prog.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHvW34DvirxR"
      },
      "source": [
        "# Download train data and unzip convert back to torch tensor\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPzO8AkfLdqq",
        "outputId": "e58e7846-eb82-4a8f-ff31-ded5e7463358"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting gdown\n",
            "  Downloading gdown-5.1.0-py3-none-any.whl.metadata (5.7 kB)\n",
            "Collecting beautifulsoup4 (from gdown)\n",
            "  Downloading beautifulsoup4-4.12.3-py3-none-any.whl.metadata (3.8 kB)\n",
            "Requirement already satisfied: filelock in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from gdown) (3.12.2)\n",
            "Requirement already satisfied: requests[socks] in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from gdown) (4.66.1)\n",
            "Collecting soupsieve>1.2 (from beautifulsoup4->gdown)\n",
            "  Downloading soupsieve-2.5-py3-none-any.whl.metadata (4.7 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from requests[socks]->gdown) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from requests[socks]->gdown) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages (from requests[socks]->gdown) (2023.7.22)\n",
            "Collecting PySocks!=1.5.7,>=1.5.6 (from requests[socks]->gdown)\n",
            "  Downloading PySocks-1.7.1-py3-none-any.whl.metadata (13 kB)\n",
            "Downloading gdown-5.1.0-py3-none-any.whl (17 kB)\n",
            "Downloading beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.9/147.9 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading PySocks-1.7.1-py3-none-any.whl (16 kB)\n",
            "Downloading soupsieve-2.5-py3-none-any.whl (36 kB)\n",
            "Installing collected packages: soupsieve, PySocks, beautifulsoup4, gdown\n",
            "Successfully installed PySocks-1.7.1 beautifulsoup4-4.12.3 gdown-5.1.0 soupsieve-2.5\n"
          ]
        }
      ],
      "source": [
        "!pip3 install gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hppq96UeMLrm",
        "outputId": "e24a17c6-220b-4f42-8aa4-22d7b04a4a4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/arpan/miniconda3/envs/prog/lib/python3.10/site-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1kGWf1Q_bi3SXzOZ822KtA6dh0CzgXvZ8\n",
            "From (redirected): https://drive.google.com/uc?id=1kGWf1Q_bi3SXzOZ822KtA6dh0CzgXvZ8&confirm=t&uuid=71f2bd6e-72dd-47f6-aca1-a4b0a8d46432\n",
            "To: /home/arpan/prog-rock/both_tensors_160_216.zip\n",
            "100%|██████████████████████████████████████| 1.99G/1.99G [00:24<00:00, 82.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 1kGWf1Q_bi3SXzOZ822KtA6dh0CzgXvZ8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLyqbSG9MfN2",
        "outputId": "7c7aa879-2c8b-40e2-f026-e402afa25e62"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  ./content/both_tensors_160_216.zip\n",
            "  inflating: ./content/non_progressive_rock_songs_tensor.pt  \n",
            "  inflating: ./content/progressive_rock_songs_tensor.pt  \n"
          ]
        }
      ],
      "source": [
        "!unzip -j ./content/both_tensors_160_216.zip -d ./content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "L07E1_ukNez7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " CUDA Version is  cuda:0\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torchsummary import summary\n",
        "\n",
        "#training tensors\n",
        "prog_tensors = torch.load('./content/progressive_rock_songs_tensor.pt')\n",
        "non_prog_tensors = torch.load('./content/non_progressive_rock_songs_tensor.pt')\n",
        "\n",
        "def get_device():\n",
        "    if torch.cuda.is_available():\n",
        "        device = 'cuda:0'\n",
        "    else:\n",
        "        device = 'cpu'\n",
        "    return device\n",
        "print(\" CUDA Version is \", get_device())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FJMqG7F3gsQT"
      },
      "outputs": [],
      "source": [
        "# prompt: non_prog_tensors reshaped to [8052, 160, 216])\n",
        "# originally (X, 216, 160)\n",
        "non_prog_tensors = non_prog_tensors.reshape(8052, 160, 216)\n",
        "prog_tensors = prog_tensors.reshape(7455, 160, 216)\n",
        "prog_tensors = prog_tensors.float()\n",
        "non_prog_tensors = non_prog_tensors.float()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9S_vHwRqi04D"
      },
      "source": [
        "# Download validation data and mess around with it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IWMKO0E5Us7m",
        "outputId": "1140a737-9f33-45ec-d2f0-22114d700f33"
      },
      "outputs": [],
      "source": [
        "# https://drive.google.com/file/d/1--QAwB8w8GVWZ1wmpPOPaj6jNkGWDI3c/view?usp=sharing\n",
        "!gdown --id 1--QAwB8w8GVWZ1wmpPOPaj6jNkGWDI3c\n",
        "!unzip ./content/both_tensors_160_216_validation_1.zip\n",
        "import torch\n",
