYN/rP/9L9CypTNrS3aJtCZDWVV09Q1s+mco8Nj6qbtXINAEEqRicNL0kzy3jfe4eDKbud62Vj2e5kNQxftlRP4VLZkgT3NBXsR1YFVRHulcSAclcPDU44OTymygtFwQFFkKK1ompZELgd8aO2Q7ZOTE8bjM6bTKcYYvvq1d3jjjTfY3dljfH6OarVHvZ0sl9uwQ7lP+fNxN3aapfT6/SjjFai8Ie1YJQvplW5BWNJ3XXhulgmPTykC6h7mCYTXhzZlYzSkidMvECI6qCRxaUTI91UbxESDIImbtJxlCVvbm24uoxRsbW9hrXBMRqMx1mMownr1pcqPSFsCj5Eg1JnPKCW8ceM6m5vrRG5AuIaWThrUBZSf443zGHvlBJ7alhf4WexivvrZ3yMQTC62DAvu3z/m8PCM0WjE2vqIXq9AKb/g4uttlA8/PDxkPB5TlguSRPKNb3ydW7duMZ0umEymaGVcM5BQBLGQwKTTSiGTNMISrq04RbVtbLKxHnALw0GtbwEOeXhY0EF0w2EWAd1fsmOcZqFjJ2ZphtZqBeUPjUHubZYNUc7J+LTDeIGUtvElQFe+dOInKf1hzu7eNutrI7TR7O7uoFpHDjK6dnMLlSJNE5q2pSorFouSJMm8o+wShEJVwCATwQ3vBNz5uGEk0B0Xv3p/BDox4kUMJF/aKyfwVPZ4B/B5pAI2tgyFKbbBJLc/OuTenRMOruy5oRapa/IZebVdIQSzecnx8Ql37tzhO7/9Haq6JMsS3vv6e/yRP/rLXLlyld/9ne8ynZZo5Xj0o9HQtc82DYPBIC74UAGwxtL4CUTWWtLEdxNKF86ngS8gZRQhDQt42fiTxYaiorfk9gvhRETDbIQkSd28AqM5Px+vUJKDPHiQCgslwaZuKPKCXtFnOp3GqCrNUozVDId9vvre29y6dYPBoI/WhvW1NeaLGpmkJIlzHq1qqaqSs9Mxp2fjlVKwAw4zksR9ISRFkbK5NeDdr36FtbVBdHbuOn6+ySS8cgJPYY/mBnwau6wl9LPxCLzAphBYEvcvqwHXyDKbzrl/74jTkzPeuLFPmkqyLEHKAb0sj2lAryioqorjk2PqpsJoRTHq89WvvYuUKbPZwglpVG5qjkRQLrTP25c8/jAfMICAAXwMLMKmbWJYm3i1XSDKi6+2/orIJrQ+3ejOKQw4Qa/oU1UVqm5RuqWL7If5BcEZBAclhKBVykUZXaKQTxGGwx47u9vcvPkG6+vrCAFKVY46rQQH+30/bERjrGYxnzOdzphOZp4bIGLHYBKcQOrKpnv7m7z51lVHDoqpE1xM5S5tG34JSMErJ/AUdnGNPu/L8iR24SqrDYIjcA5KIlFYBFpZTk/GnJ2ds1iUFEVGGrj3qXSlPaUxRgGCsiw5OxtH2a08z/nyl98Gi+v9L2s/QsuF843nA/T7PS4ntQR9QhsjDmvcAA0lfNjud/UwS3AZzifxswgMwLZTeQjRhRQyLvSmbWjb5tLPMziCqqoioxGLFxpd4gEBRxgM+mxubrC7u0uWJdS1a6s+PR2TJDl7Fk9TzshUS1PXLBYl8/kiViXw/ICgKJwkKYNhn739Hd66dTOWOpczIOjcTBdJQy/PXjmBJ9qnYwk+8l064Nflx/iUpwQIDK685P5VlhV//+/9PmU1p9dPyHN306aJk8iu65q6bqjKipPxmNuffMK9+/cQnmhT9At+5Vf+BPfvH3F6csx0MkO1itAKOxyNOiw4EXUDgiinUgohBalXG5bSDdtUWtG2tQPUgDChJ4Tqg8HAYxa+hVcueQOBtBTCeaNtbCQKn2uv13Ny4sJNGQo6AKFKED730Wjk/v6qdFyIRESV44ODPa5c3XOfhbGcno757e/8PoiENC3Q2iLFUtSk9FhAVTlBke61kWlGkuYkacprr1/jnXff5lu//DWSVC6fFEHPzz/FfOUEnsE+bTa30qRy4bEnH8uGF6wc2VWXUqxNqJqWH/34JwihGK0VCOkShgQw1i2ixaJkOptx585dJpNzwLK7v8eX3/4Sv/TOV+j3+yzmJbNZSa/XJ88Dau8Wfdxl8fME/fmFBqKyLH1rrY7lPazL1+MwkiTB2OXwj+l06t/FgYei03iz0vtvl6o+MnFS3wFgDASnrj5hcDpBOi1oFWit6fUESepkx6qqZGd3k/39XS82CvP5gg8/+th1WRZ98iyj3y+o65KmqalqN5nJWI2wEkHohUgQMiHxsmf7+ztcubrHlSu7ndHny/KgvSSiiveHWPn2wuyVE3isXc4JeFp75GLvhvePPNIl7yM6r+jIUlsrUdpSljVHh0cMR32GI5fD5llKIgRKO928ummpqtbPG3RimAcH+7zz7ju8885XUDrUwJuVEhqYTmlxubsGp5Amy91fC89B0NqNNxMSaZ3TyLIMKcQKHuAQfIlMPMTpy+2i87xQRVDKqQRleeplwPRDTMDwFfAAcE1HQVxFSkGSJl5nEdI0YTgcMBr1cfMUHaFKK02v6JFlOZal3oC11s9jUJ3HrT9nh5ckiXvPnZ0tNjfXGQz7CHtxrNgF4lc3Uuxe6xdsr5zAEyys10ddDsslu/llIGB4v8e9z9OfFcvYQGJJmU4nnByfcXZyxv7+Dnt7O2ztbLOxPsK0LdPxgvmiYlG11AoOHxwzn89Js4T33nvP9Qi8/hqHh0fM5yV1o2gaFT8D6UP0bjku7P7T6TTm7ghB5mcZGGMZDgdYa+J0IiEEmWcMdunFsZtQru6MjpzUMpvN2dzYcj3+SpGkwlOfl44gqBZ1uxGFcEpBLYq6njpZcGtBGJQ2SClY3xgwHPYoitxF6tbQH/S5efMtrL6DUpqqLCkXcxCGtmk4OTmjrGrnLNBIK8CmDtuwrvnpytV93rz1Ort7W/4Pkk5H1BrEhSv+6FTxxdsrJ/BEe/LSvIjyX3Y5Rdg5Vx68jAL8mKPasEWCtMaHkhKs4PjwhPt37rO5tkZCQrVouXf/jOn5AtM2lJMxR8en1E1LXVecHN9nXs1oTc2v/43/nrfevM6bN17n3r1DxmdjZrMSo60v7zmqblVVsUkoAHuh6ScM6siyLO6QWMtsNo3AXOY7AAMDMHxmbduS5W74RpgYJL0akQvzDVqDMS6Pd9JmIr5XKAcGPcGwS7dti9GWJFk2Nhnj0H2lNWma0Bv1+fJXbjEcDdw5G7eb94oeu7t73P7oPk3jZg00TeW4FaXnT2i7rAoI58CkSLDa0ssTrl3d5MqVTdbX+mB9KVf4AWr20eXBl10wfOUEHmufjiHodpjncwkvdwhi5fsSHbCkwtJLJbubQ1IJdVUzrzXTRGBUS1vOGY8nlFXJfDGlqhcUvYzttXVef+Mam5ubGG2YnM9oKoVVxi2gPFnZ/cPf2eXah4W8DMXd2ckkiYQf6Xdp0Znys1IiNBYrbJwTGDQHu87CWIP0EYixxgOWOPDTtzOHEiO4acKVrmO0IISTTlO6xT3FOae9PTcBqNtUZC0opT3NWqD9313XNbPZPOojCr/48Q1BbkKzG/G+v7/N+ppjbDoznlQVhEQefwe8LGfwygk80j7jtOFLhCrgEdHB074lq45AhG6h6BMsu5tD7NVt7l7f4myhmC5mHJ+XtHXjFoy1jCdjxucnnJ4c0uia16/f5L1vfI2/9G//W2glODuZcHY6xbaWxEh0q0h6kjRN4jDRgNgHkC+Sj2Yz2raN4KEQodsuj59j6PUPQ0ZDRSE4E2uhbhq0H2S6vr5OnufxL6/rOgqOut59txDdDEUTVYUDYSfLMrQar4iRtm1DVZeM1voICXmecuXKPmnmwnhj3c5e1w1HR8ediAO0TqnrlvPxJJY0hZAkMgCDjqsxGg3ZP9jlrVs32NgcusgFgfUgokU+YYm/3NTglRP4OTHHEHQ/LZFli8CgmwpbHdGzx3ztrXXOasl4rvj4wTnj8Yy6VtSN5nx8yvR8TF0uWN9Y45133+VP/PFfoakMd+484P79I6q2JskSkqxg2C/oDwsQSz2AbjQQCD+BzJPnOdvb20wm09iUMxi4acCu+WfpIAMrMMiWB0WePM8j469tW1JfSdBaM5vNfITgdQQEIP2EYutETS3EtuH5fNkq7bQPM5IUssKBgts7W1y9euCwAIKzFTSNm2j8/k8/YG/3gDzLWSwWkWZ9dHSMtcalZ8JAIpGeIZjIhFtv3+TLX36Tg4Md0sRVO7C4QSz4ycOX9n18PvbKCVxqPvf+jPaQQOQlkcGnfs/wXoSh3CH89E5A1aRCsbFWkAwS+gNDlsHpMGWxaJjPa07uZwySIXqzx9bVPb761Xe59dZbLGaOAnt2NqZta2ySkSVuboDj+69iHt3vgmWtPvx9uc/vQ1gfIobQUBR2/yRWFJxcd1iwXaFOa4k9CvGYghhtCO8sksRr/CcJTdOg/RzCRCZexSdUCyxpkpBmkrXRkI2N9Qs8HcF0MvMcCR0bobRW1LUb0T6fz50TQEbX7P4WBwxev3bA1Wv7DIZ+clJE+pdzIS9Sg5ZNWC/fXjmBJ9jzuCzdHfCxzMDHHTPcJNY5AeuEurFY2noBuiHFsDYsKETCBoK9nR7jsz6zWcV4vGB+MsLYEb3BkDe+9h7f/Pa3uHXrTX783Z9xdnLG+GxM21SQOZnwrJfFEmFXiCMCoRBD/cDTr+vazSnMc/I8jaF/+LvDQBFXq+95EDCNCkLYpUBpdARmmUKE5qGmdR2RQkqquqIo8ogjGL9oAfLUaQq4piXtuBOpE/xcX19jc2vDV3O847fW6TKOJ0gpvZqSA1On0xnT6ZT5fO7B0uAcbYxQkjTljRvXuf7aFfr93DmqSAvy7lv4a/yYm+sVWehzt0+PBTwk9vGMJZ+HqslWx39bLAaB8XGBaVtEa5E6AXrk0pIJKBJBupaxWcDVkeRK721s2iNZ2+bdP/mraCs5OT7lg48/Yb4oSRLJsN9j2B+6Jh3fFdd6PYDRaLQiGgJul57NZnEoqYVIGy6KIqoMh8ajwAAMnYMOxa8xLOv7xhiyNKM/7COEa34qy5LEjxPDlyxDSO0qFR6n8NdhqR0IWre0bcPe/o6btZAIer2cK1cPuHr1il+VQRwU7t875uhojBSS8fmZrwzUfPLJHabTmddTXOoTSunSk6Loce3aNa6/doXd3U1k4lmiAqS0WB0qAo+7N15+NPBZB5L+h0KIHwo3dPR/EUJsdn73l4UbSPojIcQ//qJO/MXZp1/8z1s34FKKkl3ePlYIBBqJRljtWmKlRLgk2Q0UsWCNAGOR1pJgsG1Dr1ewf8WFqtpoprM5VdNggSRNKbIcgcD4enwI6QMtF4iLNZbhjPH1exc+O+XiJs4VCIpBuRccDTiA0zd0/QPWLEN1gYjtxovFwkmTdQaELKcSu2MF7YFwbkmSkPmoQXjSkUxkfF0iE0Yj12KdZa6nINC6m6bhwYMjzk7HZFlO2zjnpbViNptFfYJlZAdCJFgERZFz48ZrjEZ90iwhtkHHiknnin6OvICL9lkHkv5t4GvW2q8DPwb+MoAQ4l3gzwNf9a/5z4UQCX8ILYaz/qvLZLuog/fY9/E1ZVhCggaBEM4BSAxZmnpOv3Wgk8VVo7TFKotVGqMU5aIkSVK29vZJ0oS6aZhM59RKYYUbnV1kPddo49l44PLdwPWHrhPQvhkn8dGKRftIIczyC44EllOBw0RiawPRx8uQC+GdgAMiq6ryo78dQNh1AEIIjO9ZCK93nxcRVwiRjBCQJE4XMWgbrK2NKIocmXTwGmMoy5KTkzOm03mHC9HQNC3z+cKzG11VQPiyoKsUOFr1zTdfYzAonIhJuH4A5km40BeYLGQvGUhqrf1bnX/+FvDn/M9/Fvh1a20NfCCE+Cnwx4D/77mc7Uuxl03VeAoLcAAShPH0U4cKYEHVLdpoNAYjFEZpjFbousG0TlFosaiYNJK94Q47r7+NSWB8PufO3UMmszmZTMnTjMFonbpeoI1a4QeEFmQnTgrz+dxp82cZB/sHrmzYNpxPxli77Ovv9XpR6PPs7Iw8z+n3+/T7/SgeEkqLQrj3dZiBmwDU6/XiFORQrgvy5bAEImM7sdcmNNqpIKVpEinIWikanFPb39+h3/flTH/Ngzhrv9dnY90xGeu64vx8zPn5eZyg5MhOq+kAVjIcDvjlP/I1Rut9pPRxm7BgXfkwRHRfNHsemMC/DPwN//N1nFMIFgaS/nzaE8L8x3cGPh4EfOi5j/vlQw0loZglwWpUOcWoxmMFFo0rlymj0MoNF53OFwx39+htbGGTlPl8gWoM0goS6xiNwkIqUih6GLuUBQsMv7puYj0/8dp5MkmYz+dxfl9Y9CHv7/f78XMKlYIuqcg5F4VS7vHFYuHfP6EoHLoe8vzu6wGElCTWYrR2UZIxfqcXJNKlSCFyQBBHomdpyu7utj83x7jEutLi7dv3UUpHPGE6m3B6esLp6amTLqN7W4Rx45Kt7Q2uXNnj4GCTNBWd6CQwBAVh7PhjLvTnsgU9kxMQQvx7gAL++md47RdwIOnDCP5lhZtueP/izD70zXpKSuQLWsdDV/UC41WCrZ9FqK2TzdaqpW0a5mXF9pU3KdbWMUjm85qmbkG7G88plbl+/yxN0Tbs/ktHYP1GFiKEUMorKz+kVAr6g35kDwJRXjyUBLspRdQS9ESfEBWkXoxjMBi4lEQEBWF8qrI8J+FZgstr4liFUgpXRWia+CEa44ejFjnD0ZAsT3250Qmk1HXL8dGpZy0atLZMJ+c+Cpg6zr9f9IEVGcqE29ub7O9vMxz1UKbxtGlXOYgBQCwVXnKdP0f7zE5ACPEvAv8k8Gt2uRp+7geSRgcgxHP1yk+KGi55BfEG8bnnkmzq0GytW0xd0pYzjG5xvHQ3VkxrQ1PX6KahKmsms5p33niLzb0DjLUcH59xdnrGdHqOsYY0cZwAKyzG5+pBlxB0HB8eWIMXx41JPyB0NK+Y0qAAACAASURBVBp5Ys1SOSjYxVKhA9w0ue8sFFI6CbE0pd/rs7W9FWcfplnKdDajqiqapnEYBW4JBuBQiqXkeDhGOEejDXmeMhoN2dnZJlCDAR+B1EzO55yejVGNQqmWpqn48KMPmU1mOLFRNwA1tg0LiZQpSZLx9ttv8uWvvOkalIxx10KIJUbDEre43D6/JfA0wOBDJoT4U8C/A/wZa+2i86vfAP68EKIQQrwJvA38vWc/zZdhDxOEHlXMeZrFfKkIyQWw8PHWkaMmMAZXj6ubinYxwYb/rKskGqUwbYvRmrJuqJTBpDlb+9cYrG1iNJyOJ5zPFpSNpuj1kUWOTRO0tEg/H6A/GFD0XCttIP90JcK74Xkg8NR1zXA4ZDgcRnBPCMHW1lZ8TWAWrq2t0e/1IkbQ7/cZxkYgxfl4TNvUKNWgPEAnhIhS5olMSFOnXRhoO9LrF4Z5CAHAbFo3Z6FpW2bzKefn51G9WAg4Pj7i7r17VGUVS5qnp6cOWxAiDjsxxo1TD9GQTBwucPXaAdeuX3H6AnIJBgu7jN2+CLv+ZfZZB5L+ZaAA/ra/mX/LWvuvWWu/J4T4m8D3cWnCX7S2U+D+ebHnXPJ77KGe4jnWxwCS1RZUo1pUvXBOyYecxlg3Vtsz8xqlMTKhtz6gGI6QMqWqa+bzBXWrMBZ6WY4yFm2dpHYi3G4nE6DtcAIuSYGW4KEDEFWH2Qf8/+2dWYylSXbXfxHxLXfNm5VZa1cv1d01i200Y4+ssR+MhYRk2RbYIF6MkDDLoy1hAQ8WfvGLHwwCJCSEBTKSQTaWkLGYB5AMCLCEsIU9nvGMGTwznulxT3etud3l2yOChxPx3ZtZVd3V1dWV2V15pFt562bmvZHfcuIs//P/PzBoFB9aaxJjsH0rT8aG0zSF0HZsmlqiky78bdaFG1KFvATiQE4E/eoNB7vZiYm6hFFpSXZsH9IbIRFZzBdScEykPjCfz3GO4ChUn44o5dE6IiGl7jGbbTGdjgOCI24o63P1OA7gtErSTypI+svv8PO/APzC+1nU6djT7/c/XVMyjqzChaUUrqux1RJwOORCt62jC9JYZVlRdI5kss3LL7yKSVOKsmJv74D50QLXOQb5gEGacrSY03Yto0RjtLQD27ajLAvw66JcnBFYLBb9zZxlKcaslXyOjo4AeoGR1WrF0dER0+kUoJ8riAzEcfeO7EB9pKSiRkFDVYW83zk6LzJkEGsLNmRMGmWklhCjkF5lWGsRQQWapmUymfSRgtaaumooCmlpujCncPfuPZyNqkKbdOKiKaBNyiAf8OKL15jNJuSDBNn7hM5NZgs2nMCmTzhmpxshnCMGgbPYFlTHdpKNVMBHlGCDbSq6Wub+467mrKduKsq6oKwqyLeY7Fzm+msfx5iEw4MD7t6+i0KRZxk+BaVVH74rrem6Fu/Z6PEL6MXZNSAnFvasjcM+Uv2fTMY9rVeWZQwGg35moKqq9d8XWnZtiABiqtBG+fG+z6+wyob1tH1xEh1JRDRta8NOH1mG28B72IhjdA7rRJFoMNpiNpuRBM4C5xzFquLevT3u39+ja7sgpNpxdCTtTq0DD4CSpEPwP66Pbq5c3SXNFM53QLZRNIxx26Nu8rORHpw7AeBYFPAUooHHiSjeS/NQLqfNwpLHNhW2bdZdAReERq2lawWs07QdepySjsZMtndQSrDw86O5EIgSxmCN0G1pF4qXLtBdBKQd4aKOM/3rsdx2o+ofC2Um/KxED3GHjwCizc6CD4XDiPkHoSSL/IMoadNpZOqv67oeAeicDXMNMXLw/ffiVKENAibOy+e0XUuSJkynE/m7lQqRy4LFYkUZ6gFlWfSEpeCPtQTp0411ytG0lTiAY8XAdUoQ5wXOqp07gU37gBzA+3nXdSwQ2lzh0ZRzuqbs+/beSRTQttISbJqWunMM05xkNGG8tY1ynrqsONg7pK4akiwlNRkmTVDO4lxg7wlhf5omROVi7z06MbhObi7BDQiDcJZlffsvUm2LE0io6yV1XYdd2a3JQqLcWCg2RqcjiEPbcwIoZTAGjOGYEynLloAH6klEIDx3giGQKGCtdty2HYM8Z3f3Qq+dYK3jzp27zOdz6nDT379/n/v3RYo90Uk4AeLYYhdDhWKhdY7bt2/Rdg1Gr1uGXj36vG8OYMUnp+kjzp3AUz78mxODT8dkN1E4LGFAx1uqxT62XAlQJghq2NZSVSuqsqCuG1qvubi9y2RrhtFwcP+AqqoZDIak+VB6Q0ZjMmnJudZy586dnoqrLOsembcJ0hGR0YbRaMhkOiZJM1xnsc5xcHAQgEKmjwyGwxHj8ThoAMSbuJR0IZVevg61gcFgGCINmVeIEURd18eKjVLkk9AffM9PWBQFXtFTlysfC5ESHQyHQ3Z2dvrd3HvPYrGibTq6TqjJ9vf3WC4WZOma0MTjWRf8Qm1Ai3PKBxnT6YTxZNwXaR/vzt4AgZyiF3iunUDMyJ7mTft03kut9SnUBtJECSrO2ZauLvE2lM0j+s45bNtg2w7bORyG8XSb4WgCHpbLgrYRMI5RCqcRcJFzdL7DWyEOEYCP4A3izZckSU/WoQBtpDJuAoy39jXeiSPQOgikuXUKE0FGJ1uxmzv5GkDkca7rc+7YYYicg2voruk7EkqvBUDieZDCH8Qz7b3l8OiIt966xaUr2zgrA0NlWdG2HT5MP5ZlQVmVSCogcF+BQkf8tqQIJugqXL9+jdFoKI4IwoBX/xee+HvPRh1g055LJ/AAAvApve/TciZRsNYrhZYemFyEKJy32LbGNjWq61AOKQo6H8g3amwojDmdMd3eYTyZ4a1IlDV1K/l3onEKLI66FACOty5M+uUhz9YyIxAu9iZIjHlkp4/goXgTx91XBy5BYfqJRcv1JF2syMebPt6smzd/BCENBoM+nE/S9SRhXFOSJJRliZNyfOA1tHgv64+fEb/eu3cPY+DT3/0x2q6jqkpWy0KUlpzv368sS3FXoTjZO1oIpLFSLxkOB9y8+Rrj8ajHHJy8wDYvi/cOGvvg7bl0AmvbqOCescqNj5jz8K/IiIBraprFgaCCNqKAtq2oyhWrxYK26VAqZby1w+7lq0y2ZjR1x2pVUZQ1ddMyyIakqSFRUK+qoMSjUcr0VN7rG1R49tM0xRnXpyBRB3W1WvVRwmQykfV72WWrwCeglSD70jQlN+Joovttmq7H6kdi0vXQku1nEJaLZV9HwAcGI6Vou7ZPJ5RSoTXoerYfQTy2GJNw6dJFXr95A5Tn8OCIt9++x979Q5q6DRRmCzwu8BWsUwbvQekEcKEzYDk8mJNlQ77ruz7BZCLU6t7rdSbwiEvq5Munfek9505gbe83l39aBUG54R8eq3jvZRagLtE+0IooadN1TU1dV3RNS9tayAds71zEhghguSixVlqAUSK86zqpoFuHt6FYpegv+lggjDJjceIuhuLHEYPrqn8M2SNXYDw+SmuapqWpW3Z3d0OUsCYpiYjEyF0Qd/DNkeZ1SuRp2obOCX14ugEVjh2KNXmJ6TsV0+mUK1cuoZWhKCqODhdUpcizyRjxfbrGhh7/ekZABzCUD+zDSZIym8148cXr7F7cIc3SjTs/pkGxofiw03miOHiK9hw7gbNw+N+DxYu/a7FNhfIucNZ6vO1om5qmrgQo1Fqy3LC9c5GmbinrBYcHC1prQWnRJnSe1nW0XYu3rh8TPglt3tyR402dmIQ0S/sqeQzPNzH78bE5L6C1oalbmqblSppJ+kKHUkHaPHzOZqqwWRcwiQFLX29o2rZHF8bfj9FLTCeEwITeoUwmY3YuXAAUVdmyXIieYNPUlGXB/v4+bRvSCTm6PVhIhQ6BFAQTLl++zCuvvMxsNkXpjjW4WyK4pzt98sHZc+wE4HEdwTsBP5/mafYcjwRU/2/4FNvIo63R0s+TqnzTiCBGsWJRtSwXBbPRjGsvv8zhqmBZ1BzNl6yWNQpRzbVhB3WdjOFWVUVdN6Gwp/tdeTQc9jd33OXLtiRJk54rsO/xB5ReZBuKhbtIB260ZjweM5lIrWETPzAcDDCJCJTEz27bth9Fdt6hkVqDYd0ujBaxC3mer4lKN5yY957Z9hZbW1OUVpRlI3qJnWM+X1AUBWW5ku6Gk3qCdXI2hNrcB6IQucGd09y48TKf+vR3CLw68gUQHOmjG4RP7Xp5WvZEA0QfZnsg0H6XFECdeH7yEd7kHX/vva7vgfcPDQLbNviuxXgPDmxrqeuGoqxo6oauaWnKmiSIYQ7HQw4ORKq8a5EtDEL470i0Jk0SVstVKIytp/4iPHizEJcGkQ9jTD+bPxgMjhUGYS0A6pxjOBz2WgQeFai/836HF6nzYUDwGZIkgzAwnaYZXZhmjNqA8fcmkwnGmH7nj6nJZlQQR46dkyGio8ND7t/fC5ThqkcAdl3LqliyXK76KUDhA5AJSpENi61aSQ3SbMC161e48eqLRJ4Aj+oBQ4+T6J+VOOG5cwJrO34KnnZh0G88Hn89697ypgOI5toauhYdqtPOeromsPe2Ft85bGdFoCNN8d6xKgrKssbZ9YCRC3x+EfASuQD7jwv5+yYI5ySHQGzTbQ7/9Bx+UW0ofD9GCvi1BsAmWcmxeQGQOoWHJImahgFUZNfjyVGqDNbsxPIR6wO2FjOVukdZVhweHHLnzj2c8z0EG6CuKsqykN8hOgF5vnkm5TgkjMdjLl3a4fKV3f48baAIPjSpADy36cDjn6DHwnFsFBXfX7AX4Shroo+eQcBDWy6xVYHqOrTXeCeEoG1VYzzkOiFDobKMunN84+vfxFtFYlKZ368qnLd4BdPxGNu5oNizbvPVQVMQ1irCPoTt8qf6HjK8WbzbFCKJnABpKp9rAxdB19oeL7B5sy4WC4Yh7SiKoncO8fedc9iQYkRocHRCeZ4D6zrEJnpxLVYqxzYxCbdv3+VofsjNmzfDmi3D0YCyKlkul+AFBITyG8CfGOaDNgn5YMgnPvEaL718jd2LM6yz4Fx/3t4RLvgUrpKnbc+pE4jR2uM5g81T9m5Q0IeFge/++3LrrzPO499xzuK6mrZc4ZsaHcZgy6LgcH+fcrlAq4RcJ0yGQ+4VJaXaZ3RvHwbbPRW4UooAp6Msq1D171BGh374eloQZIcdDofUVcVisWB3d5cuEJZGaO/mTh6r+FEKLL7u/Hq8NuoOREcSJc2i3FgkDIkMx0VRhCnCkvFk3EcMw+GwFzHpZ/tDCgPrKCDSkxmt8KRo48myHO8888WSvb0DmaeYz5nPj+SIhwKgCbm/QpCPon2YMMiHvHbzNcaTCZ2ViKFnDfAbQK+HXgVnywHAc+QEHjz0Txau9SjDR73/CSCI+JrH/6xeqKKfRAOcAIRcI+lAkOsRzoCmxTYdKpEbXANt16E6GxBuCrToF8bwH6UCNXiY9fcuwGv1MWlvraJcuD6GwFM6DB6F78caQdzB4w4cuwmxHhCdS7w5gX6n3+QbOJZCbBzITXqyWHM46QTWhcAY0svfq1BkaUY+TJhujShKAQktlyvKoqSuKtquI0+F8DSeM63ECagwzKSUJkkzLl+5JHyKXpQFT5zxdznHZ8ueGyewtmMlN3nlCeoB79WfP275UeGDA1h/krMdbbGEroHO4q3Ddx0pikGSUDqPbTuc9RR1g0eT50N2L17hftlhvZcpwQ0q7qquAHEAVVkFoU8pKEqNwKMSTRdIRYbDIU0jO7d3kCQZRhsJn5Xud+XhcNgLjSwWiyBkmvTvfzJy2IT+AqF7IICjyEqU5RlJao6RmB5DKW7UK2JdQgBPsXPhcQ7GkwlXru5w+fIOb791m/39fZbLJff37tGGQmcSFI/xMQVIoKcIk3pAkg64fv0aw9EgoCQjgejjFQTPmj2HTuBBe/pDP4/9yQAo3LHc//i3HbatqZaHqLZFOYu3ntV8QVuW6M6SpbmM+HqLM4YLs12Gu1fAJIxGGU3bUZUCJNJKQQzjrQVCO2wD4JMkQg5aVXUoqsnr88VCBpWsIwvzBSAkHQpDYnQfMqdpdByiHpylwko8Ho8ZDoccHh72N3MM/+OEoWj9yYEwRqMxeKP7uoNzonoUW4KwjiiyLEMpyFJNOjYsSk9Tt7RVw2Q6pqkr9vbv8rU/foOiaAAlxCnILl9HFSMVoid8SA8UxmTMZttcvXqF8WQoRKWhtO7CPME7V9rPXioAz50TOFteep3/r5MMf6zf7IUvsGtwTUXiJAqwXUexWOCbBg2keSbswrrDKcN4OmM42aK1TgjJAt+ADbP6zmhUYkApUR3K8z4/j2IiMX9vW4G/GGPCzwQQUE86Cl27HvZxYaLRORfQhWugTUw1YqQQo4LYLYhAoU1bswyt8/xNIFBsC8bP74+t92E0mjAO7bBdw6rwVA3s7R9iW09d1TjX9Wmccx5lVIBjK0z4fKkrJIE/0NO09ZrRKKRaj766zubNH+25cAIPydIfuvtvVqyfVWTgTwypelQvVqO8xbYltimlPegkDWirmvn9++RJwiDPyUcjOrxU0A+WjLcuMJhuU9QtHR02QIJt20n+34EhQ2lNnuWkScZyuezx/nFUN1J7JeFzqsWKumlo25ZBlvd5e5wbkBHjrif6tNZhdCKqQmrNXlQUBXVd94ShIvxZ96F8VDhu2xatVU/cCWu68ZhKRCdgoxPyHuUd1nuq2tE5jQYyo6iLFVUNrbcslwVNLRGSC5wDoXoarwA5Lz3oSERGnLVU1ZLDw30uXdpCMe4dwOO0Bc/WNiT2XDiBtYWQ+wO6wd8JWfgwi91n3f+2Wk8QOoutVtSrI5pqSectrqpolivKowVtXaOcMANvXbtK0bY0qxKr51id4ZIB6WhKdXSICISmXLiwRd00VE0DSlqAnZWaQGy3DYcjWYd3YVJQ9+SdgzyTG1eJNkESBEgip1/cocuyBOhfjzf6Jg351tZW/zsR4x+7Eb1+gFaMJxNAooc0TXtHNR7LzecBk6Y4xEEsVyu8tSiTkOYjEu3BhK6IhuFoxPZgQKY0B/cP2N/vSHSK84rOeSEbCYVEFY4DATBlEsO1a1f55Hfd5LVXX2Y6nSC3vj6TN/fj2hMJkm587+8ppbxS6mL4v1JK/TMlgqR/qJT6zAex6A/C3s9458lS4wOlR78hJkrc/TeijhN7iAKwjq4o6RYL7GKJL0qqxZJifsRyfgBNiw+8AR5oOkvdWjApVhlcmBFQSuOdp7NtqFvFFlgoYnn6olyU146qu977nobLOYc2QvGdpkl/00qJQR/TJIgFwKgTGEP4siyPAZNiKvAwmK9zIXqx3TFHEvEDEdREKCo65wIYSSTCEqNIEo1WDqM9aaLIs4Sd2YQXruywPc4YZZAqS9fWkjKQYElkEhCFVqC0R2upTezubPPSy9e4+forTKcj0sT0a3336+fspgRPKkiKUuol4IeAP914+UcQrYGPIepC/+L9L/Fp2en46nc79QIKOuE2PHhraVcrmoMj2oND3NGC1d4B8/09jg7uo5oGH9qDrXNUVUNZNvgkp0NjvVBhaSUDLW1bi1RYD21bo/RiPr3ZgpOKemCLD2AgY3Sg0JKbLQJqxBHEFqBnMBiQpmnP9ht5CaPOoEQMbd/XjyrF68q+jOp6RMcgth9VgC7ngwHWuR77ELkOUTLdl+cpeZ6QpAqtOoyyJAkM84TdCxNeemGXrQxGiSPXHa4tw8lIsF6cgEKRaIXRnsQo8jzlheuXufn6K3zik68zGg0kaohTmI880Wf35o/2RIKkwf4pIkDyHzde+3Hg33hxi7+jlNpWSl3z3t96Gos9q/ZuqMIHvh9QaLLfrEuD8aG8RVmLbjsWb71NdXhAV8z59pvfomoqrOu4zJDSV7hFxcXv+E5au6KoW0rvaFDo1lLcuYMygoDTWqr91snOVZWr/sKN4qBlKUNEk8kYpXQgLRUSkuFoJFTj8zkHBwcMBwPyfECaBlKPSBziPHkuN+vW1lSmFL0MK023JkHyXCYJ4+dHFuIIIKrrGo8ny5Ie/NN1HUmWkQ8GDMdjFotFHx1EuTLnXGD4c1hvaaoVXV1IYVMrVJlQLOesDj2Hb36Zy4OMy6+M6Ba7fOWbd7h7sOBo2YCHQZawOx4x37/DzsVL3Lj5Ej/45z7Lx7/jY7z88hWMCSPXeJTRZzPZf0x7opqAUurHgbe89188kV9fB97c+H8UJD01J7AZdJ8lixGAQlSrY4HQE9ltHdgO3TT4sqCaH7E6OqIKir2HqsElKUYBtiX1jsQ5ytWSg/0jpmRcfuGyCG3YDqUNtatFTtyu83dr18Qh3hOKfWG3NwbnhVhUORcIQ9sNQJTc+EL7Jbt328pIbeivBa5Bte4utJ3k+0ZYkiJHYNetx4lBqLuMMZgwm+BDSuW8h43JRSC0ISWpctaDd9Lrd5626UIVX2Gt59bblnJ+n9Za8ixlOhrwfZ/6OK/feIGjZcHte0fs7c+ZjMd84vXXuHfr24xnM669dINPfvImO5d2JS1RMt/goa/jPGjr43S2rr7j9p6dgFJqBPwDJBV4YlPPVJB0A4zzmEXBp4Ed2MTIrxlmH/KeEYsSxwVVeHQtvirpViuqxZx6JTTYbdPhVU06GTMc5bi2xtiO1Fts27JaFSSjmsEwp7UdeDCJZonQcK1vChcq8LrPwbU2MjWnIDEGZy1W/pgeS7A5SKSUIssyKTJ2QasAuRFhTSceb1ofBoGSxACiSKyNlvpGgB0naSK6A8la+VghSEjvvYCfjOlrDXVdy8yADjqJncMH5qW27QJ5SiAn6UrKuWOgDGmaMZ2MuXblChhHVTd8+9Y9vvXtO2xNZ3zPpz/F7bcvkQ5GzC5d5aVXrqPTDEXIoxW9JtT6ugnncX1qTzw5e/YkkcDrwKtAjAJeBD6vlPosZ1yQ9L3e1P2N+x4cR/gF+fLolRA7AU5CAqkLSC4gAB3fYbuab73xDQ7vvE1TLTFWYVpNUbTcW+7xwugVdrcnLO/fo14U6M7x6suvwIXL+CzjW3/6Bjs7l5lOZkyzEXVnyYMDqEpp/dW1CIV0XUMb9AoGg5w8yxiPRywWC3xgIGrqGmMM0+n02Nz+JognUpFF5yCIwbWuQNu22M6yNdvq25GiSdgFgZK6hysnSULbdSRIhBKJRiNXwTF1YiXaCcNBzuHhfbqgW1isSqq6oXOeS69dZ3uSMhkokhIGoxGYAVZnKKXJh0NuvjrmY69cR/sO4/d5+YUpfjBDb22jB/JzeIcKcm1aadp+WOBhl/FHoCZw0rz3XwIux/8rpd4Avtd7f18p9Tngp5VSvw58H3B0+vWAD94F953lE9Nx71Qx9pvPfJAYQyKFruvo6oamKpmORzT5gPnBfboS5kXJ/nJJ0TSYwYDt3V3QiqKpWdYWfXFAmqf4RNPWHU3XQKVoQp6dJsK7VxSl7PapIU1SkiSlbTvm87mAY4yg8RYBP5AYQ5okMncQyEPyPO+lxyM2AKTPL311kTKT/L8gSzO8A60TtDL9DdUFBaFIaeadwzuPRlNVNa1uSbOU0WjUzyJsyp9PJpMAcBIH1DYtLqQ5gigUHsLJeMRLL17mhYtT3vjSbYyyKCzeCycjSuGdQisDSoPyOJVg8hHZZIYNCZtS0GJgPTb0obYnEiT13j9Ki/A/AT8KfB0ogL/5lNb5nu1hAKEnfq+TQ0EbkcHJdz6GWnvkOwYKKhVzxY2xJAXeOlzb0dYNWZKSJgmuc5Rlw6ooWZQFGEOS5wyGY2prqayl6DrasuSFKxlpPqRpLW1nsbaiDupERqdhsg5hBvYyIZel0i1IUxEGUQQ1oFCx3+wktBvdhM2wfDNVMMaAB2tbutYG/QKRA4/zBFqJpkHc0ePnCHBHkIdN1aCNlPtsHvkB/LHPSlO5gaXo2IW6gMCAhdBUCqNZkjAZjbgwm/IWDo1FlJ065IZeDzspJXyCXqfodEQ2nPaRGoBVWpz3A9fAQ0bVzrifeFJB0s3v39h47oGfev/L+mhYDBLj82h95C//O/YTyouGjXdeRDKrhs56hoMRt27tcThfsChKXn39dUaDMW3j+dbhIa3JWBjN//hf/5uf+jOf4aXrL/O1r/wxq1gYszLzbjtLZj3j4Rh8QdGWVGXRE2lub01pw3jvn775pvTxk0RUjeqaqqqoqort7e2+5x9n+OOYcITzJiYhQ6OVRAhVWTEYDJhOp6Rp2sOUoxOIswQqOJbFctmLlGRZztHRPNz4miQxjEYj0jRlb2+PNKAP82xIY9rAOJQxGotjq2uDbVv2bt9CL+6iW4XJwWAxdCjniMUZj8YrkT2yZowZbjOZXcG6tShsp2QwKXZ6TkaDZ/y+P2YfccTg2k8/SZHvYWw1T7KCY//rX9iEDq2RgsqDyTLy0ZjxhQtceeUGeZ7RdTUvVS1boxHbiyWvXb9Oogz37u9j05TCahZNRzFv+LVf/Q12tne4eukSV66/QJqK7t9wMAxMQJr9/X0cHm00RVFKZ8AYdnZ2ArEHJKnui4Zt0/adgCzLelVfpUT1J0kSJpMJ0+mU2WyGUorVqqDrarqupSzLnpIsFhhjSB+5A5RSlGXZ7/RVVZGkKVmek2QpOuAHZG4goSiKfphohCdNE5TrGI1HocbqqOsVZV2xKgpad4GjwwO6w4KdLCdNc7TOsC5BBoZB40B5dGpIxlNmsxfIpheCBkTs6kgs58LZW5/PzevmUTWCs2cfcSewtncK6R9mH+TswMNUavtXvEIZjR7kDHd30FVFZ1uyu28z2xaBkK5p2ZltUXnP4eER8yxj7jR7ZUtZtnz1q99gMr5L97GO7d1dEjMMH6D66nWEwSZao5So7aDWOH0J93UYLuqo6xqUkJRG4RGQUP7kMFD8nlL0O3w8nDF1iACfk6zCMbLQWotsmdGyzp4/QAHSJZCORBffGAXCSOyFTdlaj2ftOKyzlHWBa4+4dPVCL5ICRgqz3gB/5gAACoVJREFUSuYOUAplUpLhhGw6wwyHOLV2AADa06d0jziTHxr7SDqBp30aHuUQ3rubiLlqpLOWd4lfQVIFqxRqOGB281WayYjWQPLmN5lYJcq5e/eZzSYs9+bcunOfby4WzFXKwmqWi4bWOsq6Rf3JN7nx2g0GeUqSaMqq6vP10XhEkmZoIzTg4Q8VvkJnsc7SdjWz7W2UbrBHc2ndKY1WJnQSmh74EzsF3vu+uCjcgBZtFIPhAGPWkUAcBY6RxGq1AqQLcOnSJbz3wiegTc93GGcHnLM0Td0PLY2HY/I0I01S8mFGsSqxvkMphwlAKfB0bYsvC2y1QKUXUYmWUWBtwi4vBUmUQacjRttXMdMZ3qTYwDjsN0aMHwcN+mGwj6QTWNsjbt4zVqnZxG575YN8l8PiMIOc7WtXufnZ78MWK67cu8eLb93CpCk6M2SDlHGXM7tynUIP+Padr0CqqVzDN956g1VdsO23MEpzcHCI1oZBPmBnd6cX8xiPRyLk0VnarpGOQZYyUiPaxlLXLc7L7metoPHMMKcsC1arZWgXjsiylOFwRFVVfRtvPRAER4dHtF3b049tYg1iqy/P815WLM0zmrbt3284HPZOLE0zQj0QjSbs+VSNFBITn2C7jtnWLmk2Ap1x5803mCUtF4dKcAN4rPIYGqE0DxOIJClJPmYwu0SnUsFZxJveeyJZ2sZZe8hZ/fBEBB9hJ3C2bvRNexj+wG9gixWAlzBVJznpeMro8hWagz1c20JgF87yJXmWsnNhm3Rnl4xMdraAM2jamrdvvc1okHPjxg30fIF3niaQhMbP18aguiCsoSRliGtsbE3XtiLDHVB9OqQVkYZcwnfVE5DErkKvNxB2cZMYtA8DSXZ9k0RKclSgENMKFeTFYzqxiUMQUJEMFRmtyfKMtmvEUWktxJ94tNI4DyhDlmXMl0dMR5p0MgpFW+kAOAUqQIDRUpPRWQ5mfXtE/UEfnvuYBhy719dpz4fJPlJO4Gn53tOIFNQDDgCUMugUzAjSCx3V8giVJmSDAckoZTovmAxWjC/s4HcvYxpPkiq6tsHZDm08X/7yl0m04VOf/m6Wy0L66G0rkmQbQ0TRA4lcl0wnRqYhkMnA5XIlZCJphveOwWAYsAJSA+jahtoLyYiImziqqiTPB1y4cIE0FQbiPB+wWha9SMlwOBT0nQ9kIVkmNYK66p1RhDYLsYlAjaMDmm1lMiXphUR01SzxzpEmmsOilsglSaiLQ/xgRJ5NAguSwYVOgFIeUULSmNEYPRwiroT+wuqpYB+4/z/cdYGPlBNY24Pd2/47HypXrfBJhjYpo9GY8XSL+s4dVsM3acqS3dqhSLitFV958xZv3F8ASoaPrMUA9+7t8fk/+CJH8wUvXLvGZDJhPB6xf7AviL5E9Akm4zEmMTRNy+HBHEtLkgjeP7bjrLXkWc5oPKKpKoqioGlqYIjsggqUxnkXqvsleZ6TZQmdbRlPxqGg1+JZMwHdvXuX2YUZSZqwXC0F+BNgyrH9GKHHaZoAeU8tlqYpdSPKSdY5yqLE2xAlJAndspBR4a7C1ksyM2Q2HZEmCalSGO8x3oE2oBKcSUlnl0kmO1KbCXUAhw5DAo+TCny47CPqBD5Ye2ZupIcQeBkUGoxJZ7sMrzk42mdQFeTVnPaeqAzd21vQ1jIFZ5TCK0PbWQ7nc77xzTc4Ojri6tWrvPTii4yGo4Css6I27BxY+lkCGfyB0WgQ8PsCy80HAif2YYgnziGkqaQEPd146CwMh0OZAQj1BxCWniRJMNrjnFyCsd2YJInMEgRikchwJCPFIks2Ho8DSMn1WANYC5lgNHhPF6KMzkBb1bx4/ToXLozRJmHNBhTwHFqDTiAbkgynmHzYn20fpgV8jAQemgoctw/TVnPuBD5Ae984A6+gR7VZrM7Q020GaUaXQlockC4S6m8ecXR4yMHhirqqyVKZAPQqobM1q6KgKApu375NXTdc2L7AxYsX6WxH2/keo69sZPjVAQ7sGAzzfvc2RvLv0XhEVQiIJ3Y7BoMBWitWqxV5ngesv0iMKS2k3NYKIEdpRZIm0plX0jnweKyTnT9JImuw5NjWCvRYa9U7gTRNWSzm1E1F13myLO1pzGyIRNrOMh6P6RJPZZe8+OqrjBMnHIIb5CqywRswKaQjkuGYJBtsdPqF+3GjbBPM8VGIBNT7AcI8tUUodQ9YAfdPey0bdpHz9bybnbU1na/nne0V7/2lky+eCScAoJT6Pe/99572OqKdr+fd7ayt6Xw9T2bPsSDpuZ3bucG5Ezi3c3vu7Sw5gX952gs4YefreXc7a2s6X88T2JmpCZzbuZ3b6dhZigTO7dzO7RTs1J2AUuqHlVJ/HARLfvaU1vCSUuq/K6X+r1Lqj5RSfye8/vNKqbeUUl8Ijx99hmt6Qyn1pfC5vxde21FK/Rel1NfC1wvPaC2f2DgGX1BKzZVSP/Osj496iBDOo46JEvtAhXAesZ5/pJT6f+Ezf1MptR1ev6GUKjeO1S897fU8scUBjdN4AAb4E+A1IAO+CHznKazjGvCZ8HwKfBX4TuDngb9/SsfmDeDiidf+IfCz4fnPAr94SufsNvDKsz4+wA8CnwG+/G7HBKG5+88Ivuf7gd99Ruv5ISAJz39xYz03Nn/uLD1OOxL4LPB17/03vPcN8OuIgMkzNe/9Le/958PzBfAVRC/hrNmPA78Snv8K8JdOYQ1/HvgT7/23nvUHe+9/G9g/8fKjjkkvhOO9/x1gWyl17YNej/f+t7z3gemE30EYt8+0nbYTeJRYyamZUuoG8D3A74aXfjqEdv/6WYXfwTzwW0qp31ei0QBwxa/Zm28DV57heqL9BPDvNv5/Wscn2qOOyVm4tv4WEo1Ee1Up9QdKqf+plPqzz3gtj7TTdgJnypRSE+A3gJ/x3s8RLcXXge9GVJT+8TNczg947z+D6Dv+lFLqBze/6SXGfKatHaVUBvwY8O/DS6d5fB6w0zgmjzKl1M8hNMa/Gl66Bbzsvf8e4O8Cv6aU2jqt9W3aaTuBxxYr+aBNKZUiDuBXvff/AcB7f8d7b733DvhXSPryTMx7/1b4ehf4zfDZd2JIG77efVbrCfYjwOe993fC2k7t+GzYo47JqV1bSqm/AfwF4K8Fx4T3vvbe74Xnv4/Uwj7+LNbzbnbaTuD/AB9TSr0adpmfAD73rBehZNzvl4GveO//ycbrmznkXwYekGf/gNYzVkpN43Ok2PRl5Nj8ZPixn+S4GOyzsL/KRipwWsfnhD3qmHwO+OuhS/D9PCMhHKXUDyNCvT/mvS82Xr+klDLh+WuIcvc3Puj1PJaddmUSqeJ+FfGMP3dKa/gBJIz8Q+AL4fGjwL8FvhRe/xxw7Rmt5zWkU/JF4I/icQF2gf8GfA34r8DOMzxGY2APmG289kyPD+KAbgEtkuP/7UcdE6Qr8M/DdfUlRCXrWazn60gtIl5HvxR+9q+Ec/kF4PPAXzyNa/1hj3PE4Lmd23Nup50OnNu5ndsp27kTOLdze87t3Amc27k953buBM7t3J5zO3cC53Zuz7mdO4FzO7fn3M6dwLmd23Nu507g3M7tObf/D4eFUrW/uViMAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "bMQV8EeiELN9",
+        "outputId": "d0143de0-4319-4f67-f919-161e2a645519"
