Pyramid_Hir_Transformer_Demo.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_dFGvUw047g",
        "outputId": "18d60ffd-876e-45b4-afaf-b7315d921d30"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-09-14 10:39:06.970695: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2024-09-14 10:39:07.005238: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-09-14 10:39:07.005278: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-09-14 10:39:07.006162: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-09-14 10:39:07.013083: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-09-14 10:39:07.650832: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import time\n",
        "import warnings\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy.io as sio\n",
        "import seaborn as sns\n",
        "import spectral\n",
        "import spectral.io.envi as envi\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import (accuracy_score, classification_report,\n",
        "                             cohen_kappa_score, confusion_matrix)\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import (Conv3D, Dense, Dropout, Flatten, Input, concatenate, Reshape)\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import layers, Model\n",
        "from keras.losses import categorical_crossentropy\n",
        "from keras.models import Model, Sequential\n",
        "from tensorflow.keras.layers import (Activation, Lambda, multiply)\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers import legacy\n",
        "from operator import truediv\n",
        "from keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnoyZdRF2QFl"
      },
      "source": [
        "**Load HSI Datasets**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3g1EnHI1NAj",
        "outputId": "0cd5a380-1ba9-473f-fbd1-9b3a2bc39faa"
      },
      "outputs": [],
      "source": [
        "## Loading Datasets\n",
        "def LoadHSIData(method):\n",
        "    data_path = os.path.join(os.getcwd(),'../HSI/')\n",
        "    if method == 'PU':\n",
        "        HSI = sio.loadmat(os.path.join(data_path, 'PaviaU.mat'))['paviaU']\n",
        "        GT = sio.loadmat(os.path.join(data_path, 'PaviaU_gt.mat'))['paviaU_gt']\n",
        "        Num_Classes = 9\n",
        "        target_names = ['Asphalt','Meadows','Gravel','Trees', 'Painted','Soil','Bitumen',\n",
        "                        'Bricks','Shadows']\n",
        "    elif method == 'SA':\n",
        "        HSI = sio.loadmat(os.path.join(data_path, 'Salinas_corrected.mat'))['salinas_corrected']\n",
        "        GT = sio.loadmat(os.path.join(data_path, 'Salinas_gt.mat'))['salinas_gt']\n",
        "        Num_Classes = 16\n",
        "        target_names = ['Weeds_1','Weeds_2','Fallow',\n",
        "                        'Fallow_rough_plow','Fallow_smooth', 'Stubble','Celery',\n",
        "                        'Grapes_untrained','Soil_vinyard_develop','Corn_Weeds',\n",
        "                        'Lettuce_4wk','Lettuce_5wk','Lettuce_6wk',\n",
        "                        'Lettuce_7wk', 'Vinyard_untrained','Vinyard_trellis']\n",
        "    elif method == 'UH':\n",
        "      HSI = sio.loadmat(os.path.join(data_path, 'HU.mat'))['HSI']\n",
        "      GT = sio.loadmat(os.path.join(data_path, 'HU_gt.mat'))['gt']\n",
        "      Num_Classes = 15\n",
        "      target_names = ['Healthy grass', 'Stressed grass', 'Synthetic grass', 'Trees',\n",
        "                    'Soil', 'Water', 'Residential', 'Commercial', 'Road',\n",
        "                    'Highway', 'Railway', 'Parking Lot 1', 'Parking Lot 2',\n",
        "                    'Tennis Court', 'Running Track']\n",
        "    return HSI, GT, Num_Classes, target_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gcwd4TP22U0_"
      },
      "source": [
        "**Important Internal Functions**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "mK-OGPKL2VES"
      },
      "outputs": [],
      "source": [
        "## Prediction Model\n",
        "def PreModel(Actual, model):\n",
        "  ## Validation Prediction Model\n",
        "  prediction = model.predict(Actual)\n",
