train_3Channel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n",
    "import sys\n",
    "from matplotlib import pyplot as plt\n",
    "from Datasets.MSASLDataset import MSASLDataset_3DCNN\n",
    "from Datasets.MSASL5Channel import MSAL5ChannelDataset\n",
    "from Models.Conv3d import Conv3d\n",
    "from utils.train import train\n",
    "from torchvision import transforms\n",
    "import os\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2813\n"
     ]
    }
   ],
   "source": [
    "###### loading full dataset\n",
    "root_dir = './videos/MS-ASL100'\n",
    "classes = os.listdir(root_dir)\n",
    "total = 0\n",
    "for c in classes:\n",
    "    total += len(os.listdir(os.path.join(root_dir, c)))\n",
    "print(total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_10 = ['spring', 'cousin', 'water', 'friend', 'like', 'yes', 'doctor', 'blue', 'nice', 'yellow']\n",
    "\n",
    "classes_25 = ['spring', 'cousin', 'water', 'friend', 'like', 'yes', 'doctor', 'blue', 'nice', 'yellow', \n",
    "              'again', 'deaf', 'drink', 'family', 'father', 'finish', 'grandmother', 'help', 'milk', 'mother', 'orange', 'pencil', 'school', 'sister', 'student']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 32/32 [00:03<00:00, 10.11it/s]\n",
      "100%|██████████| 37/37 [00:03<00:00, 11.43it/s]\n",
      "100%|██████████| 34/34 [00:03<00:00, 10.89it/s]\n",
      "100%|██████████| 35/35 [00:03<00:00, 10.65it/s]\n",
      "100%|██████████| 34/34 [00:03<00:00, 10.22it/s]\n",
      "100%|██████████| 34/34 [00:03<00:00, 10.67it/s]\n",
      "100%|██████████| 32/32 [00:03<00:00,  8.57it/s]\n",
      "100%|██████████| 31/31 [00:02<00:00, 11.81it/s]\n",
      "100%|██████████| 39/39 [00:03<00:00,  9.93it/s]\n",
      "100%|██████████| 31/31 [00:02<00:00, 10.77it/s]\n",
      "100%|██████████| 10/10 [00:32<00:00,  3.25s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "339"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data\n",
    "root_dir = './videos/MS-ASL100'\n",
    "classes = classes_10\n",
    "data = MSASLDataset_3DCNN(root_dir, classes, frames=10)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the loaded data if needed. Uncomment the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = [dataset[i] for i in range(len(dataset))]\n",
    "# print(len(data))\n",
    "# torch.save(data, './StoredData/conv3d10classData.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [(d[0]/255, d[1]) for d in data]\n",
    "len(dataset)\n",
    "del data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "torch.Size([10, 128, 128, 3])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example = dataset[1][0].permute(3,1,2,0)\n",
    "print(dataset[1][1])\n",
    "plt.figure(figsize=(10,10))\n",
    "print(example.shape)\n",
    "for i in range(example.shape[0]):\n",
    "    plt.subplot(1, example.shape[0], i+1)\n",
    "    plt.title(f'frame {i}')\n",
    "    plt.imshow(example[i])\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Augment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def augment_dataset(dataset, n):\n",
    "    for i in range(n):\n",
    "        print(\"augmenting\", i, \"of\", n)\n",
    "        img = dataset[i][0].permute(3,1,2,0).clone()\n",
    "        label = dataset[i][1]\n",
    "\n",
    "        for j in range(10):\n",
    "            temp_img = img.clone()\n",
    "            choices = [\"rotation\", \"flip\", \"translation\", \"noise\"]\n",
    "            choice = np.random.choice(choices)\n",
    "\n",
    "            if choice == \"rotation\":\n",
    "                angle = np.random.randint(-30, 30)\n",
    "                for i in range(10):\n",
    "                    temp_rgb = temp_img[i].permute(2, 0, 1)\n",