+      },
+      "source": [
+        "plt.imshow(x[50])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f20b1c9ecd0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7Kbn_kfSctmV"
+      },
+      "source": [
+        "def plot_images(img,labels):\n",
+        "    plt.figure(figsize=(15,10))\n",
+        "    for i in range(16):\n",
+        "        plt.subplot(4,4,i+1)\n",
+        "        plt.imshow(img[i])\n",
+        "        plt.title(classes[np.argmax(labels[i])])\n",
+        "        plt.axis('off')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 591
+        },
+        "id": "VurZy9fZctx9",
+        "outputId": "f38dccee-edf7-4031-f808-0f8a41c55c09"
+      },
+      "source": [
+        "plot_images(x,y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
diff --git a/Task1/ResNet_Arch.ipynb b/Task1/ResNet_Arch.ipynb
new file mode 100644
index 0000000..7853c20
--- /dev/null
+++ b/Task1/ResNet_Arch.ipynb
@@ -0,0 +1,925 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "ResNet Arch.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Cmy5aLFZI3Hv"
+      },
+      "source": [
+        "import os\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.datasets import cifar10\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras import models\n",
+        "from tensorflow.keras.models import Sequential, Model\n",
+        "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Input, MaxPool2D,GlobalAveragePooling2D\n",
+        "from tensorflow.keras import applications\n",
+        "from tensorflow.keras.optimizers import SGD, Adam\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "import numpy as np\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JW7X6qU5Lykc",
+        "outputId": "ad73512c-bb9c-443f-871f-931283fecef8"
+      },
+      "source": [
+        "(train_data, train_labels),(test_data, test_labels) = cifar10.load_data()\n",
+        "print(train_data.shape,train_labels.shape,test_data.shape,test_labels.shape)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "170500096/170498071 [==============================] - 3s 0us/step\n",
+            "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qSVjxWl0T2G2"
+      },
+      "source": [
+        "train_data = train_data.astype('float32')/255.0\n",
+        "test_data = test_data.astype('float32')/255.0\n",
+        "train_labels = to_categorical(train_labels)\n",
+        "test_labels = to_categorical(test_labels)"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VEuGRgFrOCie"
+      },
+      "source": [
+        "cnn_model = Sequential([\n",
+        "      Conv2D(32,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same',input_shape=(32,32,3)),\n",
+        "      MaxPooling2D((2,2)),\n",
+        "      Dropout(0.25),\n",
+        "\n",
+        "      Conv2D(32,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same'),\n",
+        "      MaxPooling2D((2,2)),\n",
+        "      Dropout(0.25),\n",
+        "    \n",
+        "      Conv2D(64,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same'),\n",
+        "      MaxPooling2D((2,2)),\n",
+        "      Dropout(0.25),\n",
+        "    \n",
+        "      Conv2D(64,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same'),\n",
+        "      Dropout(0.25),\n",
+        "    \n",
+        "      Conv2D(128,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same'),\n",
+        "      Dropout(0.25),\n",
+        "  \n",
+        "      Conv2D(128,kernel_size=(3,3),activation='relu',strides=(1,1),padding='same'),\n",
+        "      MaxPooling2D((2,2)),\n",
+        "      Dropout(0.25),\n",
+        "\n",
+        "      Flatten(),\n",
+        "      Dense(256,activation=\"relu\"),\n",
+        "      Dropout(0.25),\n",
+        "      Dense(128,activation=\"relu\"),\n",
+        "      Dropout(0.25),\n",
+        "      Dense(10,activation=\"softmax\")\n",
+        "  ])"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kCwogP0-kAMo",
+        "outputId": "f52738b8-d162-4846-92c4-aa7bd06205b4"
+      },
+      "source": [
+        "cnn_model.summary()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 16, 16, 32)        0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_1 (Conv2D)            (None, 16, 16, 32)        9248      \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0         \n",
+            "_________________________________________________________________\n",
+            "dropout_1 (Dropout)          (None, 8, 8, 32)          0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_2 (Conv2D)            (None, 8, 8, 64)          18496     \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
+            "_________________________________________________________________\n",
+            "dropout_2 (Dropout)          (None, 4, 4, 64)          0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_3 (Conv2D)            (None, 4, 4, 64)          36928     \n",
+            "_________________________________________________________________\n",
+            "dropout_3 (Dropout)          (None, 4, 4, 64)          0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_4 (Conv2D)            (None, 4, 4, 128)         73856     \n",
+            "_________________________________________________________________\n",
+            "dropout_4 (Dropout)          (None, 4, 4, 128)         0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_5 (Conv2D)            (None, 4, 4, 128)         147584    \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 128)         0         \n",
+            "_________________________________________________________________\n",
+            "dropout_5 (Dropout)          (None, 2, 2, 128)         0         \n",
+            "_________________________________________________________________\n",
+            "flatten (Flatten)            (None, 512)               0         \n",
+            "_________________________________________________________________\n",
+            "dense (Dense)                (None, 256)               131328    \n",
+            "_________________________________________________________________\n",
+            "dropout_6 (Dropout)          (None, 256)               0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 128)               32896     \n",
+            "_________________________________________________________________\n",
+            "dropout_7 (Dropout)          (None, 128)               0         \n",
+            "_________________________________________________________________\n",
+            "dense_2 (Dense)              (None, 10)                1290      \n",
+            "=================================================================\n",
+            "Total params: 452,522\n",
+            "Trainable params: 452,522\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "bfaJ2EfUkQGJ",
+        "outputId": "e4cf7743-3988-4033-9304-22e9f2968a57"
+      },
+      "source": [
+        "tf.keras.utils.plot_model(cnn_model)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a1FXjz08Gywv"
+      },
+      "source": [
+        "cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sTw8thBcTSN9",
+        "outputId": "0685881a-ab2c-4f26-8e9e-7838e02f17ff"
+      },
+      "source": [
+        "history = cnn_model.fit(train_data, train_labels, epochs=5, batch_size=128, steps_per_epoch= train_data.shape[0]//128 , validation_data=(test_data, test_labels))"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/5\n",
+            "390/390 [==============================] - 132s 334ms/step - loss: 1.9137 - accuracy: 0.2676 - val_loss: 1.6316 - val_accuracy: 0.3888\n",
+            "Epoch 2/5\n",
+            "390/390 [==============================] - 130s 333ms/step - loss: 1.5492 - accuracy: 0.4242 - val_loss: 1.4070 - val_accuracy: 0.4865\n",
+            "Epoch 3/5\n",
+            "390/390 [==============================] - 130s 334ms/step - loss: 1.3924 - accuracy: 0.4900 - val_loss: 1.4662 - val_accuracy: 0.4989\n",
+            "Epoch 4/5\n",
+            "390/390 [==============================] - 130s 333ms/step - loss: 1.3034 - accuracy: 0.5314 - val_loss: 1.2648 - val_accuracy: 0.5437\n",
+            "Epoch 5/5\n",
+            "390/390 [==============================] - 130s 332ms/step - loss: 1.2479 - accuracy: 0.5546 - val_loss: 1.1434 - val_accuracy: 0.5832\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 513
+        },
+        "id": "sjM9iK7nW_Ig",
+        "outputId": "15078741-e298-4ea8-855a-819ab855cb5e"
+      },
+      "source": [
+        "history_frame = pd.DataFrame(history.history)\n",
+        "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
+        "history_frame.loc[:, ['accuracy', 'val_accuracy']].plot();"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XZ_wUZ9r1ErW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "be535b9d-16f7-4a89-c4b6-8ce0509a7107"
+      },
+      "source": [
+        "resnet_model = applications.resnet50.ResNet50(weights= 'imagenet', include_top=False, input_shape= (32,32,3))\n",
+        "\n",
+        "res_output = resnet_model.output\n",
+        "res_output = GlobalAveragePooling2D()(res_output)\n",
+        "res_output = Dropout(0.7)(res_output)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "94773248/94765736 [==============================] - 1s 0us/step\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0f43cKv31Jeu"
+      },
+      "source": [
+        "preds = Dense(10, activation= 'softmax')(res_output)\n",
+        "model = Model(inputs = resnet_model.input, outputs = preds)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R-yZ9udI1J3_"
+      },
+      "source": [
+        "model.compile(optimizer= Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zNWQYm-U3wES",
+        "outputId": "f57dc790-270a-439a-c48d-f77b7995fc00"
+      },
+      "source": [
+        "model.summary()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"model\"\n",
+            "__________________________________________________________________________________________________\n",
+            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+            "==================================================================================================\n",
+            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
+            "__________________________________________________________________________________________________\n",
+            "conv1_pad (ZeroPadding2D)       (None, 38, 38, 3)    0           input_1[0][0]                    \n",
+            "__________________________________________________________________________________________________\n",
+            "conv1_conv (Conv2D)             (None, 16, 16, 64)   9472        conv1_pad[0][0]                  \n",
+            "__________________________________________________________________________________________________\n",
+            "conv1_bn (BatchNormalization)   (None, 16, 16, 64)   256         conv1_conv[0][0]                 \n",
+            "__________________________________________________________________________________________________\n",
+            "conv1_relu (Activation)         (None, 16, 16, 64)   0           conv1_bn[0][0]                   \n",
+            "__________________________________________________________________________________________________\n",
+            "pool1_pad (ZeroPadding2D)       (None, 18, 18, 64)   0           conv1_relu[0][0]                 \n",
+            "__________________________________________________________________________________________________\n",
+            "pool1_pool (MaxPooling2D)       (None, 8, 8, 64)     0           pool1_pad[0][0]                  \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_1_conv (Conv2D)    (None, 8, 8, 64)     4160        pool1_pool[0][0]                 \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_1_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block1_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_1_relu (Activation (None, 8, 8, 64)     0           conv2_block1_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_2_conv (Conv2D)    (None, 8, 8, 64)     36928       conv2_block1_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_2_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block1_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_2_relu (Activation (None, 8, 8, 64)     0           conv2_block1_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_0_conv (Conv2D)    (None, 8, 8, 256)    16640       pool1_pool[0][0]                 \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_3_conv (Conv2D)    (None, 8, 8, 256)    16640       conv2_block1_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_0_bn (BatchNormali (None, 8, 8, 256)    1024        conv2_block1_0_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_3_bn (BatchNormali (None, 8, 8, 256)    1024        conv2_block1_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_add (Add)          (None, 8, 8, 256)    0           conv2_block1_0_bn[0][0]          \n",
+            "                                                                 conv2_block1_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block1_out (Activation)   (None, 8, 8, 256)    0           conv2_block1_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_1_conv (Conv2D)    (None, 8, 8, 64)     16448       conv2_block1_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_1_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block2_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_1_relu (Activation (None, 8, 8, 64)     0           conv2_block2_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_2_conv (Conv2D)    (None, 8, 8, 64)     36928       conv2_block2_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_2_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block2_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_2_relu (Activation (None, 8, 8, 64)     0           conv2_block2_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_3_conv (Conv2D)    (None, 8, 8, 256)    16640       conv2_block2_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_3_bn (BatchNormali (None, 8, 8, 256)    1024        conv2_block2_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_add (Add)          (None, 8, 8, 256)    0           conv2_block1_out[0][0]           \n",
+            "                                                                 conv2_block2_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block2_out (Activation)   (None, 8, 8, 256)    0           conv2_block2_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_1_conv (Conv2D)    (None, 8, 8, 64)     16448       conv2_block2_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_1_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block3_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_1_relu (Activation (None, 8, 8, 64)     0           conv2_block3_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_2_conv (Conv2D)    (None, 8, 8, 64)     36928       conv2_block3_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_2_bn (BatchNormali (None, 8, 8, 64)     256         conv2_block3_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_2_relu (Activation (None, 8, 8, 64)     0           conv2_block3_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_3_conv (Conv2D)    (None, 8, 8, 256)    16640       conv2_block3_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_3_bn (BatchNormali (None, 8, 8, 256)    1024        conv2_block3_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_add (Add)          (None, 8, 8, 256)    0           conv2_block2_out[0][0]           \n",
+            "                                                                 conv2_block3_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2_block3_out (Activation)   (None, 8, 8, 256)    0           conv2_block3_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_1_conv (Conv2D)    (None, 4, 4, 128)    32896       conv2_block3_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block1_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_1_relu (Activation (None, 4, 4, 128)    0           conv3_block1_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_2_conv (Conv2D)    (None, 4, 4, 128)    147584      conv3_block1_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_2_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block1_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_2_relu (Activation (None, 4, 4, 128)    0           conv3_block1_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_0_conv (Conv2D)    (None, 4, 4, 512)    131584      conv2_block3_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_3_conv (Conv2D)    (None, 4, 4, 512)    66048       conv3_block1_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_0_bn (BatchNormali (None, 4, 4, 512)    2048        conv3_block1_0_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_3_bn (BatchNormali (None, 4, 4, 512)    2048        conv3_block1_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_add (Add)          (None, 4, 4, 512)    0           conv3_block1_0_bn[0][0]          \n",
+            "                                                                 conv3_block1_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block1_out (Activation)   (None, 4, 4, 512)    0           conv3_block1_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_1_conv (Conv2D)    (None, 4, 4, 128)    65664       conv3_block1_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block2_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_1_relu (Activation (None, 4, 4, 128)    0           conv3_block2_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_2_conv (Conv2D)    (None, 4, 4, 128)    147584      conv3_block2_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_2_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block2_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_2_relu (Activation (None, 4, 4, 128)    0           conv3_block2_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_3_conv (Conv2D)    (None, 4, 4, 512)    66048       conv3_block2_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_3_bn (BatchNormali (None, 4, 4, 512)    2048        conv3_block2_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_add (Add)          (None, 4, 4, 512)    0           conv3_block1_out[0][0]           \n",
+            "                                                                 conv3_block2_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block2_out (Activation)   (None, 4, 4, 512)    0           conv3_block2_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_1_conv (Conv2D)    (None, 4, 4, 128)    65664       conv3_block2_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block3_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_1_relu (Activation (None, 4, 4, 128)    0           conv3_block3_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_2_conv (Conv2D)    (None, 4, 4, 128)    147584      conv3_block3_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_2_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block3_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_2_relu (Activation (None, 4, 4, 128)    0           conv3_block3_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_3_conv (Conv2D)    (None, 4, 4, 512)    66048       conv3_block3_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_3_bn (BatchNormali (None, 4, 4, 512)    2048        conv3_block3_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_add (Add)          (None, 4, 4, 512)    0           conv3_block2_out[0][0]           \n",
+            "                                                                 conv3_block3_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block3_out (Activation)   (None, 4, 4, 512)    0           conv3_block3_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_1_conv (Conv2D)    (None, 4, 4, 128)    65664       conv3_block3_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block4_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_1_relu (Activation (None, 4, 4, 128)    0           conv3_block4_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_2_conv (Conv2D)    (None, 4, 4, 128)    147584      conv3_block4_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_2_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block4_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_2_relu (Activation (None, 4, 4, 128)    0           conv3_block4_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_3_conv (Conv2D)    (None, 4, 4, 512)    66048       conv3_block4_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_3_bn (BatchNormali (None, 4, 4, 512)    2048        conv3_block4_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_add (Add)          (None, 4, 4, 512)    0           conv3_block3_out[0][0]           \n",
