baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Galaxy Zoo - The Galaxy Challenge\n",
    "#### Baseline model\n",
    "#### Author: Ching-Yin NG\n",
    "#### Source code and report are available at https://github.com/alvinng4/GalaxyZooChallenge\n",
    "#### Baseline model score: 0.07738\n",
    "#### Baseline + CBAM: 0.07694\n",
    "#### Baseline + EMA: 0.07653"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. Is the object a smooth galaxy, a galaxy with features/disk or a star? 3 responses\\\n",
    "Q2. Is it edge-on? 2 responses\\\n",
    "Q3. Is there a bar? 2 responses\\\n",
    "Q4. Is there a spiral pattern? 2 responses\\\n",
    "Q5. How prominent is the central bulge? 4 responses\\\n",
    "Q6. Is there anything \"odd\" about the galaxy? 2 responses\\\n",
    "Q7. How round is the smooth galaxy? 3 responses\\\n",
    "Q8. What is the odd feature? 7 responses\\\n",
    "Q9. What shape is the bulge in the edge-on galaxy? 3 responses\\\n",
    "Q10. How tightly wound are the spiral arms? 3 responses\\\n",
    "Q11. How many spiral arms are there? 6 responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import itertools\n",
    "import math\n",
    "import pickle\n",
    "import timeit\n",
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import polars\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from PIL import Image\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Self-attention modules, only set one of them to True\n",
    "USE_CBAM = False\n",
    "USE_EMA = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_SIZE_BEFORE_SCALING = 256\n",
    "TRAIN_SCALING_UPPER = IMAGE_SIZE_BEFORE_SCALING + 96\n",
    "TRAIN_SCALING_LOWER = IMAGE_SIZE_BEFORE_SCALING - 72\n",
    "\n",
    "OUTPUT_NUM_ROTATIONS = 32\n",
    "OUTPUT_REFLECTION_FLAG = True\n",
    "\n",
    "BATCH_SIZE = 105\n",
    "FRACTION_FILES_VALIDATION = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 1\n",
    "\n",
    "\n",
    "def seed_everything(seed: int):\n",
    "    # random.seed(seed)\n",
    "    # os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "\n",
    "seed_everything(RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SUBCLASS_LEN = [3, 2, 2, 2, 4, 2, 3, 7, 3, 3, 6]\n",
    "SUBCLASS_LEN_CUMSUM = np.cumsum(SUBCLASS_LEN)\n",
    "CLASS_LEN = len(SUBCLASS_LEN)\n",
    "\n",
    "FILE_PREFIX = \"test\"\n",
    "OUTPUT_DIR = Path(\"output\")\n",
    "\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.is_available())\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda:0\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = Path(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data: 61578\n",
      "Some labels: \n",
      "shape: (5, 38)\n",
      "┌──────────┬──────────┬──────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ GalaxyID ┆ Class1.1 ┆ Class1.2 ┆ Class1.3 ┆ … ┆ Class11.3 ┆ Class11.4 ┆ Class11.5 ┆ Class11.6 │\n",
      "│ ---      ┆ ---      ┆ ---      ┆ ---      ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
      "│ i64      ┆ f64      ┆ f64      ┆ f64      ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
      "╞══════════╪══════════╪══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 100008   ┆ 0.383147 ┆ 0.616853 ┆ 0.0      ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.325512  │\n",
      "│ 100023   ┆ 0.327001 ┆ 0.663777 ┆ 0.009222 ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
      "│ 100053   ┆ 0.765717 ┆ 0.177352 ┆ 0.056931 ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
      "│ 100078   ┆ 0.693377 ┆ 0.238564 ┆ 0.068059 ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
      "│ 100090   ┆ 0.933839 ┆ 0.0      ┆ 0.066161 ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
      "└──────────┴──────────┴──────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
      "[[0.383147   0.616853   0.         ... 0.         0.         0.32551225]\n",
      " [0.327001   0.663777   0.009222   ... 0.         0.         0.        ]\n",
      " [0.765717   0.177352   0.056931   ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.202841   0.777376   0.019783   ... 0.         0.         0.07229626]\n",
      " [0.091      0.909      0.         ... 0.02306456 0.         0.04533378]\n",
      " [0.767      0.14       0.093      ... 0.         0.         0.02338   ]]\n",
      "(61578, 37)\n",
      "[[1 1 1 ... 0 0 5]\n",
      " [1 1 0 ... 2 2 1]\n",
      " [0 1 1 ... 0 0 0]\n",
      " ...\n",
      " [1 1 1 ... 0 1 5]\n",
      " [1 1 1 ... 0 2 1]\n",
      " [0 1 1 ... 0 0 5]]\n",
      "(61578, 11)\n"
     ]
    }
   ],
   "source": [
    "img_path = DATA_FOLDER / \"images_training_rev1\"\n",
    "file_list = sorted([str(path) for path in img_path.glob(\"*.jpg\")])\n",
    "print(f\"Number of data: {len(file_list)}\")\n",
    "\n",
    "train_sol_path = DATA_FOLDER / \"training_solutions_rev1.csv\"\n",
    "train_sol = polars.read_csv(train_sol_path)\n",
    "print(\"Some labels: \")\n",
    "print(train_sol.head())\n",
    "\n",
    "soft_labels = train_sol[:, 1:].to_numpy()\n",
    "\n",
    "one_hot_labels = np.argmax(\n",
    "    soft_labels[:, : SUBCLASS_LEN_CUMSUM[0]], axis=1\n",
    ")  # Class one\n",
    "for i in range(1, CLASS_LEN):\n",
    "    one_hot_labels = np.vstack(\n",
    "        (\n",
    "            one_hot_labels,\n",
    "            np.argmax(\n",
    "                soft_labels[:, SUBCLASS_LEN_CUMSUM[i - 1] : SUBCLASS_LEN_CUMSUM[i]],\n",
    "                axis=1,\n",
    "            ),\n",
    "        )\n",
    "    )\n",
    "one_hot_labels = one_hot_labels.T\n",
    "print(soft_labels)\n",
    "print(soft_labels.shape)\n",
    "print(one_hot_labels)\n",
    "print(one_hot_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageDataset(Dataset):\n",
    "    def __init__(self, file_list, labels, transform=None):\n",
    "        self.file_list = file_list\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        file = self.file_list[index]\n",
    "        label = self.labels[index]\n",
    "\n",
    "        image = Image.open(file)\n",
    "\n",
    "        if self.transform is not None:\n",
    "            image = self.transform(image)\n",
    "\n",
    "        return image, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "\n",
    "class TransformTrain:\n",
    "    def __init__(self):\n",
    "        self.rng = np.random.default_rng(seed=RANDOM_STATE)\n",
    "        self.random_rotate = transforms.RandomRotation(360)\n",
    "        self.transform_train = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize([128, 128]),\n",
    "                transforms.RandomHorizontalFlip(),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def __call__(self, x):\n",
    "        rotated_img = self.random_rotate(x)\n",
    "        random_crop_size = self.rng.integers(\n",
    "            low=(TRAIN_SCALING_LOWER), high=(TRAIN_SCALING_UPPER)\n",
    "        )\n",
    "        cropped_image = transforms.functional.center_crop(\n",
    "            rotated_img, [random_crop_size, random_crop_size]\n",
    "        )\n",
    "\n",
    "        return self.transform_train(cropped_image)\n",
    "\n",
    "\n",
    "class TransformValid:\n",
    "    def __init__(self):\n",
    "        self.transform_valid = transforms.Compose(\n",
    "            [\n",
    "                transforms.CenterCrop(\n",
    "                    [IMAGE_SIZE_BEFORE_SCALING, IMAGE_SIZE_BEFORE_SCALING]\n",
    "                ),\n",
    "                transforms.Resize([128, 128]),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def __call__(self, x):\n",
    "        return self.transform_valid(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of training files: 55420\n",
      "Length of validation files: 6158\n",
      "\n",
      "Class 1 distribution: \n",
      "Training dataset: Counter({1: 31343, 0: 24024, 2: 53})\n",
      "Validation dataset: Counter({1: 3483, 0: 2669, 2: 6})\n"
     ]
    }
   ],
   "source": [
    "if FRACTION_FILES_VALIDATION > 0.0:\n",
    "    (\n",
    "        train_file_list,\n",
    "        valid_file_list,\n",
    "        train_soft_labels,\n",
    "        valid_soft_labels,\n",
    "    ) = train_test_split(\n",
    "        file_list,\n",
    "        soft_labels,\n",
    "        test_size=FRACTION_FILES_VALIDATION,\n",
    "        random_state=RANDOM_STATE,\n",
    "        stratify=one_hot_labels[:, 0],\n",
    "    )\n",
    "else:\n",
    "    train_file_list = file_list\n",
    "    train_soft_labels = soft_labels\n",
    "\n",
    "    valid_file_list = []\n",
    "    valid_soft_labels = []\n",
    "\n",
    "transform_train = TransformTrain()\n",
    "train_dataset = ImageDataset(train_file_list, train_soft_labels, transform_train)\n",
    "train_one_hot_labels_class_one = np.argmax(train_soft_labels[:, :3], axis=1)\n",
    "\n",
    "if FRACTION_FILES_VALIDATION > 0.0:\n",