        "  argmax_prediction = np.argmax(prediction, axis=1) + 1  # Add 1 to the argmax result to shift the class labels\n",
        "  non_zero_classes = np.unique(argmax_prediction)  # Get the unique non-zero class labels\n",
        "  # Remove the 0 class label if it exists in the non_zero_classes array\n",
        "  if 0 in non_zero_classes:\n",
        "    non_zero_classes = non_zero_classes[non_zero_classes != 0]\n",
        "  # Filter out the non-zero classes from the argmax_prediction array\n",
        "  Pre = argmax_prediction[np.isin(argmax_prediction, non_zero_classes)]\n",
        "  return Pre\n",
        "\n",
        "## Computing the Accuracies and Confusion Matrix for Disjoint Samples\n",
        "def ClassificationReports(TeC, HSID, Te_Pre, target_names):\n",
        "  classification = classification_report(np.argmax(TeC, axis=1)+1, Te_Pre, target_names = target_names)\n",
        "  oa = accuracy_score(np.argmax(TeC, axis=1)+1, Te_Pre)\n",
        "  oa = round(oa, 4)\n",
        "  confusion = confusion_matrix(np.argmax(TeC, axis=1)+1, Te_Pre)\n",
        "  list_diag = np.diag(confusion)\n",
        "  list_raw_sum = np.sum(confusion, axis=1)\n",
        "  each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))\n",
        "  aa = np.mean(each_acc)\n",
        "  aa = round(aa, 4)\n",
        "  kappa = cohen_kappa_score(np.argmax(TeC, axis=1)+1, Te_Pre)\n",
        "  kappa = round(kappa, 4)\n",
        "  return classification, confusion, oa*100, each_acc*100, aa*100, kappa*100\n",
        "\n",
        "\n",
        "## Writing Results in CSV files\n",
        "def CSVResults(file_name, Tr_Time, Te_Time, Te_classification,\n",
        "               Te_Confusion, Te_Kappa, Te_OA, Te_AA, Te_Per_Class):\n",
        "    Te_classification = str(Te_classification)\n",
        "    Te_Confusion = str(Te_Confusion)\n",
        "    with open(file_name, 'w') as CSV_file:\n",
        "      CSV_file.write('{} Tr_Time'.format(Tr_Time))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te_Time'.format(Te_Time))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Kappa (%)'.format(Te_Kappa))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Overall (%)'.format(Te_OA))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Average (%)'.format(Te_AA))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Classification'.format(Te_classification))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Per Class'.format(Te_Per_Class))\n",
        "      CSV_file.write('\\n')\n",
        "      CSV_file.write('{} Te Confussion'.format(Te_Confusion))\n",
        "      CSV_file.write('\\n')\n",
        "    return CSV_file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OeacHTK7wBo"
      },
      "source": [
        "**Initializating Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpn6JXlA7vPZ",
        "outputId": "022bd6ee-2fac-4618-bc33-11c11bfd1335"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/ahmad/miniconda3/envs/hsi/lib/python3.10/site-packages/keras/src/optimizers/legacy/adam.py:118: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super().__init__(name, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "## Global Parameters for Loop\n",
        "HSID = \"UH\" ## UH, PU, SA\n",
        "WS = 8\n",
        "teRatio = 0.50\n",
        "vrRatio = 0.50\n",
        "k = 15\n",
        "adam = tf.keras.optimizers.legacy.Adam(lr = 0.0001, decay = 1e-06)\n",
        "epochs = 50\n",
        "batch_size = 56"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rPdFijeX2ml1"
      },
      "source": [
        "**Dimensionality Reduction and 3D Patch Process**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "kPlrRGkb2m1L"
      },
      "outputs": [],
      "source": [
        "## Different Dimensional Reduction Methods\n",
        "def DLMethod(HSI, NC = 75):\n",
        "  RHSI = np.reshape(HSI, (-1, HSI.shape[2]))\n",
        "  pca = PCA(n_components = NC, whiten = True)\n",
        "  RHSI = pca.fit_transform(RHSI)\n",
        "  RHSI = np.reshape(RHSI, (HSI.shape[0], HSI.shape[1], NC))\n",
        "  return RHSI\n",
        "\n",
        "## Creat Patches for 3D (Spatial-Spectral) Models\n",
        "def ImageCubes(HSI, GT, WS=WS, removeZeroLabels=True):\n",