    "                    temp_rgb = transforms.functional.to_pil_image(temp_rgb)\n",
    "                    temp_rgb = temp_rgb.rotate(angle, resample=Image.BICUBIC)\n",
    "                    temp_rgb = transforms.ToTensor()(temp_rgb)\n",
    "                    temp_img[i] = temp_rgb.permute(1, 2, 0)\n",
    "                \n",
    "            if choice == \"flip\":\n",
    "                \n",
    "                    for i in range(10):\n",
    "                        temp_rgb = temp_img[i].permute(2, 0, 1)\n",
    "                        temp_rgb = transforms.functional.to_pil_image(temp_rgb)\n",
    "\n",
    "                        temp_rgb = temp_rgb.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "                        temp_rgb = transforms.ToTensor()(temp_rgb)\n",
    "                        temp_img[i] = temp_rgb.permute(1, 2, 0)\n",
    "\n",
    "            if choice == \"translation\":\n",
    "                x = np.random.randint(-10, 10)\n",
    "                y = np.random.randint(-10, 10)\n",
    "                for i in range(10):\n",
    "                    temp_rgb = temp_img[i].permute(2, 0, 1)\n",
    "                    temp_rgb = transforms.functional.to_pil_image(temp_rgb)\n",
    "                    temp_rgb = transforms.functional.affine(temp_rgb, angle=0, translate=[x,y], scale=1, shear=0)\n",
    "                    temp_rgb = transforms.ToTensor()(temp_rgb)\n",
    "                    temp_img[i] = temp_rgb.permute(1, 2, 0)\n",
    "\n",
    "            if choice == \"noise\":\n",
    "                for i in range(10):\n",
    "                    random_noise = torch.normal(0, 0.1, size=temp_img[i].shape)\n",
    "                    temp_img[i] = temp_img[i] + random_noise\n",
    "            \n",
    "\n",
    "            dataset.append((temp_img.permute(3,1,2,0), label))\n",
    "    return dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "augmenting 0 of 339\n",
      "augmenting 1 of 339\n",
      "augmenting 2 of 339\n",
      "augmenting 3 of 339\n",
      "augmenting 4 of 339\n",
      "augmenting 5 of 339\n",
      "augmenting 6 of 339\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\randy\\AppData\\Local\\Temp\\ipykernel_8676\\327715493.py:27: DeprecationWarning: FLIP_LEFT_RIGHT is deprecated and will be removed in Pillow 10 (2023-07-01). Use Transpose.FLIP_LEFT_RIGHT instead.\n",
      "  temp_rgb = temp_rgb.transpose(Image.FLIP_LEFT_RIGHT)\n",
      "C:\\Users\\randy\\AppData\\Local\\Temp\\ipykernel_8676\\327715493.py:17: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.\n",
      "  temp_rgb = temp_rgb.rotate(angle, resample=Image.BICUBIC)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "augmenting 7 of 339\n",
      "augmenting 8 of 339\n",
      "augmenting 9 of 339\n",
      "augmenting 10 of 339\n",
      "augmenting 11 of 339\n",
      "augmenting 12 of 339\n",
      "augmenting 13 of 339\n",
      "augmenting 14 of 339\n",
      "augmenting 15 of 339\n",
      "augmenting 16 of 339\n",
      "augmenting 17 of 339\n",
      "augmenting 18 of 339\n",
      "augmenting 19 of 339\n",
      "augmenting 20 of 339\n",
      "augmenting 21 of 339\n",
      "augmenting 22 of 339\n",
      "augmenting 23 of 339\n",
      "augmenting 24 of 339\n",
      "augmenting 25 of 339\n",
      "augmenting 26 of 339\n",
      "augmenting 27 of 339\n",
      "augmenting 28 of 339\n",
      "augmenting 29 of 339\n",
      "augmenting 30 of 339\n",
      "augmenting 31 of 339\n",
      "augmenting 32 of 339\n",
      "augmenting 33 of 339\n",
      "augmenting 34 of 339\n",
      "augmenting 35 of 339\n",
      "augmenting 36 of 339\n",
      "augmenting 37 of 339\n",
      "augmenting 38 of 339\n",
      "augmenting 39 of 339\n",
