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checkpoints/checkpoint

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+model_checkpoint_path: "ckpt-34"
+all_model_checkpoint_paths: "ckpt-34"
+all_model_checkpoint_timestamps: 1770885364.6021488
+last_preserved_timestamp: 1770885364.6021488

checkpoints/ckpt-34.data-00000-of-00001

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef70f3bd501ffb9f7c237d49042ca432ac074beecbd4f6853ba23b4e70fb06df
+size 207904461

visible-to-thermal-f7984f (1).ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3df26cb5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:58:41.908567Z",
+     "iopub.status.busy": "2026-02-12T21:58:41.908310Z",
+     "iopub.status.idle": "2026-02-12T21:59:00.902318Z",
+     "shell.execute_reply": "2026-02-12T21:59:00.901653Z"
+    },
+    "papermill": {
+     "duration": 19.000924,
+     "end_time": "2026-02-12T21:59:00.903787",
+     "exception": false,
+     "start_time": "2026-02-12T21:58:41.902863",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-12 21:58:43.423256: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1770933523.610196      20 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1770933523.667872      20 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "I0000 00:00:1770933537.686778      20 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15511 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, Model, Input\n",
+    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, LeakyReLU, Dropout, Concatenate\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.applications import VGG19\n",
+    "\n",
+    "# -------------------- Settings --------------------\n",
+    "IMG_SIZE = 256\n",
+    "BATCH_SIZE = 1\n",
+    "EPOCHS = 190\n",
+    "BASE_DIR = \"/kaggle/input/llvip-dataset/LLVIP\"  # change if needed\n",
+    "CHECKPOINT_DIR = \"checkpoints\"\n",
+    "OUTPUT_DIR = \"outputs\"\n",
+    "LOG_INTERVAL = 100\n",
+    "SAVE_INTERVAL_EPOCHS = 10\n",
+    "\n",
+    "# Discriminator update steps per generator step (1 or 2)\n",
+    "D_steps_per_G = 1\n",
+    "\n",
+    "# -------------------- Dataset Helpers --------------------\n",
+    "def load_image_pair(visible_path, ir_path, image_size=(IMG_SIZE, IMG_SIZE)):\n",
+    "    vis = tf.io.read_file(visible_path)\n",
+    "    vis = tf.image.decode_png(vis, channels=3)\n",
+    "    vis = tf.image.resize(vis, image_size)\n",
+    "    vis = tf.cast(vis, tf.float32) / 127.5 - 1.0  # [-1,1]\n",
+    "\n",
+    "    ir = tf.io.read_file(ir_path)\n",
+    "    ir = tf.image.decode_png(ir, channels=3)\n",
+    "    ir = tf.image.resize(ir, image_size)\n",
+    "    ir = tf.cast(ir, tf.float32) / 127.5 - 1.0\n",
+    "\n",
+    "    return vis, ir\n",
+    "def augment_image(vis, ir):\n",
+    "    vis = tf.image.random_contrast(vis, lower=0.8, upper=1.2)\n",
+    "    vis = tf.image.random_brightness(vis, max_delta=0.1)\n",
+    "    ir = tf.image.random_contrast(ir, lower=0.8, upper=1.2)\n",
+    "    return vis, ir\n",
+    "\n",
+    "def make_dataset(\n",
+    "    visible_dir,\n",
+    "    ir_dir,\n",
+    "    image_size=256,\n",
+    "    batch_size=1,\n",
+    "    shuffle=False,\n",
+    "    start=None,\n",
+    "    limit=None   # NEW\n",
+    "):\n",
+    "    visible_files = sorted(glob.glob(os.path.join(visible_dir, \"*\")))\n",
+    "    ir_files = sorted(glob.glob(os.path.join(ir_dir, \"*\")))\n",
+    "\n",
+    "    print(f\"Found {len(visible_files)} visible images and {len(ir_files)} IR images\")\n",
+    "\n",
+    "    if len(visible_files) == 0 or len(ir_files) == 0:\n",
+    "        raise ValueError(\"❌ No images found! Check paths.\")\n",
+    "\n",
+    "    # ---- LIMIT DATASET SIZE ----\n",
+    "    if limit is not None:\n",
+    "        visible_files = visible_files[start:limit]\n",
+    "        ir_files = ir_files[start:limit]\n",
+    "        print(f\"Using only {limit} image pairs\")\n",
+    "\n",
+    "    dataset = tf.data.Dataset.from_tensor_slices((visible_files, ir_files))\n",
+    "\n",
+    "    if shuffle and len(visible_files) > 1:\n",
+    "        dataset = dataset.shuffle(buffer_size=len(visible_files))\n",
+    "\n",
+    "    dataset = dataset.map(\n",
+    "        lambda v, i: load_image_pair(v, i, image_size),\n",
+    "        num_parallel_calls=tf.data.AUTOTUNE\n",
+    "    )\n",
+    "\n",
+    "    dataset = dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)\n",
+    "    dataset = dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
+    "\n",
+    "    return dataset\n",
+    "\n",
+    "\n",
+    "def get_train_dataset(base_dir=BASE_DIR):\n",