+            "                                                                 conv3_block4_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv3_block4_out (Activation)   (None, 4, 4, 512)    0           conv3_block4_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_1_conv (Conv2D)    (None, 2, 2, 256)    131328      conv3_block4_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block1_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_1_relu (Activation (None, 2, 2, 256)    0           conv4_block1_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block1_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block1_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_2_relu (Activation (None, 2, 2, 256)    0           conv4_block1_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_0_conv (Conv2D)    (None, 2, 2, 1024)   525312      conv3_block4_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block1_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_0_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block1_0_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block1_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_add (Add)          (None, 2, 2, 1024)   0           conv4_block1_0_bn[0][0]          \n",
+            "                                                                 conv4_block1_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block1_out (Activation)   (None, 2, 2, 1024)   0           conv4_block1_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_1_conv (Conv2D)    (None, 2, 2, 256)    262400      conv4_block1_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block2_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_1_relu (Activation (None, 2, 2, 256)    0           conv4_block2_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block2_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block2_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_2_relu (Activation (None, 2, 2, 256)    0           conv4_block2_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block2_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block2_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_add (Add)          (None, 2, 2, 1024)   0           conv4_block1_out[0][0]           \n",
+            "                                                                 conv4_block2_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block2_out (Activation)   (None, 2, 2, 1024)   0           conv4_block2_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_1_conv (Conv2D)    (None, 2, 2, 256)    262400      conv4_block2_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block3_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_1_relu (Activation (None, 2, 2, 256)    0           conv4_block3_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block3_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block3_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_2_relu (Activation (None, 2, 2, 256)    0           conv4_block3_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block3_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block3_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_add (Add)          (None, 2, 2, 1024)   0           conv4_block2_out[0][0]           \n",
+            "                                                                 conv4_block3_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block3_out (Activation)   (None, 2, 2, 1024)   0           conv4_block3_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_1_conv (Conv2D)    (None, 2, 2, 256)    262400      conv4_block3_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block4_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_1_relu (Activation (None, 2, 2, 256)    0           conv4_block4_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block4_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block4_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_2_relu (Activation (None, 2, 2, 256)    0           conv4_block4_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block4_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block4_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_add (Add)          (None, 2, 2, 1024)   0           conv4_block3_out[0][0]           \n",
+            "                                                                 conv4_block4_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block4_out (Activation)   (None, 2, 2, 1024)   0           conv4_block4_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_1_conv (Conv2D)    (None, 2, 2, 256)    262400      conv4_block4_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block5_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_1_relu (Activation (None, 2, 2, 256)    0           conv4_block5_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block5_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block5_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_2_relu (Activation (None, 2, 2, 256)    0           conv4_block5_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block5_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block5_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_add (Add)          (None, 2, 2, 1024)   0           conv4_block4_out[0][0]           \n",
+            "                                                                 conv4_block5_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block5_out (Activation)   (None, 2, 2, 1024)   0           conv4_block5_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_1_conv (Conv2D)    (None, 2, 2, 256)    262400      conv4_block5_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_1_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block6_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_1_relu (Activation (None, 2, 2, 256)    0           conv4_block6_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_2_conv (Conv2D)    (None, 2, 2, 256)    590080      conv4_block6_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_2_bn (BatchNormali (None, 2, 2, 256)    1024        conv4_block6_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_2_relu (Activation (None, 2, 2, 256)    0           conv4_block6_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_3_conv (Conv2D)    (None, 2, 2, 1024)   263168      conv4_block6_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_3_bn (BatchNormali (None, 2, 2, 1024)   4096        conv4_block6_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_add (Add)          (None, 2, 2, 1024)   0           conv4_block5_out[0][0]           \n",
+            "                                                                 conv4_block6_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv4_block6_out (Activation)   (None, 2, 2, 1024)   0           conv4_block6_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_1_conv (Conv2D)    (None, 1, 1, 512)    524800      conv4_block6_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_1_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block1_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_1_relu (Activation (None, 1, 1, 512)    0           conv5_block1_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_2_conv (Conv2D)    (None, 1, 1, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_2_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block1_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_2_relu (Activation (None, 1, 1, 512)    0           conv5_block1_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_0_conv (Conv2D)    (None, 1, 1, 2048)   2099200     conv4_block6_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_3_conv (Conv2D)    (None, 1, 1, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_0_bn (BatchNormali (None, 1, 1, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_3_bn (BatchNormali (None, 1, 1, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_add (Add)          (None, 1, 1, 2048)   0           conv5_block1_0_bn[0][0]          \n",
+            "                                                                 conv5_block1_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block1_out (Activation)   (None, 1, 1, 2048)   0           conv5_block1_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_1_conv (Conv2D)    (None, 1, 1, 512)    1049088     conv5_block1_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_1_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block2_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_1_relu (Activation (None, 1, 1, 512)    0           conv5_block2_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_2_conv (Conv2D)    (None, 1, 1, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_2_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block2_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_2_relu (Activation (None, 1, 1, 512)    0           conv5_block2_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_3_conv (Conv2D)    (None, 1, 1, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_3_bn (BatchNormali (None, 1, 1, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_add (Add)          (None, 1, 1, 2048)   0           conv5_block1_out[0][0]           \n",
+            "                                                                 conv5_block2_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block2_out (Activation)   (None, 1, 1, 2048)   0           conv5_block2_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_1_conv (Conv2D)    (None, 1, 1, 512)    1049088     conv5_block2_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_1_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block3_1_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_1_relu (Activation (None, 1, 1, 512)    0           conv5_block3_1_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_2_conv (Conv2D)    (None, 1, 1, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_2_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block3_2_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_2_relu (Activation (None, 1, 1, 512)    0           conv5_block3_2_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_3_conv (Conv2D)    (None, 1, 1, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_3_bn (BatchNormali (None, 1, 1, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_add (Add)          (None, 1, 1, 2048)   0           conv5_block2_out[0][0]           \n",
+            "                                                                 conv5_block3_3_bn[0][0]          \n",
+            "__________________________________________________________________________________________________\n",
+            "conv5_block3_out (Activation)   (None, 1, 1, 2048)   0           conv5_block3_add[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "global_average_pooling2d (Globa (None, 2048)         0           conv5_block3_out[0][0]           \n",
+            "__________________________________________________________________________________________________\n",
+            "dropout_8 (Dropout)             (None, 2048)         0           global_average_pooling2d[0][0]   \n",
+            "__________________________________________________________________________________________________\n",
+            "dense_3 (Dense)                 (None, 10)           20490       dropout_8[0][0]                  \n",
+            "==================================================================================================\n",
+            "Total params: 23,608,202\n",
+            "Trainable params: 23,555,082\n",
+            "Non-trainable params: 53,120\n",
+            "__________________________________________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S0h5Jhzs2ByY",
+        "outputId": "cb411d52-c3ec-46c6-8b41-f41c57f42ff6"
+      },
+      "source": [
+        "hist = model.fit(train_data, train_labels, batch_size=128, steps_per_epoch= train_data.shape[0]//128, validation_data=(test_data, test_labels), epochs = 5)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/5\n",
+            "390/390 [==============================] - 3580s 9s/step - loss: 2.0744 - accuracy: 0.4199 - val_loss: 3.2027 - val_accuracy: 0.1193\n",
+            "Epoch 2/5\n",
+            "390/390 [==============================] - 3594s 9s/step - loss: 1.5828 - accuracy: 0.5412 - val_loss: 12.5717 - val_accuracy: 0.1073\n",
+            "Epoch 3/5\n",
+            "390/390 [==============================] - 3555s 9s/step - loss: 1.7548 - accuracy: 0.4928 - val_loss: 44.7999 - val_accuracy: 0.1353\n",
+            "Epoch 4/5\n",
+            "390/390 [==============================] - 3564s 9s/step - loss: 1.8785 - accuracy: 0.4504 - val_loss: 1.9969 - val_accuracy: 0.3662\n",
+            "Epoch 5/5\n",
+            "390/390 [==============================] - 3526s 9s/step - loss: 1.3859 - accuracy: 0.5534 - val_loss: 1.3679 - val_accuracy: 0.5595\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 513
+        },
+        "id": "t_Ka0yyGbDJl",
+        "outputId": "0580b9dd-885e-4f17-def4-8a1679cedf0d"
+      },
+      "source": [
+        "hist_frame = pd.DataFrame(hist.history)\n",
+        "hist_frame.loc[:, ['loss', 'val_loss']].plot()\n",
+        "hist_frame.loc[:, ['accuracy', 'val_accuracy']].plot();"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BusC9MQpbw4L",
+        "outputId": "f5b7332a-b5bd-4744-d563-01df9c818f9b"
+      },
+      "source": [
+        "def residual_module(input, n_filters):\n",
+        "\tmerge_input = input\n",
+        "\tif input.shape[-1] != n_filters:\n",
+        "\t\tmerge_input = Conv2D(n_filters, (1,1), padding='same', activation='relu')(input)\n",
+        "\t\n",
+        "\tconv1 = Conv2D(n_filters, (3,3), padding='same', activation='relu')(input)\n",
+        "\tconv2 = Conv2D(n_filters, (3,3), padding='same', activation='linear')(conv1)\n",
+        "\n",
+        "\toutput = keras.layers.add([conv2, merge_input])\n",
+        "\toutput = keras.layers.Activation('relu')(output)\n",
+        "\treturn output\n",
+        " \n",
+        "input = Input(shape=(32, 32, 3))\n",
+        "output = residual_module(input, 128)\n",
+        "res_model = Model(inputs=input, outputs=output)\n",
+        "res_model.summary()"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"model_6\"\n",
+            "__________________________________________________________________________________________________\n",
+            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+            "==================================================================================================\n",
+            "input_11 (InputLayer)           [(None, 32, 32, 3)]  0                                            \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2d_34 (Conv2D)              (None, 32, 32, 128)  3584        input_11[0][0]                   \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2d_35 (Conv2D)              (None, 32, 32, 128)  147584      conv2d_34[0][0]                  \n",
+            "__________________________________________________________________________________________________\n",
+            "conv2d_33 (Conv2D)              (None, 32, 32, 128)  512         input_11[0][0]                   \n",
+            "__________________________________________________________________________________________________\n",
+            "add_8 (Add)                     (None, 32, 32, 128)  0           conv2d_35[0][0]                  \n",
+            "                                                                 conv2d_33[0][0]                  \n",
+            "__________________________________________________________________________________________________\n",
+            "activation_5 (Activation)       (None, 32, 32, 128)  0           add_8[0][0]                      \n",
+            "==================================================================================================\n",
+            "Total params: 151,680\n",
+            "Trainable params: 151,680\n",
+            "Non-trainable params: 0\n",
+            "__________________________________________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PBINAcpEdqZP"
+      },
+      "source": [
+        "res_model.compile(optimizer= Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
+      ],
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-petLVfpeWtr"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
diff --git a/adapted_resnet_utils.py b/adapted_resnet_utils.py
new file mode 100644
index 0000000..b00fd52
--- /dev/null
+++ b/adapted_resnet_utils.py
@@ -0,0 +1,117 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# THIS CODE WAS HEAVILY ADAPTED AND DOES NOT CORRESPOND TO THE ORIGINAL TENSORFLOW IMPLEMENTATION
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
+
+slim = contrib_slim
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    pass
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):
+    if stride == 1:
+        return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate,
+                           padding='SAME', scope=scope)
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tf.pad(inputs,
+                        [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
+        return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride,
+                           rate=rate, padding='VALID', scope=scope)
+
+
+@slim.add_arg_scope
+def stack_blocks_dense(net, blocks, output_stride=None,
+                       store_non_strided_activations=False,
+                       outputs_collections=None):
+    current_stride = 1
+
+    rate = 1
+
+    for block in blocks:
+        with tf.variable_scope(block.scope, 'block', [net]) as sc:
+            block_stride = 1
+            for i, unit in enumerate(block.args):
+                if store_non_strided_activations and i == len(block.args) - 1:
+                    block_stride = unit.get('stride', 1)
+                    unit = dict(unit, stride=1)
+
+                with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
+                    if output_stride is not None and current_stride == output_stride:
+                        net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
+                        rate *= unit.get('stride', 1)
+
+                    else:
+                        net = block.unit_fn(net, rate=1, **unit)
+                        current_stride *= unit.get('stride', 1)
+                        if output_stride is not None and current_stride > output_stride:
+                            raise ValueError('The target output_stride cannot be reached.')
+
+            net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)
+
+            if output_stride is not None and current_stride == output_stride:
+                rate *= block_stride
+            else:
+                net = subsample(net, block_stride)
+                current_stride *= block_stride
+                if output_stride is not None and current_stride > output_stride:
+                    raise ValueError('The target output_stride cannot be reached.')
+
+    if output_stride is not None and current_stride != output_stride:
+        raise ValueError('The target output_stride cannot be reached.')
+
+    return net
+
+
+def resnet_arg_scope(weight_decay=0.0001,
+                     batch_norm_epsilon=1e-5,
+                     batch_norm_scale=True,
+                     activation_fn=tf.nn.relu,
+                     use_batch_norm=True):
+    batch_norm_params = {
+        'epsilon': batch_norm_epsilon,
+        'scale': batch_norm_scale
+    }
+
+    with slim.arg_scope(
+            [slim.conv2d],
+            weights_regularizer=slim.l2_regularizer(weight_decay),
+            weights_initializer=slim.variance_scaling_initializer(),
+            activation_fn=activation_fn,
+            normalizer_fn=slim.group_norm if use_batch_norm else None,
+            normalizer_params=batch_norm_params):
+        with slim.arg_scope([slim.group_norm], **batch_norm_params):
+            with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
+                return arg_sc
diff --git a/adapted_resnet_v2.py b/adapted_resnet_v2.py
new file mode 100644
index 0000000..d191d0f
--- /dev/null
+++ b/adapted_resnet_v2.py
@@ -0,0 +1,150 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# THIS CODE WAS HEAVILY ADAPTED AND DOES NOT CORRESPOND TO THE ORIGINAL TENSORFLOW IMPLEMENTATION
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
+
+import adapted_resnet_utils
+
+slim = contrib_slim
+resnet_arg_scope = adapted_resnet_utils.resnet_arg_scope
+
+
+@slim.add_arg_scope
+def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
+               outputs_collections=None, scope=None):
+    with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
+        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
+        preact = slim.group_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
+        if depth == depth_in:
+            shortcut = adapted_resnet_utils.subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
+                                   normalizer_fn=None, activation_fn=None,
+                                   scope='shortcut')
+
+        residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
+        residual = adapted_resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
+        residual = slim.conv2d(residual, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None, scope='conv3')
+
+        output = shortcut + residual
+
+        return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
+
+
+@slim.add_arg_scope
+def bottleneck_transposed(inputs, depth, depth_bottleneck, stride, outputs_collections=None, scope=None):
+    assert stride in (1, 2)
+
+    with tf.variable_scope(scope, 'bottleneck_v2_transposed', [inputs]) as sc:
+        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
+        preact = slim.group_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
+        if depth == depth_in:
+            if stride > 1:
+                raise Exception('We cannot do spatial expansion by subsampling!')