        "    num_rows, num_cols, num_bands = HSI.shape\n",
        "    margin = int(WS / 2)\n",
        "    padded_data = np.pad(HSI, ((margin, margin), (margin, margin), (0, 0)), mode='constant')\n",
        "    image_cubes = np.zeros((num_rows * num_cols, WS, WS, num_bands))\n",
        "    patchesLabels = np.zeros((num_rows * num_cols))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, num_rows + margin):\n",
        "        for c in range(margin, num_cols + margin):\n",
        "            cube = padded_data[r - margin: r + margin, c - margin: c + margin, :]\n",
        "            image_cubes[patchIndex, :, :, :] = cube\n",
        "            patchesLabels[patchIndex] = GT[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    return image_cubes, patchesLabels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Jvtcwqr2vLy"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GoXErHJO2vZX"
      },
      "outputs": [],
      "source": [
        "## Main Function to load Datasets, Dimensional Reduction and Creating Patchs for CNN\n",
        "HSI, GT, Num_Classes, target_names = LoadHSIData(HSID)\n",
        "## Reduce the Dimensionality\n",
        "start = time.time()\n",
        "RDHSI = DLMethod(HSI, NC = k)\n",
        "end = time.time()\n",
        "DL_Time = end - start\n",
        "## Create Image Cubes for Model Building\n",
        "CRDHSI, CGT = ImageCubes(RDHSI, GT, WS = WS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0L2zCZIi3BHu"
      },
      "source": [
        "**Disjoint Train/Validation/Test Sample Selection**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "vCQYpgRw3BWV",
        "outputId": "d09335d0-a795-40c4-9157-7588dfa01ff1"
      },
      "outputs": [],
      "source": [
        "## Set seed for reproducibility\n",
        "np.random.seed(42)\n",
        "## Calculate the number of rows for each sample\n",
        "num_rows, num_cols = GT.shape\n",
        "## Flatten the matrix into a 1D array\n",
        "flattened = GT.flatten()\n",
        "## Get the unique values and their counts, excluding the 0 class\n",
        "unique_values, value_counts = np.unique(flattened, return_counts=True)\n",
        "nonzero_indices = np.where(unique_values != 0)[0]\n",
        "unique_values = unique_values[nonzero_indices]\n",
        "## Create a DataFrame to store sample counts\n",
        "Samples = pd.DataFrame(columns=['Training', 'Validation', 'Test'])\n",
        "## Create lists to store the indices\n",
        "TrInd = []\n",
        "VaInd = []\n",
        "TeInd = []\n",
        "## Split the data for each class\n",
        "for value in unique_values:\n",
        "    class_indices = np.where(flattened == value)[0]\n",
        "    train_indices, test_indices = train_test_split(class_indices, test_size=teRatio)\n",
        "    train_indices, val_indices = train_test_split(train_indices, test_size=vrRatio)\n",
        "    ## Save the sample counts\n",
        "    Samples.loc[value] = [len(train_indices), len(val_indices), len(test_indices)]\n",
        "    ## Store the indices\n",
        "    TrInd.extend(train_indices)\n",
        "    VaInd.extend(val_indices)\n",
        "    TeInd.extend(test_indices)\n",
        "\n",
        "## Convert the DataFrame to a CSV file\n",
        "file_name = f\"{HSID}_{teRatio}_{vrRatio}_{k}_{WS}_Samples.csv\"\n",
        "Samples.to_csv(file_name, index_label='Class')\n",
        "## Reshape the selected samples back into the GT size\n",
        "train_matrix = np.zeros_like(GT)\n",
        "val_matrix = np.zeros_like(GT)\n",
        "test_matrix = np.zeros_like(GT)\n",
        "## Convert Train/Validation/Test samples into Matrix\n",
        "train_matrix.flat[TrInd] = 1\n",
        "val_matrix.flat[VaInd] = 1\n",
        "test_matrix.flat[TeInd] = 1\n",
        "## Select Train/Validation/Test Samples\n",
        "Tr = CRDHSI[TrInd]\n",
        "Va = CRDHSI[VaInd]\n",
        "Te = CRDHSI[TeInd]\n",
        "## Convert labels into one-hot encoding\n",
        "TrC = to_categorical(CGT[TrInd] -1)\n",
        "VaC = to_categorical(CGT[VaInd] -1)\n",
        "TeC = to_categorical(CGT[TeInd] -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "b_zanVtVldiS"
      },
      "outputs": [],