    "    transform_valid = TransformValid()\n",
    "    valid_dataset = ImageDataset(valid_file_list, valid_soft_labels, transform_valid)\n",
    "    valid_one_hot_labels_class_one = np.argmax(valid_soft_labels[:, :3], axis=1)\n",
    "\n",
    "print(f\"Length of training files: {len(train_file_list)}\")\n",
    "print(f\"Length of validation files: {len(valid_file_list)}\")\n",
    "\n",
    "print()\n",
    "print(\"Class 1 distribution: \")\n",
    "print(f\"Training dataset: {Counter(train_one_hot_labels_class_one)}\")\n",
    "if FRACTION_FILES_VALIDATION > 0.0:\n",
    "    print(f\"Validation dataset: {Counter(valid_one_hot_labels_class_one)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.8745098].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.67058825].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9529412].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.5764706].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.8352941].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.38823533].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.84313726].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.7647059].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.6313726].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5529412..1.0].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.8039216].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.7254902].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.39607847].\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_data_loader = DataLoader(train_dataset, batch_size=100, shuffle=False)\n",
    "\n",
    "img_class_zero = []\n",
    "img_class_one = []\n",
    "img_class_two = []\n",
    "img_per_class = 5\n",
    "\n",
    "iter_data_loader = iter(temp_data_loader)\n",
    "while True:\n",
    "    img_batch, label_batch = next(iter_data_loader)\n",
    "    for i in range(100):\n",
    "        img = img_batch[i]\n",
    "        label = label_batch[i]\n",
    "        one_hot_label = np.argmax(label[:3])\n",
    "\n",
    "        if (len(img_class_zero) < img_per_class) and (one_hot_label == 0):\n",
    "            img_class_zero.append(img)\n",
    "        elif (len(img_class_one) < img_per_class) and (one_hot_label == 1):\n",
    "            img_class_one.append(img)\n",
    "        elif (len(img_class_two) < img_per_class) and (one_hot_label == 2):\n",
    "            img_class_two.append(img)\n",
    "\n",
    "    if (\n",
    "        (len(img_class_zero) >= img_per_class)\n",
    "        and (len(img_class_one) >= img_per_class)\n",
    "        and (len(img_class_two) >= img_per_class)\n",
    "    ):\n",
    "        break\n",
    "\n",
    "fig1 = plt.figure(figsize=(9, 15))\n",
    "for i in range(img_per_class):\n",
    "    img = img_class_zero[i].cpu()\n",
    "    ax = fig1.add_subplot(img_per_class, 3, (i * 3) + 1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(f\"Class 1.1:\", size=15)\n",
    "    ax.imshow(img.permute(1, 2, 0))\n",
    "\n",
    "    img = img_class_one[i].cpu()\n",
    "    ax = fig1.add_subplot(img_per_class, 3, (i * 3) + 2)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(f\"Class 1.2:\", size=15)\n",
    "    ax.imshow(img.permute(1, 2, 0))\n",
    "\n",
    "    img = img_class_two[i].cpu()\n",
    "    ax = fig1.add_subplot(img_per_class, 3, (i * 3) + 3)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(f\"Class 1.3:\", size=15)\n",
    "    ax.imshow(img.permute(1, 2, 0))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-attention module (CBAM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class channel_attention(nn.Module):\n",
    "    def __init__(self, in_channels, reduction_ratio):\n",
    "        super().__init__()\n",
    "        self.max_pool = nn.AdaptiveMaxPool2d(1)\n",
    "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
    "        self.flatten = nn.Flatten()\n",
    "        hidden_layer_size = int(in_channels // reduction_ratio)\n",
    "\n",
    "        self.mlp = nn.Sequential()\n",
    "        self.mlp.add_module(\"fc1\", nn.Linear(in_channels, hidden_layer_size))\n",
    "        self.mlp.add_module(\"relu\", nn.ReLU())\n",
    "        self.mlp.add_module(\"fc2\", nn.Linear(hidden_layer_size, in_channels))\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "        max_pooled_map = self.max_pool(x)\n",
    "        avg_pooled_map = self.avg_pool(x)\n",
    "\n",
    "        max_att_map = self.mlp(self.flatten(max_pooled_map))\n",
    "        avg_att_map = self.mlp(self.flatten(avg_pooled_map))\n",
    "\n",
    "        cha_att_map = self.sigmoid(max_att_map + avg_att_map).unsqueeze(2).unsqueeze(3)\n",
    "\n",
    "        return cha_att_map\n",
    "\n",
    "\n",
    "class spatial_attention(nn.Module):\n",
    "    def __init__(self, kernel_size):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Conv2d(\n",
    "            in_channels=2,\n",
    "            out_channels=1,\n",
    "            kernel_size=kernel_size,\n",
    "            padding=((kernel_size - 1) // 2),\n",
    "        )\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "        max_pooled_map = torch.amax(x, dim=1, keepdim=True)\n",
    "        avg_pooled_map = torch.mean(x, dim=1, keepdim=True)\n",
    "\n",
    "        concat_pooled_map = torch.cat((max_pooled_map, avg_pooled_map), dim=1)\n",
    "        conv_map = self.conv(concat_pooled_map)\n",
    "        spa_att_map = self.sigmoid(conv_map)\n",
    "\n",
    "        return spa_att_map\n",
    "\n",
    "\n",
    "class CBAM(nn.Module):\n",
    "    \"\"\"\n",
    "    Convolutional Block Attention Module\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_channels, cha_att_reduction_ratio, spa_att_kernel_size):\n",
    "        super().__init__()\n",
    "        self.channel_attention_module = channel_attention(\n",
    "            in_channels, cha_att_reduction_ratio\n",
    "        )\n",
    "        self.spatial_attention_module = spatial_attention(spa_att_kernel_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        cha_att_map = self.channel_attention_module(x)\n",
    "        spa_att_map = self.spatial_attention_module(x * cha_att_map)\n",
    "\n",
    "        return x + (x * cha_att_map * spa_att_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-attention module (EMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EMA(nn.Module):\n",
    "    \"\"\"\n",
    "    Efficient Multi-Scale Attention Module with Cross-Spatial Learning\n",
    "\n",
    "    Source: https://github.com/YOLOonMe/EMA-attention-module/blob/main/EMA_attention_module\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_channels, factor=32):\n",
    "        super().__init__()\n",
    "        self.groups = factor\n",
    "        assert in_channels // self.groups > 0\n",
    "        self.softmax = nn.Softmax(-1)\n",
    "        self.agp = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.pool_h = nn.AdaptiveAvgPool2d((None, 1))\n",
    "        self.pool_w = nn.AdaptiveAvgPool2d((1, None))\n",
    "        self.gn = nn.GroupNorm(in_channels // self.groups, in_channels // self.groups)\n",
    "        self.conv1x1 = nn.Conv2d(\n",
    "            in_channels // self.groups,\n",
    "            in_channels // self.groups,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "        )\n",
    "        self.conv3x3 = nn.Conv2d(\n",
    "            in_channels // self.groups,\n",
    "            in_channels // self.groups,\n",
    "            kernel_size=3,\n",
    "            stride=1,\n",
    "            padding=1,\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, c, h, w = x.size()\n",
    "        group_x = x.reshape(b * self.groups, -1, h, w)  # b*g,c//g,h,w\n",
    "        x_h = self.pool_h(group_x)\n",
    "        x_w = self.pool_w(group_x).permute(0, 1, 3, 2)\n",
    "        hw = self.conv1x1(torch.cat([x_h, x_w], dim=2))\n",
    "        x_h, x_w = torch.split(hw, [h, w], dim=2)\n",
    "        x1 = self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())\n",
    "        x2 = self.conv3x3(group_x)\n",
    "        x11 = self.softmax(\n",
    "            self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1)\n",
    "        )\n",
    "        x12 = x2.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw\n",
    "        x21 = self.softmax(\n",
    "            self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1)\n",
    "        )\n",
    "        x22 = x1.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw\n",
    "        weights = (torch.matmul(x11, x12) + torch.matmul(x21, x22)).reshape(\n",
    "            b * self.groups, 1, h, w\n",
    "        )\n",
    "        return (group_x * weights.sigmoid()).reshape(b, c, h, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_CBAM is True:\n",
    "    model = nn.Sequential(\n",
