MEG2VEC (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The below code extracts the features of the MEG signals(Brain Waves) using a CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Installing one dnn and setting it up on gpu\n",
    "!pip install onednn-cpu-gomp\n",
    "\n",
    "import os\n",
    "\n",
    "os.environ['TF_ENABLE_ONEDNN_OPTS']='1'\n",
    "\n",
    "os.environ['TF_ENABLE_AUTO_MIXED_PRECISION']='1'\n",
    "\n",
    "os.environ['DNNL_ENGINE_LIMIT_CPU_CAPABILITIES']='0'\n",
    "\n",
    "os.environ['SYCL_DEVICE_FILTER']='opencl:gpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting the dataset\n",
    "with zipfile.ZipFile(\"organized_results-20230810T193042Z-001.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oxgRC89INuMc",
    "outputId": "0cbfb1ce-1bcf-4c20-d778-d1e4b182be5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchsummary in /usr/local/lib/python3.10/dist-packages (1.5.1)\n"
     ]
    }
   ],
   "source": [
    "pip install torchsummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib, seaborn\n",
      "Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.42.0 kiwisolver-1.4.4 matplotlib-3.7.2 pillow-10.0.0 pyparsing-3.0.9 seaborn-0.12.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Successfully installed sklearn-0.0.post7\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Successfully installed torchvision-0.15.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
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     "height": 711,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'The_Black_Widow': 0, 'cable_spool_fort': 1, 'easy_money': 2, 'lw1': 3}\n"
     ]
    },
    {
     "data": {
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       "model_id": "0d7a5611fff34d719749e6a7587ee894",
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     "name": "stdout",
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     "text": [
      "Epoch 1/20 - Loss: 102.47329723238946\n",
      "Epoch 2/20 - Loss: 17.161834960872692\n",
      "Epoch 3/20 - Loss: 3.7287267133698037\n",
      "Epoch 4/20 - Loss: 3.1207052126167922\n",
      "Epoch 5/20 - Loss: 1.193242127335667\n",
      "Epoch 6/20 - Loss: 0.23572280358404138\n",
      "Epoch 7/20 - Loss: 0.03270813832095274\n",
      "Epoch 8/20 - Loss: 0.16507467142261056\n",
      "Epoch 9/20 - Loss: 0.11646324404527753\n",
      "Epoch 10/20 - Loss: 0.004604484663264685\n",
      "Epoch 11/20 - Loss: 0.026054607358581228\n",
      "Epoch 12/20 - Loss: 0.03335053286034917\n",
      "Epoch 13/20 - Loss: 0.004315683069492193\n",
      "Epoch 14/20 - Loss: 8.369717160476853e-05\n",
      "Epoch 15/20 - Loss: 0.664363441193274\n",
      "Epoch 16/20 - Loss: 0.32344198336002805\n",
      "Epoch 17/20 - Loss: 0.19265688598401293\n",
      "Epoch 18/20 - Loss: 0.05997891511177147\n",
      "Epoch 19/20 - Loss: 0.0011360683233797796\n",
      "Epoch 20/20 - Loss: 0.0005022343385130857\n",
      "Validation Accuracy: 0.9230769230769231\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94         8\n",
      "           1       0.89      0.89      0.89         9\n",
      "           2       1.00      0.89      0.94         9\n",
      "           3       0.92      0.92      0.92        13\n",
      "\n",
      "    accuracy                           0.92        39\n",
      "   macro avg       0.93      0.93      0.92        39\n",
      "weighted avg       0.93      0.92      0.92        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# importing the required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, utils, datasets\n",
    "from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import transforms, datasets\n",
    "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Define the CNN model\n",
    "class MEG2VEC(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super(MEG2VEC, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1) # Adding the Convolution 2d layer\n",
    "        self.bn1 = nn.BatchNorm2d(32) # Adding a batch normalization layer\n",
    "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1) # Adding another Convolution 2d layer\n",
