ASL-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as datasets\n",
    "\n",
    "# Set the path to the directory containing the image folders\n",
    "data_path = \"Images/\"\n",
    "\n",
    "# Define the transforms to be applied to each image\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((28, 28)),\n",
    "    transforms.Grayscale(num_output_channels=1),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485], std=[0.229])\n",
    "])\n",
    "\n",
    "# Load the dataset using the ImageFolder class\n",
    "dataset = datasets.ImageFolder(root=data_path, transform=transform)\n",
    "\n",
    "# Define the proportions for the training, validation, and test sets\n",
    "train_prop = 0.8\n",
    "val_prop = 0.1\n",
    "test_prop = 0.1\n",
    "\n",
    "# Compute the sizes of each set\n",
    "num_data = len(dataset)\n",
    "num_train = int(train_prop * num_data)\n",
    "num_val = int(val_prop * num_data)\n",
    "num_test = num_data - num_train - num_val\n",
    "\n",
    "# Split the dataset into training, validation, and test sets\n",
    "train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(\n",
    "    dataset, [num_train, num_val, num_test])\n",
    "\n",
    "# Define the data loaders for the training, validation, and test sets\n",
    "batch_size = 8\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "alphabet_dict = {0: 'A',1: 'B',2: 'C',3: 'D',4: 'E',5: 'F',6: 'G',7: 'H',8: 'I',9: 'K',10: 'L',11: 'M',\n",
    "    12: 'N',13: 'O',14: 'P',15: 'Q',16: 'R',17: 'S',18: 'T',19: 'U',20: 'V',21: 'W',22: 'X',23: 'Y'}\n",
    "\n",
    "figure = plt.figure(figsize=(8, 8)) #defining the figure size\n",
    "cols, rows = 3, 3 #numbers of rows and colomns\n",
    "\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(dataset), size=(1,)).item() #taking a random sample from the training data\n",
    "    img, label = dataset[sample_idx] #saving the img and label of that sample\n",
    "    figure.add_subplot(rows, cols, i) #adding the subplot at the right position\n",
    "    plt.title(str(alphabet_dict[label])) #printing the lable (class) of the image\n",
    "    plt.axis(\"off\") #switching the axis off for a better result\n",
    "    plt.imshow(img.squeeze()) #visulizing the tensor as an image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN_Classification_Net(\n",
      "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (act1): ReLU()\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (bn1): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (act2): ReLU()\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (bn2): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv3): Conv2d(50, 100, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (act3): ReLU()\n",
      "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (bn3): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (linear1): Linear(in_features=100, out_features=240, bias=True)\n",
      "  (act4): ReLU()\n",
      "  (drop): Dropout(p=0.3, inplace=False)\n",
      "  (linear2): Linear(in_features=240, out_features=120, bias=True)\n",
      "  (act5): ReLU()\n",
      "  (linear3): Linear(in_features=120, out_features=60, bias=True)\n",
      "  (act6): ReLU()\n",
      "  (out): Linear(in_features=60, out_features=24, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "device = \"cuda\" if torch.has_cuda else \"cpu\"\n",
    "\n",
    "class CNN_Classification_Net(nn.Module):\n",
    "    def __init__(self, num_classes, input_shape) -> None:\n",
    "        super(CNN_Classification_Net,self).__init__()\n",
    "\n",
    "        # first convolution layer + activation + max pooling (first layer it to capture the edges)\n",
    "        self.conv1 = nn.Conv2d(in_channels = input_shape[0], out_channels = 20,\n",