        "prog_tensors_validation = torch.load('./content/progressive_rock_songs_validation_tensor.pt')\n",
        "non_prog_tensors_validation = torch.load('./content/non_progressive_rock_songs_validation_tensor.pt')\n",
        "## NOTE: the above is an array not a tensor!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPGD-qKROjWz"
      },
      "source": [
        "# running some ML and ensemble algorithms from sklearn\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDu2fczZOpAS",
        "outputId": "5c58e9c8-1431-471a-c442-7d6fcaca4374"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15507, 34560)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prompt: i have a prog_tensors torch tensor of shape (7455, 160, 216) convert to 2d array of shape (7455, 160*216)\n",
        "## Converting prog tensor to numpy array for sklearn use\n",
        "import numpy as np\n",
        "prog_2d_array = prog_tensors.reshape(7455, 160 * 216)\n",
        "prog_2d_array = prog_2d_array.detach().numpy()\n",
        "\n",
        "non_prog_2d_array = non_prog_tensors.reshape(8052, 160 * 216)\n",
        "non_prog_2d_array = non_prog_2d_array.detach().numpy()\n",
        "labels = torch.cat((torch.ones(prog_tensors.shape[0]), torch.zeros(non_prog_tensors.shape[0])), dim=0)\n",
        "\n",
        "# Convert the labels tensor to a NumPy array\n",
        "y_data = labels.detach().numpy()\n",
        "X_data = np.concatenate((prog_2d_array, non_prog_2d_array), axis=0)\n",
        "X_data.shape\n",
        "\n",
        "# (15507, 34560) -> (instances, features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKEe4EGGjXFH"
      },
      "source": [
        "# Random forest classifier on the training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "91sFXGPxR1gN",
        "outputId": "3dde8d8c-4c69-47c6-f81f-75365092e7f5"
      },
      "outputs": [
        {
          "data": {
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            ],
            "text/plain": [
              "RandomForestClassifier(n_estimators=101)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# xgboost, bagging, decision trees\n",
        "model = RandomForestClassifier(n_estimators=101)\n",
        "model.fit(X_data, y_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcNWkaQoT-9Q",
        "outputId": "5dcd6216-e31b-4a41-8b1e-890b4b246643"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RandomForestClassifier(n_estimators=101)\n",
            "Feature ranking:\n",
            "1. feature 28321 (0.000670)\n",
            "2. feature 7362 (0.000650)\n",
            "3. feature 7201 (0.000641)\n",
            "4. feature 27361 (0.000624)\n",
            "5. feature 7202 (0.000604)\n",
            "6. feature 19361 (0.000553)\n",
            "7. feature 28481 (0.000545)\n",
            "8. feature 30241 (0.000530)\n",
            "9. feature 12001 (0.000519)\n",
            "10. feature 28322 (0.000487)\n"
          ]
        }
      ],
      "source": [
        "print(model)\n",
        "importances = model.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in model.estimators_],\n",
        "             axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(10):\n",
        "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAJBrsHGUfEa",
        "outputId": "8208ef2f-6eee-4148-d0ae-f96d0f8d4f80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "29 1\n",
            "3 56\n",
            "0.9550561797752809 0.9666666666666667 0.90625\n"
          ]
        }
      ],
      "source": [
        "prog_true = 0\n",
        "prog_false = 0\n",
        "non_prog_true = 0\n",
        "non_prog_false = 0\n",
        "for obj in prog_tensors_validation: # obj collection of 10 sec snippets for each song in the validation set 1d array\n",
        "  arr = []\n",
        "  cnt = 0\n",
        "  for i in obj:\n",
        "    # print(i.shape)\n",
        "    j = i.reshape(1, 160 * 216)\n",
        "    j = j.detach().numpy()\n",
        "    prediction = model.predict(j)\n",
        "    # arr.append(j)\n",
        "    # print(prediction[0])\n",
        "    if(prediction[0] == 1.0):\n",
        "      cnt = cnt+1\n",
        "      # 100 snippetts\n",
        "      # cnt = 60\n",
        "  if(2*cnt >= len(obj)):\n",
        "    prog_true+=1\n",
        "  else:\n",
        "    prog_false+=1\n",
        "\n",
        "for obj in non_prog_tensors_validation:\n",
        "  arr = []\n",
        "  cnt = 0\n",
        "  for i in obj:\n",
        "    # print(i.shape)\n",
        "    j = i.reshape(1, 160 * 216)\n",
        "    j = j.detach().numpy()\n",
        "    prediction = model.predict(j)\n",
        "    # arr.append(j)\n",
        "    # print(prediction[0])\n",
        "    if(prediction[0] == 0):\n",
        "      cnt = cnt+1\n",
        "  if(2*cnt >= len(obj)):\n",
        "    non_prog_true+=1\n",
        "  else:\n",
        "    non_prog_false+=1\n",
        "\n",
        "print(prog_true, prog_false)\n",