      "augmenting 40 of 339\n",
      "augmenting 41 of 339\n",
      "augmenting 42 of 339\n",
      "augmenting 43 of 339\n",
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      "augmenting 45 of 339\n",
      "augmenting 46 of 339\n",
      "augmenting 47 of 339\n",
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      "augmenting 50 of 339\n",
      "augmenting 51 of 339\n",
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      "augmenting 60 of 339\n",
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      "2034\n"
     ]
    }
   ],
   "source": [
    "n = len(dataset)\n",
    "dataset = augment_dataset(dataset, n)\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### save the augmented data if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(dataset, './StoredData/conv3d10classDataAugmented.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 128, 128, 10])\n",
      "9\n",
      "torch.Size([10, 128, 128, 3])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(dataset[500][0].shape)\n",
    "aug_exmp = dataset[500][0].permute(3,1,2,0)\n",
    "print(dataset[-1][1])\n",
    "plt.figure(figsize=(10,10))\n",
    "print(aug_exmp.shape)\n",
    "for i in range(aug_exmp.shape[0]):\n",
    "    plt.subplot(1, aug_exmp.shape[0], i+1)\n",
    "    plt.title(f'frame {i}')\n",
    "    plt.imshow(aug_exmp[i])\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train size: 1627\n",
      "val size: 203\n",
      "test size: 204\n"
     ]
    }
   ],
   "source": [
    "#split the data into training, validation and test sets\n",
    "train_size = int(0.8 * len(dataset))\n",
    "val_size = int(0.1 * len(dataset))\n",
    "test_size = len(dataset) - train_size - val_size\n",
    "\n",
    "train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, val_size, test_size])\n",
    "print(\"train size:\", len(train_dataset))\n",
    "print(\"val size:\", len(val_dataset))\n",
    "print(\"test size:\", len(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "val_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)\n",
    "\n",
    "del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 128, 128, 10])\n",
      "tensor([9, 1, 8, 6, 6, 2, 2, 3, 4, 8, 0, 9, 7, 4, 7, 9, 2, 1, 9, 4, 5, 1, 1, 3,\n",
      "        5, 5, 0, 6, 8, 4, 8, 1, 5, 2, 6, 5, 2, 0, 5, 3, 2, 8, 7, 4, 8, 5, 0, 9,\n",
      "        9, 4, 8, 2, 4, 3, 3, 6, 0, 6, 3, 8, 7, 4, 4, 6])\n"
     ]
    }
   ],
   "source": [
    "for batch_idx, (data, target) in enumerate(train_loader):\n",
    "    print(data.shape)\n",
    "    print(target)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conv3d(nn.Module):\n",
    "    def __init__(self,in_channels, num_frames, num_classes=10):\n",
    "        super(Conv3d, self).__init__()\n",
    "        #input shape: (3, 128, 128, num_frames)\n",
    "        self.conv1 = nn.Conv3d(in_channels=in_channels, out_channels=64, kernel_size=(7,7,5))\n",
    "        #output shape: (64, 122, 122, num_frames-4)\n",
    "        self.bn1 = nn.BatchNorm3d(64)\n",
    "        self.pool3D1 = nn.MaxPool3d(kernel_size=(2,2,1))\n",
    "        #output shape: (64, 61, 61, num_frames-4)\n",
    "\n",
    "        self.conv2 = nn.Conv3d(in_channels=64, out_channels=32, kernel_size=(7,7,3))\n",
    "        #output shape: (32, 55, 55, num_frames-4-2)\n",
    "        self.bn2 = nn.BatchNorm3d(32)\n",
    "        self.pool3D2 = nn.MaxPool3d(kernel_size=(2,2,1))\n",
    "        #output shape: (32, 27, 27, num_frames-4-2)\n",
    "\n",
    "        self. conv3 = nn.Conv3d(in_channels=32, out_channels=16, kernel_size=(5,5,4))\n",
    "        #output shape: (16, 23, 23, num_frames-4-2-3)\n",
    "        self.bn3 = nn.BatchNorm3d(16)\n",
    "        self.pool3D3 = nn.MaxPool3d(kernel_size=(2,2,1))\n",
