model_tesing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torchvision.transforms as T\n",
    "import torchvision\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision.utils import make_grid\n",
    "from torchvision.utils import save_image\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'images/train/FAKE/'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/tr/c84cdmm15fv83pw08sylj9qh0000gn/T/ipykernel_28148/2848824531.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpath_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'images/train/FAKE/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mimg_names_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_fake\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'images/train/FAKE/'"
     ]
    }
   ],
   "source": [
    "# # THIS IS JUST FOR VIEWING IMAGES\n",
    "\n",
    "# image_size = 512\n",
    "\n",
    "# path_real = '200_images_brain/train/'\n",
    "# img_names_real = os.listdir(path_real)\n",
    "\n",
    "# path_fake = 'images/train/FAKE/'\n",
    "# img_names_fake = os.listdir(path_fake)\n",
    "\n",
    "# batch_size = 64\n",
    "\n",
    "\n",
    "# X_train_real = []\n",
    "# X_train_fake = []\n",
    "# for i in range(batch_size):\n",
    "#     name_real = img_names_real[i]\n",
    "#     img_name = path_real + name_real\n",
    "#     image = plt.imread(img_name)\n",
    "#     X_train_real.append(image)\n",
    "\n",
    "#     name_fake = img_names_fake[i]\n",
    "#     img_name = path_fake + name_fake\n",
    "#     image = plt.imread(img_name)\n",
    "#     X_train_fake.append(image)\n",
    "\n",
    "# X_train_real = np.array(X_train_real)\n",
    "# X_train_fake = np.array(X_train_fake)\n",
    "# print(f\"Shape of training data: {X_train_real.shape}\")\n",
    "# print(f\"Data type: {type(X_train_real)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actual Dataloader\n",
    "\n",
    "base_path = \"200_images_brain/\"\n",
    "batch_size = 64\n",
    "\n",
    "tranform_tensor = transform = T.Compose([\n",
    "    # you can add other transformations in this list\n",
    "    T.ToTensor(),\n",
    "    T.RandomHorizontalFlip(p=0.5),\n",
    "    T.RandomVerticalFlip(p=0.5),\n",
    "    T.RandomAdjustSharpness(sharpness_factor=1.2, p=0.3)\n",
    "])\n",
    "\n",
    "tranform_j_tensor = transform = T.Compose([\n",
    "    # you can add other transformations in this list\n",
    "    T.ToTensor(),\n",
    "    T.Resize((128, 128))\n",
    "])\n",
    "\n",
    "train_set = torchvision.datasets.ImageFolder(root=base_path + \"train/\", transform=tranform_j_tensor)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_set,\n",
    "                                           batch_size=batch_size,\n",
    "                                           shuffle=True)\n",
    "\n",
    "test_set = torchvision.datasets.ImageFolder(root=base_path + \"test/\", transform=tranform_j_tensor)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_set,\n",
    "                                           batch_size=batch_size,\n",
    "                                           shuffle=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10tensor(1.6084)\n",
      "Epoch 1/10tensor(1.5987)\n",
      "Epoch 1/10tensor(1.6002)\n",
      "Epoch 1/10tensor(1.6046)\n",
      "Epoch 1/10tensor(1.5897)\n",
      "Epoch 1/10tensor(1.5790)\n",
      "Epoch 1/10tensor(1.5982)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [01:44<15:39, 104.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10tensor(1.5719)\n",
      "Epoch 2/10tensor(1.5887)\n",
      "Epoch 2/10tensor(1.5893)\n",
      "Epoch 2/10tensor(1.5748)\n",
      "Epoch 2/10tensor(1.5622)\n",
      "Epoch 2/10tensor(1.5667)\n",
      "Epoch 2/10tensor(1.5803)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 2/10 [03:21<13:37, 102.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10tensor(1.5854)\n",
      "Epoch 3/10tensor(1.5568)\n",
      "Epoch 3/10tensor(1.5707)\n",
      "Epoch 3/10tensor(1.5859)\n",
      "Epoch 3/10tensor(1.5862)\n",
      "Epoch 3/10tensor(1.5600)\n",
      "Epoch 3/10tensor(1.5734)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 3/10 [09:41<21:39, 185.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10tensor(1.5756)\n",
      "Epoch 4/10tensor(1.5413)\n",
      "Epoch 4/10tensor(1.5756)\n",
      "Epoch 4/10tensor(1.5648)\n",
      "Epoch 4/10tensor(1.5883)\n",
      "Epoch 4/10tensor(1.5760)\n",