+            shortcut = adapted_resnet_utils.subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = slim.conv2d(
+                preact, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None,
+                scope='shortcut')
+            if stride > 1:
+                shortcut = tf.image.resize_images(shortcut,
+                                                  (shortcut.shape[1] * stride - 1, shortcut.shape[2] * stride - 1),
+                                                  method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
+
+        residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
+
+        if stride > 1:
+            residual = tf.image.resize_images(residual,
+                                              (residual.shape[1] * stride - 1, residual.shape[2] * stride - 1),
+                                              method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
+        residual = slim.conv2d(residual, depth_bottleneck, 3, stride=1, scope='conv2')
+
+        residual = slim.conv2d(residual, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None, scope='conv3')
+        output = shortcut + residual
+
+        return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
+
+
+def resnet_v2(inputs,
+              blocks,
+              num_classes=None,
+              global_pool=False,
+              output_stride=None,
+              include_root_block=True,
+              spatial_squeeze=True,
+              reuse=None,
+              scope=None,
+              checkpoint_backward_compatibility=False):
+    with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
+        end_points_collection = sc.original_name_scope + '_end_points'
+        with slim.arg_scope([slim.conv2d, bottleneck,
+                             adapted_resnet_utils.stack_blocks_dense],
+                            outputs_collections=end_points_collection):
+            with slim.arg_scope([slim.group_norm]):
+                net = inputs
+                if include_root_block:
+                    if output_stride is not None:
+                        if output_stride % 4 != 0:
+                            raise ValueError('The output_stride needs to be a multiple of 4.')
+                        output_stride /= 4
+                    with slim.arg_scope([slim.conv2d],
+                                        activation_fn=None, normalizer_fn=None):
+                        if checkpoint_backward_compatibility:
+                            res = 0
+                            res += adapted_resnet_utils.conv2d_same(net[..., :3], 64, 7, stride=2, scope='conv1')
+                            if 1 * 3 > net.shape[-1]:
+                                print(True)
+                                exit()
+                                res += adapted_resnet_utils.conv2d_same(net[..., 3:], 64, 7, stride=2, scope='conv1p')
+                        else:
+                            res = adapted_resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
+                        net = res
+                    net = slim.max_pool2d(net, [3, 3], stride=2, padding='SAME', scope='pool1')
+                net = adapted_resnet_utils.stack_blocks_dense(net, blocks, output_stride)
+                net = slim.group_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
+                end_points = slim.utils.convert_collection_to_dict(
+                    end_points_collection)
+
+                if global_pool:
+                    net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
+                    end_points['global_pool'] = net
+                if num_classes:
+                    net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                                      normalizer_fn=None, scope='logits')
+                    end_points[sc.name + '/logits'] = net
+                    if spatial_squeeze:
+                        net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+                        end_points[sc.name + '/spatial_squeeze'] = net
+                    end_points['predictions'] = slim.softmax(net, scope='predictions')
+                return net, end_points
+
+
+def resnet_v2_block(scope, base_depth, num_units, stride):
+    return adapted_resnet_utils.Block(scope, bottleneck, [{
+        'depth': base_depth * 4,
+        'depth_bottleneck': base_depth,
+        'stride': 1
+    }] * (num_units - 1) + [{
+        'depth': base_depth * 4,
+        'depth_bottleneck': base_depth,
+        'stride': stride
+    }])
diff --git a/augment_color.py b/augment_color.py
new file mode 100644
index 0000000..5db4328
--- /dev/null
+++ b/augment_color.py
@@ -0,0 +1,64 @@
+import cv2
+import numpy as np
+
+
+def augment_brightness(im, in_colorspace, rng):
+    if in_colorspace != 'rgb':
+        cv2.cvtColor(im, cv2.COLOR_HSV2RGB, dst=im)
+
+    im += rng.uniform(-0.125, 0.125)
+    return 'rgb'
+
+
+def augment_contrast(im, in_colorspace, rng):
+    if in_colorspace != 'rgb':
+        cv2.cvtColor(im, cv2.COLOR_HSV2RGB, dst=im)
+    im -= 0.5
+    im *= 1 + rng.uniform(-0.5, 0.5)
+    im += 0.5
+    return 'rgb'
+
+
+def augment_hue(im, in_colorspace, rng):
+    if in_colorspace != 'hsv':
+        np.clip(im, 0, 1, out=im)
+        cv2.cvtColor(im, cv2.COLOR_RGB2HSV, dst=im)
+    hue = im[:, :, 0]
+    hue += rng.uniform(-72, 72)
+    hue[hue < 0] += 360
+    hue[hue > 360] -= 360
+    return 'hsv'
+
+
+def augment_saturation(im, in_colorspace, rng):
+    if in_colorspace != 'hsv':
+        np.clip(im, 0, 1, out=im)
+        cv2.cvtColor(im, cv2.COLOR_RGB2HSV, dst=im)
+
+    saturation = im[:, :, 1]
+    saturation *= 1 + rng.uniform(-0.5, 0.5)
+    saturation[saturation > 1] = 1
+    return 'hsv'
+
+
+def augment_color(im, rng):
+    im += 1
+    im /= 2
+    result = np.empty_like(im, dtype=np.float32)
+    cv2.divide(im, (1, 1, 1, 1), dst=result, dtype=cv2.CV_32F)
+
+    augmentation_functions = [augment_brightness, augment_contrast, augment_hue, augment_saturation]
+    rng.shuffle(augmentation_functions)
+
+    colorspace = 'rgb'
+    for fn in augmentation_functions:
+        colorspace = fn(result, colorspace, rng)
+
+    if colorspace != 'rgb':
+        cv2.cvtColor(result, cv2.COLOR_HSV2RGB, dst=result)
+
+    np.clip(result, 0, 1, out=result)
+
+    result = result.astype(np.float32)
+
+    return result * 2 - 1
diff --git a/background_inpainter.py b/background_inpainter.py
new file mode 100644
index 0000000..0c677cf
--- /dev/null
+++ b/background_inpainter.py
@@ -0,0 +1,117 @@
+import os
+import sys
+import numpy as np
+
+import tensorflow as tf
+import tensorflow_addons as tfa
+import keras
+from keras.utils import conv_utils
+from keras.models import Model
+from keras.models import load_model
+from keras.optimizers import Adam
+from keras.layers import Input, Conv2D, UpSampling2D, Dropout, LeakyReLU, BatchNormalization, Activation, Lambda
+from keras.layers.merge import Concatenate
+from keras import backend as K
+from keras.utils.multi_gpu_utils import multi_gpu_model
+
+class PConv2D(Conv2D):
+    def build(self, input_shape):        
+        if self.data_format == 'channels_first':
+            channel_axis = 1
+        else:
+            channel_axis = -1
+            
+        self.input_dim = input_shape[0][channel_axis]
+        
+        # Image kernel
+        kernel_shape = self.kernel_size + (self.input_dim, self.filters)
+        self.kernel = self.add_weight(shape=kernel_shape, initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint)
+        # Mask kernel
+        self.kernel_mask = K.ones(shape=kernel_shape)
+
+        # Calculate padding size to achieve zero-padding
+        self.pconv_padding = ( (int((self.kernel_size[0]-1)/2), int((self.kernel_size[0]-1)/2)), (int((self.kernel_size[0]-1)/2), int((self.kernel_size[0]-1)/2)),)
+
+        # Window size - used for normalization
+        self.window_size = self.kernel_size[0] * self.kernel_size[1]
+        
+        if self.use_bias:
+            self.bias = self.add_weight(shape=(self.filters,),initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint)
+        else:
+            self.bias = None
+        
+    def call(self, inputs, mask=None):
+        images = K.spatial_2d_padding(inputs[0], self.pconv_padding, self.data_format)
+        masks  = K.spatial_2d_padding(inputs[1], self.pconv_padding, self.data_format)
+
+        mask_output = K.conv2d( masks, self.kernel_mask,  strides=self.strides, padding='valid', data_format=self.data_format, dilation_rate=self.dilation_rate)
+        img_output  = K.conv2d((images*masks), self.kernel, strides=self.strides, padding='valid', data_format=self.data_format, dilation_rate=self.dilation_rate)        
+
+        # Calculate the mask ratio on each pixel in the output mask
+        mask_ratio = self.window_size / (mask_output + 1e-8)
+
+        # Clip output to be between 0 and 1
+        mask_output = K.clip(mask_output, 0, 1)
+
+        # Remove ratio values where there are holes
+        mask_ratio = mask_ratio * mask_output
+
+        # Normalize image output
+        img_output = img_output * mask_ratio
+
+        if self.use_bias:
+            img_output = K.bias_add( img_output, self.bias, data_format=self.data_format)
+        
+        if self.activation is not None:
+            img_output = self.activation(img_output)
+            
+        return [img_output, mask_output]
+   
+img_rows=512
+img_cols=512
+inputs_img  = Input((img_rows, img_cols, 3))
+inputs_mask = Input((img_rows, img_cols, 3))
+
+def encoder(img_in, mask_in, filters, kernel_size, bn=True):
+    conv, mask = PConv2D(filters, kernel_size, strides=2, padding='same')([img_in, mask_in])
+    if bn:
+        conv = tfa.GroupNormalization(conv, training=True)
+    conv = Activation('relu')(conv)
+    return conv, mask
+    
+    e_conv1, e_mask1 = encoder(inputs_img, inputs_mask, 64, 7, bn=False)
+    e_conv2, e_mask2 = encoder(e_conv1, e_mask1, 128, 5)
+    e_conv3, e_mask3 = encoder(e_conv2, e_mask2, 256, 5)
+    e_conv4, e_mask4 = encoder(e_conv3, e_mask3, 512, 3)
+    e_conv5, e_mask5 = encoder(e_conv4, e_mask4, 512, 3)
+    e_conv6, e_mask6 = encoder(e_conv5, e_mask5, 512, 3)
+    e_conv7, e_mask7 = encoder(e_conv6, e_mask6, 512, 3)
+    e_conv8, e_mask8 = encoder(e_conv7, e_mask7, 512, 3)
+    
+    
+def decoder(img_in, mask_in, e_conv, e_mask, filters, kernel_size, bn=True):
+    up_img      = UpSampling2D(size=(2,2))(img_in)
+    up_mask     = UpSampling2D(size=(2,2))(mask_in)
+    concat_img  = Concatenate(axis=3)([e_conv,up_img])
+    concat_mask = Concatenate(axis=3)([e_mask,up_mask])
+
+    conv, mask = PConv2D(filters, kernel_size, padding='same')([concat_img, concat_mask])
+
+    if bn:
+        conv = tfa.GroupNormalization()(conv)
+    conv = LeakyReLU(alpha=0.2)(conv)
+    return conv, mask
+        
+    d_conv1, d_mask1 = decoder(e_conv8, e_mask8, e_conv7, e_mask7, 512, 3)
+    d_conv2, d_mask2 = decoder(d_conv1, d_mask1, e_conv6, e_mask6, 512, 3)
+    d_conv3, d_mask3 = decoder(d_conv2, d_mask2, e_conv5, e_mask5, 512, 3)
+    d_conv4, d_mask5 = decoder(d_conv3, d_mask3, e_conv4, e_mask4, 512, 3)
+    d_conv5, d_mask5 = decoder(d_conv4, d_mask4, e_conv3, e_mask3, 256, 3)
+    d_conv6, d_mask6 = decoder(d_conv5, d_mask5, e_conv2, e_mask2, 128, 3)
+    d_conv7, d_mask7 = decoder(d_conv6, d_mask6, e_conv1, e_mask1, 64, 3)
+    d_conv8, d_mask8 = decoder(d_conv7, d_mask7, inputs_img, inputs_mask, 3, 3, bn=False)
+    outputs = Conv2D(3, 1, activation = 'sigmoid')(d_conv8)
+    
+    model = Model(inputs=[inputs_img, inputs_mask], outputs=outputs)
+
+    return model, inputs_mask    
diff --git a/data/fashion3d/po b/data/fashion3d/po
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/data/fashion3d/po
@@ -0,0 +1 @@
+
diff --git a/data/fashion3d/poses.pkl b/data/fashion3d/poses.pkl
new file mode 100644
index 0000000..14abcfa
Binary files /dev/null and b/data/fashion3d/poses.pkl differ
diff --git a/dataset.py b/dataset.py
index e69de29..d089408 100644
--- a/dataset.py
+++ b/dataset.py
@@ -0,0 +1,432 @@
+import random
+import numpy as np
+from skimage.transform import estimate_transform
+from itertools import combinations
+from imageio import imread
+import pickle
+from utils import extract_paths_from_deep_dict, get_from_deep_dict
+from collections import namedtuple
+import cv2
+from augment_color import augment_color
+from parameters import params
+from PIL import ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+Sample = namedtuple('Sample', 'img pose')
+
+
+# several functions are adapted from https://github.com/AliaksandrSiarohin/pose-gan/
+
+# UTILS
+def give_name_to_keypoints(array, joint_order):
+    array = array.T
+    res = {}
+    for i, name in enumerate(joint_order):
+        res[name] = array[i]
+    return res
+
+
+def compute_st_distance(kp):
+    st_distance1 = np.sum((kp['rhip'] - kp['rsho']) ** 2)
+    st_distance2 = np.sum((kp['lhip'] - kp['lsho']) ** 2)
+    return np.sqrt((st_distance1 + st_distance2) / 2.0)
+
+
+def estimate_polygon(fr, to, st, inc_to, inc_from, p_to, p_from):
+    fr = fr + (fr - to) * inc_from
+    to = to + (to - fr) * inc_to
+
+    norm_vec = fr - to
+    norm_vec = np.array([-norm_vec[1], norm_vec[0]])
+    norm = np.linalg.norm(norm_vec)
+    if norm == 0:
+        return np.array([
+            fr + 1,
+            fr - 1,
+            to - 1,
+            to + 1,
+        ])
+    norm_vec = norm_vec / norm
+    return np.array([
+        fr + st * p_from * norm_vec,
+        fr - st * p_from * norm_vec,
+        to - st * p_to * norm_vec,
+        to + st * p_to * norm_vec
+    ])
+
+
+def get_array_of_points(kp, names):
+    return np.array([kp[name] for name in names])
+
+
+#
+# MASKS
+#
+def create_part_masks(pose, joint_order):
+    pose = pose.copy()
+    pose[[0, 1]] = pose[[1, 0]]
+    kp_o = give_name_to_keypoints(pose, joint_order)
+    st = np.sqrt(max(sum((kp_o['lsho'] - kp_o['rhip']) ** 2), sum((kp_o['rsho'] - kp_o['lhip']) ** 2)))
+    image_size = params['image_size']
+    size = params['volume_size']
+    pose[:2] = pose[:2] / image_size * size
+    pose[2] = pose[2] / image_size * params['depth']
+    kp = give_name_to_keypoints(pose, joint_order)
+
+    def compute_ellipse(widths):
+        ellipse = np.zeros(2 * (widths.astype(np.int16) + 1) + 1)
+        for i in range(ellipse.shape[0]):
+            for j in range(ellipse.shape[1]):
+                for k in range(ellipse.shape[2]):
+                    if sum([(x - w) ** 2 / (w + 1) ** 2 for x, w in zip([i, j, k], widths)]) <= 1:
+                        ellipse[i, j, k] = 1
+        return ellipse
+
+    def draw_thick_line(v, p1, p2, widths):
+        widths = np.array(widths)
+        ellipse = compute_ellipse(widths)
+        widths = widths.astype(np.int16) + 1
+        p1 = np.copy(p1)
+        d = p2 - p1
+        n = max(np.linalg.norm(d), 1)
+        dn = d / n
+
+        def fr(i):
+            return max(0, p[i] - widths[i])
+
+        def to(i):
+            return min(p[i] + widths[i] + 1, v.shape[i])
+
+        def efr(i):
+            return max(0, widths[i] - p[i])
+
+        def eto(i):
+            return min(ellipse.shape[i] - (p[i] + widths[i] - v.shape[i]) - 1, ellipse.shape[i])
+
+        for i in range(0, int(n)):
+            p = np.round(p1 + i * dn).astype(np.int32)
+            for j in range(3):
+                p[j] = min(v.shape[j] - 1, p[j])
+                p[j] = max(0, p[j])
+
+            v[fr(0):to(0), fr(1):to(1), fr(2):to(2)] += ellipse[efr(0):eto(0), efr(1):eto(1), efr(2):eto(2)]
+        v[v > 1] = 1
+
+    def draw_mask(vol, points, thickness, end=None):
+        for p1, p2 in combinations(points, 2):
+            draw_thick_line(vol, p1, p2, [thickness * size / image_size / 2,
+                                          thickness * size / image_size / 2,
+                                          thickness * params['depth'] / image_size / 2])
+        if end is not None:
+            draw_thick_line(vol, points[-1], points[-1], [end * size / image_size / 2,
+                                                          end * size / image_size / 2,
+                                                          end * params['depth'] / image_size / 2])
+
+    masks = np.zeros((size, size, params['depth'], 10), dtype=np.float32)
+    draw_mask(masks[..., 0], [kp[joint] for joint in ['rhip', 'lhip', 'lsho', 'rsho']], thickness=0.3 * st)
+    if params['dataset'] == 'merged':
+        draw_mask(masks[..., 1], [kp[joint] for joint in ['htop', 'head', 'neck']], thickness=0.5 * st)
+    elif params['dataset'] in ['iPER', 'fashion3d']:
+        center = 0.5 * kp['lear'] + 0.5 * kp['rear']  # head mask is capsule through ear's center
+        to_neck = 0.7 * center + 0.3 * kp['neck']  # head mask is stretched in neck-direction
+        back_neck = center - (to_neck - center)
+        draw_mask(masks[..., 1], [back_neck, to_neck], thickness=0.5 * st)
+    else:
+        raise ValueError()
+    kp['lhip'] = kp['lhip'] + 0.1 * (kp['lhip'] - kp['lsho'])  # move hips down a bit
+    kp['rhip'] = kp['rhip'] + 0.1 * (kp['rhip'] - kp['rsho'])
+    kp['lwri'] = kp['lwri'] + 0.2 * (kp['lwri'] - kp['lelb'])  # make lower arms contain hands
+    kp['rwri'] = kp['rwri'] + 0.2 * (kp['rwri'] - kp['relb'])
+    kp['lank'] = kp['lank'] + 0.2 * (kp['lank'] - kp['lkne'])  # make lower legs contain foot
+    kp['rank'] = kp['rank'] + 0.2 * (kp['rank'] - kp['rkne'])
+    draw_mask(masks[..., 2], [kp[joint] for joint in ['lsho', 'lelb']], thickness=0.2 * st)
+    draw_mask(masks[..., 4], [kp[joint] for joint in ['rsho', 'relb']], thickness=0.2 * st)
+    draw_mask(masks[..., 3], [kp[joint] for joint in ['lelb', 'lwri']], thickness=0.2 * st, end=0.4 * st)
+    draw_mask(masks[..., 5], [kp[joint] for joint in ['relb', 'rwri']], thickness=0.2 * st, end=0.4 * st)
+    draw_mask(masks[..., 6], [kp[joint] for joint in ['lhip', 'lkne']], thickness=0.2 * st)
+    draw_mask(masks[..., 8], [kp[joint] for joint in ['rhip', 'rkne']], thickness=0.2 * st)
+    draw_mask(masks[..., 7], [kp[joint] for joint in ['lkne', 'lank']], thickness=0.2 * st, end=0.4 * st)
+    draw_mask(masks[..., 9], [kp[joint] for joint in ['rkne', 'rank']], thickness=0.2 * st, end=0.4 * st)
+    if params['2d_3d_warp']:
+        masks = np.max(masks, axis=2)
+    return masks
+
+
+#
+# TRANSFORM
+#
+def estimate_transform_params(poses, joint_order):
+    return helmert_transforms_3d(poses[0], poses[1], joint_order)
+
+
+# 3D
+def helmert_transforms_3d(array1, array2, joint_order):
+    array1 = array1.copy()
+    array2 = array2.copy()
+    kp1 = give_name_to_keypoints(array1, joint_order)
+    kp2 = give_name_to_keypoints(array2, joint_order)
+
+    transforms = []
+
+    body_poly_1 = get_array_of_points(kp1, ['rhip', 'lhip', 'lsho', 'rsho'])
+    body_poly_2 = get_array_of_points(kp2, ['rhip', 'lhip', 'lsho', 'rsho'])
+    transforms.append(estimate_helmert_transform(src=body_poly_2, dst=body_poly_1))
+
+    def estimate_join(fr, to, roll=None):
+        if roll is None:
+            poly_1 = get_array_of_points(kp1, [fr, to])
+            poly_2 = get_array_of_points(kp2, [fr, to])
+        else:
+            poly_1 = get_array_of_points(kp1, [fr, to, roll])
+            poly_2 = get_array_of_points(kp2, [fr, to, roll])
+            for poly in [poly_1, poly_2]:
+                bone = poly[1] - poly[0]
+                roll = poly[2] - poly[1]
+                cross = np.cross(bone, roll)
+                cross = cross / np.linalg.norm(cross) * np.linalg.norm(roll)
+                poly[2] = cross + poly[1]
+        return estimate_helmert_transform(src=poly_2, dst=poly_1)
+
+    head_kp_names = ['neck', 'leye', 'reye', 'nose', 'lear', 'rear', 'lsho', 'rsho']
+    head_poly_1 = get_array_of_points(kp1, list(head_kp_names))
+    head_poly_2 = get_array_of_points(kp2, list(head_kp_names))
+
+    transforms.append(estimate_helmert_transform(src=head_poly_2, dst=head_poly_1))
+
+    transforms.append(estimate_join('lsho', 'lelb', roll='lwri'))
+    transforms.append(estimate_join('lwri', 'lelb', roll='lsho'))
+
+    transforms.append(estimate_join('rsho', 'relb', roll='rwri'))
+    transforms.append(estimate_join('rwri', 'relb', roll='rsho'))
+
+    transforms.append(estimate_join('lhip', 'lkne', roll='lank'))
+    transforms.append(estimate_join('lank', 'lkne', roll='lhip'))
+
+    transforms.append(estimate_join('rhip', 'rkne', roll='rank'))
+    transforms.append(estimate_join('rank', 'rkne', roll='rhip'))
+
+    return np.array(transforms)
+
+
+def estimate_helmert_transform(src, dst):
+    src = np.array(src, dtype=np.float32)
+    dst = np.array(dst, dtype=np.float32)
+    src_center = np.mean(src, axis=0)
+    dst_center = np.mean(dst, axis=0)
+    src_c = src - src_center
+    dst_c = dst - dst_center
+    h = src_c.T @ dst_c
+    u, s, vt = np.linalg.svd(h)
+    r = vt.T @ u.T
+    d = np.trace(dst_c @ r @ src_c.T) / np.trace(src_c @ src_c.T)
+    t = np.expand_dims(dst_center - d * r @ src_center, axis=-1)
+    res = np.identity(4, dtype=np.float32)
+    res[:3, :3] = d * r
+    res[:3, 3:] = t
+    return res
+
+
+def augment_transform_together(imgs, poses, flips):
+    size = imgs[0].shape[0]
+    alpha = random.uniform(-.1, .1)
+    dx = random.uniform(-20, 20)
+    dy = random.uniform(-20, 20)
+    s = 1 + random.uniform(-0.1, 0.1)
+    x = size / 2
+    y = size / 2
+    z = size / 2
+    trans = np.array([[1, 0, 0, x],
+                      [0, 1, 0, y],
+                      [0, 0, 1, z],
+                      [0, 0, 0, 1]])
+    trans2 = np.array([[1, 0, 0, -x + dx],
+                       [0, 1, 0, -y + dy],
+                       [0, 0, 1, -z],
+                       [0, 0, 0, 1]])
+    rot = np.array([[np.cos(alpha), -np.sin(alpha), 0, 0],
+                    [np.sin(alpha), np.cos(alpha), 0, 0],
+                    [0, 0, 1, 0],
+                    [0, 0, 0, 1]])
+    scale = np.array([[s, 0, 0, 0],
+                      [0, s, 0, 0],
+                      [0, 0, s, 0],
+                      [0, 0, 0, 1]])
+    transform = trans @ rot @ scale @ trans2
+
+    flip = np.random.rand() < 0.5
+
+    for i in range(len(imgs)):
+        pose = poses[i]
+        img = imgs[i]
+
+        augmented = np.ones((pose.shape[0], pose.shape[1] + 1))
+        augmented[:pose.shape[0], :pose.shape[1]] = pose
+        augmented = (transform @ augmented.T).T
+        new_pose = augmented[:, :-1]
+
+        if params['dataset'] == 'merged':
+            new_img = cv2.warpAffine(img, transform[[[0], [1]], [0, 1, 3]], (size, size),
+                                     borderMode=cv2.cv2.BORDER_CONSTANT, borderValue=(1, -1, 1))
+        elif params['dataset'] in ['iPER', 'fashion3d']:
+            new_img = cv2.warpAffine(img, transform[[[0], [1]], [0, 1, 3]], (size, size),
+                                     borderMode=cv2.BORDER_REPLICATE)
+        else:
+            raise ValueError()
+        if flip:
+            new_img = np.flip(new_img, axis=1)
+            new_pose[:, 0] = 256 - new_pose[:, 0]
+            new_pose = new_pose[flips]
+
+        poses[i] = new_pose.astype(np.float32)
+        imgs[i] = new_img
+
+    return imgs, poses
+
+
+class Dataset:
+    def __init__(self, name, persondepth, joint_order, valid, test, deterministic=False, with_to_masks=False):
+        """Dataset class.