    "        self.bn2 = nn.BatchNorm2d(16)\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(16 * 144 * 144, 512)  # 144 = 576 / 2 (due to max pooling)\n",
    "        self.fc2 = nn.Linear(512, num_classes) # Adding another linear layer\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "    # Defining the forward propagation function\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.bn1(self.conv1(x))))\n",
    "        x = self.dropout(x)\n",
    "        x = self.pool(F.relu(self.bn2(self.conv2(x))))\n",
    "        x = self.dropout(x)\n",
    "        x = x.view(-1, 16 * 144 * 144)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x\n",
    "# Preparing the dataloader function\n",
    "def get_data_loaders(data_transforms, batch_size, val_split_ratio=0.2, num_workers=4):\n",
    "    dataset = datasets.ImageFolder(root=\"BRAIN/organized_results\", transform=data_transforms[\"train\"])\n",
    "    print(dataset.class_to_idx)\n",
    "    dataset_size = len(dataset)\n",
    "    dataset_indices = list(range(dataset_size))\n",
    "    np.random.shuffle(dataset_indices) # Shuffing the index id's to ensure randomization\n",
    "    val_split_index = int(np.floor(val_split_ratio * dataset_size))\n",
    "    train_idx, val_idx = dataset_indices[val_split_index:], dataset_indices[:val_split_index]\n",
    "\n",
    "    train_sampler = SubsetRandomSampler(train_idx) # Getting the train sampler\n",
    "    val_sampler = SubsetRandomSampler(val_idx)  # Getting the test sampler\n",
    "    # Loading the training data loader\n",
    "    train_loader = DataLoader(dataset=dataset, shuffle=False, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers) \n",
    "    # Loading the testing data loader\n",
    "    val_loader = DataLoader(dataset=dataset, shuffle=False, batch_size=1, sampler=val_sampler, num_workers=num_workers)\n",
    "\n",
    "    return train_loader, val_loader\n",
    "\n",
    "    \n",
    "# Defining the training function\n",
    "def train_model(model, train_loader, criterion, optimizer, device, num_epochs=10):\n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "\n",
    "        for images, labels in train_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad() # Freezing the weights\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward() # Backpropagation\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{num_epochs} - Loss: {running_loss / len(train_loader)}\")\n",
    "        \n",
    "# Evaluating model performance\n",
    "def evaluate_model(model, val_loader, device):\n",
    "    model.eval()\n",
    "    all_labels = []\n",
    "    all_predictions = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in val_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predictions = torch.max(outputs, 1)\n",
    "\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "            all_predictions.extend(predictions.cpu().numpy())\n",
    "    accuracy = accuracy_score(all_labels, all_predictions)\n",
    "    print(\"Validation Accuracy:\", accuracy)\n",
    "    print(\"\\nClassification Report:\\n\", classification_report(all_labels, all_predictions))\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# preprocessing the training (image) data\n",
    "image_transforms = {\n",
    "    \"train\": transforms.Compose([\n",
    "        transforms.Resize((576, 576)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "    ])\n",
    "}\n",
    "\n",
    "batch_size = 4\n",
    "train_loader, val_loader = get_data_loaders(image_transforms, batch_size)\n",
    "\n",
    "num_classes = len(train_loader.dataset.classes)\n",
    "model = MEG2VEC(num_classes).to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "train_model(model, train_loader, criterion, optimizer, device, num_epochs=20)\n",
    "evaluate_model(model, val_loader, device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting torchsummary\n",
      "  Downloading torchsummary-1.5.1-py3-none-any.whl (2.8 kB)\n",
      "Installing collected packages: torchsummary\n",
      "Successfully installed torchsummary-1.5.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install torchsummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hmnyHVPrCary",