      "source": [
        "# Define the Pyramid 3D CNN model\n",
        "def pyramid_3d_cnn(WS, k, num_pyramid_levels):\n",
        "    input_shape = (WS, WS, k, 1)\n",
        "    max_patch_size = min(input_shape[:3])\n",
        "    patch_size = max_patch_size\n",
        "    inputs = layers.Input(shape=input_shape)\n",
        "    x = inputs\n",
        "    # Create pyramid levels\n",
        "    pyramid_outputs = []\n",
        "    # Create pyramid levels\n",
        "    for level in range(num_pyramid_levels):\n",
        "        scale = 2 ** level\n",
        "        scaled_input_shape = (input_shape[0] // scale, input_shape[1] // scale, input_shape[2] // scale, input_shape[3])\n",
        "        # Convolutional layers for each pyramid level\n",
        "        x = tf.keras.layers.Conv3D(32, kernel_size=(1, patch_size, patch_size), activation='relu', padding='same', input_shape=scaled_input_shape)(x)\n",
        "        x = tf.keras.layers.Conv3D(64, kernel_size=(1, patch_size, patch_size), activation='relu', padding='same')(x)\n",
        "        pyramid_outputs.append(x)\n",
        "\n",
        "    # Merge the feature maps from pyramid levels\n",
        "    merged = tf.keras.layers.concatenate(pyramid_outputs, axis=-1)\n",
        "    return Model(inputs=inputs, outputs=merged)\n",
        "\n",
        "def PyFormer(WS, k, num_classes):\n",
        "    num_layers = 2\n",
        "    num_heads = 4\n",
        "    mlp_dim = 64\n",
        "    num_pyramid_levels = 2\n",
        "    input_shape = (WS, WS, k, 1)\n",
        "    inputs = layers.Input(shape=input_shape)\n",
        "    x = pyramid_3d_cnn(WS, k, num_pyramid_levels)(inputs)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    for i in range(num_layers):\n",
        "        x = layers.LayerNormalization()(x)\n",
        "        x = layers.MultiHeadAttention(num_heads=num_heads, key_dim=k)(x, x)\n",
        "        x = layers.Add()([x, inputs])\n",
        "        y = layers.Conv3D(filters=k, kernel_size=(1, WS, WS), activation='relu', padding='same')(x)\n",
        "        y = layers.Conv3D(filters=2*mlp_dim, kernel_size=(1, WS, WS), activation='relu', padding='same')(y)\n",
        "        x = layers.Add()([x, y])\n",
        "    # Apply regularization\n",
        "    flatten_layer = layers.Flatten()(x)\n",
        "    x = layers.Dense(units=128, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l2(0.01))(flatten_layer)\n",
        "    outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "    model = Model(inputs=inputs, outputs=outputs, name='PyFormer')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hQA-MSt4lfeV"
      },
      "outputs": [],
      "source": [
        "def train_and_evaluate_model(model_name, Tr, TrC, Va, VaC, Te, TeC, adam,\n",
        "                             CRDHSI, HSID, teRatio, k, WS, RDHSI,\n",
        "                             GT,Num_Classes,batch_size,epochs, val_matrix,\n",
        "                             VaInd, test_matrix, TeInd, flattened):\n",
        "  # Calling Custom Model\n",
        "  model = model_name(WS, k, Num_Classes)\n",
        "  model.summary()\n",
        "  # Compiling the model\n",
        "  model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "  # Training the model\n",
        "  start = time.time()\n",
        "  history = model.fit(x=Tr, y=TrC, batch_size=batch_size, epochs=epochs, validation_data=(Va, VaC))\n",
        "  end = time.time()\n",
        "  Tr_Time = end - start\n",
        "\n",
        "  ## Test Prediction Model\n",
        "  start = time.time()\n",
        "  Te_Pre = PreModel(Te, model)\n",
        "  end = time.time()\n",
        "  Te_Time = end - start\n",
        "\n",
        "  ## Transform The Test Predicted Labels into GTs for Final Plot\n",
        "  Te_labels = Tranform_Labels(test_matrix, TeInd, Te_Pre)\n",
        "\n",
        "  ## Classification Report for Test Model\n",
        "  Te_classification,Te_Confusion,Te_OA,Te_Per_Class,Te_AA,Te_Kappa = ClassificationReports(TeC, HSID, Te_Pre, target_names)\n",
        "  Te_Kappa = round(Te_Kappa, 4)\n",
        "  ## Writing Results in CSV File\n",