      "Epoch 4/10tensor(1.5735)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 4/10 [11:36<16:26, 164.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10tensor(1.5490)\n",
      "Epoch 5/10tensor(1.5839)\n",
      "Epoch 5/10tensor(1.5462)\n",
      "Epoch 5/10tensor(1.5570)\n",
      "Epoch 5/10tensor(1.5577)\n",
      "Epoch 5/10tensor(1.5799)\n",
      "Epoch 5/10tensor(1.5575)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 5/10 [13:21<12:12, 146.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10tensor(1.5727)\n",
      "Epoch 6/10tensor(1.5490)\n",
      "Epoch 6/10tensor(1.5330)\n",
      "Epoch 6/10tensor(1.5668)\n",
      "Epoch 6/10tensor(1.5671)\n",
      "Epoch 6/10tensor(1.5837)\n",
      "Epoch 6/10tensor(1.5697)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 6/10 [15:09<09:00, 135.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10tensor(1.5682)\n",
      "Epoch 7/10tensor(1.5549)\n",
      "Epoch 7/10tensor(1.5657)\n",
      "Epoch 7/10tensor(1.5439)\n",
      "Epoch 7/10tensor(1.5688)\n",
      "Epoch 7/10tensor(1.5517)\n",
      "Epoch 7/10tensor(1.5863)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 7/10 [16:55<06:18, 126.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10tensor(1.5317)\n",
      "Epoch 8/10tensor(1.5788)\n",
      "Epoch 8/10tensor(1.5756)\n",
      "Epoch 8/10tensor(1.5608)\n",
      "Epoch 8/10tensor(1.5705)\n",
      "Epoch 8/10tensor(1.5595)\n",
      "Epoch 8/10tensor(1.5429)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 8/10 [18:34<03:56, 118.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10tensor(1.5360)\n",
      "Epoch 9/10tensor(1.5409)\n",
      "Epoch 9/10tensor(1.5556)\n",
      "Epoch 9/10tensor(1.5691)\n",
      "Epoch 9/10tensor(1.5838)\n",
      "Epoch 9/10tensor(1.5425)\n",
      "Epoch 9/10tensor(1.5755)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 9/10 [20:19<01:54, 114.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10tensor(1.5466)\n",
      "Epoch 10/10tensor(1.5391)\n",
      "Epoch 10/10tensor(1.5438)\n",
      "Epoch 10/10tensor(1.5444)\n",
      "Epoch 10/10tensor(1.5515)\n",
      "Epoch 10/10tensor(1.5622)\n",
      "Epoch 10/10tensor(1.5486)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [21:59<00:00, 131.93s/it]\n"
     ]
    }
   ],
   "source": [
    "from model_2 import SegmentNet\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "\n",
    "# initialize parameters\n",
    "cnn = SegmentNet()\n",
    "cnn.train()\n",
    "iterations = 10\n",
    "learning_rate = 0.005\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n",
    "\n",
    "# Train the Model\n",
    "for epoch in tqdm(range(iterations)):\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        \n",
    "        # Forward + Backward + Optimize\n",
    "        optimizer.zero_grad()\n",
    "        outputs = cnn(images)\n",
    "        \n",
    "        labels = labels\n",
    "        loss = criterion(outputs,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if (i+1)%2 == 0:\n",
    "            print(\"Epoch \" + str(epoch+1) + \"/\" + str(iterations) + str(loss.data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/tr/c84cdmm15fv83pw08sylj9qh0000gn/T/ipykernel_92863/562898924.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;31m# Forward + Backward + Optimize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/ai-image-classifier/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     54\u001b[0m       \u001b[0mlogits\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mranging\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0mto\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \"\"\"\n\u001b[0;32m---> 56\u001b[0;31m     \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfake_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    437\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 439\u001b[0;31m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m    440\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# # NEW MODELS\n",
    "\n",
    "# from model import FakeNet\n",
    "# from tqdm import tqdm\n",
    "\n",
    "\n",
    "\n",
    "# # initialize parameters\n",
    "# cnn = FakeNet()\n",
    "# cnn.train()\n",
    "# iterations = 20\n",
    "# learning_rate = 0.001 \n",
    "\n",
    "# criterion = nn.BCELoss()\n",
    "# optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n",
    "\n",
    "# # Train the Model\n",
    "# for epoch in tqdm(range(iterations)):\n",
    "#     for i, (images, labels) in enumerate(train_loader):\n",
    "        \n",