    "outputId": "6bd4d535-2151-4baf-f6a4-5bd5a7bda159"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 32, 576, 576]             896\n",
      "       BatchNorm2d-2         [-1, 32, 576, 576]              64\n",
      "         MaxPool2d-3         [-1, 32, 288, 288]               0\n",
      "           Dropout-4         [-1, 32, 288, 288]               0\n",
      "            Conv2d-5         [-1, 16, 288, 288]           4,624\n",
      "       BatchNorm2d-6         [-1, 16, 288, 288]              32\n",
      "         MaxPool2d-7         [-1, 16, 144, 144]               0\n",
      "           Dropout-8         [-1, 16, 144, 144]               0\n",
      "            Linear-9                  [-1, 512]     169,869,824\n",
      "          Dropout-10                  [-1, 512]               0\n",
      "           Linear-11                    [-1, 4]           2,052\n",
      "================================================================\n",
      "Total params: 169,877,492\n",
      "Trainable params: 169,877,492\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 3.80\n",
      "Forward/backward pass size (MB): 227.82\n",
      "Params size (MB): 648.03\n",
      "Estimated Total Size (MB): 879.65\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "model = MEG2VEC(num_classes).to(device)\n",
    "\n",
    "# Print the summary of the model\n",
    "summary(model, input_size=(3, 576, 576))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dMTxSG6IZUcx"
   },
   "outputs": [],
   "source": [
    "weights = model.fc2.weight.data.cpu().numpy()\n",
    "bias = model.fc2.bias.data.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r2FRbFnCZnzt",
    "outputId": "a5c120d7-43f9-45ee-9461-5609c557bba5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01227093, -0.02981179,  0.02494627, ...,  0.00725079,\n",
       "        -0.03682656,  0.03194759],\n",
       "       [ 0.04022798, -0.01205477, -0.00355153, ...,  0.02733205,\n",
       "         0.01657236, -0.02817695],\n",
       "       [-0.02448063,  0.00069862,  0.00521385, ...,  0.0330551 ,\n",
       "        -0.00564435, -0.03479967],\n",
       "       [-0.02820493,  0.00582488, -0.01276053, ..., -0.00148993,\n",
       "         0.00032969, -0.00429593]], dtype=float32)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TIXtDpnzgxlq",
    "outputId": "0cc5bf2b-2a3f-4982-b7d2-2c5627c11a58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512,)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xnzvfdECaHjb",
    "outputId": "a873d038-6d4f-4879-d2c6-3b28ffbc85af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.02557549, -0.02215453, -0.03439986, -0.02080782], dtype=float32)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "q4W4t5kdcCIN",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "af7d8065-99ea-4662-9567-7ac97a056b43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        dtype=float32),\n",
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       "         -0.02538352,  0.03655425,  0.01251749, -0.01853818, -0.03669619,\n",
       "         -0.02693558,  0.00590893,  0.03766856, -0.03049598, -0.0144245 ,\n",
       "         -0.01700889,  0.03051845, -0.00699128, -0.0326555 , -0.0471203 ,\n",
       "          0.03688553, -0.01876125, -0.0103715 , -0.01669098,  0.02880618,\n",
       "          0.02003923,  0.0327515 ,  0.01888885,  0.02224303,  0.03333031,\n",
       "          0.04523965,  0.00019349,  0.02048132,  0.01573549,  0.00875069,\n",
       "         -0.0165037 ,  0.01048378,  0.00417221,  0.00549939, -0.01239019,\n",
       "         -0.00428447, -0.00627801,  0.03810693, -0.02438955, -0.03397368,\n",
       "         -0.00786101,  0.02568716,  0.01579317, -0.03304963, -0.01536563,\n",
       "          0.00897957, -0.00270159, -0.00512864, -0.00721025, -0.02588633,\n",
       "          0.02083887, -0.00319728,  0.02509082,  0.02389041, -0.04225339,\n",
       "         -0.02440665, -0.0008802 ,  0.03633313, -0.01306279,  0.01727491,\n",
       "          0.01900334,  0.02604051, -0.01363038, -0.00699206, -0.02293963,\n",
       "         -0.00548882,  0.00558761, -0.02253106,  0.0210675 ,  0.01211288,\n",
       "         -0.03712302, -0.02862302,  0.02283728,  0.01356483,  0.00404105,\n",