    "        nn.Conv2d(in_channels=3, out_channels=32, kernel_size=17, padding=8),\n",
    "        nn.BatchNorm2d(num_features=32),\n",
    "        nn.ReLU(),\n",
    "        CBAM(in_channels=32, cha_att_reduction_ratio=4, spa_att_kernel_size=7),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=32, out_channels=64, kernel_size=13, padding=6),\n",
    "        nn.BatchNorm2d(num_features=64),\n",
    "        nn.ReLU(),\n",
    "        CBAM(in_channels=64, cha_att_reduction_ratio=4, spa_att_kernel_size=7),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=64, out_channels=128, kernel_size=9, padding=4),\n",
    "        nn.BatchNorm2d(num_features=128),\n",
    "        nn.ReLU(),\n",
    "        CBAM(in_channels=128, cha_att_reduction_ratio=4, spa_att_kernel_size=7),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=128, out_channels=256, kernel_size=7, padding=3),\n",
    "        nn.BatchNorm2d(num_features=256),\n",
    "        nn.ReLU(),\n",
    "        CBAM(in_channels=256, cha_att_reduction_ratio=4, spa_att_kernel_size=7),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=256, out_channels=96, kernel_size=1, padding=0),\n",
    "        nn.BatchNorm2d(num_features=96),\n",
    "        nn.ReLU(),\n",
    "        nn.Flatten(),\n",
    "        nn.Linear(6144, SUBCLASS_LEN_CUMSUM[-1]),\n",
    "        nn.Sigmoid(),\n",
    "    )\n",
    "elif USE_EMA is True:\n",
    "    model = nn.Sequential(\n",
    "        nn.Conv2d(in_channels=3, out_channels=32, kernel_size=17, padding=8),\n",
    "        nn.BatchNorm2d(num_features=32),\n",
    "        nn.ReLU(),\n",
    "        EMA(in_channels=32),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=32, out_channels=64, kernel_size=13, padding=6),\n",
    "        nn.BatchNorm2d(num_features=64),\n",
    "        nn.ReLU(),\n",
    "        EMA(in_channels=64),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=64, out_channels=128, kernel_size=9, padding=4),\n",
    "        nn.BatchNorm2d(num_features=128),\n",
    "        nn.ReLU(),\n",
    "        EMA(in_channels=128),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=128, out_channels=256, kernel_size=7, padding=3),\n",
    "        nn.BatchNorm2d(num_features=256),\n",
    "        nn.ReLU(),\n",
    "        EMA(in_channels=256),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=256, out_channels=96, kernel_size=1, padding=0),\n",
    "        nn.BatchNorm2d(num_features=96),\n",
    "        nn.ReLU(),\n",
    "        nn.Flatten(),\n",
    "        nn.Linear(6144, SUBCLASS_LEN_CUMSUM[-1]),\n",
    "        nn.Sigmoid(),\n",
    "    )\n",
    "else:\n",
    "    model = nn.Sequential(\n",
    "        nn.Conv2d(in_channels=3, out_channels=32, kernel_size=17, padding=8),\n",
    "        nn.BatchNorm2d(num_features=32),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=32, out_channels=64, kernel_size=13, padding=6),\n",
    "        nn.BatchNorm2d(num_features=64),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=64, out_channels=128, kernel_size=9, padding=4),\n",
    "        nn.BatchNorm2d(num_features=128),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=128, out_channels=256, kernel_size=7, padding=3),\n",
    "        nn.BatchNorm2d(num_features=256),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(kernel_size=2),\n",
    "        nn.Conv2d(in_channels=256, out_channels=96, kernel_size=1, padding=0),\n",
    "        nn.BatchNorm2d(num_features=96),\n",
    "        nn.ReLU(),\n",
    "        nn.Flatten(),\n",
    "        nn.Linear(6144, SUBCLASS_LEN_CUMSUM[-1]),\n",
    "        nn.Sigmoid(),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 37])\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones((4, 3, 128, 128))\n",
    "print(model(x).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(3, 32, kernel_size=(17, 17), stride=(1, 1), padding=(8, 8))\n",
      "  (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): ReLU()\n",
      "  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (4): Conv2d(32, 64, kernel_size=(13, 13), stride=(1, 1), padding=(6, 6))\n",
      "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (6): ReLU()\n",
      "  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (8): Conv2d(64, 128, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4))\n",
      "  (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (10): ReLU()\n",
      "  (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (12): Conv2d(128, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
      "  (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (14): ReLU()\n",
      "  (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (16): Conv2d(256, 96, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (17): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (18): ReLU()\n",
      "  (19): Flatten(start_dim=1, end_dim=-1)\n",
      "  (20): Linear(in_features=6144, out_features=37, bias=True)\n",
      "  (21): Sigmoid()\n",
      ")\n",
      "Number of parameters: 2896709\n"
     ]
    }
   ],
   "source": [
    "print(model)\n",
    "\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print(f\"Number of parameters: {total_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = [[], [], [], []]\n",
    "lr_hist = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(\n",
    "    model,\n",
    "    num_epochs,\n",
    "    lr_scheduler,\n",
    "    optimizer,\n",
    "    train_dl,\n",
    "    valid_dl,\n",
    "    batch_size,\n",
    "    hist,\n",
    "    lr_hist,\n",
    "    save_every_n_epochs,\n",
    "):\n",
    "    loss_fn = nn.MSELoss()\n",
    "    previous_epochs = len(hist[0])\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        start = timeit.default_timer()\n",
    "        model.train()\n",
    "        i = 0\n",
    "        loss_hist_train = 0\n",
    "        rmse_hist_train = 0\n",
    "        for x_batch, y_batch in train_dl:\n",
    "            i += 1\n",
    "            if i % 5 == 0:\n",
    "                print(\n",
    "                    f\"Progress: epoch: {previous_epochs + epoch + 1}/{previous_epochs + num_epochs}, batch: {i}/{len(train_dl)}\",\n",
    "                    end=\"\\r\",\n",
    "                )\n",
    "            x_batch = x_batch.to(device)\n",
    "            y_batch = y_batch.to(device)\n",
    "            pred = model(x_batch)\n",
    "            loss = loss_fn(pred.float(), y_batch.float())\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            loss_hist_train += loss.item() * y_batch.size(0)\n",
    "            rmse_hist_train += mean_squared_error(\n",
    "                y_true=y_batch.cpu(), y_pred=pred.detach().cpu()\n",
    "            )\n",
    "\n",
    "        loss_hist_train /= len(train_dl.dataset)\n",
    "        rmse_hist_train /= len(train_dl.dataset) / batch_size\n",
    "        rmse_hist_train = math.sqrt(rmse_hist_train)\n",
    "\n",
    "        if FRACTION_FILES_VALIDATION > 0.0:\n",
    "            model.eval()\n",
    "            loss_hist_valid = 0\n",
    "            rmse_hist_valid = 0\n",
    "            with torch.no_grad():\n",
    "                for x_batch, y_batch in valid_dl:\n",
    "                    x_batch = x_batch.to(device)\n",
    "                    y_batch = y_batch.to(device)\n",
    "                    pred = model(x_batch)\n",
    "                    loss = loss_fn(pred.float(), y_batch.float())\n",
    "\n",
    "                    loss_hist_valid += loss.item() * y_batch.size(0)\n",
    "                    rmse_hist_valid += mean_squared_error(\n",
    "                        y_true=y_batch.cpu(), y_pred=pred.detach().cpu()\n",
    "                    )\n",
    "\n",
    "            loss_hist_valid /= len(valid_dl.dataset)\n",
    "            rmse_hist_valid /= len(valid_dl.dataset) / batch_size\n",
    "            rmse_hist_valid = math.sqrt(rmse_hist_valid)\n",
    "        else:\n",
    "            loss_hist_valid = 0.0\n",
    "            rmse_hist_valid = 0.0\n",
    "\n",
    "        end = timeit.default_timer()\n",
    "\n",
    "        print_str = (\n",
    "            f\"Epoch: {previous_epochs + epoch + 1} \"\n",
    "            + f\"train rmse: {rmse_hist_train:.4f} \"\n",
    "            + f\"train loss: {loss_hist_train:.4f} \"\n",
    "            + f\"val rmse: {rmse_hist_valid:.4f} \"\n",
    "            + f\"val loss: {loss_hist_valid:.4f} \"\n",
    "            + f\"lr: {lr_scheduler.get_last_lr()[0]} \"\n",
    "            + f\"Time taken: {end - start:.3f}s\"\n",
    "        )\n",
    "        print(print_str)\n",
    "\n",
    "        if (previous_epochs + epoch + 1) % save_every_n_epochs == 0:\n",
    "            torch.save(\n",
    "                model,\n",
    "                OUTPUT_DIR\n",
    "                / f\"{FILE_PREFIX}_model_epoch{previous_epochs + epoch + 1}.pth\",\n",