+
+        Args:
+          name: name of the dataset, used for the dataset path
+          persondepth: how many subfolders of the dataset decide the person / clothing layout, if a single file of the
+            dataset has for example a path `person/clothing/action/0103.png` the persondepth is 2, since every file
+            after the first 2 folders belongs to the same person / clothing layout
+          joint_order: a list with the order of joints, these are used for estimating transfomations and creating masks
+          valid: a list of all persons of the validation set, given as `person-clothing`
+          test: a list of all persons of the test set, given as `person-clothing`
+          deterministic: usually the dataset resturns a random sample, this enforces the same order every time
+          with_to_masks: whether or not the dataset should also return masks for the target pose
+        """
+        print('initialize', name, 'dataset')
+        self.name = name
+        self.poses = self.init_poses()
+        self.joint_order = joint_order
+        self.with_to_masks = with_to_masks
+
+        self.train, self.valid, self.test = self.init_selectable(persondepth, valid, test)
+        if deterministic:
+            random.seed(0)
+        else:
+            random.seed()
+
+        self.flips = []
+        for i, joint in enumerate(self.joint_order):
+            if joint.startswith('l'):
+                other = 'r' + joint[1:]
+                if other in self.joint_order:
+                    self.flips.append(self.joint_order.index(other))
+                else:
+                    self.flips.append(i)
+            elif joint.startswith('r'):
+                other = 'l' + joint[1:]
+                if other in self.joint_order:
+                    self.flips.append(self.joint_order.index(other))
+                else:
+                    self.flips.append(i)
+            else:
+                self.flips.append(i)
+
+    def init_selectable(self, persondepth, valid, test):
+        selectable = {}
+        keys_list = extract_paths_from_deep_dict(self.poses)
+        keys_list = [[str(key) for key in keys] for keys in keys_list]
+        for keys in keys_list:
+            key = '-'.join(keys[:persondepth])
+            if key not in selectable:
+                selectable[key] = []
+            selectable[key].append(keys)
+
+        singles = []
+        for person in selectable:
+            if len(selectable[person]) < 2:
+                singles.append(person)
+        for person in singles:
+            selectable.pop(person)
+
+        valid = set(valid)
+        test = set(test)
+        tr = {}
+        va = {}
+        te = {}
+        for person in selectable:
+            if person in valid:
+                va[person] = selectable[person]
+                if params['with_valid']:
+                    tr[person] = selectable[person]
+            elif person in test:
+                te[person] = selectable[person]
+            else:
+                tr[person] = selectable[person]
+        return tr, va, te
+
+    def init_poses(self):
+        pose_file = params['data_dir'] + '/' + self.name + '/poses.pkl'
+        with open(pose_file, 'rb') as f:
+            poses = pickle.load(f)
+        return poses
+
+    def next_train_sample(self):
+        while True:
+            yield self.uncached_sample(self.train, train=True)
+
+    def next_valid_sample(self):
+        while True:
+            yield self.uncached_sample(self.valid)
+
+    def next_test_sample(self):
+        while True:
+            yield self.uncached_sample(self.test)
+
+    def uncached_sample(self, selectable, train=False):
+        person = random.choice(list(selectable.keys()))
+        if len(selectable[person]) <= 2:
+            samples = selectable[person]
+        else:
+            samples = random.sample(selectable[person], 1)
+        # if len(samples) == 1:
+        #     samples.append(samples[0])
+        fr = self.load(samples[0])
+        
+        person = random.choice(list(selectable.keys()))
+        if len(selectable[person]) <= 2:
+            samples = selectable[person]
+        else:
+            samples = random.sample(selectable[person], 1)
+        to = self.load(samples[0])
+        return self.get_sample_from_loaded(fr, to, train)
+
+    def get_sample_from_loaded(self, fr, to, train):
+        imgs = np.concatenate([fr.img, to.img])
+        if params['augment_color'] and train:
+            imgs = augment_color(imgs, random)
+        splits = np.vsplit(imgs, 2)
+        fr_img, to_img = splits[0], splits[1]
+        fr_pose, to_pose = fr.pose, to.pose
+        if params['augment_transform'] and train:
+            fr_pose, to_pose = np.transpose(fr_pose), np.transpose(to_pose)
+            (fr_img, to_img), (fr_pose, to_pose) = augment_transform_together([fr_img, to_img], [fr_pose, to_pose],
+                                                                              self.flips)
+            fr_pose, to_pose = np.transpose(fr_pose), np.transpose(to_pose)
+            to_pose[2] += random.uniform(-.5, .5)
+        fr_masks = create_part_masks(fr_pose, self.joint_order)
+
+        transform_params = estimate_transform_params([fr_pose, to_pose], self.joint_order)
+
+        if self.with_to_masks:
+            to_masks = create_part_masks(to_pose, self.joint_order)
+            return fr_img, to_img, fr_masks, to_masks, transform_params, fr_pose, to_pose
+        else:
+            return fr_img, to_img, fr_masks, transform_params, fr_pose, to_pose
+
+    def load(self, keys):
+        img = '/'.join([params['data_dir'], self.name, 'images'] + keys[0:4]) +'_'+ keys[4] + '.jpg'
+        img = np.array(imread(img), dtype=np.float32)
+        img = img / 127.5 - 1
+        
+        pose = get_from_deep_dict(self.poses, keys).copy().astype(np.float32)
+        pose[:, 2] += params['image_size'] / 2  # move z coordinate to range (0, image_size)
+        img, pose = img.copy(), pose.copy()
+        pose = np.transpose(pose)
+        return Sample(img=img, pose=pose)
diff --git a/decoder.py b/decoder.py
index e69de29..9f182a2 100644
--- a/decoder.py
+++ b/decoder.py
@@ -0,0 +1,48 @@
+def create_resnet_dencoder():
+    inputen, outputen = create_resnet_encoder()
+   
+
+    t = outputen
+    num_blocks_list = [2]
+    for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block3d(t, downsample=0, filters=64)
+    
+    t = layers.Conv3D(10, (3, 3, 3), padding='same', strides=(1, 1,  1))(t)
+    t = layers.Conv3D(2, (3, 3, 3), padding='same', strides=(1, 1,  1))(t)
+    t = layers.Reshape((32, 32, 128))(t)
+    t = layers.Conv2DTranspose(64,
+                            kernel_size=4,
+                            strides=2,
+                            padding="same",
+                            use_bias=False)(t)
+    t = relu_bn(t)
+    
+    num_blocks_list = [2, 2]
+    for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block(t, downsample=0, filters=64)
+        t = layers.Conv2DTranspose(64,
+                            kernel_size=4,
+                            strides=2,
+                            padding="same",
+                            use_bias=False)(t)
+      
+    
+    t = Conv2D(kernel_size=3,
+               strides=1,
+               filters=128,
+               padding="same")(t)
+    t = relu_bn(t)
+
+    t = layers.Conv2D(filters=64, kernel_size=3, padding='same')(t)
+    t = layers.Conv2D(filters=32, kernel_size=3, padding='same')(t)
+    t = layers.Conv2D(filters=16, kernel_size=3, padding='same')(t)
+    t = layers.Conv2D(filters=3, kernel_size=3, padding='same')(t)
+    outputs = t 
+  
+    model = Model(inputen, outputs)
+
+    return model
diff --git a/discriminator.py b/discriminator.py
new file mode 100644
index 0000000..26fc503
--- /dev/null
+++ b/discriminator.py
@@ -0,0 +1,48 @@
+def Discriminator():
+  
+  ini = layers.Input(shape=[256, 256, 3], name="input_img")
+    
+  
+  t = Conv2D(kernel_size=3,
+               strides=1,
+               filters=64,
+               padding="same")(ini)
+  t = relu_bn(t)
+    
+  num_blocks_list = [2,5, 5,2]
+  for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block(t, downsample=(j==0 and i!=0), filters=64)
+    
+    
+  output3 = t
+
+  zero_pad1 = tf.keras.layers.ZeroPadding2D()(output3) # (bs, 34, 34, 256)
+
+  initializer = tf.random_normal_initializer(0., 0.02)
+  conv = tf.keras.layers.Conv2D(512, 
+                                kernel_size=4, 
+                                strides=1,
+                                kernel_initializer=initializer,
+                                use_bias=False)(zero_pad1) # (bs, 31, 31, 512)
+
+  gamma_init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02)
+  norm1 = tfa.layers.InstanceNormalization(gamma_initializer=gamma_init)(conv)
+
+  leaky_relu = tf.keras.layers.LeakyReLU()(norm1)
+
+  zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)
+
+  last = tf.keras.layers.Conv2D(1, 
+                                kernel_size=4, 
+                                strides=1,
+                                kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)
+  last=tf.keras.layers.Flatten()(last)
+  last=tf.keras.layers.Dense(128)(last)
+  last=tf.keras.layers.Dense(64)(last) 
+  last=tf.keras.layers.Dense(32)(last)  
+  last=tf.keras.layers.Dense(1)(last)                                     
+                                   
+
+  return keras.Model(inputs=ini, outputs=last)
diff --git a/encoder.py b/encoder.py
index e69de29..b14bc88 100644
--- a/encoder.py
+++ b/encoder.py
@@ -0,0 +1,111 @@
+#Function for 2D Residual Block
+
+def relu_bn(inputs: Tensor) -> Tensor:
+    relu = ReLU()(inputs)
+    bn = BatchNormalization()(relu)
+    return bn
+
+def residual_block(x: Tensor, downsample: bool, filters: int, kernel_size: int = 3) -> Tensor:
+    y = Conv2D(kernel_size=kernel_size,
+               strides= (1 if not downsample else 2),
+               filters=filters,
+               padding="same")(x)
+    y = relu_bn(y)
+    y = Conv2D(kernel_size=kernel_size,
+               strides=1,
+               filters=filters,
+               padding="same")(y)
+  
+    if downsample:
+        x = Conv2D(kernel_size=1,
+                   strides=2,
+                   filters=filters,
+                   padding="same")(x)
+    out = Add()([x, y])
+    out = relu_bn(out)
+    return out
+
+def residual_block_decode(x: Tensor, downsample: bool, filters: int, kernel_size: int = 3) -> Tensor:
+    y = layers.Conv2DTranspose(kernel_size=kernel_size,
+               strides= (1 if not downsample else 2),
+               filters=filters,
+               padding="same")(x)
+    y = relu_bn(y)
+    y = layers.Conv2DTranspose(kernel_size=kernel_size,
+               strides=1,
+               filters=filters,
+               padding="same")(y)
+  
+    if downsample:
+        x = layers.Conv2DTranspose(kernel_size=1,
+                   strides=2,
+                   filters=filters,
+                   padding="same")(x)
+    out = Add()([x, y])
+    out = relu_bn(out)
+    return out
+
+
+#Function for 3D Residual Block
+
+def relu_bn3d(inputs: Tensor) -> Tensor:
+    relu = ReLU()(inputs)
+    bn = GroupNormalization()(relu)
+    return bn
+
+def residual_block3d(x: Tensor, downsample: bool, filters: int, kernel_size: int = 3) -> Tensor:
+    y = Conv3D(kernel_size=kernel_size,
+               strides= (1 if not downsample else 2),
+               filters=filters,
+               padding="same")(x)
+    y = relu_bn3d(y)
+    y = Conv3D(kernel_size=kernel_size,
+               strides=1,
+               filters=filters,
+               padding="same")(y)
+  
+    if downsample:
+        x = Conv3D(kernel_size=1,
+                   strides=2,
+                   filters=filters,
+                   padding="same")(x)
+    out = Add()([x, y])
+    out = relu_bn3d(out)
+    return out
+
+
+# Function to create Encoder model
+
+def create_resnet_encoder():
+    
+    inputs = Input(shape=(256, 256, 3))
+    t = BatchNormalization()(inputs)
+    t = Conv2D(kernel_size=3,
+               strides=1,
+               filters=64,
+               padding="same")(t)
+    t = relu_bn(t)
+    
+    num_blocks_list = [2, 2, 3, 3]
+    for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block(t, downsample=(j==0 and i!=0), filters=64)
+    
+    t = Conv2D(kernel_size=3,
+               strides=1,
+               filters=128,
+               padding="same")(t)
+    t = layers.Reshape((32, 32, 64, 2))(t)
+    t = layers.Conv3D(64, (3, 3, 3), padding='same', strides=(1, 1,  1))(t)
+
+    num_blocks_list = [2]
+    for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block3d(t, downsample=(j==0 and i!=0), filters=64)
+
+    outputs = t 
+  
+    return (inputs,outputs)
+
diff --git a/extracting_dataset.py b/extracting_dataset.py
new file mode 100644
index 0000000..f3d9b62
--- /dev/null
+++ b/extracting_dataset.py
@@ -0,0 +1,86 @@
+import numpy as np
+import tensorflow as tf
+import pickle
+import matplotlib.pyplot as plt
+from matplotlib import image 
+import glob
+import os
+from PIL import Image
+from numpy import asarray
+import PIL
+import pathlib
+import tensorflow_datasets as tfds
+# import tensorflow.keras.datasets.cifar10 as cf
+
+
+!unzip '/content/drive/MyDrive/DeepFashion/In-shop Clothes Retrieval Benchmark/Img/img.zip'
+
+infile = open('/content/drive/MyDrive/poses_fashion3d.pkl','rb')
+poses = pickle.load(infile)
+
+
+
+#Function For Converting image into numpy array
+
+def img_to_tensor (path):
+  # load the image
+  image = Image.open(path)
+  # convert image to numpy array
+  data = asarray(image)
+  # print(type(data))
+  # # summarize shape
+  # print(data.shape)
+
+  # # create Pillow image
+  # image2 = Image.fromarray(data)
+  # print(type(image2))
+
+  # # summarize image details
+  # print(image2.mode)
+  # print(image2.size)
+  data.reshape(256,256,3)
+  return data
+
+
+
+data = {}
+for n in poses:
+  for i in poses[n]:
+  #data_men[i] = {} 
+    for j in poses[n][i]:
+    #data_men[i][j]={}
+      for k in poses[n][i][j]:
+      #data_men[i][j][k]={}
+        for l in poses[n][i][j][k]:
+          path = '/content/img/'+n+'/'+i+'/'+j+'/'+k+'_'+l+'.jpg'
+          x = img_to_tensor(path)
+          data.update({path : x})
+
+
+
+data_pose={}
+for n in poses:
+  for i in poses[n]:
+    for j in poses[n][i]:
+      for k in poses[n][i][j]:
+        for l in poses[n][i][j][k]:
+          #for m in poses[n][i][j][k][l]:
+          path = '/content/img/'+n+'/'+i+'/'+j+'/'+k+'_'+l+'.jpg'
+          data_pose.update({path : poses[n][i][j][k][l]})
+
+
+
+joint_order=['neck', 'nose', 'lsho', 'lelb', 'lwri', 'lhip', 'lkne', 'lank', 'rsho', 'relb', 'rwri', 'rhip', 'rkne', 'rank', 'leye', 'lear', 'reye', 'rear', 'pelv']
+
+def give_name_to_keypoints(array, joint_order):
+    res = {}
+    for i, name in enumerate(joint_order):
+        res[name] = array[i]
+    return res
+
+
+data_with_joints={}
+for path,image in data.items():
+  array=data_pose.get(path)
+  data_with_joints[path]=give_name_to_keypoints(data_pose.get(path), joint_order)
+
diff --git a/loss.py b/loss.py
index e69de29..ac2c41c 100644
--- a/loss.py
+++ b/loss.py
@@ -0,0 +1,72 @@
+import tensorflow as tf
+from tensorflow.keras.applications import vgg19
+from tensorflow.keras.models import Model
+import time
+from pose3d_minimal.main import predict_pose3d
+import tensorflow_gan as tfgan
+perception_model = None
+def init_perception_model():
+    global perception_model
+    start = time.time()
+    with tf.name_scope('Perceptual'):
+        vgg = vgg19.VGG19(weights='imagenet', include_top=False)
+        perception_model = Model(inputs=vgg.input, outputs=[
+            vgg.get_layer('block1_conv2').output,
+            vgg.get_layer('block2_conv2').output,
+            vgg.get_layer('block3_conv2').output,
+            vgg.get_layer('block4_conv2').output,
+            vgg.get_layer('block5_conv2').output
+        ])
+        for layer in perception_model.layers:
+            layer.trainable = False
+
+    print('Loaded perception model:', time.time() - start)
+
+def perception_output(x):
+    if perception_model is None:
+        raise RuntimeError('perception model is not initialized')
+
+    def preprocess_for_vgg(x):
+        x = 255 * (x + 1) / 2
+        mean = tf.constant([103.939, 116.779, 123.68])
+        mean = tf.reshape(mean, (1, 1, 1, 3))
+        x = x - mean
+        x = x[..., ::-1]
+        return x
+
+    x = preprocess_for_vgg(x)
+    x = perception_model(x)
+    return x
+
+
+def get_feature_loss(target, generated):
+    target = perception_output(target)
+    generated = perception_output(generated)
+    loss = 0
+    for t, g, w in zip(target, generated, [1. / 32, 1. / 16, 1. / 8, 1. / 4, 1.]):
+        loss += w * tf.reduce_mean(tf.abs(tf.subtract(t, g)))
+    return loss
+
+
+def init_pose_model(sess, weight_path):
+    start = time.time()
+    checkpoint_scope = f'MainPart/resnet_v2_50'
+    loaded_scope = f'Pose/MainPart/resnet_v2_50'
+    do_not_load = ['Adam', 'Momentum']
+
+    var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=loaded_scope)
+    var_dict = {v.op.name[v.op.name.index(checkpoint_scope):]: v for v in var_list}
+    var_dict = {k: v for k, v in var_dict.items() if not any(excl in k for excl in do_not_load)}
+
+    saver = tf.train.Saver(var_list=var_dict)
+    saver.restore(sess, weight_path)
+    print('Loaded pose model:', time.time() - start)
+
+
+def get_pose_loss(target, generated):
+    with tf.variable_scope('Pose', reuse=tf.AUTO_REUSE):
+        target = tf.transpose(target, (0, 3, 1, 2)) / 2
+        generated = tf.transpose(generated, (0, 3, 1, 2)) / 2
+        target, target_logits = predict_pose3d(target)
+        generated, generated_logits = predict_pose3d(generated)
+        return tf.reduce_mean(tf.abs(target - generated)) * 2.2
diff --git a/model.py b/model.py
new file mode 100644
index 0000000..aae3b2c
--- /dev/null
+++ b/model.py
@@ -0,0 +1,365 @@
+import tensorflow as tf
+from parameters import params
+import numpy as np
+from utils import extend_spatial_sizes, reduce_spatial_sizes
+from adapted_resnet_v2 import resnet_v2_block, resnet_v2, resnet_arg_scope, bottleneck_transposed
+
+
+def group_norm(x):
+    if x.shape[-1] >= 32:
+        return tf.contrib.layers.group_norm(x)
+    else:
+        return tf.contrib.layers.layer_norm(x)
+
+
+def build_coords(shape):
+    xx, yy, zz = tf.meshgrid(tf.range(shape[1]), tf.range(shape[0]), tf.range(shape[2]))  # in image notation
+    ww = tf.ones(xx.shape)
+    coords = tf.concat([tf.expand_dims(tf.cast(a, tf.float32), -1) for a in [xx, yy, zz, ww]], axis=-1)
+    return coords
+
+
+# input in matrix notation
+def transform_single(volume, transform, interpolation):
+    volume = tf.transpose(volume, [1, 0, 2, 3])  # switch to image notation
+    coords = build_coords(volume.shape[:3])
+    coords_shape = coords.shape
+    coords_reshaped = tf.reshape(coords, [-1, 4])
+    pointers_reshaped = tf.linalg.matmul(transform, coords_reshaped, transpose_b=True)
+    pointers = tf.reshape(tf.transpose(pointers_reshaped, [1, 0]), coords_shape)  # undo transpose_b
+    pointers = pointers[:, :, :, :3]
+    if interpolation == 'NEAREST':
+        pointers = tf.cast(tf.math.round(pointers), dtype=tf.int32)
+        with tf.device('/gpu:0'):
+            res = tf.gather_nd(volume, pointers)
+    elif interpolation == 'TRILINEAR':
+        c3s = {}
+        for c in [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]:
+            c3s[c] = tf.gather_nd(volume, tf.cast(tf.floor(pointers), dtype=tf.int32) + c)
+        d = pointers - tf.floor(pointers)
+        c2s = {}
+        for c in [(0, 0), (0, 1), (1, 0), (1, 1)]:
+            c2s[c] = c3s[(0,) + c] * (1 - d[:, :, :, 0:1]) + c3s[(1,) + c] * (d[:, :, :, 0:1])
+        c1s = {}
+        for c in [(0,), (1,)]:
+            c1s[c] = c2s[(0,) + c] * (1 - d[:, :, :, 1:2]) + c2s[(1,) + c] * (d[:, :, :, 1:2])
+        res = c1s[(0,)] * (1 - d[:, :, :, 2:3]) + c1s[(1,)] * (d[:, :, :, 2:3])
+    else:
+        raise ValueError
+    return res
+
+
+def volumetric_transform(volumes, transforms, interpolation='NEAREST'):
+    return tf.map_fn(lambda x: transform_single(x[0], x[1], interpolation), (volumes, transforms), dtype=tf.float32,
+                     parallel_iterations=128)
+
+
+def warp_3d(vol_batch, masks_batch, transform_batch, reduce=True):
+    n, h, w, d, c = vol_batch.get_shape().as_list()
+    with tf.name_scope('warp_3d'):
+        net = {}
+
+        part_count = transform_batch.shape[1]
+
+        net['bodypart_masks'] = masks_batch
+
+        init_volume_size = (params['image_size'], params['image_size'], params['image_size'])
+        z_scale = (d - 1) / (h - 1)
+        v_scale = (h - 1) / init_volume_size[0]
+        affine_mul = [[1, 1, 1 / z_scale, v_scale],
+                      [1, 1, 1 / z_scale, v_scale],
+                      [z_scale, z_scale, 1, v_scale * z_scale],
+                      [1, 1, 1 / z_scale, 1]]
+        affine_mul = np.array(affine_mul).reshape((1, 1, 4, 4))
+        affine_transforms = transform_batch * affine_mul
+        affine_transforms = tf.reshape(affine_transforms, (-1, 4, 4))
+
+        expanded_tensor = tf.expand_dims(vol_batch, -1)
+        multiples = [1, part_count, 1, 1, 1, 1]
+        tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)
+        repeated_tensor = tf.reshape(tiled_tensor, (
+            n * part_count, h, w, d, c))
+
+        transposed_masks = tf.transpose(masks_batch, [0, 4, 1, 2, 3])
+        reshaped_masks = tf.reshape(transposed_masks, [n * part_count, h, w, d])
+        repeated_tensor = repeated_tensor * tf.expand_dims(reshaped_masks, axis=-1)
+
+        net['masked_bodyparts'] = repeated_tensor
+        warped = volumetric_transform(repeated_tensor, affine_transforms, interpolation='TRILINEAR')
+        net['masked_bodyparts_warped'] = warped
+
+        res = tf.reshape(warped, [-1, part_count, h, w, d, c])
+        res = tf.transpose(res, [0, 2, 3, 4, 1, 5])
+        if reduce:
+            res = tf.reduce_max(res, reduction_indices=[-2])
+        return res, net
+
+
+def residual_unit_3d(x):
+    filters = x.shape[-1]
+    r = x
+    for i in range(2):
+        r = group_norm(r)
+        r = tf.nn.relu(r)
+        r = tf.layers.conv3d(r, filters, kernel_size=3, padding='SAME')
+    return x + r
+
+
+def tf_pose_map_3d(poses, shape):
+    y = tf.unstack(poses, axis=1)
+    y[0], y[1] = y[1], y[0]
+    poses = tf.stack(y, axis=1)
+    image_size = tf.constant(params['image_size'], tf.float32)
+    shape = tf.constant(shape, tf.float32)
+    sigma = tf.constant(6, tf.float32)
+    poses = tf.unstack(poses, axis=0)
+    pose_mapss = []
+    for pose in poses:
+        pose = pose / image_size * shape[:, tf.newaxis]
+        joints = tf.unstack(pose, axis=-1)
+        pose_maps = []
+        for joint in joints:
+            xx, yy, zz = tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]), tf.range(shape[2]), indexing='ij')
+            mesh = tf.cast(tf.stack([xx, yy, zz]), dtype=tf.float32)
+            pose_map = mesh - joint[:, tf.newaxis, tf.newaxis, tf.newaxis]
+            pose_map = pose_map / shape[:, tf.newaxis, tf.newaxis, tf.newaxis] * image_size
+            pose_map = tf.exp(-tf.reduce_sum(pose_map ** 2, axis=0) / (2 * sigma ** 2))
+            pose_maps.append(pose_map)
+        pose_map = tf.stack(pose_maps, axis=-1)
+        if params['2d_3d_pose']:
+            pose_map = tf.reduce_max(pose_map, axis=2, keepdims=True)
+            pose_map = tf.tile(pose_map, [1, 1, params['depth'], 1])
+        pose_mapss.append(pose_map)
+    return tf.stack(pose_mapss, axis=0)
+
+
+def resnet_encoder(img_batch):
+    blocks = [
+        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+        resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+        resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
+        resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+    ]
+    with tf.contrib.slim.arg_scope(resnet_arg_scope()):
+        x, network = resnet_v2(img_batch, blocks, output_stride=4, scope='resnet_v2_50', spatial_squeeze=False,
+                               reuse=tf.AUTO_REUSE, checkpoint_backward_compatibility=True)
+        # checkpoint_backward_compatibility must be set to True if the provided generator checkpoints should work
+        # if a new model should be trained, it can be set to False
+
+    return network['GAN/Generator/resnet_v2_50/block2']
+
+
+def pose_encoder(pose_batch, features=32):
+    x = tf.layers.conv3d(pose_batch, features, kernel_size=3, padding='SAME')