+    "    train_visible = os.path.join(base_dir, \"visible/train\")\n",
+    "    train_ir = os.path.join(base_dir, \"infrared/train\")\n",
+    "\n",
+    "    return make_dataset(\n",
+    "        train_visible,\n",
+    "        train_ir,\n",
+    "        (IMG_SIZE, IMG_SIZE),\n",
+    "        batch_size=BATCH_SIZE,\n",
+    "        start=0,\n",
+    "        limit=12025   # 🔥 10K TRAIN\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def get_val_dataset(base_dir=BASE_DIR):\n",
+    "    val_visible = os.path.join(base_dir, \"visible/train\")\n",
+    "    val_ir = os.path.join(base_dir, \"infrared/train\")\n",
+    "\n",
+    "    return make_dataset(\n",
+    "        val_visible,\n",
+    "        val_ir,\n",
+    "        (IMG_SIZE, IMG_SIZE),\n",
+    "        batch_size=1,\n",
+    "        shuffle=True,\n",
+    "        start=10000,\n",
+    "        limit=12025   # 🔥 2.5K TEST\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# -------------------- Instance Normalization (per-channel) --------------------\n",
+    "class InstanceNormalization(layers.Layer):\n",
+    "    def __init__(self, epsilon=1e-5, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.epsilon = epsilon\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        channels = int(input_shape[-1])\n",
+    "        self.gamma = self.add_weight(name='gamma', shape=(1,1,1,channels), initializer='ones', trainable=True)\n",
+    "        self.beta = self.add_weight(name='beta', shape=(1,1,1,channels), initializer='zeros', trainable=True)\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        mean, var = tf.nn.moments(inputs, axes=[1,2], keepdims=True)\n",
+    "        normalized = (inputs - mean) / tf.sqrt(var + self.epsilon)\n",
+    "        return self.gamma * normalized + self.beta\n",
+    "\n",
+    "from tensorflow.keras.layers import Layer\n",
+    "\n",
+    "class AddNoise(Layer):\n",
+    "    def __init__(self, stddev=0.02, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.stddev = stddev\n",
+    "\n",
+    "    def call(self, inputs, training=None):\n",
+    "        # you might choose to only add noise during training, or always\n",
+    "        noise = tf.random.normal(tf.shape(inputs), mean=0.0, stddev=self.stddev)\n",
+    "        return inputs + noise\n",
+    "\n",
+    "    def get_config(self):\n",
+    "        config = super().get_config()\n",
+    "        config.update({\"stddev\": self.stddev})\n",
+    "        return config\n",
+    "\n",
+    "\n",
+    "def build_generator_attention(img_size=256):\n",
+    "    inputs = Input(shape=(img_size, img_size, 3))\n",
+    "    x = AddNoise(stddev=0.02)(inputs)\n",
+    "\n",
+    "    e1 = Conv2D(64, 4, strides=2, padding='same')(x)\n",
+    "    e1 = LeakyReLU(0.2)(e1)\n",
+    "\n",
+    "    e2 = Conv2D(128, 4, strides=2, padding='same')(e1)\n",
+    "    e2 = LeakyReLU(0.2)(e2)\n",
+    "\n",
+    "    e3 = Conv2D(256, 4, strides=2, padding='same')(e2)\n",
+    "    e3 = LeakyReLU(0.2)(e3)\n",
+    "\n",
+    "    e4 = Conv2D(512, 4, strides=2, padding='same')(e3)\n",
+    "    e4 = LeakyReLU(0.2)(e4)\n",
+    "\n",
+    "    # Bottleneck + Attention block (optional)\n",
+    "    b = Conv2D(512, 4, strides=2, padding='same')(e4)\n",
+    "    b = LeakyReLU(0.2)(b)\n",
+    "\n",
+    "    # Decoder\n",
+    "    d1 = Conv2DTranspose(512, 4, strides=2, padding='same')(b)\n",
+    "    d1 = tf.keras.layers.ReLU()(d1)\n",
+    "    d1 = Concatenate()([d1, e4])\n",
+    "\n",
+    "    d2 = Conv2DTranspose(256, 4, strides=2, padding='same')(d1)\n",
+    "    d2 = tf.keras.layers.ReLU()(d2)\n",
+    "    d2 = Concatenate()([d2, e3])\n",
+    "\n",
+    "    d3 = Conv2DTranspose(128, 4, strides=2, padding='same')(d2)\n",
+    "    d3 = tf.keras.layers.ReLU()(d3)\n",
+    "    d3 = Concatenate()([d3, e2])\n",
+    "\n",
+    "    d4 = Conv2DTranspose(64, 4, strides=2, padding='same')(d3)\n",
+    "    d4 = tf.keras.layers.ReLU()(d4)\n",
+    "    d4 = Concatenate()([d4, e1])\n",
+    "\n",
+    "    output = Conv2DTranspose(3, 4, strides=2, padding='same', activation='tanh')(d4)\n",
+    "\n",
+    "    return Model(inputs, output, name='Generator')\n",
+    "\n",
+    "\n",
+    "# -------------------- Stronger PatchGAN Discriminator --------------------\n",
+    "def build_patch_discriminator(img_size=IMG_SIZE):\n",
+    "    inp = Input(shape=(img_size, img_size, 3))\n",
+    "    tar = Input(shape=(img_size, img_size, 3))\n",
+    "    x = Concatenate()([inp, tar])  # condition on input\n",
+    "\n",
+    "    x = Conv2D(64, 4, strides=2, padding='same')(x)\n",