        "  file_name = f\"{HSID}_{teRatio}_{vrRatio}_{WS}_{model_name}_Classification_Report.csv\"\n",
        "  CSV_file = CSVResults(file_name, Tr_Time, Te_Time, Te_classification, Te_Confusion,\n",
        "                        Te_Kappa, Te_OA, Te_AA, Te_Per_Class)\n",
        "\n",
        "  ## Print Ground Truths\n",
        "  cmap = 'jet'\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.imshow(Te_labels, cmap=cmap, interpolation='nearest')\n",
        "  plt.title(f'Dis_Test: {Te_Kappa}%', fontsize=10, fontweight='bold')\n",
        "  plt.axis('off')\n",
        "  plt.tight_layout()\n",
        "  ## Save the plot\n",
        "  file_name = f\"{HSID}_{teRatio}_{vrRatio}_{WS}_{model_name}_Predicted_GTs.png\"\n",
        "  plt.savefig(file_name, dpi=500, format='png', bbox_inches='tight', pad_inches=0)\n",
        "  return history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ogdW6LxIlfh8",
        "outputId": "bc163707-e96e-4088-d7bb-b3029091cf18"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-09-14 10:39:28.477215: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:28.499938: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:28.499979: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:28.510870: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:28.510964: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:28.510996: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:30.219813: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:30.219865: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:30.219871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
            "2024-09-14 10:39:30.219908: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
            "Your kernel may have been built without NUMA support.\n",
            "2024-09-14 10:39:30.219930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5578 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"PyFormer\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 8, 8, 15, 1)]        0         []                            \n",
            "                                                                                                  \n",
            " model (Functional)          (None, 8, 8, 15, 128)        395456    ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 8, 8, 15, 128)        0         ['model[0][0]']               \n",
            "                                                                                                  \n",
            " layer_normalization (Layer  (None, 8, 8, 15, 128)        256       ['activation[0][0]']          \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " multi_head_attention (Mult  (None, 8, 8, 15, 128)        31028     ['layer_normalization[0][0]', \n",
            " iHeadAttention)                                                     'layer_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 8, 8, 15, 128)        0         ['multi_head_attention[0][0]',\n",
            "                                                                     'input_1[0][0]']             \n",
            "                                                                                                  \n",
            " conv3d_4 (Conv3D)           (None, 8, 8, 15, 15)         122895    ['add[0][0]']                 \n",
            "                                                                                                  \n",
            " conv3d_5 (Conv3D)           (None, 8, 8, 15, 128)        123008    ['conv3d_4[0][0]']            \n",
            "                                                                                                  \n",
            " add_1 (Add)                 (None, 8, 8, 15, 128)        0         ['add[0][0]',                 \n",
            "                                                                     'conv3d_5[0][0]']            \n",
            "                                                                                                  \n",
            " layer_normalization_1 (Lay  (None, 8, 8, 15, 128)        256       ['add_1[0][0]']               \n",
            " erNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " multi_head_attention_1 (Mu  (None, 8, 8, 15, 128)        31028     ['layer_normalization_1[0][0]'\n",