+    x = residual_unit_3d(x)
+    x = residual_unit_3d(x)
+    x = residual_unit_3d(x)
+    return x
+
+
+def resnet_decoder(x):
+    x = bottleneck_transposed(x, depth=256 * 4, depth_bottleneck=256, stride=1)
+    x = bottleneck_transposed(x, depth=256 * 4, depth_bottleneck=256, stride=1)
+    x = bottleneck_transposed(x, depth=128 * 4, depth_bottleneck=128, stride=2)
+    x = bottleneck_transposed(x, depth=128 * 4, depth_bottleneck=128, stride=1)
+    x = bottleneck_transposed(x, depth=128 * 4, depth_bottleneck=128, stride=1)
+    x = bottleneck_transposed(x, depth=64 * 4, depth_bottleneck=64, stride=2)
+    return x
+
+
+def background_inpainter(img_batch, masks_batch):
+    # adapted from https://github.com/MathiasGruber/PConv-Keras
+    if params['2d_3d_warp']:
+        bg_mask = masks_batch
+    else:
+        bg_mask = tf.reduce_max(masks_batch, axis=3)
+    bg_mask = bg_mask[:, :-1, :-1]
+    bg_mask = tf.image.resize_images(bg_mask, (params['image_size'], params['image_size']),
+                                     method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+    bg_mask = tf.reduce_max(bg_mask, axis=3)
+    bg_mask = 1 - bg_mask
+    bg_mask = tf.pad(bg_mask, [[0, 0]] + [[0, 1]] * (len(bg_mask.shape) - 1))
+    img_batch = img_batch * bg_mask[..., tf.newaxis]
+
+    def pconv(imgs, masks, filters, kernel_size, strides=1):
+        with tf.variable_scope(None, default_name='pconv'):
+            ps = int((kernel_size - 1) / 2)
+            imgs = tf.pad(imgs, [[0, 0], [ps, ps], [ps, ps], [0, 0]])
+            masks = tf.pad(masks, [[0, 0], [ps, ps], [ps, ps], [0, 0]])
+
+            input_dim = int(imgs.shape[-1])
+            kernel = tf.get_variable('kernel', shape=(kernel_size, kernel_size, input_dim, filters),
+                                     initializer=tf.initializers.glorot_uniform())
+            imgs = tf.nn.conv2d(imgs, kernel, strides=(1, strides, strides, 1), padding='VALID')
+
+            mask_kernel = tf.ones((kernel_size, kernel_size, input_dim, filters), dtype=tf.float32,
+                                  name='mask_kernel')
+            masks = tf.nn.conv2d(masks, mask_kernel, strides=(1, strides, strides, 1), padding='VALID')
+
+            mask_ratio = kernel_size ** 2 / (masks + 1e-8)
+
+            masks = tf.clip_by_value(masks, 0, 1)
+
+            mask_ratio = mask_ratio * masks
+
+            imgs = imgs * mask_ratio
+
+            bias = tf.get_variable('bias', shape=(filters,), initializer=tf.initializers.zeros())
+            imgs = tf.nn.bias_add(imgs, bias)
+
+        return [imgs, masks]
+
+    bg_mask = bg_mask[..., tf.newaxis]
+    bg_mask = tf.tile(bg_mask, [1, 1, 1, img_batch.shape[-1]])
+
+    img_batch = img_batch / 2 + .5
+    img_batch = img_batch + (1 - bg_mask)
+
+    def encoder_layer(img_in, mask_in, filters, kernel_size, gn=True):
+        img_in = mask_in * img_in
+        conv, mask = pconv(img_in, mask_in, filters, kernel_size, strides=2)
+        if gn:
+            conv = group_norm(conv)
+        conv = tf.nn.relu(conv)
+        return conv, mask
+
+    e_conv1, e_mask1 = encoder_layer(img_batch, bg_mask, 64, 7, gn=False)
+    e_conv2, e_mask2 = encoder_layer(e_conv1, e_mask1, 128, 5)
+    e_conv3, e_mask3 = encoder_layer(e_conv2, e_mask2, 256, 5)
+    e_conv4, e_mask4 = encoder_layer(e_conv3, e_mask3, 512, 3)
+    e_conv5, e_mask5 = encoder_layer(e_conv4, e_mask4, 512, 3)
+    e_conv6, e_mask6 = encoder_layer(e_conv5, e_mask5, 512, 3)
+    e_conv7, e_mask7 = encoder_layer(e_conv6, e_mask6, 512, 3)
+
+    def decoder_layer(img_in, mask_in, e_conv, e_mask, filters, kernel_size, gn=True, activation=tf.nn.leaky_relu):
+        img_in = img_in * mask_in
+        up_img = tf.image.resize_images(img_in, (img_in.shape[1] * 2 - 1, img_in.shape[2] * 2 - 1),
+                                        method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
+        up_mask = tf.image.resize_images(mask_in, (mask_in.shape[1] * 2 - 1, mask_in.shape[2] * 2 - 1),
+                                         method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
+
+        concat_img = tf.concat([e_conv, up_img], axis=3)
+        concat_mask = tf.concat([e_mask, up_mask], axis=3)
+        conv, mask = pconv(concat_img, concat_mask, filters, kernel_size)
+        if gn:
+            conv = group_norm(conv)
+        if activation:
+            conv = activation(conv)
+        return conv, mask
+
+    d_conv10, d_mask10 = decoder_layer(e_conv7, e_mask7, e_conv6, e_mask6, 512, 3)
+    d_conv11, d_mask11 = decoder_layer(d_conv10, d_mask10, e_conv5, e_mask5, 512, 3)
+    d_conv12, d_mask12 = decoder_layer(d_conv11, d_mask11, e_conv4, e_mask4, 512, 3)
+    d_conv13, d_mask13 = decoder_layer(d_conv12, d_mask12, e_conv3, e_mask3, 256, 3)
+    d_conv14, d_mask14 = decoder_layer(d_conv13, d_mask13, e_conv2, e_mask2, 128, 3)
+    d_conv15, d_mask15 = decoder_layer(d_conv14, d_mask14, e_conv1, e_mask1, 64, 3)
+    x, _ = decoder_layer(d_conv15, d_mask15, img_batch, bg_mask, 3, 3, gn=False)
+
+    x = tf.nn.tanh(x)
+    return x
+
+
+def warp3d_generator(img_batch, masks_batch, params_batch, pose_batch):
+    if params['volume_size'] != 64:
+        raise ValueError('parameter volume_size must be 64 instead of', params['volume_size'])
+    n, h, w, c = img_batch.shape
+    noise = tf.random.normal(img_batch[..., :1].shape)
+    img_batch = tf.concat([img_batch, noise], axis=-1)
+
+    net = {}
+    x = resnet_encoder(img_batch)
+    p = pose_encoder(pose_batch)
+    b = background_inpainter(img_batch, masks_batch)
+
+    # convert image stream to 3D
+    x = group_norm(x)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, (params['depth'] + 1) * params['residual_channels'], kernel_size=3, padding='SAME')
+
+    x = tf.reshape(x, (n, x.shape[1], x.shape[2], params['depth'] + 1, params['residual_channels']))
+
+    net['first_3d'] = x
+
+    # network center
+    for i in range(params['before_count']):
+        x = residual_unit_3d(x)
+
+    net['volume'] = x
+
+    net['masks'] = masks_batch
+
+    
+    x, subnet = warp_3d(x, masks_batch, params_batch)
+
+    net = {**net, **subnet}
+
+    net['warped'] = x
+    x = tf.concat([x, p], axis=-1)
+
+    for i in range(params['after_count']):
+        x = residual_unit_3d(x)
+
+    net['last_3d'] = x
+
+    # convert to 2D
+    x = tf.reshape(x, (n, x.shape[1], x.shape[2], -1))
+
+    x = resnet_decoder(x)
+
+    x = group_norm(x)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, 4, kernel_size=3, padding='SAME')
+    mask = tf.nn.sigmoid(x[..., 3])[..., tf.newaxis]
+    x = tf.nn.tanh(x[..., :3])
+    net['foreground'] = x
+    x = mask * x + (1 - mask) * b
+    net['background'] = b
+    net['foreground_mask'] = mask
+    return x, net
+
+
+def resnet_discriminator(x):
+    blocks = [
+        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+        resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+        resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
+        resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+    ]
+    with tf.contrib.slim.arg_scope(resnet_arg_scope()):
+        x, network = resnet_v2(x, blocks, output_stride=None, scope='resnet_v2_50')
+    x = network['GAN/Discriminator/resnet_v2_50/block3']
+    x = group_norm(x)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, 1, kernel_size=3, padding='SAME')
+    x = tf.reduce_mean(x)
+    return x
+
+
+def generator(x):
+    img_batch, masks_batch, params_batch, input_pose_batch, target_pose_batch = x
+
+    img_batch = extend_spatial_sizes(img_batch)
+
+    masks_batch = extend_spatial_sizes(masks_batch)
+    target_pose_batch = tf_pose_map_3d(target_pose_batch,
+                                       [params['volume_size'], params['volume_size'], params['depth']])
+    target_pose_batch = extend_spatial_sizes(target_pose_batch)
+
+    x, net = warp3d_generator(img_batch, masks_batch, params_batch, target_pose_batch)
+    return reduce_spatial_sizes(x), net
+
+
+def discriminator(generated, x):
+    if isinstance(generated, tuple):
+        generated = generated[0]
+    N, H, W, C = generated.shape
+    generated = extend_spatial_sizes(generated)
+    img_batch, _, _, _, pose_batch = x
+    img_batch = extend_spatial_sizes(img_batch)
+    pose_batch = tf_pose_map_3d(pose_batch, [params['image_size'], params['image_size'], params['depth']])
+    pose_batch = tf.reshape(pose_batch,
+                            (N, params['image_size'], params['image_size'], -1))
+    pose_batch = extend_spatial_sizes(pose_batch)
+    x = tf.concat([img_batch, generated, pose_batch], axis=-1)
+
+    return reduce_spatial_sizes(resnet_discriminator(x))
diff --git a/models.py b/models.py
deleted file mode 100644
index e69de29..0000000
diff --git a/params.py b/params.py
index e69de29..8046c25 100644
--- a/params.py
+++ b/params.py
@@ -0,0 +1,132 @@
+import sys
+
+params = {}
+
+
+def init():
+    # paths
+    params['data_dir'] = 'data'
+    params['tb_dir'] = 'tensorboard_events/'
+    params['check_dir'] = 'checkpoints/'
+
+    # train configuration
+    params['batch_size'] = 2
+    params['batches'] = 150000
+
+    # datset information
+    params['dataset'] = 'fashion3d'
+    params['image_size'] = 256
+    params['volume_size'] = 64  # height/width of volume, used by dataset to generate masks, must be 64 for our model
+    params['data_workers'] = 7  # parallel workers for bodypart-mask generation and transformation estimation
+
+    # augmentation
+    params['augment_color'] = True
+    params['augment_transform'] = True
+
+    # volume architecture, change these to create a smaller or larger model
+    params['before_count'] = 3  # number of 3D residual blocks before warping
+    params['after_count'] = 3  # number of 3D residual blocks after warping
+    params['residual_channels'] = 64  # number of 3D channels
+    params['depth'] = 32  # depth of the volume
+
+    # ablation models
+    params['2d_3d_warp'] = False
+    params['2d_3d_pose'] = False
+
+    # adam parameters
+    params['alpha'] = 2e-4
+    params['beta1'] = 0.5
+    params['beta2'] = 0.999
+
+    # loss weighting
+    params['feature_loss_weight'] = 3.
+
+    # checkpoints and tensorboard output
+    params['steps_per_checkpoint'] = 1000
+    params['steps_per_validation'] = 1000
+    params['steps_per_scalar_summary'] = 20
+    params['steps_per_image_summary'] = 200
+
+    # validation configuration
+    params['with_valid'] = False  # if True, training is performed on train and valid and tb outputs are on test split
+    params['valid_count'] = 3  # number of samples validation is based on
+
+    params['name'] = 'unnamed'  # name will be appended to both the checkpoint directory and the tebsorboard directory
+    params['JOB_ID'] = -1
+
+
+def load_id(job_id):
+    if job_id == 1:
+        params['dataset'] = 'fashio3d'
+        params['with_valid'] = True
+        params['name'] = 'fash-3d_w-3d_p-fash'
+    elif job_id == 2:
+        params['dataset'] = 'fashion3d'
+        params['2d_3d_pose'] = True
+        params['with_valid'] = True
+        params['name'] = 'fash-3d_w-2d_p-fash'
+    elif job_id == 3:
+        params['dataset'] = 'fashion3d'
+        params['with_valid'] = True
+        params['name'] = 'fash-2d_w-3d_p-fash'
+    elif job_id == 4:
+        params['dataset'] = 'fashion3d'
+        params['2d_3d_pose'] = True
+        params['2d_3d_warp'] = True
+        params['with_valid'] = True
+        params['name'] = 'fash-2d_w-2d_p=fash'
+
+    elif job_id == 5:
+        params['dataset'] = 'fashion3d'
+        params['with_valid'] = True
+        params['name'] = 'fash-3d_w-3d_p-fash'
+    elif job_id == 6:
+        params['dataset'] = 'fashion3d'
+        params['2d_3d_pose'] = True
+        params['with_valid'] = True
+        params['name'] = 'fash-3d_w-2d_p-fash'
+    elif job_id == 7:
+        params['dataset'] = 'fashion3d'
+        params['2d_3d_warp'] = True
+        params['with_valid'] = True
+        params['name'] = 'fash-2d_w-3d_p-fash'
+    elif job_id == 8:
+        params['dataset'] = 'fashion3d'
+        params['2d_3d_pose'] = True
+        params['2d_3d_warp'] = True
+        params['with_valid'] = True
+        params['name'] = 'fash-2d_w-2d_p-fash'
+
+    else:
+        raise ValueError()
+
+
+init()
+
+if len(sys.argv) == 2:
+    if sys.argv[1] == 'params':
+        for p, v in params.items():
+            print('{}:\t{}'.format(p, v))
+        raise ValueError
+
+par_names = sys.argv[1::2]
+par_vals = sys.argv[2::2]
+
+if len(par_names) != len(par_vals):
+    raise ValueError('Number of inputs must be even')
+
+for name, val in zip(par_names, par_vals):
+    if name not in params:
+        if name == '-f':
+            continue
+        raise ValueError(f'{name} is not a valid parameter')
+    if type(params[name]) == bool:
+        params[name] = val == 'True'
+    else:
+        params[name] = type(params[name])(val)
+
+if params['JOB_ID'] != -1:
+    load_id(params['JOB_ID'])
+
+params['tb_dir'] += params['name'] + '/'
+params['check_dir'] += params['name'] + '/'
diff --git a/pose3d_minimal/main.py b/pose3d_minimal/main.py
new file mode 100644
index 0000000..7fb98d9
--- /dev/null
+++ b/pose3d_minimal/main.py
@@ -0,0 +1,284 @@
+from . import tfutil
+import tensorflow as tf
+from . import resnet_v2
+import numpy as np
+import tensorflow.contrib.slim as slim
+import imageio
+
+import cv2
+
+
+def get_iper_intrinsics():
+    intrinsics = np.eye(3, dtype=np.float32)
+    intrinsics[0, 0] = 1125 * 257 / 1024
+    intrinsics[1, 1] = 1125 * 257 / 1024
+    intrinsics[:2, 2] = 257 / 2
+    intrinsics = np.expand_dims(intrinsics, 0)
+    return intrinsics
+
+
+def main():
+    im = imageio.imread('/globalwork/datasets/iper/images/001_1_1/frame_000000.jpg')
+    im = cv2.resize(im, (257, 257))[np.newaxis].astype(np.float32) / 255 * 2 - 1
+    im = np.transpose(im, [0, 3, 1, 2])
+
+    # Simpler, unscaled ResNet50 root-relative estimation
+    # pose = predict_pose3d(tf.convert_to_tensor(im))
+    # path = ('/globalwork/sarandi/trainings/2020-02-01/iper/metric_nocrop_seed1/model.ckpt-160684')
+    # with tf.Session() as sess:
+    #     load_pretrained(sess, path, 'resnet_v2_50')
+    #     print(sess.run(pose))
+
+    # Absolute non-root-relative estimation in millimeters
+    pose = predict_pose3d_abs(tf.convert_to_tensor(im), tf.convert_to_tensor(get_iper_intrinsics()))
+
+    # Or by setting intrinsics=None it assumes 60 degree field of view by default:
+    # pose = predict_pose3d_abs(tf.convert_to_tensor(im), intrinsics=None)
+
+    model_path = ('/globalwork/sarandi/trainings/2020-02-01/'
+                  'merged/with_coco_resnet152_fulldata_upperbodyaug/model.ckpt-515515')
+    with tf.Session() as sess:
+        load_weights(sess, model_path, 'resnet_v2_152')
+        pose_arr = sess.run(pose)
+        joint_names = ['neck', 'nose', 'lsho', 'lelb', 'lwri', 'lhip', 'lkne', 'lank', 'rsho',
+                       'relb', 'rwri', 'rhip', 'rkne', 'rank', 'leye', 'lear', 'reye', 'rear',
+                       'pelv']
+        edges = [(1, 0), (0, 18), (0, 2), (2, 3), (3, 4), (0, 8), (8, 9), (9, 10), (18, 5), (5, 6),
+                 (6, 7), (18, 11), (11, 12), (12, 13), (15, 14), (14, 1), (17, 16), (16, 1)]
+        visualize_pose(image=np.transpose(im, [0, 2, 3, 1])[0], coords=pose_arr[0], edges=edges)
+
+
+def predict_pose3d_abs(im, intrinsics=None):
+    proc_side = 257
+
+    if intrinsics is None:
+        # Assume a default of 60 degree FOV
+        fov = np.deg2rad(60)
+        f = proc_side / (2 * np.tan(fov / 2))
+        intrinsics_np = np.array([[f, 0, proc_side / 2], [0, f, proc_side / 2], [0, 0, 1]])
+        intrinsics = tf.convert_to_tensor(intrinsics_np, dtype=tf.float32)
+        intrinsics = tf.tile(tf.expand_dims(intrinsics, 0), [tf.shape(im)[0], 1, 1])
+
+    mirror_joint_mapping = [
+        0, 1, 8, 9, 10, 11, 12, 13, 2, 3, 4, 5, 6, 7, 16, 17, 14, 15, 18, 19, 20, 22, 21, 24, 23,
+        25, 26, 27, 29, 28, 30, 31, 32, 33, 35, 34, 36]
+    coords2d, coords3d, weights = image_to_unscaled_coords(im)
+
+    # Horizontal flip augmentation
+    coords2d_flip, coords3d_flip, weights_flip = image_to_unscaled_coords(im[:, :, :, ::-1])
+    # To get back to the original (non-flipped) coordinate frame, we need to swap
+    # left and right joints along the joint ID axis...
+    coords2d_flip = tf.gather(coords2d_flip, mirror_joint_mapping, axis=1)
+    coords3d_flip = tf.gather(coords3d_flip, mirror_joint_mapping, axis=1)
+    # ... and subtract the x coordinates from 1
+    coords2d_flip = tf.concat([1 - coords2d_flip[..., :1], coords2d_flip[..., 1:]], axis=-1)
+    coords3d_flip = tf.concat([1 - coords3d_flip[..., :1], coords3d_flip[..., 1:]], axis=-1)
+
+    # Then average the flipped and non-flipped results
+    weights_flip = tf.gather(weights_flip, mirror_joint_mapping, axis=1)
+    coords2d = (coords2d + coords2d_flip) * 0.5
+    coords3d = (coords3d + coords3d_flip) * 0.5
+    weights = (weights + weights_flip) * 0.5
+
+    # Scale the coordinates to be in pixels (2D) and millimeters (3D)
+    box_size = 2200
+    coords3d_rel = tf.concat([
+        coords3d[:, :, :2] * box_size * (proc_side - 1) / proc_side,
+        coords3d[:, :, 2:] * box_size], axis=-1)
+    coords2d = coords2d * (proc_side - 1)
+
+    # Normalize the image coordinates to be intrinsics invariant
+    inv_intrinsics = tf.linalg.inv(intrinsics)
+    coords2d_homog = tf.concat([coords2d, tf.ones_like(coords2d[..., :1])], axis=-1)
+    coords2d_normalized = tf.einsum('Bij,BCj->BCi', inv_intrinsics, coords2d_homog)
+
+    # Reconstruct the unknown reference point
+    coords_abs_3d_based, ref = reconstruct_ref(coords2d_normalized[:, :, :2], coords3d_rel, weights)
+
+    # Reproject the result into image coordinates
+    coords2d_reprojected = coords_abs_3d_based / coords_abs_3d_based[..., 2:]
+    coords2d_reproj_pixels = tf.einsum('Bij,BCj->BCi', intrinsics[:, :2], coords2d_reprojected)
+
+    # Check if the reprojected joints are within the image boundary (field-of-view)
+    is_predicted_to_be_in_fov = tf.reduce_all(
+        tf.logical_and(coords2d_reproj_pixels >= 0, coords2d_reproj_pixels < proc_side),
+        axis=-1, keepdims=True)
+    is_predicted_to_be_in_fov = tf.tile(is_predicted_to_be_in_fov, [1, 1, 3])
+
+    # Back-project the 2D head's predicted coordinates according to the depths from the
+    # 3D head and reconstruction.
+    coords_abs_2d_based = (coords2d_normalized *
+                           (coords3d_rel[:, :, 2:] + tf.expand_dims(ref[:, 2:], 1)))
+
+    # Prefer the backprojected variant for joints within the image
+    coords_abs = tf.where(is_predicted_to_be_in_fov, coords_abs_2d_based, coords_abs_3d_based)
+
+    # Return the first 19 joints, these correspond to the COCO/OpenPose/CMU-Panoptic joints
+    return coords_abs[:, :19]
+
+
+def reconstruct_ref(normalized_2d, coords3d_delta, weights):
+    """Reconstructs the reference point location.
+
+    Args:
+      normalized_2d: normalized image coordinates of the joints
+         (without intrinsics applied), shape [batch_size, n_points, 2]
+      coords3d_delta: 3D camera coordinate offsets from the unknown reference
+         point which we want to reconstruct, shape [batch_size, n_points, 3]
+      weights: how important each joint should be in the weighted linear least squares optimization
+         shape [batch_size, n_points]
+
+    Returns:
+      The 3D reference point in camera coordinates, shape [batch_size, 3]
+    """
+
+    def root_mean_square(x):
+        return tf.sqrt(tf.reduce_mean(tf.square(x)))
+
+    n_batch = tf.shape(normalized_2d)[0]
+    n_points = normalized_2d.get_shape().as_list()[1]
+    reshaped2d = tf.reshape(normalized_2d, [-1, n_points * 2, 1])
+    scale2d = root_mean_square(reshaped2d)
+    eyes = tf.tile(tf.expand_dims(tf.eye(2, 2), 0), [n_batch, n_points, 1])
+    expanded_weights = tf.sqrt(tf.reshape(
+        tf.tile(tf.expand_dims(weights, axis=-1), [1, 1, 2]), [-1, n_points * 2, 1]))
+
+    A = tf.concat([eyes, -reshaped2d / scale2d], axis=2)
+    rel_backproj = normalized_2d * coords3d_delta[:, :, 2:] - coords3d_delta[:, :, :2]
+    b = tf.reshape(rel_backproj, [-1, n_points * 2, 1])
+    scale_b = root_mean_square(b)
+    b = b / scale_b
+    ref = tf.linalg.lstsq(
+        A * expanded_weights,
+        b * expanded_weights, fast=True)
+
+    ref = tf.concat([ref[:, :2], ref[:, 2:] / scale2d], 1) * scale_b
+    ref = tf.squeeze(ref, -1)
+    coords_abs = coords3d_delta + tf.reshape(ref, [-1, 1, 3])
+    return coords_abs, ref
+
+
+def image_to_unscaled_coords(im):
+    depth = 8
+    n_joints = 37
+    stride = 32
+    n_outs = [n_joints, n_joints + depth * n_joints]
+    net_outputs = resnet_v2_spatial(
+        im, resnet_v2.resnet_v2_152, n_out=n_outs, stride=stride, split_after_block=3,
+        is_training=False, reuse=tf.AUTO_REUSE, scope='MainPart')
+    side = net_outputs[0].get_shape().as_list()[2]
+
+    # 3D head
+    logits3d = net_outputs[1][:, :-n_joints]
+    logits3d = tf.reshape(logits3d, [-1, depth, n_joints, side, side])
+    logits3d = tf.transpose(logits3d, [0, 2, 3, 4, 1])
+    softmaxed3d = tfutil.softmax(logits3d, axis=[2, 3, 4])
+    coords3d = tf.stack(tfutil.decode_heatmap(softmaxed3d, [3, 2, 4]), axis=-1)
+
+    # 2D head
+    logits2d = net_outputs[0]
+    softmaxed2d = tfutil.softmax(logits2d, axis=[2, 3])
+    coords2d = tf.stack(tfutil.decode_heatmap(softmaxed2d, [3, 2]), axis=-1)
+
+    # Weight head
+    confidences = net_outputs[1][:, -n_joints:]
+    confidences = tf.reduce_sum(confidences * tf.reduce_sum(softmaxed3d, axis=4), axis=[2, 3])
+    weights = tfutil.softmax(confidences, axis=1)
+    return coords2d, coords3d, weights
+
+
+def predict_pose3d(im):
+    depth = 8
+    n_joints = 19
+    stride = 32
+    net_output = resnet_v2_spatial(
+        im, resnet_v2.resnet_v2_50, n_out=[depth * n_joints], stride=stride, is_training=False,
+        reuse=tf.AUTO_REUSE, scope='MainPart')[0]
+    n, c, h, w = net_output.get_shape().as_list()
+    reshaped = tf.reshape(net_output, [-1, depth, n_joints, h, w])
+    transposed = tf.transpose(reshaped, [0, 2, 3, 4, 1])
+    return root_relative(tfutil.soft_argmax(transposed, [3, 2, 4])), transposed
+
+
+def root_relative(coords):
+    return coords - coords[:, -1:]
+
+
+def load_weights(sess, weight_path, resnet_name='resnet_v2_50'):
+    checkpoint_scope = f'MainPart/{resnet_name}'
+    loaded_scope = f'MainPart/{resnet_name}'
+    do_not_load = ['Adam', 'Momentum']
+
+    var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=loaded_scope)
+    var_dict = {v.op.name[v.op.name.index(checkpoint_scope):]: v for v in var_list}
+    var_dict = {k: v for k, v in var_dict.items() if
+                not any(excl in k for excl in do_not_load)}
+    saver = tf.train.Saver(var_list=var_dict)
+    saver.restore(sess, weight_path)
+
+
+def resnet_arg_scope(is_training):
+    batch_norm_params = dict(
+        decay=0.997, epsilon=1e-5, scale=True, is_training=is_training, fused=True,
+        data_format='NCHW')
+
+    with slim.arg_scope(
+            [slim.conv2d, slim.conv3d],
+            weights_regularizer=slim.l2_regularizer(1e-4),
+            weights_initializer=slim.variance_scaling_initializer(),
+            activation_fn=tf.nn.relu,
+            normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):
+        with slim.arg_scope(
+                [slim.conv2d, slim.conv3d, slim.conv3d_transpose, slim.conv2d_transpose,
+                 slim.avg_pool2d, slim.separable_conv2d, slim.max_pool2d, slim.batch_norm,
+                 slim.spatial_softmax],
+                data_format='NCHW'):
+            with slim.arg_scope([slim.batch_norm], **batch_norm_params):
+                with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
+                    return arg_sc
+
+
+@tfutil.in_variable_scope('Resnet_spatial', mixed_precision=True)
+def resnet_v2_spatial(inp, resnet_fn, n_out, stride, is_training, split_after_block=5):
+    with slim.arg_scope(resnet_arg_scope(is_training)):
+        x = tf.cast(inp, tf.float16)
+        xs, end_points = resnet_fn(
+            x, num_classes=n_out, is_training=is_training, global_pool=False,
+            output_stride=stride, split_after_block=split_after_block)
+        xs = [tf.cast(x, tf.float32) for x in xs]
+        return xs
+
+
+def visualize_pose(image, coords, edges):
+    import matplotlib.pyplot as plt
+    plt.switch_backend('TkAgg')
+    # noinspection PyUnresolvedReferences
+    from mpl_toolkits.mplot3d import Axes3D
+
+    # Matplotlib interprets the Z axis as vertical, but our pose
+    # has Y as the vertical axis.
