{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Move File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Used to copy random 5k image from 10k image source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 5000\n",
    "source = 'mnist_images_fid'\n",
    "destination = 'mnist_images_is'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "folders_root_dir = os.listdir(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = np.random.choice(a=len(folders_root_dir),\n",
    "                        size=num,\n",
    "                        replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in index:\n",
    "    file = folders_root_dir[i]\n",
    "    shutil.copyfile(os.path.join(source, file), os.path.join(destination, file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inception Score (5k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inception_score_mean: 2.142887\n",
      "inception_score_std: 0.04756409\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['logits_unbiased']\n",
      "Extracting features from input1\n",
      "Looking for samples non-recursivelty in \"resnet/sample_is\" with extensions png,jpg,jpeg\n",
      "Found 5000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/5000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "Processing samples:  88%|████████▊ | 4416/5000 [00:36<00:01, 392.23samples/s]\n",
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      "Processing samples:  92%|█████████▏| 4608/5000 [00:37<00:01, 390.35samples/s]\n",
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      "Processing samples: 100%|█████████▉| 4992/5000 [00:38<00:00, 396.84samples/s]\n",
      "                                                                             \n",
      "Processing samples\n",
      "Inception Score: 2.142887028328697 ± 0.04756408949604466\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --isc --input1 resnet/sample_is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inception_score_mean: 2.814816\n",
      "inception_score_std: 0.03961252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['logits_unbiased']\n",
      "Extracting features from input1\n",
      "Looking for samples non-recursivelty in \"resnet_wo_t/sample_is\" with extensions png,jpg,jpeg\n",
      "Found 5000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/5000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "Processing samples:  93%|█████████▎| 4672/5000 [00:21<00:00, 430.88samples/s]\n",
      "Processing samples:  95%|█████████▍| 4736/5000 [00:21<00:00, 431.69samples/s]\n",
      "Processing samples:  96%|█████████▌| 4800/5000 [00:21<00:00, 429.63samples/s]\n",
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      "Processing samples: 100%|█████████▉| 4992/5000 [00:21<00:00, 424.95samples/s]\n",
      "                                                                             \n",
      "Processing samples\n",
      "Inception Score: 2.8148155834096182 ± 0.03961251861943447\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --isc --input1 resnet_wo_t/sample_is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inception_score_mean: 2.108094\n",
      "inception_score_std: 0.03917094\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['logits_unbiased']\n",
      "Extracting features from input1\n",
      "Looking for samples non-recursivelty in \"unet/sample_is\" with extensions png,jpg,jpeg\n",
      "Found 5000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/5000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "Processing samples: 100%|█████████▉| 4992/5000 [00:22<00:00, 434.61samples/s]\n",
      "                                                                             \n",
      "Processing samples\n",
      "Inception Score: 2.1080944824344976 ± 0.03917093830568066\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --isc --input1 unet/sample_is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inception_score_mean: 2.882274\n",
      "inception_score_std: 0.04135839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['logits_unbiased']\n",
      "Extracting features from input1\n",
      "Looking for samples non-recursivelty in \"unet_wo_t/sample_is\" with extensions png,jpg,jpeg\n",
      "Found 5000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/5000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "                                                                             \n",
      "Processing samples\n",
      "Inception Score: 2.8822742883553216 ± 0.04135839168401501\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --isc --input1 unet_wo_t/sample_is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inception Score:\n",
    "* Res-Net: 2.143 ± 0.0476\n",
    "* Res-Net without $t$: 2.815 ± 0.04\n",
    "* U-Net: 2.108 ± 0.0392\n",
    "* U-Net without $t$: 2.882 ± 0.0414"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FID (10k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frechet_inception_distance: 6.429977\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['2048']\n",
      "Extracting statistics from input 1\n",
      "Looking for samples non-recursivelty in \"resnet/sample_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Extracting statistics from input 2\n",
      "Looking for samples non-recursivelty in \"mnist_images_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Frechet Inception Distance: 6.429976923460202\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --fid --input1 resnet/sample_fid --input2 mnist_images_fid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frechet_inception_distance: 99.56072\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['2048']\n",
      "Extracting statistics from input 1\n",
      "Looking for samples non-recursivelty in \"resnet_wo_t/sample_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Extracting statistics from input 2\n",
      "Looking for samples non-recursivelty in \"mnist_images_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Frechet Inception Distance: 99.56071685176408\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --fid --input1 resnet_wo_t/sample_fid --input2 mnist_images_fid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frechet_inception_distance: 8.795097\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['2048']\n",
      "Extracting statistics from input 1\n",
      "Looking for samples non-recursivelty in \"unet/sample_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Extracting statistics from input 2\n",
      "Looking for samples non-recursivelty in \"mnist_images_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Frechet Inception Distance: 8.795096903230046\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --fid --input1 unet/sample_fid --input2 mnist_images_fid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frechet_inception_distance: 107.4555\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating feature extractor \"inception-v3-compat\" with features ['2048']\n",
      "Extracting statistics from input 1\n",
      "Looking for samples non-recursivelty in \"unet_wo_t/sample_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Extracting statistics from input 2\n",
      "Looking for samples non-recursivelty in \"mnist_images_fid\" with extensions png,jpg,jpeg\n",
      "Found 10000 samples\n",
      "\n",
      "Processing samples:   0%|          | 0/10000 [00:00<?, ?samples/s]D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
      "D:\\Anaconda\\lib\\site-packages\\torch_fidelity\\datasets.py:16: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)\n",
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      "                                                                              \n",
      "Processing samples\n",
      "Frechet Inception Distance: 107.45548609810362\n"
     ]
    }
   ],
   "source": [
    "!fidelity --gpu 0 --fid --input1 unet_wo_t/sample_fid --input2 mnist_images_fid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frechet Inception Distance:\n",
    "* Res-Net: 6.43\n",
    "* Res-Net without $t$: 99.561\n",
    "* U-Net: 8.795\n",
    "* U-Net without $t$: 107.455"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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