            " ltiHeadAttention)                                                  , 'layer_normalization_1[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 8, 8, 15, 128)        0         ['multi_head_attention_1[0][0]\n",
            "                                                                    ',                            \n",
            "                                                                     'input_1[0][0]']             \n",
            "                                                                                                  \n",
            " conv3d_6 (Conv3D)           (None, 8, 8, 15, 15)         122895    ['add_2[0][0]']               \n",
            "                                                                                                  \n",
            " conv3d_7 (Conv3D)           (None, 8, 8, 15, 128)        123008    ['conv3d_6[0][0]']            \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 8, 8, 15, 128)        0         ['add_2[0][0]',               \n",
            "                                                                     'conv3d_7[0][0]']            \n",
            "                                                                                                  \n",
            " flatten (Flatten)           (None, 122880)               0         ['add_3[0][0]']               \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 128)                  1572876   ['flatten[0][0]']             \n",
            "                                                          8                                       \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 15)                   1935      ['dense[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 16680533 (63.63 MB)\n",
            "Trainable params: 16680533 (63.63 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/50\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-09-14 10:39:32.222907: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904\n",
            "2024-09-14 10:39:32.410251: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
            "2024-09-14 10:39:32.505554: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "68/68 [==============================] - 71s 886ms/step - loss: 3.1620 - accuracy: 0.8817 - val_loss: 2.6957 - val_accuracy: 0.9572\n",
            "Epoch 2/50\n",
            "68/68 [==============================] - 54s 798ms/step - loss: 2.5065 - accuracy: 0.9805 - val_loss: 2.5131 - val_accuracy: 0.9617\n",
            "Epoch 3/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 2.2052 - accuracy: 0.9920 - val_loss: 2.1354 - val_accuracy: 0.9798\n",
            "Epoch 4/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 1.9840 - accuracy: 0.9891 - val_loss: 1.9023 - val_accuracy: 0.9824\n",
            "Epoch 5/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 1.7299 - accuracy: 0.9949 - val_loss: 1.7100 - val_accuracy: 0.9808\n",
            "Epoch 6/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 1.5579 - accuracy: 0.9907 - val_loss: 1.5779 - val_accuracy: 0.9774\n",
            "Epoch 7/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 1.5181 - accuracy: 0.9782 - val_loss: 1.4978 - val_accuracy: 0.9649\n",
            "Epoch 8/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 1.4235 - accuracy: 0.9800 - val_loss: 1.4655 - val_accuracy: 0.9585\n",
            "Epoch 9/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 1.1920 - accuracy: 0.9872 - val_loss: 1.3151 - val_accuracy: 0.9792\n",
            "Epoch 10/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 1.0343 - accuracy: 0.9947 - val_loss: 1.5224 - val_accuracy: 0.9633\n",
            "Epoch 11/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.9157 - accuracy: 0.9939 - val_loss: 0.9471 - val_accuracy: 0.9824\n",
            "Epoch 12/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.7745 - accuracy: 0.9968 - val_loss: 0.8444 - val_accuracy: 0.9824\n",
            "Epoch 13/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.6541 - accuracy: 0.9997 - val_loss: 0.6881 - val_accuracy: 0.9875\n",