    "            )\n",
    "\n",
    "        results = [loss_hist_train, loss_hist_valid, rmse_hist_train, rmse_hist_valid]\n",
    "        for i in range(len(hist)):\n",
    "            hist[i].append(results[i])\n",
    "        lr_hist.append(lr_scheduler.get_last_lr()[0])\n",
    "\n",
    "        lr_scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/cyng/.conda/envs/adc/lib/python3.11/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    }
   ],
   "source": [
    "num_workers = (\n",
    "    8  # num_workers must set to zero if the images are already transferred to the GPU\n",
    ")\n",
    "\n",
    "if num_workers == 0:\n",
    "    train_dl = DataLoader(train_dataset, BATCH_SIZE, num_workers=0, shuffle=True)\n",
    "\n",
    "    if FRACTION_FILES_VALIDATION > 0.0:\n",
    "        valid_dl = DataLoader(valid_dataset, BATCH_SIZE, num_workers=0, shuffle=False)\n",
    "    else:\n",
    "        valid_dl = None\n",
    "else:\n",
    "    train_dl = DataLoader(\n",
    "        train_dataset,\n",
    "        BATCH_SIZE,\n",
    "        num_workers=num_workers,\n",
    "        persistent_workers=True,\n",
    "        pin_memory=True,\n",
    "        shuffle=True,\n",
    "    )\n",
    "    if FRACTION_FILES_VALIDATION > 0.0:\n",
    "        valid_dl = DataLoader(\n",
    "            valid_dataset,\n",
    "            BATCH_SIZE,\n",
    "            num_workers=num_workers,\n",
    "            persistent_workers=True,\n",
    "            pin_memory=True,\n",
    "            shuffle=False,\n",
    "        )\n",
    "    else:\n",
    "        valid_dl = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "learning_rate = 0.0001\n",
    "num_epochs = 240\n",
    "save_every_n_epochs = 10\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
    "lr_scheduler = CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=1e-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: epoch: 1/240, batch: 525/528\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/cyng/.conda/envs/adc/lib/python3.11/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 train rmse: 0.1335 train loss: 0.0178 val rmse: 0.1408 val loss: 0.0197 lr: 0.0001 Time taken: 132.415s\n",
      "Epoch: 2 train rmse: 0.1145 train loss: 0.0131 val rmse: 0.1214 val loss: 0.0147 lr: 9.999571680706248e-05 Time taken: 127.028s\n",
      "Epoch: 3 train rmse: 0.1085 train loss: 0.0118 val rmse: 0.1119 val loss: 0.0124 lr: 9.998286796215299e-05 Time taken: 126.655s\n",
      "Epoch: 4 train rmse: 0.1051 train loss: 0.0111 val rmse: 0.1164 val loss: 0.0135 lr: 9.996145566685496e-05 Time taken: 126.072s\n",
      "Epoch: 5 train rmse: 0.1031 train loss: 0.0106 val rmse: 0.1055 val loss: 0.0111 lr: 9.993148359005493e-05 Time taken: 126.480s\n",
      "Epoch: 6 train rmse: 0.1016 train loss: 0.0103 val rmse: 0.1035 val loss: 0.0107 lr: 9.989295686731399e-05 Time taken: 126.795s\n",
      "Epoch: 7 train rmse: 0.1002 train loss: 0.0100 val rmse: 0.1006 val loss: 0.0100 lr: 9.984588209998776e-05 Time taken: 126.738s\n",
      "Epoch: 8 train rmse: 0.0995 train loss: 0.0099 val rmse: 0.1048 val loss: 0.0109 lr: 9.979026735409523e-05 Time taken: 126.638s\n",
      "Epoch: 9 train rmse: 0.0984 train loss: 0.0097 val rmse: 0.1021 val loss: 0.0104 lr: 9.972612215893684e-05 Time taken: 125.920s\n",
      "Epoch: 10 train rmse: 0.0974 train loss: 0.0095 val rmse: 0.0997 val loss: 0.0099 lr: 9.965345750546156e-05 Time taken: 126.773s\n",
      "Epoch: 11 train rmse: 0.0968 train loss: 0.0094 val rmse: 0.1021 val loss: 0.0104 lr: 9.957228584438367e-05 Time taken: 124.717s\n",
      "Epoch: 12 train rmse: 0.0962 train loss: 0.0093 val rmse: 0.1103 val loss: 0.0121 lr: 9.948262108404943e-05 Time taken: 124.672s\n",
      "Epoch: 13 train rmse: 0.0955 train loss: 0.0091 val rmse: 0.1059 val loss: 0.0111 lr: 9.938447858805392e-05 Time taken: 125.989s\n",
      "Epoch: 14 train rmse: 0.0953 train loss: 0.0091 val rmse: 0.1106 val loss: 0.0122 lr: 9.927787517260861e-05 Time taken: 126.400s\n",
      "Epoch: 15 train rmse: 0.0943 train loss: 0.0089 val rmse: 0.1093 val loss: 0.0119 lr: 9.916282910365992e-05 Time taken: 126.117s\n",
      "Epoch: 16 train rmse: 0.0939 train loss: 0.0088 val rmse: 0.1018 val loss: 0.0103 lr: 9.903936009375951e-05 Time taken: 125.580s\n",
      "Epoch: 17 train rmse: 0.0934 train loss: 0.0087 val rmse: 0.1011 val loss: 0.0102 lr: 9.890748929868662e-05 Time taken: 124.205s\n",
      "Epoch: 18 train rmse: 0.0930 train loss: 0.0086 val rmse: 0.0995 val loss: 0.0098 lr: 9.87672393138231e-05 Time taken: 124.481s\n",
      "Epoch: 19 train rmse: 0.0925 train loss: 0.0086 val rmse: 0.1111 val loss: 0.0123 lr: 9.861863417028185e-05 Time taken: 124.979s\n",
      "Epoch: 20 train rmse: 0.0919 train loss: 0.0084 val rmse: 0.0922 val loss: 0.0084 lr: 9.846169933078919e-05 Time taken: 125.823s\n",
      "Epoch: 21 train rmse: 0.0915 train loss: 0.0084 val rmse: 0.0968 val loss: 0.0093 lr: 9.829646168532197e-05 Time taken: 126.340s\n",
      "Epoch: 22 train rmse: 0.0910 train loss: 0.0083 val rmse: 0.1070 val loss: 0.0114 lr: 9.81229495465001e-05 Time taken: 125.672s\n",
      "Epoch: 23 train rmse: 0.0907 train loss: 0.0082 val rmse: 0.1077 val loss: 0.0115 lr: 9.794119264473532e-05 Time taken: 126.968s\n",
      "Epoch: 24 train rmse: 0.0905 train loss: 0.0082 val rmse: 0.0932 val loss: 0.0086 lr: 9.775122212313706e-05 Time taken: 126.831s\n",
      "Epoch: 25 train rmse: 0.0900 train loss: 0.0081 val rmse: 0.0948 val loss: 0.0089 lr: 9.755307053217622e-05 Time taken: 126.871s\n",
      "Epoch: 26 train rmse: 0.0897 train loss: 0.0080 val rmse: 0.0955 val loss: 0.0091 lr: 9.734677182410783e-05 Time taken: 126.693s\n",
      "Epoch: 27 train rmse: 0.0893 train loss: 0.0080 val rmse: 0.0905 val loss: 0.0081 lr: 9.713236134715347e-05 Time taken: 126.860s\n",
      "Epoch: 28 train rmse: 0.0891 train loss: 0.0079 val rmse: 0.0902 val loss: 0.0081 lr: 9.69098758394446e-05 Time taken: 126.931s\n",
      "Epoch: 29 train rmse: 0.0888 train loss: 0.0079 val rmse: 0.0925 val loss: 0.0085 lr: 9.667935342272761e-05 Time taken: 126.711s\n",
      "Epoch: 30 train rmse: 0.0886 train loss: 0.0078 val rmse: 0.0978 val loss: 0.0095 lr: 9.644083359583187e-05 Time taken: 126.830s\n",
      "Epoch: 31 train rmse: 0.0884 train loss: 0.0078 val rmse: 0.0899 val loss: 0.0080 lr: 9.61943572279018e-05 Time taken: 126.014s\n",
      "Epoch: 32 train rmse: 0.0880 train loss: 0.0077 val rmse: 0.0939 val loss: 0.0088 lr: 9.593996655139418e-05 Time taken: 126.302s\n",
      "Epoch: 33 train rmse: 0.0879 train loss: 0.0077 val rmse: 0.0991 val loss: 0.0098 lr: 9.567770515484182e-05 Time taken: 126.794s\n",
      "Epoch: 34 train rmse: 0.0877 train loss: 0.0077 val rmse: 0.1029 val loss: 0.0105 lr: 9.540761797538495e-05 Time taken: 126.245s\n",
      "Epoch: 35 train rmse: 0.0874 train loss: 0.0076 val rmse: 0.0933 val loss: 0.0086 lr: 9.51297512910713e-05 Time taken: 126.072s\n",
      "Epoch: 36 train rmse: 0.0873 train loss: 0.0076 val rmse: 0.0953 val loss: 0.0090 lr: 9.484415271292675e-05 Time taken: 126.848s\n",
      "Epoch: 37 train rmse: 0.0871 train loss: 0.0076 val rmse: 0.0941 val loss: 0.0088 lr: 9.455087117679744e-05 Time taken: 125.881s\n",
      "Epoch: 38 train rmse: 0.0870 train loss: 0.0076 val rmse: 0.0879 val loss: 0.0077 lr: 9.424995693496477e-05 Time taken: 126.966s\n",
      "Epoch: 39 train rmse: 0.0867 train loss: 0.0075 val rmse: 0.0884 val loss: 0.0078 lr: 9.394146154753495e-05 Time taken: 127.052s\n",
      "Epoch: 40 train rmse: 0.0867 train loss: 0.0075 val rmse: 0.1053 val loss: 0.0110 lr: 9.36254378736045e-05 Time taken: 126.955s\n",
      "Epoch: 41 train rmse: 0.0865 train loss: 0.0075 val rmse: 0.0887 val loss: 0.0078 lr: 9.330194006220301e-05 Time taken: 126.250s\n",
      "Epoch: 42 train rmse: 0.0862 train loss: 0.0074 val rmse: 0.0922 val loss: 0.0085 lr: 9.297102354301513e-05 Time taken: 125.748s\n",
      "Epoch: 43 train rmse: 0.0861 train loss: 0.0074 val rmse: 0.1003 val loss: 0.0100 lr: 9.263274501688284e-05 Time taken: 125.224s\n",
      "Epoch: 44 train rmse: 0.0858 train loss: 0.0074 val rmse: 0.0991 val loss: 0.0098 lr: 9.228716244609014e-05 Time taken: 124.843s\n",
      "Epoch: 45 train rmse: 0.0858 train loss: 0.0074 val rmse: 0.0909 val loss: 0.0082 lr: 9.193433504443147e-05 Time taken: 126.029s\n",
      "Epoch: 46 train rmse: 0.0856 train loss: 0.0073 val rmse: 0.0869 val loss: 0.0075 lr: 9.157432326706575e-05 Time taken: 126.614s\n",
      "Epoch: 47 train rmse: 0.0856 train loss: 0.0073 val rmse: 0.0891 val loss: 0.0079 lr: 9.120718880015766e-05 Time taken: 126.462s\n",
      "Epoch: 48 train rmse: 0.0854 train loss: 0.0073 val rmse: 0.0864 val loss: 0.0074 lr: 9.083299455030813e-05 Time taken: 124.670s\n",
      "Epoch: 49 train rmse: 0.0854 train loss: 0.0073 val rmse: 0.1012 val loss: 0.0102 lr: 9.04518046337755e-05 Time taken: 125.744s\n",
