testing.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bunch of imports\n",
    "# System path should be changed to where the git repo is.\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import models as models\n",
    "import lattice_filter_op_loader\n",
    "\n",
    "custom_module = lattice_filter_op_loader.module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set GPU and batch size\n",
    "# Can set a different batch size, or skip if CPU\n",
    "strategy = models.set_GPU()\n",
    "BATCH_SIZE_PER_REPLICA = 1\n",
    "BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some information needed\n",
    "\n",
    "# despite the name input_dir can be a single file\n",
    "# be sure to give a output_dir as a file_name too\n",
    "# if input_dir is a single file\n",
    "input_dir = ''\n",
    "output_dir = ''\n",
    "model_type = 'evac_plus'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained model\n",
    "model = models.load_model(version='trained', model_type=model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the model\n",
    "# The outputs will be saved in the given output directory\n",
    "models.test_model(model, input_dir, output_dir, batch_size=1)"
   ]
  }
 ],
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