+    # Therefore we do a 90 degree rotation around the horizontal (X) axis
+    coords2 = coords.copy()
+    coords[:, 1], coords[:, 2] = coords2[:, 2], -coords2[:, 1]
+
+    fig = plt.figure()
+    image_ax = fig.add_subplot(1, 2, 1)
+    image_ax.set_title('Input')
+    image_ax.imshow(image)
+
+    pose_ax = fig.add_subplot(1, 2, 2, projection='3d')
+    pose_ax.set_title('Prediction')
+    range_ = 800
+    pose_ax.set_xlim3d(-range_, range_)
+    pose_ax.set_ylim3d(-range_, range_)
+    pose_ax.set_zlim3d(-range_, range_)
+
+    for i_start, i_end in edges:
+        pose_ax.plot(*zip(coords[i_start], coords[i_end]), marker='o', markersize=2)
+
+    fig.tight_layout()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pose3d_minimal/resnet_utils.py b/pose3d_minimal/resnet_utils.py
new file mode 100644
index 0000000..ef723fb
--- /dev/null
+++ b/pose3d_minimal/resnet_utils.py
@@ -0,0 +1,416 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains building blocks for various versions of Residual Networks.
+
+Residual networks (ResNets) were proposed in:
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015
+
+More variants were introduced in:
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016
+
+We can obtain different ResNet variants by changing the network depth, width,
+and form of residual unit. This module implements the infrastructure for
+building them. Concrete ResNet units and full ResNet networks are implemented in
+the accompanying resnet_v1.py and resnet_v2.py modules.
+
+Compared to https://github.com/KaimingHe/deep-residual-networks, in the current
+implementation we subsample the output activations in the last residual unit of
+each block, instead of subsampling the input activations in the first residual
+unit of each block. The two implementations give identical results but our
+implementation is more memory efficient.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+from tensorflow.contrib import layers as layers_lib
+from tensorflow.contrib.framework.python.ops import add_arg_scope
+from tensorflow.contrib.framework.python.ops import arg_scope
+from tensorflow.contrib.layers.python.layers import initializers
+from tensorflow.contrib.layers.python.layers import layers
+from tensorflow.contrib.layers.python.layers import regularizers
+from tensorflow.contrib.layers.python.layers import utils
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import variable_scope
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def subsample(inputs, factor, scope=None):
+    """Subsamples the input along the spatial dimensions.
+
+    Args:
+      inputs: A `Tensor` of size [batch, height_in, width_in, channels].
+      factor: The subsampling factor.
+      scope: Optional variable_scope.
+
+    Returns:
+      output: A `Tensor` of size [batch, height_out, width_out, channels] with the
+        input, either intact (if factor == 1) or subsampled (if factor > 1).
+    """
+    if factor == 1:
+        return inputs
+    else:
+        return layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
+
+
+def conv2d_same(
+        inputs, num_outputs, kernel_size, stride=1, rate=1, scope=None, **kwargs):
+    """Strided 2-D convolution with 'SAME' padding.
+
+    When stride > 1, then we do explicit zero-padding, followed by conv2d with
+    'VALID' padding.
+
+    Note that
+
+       net = conv2d_same(inputs, num_outputs, 3, stride=stride)
+
+    is equivalent to
+
+       net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1,
+       padding='SAME')
+       net = subsample(net, factor=stride)
+
+    whereas
+
+       net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride,
+       padding='SAME')
+
+    is different when the input's height or width is even, which is why we add the
+    current function. For more details, see ResnetUtilsTest.testConv2DSameEven().
+
+    Args:
+      inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
+      num_outputs: An integer, the number of output filters.
+      kernel_size: An int with the kernel_size of the filters.
+      stride: An integer, the output stride.
+      rate: An integer, rate for atrous convolution.
+      scope: Scope.
+
+    Returns:
+      output: A 4-D tensor of size [batch, height_out, width_out, channels] with
+        the convolution output.
+    """
+    if stride == 1:
+        return layers_lib.conv2d(
+            inputs, num_outputs, kernel_size, stride=stride, rate=rate, padding='SAME', scope=scope,
+            **kwargs)
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = array_ops.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
+        return layers_lib.conv2d(
+            inputs, num_outputs, kernel_size, stride=stride, rate=rate, padding='VALID',
+            scope=scope, **kwargs)
+
+
+def max_pool2d_same(
+        inputs, kernel_size, stride, scope=None):
+    """Strided 2-D max pool with 'SAME' padding.
+
+    When stride > 1, then we do explicit zero-padding, followed by max_pool2d with
+    'VALID' padding.
+
+    Note that
+
+       net = max_pool2d_same(inputs, num_outputs, 3, stride=stride)
+
+    is equivalent to
+
+       net = tf.contrib.layers.max_pool2d(inputs, num_outputs, 3, stride=1,
+       padding='SAME')
+       net = subsample(net, factor=stride)
+
+    whereas
+
+       net = tf.contrib.layers.max_pool2d(inputs, num_outputs, 3, stride=stride,
+       padding='SAME')
+
+    is different when the input's height or width is even, which is why we add the
+    current function. For more details, see ResnetUtilsTest.testConv2DSameEven().
+
+    Args:
+      inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
+      kernel_size: An int with the kernel_size of the filters.
+      stride: An integer, the output stride.
+      scope: Scope.
+
+    Returns:
+      output: A 4-D tensor of size [batch, height_out, width_out, channels] with
+        the convolution output.
+    """
+    if stride == 1:
+        return layers_lib.max_pool2d(
+            inputs, kernel_size, stride=stride, padding='SAME', scope=scope)
+    else:
+        pad_total = kernel_size - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = array_ops.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
+        return layers_lib.max_pool2d(
+            inputs, kernel_size, stride=stride, padding='VALID', scope=scope)
+
+
+# @add_arg_scope
+# def stack_blocks_dense(net,
+#                        blocks,
+#                        output_stride=None,
+#                        outputs_collections=None):
+#     """Stacks ResNet `Blocks` and controls output feature density.
+#
+#     First, this function creates scopes for the ResNet in the form of
+#     'block_name/unit_1', 'block_name/unit_2', etc.
+#
+#     Second, this function allows the user to explicitly control the ResNet
+#     output_stride, which is the ratio of the input to output spatial resolution.
+#     This is useful for dense prediction tasks such as semantic segmentation or
+#     object detection.
+#
+#     Most ResNets consist of 4 ResNet blocks and subsample the activations by a
+#     factor of 2 when transitioning between consecutive ResNet blocks. This results
+#     to a nominal ResNet output_stride equal to 8. If we set the output_stride to
+#     half the nominal network stride (e.g., output_stride=4), then we compute
+#     responses twice.
+#
+#     Control of the output feature density is implemented by atrous convolution.
+#
+#     Args:
+#       net: A `Tensor` of size [batch, height, width, channels].
+#       blocks: A list of length equal to the number of ResNet `Blocks`. Each
+#         element is a ResNet `Block` object describing the units in the `Block`.
+#       output_stride: If `None`, then the output will be computed at the nominal
+#         network stride. If output_stride is not `None`, it specifies the requested
+#         ratio of input to output spatial resolution, which needs to be equal to
+#         the product of unit strides from the start up to some level of the ResNet.
+#         For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
+#         then valid values for the output_stride are 1, 2, 6, 24 or None (which
+#         is equivalent to output_stride=24).
+#       outputs_collections: Collection to add the ResNet block outputs.
+#
+#     Returns:
+#       net: Output tensor with stride equal to the specified output_stride.
+#
+#     Raises:
+#       ValueError: If the target output_stride is not valid.
+#     """
+#     # The current_stride variable keeps track of the effective stride of the
+#     # activations. This allows us to invoke atrous convolution whenever applying
+#     # the next residual unit would result in the activations having stride larger
+#     # than the target output_stride.
+#     current_stride = 1
+#
+#     # The atrous convolution rate parameter.
+#     rate = 1
+#
+#     for block in blocks:
+#         with variable_scope.variable_scope(block.scope, 'block', [net]) as sc:
+#             for i, unit in enumerate(block.args):
+#                 if output_stride is not None and current_stride > output_stride:
+#                     raise ValueError('The target output_stride cannot be reached.')
+#
+#                 with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]):
+#                     # If we have reached the target output_stride, then we need to employ
+#                     # atrous convolution with stride=1 and multiply the atrous rate by the
+#                     # current unit's stride for use in subsequent layers.
+#                     if output_stride is not None and current_stride == output_stride:
+#                         net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
+#                         rate *= unit.get('stride', 1)
+#                     else:
+#                         net = block.unit_fn(net, rate=1, **unit)
+#                         current_stride *= unit.get('stride', 1)
+#             net = utils.collect_named_outputs(outputs_collections, sc.name, net)
+#
+#     if output_stride is not None and current_stride != output_stride:
+#         raise ValueError('The target output_stride cannot be reached.')
+#
+#     return net
+
+
+@add_arg_scope
+def stack_blocks_dense(
+        net, blocks, output_stride=None, n_branches=1, split_after_block=5,
+        store_non_strided_activations=False, outputs_collections=None):
+    """Stacks ResNet `Blocks` and controls output feature density.
+    First, this function creates scopes for the ResNet in the form of
+    'block_name/unit_1', 'block_name/unit_2', etc.
+    Second, this function allows the user to explicitly control the ResNet
+    output_stride, which is the ratio of the input to output spatial resolution.
+    This is useful for dense prediction tasks such as semantic segmentation or
+    object detection.
+    Most ResNets consist of 4 ResNet blocks and subsample the activations by a
+    factor of 2 when transitioning between consecutive ResNet blocks. This results
+    to a nominal ResNet output_stride equal to 8. If we set the output_stride to
+    half the nominal network stride (e.g., output_stride=4), then we compute
+    responses twice.
+    Control of the output feature density is implemented by atrous convolution.
+    Args:
+      net: A `Tensor` of size [batch, height, width, channels].
+      blocks: A list of length equal to the number of ResNet `Blocks`. Each
+        element is a ResNet `Block` object describing the units in the `Block`.
+      output_stride: If `None`, then the output will be computed at the nominal
+        network stride. If output_stride is not `None`, it specifies the requested
+        ratio of input to output spatial resolution, which needs to be equal to
+        the product of unit strides from the start up to some level of the ResNet.
+        For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
+        then valid values for the output_stride are 1, 2, 6, 24 or None (which
+        is equivalent to output_stride=24).
+      store_non_strided_activations: If True, we compute non-strided (undecimated)
+        activations at the last unit of each block and store them in the
+        `outputs_collections` before subsampling them. This gives us access to
+        higher resolution intermediate activations which are useful in some
+        dense prediction problems but increases 4x the computation and memory cost
+        at the last unit of each block.
+      outputs_collections: Collection to add the ResNet block outputs.
+    Returns:
+      net: Output tensor with stride equal to the specified output_stride.
+    Raises:
+      ValueError: If the target output_stride is not valid.
+    """
+    # The current_stride variable keeps track of the effective stride of the
+    # activations. This allows us to invoke atrous convolution whenever applying
+    # the next residual unit would result in the activations having stride larger
+    # than the target output_stride.
+    current_strides = [1]
+
+    # The atrous convolution rate parameter.
+    rates = [1]
+    nets = [net]
+
+    for i_block, block in enumerate(blocks):
+        if i_block == split_after_block:
+            # We make the split here from a single "net" to multiple branches
+            current_strides, nets, rates = zip(*[resnet_block_fn(
+                block, current_strides[0], nets[0], output_stride, outputs_collections, rates[0],
+                store_non_strided_activations, f'_copy{i_branch}')
+                for i_branch in range(n_branches)])
+        else:
+            # Otherwise just push each "net" through its own copy of the current block
+            current_strides, nets, rates = zip(*[resnet_block_fn(
+                block, current_stride, net, output_stride, outputs_collections, rate,
+                store_non_strided_activations, f'_copy{i_branch}')
+                for i_branch, (net, current_stride, rate) in
+                enumerate(zip(nets, current_strides, rates))])
+
+    if output_stride is not None and current_strides[0] != output_stride:
+        raise ValueError('The target output_stride cannot be reached.')
+
+    return nets
+
+
+def resnet_block_fn(
+        block, current_stride, net, output_stride, outputs_collections, rate,
+        store_non_strided_activations, scope_suffix):
+    with variable_scope.variable_scope(block.scope, f'block{scope_suffix}', [net]) as sc:
+        block_stride = 1
+        for i, unit in enumerate(block.args):
+            if store_non_strided_activations and i == len(block.args) - 1:
+                # Move stride from the block's last unit to the end of the block.
+                block_stride = unit.get('stride', 1)
+                unit = dict(unit, stride=1)
+
+            with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]):
+                # If we have reached the target output_stride, then we need to employ
+                # atrous convolution with stride=1 and multiply the atrous rate by the
+                # current unit's stride for use in subsequent layers.
+                if output_stride is not None and current_stride == output_stride:
+                    net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
+                    rate *= unit.get('stride', 1)
+
+                else:
+                    net = block.unit_fn(net, rate=1, **unit)
+                    current_stride *= unit.get('stride', 1)
+                    if output_stride is not None and current_stride > output_stride:
+                        raise ValueError('The target output_stride cannot be reached.')
+
+        # Collect activations at the block's end before performing subsampling.
+        net = utils.collect_named_outputs(outputs_collections, sc.name, net)
+
+        # Subsampling of the block's output activations.
+        if output_stride is not None and current_stride == output_stride:
+            rate *= block_stride
+        else:
+            net = subsample(net, block_stride)
+            current_stride *= block_stride
+            if output_stride is not None and current_stride > output_stride:
+                raise ValueError('The target output_stride cannot be reached.')
+    return current_stride, net, rate
+
+
+def resnet_arg_scope(weight_decay=0.0001,
+                     batch_norm_decay=0.997,
+                     batch_norm_epsilon=1e-5,
+                     batch_norm_scale=True):
+    """Defines the default ResNet arg scope.
+
+    TODO(gpapan): The batch-normalization related default values above are
+      appropriate for use in conjunction with the reference ResNet models
+      released at https://github.com/KaimingHe/deep-residual-networks. When
+      training ResNets from scratch, they might need to be tuned.
+
+    Args:
+      weight_decay: The weight decay to use for regularizing the model.
+      batch_norm_decay: The moving average decay when estimating layer activation
+        statistics in batch normalization.
+      batch_norm_epsilon: Small constant to prevent division by zero when
+        normalizing activations by their variance in batch normalization.
+      batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
+        activations in the batch normalization layer.
+
+    Returns:
+      An `arg_scope` to use for the resnet models.
+    """
+    batch_norm_params = {
+        'decay': batch_norm_decay,
+        'epsilon': batch_norm_epsilon,
+        'scale': batch_norm_scale,
+        'updates_collections': ops.GraphKeys.UPDATE_OPS,
+    }
+
+    with arg_scope(
+            [layers_lib.conv2d],
+            weights_regularizer=regularizers.l2_regularizer(weight_decay),
+            weights_initializer=initializers.variance_scaling_initializer(),
+            activation_fn=nn_ops.relu,
+            normalizer_fn=layers.batch_norm,
+            normalizer_params=batch_norm_params):
+        with arg_scope([layers.batch_norm], **batch_norm_params):
+            # The following implies padding='SAME' for pool1, which makes feature
+            # alignment easier for dense prediction tasks. This is also used in
+            # https://github.com/facebook/fb.resnet.torch. However the accompanying
+            # code of 'Deep Residual Learning for Image Recognition' uses
+            # padding='VALID' for pool1. You can switch to that choice by setting
+            # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID').
+            with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc:
+                return arg_sc
diff --git a/pose3d_minimal/resnet_v2.py b/pose3d_minimal/resnet_v2.py
new file mode 100644
index 0000000..5b18060
--- /dev/null
+++ b/pose3d_minimal/resnet_v2.py
@@ -0,0 +1,330 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Changed by Istvan:
+  - NCHW order supported
+  - Centered striding
+  - Mask channel input
+
+Contains definitions for the preactivation form of Residual Networks.
+
+Residual networks (ResNets) were originally proposed in:
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Deep Residual Learning for Image Recognition. arXiv:1512.03385
+
+The full preactivation 'v2' ResNet variant implemented in this module was
+introduced by:
+[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
+
+The key difference of the full preactivation 'v2' variant compared to the
+'v1' variant in [1] is the use of batch normalization before every weight layer.
+
+Typical use:
+
+   from tensorflow.contrib.slim.python.slim.nets import
+   resnet_v2
+
+ResNet-101 for image classification into 1000 classes:
+
+   # inputs has shape [batch, 224, 224, 3]
+   with slim.arg_scope(resnet_v2.resnet_arg_scope()):
+      net, end_points = resnet_v2.resnet_v2_101(inputs, 1000, is_training=False)
+
+ResNet-101 for semantic segmentation into 21 classes:
+
+   # inputs has shape [batch, 513, 513, 3]
+   with slim.arg_scope(resnet_v2.resnet_arg_scope()):
+      net, end_points = resnet_v2.resnet_v2_101(inputs,
+                                                21,
+                                                is_training=False,
+                                                global_pool=False,
+                                                output_stride=16)
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from tensorflow.contrib import layers as layers_lib
+from tensorflow.contrib.framework.python.ops import add_arg_scope
+from tensorflow.contrib.framework.python.ops import arg_scope
+from tensorflow.contrib.layers.python.layers import layers
+from tensorflow.contrib.layers.python.layers import utils
+from . import resnet_utils
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import variable_scope
+import tensorflow.contrib.slim as slim
+
+resnet_arg_scope = resnet_utils.resnet_arg_scope
+
+
+@add_arg_scope
+def bottleneck(
+        inputs, depth, depth_bottleneck, stride, rate=1, outputs_collections=None, scope=None):
+    """Bottleneck residual unit variant with BN before convolutions.
+
+    This is the full preactivation residual unit variant proposed in [2]. See
+    Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
+    variant which has an extra bottleneck layer.
+
+    When putting together two consecutive ResNet blocks that use this unit, one
+    should use stride = 2 in the last unit of the first block.
+
+    Args:
+      inputs: A tensor of size [batch, height, width, channels] for NHWC or permuted for NCHW.
+      depth: The depth of the ResNet unit output.
+      depth_bottleneck: The depth of the bottleneck layers.
+      stride: The ResNet unit's stride. Determines the amount of downsampling of
+        the units output compared to its input.
+      rate: An integer, rate for atrous convolution.
+      outputs_collections: Collection to add the ResNet unit output.
+      scope: Optional variable_scope.
+
+    Returns:
+      The ResNet unit's output.
+    """
+
+    with variable_scope.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
+        depth_in = inputs.get_shape().as_list()[1]
+        preact = layers.batch_norm(
+            inputs, activation_fn=nn_ops.relu, scope='preact')
+        if depth == depth_in:
+            shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = layers_lib.conv2d(
+                preact, depth, [1, 1], stride=stride, normalizer_fn=None,
+                activation_fn=None, scope='shortcut')
+
+        residual = layers_lib.conv2d(
+            preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
+        residual = resnet_utils.conv2d_same(
+            residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
+        residual = layers_lib.conv2d(
+            residual, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None,
+            scope='conv3')
+
+        output = shortcut + residual
+        return utils.collect_named_outputs(outputs_collections, sc.name, output)
+
+
+def resnet_v2(inputs,
+              blocks,
+              num_classes=None,
+              is_training=True,
+              global_pool=True,
+              output_stride=None,
+              include_root_block=True,
+              split_after_block=5,
+              reuse=None,
+              scope=None):
+    """Generator for v2 (preactivation) ResNet models.
+
+    This function generates a family of ResNet v2 models. See the resnet_v2_*()
+    methods for specific model instantiations, obtained by selecting different
+    block instantiations that produce ResNets of various depths.
+
+    Training for image classification on Imagenet is usually done with [224, 224]
+    inputs, resulting in [7, 7] feature maps at the output of the last ResNet
+    block for the ResNets defined in [1] that have nominal stride equal to 32.
+    However, for dense prediction tasks we advise that one uses inputs with
+    spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
+    this case the feature maps at the ResNet output will have spatial shape
+    [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
+    and corners exactly aligned with the input image corners, which greatly
+    facilitates alignment of the features to the image. Using as input [225, 225]
+    images results in [8, 8] feature maps at the output of the last ResNet block.
+
+    For dense prediction tasks, the ResNet needs to run in fully-convolutional
+    (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
+    have nominal stride equal to 32 and a good choice in FCN mode is to use
+    output_stride=16 in order to increase the density of the computed features at
+    small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
+
+    Args:
+      inputs: A tensor of size [batch, height_in, width_in, channels].
+      blocks: A list of length equal to the number of ResNet blocks. Each element
+        is a resnet_utils.Block object describing the units in the block.
+      num_classes: Number of predicted classes for classification tasks. If None
+        we return the features before the logit layer.
+      is_training: whether batch_norm layers are in training mode.
+      global_pool: If True, we perform global average pooling before computing the
+        logits. Set to True for image classification, False for dense prediction.
+      output_stride: If None, then the output will be computed at the nominal
+        network stride. If output_stride is not None, it specifies the requested
+        ratio of input to output spatial resolution.
+      include_root_block: If True, include the initial convolution followed by
+        max-pooling, if False excludes it. If excluded, `inputs` should be the
+        results of an activation-less convolution.
+      reuse: whether or not the network and its variables should be reused. To be
+        able to reuse 'scope' must be given.
+      scope: Optional variable_scope.
+
+
+    Returns:
+      net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
+        If global_pool is False, then height_out and width_out are reduced by a
+        factor of output_stride compared to the respective height_in and width_in,
+        else both height_out and width_out equal one. If num_classes is None, then
+        net is the output of the last ResNet block, potentially after global
+        average pooling. If num_classes is not None, net contains the pre-softmax
+        activations.
+      end_points: A dictionary from components of the network to the corresponding
+        activation.
+
+    Raises:
+      ValueError: If the target output_stride is not valid.
+    """
+
+    with variable_scope.variable_scope(
+            scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
+        end_points_collection = sc.original_name_scope + '_end_points'
+        with arg_scope(
+                [layers_lib.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
+                outputs_collections=end_points_collection):
+            with arg_scope([layers.batch_norm], is_training=is_training):
+                net = inputs
+                if include_root_block:
+                    if output_stride is not None:
+                        if output_stride % 4 != 0:
+                            raise ValueError('The output_stride needs to be a multiple of 4.')
+                        output_stride /= 4
+                    # We do not include batch normalization or activation functions in
+                    # conv1 because the first ResNet unit will perform these. Cf.
+                    # Appendix of [2].
+                    with arg_scope([layers_lib.conv2d], activation_fn=None, normalizer_fn=None):
+                        net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
+
+                    net = resnet_utils.max_pool2d_same(net, 3, stride=2, scope='pool1')
+                nets = resnet_utils.stack_blocks_dense(
+                    net, blocks, output_stride, n_branches=len(num_classes),
+                    split_after_block=split_after_block)
+                # This is needed because the pre-activation variant does not have batch
+                # normalization or activation functions in the residual unit output. See
+                # Appendix of [2].
+                nets = [slim.batch_norm(
+                    net, activation_fn=nn_ops.relu,
+                    scope=f'postnorm' + (f'_copy{i_branch}' if len(nets) > 1 else ''))
+                    for i_branch, net in enumerate(nets)]
+
+                if global_pool:
+                    raise NotImplementedError
+
+                if num_classes is not None:
+                    nets = [layers_lib.conv2d(
+                        net, num_classes_, [1, 1], activation_fn=None, normalizer_fn=None,
+                        scope=f'logits' + (f'_copy{i_branch}' if len(nets) > 1 else ''))
+                        for i_branch, (net, num_classes_) in enumerate(zip(nets, num_classes))]
+                # Convert end_points_collection into a dictionary of end_points.
+                end_points = utils.convert_collection_to_dict(end_points_collection)
+                return nets, end_points
+
+
+resnet_v2.default_image_size = 224
+
+
+def resnet_v2_block(scope, base_depth, num_units, stride):
+    """Helper function for creating a resnet_v2 bottleneck block.
+
+    Args:
+      scope: The scope of the block.
+      base_depth: The depth of the bottleneck layer for each unit.
+      num_units: The number of units in the block.
+      stride: The stride of the block, implemented as a stride in the last unit.
+        All other units have stride=1.
+
+    Returns:
+      A resnet_v2 bottleneck block.
+    """
+    return resnet_utils.Block(scope, bottleneck, [{
+        'depth': base_depth * 4,
+        'depth_bottleneck': base_depth,
+        'stride': 1,
+    }] * (num_units - 1) + [{
+        'depth': base_depth * 4,
+        'depth_bottleneck': base_depth,
+        'stride': stride,
+    }])
+
+
+def resnet_v2_50(
+        inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None,
+        split_after_block=5, reuse=None, scope='resnet_v2_50'):
+    """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
+
+    if output_stride < 64:
+        blocks = [
+            resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+            resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+            resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
+            resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+        ]
+    else:
+        blocks = [
+            resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+            resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+            resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
+            resnet_v2_block('block4', base_depth=512, num_units=3, stride=2),
+        ]
+    return resnet_v2(
+        inputs, blocks, num_classes, is_training, global_pool, output_stride,
+        include_root_block=True, split_after_block=split_after_block, reuse=reuse, scope=scope)
+
+
+def resnet_v2_101(
+        inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None,
+        split_after_block=5, reuse=None, scope='resnet_v2_101'):
+    """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
+
+    blocks = [
+        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+        resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+        resnet_v2_block('block3', base_depth=256, num_units=23, stride=2),
+        resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+    ]
+    return resnet_v2(
+        inputs, blocks, num_classes, is_training, global_pool, output_stride,
+        include_root_block=True, split_after_block=split_after_block, reuse=reuse, scope=scope)
+
+
+def resnet_v2_152(
+        inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None,
+        split_after_block=5, reuse=None, scope='resnet_v2_152'):
+    """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""
+
+    blocks = [
+        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+        resnet_v2_block('block2', base_depth=128, num_units=8, stride=2),
+        resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),
+        resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+    ]
+    return resnet_v2(
+        inputs, blocks, num_classes, is_training, global_pool, output_stride,
+        include_root_block=True, split_after_block=split_after_block, reuse=reuse, scope=scope)
+
+
+def resnet_v2_200(
+        inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None,
+        split_after_block=5, reuse=None, scope='resnet_v2_200'):
+    """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""
+
+    blocks = [
+        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+        resnet_v2_block('block2', base_depth=128, num_units=24, stride=2),
+        resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),
+        resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+    ]
+    return resnet_v2(
+        inputs, blocks, num_classes, is_training, global_pool, output_stride,
+        include_root_block=True, reuse=reuse, scope=scope)
diff --git a/pose3d_minimal/tfutil.py b/pose3d_minimal/tfutil.py
new file mode 100644
index 0000000..4a50dfb
--- /dev/null
+++ b/pose3d_minimal/tfutil.py
@@ -0,0 +1,91 @@
+import tensorflow as tf
+import functools
+
+
+def soft_argmax(inp, axis):
+    softmaxed = softmax(inp, axis=axis)
+    return tf.stack(decode_heatmap(softmaxed, axis=axis), axis=-1)
+
+
+def softmax(target, axis=-1, name=None):
+    with tf.name_scope(name, 'softmax', values=[target]):
+        max_along_axis = tf.reduce_max(target, axis, keepdims=True)
+        exponentiated = tf.exp(target - max_along_axis)
+        normalizer_denominator = tf.reduce_sum(exponentiated, axis, keepdims=True)
+        return exponentiated / normalizer_denominator
+
+
+def jensen_shannon_loss(logit1, logit2, axis=-1, name=None):
+    with tf.name_scope(name, 'jensen_shannon', values=[logit1, logit2]):
+        probability1 = softmax(logit1, axis=axis)
+        probability2 = softmax(logit2, axis=axis)
+        logsumexp1 = tf.reduce_logsumexp(logit1, axis=axis, keepdims=True)
+        logsumexp2 = tf.reduce_logsumexp(logit2, axis=axis, keepdims=True)
+        logit_diff = logit1 - logit2
+        probability_diff = probability1 - probability2
+        logsumexp_diff = logsumexp1 - logsumexp2
+        return 0.5 * tf.reduce_sum(probability_diff * (logit_diff - logsumexp_diff), axis=axis)
+
+
+def decode_heatmap(inp, axis=-1):
+    shape = inp.get_shape().as_list()
+    ndims = inp.get_shape().ndims
+
+    def relative_coords_along_axis(ax):
+        grid_shape = [1] * ndims
+        grid_shape[ax] = shape[ax]
+        grid = tf.reshape(tf.linspace(0.0, 1.0, shape[ax]), grid_shape)
+        return tf.cast(grid, inp.dtype)
+
+    # Single axis:
+    if not isinstance(axis, (tuple, list)):
+        return tf.reduce_sum(relative_coords_along_axis(axis) * inp, axis=axis)
+
+    # Multiple axes.