      "Epoch: 50 train rmse: 0.0853 train loss: 0.0073 val rmse: 0.0909 val loss: 0.0082 lr: 9.006368436548958e-05 Time taken: 125.868s\n",
      "Epoch: 51 train rmse: 0.0850 train loss: 0.0072 val rmse: 0.1016 val loss: 0.0103 lr: 8.96687002478603e-05 Time taken: 126.734s\n",
      "Epoch: 52 train rmse: 0.0848 train loss: 0.0072 val rmse: 0.1052 val loss: 0.0110 lr: 8.926691995938284e-05 Time taken: 126.107s\n",
      "Epoch: 53 train rmse: 0.0847 train loss: 0.0072 val rmse: 0.1013 val loss: 0.0102 lr: 8.885841234304126e-05 Time taken: 126.194s\n",
      "Epoch: 54 train rmse: 0.0846 train loss: 0.0072 val rmse: 0.0927 val loss: 0.0085 lr: 8.844324739451261e-05 Time taken: 125.520s\n",
      "Epoch: 55 train rmse: 0.0846 train loss: 0.0071 val rmse: 0.0866 val loss: 0.0075 lr: 8.802149625017355e-05 Time taken: 126.591s\n",
      "Epoch: 56 train rmse: 0.0844 train loss: 0.0071 val rmse: 0.0981 val loss: 0.0096 lr: 8.759323117491148e-05 Time taken: 126.146s\n",
      "Epoch: 57 train rmse: 0.0843 train loss: 0.0071 val rmse: 0.0875 val loss: 0.0076 lr: 8.715852554974232e-05 Time taken: 124.996s\n",
      "Epoch: 58 train rmse: 0.0841 train loss: 0.0071 val rmse: 0.1022 val loss: 0.0104 lr: 8.671745385923709e-05 Time taken: 126.475s\n",
      "Epoch: 59 train rmse: 0.0841 train loss: 0.0071 val rmse: 0.0969 val loss: 0.0093 lr: 8.627009167875931e-05 Time taken: 125.519s\n",
      "Epoch: 60 train rmse: 0.0840 train loss: 0.0071 val rmse: 0.0987 val loss: 0.0097 lr: 8.581651566151558e-05 Time taken: 125.586s\n",
      "Epoch: 61 train rmse: 0.0837 train loss: 0.0070 val rmse: 0.1059 val loss: 0.0111 lr: 8.535680352542144e-05 Time taken: 125.190s\n",
      "Epoch: 62 train rmse: 0.0837 train loss: 0.0070 val rmse: 0.0928 val loss: 0.0086 lr: 8.489103403978481e-05 Time taken: 125.960s\n",
      "Epoch: 63 train rmse: 0.0838 train loss: 0.0070 val rmse: 0.0944 val loss: 0.0089 lr: 8.441928701180924e-05 Time taken: 126.299s\n",
      "Epoch: 64 train rmse: 0.0836 train loss: 0.0070 val rmse: 0.1013 val loss: 0.0102 lr: 8.394164327291944e-05 Time taken: 125.263s\n",
      "Epoch: 65 train rmse: 0.0836 train loss: 0.0070 val rmse: 0.0911 val loss: 0.0083 lr: 8.345818466491113e-05 Time taken: 125.679s\n",
      "Epoch: 66 train rmse: 0.0833 train loss: 0.0069 val rmse: 0.0934 val loss: 0.0087 lr: 8.296899402592796e-05 Time taken: 126.502s\n",
      "Epoch: 67 train rmse: 0.0832 train loss: 0.0069 val rmse: 0.0878 val loss: 0.0077 lr: 8.247415517626754e-05 Time taken: 125.712s\n",
      "Epoch: 68 train rmse: 0.0831 train loss: 0.0069 val rmse: 0.0901 val loss: 0.0081 lr: 8.197375290401936e-05 Time taken: 126.079s\n",
      "Epoch: 69 train rmse: 0.0832 train loss: 0.0069 val rmse: 0.0889 val loss: 0.0079 lr: 8.146787295053664e-05 Time taken: 125.748s\n",
      "Epoch: 70 train rmse: 0.0831 train loss: 0.0069 val rmse: 0.0937 val loss: 0.0087 lr: 8.095660199574517e-05 Time taken: 125.433s\n",
      "Epoch: 71 train rmse: 0.0830 train loss: 0.0069 val rmse: 0.0974 val loss: 0.0094 lr: 8.0440027643291e-05 Time taken: 125.815s\n",
      "Epoch: 72 train rmse: 0.0828 train loss: 0.0068 val rmse: 0.0907 val loss: 0.0082 lr: 7.991823840553012e-05 Time taken: 125.038s\n",
      "Epoch: 73 train rmse: 0.0828 train loss: 0.0069 val rmse: 0.0943 val loss: 0.0088 lr: 7.939132368836222e-05 Time taken: 123.972s\n",
      "Epoch: 74 train rmse: 0.0827 train loss: 0.0068 val rmse: 0.0910 val loss: 0.0082 lr: 7.885937377591152e-05 Time taken: 125.567s\n",
      "Epoch: 75 train rmse: 0.0826 train loss: 0.0068 val rmse: 0.0912 val loss: 0.0083 lr: 7.832247981505703e-05 Time taken: 126.037s\n",
      "Epoch: 76 train rmse: 0.0825 train loss: 0.0068 val rmse: 0.0845 val loss: 0.0071 lr: 7.778073379981502e-05 Time taken: 126.597s\n",
      "Epoch: 77 train rmse: 0.0824 train loss: 0.0068 val rmse: 0.1125 val loss: 0.0126 lr: 7.72342285555763e-05 Time taken: 126.199s\n",
      "Epoch: 78 train rmse: 0.0823 train loss: 0.0068 val rmse: 0.1063 val loss: 0.0112 lr: 7.668305772320103e-05 Time taken: 126.762s\n",
      "Epoch: 79 train rmse: 0.0822 train loss: 0.0068 val rmse: 0.0854 val loss: 0.0073 lr: 7.612731574297388e-05 Time taken: 126.376s\n",
      "Epoch: 80 train rmse: 0.0823 train loss: 0.0068 val rmse: 0.0893 val loss: 0.0079 lr: 7.556709783842224e-05 Time taken: 125.262s\n",
      "Epoch: 81 train rmse: 0.0821 train loss: 0.0067 val rmse: 0.1179 val loss: 0.0138 lr: 7.500250000000002e-05 Time taken: 125.155s\n",
      "Epoch: 82 train rmse: 0.0821 train loss: 0.0067 val rmse: 0.0911 val loss: 0.0082 lr: 7.44336189686403e-05 Time taken: 125.868s\n",
      "Epoch: 83 train rmse: 0.0819 train loss: 0.0067 val rmse: 0.0935 val loss: 0.0087 lr: 7.386055221917916e-05 Time taken: 126.480s\n",
      "Epoch: 84 train rmse: 0.0820 train loss: 0.0067 val rmse: 0.0906 val loss: 0.0082 lr: 7.328339794365398e-05 Time taken: 126.594s\n",
      "Epoch: 85 train rmse: 0.0818 train loss: 0.0067 val rmse: 0.0970 val loss: 0.0093 lr: 7.270225503447868e-05 Time taken: 126.417s\n",
      "Epoch: 86 train rmse: 0.0818 train loss: 0.0067 val rmse: 0.0930 val loss: 0.0086 lr: 7.211722306749902e-05 Time taken: 126.586s\n",
      "Epoch: 87 train rmse: 0.0817 train loss: 0.0067 val rmse: 0.0843 val loss: 0.0071 lr: 7.152840228493076e-05 Time taken: 126.163s\n",
      "Epoch: 88 train rmse: 0.0816 train loss: 0.0066 val rmse: 0.1041 val loss: 0.0108 lr: 7.093589357818376e-05 Time taken: 124.384s\n",
      "Epoch: 89 train rmse: 0.0815 train loss: 0.0066 val rmse: 0.0933 val loss: 0.0086 lr: 7.033979847057468e-05 Time taken: 125.455s\n",
      "Epoch: 90 train rmse: 0.0815 train loss: 0.0066 val rmse: 0.1034 val loss: 0.0106 lr: 6.974021909993148e-05 Time taken: 123.810s\n",
      "Epoch: 91 train rmse: 0.0815 train loss: 0.0066 val rmse: 0.1002 val loss: 0.0100 lr: 6.913725820109271e-05 Time taken: 126.041s\n",
      "Epoch: 92 train rmse: 0.0813 train loss: 0.0066 val rmse: 0.0979 val loss: 0.0095 lr: 6.85310190883043e-05 Time taken: 124.517s\n",
      "Epoch: 93 train rmse: 0.0812 train loss: 0.0066 val rmse: 0.0876 val loss: 0.0076 lr: 6.792160563751734e-05 Time taken: 123.964s\n",
      "Epoch: 94 train rmse: 0.0813 train loss: 0.0066 val rmse: 0.0934 val loss: 0.0087 lr: 6.73091222685893e-05 Time taken: 124.835s\n",
      "Epoch: 95 train rmse: 0.0812 train loss: 0.0066 val rmse: 0.0991 val loss: 0.0098 lr: 6.669367392739241e-05 Time taken: 124.065s\n",
      "Epoch: 96 train rmse: 0.0810 train loss: 0.0066 val rmse: 0.0875 val loss: 0.0076 lr: 6.60753660678316e-05 Time taken: 125.343s\n",
      "Epoch: 97 train rmse: 0.0810 train loss: 0.0066 val rmse: 0.0905 val loss: 0.0081 lr: 6.545430463377554e-05 Time taken: 124.253s\n",
      "Epoch: 98 train rmse: 0.0811 train loss: 0.0066 val rmse: 0.1047 val loss: 0.0109 lr: 6.483059604090371e-05 Time taken: 125.767s\n",
      "Epoch: 99 train rmse: 0.0810 train loss: 0.0066 val rmse: 0.0837 val loss: 0.0070 lr: 6.420434715847266e-05 Time taken: 126.488s\n",
      "Epoch: 100 train rmse: 0.0808 train loss: 0.0065 val rmse: 0.0949 val loss: 0.0089 lr: 6.357566529100442e-05 Time taken: 125.789s\n",
      "Epoch: 101 train rmse: 0.0808 train loss: 0.0065 val rmse: 0.1016 val loss: 0.0103 lr: 6.294465815990056e-05 Time taken: 125.747s\n",
      "Epoch: 102 train rmse: 0.0808 train loss: 0.0065 val rmse: 0.0891 val loss: 0.0079 lr: 6.231143388498455e-05 Time taken: 125.684s\n",
      "Epoch: 103 train rmse: 0.0807 train loss: 0.0065 val rmse: 0.0839 val loss: 0.0070 lr: 6.167610096597603e-05 Time taken: 125.469s\n",
      "Epoch: 104 train rmse: 0.0805 train loss: 0.0065 val rmse: 0.0846 val loss: 0.0071 lr: 6.10387682639e-05 Time taken: 123.721s\n",
      "Epoch: 105 train rmse: 0.0805 train loss: 0.0065 val rmse: 0.0973 val loss: 0.0094 lr: 6.0399544982433905e-05 Time taken: 124.104s\n",
      "Epoch: 106 train rmse: 0.0805 train loss: 0.0065 val rmse: 0.1049 val loss: 0.0109 lr: 5.975854064919638e-05 Time taken: 124.242s\n",
      "Epoch: 107 train rmse: 0.0803 train loss: 0.0065 val rmse: 0.0877 val loss: 0.0076 lr: 5.911586509697995e-05 Time taken: 125.381s\n",
      "Epoch: 108 train rmse: 0.0803 train loss: 0.0064 val rmse: 0.1120 val loss: 0.0125 lr: 5.847162844493202e-05 Time taken: 126.329s\n",
      "Epoch: 109 train rmse: 0.0803 train loss: 0.0064 val rmse: 0.0826 val loss: 0.0068 lr: 5.7825941079686376e-05 Time taken: 126.181s\n",
      "Epoch: 110 train rmse: 0.0802 train loss: 0.0064 val rmse: 0.0840 val loss: 0.0070 lr: 5.7178913636449055e-05 Time taken: 124.468s\n",
      "Epoch: 111 train rmse: 0.0801 train loss: 0.0064 val rmse: 0.0938 val loss: 0.0087 lr: 5.653065698004151e-05 Time taken: 125.977s\n",
      "Epoch: 112 train rmse: 0.0802 train loss: 0.0064 val rmse: 0.0922 val loss: 0.0084 lr: 5.588128218590463e-05 Time taken: 126.428s\n",
      "Epoch: 113 train rmse: 0.0800 train loss: 0.0064 val rmse: 0.0837 val loss: 0.0070 lr: 5.523090052106637e-05 Time taken: 124.825s\n",