        "print(non_prog_false,non_prog_true)\n",
        "  # prediction = model.predict([arr])\n",
        "accuracy = (prog_true + non_prog_true) / (prog_true + prog_false + non_prog_true + non_prog_false)\n",
        "precision = prog_true / (prog_true + prog_false)\n",
        "recall = prog_true / (prog_true + non_prog_false)\n",
        "print(accuracy, precision, recall)\n",
        "\n",
        "# pretty good idk how"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3Pnzw8Yjjz_"
      },
      "source": [
        "# XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kN5kf_eijmd_"
      },
      "outputs": [],
      "source": [
        "# use X_data, y_data and create an XGBoost classifier\n",
        "## crashed so maybe use PCA and reduce dimensions and then try classifying\n",
        "import xgboost as xgb\n",
        "\n",
        "# Create an XGBoost classifier\n",
        "model_xgb = xgb.XGBClassifier()\n",
        "\n",
        "# Train the model\n",
        "model_xgb.fit(X_data, y_data)\n",
        "\n",
        "# Print the model\n",
        "print(model_xgb)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Amf1ryiJOpm6"
      },
      "source": [
        "# Deep Learning starts here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "J1IKd2fNOdxb"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Concatenate the tensors and create labels\n",
        "data = torch.cat((prog_tensors, non_prog_tensors), dim=0)\n",
        "labels = torch.cat((torch.ones(prog_tensors.shape[0]), torch.zeros(non_prog_tensors.shape[0])), dim=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-15TtZU7P7uh"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([15507, 216, 160])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch_size = 32  # Define your desired batch size\n",
        "data.shape\n",
        "# (instances, timesteps, features) [15507, 216, 160]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "eNMM6ub5VBGo"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "# Create TensorDataset from your data and labels\n",
        "train_dataset = TensorDataset(data, labels)\n",
        "# val_dataset = TensorDataset(val_data, val_labels)\n",
        "# test_dataset = TensorDataset(test_data, test_labels)\n",
        "\n",
        "# Create DataLoader for each dataset with the specified batch size\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "# val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
        "# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "K2UiIFw8VEBf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MyConvNet(\n",
            "  (conv1): Conv1d(160, 320, kernel_size=(3,), stride=(1,), padding=(1,))\n",
            "  (batchnorm1): BatchNorm1d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv2): Conv1d(320, 640, kernel_size=(5,), stride=(1,), padding=(1,))\n",
            "  (batchnorm2): BatchNorm1d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv3): Conv1d(640, 256, kernel_size=(5,), stride=(1,), padding=(1,))\n",
            "  (batchnorm3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv4): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
            "  (batchnorm4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (fc1): Linear(in_features=27136, out_features=200, bias=True)\n",
            "  (fc2): Linear(in_features=200, out_features=20, bias=True)\n",
            "  (fc3): Linear(in_features=20, out_features=2, bias=True)\n",
            "  (relu): ReLU()\n",
            "  (dropout): Dropout(p=0.25, inplace=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "# nn.Conv1d expects a batched 3-dimensional input in the shape [batch_size, in_channels, seq_length] or an unbatched 2-dimensional input in the shape [in_channels, seq_length]\n",
        "class MyConvNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyConvNet, self).__init__()\n",
        "\n",
        "        # Define convolutional layers\n",
        "        self.conv1 = nn.Conv1d(in_channels=160, out_channels=320, kernel_size=3, stride=1, padding=1)\n",
        "        self.batchnorm1 = nn.BatchNorm1d(320)\n",
        "        self.conv2 = nn.Conv1d(in_channels=320, out_channels=640, kernel_size=5, stride=1, padding=1)\n",
        "        self.batchnorm2 = nn.BatchNorm1d(640)\n",
        "        self.conv3 = nn.Conv1d(in_channels=640, out_channels=256, kernel_size=5, stride=1, padding=1)\n",
        "        self.batchnorm3 = nn.BatchNorm1d(256)\n",
        "        self.conv4 = nn.Conv1d(in_channels=256, out_channels=128, kernel_size=3, stride=1, padding=1)\n",
        "        self.batchnorm4 = nn.BatchNorm1d(128)\n",
        "\n",
        "        # Define fully connected layers\n",
        "        self.fc1 = nn.Linear(212*128, 200)  \n",
        "        self.fc2 = nn.Linear(200, 20)\n",
        "        self.fc3 = nn.Linear(20, 2)\n",
        "\n",
