DeOldify/DeFadeVisualization.ipynb

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   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing\n",
    "import os\n",
    "from torch import autograd\n",
    "from fastai.transforms import TfmType\n",
    "from fasterai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fasterai.images import *\n",
    "from fasterai.dataset import *\n",
    "from fasterai.visualize import *\n",
    "from fasterai.callbacks import *\n",
    "from fasterai.loss import *\n",
    "from fasterai.modules import *\n",
    "from fasterai.training import *\n",
    "from fasterai.generators import *\n",
    "from fasterai.filters import *\n",
    "from fastai.torch_imports import *\n",
    "from pathlib import Path\n",
    "from itertools import repeat\n",
    "import tensorboardX\n",
    "torch.cuda.set_device(3)\n",
    "plt.style.use('dark_background')\n",
    "torch.backends.cudnn.benchmark=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGENET = Path('data/imagenet/ILSVRC/Data/CLS-LOC/train')\n",
    "defader_path = IMAGENET.parent/('defade_rc_gen_192.h5')\n",
    "\n",
    "#The higher the render_factor, the more GPU memory will be used and generally images will look better.  \n",
    "#11GB can take a factor of 42 max.  Performance generally gracefully degrades with lower factors, \n",
    "#though you may also find that certain images will actually render better at lower numbers.  \n",
    "#This tends to be the case with the oldest photos.\n",
    "render_factor=41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filters = [DeFader(gpu=3, weights_path=defader_path)]\n",
    "vis = ModelImageVisualizer(filters, render_factor=render_factor, results_dir='result_images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "vis.plot_transformed_image(\"test_images/FadedOvermiller.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.plot_transformed_image(\"test_images/FadedSphynx.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.plot_transformed_image(\"test_images/FadedRacket.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.plot_transformed_image(\"test_images/FadedDutchBabies.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.plot_transformed_image(\"test_images/FadedDelores.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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