    "        #output shape: (16, 11, 11, num_frames-4-2-3)\n",
    "\n",
    "\n",
    "        self.fc1 = nn.Linear(16*11*11*(num_frames-9), 128)\n",
    "        self.dropout1 = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "\n",
    "\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool3D1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool3D2(x)\n",
    "\n",
    "        x = self.conv3(x)\n",
    "        x = self.bn3(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool3D3(x)\n",
    "\n",
    "\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def train(model, train_loader, test_loader, criterion, optimizer, device, epochs, model_name):\n",
    "    start_time = time.time()\n",
    "    model.train()\n",
    "\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    stop_cnt = 0\n",
    "    best_train_loss = 1000\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        \n",
    "        trn_corr = 0\n",
    "        total = 0\n",
    "        epoch_train_loss = 0\n",
    "\n",
    "        print(f'Epoch {epoch+1}:')\n",
    "        for batch_idx, (X_train, y_train) in enumerate(train_loader):\n",
    "            batch_idx += 1\n",
    "            X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "            y_pred = model(X_train)\n",
    "\n",
    "            loss = criterion(y_pred, y_train)\n",
    "\n",
    "            predicted = torch.max(y_pred.data, 1)[1]\n",
    "            trn_corr += (predicted == y_train).sum()\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            \n",
    "            epoch_train_loss += loss.item()\n",
    "            total += len(y_train)\n",
    "            if batch_idx % 10 == 0:\n",
    "                print(f'\\tBatch {batch_idx}, loss: {loss.item():7.3f} running accuracy: {trn_corr.item()*100/total:7.3f}%')\n",
    "            \n",
    "\n",
    "        avg_epoch_train_loss = epoch_train_loss / len(train_loader)\n",
    "        train_losses.append(avg_epoch_train_loss)     \n",
    "\n",
    "        # Run the testing batches\n",
    "        tst_corr = 0\n",
    "        epoch_test_loss = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for batch_idx, (X_test, y_test) in enumerate(test_loader):\n",
    "                X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "                y_val = model(X_test)\n",
    "                predicted = torch.max(y_val.data, 1)[1]\n",
    "                tst_corr += (predicted == y_test).sum()\n",
    "\n",
    "                loss = criterion(y_val, y_test)\n",
    "                epoch_test_loss += loss.item()\n",
    "                \n",
    "\n",
    "        avg_epoch_test_loss = epoch_test_loss / len(test_loader)\n",
    "        val_losses.append(avg_epoch_test_loss)\n",
    "\n",
    "        print(f'\\n\\tEpoch {epoch+1}, Training Loss: {avg_epoch_train_loss:.3f}, val Loss: {avg_epoch_test_loss:.3f}')\n",
    "        \n",
    "        if (epoch+1) % 10 == 0:\n",
    "            torch.save(model, f'./SavedModels/{model_name}_epoch{epoch+1}.pt')\n",
    "            plt.figure()\n",
    "            plt.plot(train_losses, label='Training loss')\n",
    "            plt.plot(val_losses, label='Validation loss')\n",
    "            plt.legend()\n",
    "            plt.savefig(f'./Figures/{model_name}_losses_epoch{epoch+1}.png')\n",
    "            \n",
    "        if avg_epoch_train_loss < best_train_loss:\n",
    "            best_train_loss = avg_epoch_train_loss\n",
    "            stop_cnt = 0\n",
    "        else:\n",
    "            stop_cnt += 1\n",
    "            if stop_cnt == 3:\n",
    "                print(f'\\nEarly stopping at epoch {epoch+1}')\n",
    "                break\n",
    "        \n",