            "Epoch 14/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.5468 - accuracy: 1.0000 - val_loss: 0.5852 - val_accuracy: 0.9875\n",
            "Epoch 15/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.4568 - accuracy: 1.0000 - val_loss: 0.4960 - val_accuracy: 0.9872\n",
            "Epoch 16/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.3800 - accuracy: 1.0000 - val_loss: 0.4199 - val_accuracy: 0.9870\n",
            "Epoch 17/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 0.3149 - accuracy: 1.0000 - val_loss: 0.3556 - val_accuracy: 0.9867\n",
            "Epoch 18/50\n",
            "68/68 [==============================] - 54s 793ms/step - loss: 0.2600 - accuracy: 1.0000 - val_loss: 0.3003 - val_accuracy: 0.9872\n",
            "Epoch 19/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.2141 - accuracy: 1.0000 - val_loss: 0.2523 - val_accuracy: 0.9883\n",
            "Epoch 20/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.1760 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9880\n",
            "Epoch 21/50\n",
            "68/68 [==============================] - 54s 799ms/step - loss: 0.1445 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9875\n",
            "Epoch 22/50\n",
            "68/68 [==============================] - 54s 802ms/step - loss: 0.1184 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9859\n",
            "Epoch 23/50\n",
            "68/68 [==============================] - 54s 801ms/step - loss: 0.0972 - accuracy: 1.0000 - val_loss: 0.1474 - val_accuracy: 0.9848\n",
            "Epoch 24/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.6616 - accuracy: 0.9350 - val_loss: 1.3832 - val_accuracy: 0.9045\n",
            "Epoch 25/50\n",
            "68/68 [==============================] - 54s 803ms/step - loss: 1.0654 - accuracy: 0.9619 - val_loss: 1.0460 - val_accuracy: 0.9691\n",
            "Epoch 26/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 0.7954 - accuracy: 0.9851 - val_loss: 0.9505 - val_accuracy: 0.9673\n",
            "Epoch 27/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.7261 - accuracy: 0.9917 - val_loss: 1.2519 - val_accuracy: 0.9681\n",
            "Epoch 28/50\n",
            "68/68 [==============================] - 54s 799ms/step - loss: 0.7334 - accuracy: 0.9933 - val_loss: 0.6893 - val_accuracy: 0.9779\n",
            "Epoch 29/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.5214 - accuracy: 0.9955 - val_loss: 0.5780 - val_accuracy: 0.9846\n",
            "Epoch 30/50\n",
            "68/68 [==============================] - 54s 802ms/step - loss: 0.4273 - accuracy: 0.9968 - val_loss: 0.4882 - val_accuracy: 0.9883\n",
            "Epoch 31/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.3395 - accuracy: 1.0000 - val_loss: 0.3993 - val_accuracy: 0.9883\n",
            "Epoch 32/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.2716 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9872\n",
            "Epoch 33/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.2172 - accuracy: 1.0000 - val_loss: 0.2767 - val_accuracy: 0.9878\n",
            "Epoch 34/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.1736 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9886\n",
            "Epoch 35/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.1388 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9883\n",
            "Epoch 36/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.1110 - accuracy: 1.0000 - val_loss: 0.1585 - val_accuracy: 0.9886\n",
            "Epoch 37/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.0889 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9883\n",
            "Epoch 38/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 0.0713 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9886\n",
            "Epoch 39/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.0575 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9883\n",
            "Epoch 40/50\n",
            "68/68 [==============================] - 54s 798ms/step - loss: 0.0465 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9891\n",
            "Epoch 41/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9891\n",