+    "    x = LeakyReLU(0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(128, 4, strides=2, padding='same', use_bias=False)(x)\n",
+    "    x = InstanceNormalization()(x)\n",
+    "    x = LeakyReLU(0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(256, 4, strides=2, padding='same', use_bias=False)(x)\n",
+    "    x = InstanceNormalization()(x)\n",
+    "    x = LeakyReLU(0.2)(x)\n",
+    "\n",
+    "    out = Conv2D(1, 4, strides=1, padding='same')(x)\n",
+    "    return Model([inp, tar], out, name='PatchDiscriminator')\n",
+    "\n",
+    "\n",
+    "# -------------------- Losses & VGG perceptual --------------------\n",
+    "\n",
+    "\n",
+    "# -------------------- Perceptual model setup --------------------\n",
+    "from tensorflow.keras.applications.vgg19 import VGG19, preprocess_input\n",
+    "from tensorflow.keras import Model\n",
+    "\n",
+    "from tensorflow.keras.applications import VGG19\n",
+    "from tensorflow.keras.models import Model\n",
+    "\n",
+    "# Initialize VGG19 without top layers and no weights\n",
+    "vgg_model = VGG19(include_top=False, weights=None, input_shape=(256, 256, 3))\n",
+    "\n",
+    "# Load the manually downloaded weights\n",
+    "weights_path = '/kaggle/input/models/saisumathappala/vgg19-base-model/tensorflow2/default/1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
+    "vgg_model.load_weights(weights_path)\n",
+    "\n",
+    "# Freeze all layers\n",
+    "vgg_model.trainable = False\n",
+    "\n",
+    "# Use block4_conv2 output for perceptual loss\n",
+    "perceptual_model = Model(inputs=vgg_model.input,\n",
+    "                         outputs=vgg_model.get_layer('block4_conv2').output)\n",
+    "# from tensorflow.keras.applications import VGG19\n",
+    "# from tensorflow.keras.models import Model\n",
+    "# from tensorflow.keras.applications.vgg19 import preprocess_input\n",
+    "\n",
+    "# base_model = tf.keras.applications.VGG19(\n",
+    "#     weights='imagenet',\n",
+    "#     input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
+    "#     include_top=False)\n",
+    "\n",
+    "# base_model.trainable = False\n",
+    "\n",
+    "# Perceptual layer\n",
+    "\n",
+    "\n",
+    "bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+    "def discriminator_loss_fn(real_logits, fake_logits):\n",
+    "    real_loss = bce(tf.ones_like(real_logits), real_logits)\n",
+    "    fake_loss = bce(tf.zeros_like(fake_logits), fake_logits)\n",
+    "    total_loss = (real_loss + fake_loss) * 0.5\n",
+    "    return total_loss\n",
+    "\n",
+    "# ---------------- GAN loss ----------------\n",
+    "def generator_adv_loss(d_outs):\n",
+    "    loss = 0.0\n",
+    "    for out in d_outs:\n",
+    "        loss += bce(tf.ones_like(out), out)\n",
+    "    return loss / len(d_outs)\n",
+    "\n",
+    "\n",
+    "# ---------------- Weighted L1 ----------------\n",
+    "def weighted_l1_loss(target, gen):\n",
+    "    t = (target + 1.0) / 2.0\n",
+    "    g = (gen + 1.0) / 2.0\n",
+    "    weight = tf.clip_by_value(t ** 2 * 4.0, 1.0, 4.0)\n",
+    "    return tf.reduce_mean(weight * tf.abs(t - g))\n",
+    "\n",
+    "\n",
+    "# ---------------- Perceptual ----------------\n",
+    "from tensorflow.keras.applications.vgg19 import preprocess_input\n",
+    "\n",
+    "def perceptual_loss(target, gen):\n",
+    "    target_rgb = preprocess_input((target + 1.0) * 127.5)\n",
+    "    gen_rgb = preprocess_input((gen + 1.0) * 127.5)\n",
+    "\n",
+    "    f_t = perceptual_model(target_rgb)\n",
+    "    f_g = perceptual_model(gen_rgb)\n",
+    "\n",
+    "    return tf.reduce_mean(tf.abs(f_t - f_g))\n",
+    "\n",
+    "def generator_total_loss(d_out, gen_out, target):\n",
+    "\n",
+    "    adv  = generator_adv_loss([d_out])   # wrap in list\n",
+    "    wl1  = weighted_l1_loss(target, gen_out)\n",
+    "    perc = perceptual_loss(target, gen_out)\n",
+    "\n",
+    "    # Recommended weights for LLVIP\n",
+    "    total_loss = (\n",
+    "        1.0   * adv +     # realism\n",
+    "        100.0 * wl1 +     # alignment\n",
+    "        10.0  * perc      # structure\n",
+    "    )\n",
+    "\n",
+    "    return total_loss, adv, wl1, perc\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "generator = build_generator_attention(IMG_SIZE)\n",
+    "D_full = build_patch_discriminator(IMG_SIZE)\n",
+    "\n",
+    "# TTUR: different LRs for G and D\n",
+    "generator_optimizer = Adam(2e-4, beta_1=0.5)\n",
+    "d_optimizer = Adam(5e-5, beta_1=0.5)\n",
+    "\n",
+    "# Checkpoints\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# -------------------- Checkpoint Paths --------------------\n",