      "Epoch: 114 train rmse: 0.0800 train loss: 0.0064 val rmse: 0.1232 val loss: 0.0151 lr: 5.457962342507685e-05 Time taken: 125.191s\n",
      "Epoch: 115 train rmse: 0.0800 train loss: 0.0064 val rmse: 0.0883 val loss: 0.0077 lr: 5.392756249091365e-05 Time taken: 127.157s\n",
      "Epoch: 116 train rmse: 0.0798 train loss: 0.0064 val rmse: 0.0905 val loss: 0.0081 lr: 5.327482944586104e-05 Time taken: 126.548s\n",
      "Epoch: 117 train rmse: 0.0799 train loss: 0.0064 val rmse: 0.0983 val loss: 0.0096 lr: 5.2621536132366004e-05 Time taken: 126.875s\n",
      "Epoch: 118 train rmse: 0.0798 train loss: 0.0064 val rmse: 0.0947 val loss: 0.0089 lr: 5.196779448887467e-05 Time taken: 126.639s\n",
      "Epoch: 119 train rmse: 0.0798 train loss: 0.0064 val rmse: 0.1054 val loss: 0.0110 lr: 5.131371653065215e-05 Time taken: 126.070s\n",
      "Epoch: 120 train rmse: 0.0797 train loss: 0.0064 val rmse: 0.0887 val loss: 0.0078 lr: 5.065941433058941e-05 Time taken: 125.693s\n",
      "Epoch: 121 train rmse: 0.0796 train loss: 0.0063 val rmse: 0.0946 val loss: 0.0089 lr: 5.000500000000004e-05 Time taken: 126.143s\n",
      "Epoch: 122 train rmse: 0.0796 train loss: 0.0063 val rmse: 0.0817 val loss: 0.0066 lr: 4.935058566941066e-05 Time taken: 127.178s\n",
      "Epoch: 123 train rmse: 0.0795 train loss: 0.0063 val rmse: 0.0867 val loss: 0.0075 lr: 4.869628346934792e-05 Time taken: 126.997s\n",
      "Epoch: 124 train rmse: 0.0795 train loss: 0.0063 val rmse: 0.0927 val loss: 0.0085 lr: 4.8042205511125396e-05 Time taken: 126.105s\n",
      "Epoch: 125 train rmse: 0.0794 train loss: 0.0063 val rmse: 0.0910 val loss: 0.0082 lr: 4.7388463867634057e-05 Time taken: 126.129s\n",
      "Epoch: 126 train rmse: 0.0793 train loss: 0.0063 val rmse: 0.0816 val loss: 0.0066 lr: 4.673517055413903e-05 Time taken: 126.617s\n",
      "Epoch: 127 train rmse: 0.0794 train loss: 0.0063 val rmse: 0.1127 val loss: 0.0126 lr: 4.6082437509086424e-05 Time taken: 126.185s\n",
      "Epoch: 128 train rmse: 0.0792 train loss: 0.0063 val rmse: 0.1041 val loss: 0.0108 lr: 4.5430376574923226e-05 Time taken: 126.624s\n",
      "Epoch: 129 train rmse: 0.0792 train loss: 0.0063 val rmse: 0.0902 val loss: 0.0081 lr: 4.47790994789337e-05 Time taken: 126.713s\n",
      "Epoch: 130 train rmse: 0.0792 train loss: 0.0063 val rmse: 0.0849 val loss: 0.0072 lr: 4.4128717814095444e-05 Time taken: 125.854s\n",
      "Epoch: 131 train rmse: 0.0791 train loss: 0.0063 val rmse: 0.0848 val loss: 0.0071 lr: 4.347934301995856e-05 Time taken: 125.228s\n",
      "Epoch: 132 train rmse: 0.0792 train loss: 0.0063 val rmse: 0.0863 val loss: 0.0074 lr: 4.283108636355102e-05 Time taken: 126.045s\n",
      "Epoch: 133 train rmse: 0.0791 train loss: 0.0063 val rmse: 0.0850 val loss: 0.0072 lr: 4.218405892031369e-05 Time taken: 126.397s\n",
      "Epoch: 134 train rmse: 0.0790 train loss: 0.0062 val rmse: 0.1104 val loss: 0.0121 lr: 4.1538371555068045e-05 Time taken: 125.800s\n",
      "Epoch: 135 train rmse: 0.0790 train loss: 0.0062 val rmse: 0.0866 val loss: 0.0074 lr: 4.089413490302012e-05 Time taken: 125.527s\n",
      "Epoch: 136 train rmse: 0.0788 train loss: 0.0062 val rmse: 0.0926 val loss: 0.0085 lr: 4.0251459350803696e-05 Time taken: 126.832s\n",
      "Epoch: 137 train rmse: 0.0789 train loss: 0.0062 val rmse: 0.1061 val loss: 0.0112 lr: 3.961045501756615e-05 Time taken: 125.207s\n",
      "Epoch: 138 train rmse: 0.0787 train loss: 0.0062 val rmse: 0.0986 val loss: 0.0097 lr: 3.897123173610007e-05 Time taken: 125.758s\n",
      "Epoch: 139 train rmse: 0.0787 train loss: 0.0062 val rmse: 0.0862 val loss: 0.0074 lr: 3.833389903402404e-05 Time taken: 125.461s\n",
      "Epoch: 140 train rmse: 0.0787 train loss: 0.0062 val rmse: 0.0817 val loss: 0.0066 lr: 3.769856611501552e-05 Time taken: 124.658s\n",
      "Epoch: 141 train rmse: 0.0788 train loss: 0.0062 val rmse: 0.1064 val loss: 0.0112 lr: 3.7065341840099506e-05 Time taken: 125.424s\n",
      "Epoch: 142 train rmse: 0.0786 train loss: 0.0062 val rmse: 0.0982 val loss: 0.0096 lr: 3.643433470899564e-05 Time taken: 126.336s\n",
      "Epoch: 143 train rmse: 0.0785 train loss: 0.0062 val rmse: 0.0929 val loss: 0.0086 lr: 3.5805652841527406e-05 Time taken: 125.714s\n",
      "Epoch: 144 train rmse: 0.0784 train loss: 0.0061 val rmse: 0.0841 val loss: 0.0070 lr: 3.517940395909634e-05 Time taken: 126.672s\n",
      "Epoch: 145 train rmse: 0.0785 train loss: 0.0062 val rmse: 0.0875 val loss: 0.0076 lr: 3.4555695366224525e-05 Time taken: 126.744s\n",
      "Epoch: 146 train rmse: 0.0785 train loss: 0.0062 val rmse: 0.0826 val loss: 0.0068 lr: 3.393463393216847e-05 Time taken: 126.604s\n",
      "Epoch: 147 train rmse: 0.0784 train loss: 0.0061 val rmse: 0.0866 val loss: 0.0075 lr: 3.3316326072607647e-05 Time taken: 126.092s\n",
      "Epoch: 148 train rmse: 0.0783 train loss: 0.0061 val rmse: 0.0843 val loss: 0.0071 lr: 3.270087773141076e-05 Time taken: 126.964s\n",
      "Epoch: 149 train rmse: 0.0782 train loss: 0.0061 val rmse: 0.0921 val loss: 0.0084 lr: 3.2088394362482735e-05 Time taken: 126.218s\n",
      "Epoch: 150 train rmse: 0.0782 train loss: 0.0061 val rmse: 0.0867 val loss: 0.0075 lr: 3.147898091169576e-05 Time taken: 127.023s\n",
      "Epoch: 151 train rmse: 0.0782 train loss: 0.0061 val rmse: 0.0868 val loss: 0.0075 lr: 3.087274179890736e-05 Time taken: 126.864s\n",
      "Epoch: 152 train rmse: 0.0782 train loss: 0.0061 val rmse: 0.0819 val loss: 0.0067 lr: 3.0269780900068585e-05 Time taken: 126.529s\n",
      "Epoch: 153 train rmse: 0.0781 train loss: 0.0061 val rmse: 0.1021 val loss: 0.0104 lr: 2.9670201529425396e-05 Time taken: 124.609s\n",
      "Epoch: 154 train rmse: 0.0781 train loss: 0.0061 val rmse: 0.0949 val loss: 0.0089 lr: 2.9074106421816292e-05 Time taken: 125.878s\n",
      "Epoch: 155 train rmse: 0.0779 train loss: 0.0061 val rmse: 0.0933 val loss: 0.0086 lr: 2.8481597715069314e-05 Time taken: 125.841s\n",
      "Epoch: 156 train rmse: 0.0779 train loss: 0.0061 val rmse: 0.0823 val loss: 0.0067 lr: 2.7892776932501058e-05 Time taken: 126.384s\n",
      "Epoch: 157 train rmse: 0.0779 train loss: 0.0061 val rmse: 0.0845 val loss: 0.0071 lr: 2.7307744965521374e-05 Time taken: 125.591s\n",
      "Epoch: 158 train rmse: 0.0780 train loss: 0.0061 val rmse: 0.0981 val loss: 0.0096 lr: 2.672660205634607e-05 Time taken: 126.947s\n",
      "Epoch: 159 train rmse: 0.0778 train loss: 0.0060 val rmse: 0.0944 val loss: 0.0089 lr: 2.614944778082089e-05 Time taken: 126.404s\n",
      "Epoch: 160 train rmse: 0.0779 train loss: 0.0061 val rmse: 0.0823 val loss: 0.0067 lr: 2.5576381031359744e-05 Time taken: 124.908s\n",
      "Epoch: 161 train rmse: 0.0778 train loss: 0.0060 val rmse: 0.0918 val loss: 0.0084 lr: 2.500750000000002e-05 Time taken: 125.069s\n",
      "Epoch: 162 train rmse: 0.0777 train loss: 0.0060 val rmse: 0.0916 val loss: 0.0083 lr: 2.4442902161577797e-05 Time taken: 124.911s\n",
      "Epoch: 163 train rmse: 0.0777 train loss: 0.0060 val rmse: 0.1010 val loss: 0.0101 lr: 2.3882684257026148e-05 Time taken: 126.391s\n",
      "Epoch: 164 train rmse: 0.0778 train loss: 0.0060 val rmse: 0.1002 val loss: 0.0100 lr: 2.3326942276799014e-05 Time taken: 126.015s\n",
      "Epoch: 165 train rmse: 0.0776 train loss: 0.0060 val rmse: 0.0913 val loss: 0.0083 lr: 2.2775771444423748e-05 Time taken: 126.584s\n",
      "Epoch: 166 train rmse: 0.0775 train loss: 0.0060 val rmse: 0.0952 val loss: 0.0090 lr: 2.222926620018501e-05 Time taken: 126.701s\n",
      "Epoch: 167 train rmse: 0.0776 train loss: 0.0060 val rmse: 0.0817 val loss: 0.0066 lr: 2.1687520184942983e-05 Time taken: 126.546s\n",
      "Epoch: 168 train rmse: 0.0775 train loss: 0.0060 val rmse: 0.0806 val loss: 0.0065 lr: 2.1150626224088504e-05 Time taken: 126.267s\n",
      "Epoch: 169 train rmse: 0.0775 train loss: 0.0060 val rmse: 0.0812 val loss: 0.0066 lr: 2.061867631163782e-05 Time taken: 125.236s\n",
      "Epoch: 170 train rmse: 0.0774 train loss: 0.0060 val rmse: 0.0852 val loss: 0.0072 lr: 2.009176159446991e-05 Time taken: 126.342s\n",
      "Epoch: 171 train rmse: 0.0774 train loss: 0.0060 val rmse: 0.0973 val loss: 0.0094 lr: 1.956997235670902e-05 Time taken: 126.460s\n",
      "Epoch: 172 train rmse: 0.0774 train loss: 0.0060 val rmse: 0.0886 val loss: 0.0078 lr: 1.905339800425487e-05 Time taken: 126.291s\n",
      "Epoch: 173 train rmse: 0.0773 train loss: 0.0060 val rmse: 0.0845 val loss: 0.0071 lr: 1.8542127049463394e-05 Time taken: 125.950s\n",
      "Epoch: 174 train rmse: 0.0773 train loss: 0.0060 val rmse: 0.0830 val loss: 0.0068 lr: 1.8036247095980678e-05 Time taken: 125.994s\n",
      "Epoch: 175 train rmse: 0.0773 train loss: 0.0060 val rmse: 0.0804 val loss: 0.0064 lr: 1.7535844823732483e-05 Time taken: 125.779s\n",
      "Epoch: 176 train rmse: 0.0773 train loss: 0.0060 val rmse: 0.0816 val loss: 0.0066 lr: 1.7041005974072082e-05 Time taken: 126.426s\n",