        "        # Define activation function\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "        self.dropout = nn.Dropout(p=0.25)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Convolutional layers with ReLU activation\n",
        "        x = self.relu(self.batchnorm1(self.conv1(x)))\n",
        "        x = self.dropout(x)\n",
        "        x = self.relu(self.batchnorm2(self.conv2(x)))\n",
        "        x = self.dropout(x)\n",
        "        x = self.relu(self.batchnorm3(self.conv3(x)))\n",
        "        x = self.dropout(x)\n",
        "        x = self.relu(self.batchnorm4(self.conv4(x)))\n",
        "        x = self.dropout(x)\n",
        "\n",
        "        # Flatten the output of the convolutional layers\n",
        "        x = x.view(x.size(0), -1)\n",
        "\n",
        "        # Fully connected layers with ReLU activation\n",
        "        x = self.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.relu(self.fc2(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc3(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "# Instantiate the model\n",
        "model = MyConvNet()\n",
        "print(model)\n",
        "\n",
        "# Define loss function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Define optimizer (e.g., Adam optimizer with learning rate 0.001)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_n_params(model):\n",
        "    pp=0\n",
        "    for p in list(model.parameters()):\n",
        "        nn=1\n",
        "        for s in list(p.size()):\n",
        "            nn = nn*s\n",
        "        pp += nn\n",
        "    return pp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "INOn4Qbzd3Ko"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MyConvNet(\n",
            "  (conv1): Conv1d(160, 320, kernel_size=(3,), stride=(1,), padding=(1,))\n",
            "  (batchnorm1): BatchNorm1d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv2): Conv1d(320, 640, kernel_size=(5,), stride=(1,), padding=(1,))\n",
            "  (batchnorm2): BatchNorm1d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv3): Conv1d(640, 256, kernel_size=(5,), stride=(1,), padding=(1,))\n",
            "  (batchnorm3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv4): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
            "  (batchnorm4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (fc1): Linear(in_features=27136, out_features=200, bias=True)\n",
            "  (fc2): Linear(in_features=200, out_features=20, bias=True)\n",
            "  (fc3): Linear(in_features=20, out_features=2, bias=True)\n",
            "  (relu): ReLU()\n",
            ")\n",
            "Epoch: 0, Batch: 1, Loss: 0.6855, Accuracy: 54.69%, Precision: 60.61%\n",
            "Epoch: 0, Batch: 2, Loss: 0.9159, Accuracy: 54.69%, Precision: 63.16%\n",
            "Epoch: 0, Batch: 3, Loss: 2.4419, Accuracy: 55.73%, Precision: 59.80%\n",
            "Epoch: 0, Batch: 4, Loss: 1.1541, Accuracy: 55.08%, Precision: 57.23%\n",
            "Epoch: 0, Batch: 5, Loss: 0.6493, Accuracy: 55.00%, Precision: 56.73%\n",
            "Epoch: 0, Batch: 6, Loss: 0.6436, Accuracy: 55.99%, Precision: 56.73%\n",
            "Epoch: 0, Batch: 7, Loss: 0.7767, Accuracy: 56.47%, Precision: 56.73%\n",
            "Epoch: 0, Batch: 8, Loss: 0.6281, Accuracy: 56.25%, Precision: 56.73%\n",
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            "7530598\n"
          ]
        },
        {
          "ename": "RuntimeError",
          "evalue": "Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [2, 64, 216, 160]",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[17], line 74\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pp\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28mprint\u001b[39m(get_n_params(model))\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msummary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m216\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m160\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     75\u001b[0m \u001b[38;5;66;03m# for name, param in model.named_parameters():\u001b[39;00m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;66;03m#     print(name, param.data)\u001b[39;00m\n",
            "File \u001b[0;32m~/miniconda3/envs/prog/lib/python3.10/site-packages/torchsummary/torchsummary.py:72\u001b[0m, in \u001b[0;36msummary\u001b[0;34m(model, input_size, batch_size, device)\u001b[0m\n\u001b[1;32m     68\u001b[0m model\u001b[38;5;241m.\u001b[39mapply(register_hook)\n\u001b[1;32m     70\u001b[0m \u001b[38;5;66;03m# make a forward pass\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# print(x.shape)\u001b[39;00m\n\u001b[0;32m---> 72\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# remove these hooks\u001b[39;00m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m hooks:\n",
            "File \u001b[0;32m~/miniconda3/envs/prog/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