+    "\n",
+    "# 1️⃣ Pretrained checkpoint (read-only Kaggle input)\n",
+    "PRETRAINED_DIR = \"/kaggle/input/models/saisumathappala/image-to-ir-gan/tensorflow2/default/2\"\n",
+    "\n",
+    "# 2️⃣ Training checkpoint (writeable)\n",
+    "CHECKPOINT_DIR = \"checkpoints\"\n",
+    "\n",
+    "os.makedirs(CHECKPOINT_DIR, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "# -------------------- Checkpoint Objects --------------------\n",
+    "ckpt = tf.train.Checkpoint(\n",
+    "    generator=generator,\n",
+    "    D_full=D_full,\n",
+    "    generator_optimizer=generator_optimizer,\n",
+    "    d_optimizer=d_optimizer\n",
+    ")\n",
+    "\n",
+    "# Manager for TRAINING checkpoints\n",
+    "ckpt_manager = tf.train.CheckpointManager(\n",
+    "    ckpt,\n",
+    "    CHECKPOINT_DIR,\n",
+    "    max_to_keep=5\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0c5c2773",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:00.912705Z",
+     "iopub.status.busy": "2026-02-12T21:59:00.911688Z",
+     "iopub.status.idle": "2026-02-12T21:59:01.885299Z",
+     "shell.execute_reply": "2026-02-12T21:59:01.884435Z"
+    },
+    "papermill": {
+     "duration": 0.979,
+     "end_time": "2026-02-12T21:59:01.886677",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:00.907677",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No training checkpoint found. Loading pretrained weights...\n",
+      "✅ Restored pretrained weights from: /kaggle/input/models/saisumathappala/image-to-ir-gan/tensorflow2/default/2/ckpt-34\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -------------------- Restore Logic --------------------\n",
+    "\n",
+    "if ckpt_manager.latest_checkpoint:\n",
+    "    # Case 1 → Resume training normally\n",
+    "    ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n",
+    "    print(f\"✅ Resumed from training checkpoint: {ckpt_manager.latest_checkpoint}\")\n",
+    "\n",
+    "else:\n",
+    "    # Case 2 → First run → Load pretrained weights ONCE\n",
+    "    print(\"No training checkpoint found. Loading pretrained weights...\")\n",
+    "\n",
+    "    pretrained_ckpt = tf.train.latest_checkpoint(PRETRAINED_DIR)\n",
+    "\n",
+    "    if pretrained_ckpt:\n",
+    "        ckpt.restore(pretrained_ckpt).expect_partial()\n",
+    "        print(f\"✅ Restored pretrained weights from: {pretrained_ckpt}\")\n",
+    "    else:\n",
+    "        print(\"⚠️ No pretrained checkpoint found. Training from scratch.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "358eb256",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:01.895018Z",
+     "iopub.status.busy": "2026-02-12T21:59:01.894351Z",
+     "iopub.status.idle": "2026-02-12T21:59:01.900980Z",
+     "shell.execute_reply": "2026-02-12T21:59:01.900207Z"
+    },
+    "papermill": {
+     "duration": 0.011908,
+     "end_time": "2026-02-12T21:59:01.902226",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:01.890318",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def d_train_step(input_vis, target_ir, gen_out):\n",
+    "\n",
+    "    with tf.GradientTape() as tape:\n",
+    "\n",
+    "        d_real = D_full([input_vis, target_ir], training=True)\n",
+    "        d_fake = D_full([input_vis, gen_out], training=True)\n",
+    "\n",
+    "        d_loss = discriminator_loss_fn(d_real, d_fake)\n",
+    "\n",
+    "    d_vars  = D_full.trainable_variables\n",
+    "    d_grads = tape.gradient(d_loss, d_vars)\n",
+    "\n",
+    "    d_optimizer.apply_gradients(zip(d_grads, d_vars))\n",
+    "\n",
+    "    return d_loss\n",
+    "\n",
+    "# -------------------- Generator Train Step --------------------\n",
+    "@tf.function\n",
+    "def g_train_step(input_vis, target_ir):\n",
+    "\n",
+    "    with tf.GradientTape() as tape:\n",
+    "\n",
+    "        gen_out = generator(input_vis, training=True)\n",
+    "\n",
+    "        d_fake = D_full([input_vis, gen_out], training=True)\n",
+    "\n",
+    "        total_g_loss, adv_loss, wl1_loss, perc_loss = \\\n",
+    "            generator_total_loss(d_fake, gen_out, target_ir)\n",
+    "\n",
+    "    g_vars  = generator.trainable_variables\n",
+    "    g_grads = tape.gradient(total_g_loss, g_vars)\n",
+    "\n",
+    "    generator_optimizer.apply_gradients(zip(g_grads, g_vars))\n",
+    "\n",
+    "    return total_g_loss, adv_loss, wl1_loss, perc_loss, gen_out\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f2e569db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:01.910320Z",
+     "iopub.status.busy": "2026-02-12T21:59:01.909856Z",