    "        \n",
    "    print(f'\\nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed\n",
    "    \n",
    "\n",
    "    return train_losses, val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:\n",
      "\tBatch 10, loss:   2.289 running accuracy:  10.000%\n",
      "\tBatch 20, loss:   2.314 running accuracy:  12.578%\n",
      "\n",
      "\tEpoch 1, Training Loss: 2.301, val Loss: 2.270\n",
      "Epoch 2:\n",
      "\tBatch 10, loss:   2.219 running accuracy:  20.156%\n",
      "\tBatch 20, loss:   2.171 running accuracy:  21.875%\n",
      "\n",
      "\tEpoch 2, Training Loss: 2.188, val Loss: 2.191\n",
      "Epoch 3:\n",
      "\tBatch 10, loss:   2.085 running accuracy:  30.469%\n",
      "\tBatch 20, loss:   1.972 running accuracy:  32.188%\n",
      "\n",
      "\tEpoch 3, Training Loss: 2.063, val Loss: 2.071\n",
      "Epoch 4:\n",
      "\tBatch 10, loss:   1.889 running accuracy:  38.281%\n",
      "\tBatch 20, loss:   1.945 running accuracy:  37.422%\n",
      "\n",
      "\tEpoch 4, Training Loss: 1.910, val Loss: 1.877\n",
      "Epoch 5:\n",
      "\tBatch 10, loss:   1.714 running accuracy:  37.969%\n",
      "\tBatch 20, loss:   1.732 running accuracy:  42.500%\n",
      "\n",
      "\tEpoch 5, Training Loss: 1.746, val Loss: 1.817\n",
      "Epoch 6:\n",
      "\tBatch 10, loss:   1.668 running accuracy:  44.219%\n",
      "\tBatch 20, loss:   1.551 running accuracy:  47.812%\n",
      "\n",
      "\tEpoch 6, Training Loss: 1.588, val Loss: 1.630\n",
      "Epoch 7:\n",
      "\tBatch 10, loss:   1.414 running accuracy:  51.719%\n",
      "\tBatch 20, loss:   1.376 running accuracy:  53.906%\n",
      "\n",
      "\tEpoch 7, Training Loss: 1.434, val Loss: 1.537\n",
      "Epoch 8:\n",
      "\tBatch 10, loss:   1.180 running accuracy:  64.062%\n",
      "\tBatch 20, loss:   1.189 running accuracy:  61.328%\n",
      "\n",
      "\tEpoch 8, Training Loss: 1.304, val Loss: 1.573\n",
      "Epoch 9:\n",
      "\tBatch 10, loss:   1.081 running accuracy:  63.906%\n",
      "\tBatch 20, loss:   1.377 running accuracy:  65.391%\n",
      "\n",
      "\tEpoch 9, Training Loss: 1.154, val Loss: 1.281\n",
      "Epoch 10:\n",
      "\tBatch 10, loss:   0.955 running accuracy:  73.906%\n",
      "\tBatch 20, loss:   0.953 running accuracy:  73.359%\n",
      "\n",
      "\tEpoch 10, Training Loss: 0.953, val Loss: 1.252\n",
      "Epoch 11:\n",
      "\tBatch 10, loss:   0.815 running accuracy:  78.125%\n",
      "\tBatch 20, loss:   0.838 running accuracy:  76.484%\n",
      "\n",
      "\tEpoch 11, Training Loss: 0.852, val Loss: 1.211\n",
      "Epoch 12:\n",
      "\tBatch 10, loss:   0.768 running accuracy:  82.188%\n",
      "\tBatch 20, loss:   0.566 running accuracy:  82.578%\n",
      "\n",
      "\tEpoch 12, Training Loss: 0.735, val Loss: 1.136\n",
      "Epoch 13:\n",
      "\tBatch 10, loss:   0.753 running accuracy:  83.906%\n",
      "\tBatch 20, loss:   0.658 running accuracy:  84.688%\n",
      "\n",
      "\tEpoch 13, Training Loss: 0.641, val Loss: 0.990\n",
      "Epoch 14:\n",
      "\tBatch 10, loss:   0.494 running accuracy:  84.062%\n",
      "\tBatch 20, loss:   0.516 running accuracy:  85.938%\n",
      "\n",
      "\tEpoch 14, Training Loss: 0.545, val Loss: 0.782\n",
      "Epoch 15:\n",
      "\tBatch 10, loss:   0.546 running accuracy:  90.938%\n",
      "\tBatch 20, loss:   0.473 running accuracy:  90.234%\n",
      "\n",
      "\tEpoch 15, Training Loss: 0.445, val Loss: 0.724\n",
      "Epoch 16:\n",
      "\tBatch 10, loss:   0.410 running accuracy:  92.656%\n",
      "\tBatch 20, loss:   0.348 running accuracy:  92.578%\n",
      "\n",
      "\tEpoch 16, Training Loss: 0.391, val Loss: 0.675\n",
      "Epoch 17:\n",