            "Epoch 42/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.0312 - accuracy: 1.0000 - val_loss: 0.0630 - val_accuracy: 0.9904\n",
            "Epoch 43/50\n",
            "68/68 [==============================] - 54s 796ms/step - loss: 0.0257 - accuracy: 1.0000 - val_loss: 0.0549 - val_accuracy: 0.9912\n",
            "Epoch 44/50\n",
            "68/68 [==============================] - 54s 795ms/step - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 0.9920\n",
            "Epoch 45/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.0455 - val_accuracy: 0.9923\n",
            "Epoch 46/50\n",
            "68/68 [==============================] - 54s 794ms/step - loss: 0.0165 - accuracy: 0.9997 - val_loss: 0.2387 - val_accuracy: 0.9665\n",
            "Epoch 47/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 1.1801 - accuracy: 0.9270 - val_loss: 0.9685 - val_accuracy: 0.9763\n",
            "Epoch 48/50\n",
            "68/68 [==============================] - 54s 798ms/step - loss: 0.8220 - accuracy: 0.9853 - val_loss: 0.7692 - val_accuracy: 0.9870\n",
            "Epoch 49/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.7848 - accuracy: 0.9915 - val_loss: 0.7374 - val_accuracy: 0.9856\n",
            "Epoch 50/50\n",
            "68/68 [==============================] - 54s 797ms/step - loss: 0.6149 - accuracy: 0.9925 - val_loss: 0.6735 - val_accuracy: 0.9822\n",
            "235/235 [==============================] - 35s 141ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# # Define a list of model names\n",
        "# model_names = [AttentionGCN, Xtreme_Exception, HybIN, SCSNet, PyFormer]\n",
        "model_names = [PyFormer]\n",
        "# Loop over the model names and run train_and_evaluate_model() for each model\n",
        "history_list = []\n",
        "for model_name in model_names:\n",
        "    history = train_and_evaluate_model(model_name, Tr, TrC, Va, VaC, Te, TeC,\n",
        "                                       adam, CRDHSI, HSID, teRatio, k, WS,\n",
        "                                       RDHSI, GT, Num_Classes, batch_size,\n",
        "                                       epochs, val_matrix, VaInd,\n",
        "                                       test_matrix, TeInd, flattened)\n",
        "    history_list.append(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "u9VE9ay8lfwN"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation loss and accuracy for each model on one graph\n",
        "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
        "# colors = ['brown', 'gray', 'blue', 'green', 'purple', 'orange', 'yellow', 'red', 'YlOrBr', 'RdPu', 'YlGn']\n",
        "\n",
        "for i, history in enumerate(history_list):\n",
        "    # Plot loss\n",
        "    axs[0].plot(history.history['loss'], label=f'{model_names[i].__name__} Train')\n",
        "    axs[0].plot(history.history['val_loss'], label=f'{model_names[i].__name__} Val', linestyle='--')\n",
        "    # Plot accuracy\n",
        "    axs[1].plot(history.history['accuracy'], label=f'{model_names[i].__name__} Train')\n",
        "    axs[1].plot(history.history['val_accuracy'], label=f'{model_names[i].__name__} Val', linestyle='--')\n",
        "# Add labels and legend\n",
        "axs[0].set_title('Loss', fontsize=18)\n",
        "axs[0].set_xlabel('Epoch', fontsize=18)\n",
        "# axs[0].set_ylabel('Loss', fontsize=18)\n",
        "# axs[0].legend(fontsize=18)\n",
        "\n",
        "axs[1].set_title('Accuracy', fontsize=18)\n",
        "axs[1].set_xlabel('Epoch', fontsize=18)\n",
        "# axs[1].set_ylabel('Accuracy', fontsize=18)\n",
        "axs[1].legend(fontsize=6)\n",
        "\n",
        "# Add grid lines and set background color\n",
        "axs[0].grid(True)\n",
        "axs[1].grid(True)\n",
        "# fig.patch.set_facecolor('#f2f2f2')\n",
        "plt.tight_layout()\n",
        "file_name = f\"{HSID}_{teRatio}_{k}_{WS}_acc_loss_curve_all_models.png\"\n",
        "plt.savefig(file_name, dpi=500, format='png', bbox_inches='tight', pad_inches=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kONuy4QMlfzo"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.14"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}