+     "iopub.status.idle": "2026-02-12T21:59:01.916085Z",
+     "shell.execute_reply": "2026-02-12T21:59:01.915351Z"
+    },
+    "papermill": {
+     "duration": 0.011612,
+     "end_time": "2026-02-12T21:59:01.917295",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:01.905683",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def train(train_ds, val_ds=None, epochs=EPOCHS):\n",
+    "\n",
+    "    global_step = 0\n",
+    "\n",
+    "    for epoch in range(1, epochs + 1):\n",
+    "\n",
+    "        start = time.time()\n",
+    "        print(f\"\\nEpoch {epoch}/{epochs}\")\n",
+    "\n",
+    "        for batch, (inp, tar) in enumerate(train_ds):\n",
+    "\n",
+    "            # --------------------\n",
+    "            # Generator forward\n",
+    "            # --------------------\n",
+    "            gen_out = generator(inp, training=True)\n",
+    "\n",
+    "            # --------------------\n",
+    "            # Train Discriminator\n",
+    "            # --------------------\n",
+    "            for _ in range(D_steps_per_G):\n",
+    "                d_loss_val = d_train_step(inp, tar, gen_out)\n",
+    "\n",
+    "            # --------------------\n",
+    "            # Train Generator\n",
+    "            # --------------------\n",
+    "            g_total, g_adv, g_l1, g_perc, gen_out = \\\n",
+    "                g_train_step(inp, tar)\n",
+    "\n",
+    "            global_step += 1\n",
+    "\n",
+    "            # Step logging\n",
+    "            if batch % 50 == 0:\n",
+    "                print(\n",
+    "                    f\"Step {global_step}: \"\n",
+    "                    f\"D={d_loss_val:.4f}, \"\n",
+    "                    f\"G={g_total:.4f}, \"\n",
+    "                    f\"Adv={g_adv:.4f}, \"\n",
+    "                    f\"L1={g_l1:.4f}, \"\n",
+    "                    f\"Perc={g_perc:.4f}\"\n",
+    "                )\n",
+    "\n",
+    "        # --------------------\n",
+    "        # Visualization (TEST SET)\n",
+    "        # --------------------\n",
+    "        if val_ds is not None:\n",
+    "            print(\"🖼 Generating validation samples...\")\n",
+    "            generate_and_save_images(generator, val_ds, epoch)\n",
+    "\n",
+    "        # --------------------\n",
+    "        # Checkpoint\n",
+    "        # --------------------\n",
+    "        if epoch % SAVE_INTERVAL_EPOCHS == 0:\n",
+    "            path = ckpt_manager.save()\n",
+    "            print(f\" Saved checkpoint: {path}\")\n",
+    "\n",
+    "        print(f\" Epoch {epoch} finished in {time.time()-start:.1f}s\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bb729cec",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:01.924705Z",
+     "iopub.status.busy": "2026-02-12T21:59:01.924476Z",
+     "iopub.status.idle": "2026-02-12T21:59:01.931295Z",
+     "shell.execute_reply": "2026-02-12T21:59:01.930717Z"
+    },
+    "papermill": {
+     "duration": 0.011607,
+     "end_time": "2026-02-12T21:59:01.932316",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:01.920709",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# -------------------- Visualization Utilities --------------------\n",
+    "def to_uint8(x):\n",
+    "    \"\"\"Convert tensor from [-1,1] → uint8 [0,255].\"\"\"\n",
+    "    x = (x + 1.0) * 127.5\n",
+    "    x = tf.clip_by_value(x, 0, 255)\n",
+    "    return tf.cast(x, tf.uint8)\n",
+    "\n",
+    "def generate_and_save_images(model, val_ds, epoch, out_dir=OUTPUT_DIR, num_rows=5):\n",
+    "    \"\"\"\n",
+    "    Creates a 5-row image where each row = [Visible | Real IR | Generated IR].\n",
+    "    \"\"\"\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "    # Collect up to num_rows samples\n",
+    "    rows = []\n",
+    "    for i, (v_inp, v_tar) in enumerate(val_ds.take(num_rows)):\n",
+    "        pred = model(v_inp, training=False)\n",
+    "\n",
+    "        vis = to_uint8(v_inp[0])\n",
+    "        targ = to_uint8(v_tar[0])\n",
+    "        gen = to_uint8(pred[0])\n",
+    "\n",
+    "        # Ensure same height\n",
+    "        h = min(vis.shape[0], targ.shape[0], gen.shape[0])\n",
+    "        w = min(vis.shape[1], targ.shape[1], gen.shape[1])\n",
+    "        vis, targ, gen = vis[:h, :w], targ[:h, :w], gen[:h, :w]\n",
+    "\n",
+    "        # Concatenate horizontally\n",
+    "        row = tf.concat([vis, targ, gen], axis=1)\n",
+    "        rows.append(row)\n",
+    "\n",
+    "    # Stack vertically → 5-row image\n",
+    "    grid = tf.concat(rows, axis=0)\n",
+    "\n",
+    "    out_path = os.path.join(out_dir, f\"epoch_{epoch:03d}.png\")\n",
