notebooks/enhance_me_train.ipynb

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        "<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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        "outputId": "4fac7fb6-787c-4a1b-f6ef-12ec48024619"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/soumik12345/enhance-me -b mirnet\n",
        "!pip install wandb streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G_c4VtXWHR5l"
      },
      "outputs": [],
      "source": [
        "import sys\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_128' #@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",
        "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": 124
        },
        "id": "IVRoedqBIMuH",
        "outputId": "388a806f-f41f-420c-9c03-01024decb2d3"
      },
      "outputs": [],
      "source": [
        "mirnet = MIRNet(\n",
        "    experiment_name=experiment_name,\n",
        "    image_size=image_size,\n",
        "    dataset_label=dataset_label,\n",
        "    val_split=val_split,\n",
        "    batch_size=batch_size,\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",
        "    wandb_api_key=None if wandb_api_key == '' else wandb_api_key\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tsfKrBCsL_Bb"
      },
      "outputs": [],
      "source": [
        "mirnet.build_model(\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": "65e7ba4d-1607-4c14-d5d7-e55c4641ad0a"
      },
      "outputs": [],
      "source": [
        "history = mirnet.train(epochs=epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "daFKbgBkiyzc",
        "outputId": "38c3fc7a-8cef-4332-8efe-35103c75f1a3"
      },
      "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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      "name": "enhance-me-train.ipynb",
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