    "                                kernel_size=(5, 5)) #Resulting tensor size (20,24,24)\n",
    "        self.act1 = nn.ReLU()#Keeping in mind that both ReLU and Pooling are doing the same thing (max(f(x),f(x2)) = f(max(x1,x2))) so the order doesn't matter.\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size= 2) #Resulting tensor size (20,12,12)\n",
    "        self.bn1 = nn.BatchNorm2d(num_features = 20) #Batch normalization\n",
    "\n",
    "        # second convolution layer + activation + max pooling (Second layer is for blobs and sphirical shapes)\n",
    "        self.conv2 = nn.Conv2d(in_channels = 20, out_channels = 50, \n",
    "                                kernel_size = (5, 5)) #Resulting tensor (50,8,8)\n",
    "        self.act2 = nn.ReLU()\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size = 2) #Resulting tensor (50,4,4)\n",
    "        self.bn2 = nn.BatchNorm2d(num_features = 50) #Batch normalization\n",
    "\n",
    "        # second convolution layer + activation + max pooling (Second layer is for blobs and sphirical shapes)\n",
    "        self.conv3 = nn.Conv2d(in_channels = 50, out_channels = 100, \n",
    "                                kernel_size = (3, 3)) #Resulting tensor (100,2,2)\n",
    "        self.act3 = nn.ReLU()\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size = 2) #Resulting tensor (100,1,1)\n",
    "        self.bn3 = nn.BatchNorm2d(num_features = 100) #Batch normalization\n",
    "\n",
    "        # first linear layer + activation\n",
    "        self.conv_output_len = (100*1*1)\n",
    "        self.linear1 = nn.Linear(in_features = self.conv_output_len, out_features = 240) \n",
    "        self.act4 = nn.ReLU()\n",
    "\n",
    "        # dropout between the linear layers\n",
    "        self.drop = nn.Dropout(p=0.3)\n",
    "        \n",
    "        # second linear layer + activation\n",
    "        self.linear2 = nn.Linear(in_features = 240, out_features = 120)\n",
    "        self.act5 = nn.ReLU()\n",
    "\n",
    "        # dropout between the linear layers\n",
    "        self.drop = nn.Dropout(p=0.3)\n",
    "\n",
    "        # third linear layer + activation\n",
    "        self.linear3 = nn.Linear(in_features = 120, out_features = 60)\n",
    "        self.act6 = nn.ReLU()\n",
    "\n",
    "        # final linear layer to produce the correct number of outputs\n",
    "        self.out = nn.Linear(in_features = 60, out_features = num_classes)\n",
    "    \n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        # perform convolution -> activation -> pooling\n",
    "        x = self.bn1(self.pool1(self.act1(self.conv1(x))))\n",
    "        x = self.bn2(self.pool2(self.act2(self.conv2(x))))\n",
    "        x = self.bn3(self.pool3(self.act3(self.conv3(x))))\n",
    "\n",
    "        # flatten the features obtained from the conv layers: (BATCH_SIZE, 50, 4, 4) -> (BATCH_SIZE, 50*4*4=800)\n",
    "        batch_size = x.shape[0]\n",
    "        x = x.view(batch_size, self.conv_output_len)\n",
    "\n",
    "        # final linear layers and activations\n",
    "        x = self.act4(self.linear1(x))\n",
    "        x = self.act5(self.linear2(x))\n",
    "        x = self.act6(self.linear3(x))\n",
    "        x = self.out(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "CNN_Model = CNN_Classification_Net(24,(1,28,28)).to(device)\n",
    "print(CNN_Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 3.155305  [    0/19200]\n",
      "loss: 0.056001  [ 2400/19200]\n",
      "loss: 0.318514  [ 4800/19200]\n",
      "loss: 0.037689  [ 7200/19200]\n",
      "loss: 0.013060  [ 9600/19200]\n",
      "loss: 0.322616  [12000/19200]\n",
      "loss: 0.034918  [14400/19200]\n",
      "loss: 0.002865  [16800/19200]\n",