    "#         # Forward + Backward + Optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         outputs = cnn(images)\n",
    "#         labels = labels.unsqueeze(1).float()\n",
    "#         loss = criterion(outputs,labels)\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "\n",
    "#         if (i+1)%31 == 0:\n",
    "#             print(\"Epoch \" + str(epoch+1) + \"/\" + str(iterations) + str(loss.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3])\n",
      "tensor([2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2,\n",
      "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])\n",
      "tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])\n",
      "tensor([2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2,\n",
      "        2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2])\n",
      "Test Accuracy of the model on test images: 22.000000%\n"
     ]
    }
   ],
   "source": [
    "cnn.eval()  # Change model to 'eval' mode (BN uses moving mean/var)\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "for images, labels in test_loader:\n",
    "    outputs = cnn(images)\n",
    "    predicted = torch.argmax(outputs.data, dim=1)\n",
    "    # print(predicted)\n",
    "    # print(labels)\n",
    "    total += labels.size(0)\n",
    "    print(labels)\n",
    "    print(predicted)\n",
    "    correct += (predicted.numpy().T == np.array(labels)).sum()\n",
    "    if total % 256 == 0:\n",
    "        print(correct)\n",
    "        print(total)\n",
    "print('Test Accuracy of the model on test images: %.6f%%' % (100.0*correct/total))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "low >= high",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/tr/c84cdmm15fv83pw08sylj9qh0000gn/T/ipykernel_28148/463914724.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mimg_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4894\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mimg_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10755\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mreal_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Real\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mimg_index\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10000\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"Fake\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpredicted_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_tl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg_index\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg_index\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mnumpy/random/mtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.randint\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnumpy/random/_bounded_integers.pyx\u001b[0m in \u001b[0;36mnumpy.random._bounded_integers._rand_int64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: low >= high"
     ]
    }
   ],
   "source": [
    "# img_index = np.random.randint(10000, len(test_set))\n",
    "# img_index = 4894\n",
    "# img_index = 10755\n",
    "# real_label = \"Real\" if img_index < 10000 else \"Fake\"\n",
    "# predicted_label = torch.round(cnn(list_tl[img_index // 64][0]))[img_index % 64].detach().numpy()[0]\n",
    "# predicted_label = \"Real\" if predicted_label < 0.5 else \"Fake\"\n",
    "\n",
    "# plt.imshow(test_imgs[img_index])\n",
    "# plt.title(\"Actual Label: \" + real_label + \"\\nPredicted Label: \"\n",
    "#           + predicted_label + \"\\nImage Number: \" + str(img_index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Image Number Fake: 9')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_index = np.random.randint(0, batch_size)\n",
    "img_2_index = np.random.randint(0, batch_size)\n",
    "\n",
    "\n",
    "figure, axis = plt.subplots(2, 2) \n",
    "\n",
    "axis[0, 0].imshow(X_train_real[img_index], interpolation='nearest')\n",
    "axis[0, 0].set_title(\"Image Number Real: \" + str(img_index)) \n",
    "  \n",
    "axis[0, 1].imshow(X_train_fake[img_index], interpolation='nearest')\n",
    "axis[0, 1].set_title(\"Image Number Fake: \" + str(img_index)) \n",
    "  \n",
    "axis[1, 0].imshow(X_train_real[img_2_index], interpolation='nearest')\n",
    "axis[1, 0].set_title(\"Image Number Real: \" + str(img_2_index)) \n",
    "  \n",
    "axis[1, 1].imshow(X_train_fake[img_2_index], interpolation='nearest')\n",
    "axis[1, 1].set_title(\"Image Number Fake: \" + str(img_2_index)) \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}