            "Cell \u001b[0;32mIn[9], line 28\u001b[0m, in \u001b[0;36mMyConvNet.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;66;03m# Convolutional layers with ReLU activation\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatchnorm1(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m     29\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatchnorm2(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2(x)))\n\u001b[1;32m     30\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatchnorm3(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv3(x)))\n",
            "File \u001b[0;32m~/miniconda3/envs/prog/lib/python3.10/site-packages/torch/nn/modules/module.py:1538\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1535\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m hooks\u001b[38;5;241m.\u001b[39mBackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[1;32m   1536\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[0;32m-> 1538\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[1;32m   1540\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m   1541\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[1;32m   1542\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[1;32m   1543\u001b[0m     ):\n",
            "File \u001b[0;32m~/miniconda3/envs/prog/lib/python3.10/site-packages/torch/nn/modules/conv.py:313\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 313\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/miniconda3/envs/prog/lib/python3.10/site-packages/torch/nn/modules/conv.py:309\u001b[0m, in \u001b[0;36mConv1d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv1d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    307\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    308\u001b[0m                     _single(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 309\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    310\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [2, 64, 216, 160]"
          ]
        }
      ],
      "source": [
        "print(model)\n",
        "device = get_device()\n",
        "model.to(device)\n",
        "model.train()\n",
        "loss_hist = []\n",
        "correct_predictions = 0\n",
        "total_predictions = 0\n",
        "true_positives = 0\n",
        "false_positives = 0\n",
        "true_negatives = 0\n",
        "false_negatives = 0\n",
        "epochs = 15\n",
        "\n",
        "for e in range(epochs):\n",
        "    i = 1\n",
        "    loss_per_epoch = 0\n",
        "    for inputs, labels in train_loader:\n",
        "        # Perform forward pass, compute loss, and update the model\n",
        "        inputs = inputs.unsqueeze(1)\n",
        "        labels = labels.long()\n",
        "        bs, c, time, feats = inputs.shape\n",
        "        inputs = inputs.reshape(bs, time, feats)\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        outputs = model(inputs)\n",
        "\n",
        "        # Calculate loss\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss_per_epoch += loss.item()\n",
        "        # Calculate accuracy\n",
        "        # print('a',outputs.data)\n",
        "        # print('b',torch.max(outputs.data, 1))\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total_predictions += labels.size(0)\n",
        "        correct_predictions += (predicted == labels).sum().item()\n",
        "\n",
        "        # Calculate precision\n",
        "        true_positives += ((predicted == 1) & (labels == 1)).sum().item()\n",
        "        false_positives += ((predicted == 1) & (labels == 0)).sum().item()\n",
        "        true_negatives += ((predicted == 0) & (labels == 0)).sum().item()\n",
        "        false_negatives += ((predicted == 0) & (labels == 1)).sum().item()\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Print metrics\n",
        "        print(\"Epoch: {}, Batch: {}, Loss: {:.4f}, Accuracy: {:.2f}%, Precision: {:.2f}%\".format(\n",
        "            e, i, loss.item(), (correct_predictions / total_predictions) * 100,\n",
        "            (true_positives / (true_positives + false_positives + 1e-12)) * 100  # Add small epsilon to avoid division by zero\n",
        "        ))\n",
        "\n",
        "        i += 1\n",
        "    loss_hist.append(loss_per_epoch/i)\n",
        "torch.save(model.state_dict(), \"./model/model_conv1d_norm_3_dropout.pt\")\n",
        "\n",
        "def get_n_params(model):\n",
        "    pp=0\n",
        "    for p in list(model.parameters()):\n",
        "        nn=1\n",
        "        for s in list(p.size()):\n",
        "            nn = nn*s\n",
        "        pp += nn\n",
        "    return pp\n",
        "print(get_n_params(model))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "y5NIwYzNFc3n"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a list of epochs\n",
        "epochs = range(1, len(loss_hist) + 1)\n",
        "\n",
        "# Plot the training loss as a curve\n",
        "plt.plot(epochs, loss_hist, 'b-', label='Training loss')\n",
        "\n",