+    "    tf.keras.preprocessing.image.save_img(out_path, grid.numpy())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "302e6898",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:01.939607Z",
+     "iopub.status.busy": "2026-02-12T21:59:01.939374Z",
+     "iopub.status.idle": "2026-02-12T21:59:01.945225Z",
+     "shell.execute_reply": "2026-02-12T21:59:01.944630Z"
+    },
+    "papermill": {
+     "duration": 0.010711,
+     "end_time": "2026-02-12T21:59:01.946333",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:01.935622",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def train(train_ds, val_ds=None, epochs=EPOCHS):\n",
+    "    global_step = 0\n",
+    "    for epoch in range(1, epochs + 1):\n",
+    "        start = time.time()\n",
+    "        print(f\"\\nEpoch {epoch}/{epochs}\")\n",
+    "\n",
+    "        for batch, (inp, tar) in enumerate(train_ds):\n",
+    "            gen_out = generator(inp, training=True)\n",
+    "\n",
+    "            # Train Discriminator\n",
+    "            for _ in range(D_steps_per_G):\n",
+    "                d_loss_val = d_train_step(inp, tar, gen_out)\n",
+    "\n",
+    "            # Train Generator\n",
+    "            g_total_loss, g_adv, g_l1,g_prec, gen_out2 = g_train_step(inp, tar)\n",
+    "            global_step += 1\n",
+    "        print(f\" step {global_step}: \"\n",
+    "                      f\"D={d_loss_val:.4f}, G={g_total_loss:.4f}, Adv={g_adv:.4f}, L1={g_l1:.4f} , prec={g_prec:.4f}\")\n",
+    "\n",
+    "        # Save visualization\n",
+    "        if val_ds is not None:\n",
+    "            print(f\"🖼 Generating sample output for epoch {epoch}...\")\n",
+    "            generate_and_save_images(generator, val_ds, epoch)\n",
+    "\n",
+    "        # Save checkpoint\n",
+    "        if epoch % SAVE_INTERVAL_EPOCHS == 0:\n",
+    "            path = ckpt_manager.save()\n",
+    "            print(f\" Saved checkpoint: {path}\")\n",
+    "\n",
+    "        print(f\" Epoch {epoch} finished in {time.time() - start:.1f}s\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2581acd4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:01.953697Z",
+     "iopub.status.busy": "2026-02-12T21:59:01.953474Z",
+     "iopub.status.idle": "2026-02-12T21:59:02.537240Z",
+     "shell.execute_reply": "2026-02-12T21:59:02.536482Z"
+    },
+    "papermill": {
+     "duration": 0.589004,
+     "end_time": "2026-02-12T21:59:02.538691",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:01.949687",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 12025 visible images and 12025 IR images\n",
+      "Using only 12025 image pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_ds = get_train_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9f18cfa2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:02.548373Z",
+     "iopub.status.busy": "2026-02-12T21:59:02.547591Z",
+     "iopub.status.idle": "2026-02-12T21:59:02.551403Z",
+     "shell.execute_reply": "2026-02-12T21:59:02.550725Z"
+    },
+    "papermill": {
+     "duration": 0.009773,
+     "end_time": "2026-02-12T21:59:02.552655",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:02.542882",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# val_ds = train_ds.take(75)\n",
+    "# train_ds = train_ds.skip(75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7b2a39df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:02.561724Z",
+     "iopub.status.busy": "2026-02-12T21:59:02.561429Z",
+     "iopub.status.idle": "2026-02-12T21:59:02.573514Z",
+     "shell.execute_reply": "2026-02-12T21:59:02.572847Z"
+    },
+    "papermill": {
+     "duration": 0.018255,
+     "end_time": "2026-02-12T21:59:02.575056",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:02.556801",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "def to_uint8(x):\n",
+    "    \"\"\"Convert tensor from [-1,1] → uint8 [0,255].\"\"\"\n",
+    "    x = (x + 1.0) * 127.5\n",
+    "    x = tf.clip_by_value(x, 0, 255)\n",
+    "    return tf.cast(x, tf.uint8)\n",
+    "\n",
+    "# Shuffle the dataset to take random samples\n",
+    "train_ds_shuffled = train_ds.shuffle(buffer_size=1000, reshuffle_each_iteration=True)\n",
+    "\n",
+    "# Take 10 images\n",
+    "sample_ds = train_ds_shuffled.take(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4f876063",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:02.584167Z",
+     "iopub.status.busy": "2026-02-12T21:59:02.583876Z",
+     "iopub.status.idle": "2026-02-12T21:59:02.587381Z",
+     "shell.execute_reply": "2026-02-12T21:59:02.586759Z"
+    },
+    "papermill": {
+     "duration": 0.009294,