       "          0.01496809, -0.04933555,  0.0170941 ,  0.01846751, -0.02350968,\n",
       "          0.02813488,  0.01022051,  0.03396665,  0.01538002,  0.03360557,\n",
       "          0.03341097,  0.01038828, -0.01184873, -0.01479299, -0.00671871,\n",
       "         -0.01150206,  0.02529375,  0.00272079,  0.01914375,  0.03652456,\n",
       "         -0.00670267, -0.00866978, -0.02829457,  0.03308127, -0.02246591,\n",
       "          0.03327187, -0.0123024 , -0.03314544,  0.01657835, -0.03144111,\n",
       "         -0.00759612, -0.03057719,  0.00445343,  0.02252135,  0.00525847,\n",
       "         -0.0256362 ,  0.00657149, -0.00506198,  0.02659237,  0.00119969,\n",
       "          0.00362076, -0.02605967, -0.03116027, -0.00133443,  0.00116955,\n",
       "          0.01391009,  0.02511956,  0.01379241,  0.00407379, -0.04659564,\n",
       "          0.0084238 ,  0.02834965,  0.03408528,  0.02431898, -0.01083855,\n",
       "         -0.03101524,  0.01919202, -0.01059567, -0.02768873, -0.02787521,\n",
       "          0.02377659, -0.03594232, -0.0141609 ,  0.00269605, -0.02618276,\n",
       "         -0.02306769, -0.03748877, -0.02638421, -0.00187111,  0.04192479,\n",
       "         -0.02730829, -0.00235787, -0.00969385, -0.04233446, -0.03609701,\n",
       "          0.01279111,  0.01088084, -0.00240274, -0.0356428 ,  0.00939559,\n",
       "          0.02566423, -0.00408211, -0.04033069,  0.02311841,  0.00427421,\n",
       "         -0.0228132 ,  0.02481001,  0.05387851,  0.00168748,  0.0062698 ,\n",
       "         -0.01834958, -0.01067701,  0.00573584, -0.02739411,  0.00596002,\n",
       "          0.00065547, -0.0180605 ,  0.0231221 , -0.00734542,  0.02792973,\n",
       "          0.01396274,  0.02788134, -0.00316719,  0.00399956,  0.00513398,\n",
       "         -0.00338087, -0.03827517,  0.01311853,  0.02028898,  0.00024471,\n",
       "         -0.00908432, -0.00546087, -0.02329523, -0.0366536 , -0.04301041,\n",
       "          0.00108284,  0.03547457,  0.00861654, -0.03400485, -0.01442325,\n",
       "          0.0279668 , -0.00436938, -0.03579782,  0.00446308, -0.0247374 ,\n",
       "         -0.01106096,  0.00390347, -0.00089929,  0.03579989,  0.03194984,\n",
       "         -0.01523555,  0.03455102, -0.00773469,  0.01989388, -0.02839345,\n",
       "          0.0056737 ,  0.02950361, -0.03066433,  0.0297781 , -0.02260839,\n",
       "         -0.00144246,  0.01228098, -0.00296107,  0.00091887, -0.00533964,\n",
       "         -0.02546962, -0.03615125,  0.04396416,  0.02441524,  0.03065296,\n",
       "          0.01661453, -0.02623446,  0.01706551, -0.01333173,  0.03388662,\n",
       "         -0.02672015,  0.02884275, -0.01259095, -0.03236179,  0.00217425,\n",
       "          0.02347279, -0.02026643,  0.03142422, -0.011109  , -0.01582126,\n",
       "         -0.01711189, -0.02478121,  0.00176732,  0.0081755 , -0.02806674,\n",
       "          0.02093151,  0.02449333,  0.02399057, -0.01224085, -0.01631933,\n",
       "         -0.04937182, -0.0251485 , -0.02992636,  0.01451072,  0.00867647,\n",
       "         -0.03532818, -0.01497247,  0.03231126,  0.0342395 ,  0.03582424,\n",
       "         -0.03668262, -0.01461389,  0.02822223, -0.00195822, -0.00999993,\n",
       "          0.03179337, -0.02966141,  0.01115922,  0.03006454,  0.00707727,\n",
       "          0.00626382, -0.02492006,  0.03229028,  0.03187933, -0.0205677 ,\n",
       "          0.0434527 ,  0.01337955,  0.02964845,  0.0317061 , -0.02739162,\n",
       "         -0.03040915,  0.01193399, -0.02581206, -0.03554318, -0.02093325,\n",
       "          0.02396888, -0.00277852,  0.00576377,  0.03201981, -0.04149376,\n",
       "         -0.01909781,  0.01364124, -0.02741598,  0.00300501, -0.03351637,\n",
       "          0.03505742,  0.02200602,  0.02295278,  0.00619481,  0.005259  ,\n",
       "          0.01295122,  0.00046631,  0.03632262,  0.03087412, -0.00148993,\n",
       "          0.00032969, -0.00429593], dtype=float32),\n",
       "  'bias': -0.020807821}}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the weights of the last hidden layer\n",
    "final_dict = {\n",