      "Epoch: 177 train rmse: 0.0771 train loss: 0.0059 val rmse: 0.0803 val loss: 0.0064 lr: 1.6551815335088908e-05 Time taken: 125.465s\n",
      "Epoch: 178 train rmse: 0.0772 train loss: 0.0060 val rmse: 0.0930 val loss: 0.0086 lr: 1.6068356727080583e-05 Time taken: 126.519s\n",
      "Epoch: 179 train rmse: 0.0771 train loss: 0.0059 val rmse: 0.0806 val loss: 0.0065 lr: 1.559071298819077e-05 Time taken: 126.654s\n",
      "Epoch: 180 train rmse: 0.0772 train loss: 0.0060 val rmse: 0.0863 val loss: 0.0074 lr: 1.5118965960215216e-05 Time taken: 126.770s\n",
      "Epoch: 181 train rmse: 0.0771 train loss: 0.0059 val rmse: 0.0810 val loss: 0.0065 lr: 1.4653196474578565e-05 Time taken: 126.377s\n",
      "Epoch: 182 train rmse: 0.0770 train loss: 0.0059 val rmse: 0.0807 val loss: 0.0065 lr: 1.4193484338484415e-05 Time taken: 125.690s\n",
      "Epoch: 183 train rmse: 0.0770 train loss: 0.0059 val rmse: 0.0805 val loss: 0.0064 lr: 1.3739908321240685e-05 Time taken: 126.655s\n",
      "Epoch: 184 train rmse: 0.0770 train loss: 0.0059 val rmse: 0.0939 val loss: 0.0087 lr: 1.3292546140762918e-05 Time taken: 125.729s\n",
      "Epoch: 185 train rmse: 0.0769 train loss: 0.0059 val rmse: 0.0885 val loss: 0.0078 lr: 1.2851474450257691e-05 Time taken: 125.408s\n",
      "Epoch: 186 train rmse: 0.0769 train loss: 0.0059 val rmse: 0.0813 val loss: 0.0066 lr: 1.2416768825088536e-05 Time taken: 126.385s\n",
      "Epoch: 187 train rmse: 0.0769 train loss: 0.0059 val rmse: 0.0845 val loss: 0.0071 lr: 1.1988503749826459e-05 Time taken: 125.634s\n",
      "Epoch: 188 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0811 val loss: 0.0065 lr: 1.1566752605487399e-05 Time taken: 125.485s\n",
      "Epoch: 189 train rmse: 0.0769 train loss: 0.0059 val rmse: 0.0801 val loss: 0.0064 lr: 1.1151587656958745e-05 Time taken: 126.511s\n",
      "Epoch: 190 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0836 val loss: 0.0069 lr: 1.0743080040617157e-05 Time taken: 125.540s\n",
      "Epoch: 191 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0799 val loss: 0.0063 lr: 1.0341299752139698e-05 Time taken: 125.913s\n",
      "Epoch: 192 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0803 val loss: 0.0064 lr: 9.946315634510434e-06 Time taken: 126.076s\n",
      "Epoch: 193 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0799 val loss: 0.0063 lr: 9.558195366224514e-06 Time taken: 126.660s\n",
      "Epoch: 194 train rmse: 0.0767 train loss: 0.0059 val rmse: 0.0908 val loss: 0.0082 lr: 9.17700544969187e-06 Time taken: 126.385s\n",
      "Epoch: 195 train rmse: 0.0768 train loss: 0.0059 val rmse: 0.0801 val loss: 0.0064 lr: 8.802811199842342e-06 Time taken: 125.799s\n",
      "Epoch: 196 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0805 val loss: 0.0064 lr: 8.435676732934267e-06 Time taken: 125.044s\n",
      "Epoch: 197 train rmse: 0.0767 train loss: 0.0059 val rmse: 0.0800 val loss: 0.0064 lr: 8.075664955568535e-06 Time taken: 125.197s\n",
      "Epoch: 198 train rmse: 0.0767 train loss: 0.0059 val rmse: 0.0803 val loss: 0.0064 lr: 7.722837553909865e-06 Time taken: 125.988s\n",
      "Epoch: 199 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0804 val loss: 0.0064 lr: 7.377254983117167e-06 Time taken: 125.828s\n",
      "Epoch: 200 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0799 val loss: 0.0063 lr: 7.038976456984878e-06 Time taken: 126.611s\n",
      "Epoch: 201 train rmse: 0.0765 train loss: 0.0059 val rmse: 0.0798 val loss: 0.0063 lr: 6.708059937796992e-06 Time taken: 126.276s\n",
      "Epoch: 202 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0801 val loss: 0.0064 lr: 6.38456212639551e-06 Time taken: 126.193s\n",
      "Epoch: 203 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0813 val loss: 0.0066 lr: 6.068538452465051e-06 Time taken: 126.076s\n",
      "Epoch: 204 train rmse: 0.0765 train loss: 0.0058 val rmse: 0.0817 val loss: 0.0066 lr: 5.760043065035232e-06 Time taken: 126.581s\n",
      "Epoch: 205 train rmse: 0.0766 train loss: 0.0059 val rmse: 0.0802 val loss: 0.0064 lr: 5.459128823202557e-06 Time taken: 126.834s\n",
      "Epoch: 206 train rmse: 0.0765 train loss: 0.0059 val rmse: 0.0801 val loss: 0.0064 lr: 5.1658472870732555e-06 Time taken: 126.823s\n",
      "Epoch: 207 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0835 val loss: 0.0069 lr: 4.880248708928734e-06 Time taken: 126.126s\n",
      "Epoch: 208 train rmse: 0.0765 train loss: 0.0059 val rmse: 0.0805 val loss: 0.0064 lr: 4.602382024615073e-06 Time taken: 126.031s\n",
      "Epoch: 209 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0798 val loss: 0.0063 lr: 4.332294845158169e-06 Time taken: 126.432s\n",
      "Epoch: 210 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0798 val loss: 0.0063 lr: 4.070033448605828e-06 Time taken: 126.537s\n",
      "Epoch: 211 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0806 val loss: 0.0065 lr: 3.815642772098224e-06 Time taken: 126.754s\n",
      "Epoch: 212 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 3.569166404168152e-06 Time taken: 126.142s\n",
      "Epoch: 213 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 3.330646577272403e-06 Time taken: 126.328s\n",
      "Epoch: 214 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0832 val loss: 0.0069 lr: 3.1001241605554086e-06 Time taken: 126.449s\n",
      "Epoch: 215 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 2.8776386528465487e-06 Time taken: 126.724s\n",
      "Epoch: 216 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0798 val loss: 0.0063 lr: 2.6632281758922e-06 Time taken: 127.176s\n",
      "Epoch: 217 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 2.4569294678238022e-06 Time taken: 126.291s\n",
      "Epoch: 218 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0798 val loss: 0.0063 lr: 2.2587778768629628e-06 Time taken: 125.034s\n",
      "Epoch: 219 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 2.0688073552646966e-06 Time taken: 125.608s\n",
      "Epoch: 220 train rmse: 0.0764 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.8870504534999068e-06 Time taken: 125.700s\n",
      "Epoch: 221 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.713538314678032e-06 Time taken: 126.150s\n",
      "Epoch: 222 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0798 val loss: 0.0063 lr: 1.548300669210821e-06 Time taken: 125.480s\n",
      "Epoch: 223 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.3913658297181623e-06 Time taken: 125.106s\n",
      "Epoch: 224 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.2427606861769112e-06 Time taken: 125.695s\n",
      "Epoch: 225 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.102510701313386e-06 Time taken: 125.228s\n",
      "Epoch: 226 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 9.70639906240496e-07 Time taken: 124.586s\n",
      "Epoch: 227 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 8.471708963400957e-07 Time taken: 125.355s\n",
      "Epoch: 228 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 7.321248273914039e-07 Time taken: 125.789s\n",
      "Epoch: 229 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 6.255214119460936e-07 Time taken: 126.505s\n",
      "Epoch: 230 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 5.273789159505924e-07 Time taken: 125.175s\n",
      "Epoch: 231 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0797 val loss: 0.0063 lr: 4.3771415561635046e-07 Time taken: 125.529s\n",
      "Epoch: 232 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 3.5654249453846036e-07 Time taken: 126.069s\n",
      "Epoch: 233 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 2.8387784106317164e-07 Time taken: 126.376s\n",
      "Epoch: 234 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 2.1973264590479065e-07 Time taken: 125.339s\n",
      "Epoch: 235 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.6411790001226763e-07 Time taken: 127.239s\n",
      "Epoch: 236 train rmse: 0.0763 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.1704313268601918e-07 Time taken: 125.853s\n",
      "Epoch: 237 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 7.851640994508127e-08 Time taken: 125.386s\n",
      "Epoch: 238 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 4.8544333145058606e-08 Time taken: 126.156s\n",
      "Epoch: 239 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 2.7132037847014812e-08 Time taken: 126.863s\n",
      "Epoch: 240 train rmse: 0.0762 train loss: 0.0058 val rmse: 0.0796 val loss: 0.0063 lr: 1.4283192937520027e-08 Time taken: 125.995s\n"
     ]
    }
   ],
   "source": [
    "train(\n",
    "    model,\n",
    "    num_epochs,\n",
    "    lr_scheduler,\n",
    "    optimizer,\n",
    "    train_dl,\n",
    "    valid_dl,\n",
    "    BATCH_SIZE,\n",
    "    hist,\n",
