StyleGAN_cam.ipynb

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   "source": [
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "\n",
    "# import importlib\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torchvision.models.resnet as resnet\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torch.backends import cudnn\n",
    "cudnn.benchmark = True\n",
    "\n",
    "from stylegan2.solver import Solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class ResNet(resnet.ResNet):\n",
    "#     def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "#         return torch.sigmoid(self._forward_impl(x))"
   ]
  },
  {
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   "execution_count": 7,
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   "source": [
    "batch_size = 16\n",
    "num_workers = 1\n",
    "\n",
    "# X_val = torch.from_numpy((np.concatenate([np.load('X0_val_clean_48.npy')[:, :, 8:40, 8:40], \n",
    "#                                           np.load('X1_val_clean_48.npy')[:, :, 8:40, 8:40]]) - .5) / .5)\n",
    "# y_val = torch.from_numpy(np.concatenate([np.load('y0_val_clean_48.npy'), np.load('y1_val_clean_48.npy')]))\n",
    "\n",
    "# valloader = DataLoader(TensorDataset(X_val, y_val), batch_size=batch_size, shuffle=True, num_workers=1, pin_memory=True, drop_last=True)\n",
    "\n",
    "# Clf = ResNet(resnet.BasicBlock, [2, 2, 2, 2], num_classes=1)\n",
    "# Clf.load_state_dict(torch.load('results/clf_resnet18_32/best_model.pth'));\n",
    "\n",
    "X_train = (np.concatenate([np.load('X0_train_clean_48.npy'), np.load('X1_train_clean_48.npy')]) - .5) / .5\n",
    "\n",
    "trainloader = DataLoader(TensorDataset(torch.from_numpy(X_train[:, :, 8:40, 8:40])), batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True, drop_last=True)\n",
    "X_train = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = Solver('StyleGAN_cam48', '0', 32)"
   ]
  },
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       "Find user logs for this run at: <code>/mnt/3TB/Projects/pathology_gan/wandb/run-20210311_224623-1hxyousp/logs/debug.log</code>"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">gallant-sunset-1</strong>: <a href=\"https://wandb.ai/array/StyleGAN_cam48/runs/1hxyousp\" target=\"_blank\">https://wandb.ai/array/StyleGAN_cam48/runs/1hxyousp</a><br/>\n",
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.22 is available!  To upgrade, please run:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
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       "\n",
       "                Tracking run with wandb version 0.10.13<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">clear-donkey-2</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/array/StyleGAN_cam48\" target=\"_blank\">https://wandb.ai/array/StyleGAN_cam48</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/array/StyleGAN_cam48/runs/dd7qj4od\" target=\"_blank\">https://wandb.ai/array/StyleGAN_cam48/runs/dd7qj4od</a><br/>\n",
       "                Run data is saved locally in <code>/mnt/3TB/Projects/pathology_gan/wandb/run-20210311_225410-dd7qj4od</code><br/><br/>\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling run.save without any arguments is deprecated.Changes to attributes are automatically persisted.\n"
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   "source": [
    "solver.train(trainloader, batch_size)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver.save_model()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import math\n",
    "int(math.log(32, 2))"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 4, 3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "list(range(5, 2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 8, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**4, 2**3, 2**2"
   ]
  }
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