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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"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>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1JryaVhtBHij",
"outputId": "97ee6a4a-2479-4124-e96a-f0a792bdec46"
},
"outputs": [],
"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"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"sys.path.append(\"..\")\n",
"\n",
"from PIL import Image\n",
"from enhance_me import commons\n",
"from enhance_me.mirnet import MIRNet\n",
"from enhance_me.zero_dce import ZeroDCE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZpBHbYaMIqP_"
},
"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, enhanced_image],\n",
" [\"Original Image\", \"Ground Truth\", \"Enhanced Image\"],\n",
" (18, 18),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dO-IbNQHkB3R"
},
"outputs": [],
"source": [
"# @title Zero-DCE Train Configs\n",
"\n",
"experiment_name = \"unpaired_low_light_256_resize\" # @param {type:\"string\"}\n",
"image_size = 256 # @param {type:\"integer\"}\n",
"dataset_label = \"unpaired\" # @param [\"lol\", \"unpaired\"]\n",
"use_mixed_precision = False # @param {type:\"boolean\"}\n",
"apply_resize = True # @param {type:\"boolean\"}\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 = 16 # @param {type:\"integer\"}\n",
"learning_rate = 1e-4 # @param {type:\"number\"}\n",
"epsilon = 1e-3 # @param {type:\"number\"}\n",
"epochs = 100 # @param {type:\"slider\", min:10, max:100, step:5}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"zero_dce = ZeroDCE(\n",
" experiment_name=experiment_name,\n",
" wandb_api_key=None if wandb_api_key == \"\" else wandb_api_key,\n",
" use_mixed_precision=use_mixed_precision\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"zero_dce.build_datasets(\n",
" image_size=image_size,\n",
" dataset_label=dataset_label,\n",
" apply_resize=apply_resize,\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": {
"scrolled": false
},
"outputs": [],
"source": [
"zero_dce.compile(learning_rate=learning_rate)\n",
"history = zero_dce.train(epochs=epochs)\n",
"zero_dce.save_weights(os.path.join(experiment_name, \"weights.h5\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for index, low_image_file in enumerate(zero_dce.test_low_images):\n",
" original_image = Image.open(low_image_file)\n",
" enhanced_image = zero_dce.infer(original_image)\n",
" commons.plot_results(\n",
" [original_image, enhanced_image],\n",
" [\"Original Image\", \"Enhanced Image\"],\n",
" (18, 18),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"authorship_tag": "ABX9TyN4LuJh6kWhbqxzA5s9sp7k",
"collapsed_sections": [],
"include_colab_link": true,
"machine_shape": "hm",
"name": "enhance-me-train.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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