        "# Add a title and axis labels\n",
        "plt.title(\"Training Loss History\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "\n",
        "# Add a legend\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'summary' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msummary\u001b[49m(model, (\u001b[38;5;241m160\u001b[39m,\u001b[38;5;241m216\u001b[39m), \u001b[38;5;241m32\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
            "\u001b[0;31mNameError\u001b[0m: name 'summary' is not defined"
          ]
        }
      ],
      "source": [
        "print(summary(model, (160,216), 64, \"cuda\"))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "aYojwtoSBPF6"
      },
      "outputs": [],
      "source": [
        "# https://drive.google.com/file/d/1--QAwB8w8GVWZ1wmpPOPaj6jNkGWDI3c/view?usp=sharing\n",
        "# !gdown --id 1--QAwB8w8GVWZ1wmpPOPaj6jNkGWDI3c\n",
        "# !unzip /content/both_tensors_160_216_validation_1.zip\n",
        "import torch \n",
        "# path warning!! Colab dir are different\n",
        "prog_tensors_validation = torch.load('./content/progressive_rock_songs_validation_tensor.pt')\n",
        "non_prog_tensors_validation = torch.load('./content/non_progressive_rock_songs_validation_tensor.pt')\n",
        "# non_prog_tensors_validation = non_prog_tensors_validation.reshape(1530, 216, 160)\n",
        "# prog_tensors_validation = prog_tensors_validation.reshape(1938, 216, 160)\n",
        "# prog_tensors_validation = prog_tensors_validation.float()\n",
        "# non_prog_tensors_validation = non_prog_tensors_validation.float()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu2hNB4GY1Yt"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "j_4ukuwaC9pq"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 216, 160])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "non_prog_tensors_validation[0][0].shape\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB-KO4o8Y3Ma"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "PZpCheS0kAUL"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "28 2\n",
            "2 57\n",
            "1739 199\n",
            "206 1324\n"
          ]
        }
      ],
      "source": [
        "model.eval()  # Set the model to evaluation mode\n",
        "## For the song\n",
        "device = get_device()\n",
        "model.to(\"cpu\")\n",
        "prog_true = 0\n",
        "prog_false = 0\n",
        "non_prog_true = 0\n",
        "non_prog_false = 0\n",
        "# for the individual snippets\n",
        "total_prog_true = 0\n",
        "total_prog_false = 0\n",
        "total_non_prog_true = 0\n",
        "total_non_prog_false = 0\n",
        "with torch.no_grad():  # Disable gradients during validation\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  for obj in prog_tensors_validation:\n",
        "    total = len(obj)\n",
        "    it = 0\n",
        "    for i in obj:\n",
        "      i = i.reshape(1,160,216)\n",
        "      i = i.to(\"cpu\")\n",
        "      outputs = model(i)\n",
        "      _, predicted = torch.max(outputs.data, 1)\n",
        "      # print(predicted)\n",
        "      if(predicted.item() == 1):\n",
        "        it = it+1\n",
        "    \n",
        "    total_prog_true += it\n",
        "    total_prog_false += (total-it)\n",
        "    if(2*it >= total):\n",
        "      prog_true +=1\n",
        "    else:\n",
        "      prog_false +=1\n",
        "  for obj in non_prog_tensors_validation:\n",
        "    total = len(obj)\n",
        "    it = 0\n",
        "    for i in obj:\n",
        "      i = i.reshape(1,160,216)\n",
        "      outputs = model(i)\n",
        "      _, predicted = torch.max(outputs.data, 1)\n",
        "      # print(predicted)\n",
        "      if(predicted.item() == 0):\n",
        "        it = it+1\n",
        "    total_non_prog_true += it\n",
        "    total_non_prog_false += (total-it)\n",
        "    if(2*it >= total):\n",
        "      non_prog_true +=1\n",
        "    else:\n",
        "      non_prog_false +=1\n",
        "\n",
        "\n",
        "print(prog_true, prog_false)\n",
        "print(non_prog_false, non_prog_true)\n",
        "\n",
        "print(total_prog_true, total_prog_false)\n",
        "print(total_non_prog_false, total_non_prog_true)\n",
        "# with torch.no_grad():  # Disable gradients during validation\n",
        "#   correct = 0\n",
        "#   total = 0\n",
        "#   for inputs, labels in val_loader:\n",
        "#     inputs.shape\n",
        "#     outputs = model(inputs)\n",
        "#     _, predicted = torch.max(outputs.data, 1)\n",
        "#     total += labels.size(0)\n",
        "#     print('label',labels)\n",
        "#     print('predicted',predicted)\n",
        "#     correct += (predicted == labels).sum().item()\n",
        "#     print(correct / total)\n",
        "#   val_accuracy = correct / total\n",