      "\tBatch 10, loss:   0.339 running accuracy:  95.469%\n",
      "\tBatch 20, loss:   0.378 running accuracy:  94.062%\n",
      "\n",
      "\tEpoch 17, Training Loss: 0.340, val Loss: 0.631\n",
      "Epoch 18:\n",
      "\tBatch 10, loss:   0.225 running accuracy:  95.938%\n",
      "\tBatch 20, loss:   0.229 running accuracy:  94.766%\n",
      "\n",
      "\tEpoch 18, Training Loss: 0.292, val Loss: 0.526\n",
      "Epoch 19:\n",
      "\tBatch 10, loss:   0.298 running accuracy:  96.094%\n",
      "\tBatch 20, loss:   0.268 running accuracy:  96.328%\n",
      "\n",
      "\tEpoch 19, Training Loss: 0.240, val Loss: 0.512\n",
      "Epoch 20:\n",
      "\tBatch 10, loss:   0.196 running accuracy:  97.188%\n",
      "\tBatch 20, loss:   0.246 running accuracy:  97.188%\n",
      "\n",
      "\tEpoch 20, Training Loss: 0.208, val Loss: 0.559\n",
      "\n",
      "Duration: 160 seconds\n"
     ]
    }
   ],
   "source": [
    "model = Conv3d(in_channels=3, num_frames = 10, num_classes=len(classes))\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.0001)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0001)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "train_losses, test_losses = train(\n",
    "    model = model, \n",
    "    device = device, \n",
    "    train_loader = train_loader, \n",
    "    test_loader=val_loader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    epochs=20,\n",
    "    model_name=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses, label='Training loss')\n",
    "plt.plot(test_losses, label='Validation loss')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 204 test images: 90.19607843137256%\n"
     ]
    }
   ],
   "source": [
    "model.to(device)\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for i, (data, target) in enumerate(test_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        outputs = model(data)\n",
    "        predicted = torch.max(outputs, 1)[1]\n",
    "        total += target.size(0)\n",
    "        correct += (predicted == target).sum().item()\n",
    "\n",
    "print(f'Accuracy of the network on the {total} test images: {100 * correct / total}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14/14 [00:01<00:00,  8.41it/s]\n",
      "100%|██████████| 11/11 [00:01<00:00,  9.21it/s]\n",
      "100%|██████████| 10/10 [00:00<00:00, 10.61it/s]\n",
      "100%|██████████| 8/8 [00:00<00:00,  9.59it/s]]\n",
      "100%|██████████| 17/17 [00:01<00:00,  8.65it/s]\n",
      "100%|██████████| 16/16 [00:01<00:00, 10.35it/s]\n",
      "100%|██████████| 15/15 [00:02<00:00,  7.46it/s]\n",
      "100%|██████████| 7/7 [00:00<00:00, 14.50it/s]]\n",
      "100%|██████████| 17/17 [00:01<00:00,  8.76it/s]\n",
      "100%|██████████| 8/8 [00:00<00:00, 11.61it/s]]\n",
      "100%|██████████| 10/10 [00:13<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 123 new test images: 17.88617886178862%\n"
     ]
    }
   ],
   "source": [
    "new_data = MSASLDataset_3DCNN(\"./videos/test-MS-ASL100\", classes, frames=10)\n",
    "new_data = [(d[0]/255, d[1]) for d in new_data]\n",
    "new_loader = DataLoader(new_data, batch_size=64, shuffle=True)\n",
    "del new_data\n",
    "model.to(device)\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i, (data, target) in enumerate(new_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        outputs = model(data)\n",
    "        predicted = torch.max(outputs, 1)[1]\n",
    "        total += target.size(0)\n",
    "        correct += (predicted == target).sum().item()\n",
    "\n",
    "print(f'Accuracy of the network on the {total} new test images: {100 * correct / total}%')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cvFinal",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}