+    # Convert negative axes to the corresponding positive index (e.g. -1 means last axis)
+    heatmap_axes = [ax if ax >= 0 else ndims + ax + 1 for ax in axis]
+    result = []
+    for ax in heatmap_axes:
+        other_heatmap_axes = tuple(set(heatmap_axes) - {ax})
+        summed_over_other_axes = tf.reduce_sum(inp, axis=other_heatmap_axes, keepdims=True)
+        coords = relative_coords_along_axis(ax)
+        decoded = tf.reduce_sum(coords * summed_over_other_axes, axis=ax, keepdims=True)
+        result.append(tf.squeeze(decoded, heatmap_axes))
+
+    return result
+
+
+def in_variable_scope(default_name, mixed_precision=True):
+    """Puts the decorated function in a TF variable scope with the provided default name.
+    The function also gains two extra arguments: "scope" and "reuse" which get passed to
+    tf.variable_scope.
+    """
+
+    def decorator(f):
+        @functools.wraps(f)
+        def decorated(*args, scope=None, reuse=None, **kwargs):
+            with tf.variable_scope(
+                    scope, default_name, reuse=reuse,
+                    custom_getter=mixed_precision_getter if mixed_precision else None):
+                return f(*args, **kwargs)
+
+        return decorated
+
+    return decorator
+
+
+def mixed_precision_getter(
+        getter, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True,
+        *args, **kwargs):
+    """Custom variable getter that forces trainable variables to be stored in
+    float32 precision and then casts them to the compute precision."""
+    # print(f'mixed prec asked for {dtype} ({name})')
+    storage_dtype = tf.float32 if trainable else dtype
+    variable = getter(
+        name, shape, dtype=storage_dtype, initializer=initializer, regularizer=regularizer,
+        trainable=trainable, *args, **kwargs)
+
+    if storage_dtype != dtype:
+        return tf.cast(variable, dtype)
+
+    return variable
diff --git a/pose_encoder.py b/pose_encoder.py
new file mode 100644
index 0000000..abb539a
--- /dev/null
+++ b/pose_encoder.py
@@ -0,0 +1,53 @@
+from tensorflow_addons.layers import GroupNormalization
+
+model = models.Sequential()
+model.add(layers.Conv3D(64, (3, 3, 3), padding='same', strides=(1, 1,  1)))
+
+def relu_bn(inputs: Tensor) -> Tensor:
+    relu = ReLU()(inputs)
+    bn = GroupNormalization()(relu)
+    return bn
+
+def residual_block(x: Tensor, downsample: bool, filters: int, kernel_size: int = 3) -> Tensor:
+    y = Conv3D(kernel_size=kernel_size,
+               strides= (1 if not downsample else 2),
+               filters=filters,
+               padding="same")(x)
+    y = relu_bn(y)
+    y = Conv3D(kernel_size=kernel_size,
+               strides=1,
+               filters=filters,
+               padding="same")(y)
+  
+    if downsample:
+        x = Conv3D(kernel_size=1,
+                   strides=2,
+                   filters=filters,
+                   padding="same")(x)
+    out = Add()([x, y])
+    out = relu_bn(out)
+    return out
+
+def create_res_net():
+    
+    inputs = Input(shape=(32, 32, 64, 64))
+    
+    t = GroupNormalization()(inputs)
+    t = Conv3D(kernel_size=3,
+               strides=1,
+               filters=64,
+               padding="same")(t)
+    t = relu_bn(t)
+    
+    num_blocks_list = [1, 1, 1]
+    for i in range(len(num_blocks_list)):
+        num_blocks = num_blocks_list[i]
+        for j in range(num_blocks):
+            t = residual_block(t, downsample=(j==0 and i!=0), filters=64)
+    
+    
+    t = AveragePooling3D(4)(t)
+    outputs = t
+    model = Model(inputs, outputs)
+
+    return model
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..c924f30
--- /dev/null
+++ b/test.py
@@ -0,0 +1,160 @@
+from dataset_definitions import get_dataset
+from model import generator, discriminator
+from parallel_map import parallel_map_as_tf_dataset
+from losses import init_perception_model, get_pose_loss, init_pose_model
+import tensorflow as tf
+from utils import initialize_uninitialized, make_pretrained_weight_loader, ssim
+from io import BytesIO
+import matplotlib.pyplot as plt
+import time
+from parameters import params
+import numpy as np
+import tensorflow_gan as tfgan
+
+backend = tf.keras.backend
+
+if __name__ == '__main__':
+    print('Hyperparams:')
+    for name, val in params.items():
+        print('{}:\t{}'.format(name, val))
+
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    sess = tf.Session(config=config)
+
+    backend.set_session(sess)
+    init_perception_model()
+
+    # VALIDATION GRAPH
+    print('build validation graph')
+
+    # the validation dataset consists of the same samples every time, so results are comparable
+    valid_count = params['valid_count']
+
+    valid_dataset = get_dataset(params['dataset'], deterministic=True, with_to_masks=True)
+    valid_data = []
+    if params['with_valid']:  # if we train with valid, we use the test set instead of the valid set for validation
+        for valid_sample in valid_dataset.next_test_sample():
+            valid_data.append(valid_sample)
+            if len(valid_data) == valid_count:
+                break
+    else:
+        for valid_sample in valid_dataset.next_valid_sample():
+            valid_data.append(valid_sample)
+            if len(valid_data) == valid_count:
+                break
+
+    def valid_gen():
+        while True:
+            for sample in valid_data:
+                yield sample
+
+
+    valid_dataset = parallel_map_as_tf_dataset(None, valid_gen(), n_workers=1, deterministic=True)
+    valid_dataset = valid_dataset.batch(1, drop_remainder=True)
+    valid_iterator = valid_dataset.make_one_shot_iterator()
+    (valid_img_from, valid_img_to, valid_mask_from, valid_mask_to, valid_transform_params, valid_3d_input_pose,
+      valid_3d_target_pose) = valid_iterator.get_next()
+
+    print('- GAN')
+    with tf.variable_scope('GAN', reuse=False):
+        pose_gan = tfgan.gan_model(
+            generator,
+            discriminator,
+            real_data= valid_img_to,
+            generator_inputs=[valid_img_from, valid_mask_from, valid_transform_params, valid_3d_input_pose,
+                              valid_3d_target_pose],
+            check_shapes=False
+        )
+
+        # 2D mask for target pose to compute foreground SSIM
+    if params['2d_3d_warp']:
+        valid_fg_mask = valid_mask_to
+    else:
+        valid_fg_mask = tf.reduce_max(valid_mask_to, axis=3)
+    valid_fg_mask = valid_fg_mask[:, :-1, :-1]
+    valid_fg_mask = tf.image.resize_images(valid_fg_mask, (params['image_size'], params['image_size']),
+                                           method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+    valid_fg_mask = tf.reduce_max(valid_fg_mask, axis=3)
+
+    with tf.variable_scope('GAN/Generator', reuse=True):
+        valid_model = pose_gan.generator_fn([valid_img_from, valid_mask_from, valid_transform_params, valid_3d_input_pose, valid_3d_target_pose])
+
+    valid_pose_loss = get_pose_loss(valid_img_to, valid_model[0])
+
+    init_pose_model(sess, 'pose3d_minimal/checkpoint/model.ckpt-160684')
+
+    start = time.time()
+    checkpoint = tf.train.latest_checkpoint(params['check_dir'])
+    summary_writer = tf.summary.FileWriter(params['tb_dir'])
+    if checkpoint is not None:
+        start_step = int(checkpoint.split('-')[-1])
+        init_fn = make_pretrained_weight_loader(checkpoint, 'GAN', 'GAN', ['Adam', 'Momentum'], [])
+        init_fn(sess)
+        global_step = tf.Variable(start_step, trainable=False, name='global_step')
+        initialize_uninitialized(sess)
+        print(f'Loaded checkpoint from step {start_step}:', time.time() - start)
+
+        print('Performing validation')
+        val_start = time.time()
+        v_inp = []
+        v_tar = []
+        v_gen = []
+        v_pl = []
+        v_bg = []
+        v_bg_mask = []
+        v_fg = []
+        v_fg_m = []
+        valid_generated = tf.clip_by_value(valid_model[0], -1, 1)
+        print('- generating images')
+        for _ in range(valid_count):
+            inp, tar, gen, pl, bg, bg_mask, fg, fg_m = sess.run(
+                [valid_img_from, valid_img_to, valid_generated, valid_pose_loss, valid_model[1]['background'],
+                  valid_model[1]['foreground_mask'], valid_model[1]['foreground'], valid_fg_mask])
+            v_inp.append(inp[0, :256, :256] / 2 + .5)
+            v_tar.append(tar[0, :256, :256] / 2 + .5)
+            v_gen.append(gen[0, :256, :256] / 2 + .5)
+            v_pl += [pl]
+            v_bg.append(bg[0, :256, :256] / 2 + .5)
+            v_bg_mask.append(np.tile(bg_mask[0, :256, :256], [1, 1, 3]))
+            v_fg.append(fg[0, :256, :256] / 2 + .5)
+            v_fg_m.append(fg_m[0, ..., np.newaxis])
+
+        prefix = 'test' if params['with_valid'] else 'val'
+        print('- computing SSIM scores')
+        ssim_score, ssim_fg, ssim_bg = ssim(v_tar, v_gen, masks=v_fg_m)
+        summary = tf.Summary(value=[tf.Summary.Value(tag=f'{prefix}_metrics/ssim', simple_value=ssim_score)])
+        summary_writer.add_summary(summary, 0)
+        summary = tf.Summary(value=[tf.Summary.Value(tag=f'{prefix}_metrics/ssim_fg', simple_value=ssim_fg)])
+        summary_writer.add_summary(summary, 0)
+        summary = tf.Summary(value=[tf.Summary.Value(tag=f'{prefix}_metrics/ssim_bg', simple_value=ssim_bg)])
+        summary_writer.add_summary(summary, 0)
+
+        print('- computing pose score')
+        pl = np.mean(v_pl)
+        summary = tf.Summary(value=[tf.Summary.Value(tag=f'{prefix}_metrics/pose_loss', simple_value=pl)])
+        summary_writer.add_summary(summary, 0)
+
+        print('- creating images for tensorboard')
+        v_inp = np.concatenate(v_inp[:16], axis=0)
+        v_tar = np.concatenate(v_tar[:16], axis=0)
+        v_gen = np.concatenate(v_gen[:16], axis=0)
+        v_bg = np.concatenate(v_bg[:16], axis=0)
+        v_bg_mask = np.concatenate(v_bg_mask[:16], axis=0)
+        v_fg = np.concatenate(v_fg[:16], axis=0)
+        res = np.concatenate([v_inp, v_tar, v_gen, v_bg, v_bg_mask, v_fg], axis=1)
+        plt.imsave('output/res_with_mask.png', res, format='png')
+        s = BytesIO()
+        plt.imsave(s, res, format='png')
+        res = tf.Summary.Image(encoded_image_string=s.getvalue(), height=res.shape[0], width=res.shape[1])
+        summary = tf.Summary(value=[tf.Summary.Value(tag=f'{prefix}_img', image=res)])
+        summary_writer.add_summary(summary, 0)
+        summary_writer.flush()
+        print('Performed validation:', time.time() - val_start)
+
+        res2 = np.concatenate([v_inp, v_tar, v_gen], axis=1)
+        plt.imsave('output/res1.png', res2, format='png')
+
+    else:
+        print("No Model Found!!")
+
diff --git a/train_old.py b/train_old.py
new file mode 100644
index 0000000..6d1fa00
--- /dev/null
+++ b/train_old.py
@@ -0,0 +1,72 @@
+cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)      
+def discriminator_loss(real_output, fake_output):
+    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
+    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
+    total_loss = real_loss + fake_loss
+    return total_loss
+   
+def generator_loss(fake_output):
+    return cross_entropy(tf.ones_like(fake_output), fake_output)
+    
+generator_optimizer = tf.keras.optimizers.Adam(1e-4)
+discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
+
+checkpoint_dir = './training_checkpoints'
+checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
+checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
+                                 discriminator_optimizer=discriminator_optimizer,
+                                 generator=generator_g,
+                                 discriminator=discriminator_y)
+   
+epochs=1
+num_examples_to_generate = 16
+
+# Notice the use of `tf.function`
+# This annotation causes the function to be "compiled".
+@tf.function
+def train_step(images):
+  
+
+    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
+      generated_images = generator_g(images, training=True)
+
+      real_output = discriminator_y(images, training=True)
+      fake_output = discriminator_y(generated_images, training=True)
+
+      gen_loss = generator_loss(fake_output)
+      disc_loss = discriminator_loss(real_output, fake_output)
+     # print(gen_loss,disc_loss,real_output,fake_output)
+    gradients_of_generator = gen_tape.gradient(gen_loss, generator_g.trainable_variables)
+    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator_y.trainable_variables)
+
+    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator_g.trainable_variables))
+    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator_y.trainable_variables))
+    
+    
+def train(datasetGen, epochs):
+  for epoch in range(epochs):
+    start = time.time()
+ 
+    for image,labels in datasetGen:
+      train_step(image)
+ 
+     # Produce images for the GIF as you go
+    display.clear_output(wait=True)
+    generate_and_save_images(generator_g,
+                              epoch + 1,                                         
+                              )
+ 
+    # Save the model every 15 epochs
+    if (epoch + 1) % 1 == 0:
+      checkpoint.save(file_prefix = checkpoint_prefix)
+ 
+    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))
+ 
+  # Generate after the final epoch
+  display.clear_output(wait=True)
+  generate_and_save_images(generator_g,
+                           epochs,
+                           seed)
+                           
+                           
+ train(datasetGen, epochs)     
diff --git a/utils.py b/utils.py
deleted file mode 100644
index e69de29..0000000
diff --git a/warping_module.py b/warping_module.py
index e69de29..eb59862 100644
--- a/warping_module.py
+++ b/warping_module.py
@@ -0,0 +1,114 @@
+import tensorflow as tf
+from parameters import params
+import numpy as np
+from utils import extend_spatial_sizes, reduce_spatial_sizes
+
+def build_coords(shape):
+    xx, yy, zz = tf.meshgrid(tf.range(shape[1]), tf.range(shape[0]), tf.range(shape[2]))  # in image notation
+    ww = tf.ones(xx.shape)
+    coords = tf.concat([tf.expand_dims(tf.cast(a, tf.float32), -1) for a in [xx, yy, zz, ww]], axis=-1)
+    return coords
+
+
+# input in matrix notation
+def transform_single(volume, transform, interpolation):
+    volume = tf.transpose(volume, [1, 0, 2, 3])  # switch to image notation
+    coords = build_coords(volume.shape[:3])
+    coords_shape = coords.shape
+    coords_reshaped = tf.reshape(coords, [-1, 4])
+    pointers_reshaped = tf.linalg.matmul(transform, coords_reshaped, transpose_b=True)
+    pointers = tf.reshape(tf.transpose(pointers_reshaped, [1, 0]), coords_shape)  # undo transpose_b
+    pointers = pointers[:, :, :, :3]
+    if interpolation == 'NEAREST':
+        pointers = tf.cast(tf.math.round(pointers), dtype=tf.int32)
+        with tf.device('/gpu:0'):
+            res = tf.gather_nd(volume, pointers)
+    elif interpolation == 'TRILINEAR':
+        c3s = {}
+        for c in [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]:
+            c3s[c] = tf.gather_nd(volume, tf.cast(tf.floor(pointers), dtype=tf.int32) + c)
+        d = pointers - tf.floor(pointers)
+        c2s = {}
+        for c in [(0, 0), (0, 1), (1, 0), (1, 1)]:
+            c2s[c] = c3s[(0,) + c] * (1 - d[:, :, :, 0:1]) + c3s[(1,) + c] * (d[:, :, :, 0:1])
+        c1s = {}
+        for c in [(0,), (1,)]:
+            c1s[c] = c2s[(0,) + c] * (1 - d[:, :, :, 1:2]) + c2s[(1,) + c] * (d[:, :, :, 1:2])
+        res = c1s[(0,)] * (1 - d[:, :, :, 2:3]) + c1s[(1,)] * (d[:, :, :, 2:3])
+    else:
+        raise ValueError
+    return res
+
+
+def volumetric_transform(volumes, transforms, interpolation='NEAREST'):
+    return tf.map_fn(lambda x: transform_single(x[0], x[1], interpolation), (volumes, transforms), dtype=tf.float32,
+                     parallel_iterations=128)
+
+
+def warp_3d(vol_batch, masks_batch, transform_batch, reduce=True):
+    n, h, w, d, c = vol_batch.get_shape().as_list()
+    with tf.name_scope('warp_3d'):
+        net = {}
+
+        part_count = transform_batch.shape[1]
+
+        net['bodypart_masks'] = masks_batch
+
+        init_volume_size = (params['image_size'], params['image_size'], params['image_size'])
+        z_scale = (d - 1) / (h - 1)
+        v_scale = (h - 1) / init_volume_size[0]
+        affine_mul = [[1, 1, 1 / z_scale, v_scale],
+                      [1, 1, 1 / z_scale, v_scale],
+                      [z_scale, z_scale, 1, v_scale * z_scale],
+                      [1, 1, 1 / z_scale, 1]]
+        affine_mul = np.array(affine_mul).reshape((1, 1, 4, 4))
+        affine_transforms = transform_batch * affine_mul
+        affine_transforms = tf.reshape(affine_transforms, (-1, 4, 4))
+
+        expanded_tensor = tf.expand_dims(vol_batch, -1)
+        multiples = [1, part_count, 1, 1, 1, 1]
+        tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)
+        repeated_tensor = tf.reshape(tiled_tensor, (
+            n * part_count, h, w, d, c))
+
+        transposed_masks = tf.transpose(masks_batch, [0, 4, 1, 2, 3])
+        reshaped_masks = tf.reshape(transposed_masks, [n * part_count, h, w, d])
+        repeated_tensor = repeated_tensor * tf.expand_dims(reshaped_masks, axis=-1)
+
+        net['masked_bodyparts'] = repeated_tensor
+        warped = volumetric_transform(repeated_tensor, affine_transforms, interpolation='TRILINEAR')
+        net['masked_bodyparts_warped'] = warped
+
+        res = tf.reshape(warped, [-1, part_count, h, w, d, c])
+        res = tf.transpose(res, [0, 2, 3, 4, 1, 5])
+        if reduce:
+            res = tf.reduce_max(res, reduction_indices=[-2])
+        return res, net
+
+
+def tf_pose_map_3d(poses, shape):
+    y = tf.unstack(poses, axis=1)
+    y[0], y[1] = y[1], y[0]
+    poses = tf.stack(y, axis=1)
+    image_size = tf.constant(params['image_size'], tf.float32)
+    shape = tf.constant(shape, tf.float32)
+    sigma = tf.constant(6, tf.float32)
+    poses = tf.unstack(poses, axis=0)
+    pose_mapss = []
+    for pose in poses:
+        pose = pose / image_size * shape[:, tf.newaxis]
+        joints = tf.unstack(pose, axis=-1)
+        pose_maps = []
+        for joint in joints:
+            xx, yy, zz = tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]), tf.range(shape[2]), indexing='ij')
+            mesh = tf.cast(tf.stack([xx, yy, zz]), dtype=tf.float32)
+            pose_map = mesh - joint[:, tf.newaxis, tf.newaxis, tf.newaxis]
+            pose_map = pose_map / shape[:, tf.newaxis, tf.newaxis, tf.newaxis] * image_size
+            pose_map = tf.exp(-tf.reduce_sum(pose_map ** 2, axis=0) / (2 * sigma ** 2))
+            pose_maps.append(pose_map)
+        pose_map = tf.stack(pose_maps, axis=-1)
+        if params['2d_3d_pose']:
+            pose_map = tf.reduce_max(pose_map, axis=2, keepdims=True)
+            pose_map = tf.tile(pose_map, [1, 1, params['depth'], 1])
+        pose_mapss.append(pose_map)
+    return tf.stack(pose_mapss, axis=0)
diff --git a/warping_module3d.py b/warping_module3d.py
new file mode 100644
index 0000000..384a18f
--- /dev/null
+++ b/warping_module3d.py
@@ -0,0 +1,96 @@
+import tensorflow as tf
+import numpy as np
+import time
+
+
+
+
+#for main body mask and head mask (rotation of plane)
+#mask input shape=(3,:)
+def rotation_estimation_3joint(joint1i,joint2i,joint3i,joint1f,joint2f,joint3f,mask):
+  midi=(joint1i+joint2i)/2
+  midf=(joint1f+joint2f)/2
+  
+
+  ai=joint1i-midi
+  af=joint1f-midf
+  bi=joint2i-midi
+  bf=joint2f-midf
+  ci=joint3i-midi
+  cf=joint3f-midf
+  
+  
+  scle=tf.norm(bi-ai)/tf.norm(bf-af)
+
+  Mi=np.column_stack((ai*scle,bi*scle,ci*scle))
+  Mf=np.column_stack((af,bf,cf))
+
+  rotation_mat=tf.linalg.matmul((Mf),tf.linalg.pinv(Mi)) #s*R(ji-mi)=jf-mf
+  midi=np.reshape(midi,(3,-1))
+
+  mask=np.reshape(mask,(3,-1))
+  
+ 
+  midf=np.reshape(midf,(3,-1))
+  maskf=np.matmul(rotation_mat,mask-midi)*scle+midf
+  
+  return maskf #mask's final coordinate
+
+def rotation_estimation(joint1i,joint2i,joint1f,joint2f,mask):
+  
+  a=joint1i-joint2i
+  b=joint1f-joint2f
+  
+  
+  scle=np.linalg.norm([b[0],b[1]])/np.linalg.norm([a[0],a[1]])
+  
+  angle=tf.math.atan(b[1]/b[0])-tf.math.atan(a[1]/a[0])
+  si=tf.math.sin(angle)
+  co=tf.math.cos(angle)
+  
+  rotation_mat=np.array([[co,-si],[si,co]])
+  
+  mxy=np.array([mask[0,:],mask[1,:]])
+  j2i=np.array([[joint2i[0]],[joint2i[1]]])
+  j2f=np.array([[joint2f[0]],[joint2f[1]]])
+  
+  xymask=np.matmul(rotation_mat,mxy-j2i)*scle+j2f
+  
+  
+  zmask=((xymask[0,:]-joint2f[0])*b[2]/b[0])+joint2f[2]
+  
+  zmask=np.reshape(zmask,(1,-1))
+  maskf=np.concatenate((xymask,zmask),axis=0)
+  
+  return maskf
+
+def warpingModule(mask,transform,joint):
+    warped_mask=[]
+
+    warped_mask.append(rotation_estimation(joint['lsho'],joint['lelb'],transform['lsho'],transform['lelb'],mask[0]))
+    warped_mask.append(rotation_estimation(joint['rsho'],joint['relb'],transform['rsho'],transform['relb'],mask[1]))
+    warped_mask.append(rotation_estimation(joint['lelb'],joint['lwri'],transform['lelb'],transform['lwri'],mask[2]))
+    warped_mask.append(rotation_estimation(joint['relb'],joint['rwri'],transform['relb'],transform['rwri'],mask[3]))
+    warped_mask.append(rotation_estimation(joint['lhip'],joint['lkne'],transform['lhip'],transform['lkne'],mask[4]))
+    warped_mask.append(rotation_estimation(joint['rhip'],joint['rkne'],transform['rhip'],transform['rkne'],mask[5]))
+    warped_mask.append(rotation_estimation(joint['lkne'],joint['lank'],transform['lkne'],transform['lank'],mask[6]))
+    warped_mask.append(rotation_estimation(joint['rkne'],joint['rank'],transform['rkne'],transform['rank'],mask[7]))
+    warped_mask.append(rotation_estimation(joint['lear'],joint['rear'],joint['reye'],transform['lear'],transform['rear'],transform['reye'],mask[8]))
+    warped_mask.append(rotation_estimation(joint['neck'],joint['pelv'],joint['rsho'],transform['neck'],transform['pelv'],transform['rsho'],mask[9]))
+
+    return warped_mask
+
+
+#Testing:
+#dt = time.time()
+
+#ji=np.array([3,-4,3])
+#jf=np.array([4.99,6.26,11.9])
+#j2i=np.array([-5,-7,-4])
+#j2f=np.array([3,-2,-6])
+#mask=np.array([[2.29,5.38,3.11,3],[0.07,2.43,3.92,2.5],[6.79,8.66,10,7.5]])
+#c=rotation_estimation(ji,jf,j2i,j2f,mask)
+#print(c)
+#df = time.time()
+
+#print('1 mask coordinate is generated in:',(df-dt)/4,'ms')