    "    'The_Black_Widow' : {'weights' : weights[0], 'bias': bias[0]},\n",
    "    'cable_spool_fort' : {'weights' : weights[1], 'bias': bias[1]},\n",
    "    'easy_money' : {'weights' : weights[2], 'bias': bias[2]},\n",
    "    'lw1' : {'weights' : weights[3], 'bias': bias[3]}\n",
    "}\n",
    "final_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L39cll54dFMw"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('/content/drive/MyDrive/BV/organized_results/stories_data.pkl', 'wb') as f:\n",
    "    pickle.dump(final_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Pj8di7P6YrrY"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "4tzCSbd2PohA",
    "outputId": "1521e3cc-3e10-475f-83e5-0a868514a9cd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "# Load an example image and preprocess it\n",
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "\n",
    "image_path = '/content/drive/MyDrive/BV/organized_results/The_Black_Widow/sub-01_ses-0_task-3.png'\n",
    "image = Image.open(image_path)\n",
    "story = image_path.split('/content/drive/MyDrive/BV/organized_results/')\n",
    "story = story[1].split('/')\n",
    "story = story[0]\n",
    "\n",
    "plt.imshow(image)\n",
    "plt.axis('off')\n",
    "plt.title('Original Image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QUaIYlSigGi1"
   },
   "outputs": [],
   "source": [
    "final_output = output * weights[0] + bias[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177,
     "referenced_widgets": [
      "4b3e743158d04ea189f52f6d1ee45993",
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    "# Defining a CNN architecture to retrieve the MEG embeddings\n",
    "def mv_model(x):\n",
    "    conv1 = nn.Conv2d(in_channels=4, out_channels=32, kernel_size=3, padding=1)\n",
    "    bn1 = nn.BatchNorm2d(32)\n",
    "    conv2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1)\n",
    "    bn2 = nn.BatchNorm2d(16)\n",
    "    pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "    fc1 = nn.Linear(16 * 144 * 144, 512)\n",
    "    dropout = nn.Dropout(0.1)\n",
    "\n",
    "    x = pool(F.relu(bn1(conv1(x))))\n",
    "    x = dropout(x)\n",
    "    x = pool(F.relu(bn2(conv2(x))))\n",
    "    x = dropout(x)\n",
    "    x = x.view(-1, 16 * 144 * 144)\n",
    "    x = fc1(x)\n",
    "\n",
    "    return x\n",
    "import pickle\n",
    "\n",
    "# Specify the path to your pickle file\n",
    "pickle_file_path = '/content/drive/MyDrive/BV/stories_data.pkl'\n",
    "\n",
    "# Load data from the pickle file\n",
    "with open(pickle_file_path, 'rb') as f:\n",
    "    story = pickle.load(f)\n",
    "\n",
    "# preprocessing the data(image)\n",
    "preprocess = transforms.Compose([\n",
    "        transforms.Lambda(lambda img: img.crop((60, 0, 530, img.size[1]))),\n",
    "        transforms.Resize((576, 576)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]),\n",
    "\n",
    "    ])\n",
    "\n",
    "wave_embeddings = {}\n",
    "\n",
    "# Path to the folder containing the images\n",
    "folder_path = \"/content/drive/MyDrive/BV/organized_results/\"\n",
    "# Iterate through all files in the folder\n",
    "for story_name in tqdm(os.listdir(folder_path)):\n",
    "  wave = []\n",
    "  file_path = os.path.join(folder_path, story_name)\n",
    "  for f in tqdm(os.listdir(file_path)):\n",
    "    img_path = os.path.join(file_path, f)\n",
    "    image = Image.open(image_path)\n",
    "    input_tensor = preprocess(image)\n",
    "    input_tensor = input_tensor.unsqueeze(0)  # Add a batch dimension\n",
    "    output = mv_model(input_tensor) # Calling the model\n",
    "    output = output.detach().numpy()\n",
    "    output = output[0]\n",
    "    final_output = output * story[story_name]['weights'] + story[story_name]['bias'] # getting the MEG embeddings\n",
    "    wave.append(final_output)\n",
    "  wave_embeddings[story_name] = wave\n",
    "\n"
   ]
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   "source": [
    "import pickle\n",
    "#N Saving the MEG embeddings\n",
    "with open('/content/drive/MyDrive/BV/wave_embeddings.pkl', 'wb') as f:\n",
    "    pickle.dump(wave_embeddings, f)"
   ]
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