        "#   print(f\"Validation accuracy: {val_accuracy:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch \n",
        "# path warning!! Colab dir are different\n",
        "prog_tensors_validation = torch.load('./test-tensors/progressive_rock_songs_test_tensor.pt')\n",
        "non_prog_tensors_validation = torch.load('./test-tensors/non_progressive_rock_songs_test_tensor.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model state dictionary keys:\n",
            "conv1.weight\n",
            "conv1.bias\n",
            "batchnorm1.weight\n",
            "batchnorm1.bias\n",
            "batchnorm1.running_mean\n",
            "batchnorm1.running_var\n",
            "batchnorm1.num_batches_tracked\n",
            "conv2.weight\n",
            "conv2.bias\n",
            "batchnorm2.weight\n",
            "batchnorm2.bias\n",
            "batchnorm2.running_mean\n",
            "batchnorm2.running_var\n",
            "batchnorm2.num_batches_tracked\n",
            "conv3.weight\n",
            "conv3.bias\n",
            "batchnorm3.weight\n",
            "batchnorm3.bias\n",
            "batchnorm3.running_mean\n",
            "batchnorm3.running_var\n",
            "batchnorm3.num_batches_tracked\n",
            "conv4.weight\n",
            "conv4.bias\n",
            "batchnorm4.weight\n",
            "batchnorm4.bias\n",
            "batchnorm4.running_mean\n",
            "batchnorm4.running_var\n",
            "batchnorm4.num_batches_tracked\n",
            "fc1.weight\n",
            "fc1.bias\n",
            "fc2.weight\n",
            "fc2.bias\n",
            "fc3.weight\n",
            "fc3.bias\n",
            "\n",
            "Model layer details:\n",
            "Layer: conv1.weight\n",
            " Shape: torch.Size([320, 160, 3])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: conv1.bias\n",
            " Shape: torch.Size([320])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm1.weight\n",
            " Shape: torch.Size([320])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm1.bias\n",
            " Shape: torch.Size([320])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm1.running_mean\n",
            " Shape: torch.Size([320])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm1.running_var\n",
            " Shape: torch.Size([320])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm1.num_batches_tracked\n",
            " Shape: torch.Size([])\n",
            " Data type: torch.int64\n",
            " Device: cpu\n",
            "Layer: conv2.weight\n",
            " Shape: torch.Size([640, 320, 5])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: conv2.bias\n",
            " Shape: torch.Size([640])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm2.weight\n",
            " Shape: torch.Size([640])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm2.bias\n",
            " Shape: torch.Size([640])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm2.running_mean\n",
            " Shape: torch.Size([640])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm2.running_var\n",
            " Shape: torch.Size([640])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm2.num_batches_tracked\n",
            " Shape: torch.Size([])\n",
            " Data type: torch.int64\n",
            " Device: cpu\n",
            "Layer: conv3.weight\n",
            " Shape: torch.Size([256, 640, 5])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: conv3.bias\n",
            " Shape: torch.Size([256])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm3.weight\n",
            " Shape: torch.Size([256])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm3.bias\n",
            " Shape: torch.Size([256])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm3.running_mean\n",
            " Shape: torch.Size([256])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm3.running_var\n",
            " Shape: torch.Size([256])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm3.num_batches_tracked\n",
            " Shape: torch.Size([])\n",
            " Data type: torch.int64\n",
            " Device: cpu\n",
            "Layer: conv4.weight\n",
            " Shape: torch.Size([128, 256, 3])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: conv4.bias\n",
            " Shape: torch.Size([128])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm4.weight\n",
            " Shape: torch.Size([128])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm4.bias\n",
            " Shape: torch.Size([128])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm4.running_mean\n",
            " Shape: torch.Size([128])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm4.running_var\n",
            " Shape: torch.Size([128])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: batchnorm4.num_batches_tracked\n",
            " Shape: torch.Size([])\n",
            " Data type: torch.int64\n",
            " Device: cpu\n",
            "Layer: fc1.weight\n",
            " Shape: torch.Size([200, 27136])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: fc1.bias\n",