    "    lr_hist,\n",
    "    save_every_n_epochs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_arr = np.arange(len(hist[0])) + 1\n",
    "\n",
    "fig = plt.figure(figsize=(12, 4))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.plot(x_arr, hist[0], \"-o\", label=\"Train loss\")\n",
    "if FRACTION_FILES_VALIDATION > 0.0:\n",
    "    ax.plot(x_arr, hist[1], \"--<\", label=\"Validation loss\")\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel(\"Epoch\", size=15)\n",
    "ax.set_ylabel(\"Loss\", size=15)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.plot(x_arr, hist[2], \"-o\", label=\"Train RMSE\")\n",
    "if FRACTION_FILES_VALIDATION > 0.0:\n",
    "    ax.plot(x_arr, hist[3], \"--<\", label=\"Validation RMSE\")\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel(\"Epoch\", size=15)\n",
    "ax.set_ylabel(\"RMSE\", size=15)\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "ax.plot(x_arr, lr_hist, \"-o\", label=\"lr rate\")\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel(\"Epoch\", size=15)\n",
    "ax.set_ylabel(\"learning rate\", size=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / f\"{FILE_PREFIX}_lr{learning_rate}.png\")\n",
    "plt.show()\n",
    "\n",
    "with open(OUTPUT_DIR / f\"{FILE_PREFIX}_hist.pkl\", \"wb\") as file:\n",
    "    pickle.dump(hist, file)\n",
    "\n",
    "with open(OUTPUT_DIR / f\"{FILE_PREFIX}_lr_hist.pkl\", \"wb\") as file:\n",
    "    pickle.dump(lr_hist, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of test files: 79975\n"
     ]
    }
   ],
   "source": [
    "final_test_img_path = DATA_FOLDER / \"images_test_rev1\"\n",
    "final_test_file_list = sorted([str(path) for path in final_test_img_path.glob(\"*.jpg\")])\n",
    "print(f\"Number of test files: {len(final_test_file_list)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FinalTestImageDataset(Dataset):\n",
    "    def __init__(self, file_list, transform=None):\n",
    "        self.file_list = file_list\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        file = self.file_list[index]\n",
    "        image = Image.open(file)\n",
    "\n",
    "        if self.transform is not None:\n",
    "            image = self.transform(image)\n",
    "\n",
    "        gxy_id = Path(file).stem\n",
    "\n",
    "        return image, gxy_id\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.file_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FixedRotation:\n",
    "    def __init__(self, angle):\n",
    "        self.angle = angle\n",
    "\n",
    "    def __call__(self, img):\n",
    "        return transforms.functional.rotate(img, self.angle)\n",
    "\n",
    "\n",
    "class HorizontalFlip:\n",
    "    def __call__(self, img):\n",
    "        return transforms.functional.hflip(img)\n",
    "\n",
    "\n",
    "class Identity:\n",
    "    def __call__(self, x):\n",
    "        return x\n",
    "\n",
    "\n",
    "class TransformOutput:\n",
    "    def __init__(self):\n",
    "        fixed_rotates = [\n",
    "            FixedRotation((360.0 / OUTPUT_NUM_ROTATIONS) * i)\n",
    "            for i in range(OUTPUT_NUM_ROTATIONS)\n",
    "        ]\n",
    "        hflip = HorizontalFlip()\n",
    "        identity = Identity()\n",
    "        transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.CenterCrop([IMAGE_SIZE_BEFORE_SCALING, IMAGE_SIZE_BEFORE_SCALING]),\n",
    "                transforms.Resize([128, 128]),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        if OUTPUT_REFLECTION_FLAG is True:\n",
    "            all_combinations = list(itertools.product(fixed_rotates, [hflip, identity]))\n",
    "        else:\n",
    "            all_combinations = list(itertools.product(fixed_rotates))\n",
    "        self.all_transforms = [\n",
    "            transforms.Compose([*comb, transform]) for comb in all_combinations\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 79906 / 79975\n",
      "Writing test results to submission file...\n",
      "Progress: 79975 / 79975\n",
      "Done!\n",
      "Getting test results from model...\n",
      "Progress: 78436 / 79975\r"
     ]
    }
   ],
   "source": [
    "num_output_files = 0\n",
    "for transform in TransformOutput().all_transforms:\n",
    "    final_test_dataset = FinalTestImageDataset(final_test_file_list, transform)\n",
    "    final_test_dl = DataLoader(\n",
    "        final_test_dataset,\n",
    "        num_workers=0,\n",
    "        persistent_workers=False,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=False,\n",
    "    )\n",
    "\n",
    "    file_name = f\"submission{int(num_output_files)}.csv\"\n",
    "    with open(file_name, \"w\", newline=\"\") as submission_file:\n",
    "        writer = csv.writer(submission_file)\n",
    "        writer.writerow(\n",
    "            [\n",
    "                \"GalaxyID\",\n",
    "                \"Class1.1\",\n",
    "                \"Class1.2\",\n",
    "                \"Class1.3\",\n",
    "                \"Class2.1\",\n",
    "                \"Class2.2\",\n",
    "                \"Class3.1\",\n",
    "                \"Class3.2\",\n",
    "                \"Class4.1\",\n",
    "                \"Class4.2\",\n",
    "                \"Class5.1\",\n",
    "                \"Class5.2\",\n",
    "                \"Class5.3\",\n",
    "                \"Class5.4\",\n",
    "                \"Class6.1\",\n",
    "                \"Class6.2\",\n",
    "                \"Class7.1\",\n",
    "                \"Class7.2\",\n",
    "                \"Class7.3\",\n",
    "                \"Class8.1\",\n",
    "                \"Class8.2\",\n",
    "                \"Class8.3\",\n",
    "                \"Class8.4\",\n",
    "                \"Class8.5\",\n",
    "                \"Class8.6\",\n",
    "                \"Class8.7\",\n",
    "                \"Class9.1\",\n",
    "                \"Class9.2\",\n",
    "                \"Class9.3\",\n",
    "                \"Class10.1\",\n",
    "                \"Class10.2\",\n",
    "                \"Class10.3\",\n",
    "                \"Class11.1\",\n",
    "                \"Class11.2\",\n",
    "                \"Class11.3\",\n",
    "                \"Class11.4\",\n",
    "                \"Class11.5\",\n",
    "                \"Class11.6\",\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        print(\"Getting test results from model...\")\n",
    "        model.eval()\n",
    "        all_preds_soft_labels = np.zeros(\n",
    "            (len(final_test_dataset), SUBCLASS_LEN_CUMSUM[-1])\n",
    "        )\n",
    "        gxy_ids = np.zeros((len(final_test_dataset),))\n",
    "        i = 0\n",
    "        with torch.no_grad():\n",
    "            for img_batch, gxy_id_batch in final_test_dl:\n",
    "                print(f\"Progress: {i + 1} / {len(all_preds_soft_labels)}\", end=\"\\r\")\n",
    "                img_batch = img_batch.to(device)\n",
    "                pred = model(img_batch)\n",
    "                pred_soft_labels = pred.cpu().numpy()\n",
    "\n",
    "                if len(pred_soft_labels) == BATCH_SIZE:\n",
    "                    all_preds_soft_labels[i : (i + BATCH_SIZE)] = pred_soft_labels\n",
    "                    gxy_ids[i : (i + BATCH_SIZE)] = gxy_id_batch\n",
    "                    i += BATCH_SIZE\n",
    "                else:\n",
    "                    all_preds_soft_labels[i:] = pred_soft_labels\n",
    "                    gxy_ids[i:] = gxy_id_batch\n",
    "                    i += len(pred_soft_labels)\n",
    "                    break\n",
    "\n",
    "        print()\n",
    "        print(\"Writing test results to submission file...\")\n",
    "        for i in range(len(all_preds_soft_labels)):\n",
    "            print(f\"Progress: {i + 1} / {len(all_preds_soft_labels)}\", end=\"\\r\")\n",
    "            output_list = [int(gxy_ids[i])] + all_preds_soft_labels[i].tolist()\n",
    "            writer.writerow(output_list)\n",
    "\n",
    "        print()\n",
    "        print(\"Done!\")\n",
    "        num_output_files += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Averaging outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_averaging(file_list):\n",
    "    n_files = len(file_list)\n",
    "    avg_df = pd.read_csv(file_list[0])\n",
    "    first_column = avg_df[[\"GalaxyID\"]].copy()\n",
    "    for i in range(1, n_files):\n",
    "        avg_df += pd.read_csv(file_list[i])\n",
    "    avg_df /= n_files\n",
    "    avg_df = avg_df.drop(columns=[\"GalaxyID\"])\n",
    "    avg_df = pd.concat([first_column, avg_df], axis=1)\n",
    "    avg_df.to_csv(f\"submission.csv\", index=False)\n",
    "\n",
    "\n",
    "file_list = [f\"submission{int(i)}.csv\" for i in range(64)]\n",
    "simple_averaging(file_list)"
   ]
  }
 ],
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