notebooks/enhance_me_train.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/soumik12345/enhance-me/blob/mirnet/notebooks/enhance_me_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "!git clone https://github.com/soumik12345/enhance-me\n",
    "!pip install -qqq wandb streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G_c4VtXWHR5l"
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   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"./enhance-me\")\n",
    "\n",
    "from PIL import Image\n",
    "from enhance_me import commons\n",
    "from enhance_me.mirnet import MIRNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZpBHbYaMIqP_"
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   "outputs": [],
   "source": [
    "# @title MIRNet Train Configs\n",
    "\n",
    "experiment_name = \"lol_dataset_256\"  # @param {type:\"string\"}\n",
    "image_size = 128  # @param {type:\"integer\"}\n",
    "dataset_label = \"lol\"  # @param [\"lol\"]\n",
    "apply_random_horizontal_flip = True  # @param {type:\"boolean\"}\n",
    "apply_random_vertical_flip = True  # @param {type:\"boolean\"}\n",
    "apply_random_rotation = True  # @param {type:\"boolean\"}\n",
    "use_mixed_precision = True  # @param {type:\"boolean\"}\n",
    "wandb_api_key = \"\"  # @param {type:\"string\"}\n",
    "val_split = 0.1  # @param {type:\"slider\", min:0.1, max:1.0, step:0.1}\n",
    "batch_size = 4  # @param {type:\"integer\"}\n",
    "num_recursive_residual_groups = 3  # @param {type:\"slider\", min:1, max:5, step:1}\n",
    "num_multi_scale_residual_blocks = 2  # @param {type:\"slider\", min:1, max:5, step:1}\n",
    "learning_rate = 1e-4  # @param {type:\"number\"}\n",
    "epsilon = 1e-3  # @param {type:\"number\"}\n",
    "epochs = 50  # @param {type:\"slider\", min:10, max:100, step:5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "IVRoedqBIMuH",
    "outputId": "53ca5beb-871a-4ec3-b757-173e09a15331"
   },
   "outputs": [],
   "source": [
    "mirnet = MIRNet(\n",
    "    experiment_name=experiment_name,\n",
    "    wandb_api_key=None if wandb_api_key == \"\" else wandb_api_key,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O66Iwzx8IsGh",
    "outputId": "0b6f1683-65d1-4737-a32f-d36b331d2bc2"
   },
   "outputs": [],
   "source": [
    "mirnet.build_datasets(\n",
    "    image_size=image_size,\n",
    "    dataset_label=dataset_label,\n",
    "    apply_random_horizontal_flip=apply_random_horizontal_flip,\n",
    "    apply_random_vertical_flip=apply_random_vertical_flip,\n",
    "    apply_random_rotation=apply_random_rotation,\n",
    "    val_split=val_split,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tsfKrBCsL_Bb"
   },
   "outputs": [],
   "source": [
    "mirnet.build_model(\n",
    "    use_mixed_precision=use_mixed_precision,\n",
    "    num_recursive_residual_groups=num_recursive_residual_groups,\n",
    "    num_multi_scale_residual_blocks=num_multi_scale_residual_blocks,\n",
    "    learning_rate=learning_rate,\n",
    "    epsilon=epsilon,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y3L9wlpkNziL",
    "outputId": "5149f0e7-91f4-450f-c43a-1b6028692bbc"
   },
   "outputs": [],
   "source": [
    "history = mirnet.train(epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mirnet.load_weights(os.path.join(mirnet.experiment_name, \"weights.h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "daFKbgBkiyzc"
   },
   "outputs": [],
   "source": [
    "for index, low_image_file in enumerate(mirnet.test_low_images):\n",
    "    original_image = Image.open(low_image_file)\n",
    "    enhanced_image = mirnet.infer(original_image)\n",
    "    ground_truth = Image.open(mirnet.test_enhanced_images[index])\n",
    "    commons.plot_results(\n",
    "        [original_image, ground_truth, ground_truth],\n",
    "        [\"Original Image\", \"Ground Truth\", \"Enhanced Image\"],\n",
    "        (18, 18),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dO-IbNQHkB3R"
   },
   "outputs": [],
   "source": []
  }
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