+     "end_time": "2026-02-12T21:59:02.588465",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:02.579171",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# train(train_ds, val_ds, epochs=EPOCHS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4a82990",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003627,
+     "end_time": "2026-02-12T21:59:02.596030",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:02.592403",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bb6cd8e0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T21:59:02.604728Z",
+     "iopub.status.busy": "2026-02-12T21:59:02.604438Z",
+     "iopub.status.idle": "2026-02-12T22:08:25.100908Z",
+     "shell.execute_reply": "2026-02-12T22:08:25.099998Z"
+    },
+    "papermill": {
+     "duration": 562.502476,
+     "end_time": "2026-02-12T22:08:25.102199",
+     "exception": false,
+     "start_time": "2026-02-12T21:59:02.599723",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No training checkpoint found. Loading pretrained weights...\n",
+      "✅ Restored pretrained weights from: /kaggle/input/models/saisumathappala/image-to-ir-gan/tensorflow2/default/2/ckpt-34\n",
+      "No checkpoint found, using uninitialized model\n",
+      "Found 12025 visible images and 12025 IR images\n",
+      "Using only 12025 image pairs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/12025 [00:00<?, ?it/s]I0000 00:00:1770933543.075318      20 cuda_dnn.cc:529] Loaded cuDNN version 90300\n",
+      "100%|██████████| 12025/12025 [09:22<00:00, 21.39it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==== Test Dataset Metrics ====\n",
+      "L1 Loss   : 0.1371\n",
+      "PSNR      : 20.8206\n",
+      "SSIM      : 0.5706\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "# -------------------- Load Checkpoint --------------------\n",
+    "ckpt = tf.train.Checkpoint(generator=generator,\n",
+    "                           D_full=D_full,\n",
+    "                           generator_optimizer=generator_optimizer,\n",
+    "                           d_optimizer=d_optimizer)\n",
+    "ckpt_manager = tf.train.CheckpointManager(ckpt, CHECKPOINT_DIR, max_to_keep=1)\n",
+    "# -------------------- Restore Logic --------------------\n",
+    "\n",
+    "if ckpt_manager.latest_checkpoint:\n",
+    "    # Case 1 → Resume training normally\n",
+    "    ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n",
+    "    print(f\"✅ Resumed from training checkpoint: {ckpt_manager.latest_checkpoint}\")\n",
+    "\n",
+    "else:\n",
+    "    # Case 2 → First run → Load pretrained weights ONCE\n",
+    "    print(\"No training checkpoint found. Loading pretrained weights...\")\n",
+    "\n",
+    "    pretrained_ckpt = tf.train.latest_checkpoint(PRETRAINED_DIR)\n",
+    "\n",
+    "    if pretrained_ckpt:\n",
+    "        ckpt.restore(pretrained_ckpt).expect_partial()\n",
+    "        print(f\"✅ Restored pretrained weights from: {pretrained_ckpt}\")\n",
+    "    else:\n",
+    "        print(\"⚠️ No pretrained checkpoint found. Training from scratch.\")\n",
+    "\n",
+    "\n",
+    "# Restore latest checkpoint\n",
+    "if ckpt_manager.latest_checkpoint:\n",
+    "    ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n",
+    "    print(f\"Restored from {ckpt_manager.latest_checkpoint}\")\n",
+    "else:\n",
+    "    print(\"No checkpoint found, using uninitialized model\")\n",
+    "\n",
+    "# -------------------- Dataset Evaluation --------------------\n",
+    "def l1_loss(y_true, y_pred):\n",
+    "    return tf.reduce_mean(tf.abs(y_true - y_pred))\n",
+    "\n",
+    "def evaluate(test_ds):\n",
+    "    l1_list, psnr_list, ssim_list = [], [], []\n",
+    "    for vis, ir in tqdm(test_ds):\n",
+    "        pred = generator(vis, training=False)\n",
+    "        l1 = l1_loss(ir, pred).numpy()\n",
+    "        psnr = tf.image.psnr(ir, pred, max_val=2.0).numpy()\n",
+    "        ssim = tf.image.ssim(ir, pred, max_val=2.0).numpy()\n",
+    "\n",
+    "        l1_list.append(l1)\n",
+    "        psnr_list.append(np.mean(psnr))\n",
+    "        ssim_list.append(np.mean(ssim))\n",
+    "\n",
+    "    print(\"==== Test Dataset Metrics ====\")\n",
+    "    print(f\"L1 Loss   : {np.mean(l1_list):.4f}\")\n",
+    "    print(f\"PSNR      : {np.mean(psnr_list):.4f}\")\n",
+    "    print(f\"SSIM      : {np.mean(ssim_list):.4f}\")\n",
+    "\n",
+    "# Example usage\n",
+    "test_ds = get_val_dataset()  # Define this function\n",
+    "evaluate(train_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "268eb52f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T22:08:25.423242Z",