            " Shape: torch.Size([200])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: fc2.weight\n",
            " Shape: torch.Size([20, 200])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: fc2.bias\n",
            " Shape: torch.Size([20])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: fc3.weight\n",
            " Shape: torch.Size([2, 20])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "Layer: fc3.bias\n",
            " Shape: torch.Size([2])\n",
            " Data type: torch.float32\n",
            " Device: cpu\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# model/model_conv1d_norm_1_large.pt\n",
        "model_path = \"./model/model_conv1d_norm_1_large.pt\"\n",
        "model_state_dict = torch.load(model_path)\n",
        "# Print model details\n",
        "print(\"Model state dictionary keys:\")\n",
        "for key in model_state_dict.keys():\n",
        "    print(key)\n",
        "print(\"\\nModel layer details:\")\n",
        "for key, value in model_state_dict.items():\n",
        "    print(f\"Layer: {key}\")\n",
        "    print(f\" Shape: {value.shape}\")\n",
        "    print(f\" Data type: {value.dtype}\")\n",
        "    print(f\" Device: {value.device}\")\n",
        "print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'collections.OrderedDict' object has no attribute 'eval'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model \u001b[38;5;241m=\u001b[39m model_state_dict\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m()  \u001b[38;5;66;03m# Set the model to evaluation mode\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m## For the song\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# device = get_device()\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# model.to(\"cpu\")\u001b[39;00m\n\u001b[1;32m      6\u001b[0m prog_true \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'collections.OrderedDict' object has no attribute 'eval'"
          ]
        }
      ],
      "source": [
        "model = model_state_dict\n",
        "model.eval()  # Set the model to evaluation mode\n",
        "## For the song\n",
        "# device = get_device()\n",
        "# model.to(\"cpu\")\n",
        "prog_true = 0\n",
        "prog_false = 0\n",
        "non_prog_true = 0\n",
        "non_prog_false = 0\n",
        "# for the individual snippets\n",
        "total_prog_true = 0\n",
        "total_prog_false = 0\n",
        "total_non_prog_true = 0\n",
        "total_non_prog_false = 0\n",
        "with torch.no_grad():  # Disable gradients during validation\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  for obj in prog_tensors_validation:\n",
        "    total = len(obj)\n",
        "    it = 0\n",
        "    for i in obj:\n",
        "      i = i.reshape(1,160,216)\n",
        "      i = i.to(\"cpu\")\n",
        "      outputs = model(i)\n",
        "      _, predicted = torch.max(outputs.data, 1)\n",
        "      # print(predicted)\n",
        "      if(predicted.item() == 1):\n",
        "        it = it+1\n",
        "    \n",
        "    total_prog_true += it\n",
        "    total_prog_false += (total-it)\n",
        "    if(2*it >= total):\n",
        "      prog_true +=1\n",
        "    else:\n",
        "      prog_false +=1\n",
        "  for obj in non_prog_tensors_validation:\n",
        "    total = len(obj)\n",
        "    it = 0\n",
        "    for i in obj:\n",
        "      i = i.reshape(1,160,216)\n",
        "      outputs = model(i)\n",
        "      _, predicted = torch.max(outputs.data, 1)\n",
        "      # print(predicted)\n",
        "      if(predicted.item() == 0):\n",
        "        it = it+1\n",
        "    total_non_prog_true += it\n",
        "    total_non_prog_false += (total-it)\n",
        "    if(2*it >= total):\n",
        "      non_prog_true +=1\n",
        "    else:\n",
        "      non_prog_false +=1\n",
        "\n",
        "\n",
        "print(prog_true, prog_false)\n",
        "print(non_prog_false, non_prog_true)\n",
        "\n",
        "print(total_prog_true, total_prog_false)\n",
        "print(total_non_prog_false, total_non_prog_true)\n",
        "# with torch.no_grad():  # Disable gradients during validation\n",
        "#   correct = 0\n",
        "#   total = 0\n",
        "#   for inputs, labels in val_loader:\n",
        "#     inputs.shape\n",
        "#     outputs = model(inputs)\n",
        "#     _, predicted = torch.max(outputs.data, 1)\n",
        "#     total += labels.size(0)\n",
        "#     print('label',labels)\n",
        "#     print('predicted',predicted)\n",
        "#     correct += (predicted == labels).sum().item()\n",
        "#     print(correct / total)\n",
        "#   val_accuracy = correct / total\n",
        "#   print(f\"Validation accuracy: {val_accuracy:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "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
}