+     "iopub.status.busy": "2026-02-12T22:08:25.422959Z",
+     "iopub.status.idle": "2026-02-12T22:08:25.430078Z",
+     "shell.execute_reply": "2026-02-12T22:08:25.429489Z"
+    },
+    "papermill": {
+     "duration": 0.166727,
+     "end_time": "2026-02-12T22:08:25.431112",
+     "exception": false,
+     "start_time": "2026-02-12T22:08:25.264385",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "OUTPUT_DIR = \"outputs\"\n",
+    "\n",
+    "\n",
+    "# -------------------- Utils --------------------\n",
+    "def to_uint8(x):\n",
+    "    \"\"\"\n",
+    "    Convert tensor from [-1,1] → uint8 [0,255]\n",
+    "    \"\"\"\n",
+    "    x = (x + 1.0) * 127.5\n",
+    "    x = tf.clip_by_value(x, 0, 255)\n",
+    "    return tf.cast(x, tf.uint8)\n",
+    "\n",
+    "\n",
+    "# -------------------- Save 3 Images in 1 --------------------\n",
+    "def generate(\n",
+    "        model,\n",
+    "        dataset,\n",
+    "        epoch,\n",
+    "        out_dir=OUTPUT_DIR):\n",
+    "\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "    for idx, (visible, real_ir) in enumerate(dataset):\n",
+    "        if idx%5==0:\n",
+    "\n",
+    "            # Generate fake IR\n",
+    "            fake_ir = model(visible, training=False)\n",
+    "    \n",
+    "            # Convert → uint8\n",
+    "            vis  = to_uint8(visible[0])\n",
+    "            real = to_uint8(real_ir[0])\n",
+    "            fake = to_uint8(fake_ir[0])\n",
+    "    \n",
+    "            # Safety crop (if any mismatch)\n",
+    "            h = min(vis.shape[0], real.shape[0], fake.shape[0])\n",
+    "            w = min(vis.shape[1], real.shape[1], fake.shape[1])\n",
+    "    \n",
+    "            vis  = vis[:h, :w]\n",
+    "            real = real[:h, :w]\n",
+    "            fake = fake[:h, :w]\n",
+    "\n",
+    "            # -------- Concatenate 3 images horizontally --------\n",
+    "            trio = tf.concat([vis, real, fake], axis=1)\n",
+    "    \n",
+    "            # Save path\n",
+    "            save_path = os.path.join(\n",
+    "                out_dir,\n",
+    "                f\"img_{idx:05d}.png\"\n",
+    "            )\n",
+    "    \n",
+    "            # Save image\n",
+    "            tf.keras.preprocessing.image.save_img(\n",
+    "                save_path,\n",
+    "                trio.numpy()\n",
+    "            )\n",
+    "            print(idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "161508e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-12T22:08:25.750226Z",
+     "iopub.status.busy": "2026-02-12T22:08:25.749948Z",
+     "iopub.status.idle": "2026-02-12T22:12:22.229019Z",
+     "shell.execute_reply": "2026-02-12T22:12:22.228225Z"
+    },
+    "papermill": {
+     "duration": 236.642335,
+     "end_time": "2026-02-12T22:12:22.230235",
+     "exception": false,
+     "start_time": "2026-02-12T22:08:25.587900",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
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+     ]
+    }
+   ],
+   "source": [
+    "# test_ds = get_val_dataset()\n",
+    "# # Run evaluation\n",
+    "# print(test_ds)\n",
+    "generate(\n",
+    "        model=generator,\n",
+    "        dataset=train_ds,\n",
+    "        epoch=150  # change if you want more rows\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1bbabe5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.191322,
+     "end_time": "2026-02-12T22:12:22.615266",
+     "exception": false,
+     "start_time": "2026-02-12T22:12:22.423944",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "619ef3ab",
+   "metadata": {
+    "papermill": {
+     "duration": 0.201882,
+     "end_time": "2026-02-12T22:12:23.012230",
+     "exception": false,
+     "start_time": "2026-02-12T22:12:22.810348",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 8436032,
+     "sourceId": 13308561,
+     "sourceType": "datasetVersion"
+    },
+    {
+     "isSourceIdPinned": false,
+     "modelId": 583820,
+     "modelInstanceId": 571510,
+     "sourceId": 748364,
+     "sourceType": "modelInstanceVersion"
+    },
+    {
+     "isSourceIdPinned": true,
+     "modelId": 584139,
+     "modelInstanceId": 571806,
+     "sourceId": 749667,
+     "sourceType": "modelInstanceVersion"
+    }
+   ],
+   "dockerImageVersionId": 31154,
+   "isGpuEnabled": true,
+   "isInternetEnabled": false,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 828.406063,
+   "end_time": "2026-02-12T22:12:26.638920",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2026-02-12T21:58:38.232857",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}