      "Test Error: \n",
      " Accuracy: 99.7%, Avg loss: 0.011802 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.061216  [    0/19200]\n",
      "loss: 0.002647  [ 2400/19200]\n",
      "loss: 0.003115  [ 4800/19200]\n",
      "loss: 0.010920  [ 7200/19200]\n",
      "loss: 0.000121  [ 9600/19200]\n",
      "loss: 0.002026  [12000/19200]\n",
      "loss: 0.000795  [14400/19200]\n",
      "loss: 0.007764  [16800/19200]\n",
      "Test Error: \n",
      " Accuracy: 99.8%, Avg loss: 0.006515 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.000478  [    0/19200]\n",
      "loss: 0.015046  [ 2400/19200]\n",
      "loss: 0.003008  [ 4800/19200]\n",
      "loss: 0.004945  [ 7200/19200]\n",
      "loss: 0.000530  [ 9600/19200]\n",
      "loss: 0.000006  [12000/19200]\n",
      "loss: 0.000294  [14400/19200]\n",
      "loss: 0.040295  [16800/19200]\n",
      "Test Error: \n",
      " Accuracy: 100.0%, Avg loss: 0.000669 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision.transforms import ToTensor,RandomPerspective,RandomRotation\n",
    "\n",
    "#Implementing both training and testing sets\n",
    "def train_loop(dataloader, model, loss_fn, optimizer):\n",
    "    model.train()\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    avg_loss = 0\n",
    "\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        #Putting both tensors on the GPU\n",
    "        X=X.to(device)\n",
    "        y=y.to(device)\n",
    "\n",
    "        # Compute prediction and loss\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "        avg_loss += loss.detach().cpu().numpy().item()\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()#reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.\n",
    "        loss.backward()#Backpropagate the prediction loss\n",
    "        optimizer.step()#adjust the parameters by the gradients collected in the backward pass.\n",
    "\n",
    "        if batch % 300 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
    "\n",
    "    avg_loss /= num_batches\n",
    "    train_losses.append(avg_loss)\n",
    "\n",
    "\n",
    "def val_loop(dataloader, model, loss_fn):\n",
    "    model.eval()\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    val_loss, correct = 0, 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X=X.to(device)\n",
    "            y=y.to(device)\n",
    "            pred = model(X) \n",
    "            val_loss += loss_fn(pred, y).detach().cpu().numpy().item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "\n",
    "    val_loss /= num_batches\n",
    "    val_losses.append(val_loss)\n",
    "    correct /= size\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {val_loss:>8f} \\n\")\n",
    "\n",
    "#Defining the Training Parameters\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.AdamW(CNN_Model.parameters(),lr = 0.001,weight_decay = 0.001)\n",
    "epochs = 3\n",
    "\n",
    "#Starting the training and testing\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
    "    train_loop(train_loader, CNN_Model, loss_fn,optimizer)\n",
    "    val_loop(val_loader, CNN_Model, loss_fn)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G077vvvsObm5ueOyxx+Dv749JkyahS5cu8PX1xfz589G2bVts375dwqofjDMs9UCjUuDzsT3w1GcHcOlGHv695RhWhgVCoRCkLo2IiCyhtr8z2yHF97XAmDFj8PLLL+Pzzz+HVqvFhg0bMGrUKCgUCuTm5mLevHnYuXMnUlJSUFJSgoKCAiQlJdVR8bWDgaWeNHPQYUVYIJ5bEYO9Z9LwSdQF/PuJ9lKXRURElhCEKl+akdKQIUMgiiJ27tyJnj174rfffsPHH38MAJg+fTr27NmDiIgItGvXDnZ2dnj22Weh1+slrrpyDCz1yN/bGe8N64IZ3x3HJ1EX0MnLEaGdK/+UayIiIkvpdDo8/fTT2LBhAy5evAg/Pz/06NEDAHDgwAGMHz8ew4cPB3DnM/0uX74sYbVVwzUs9ey5IG+M7+MDAAjfkoALaTnSFkRERDZpzJgx2LlzJ1avXo0xY8aY9vv6+uKHH35AQkICjh07hueff77MO4rkiIFFAv95siN6t2mCPL0BL6+LRVYBF+ESEVHt+tvf/gZXV1ecO3cOzz//vGn/Rx99BBcXF/Tp0wdDhgxBaGioafZFzvjhhxLJyC3CU0sP4FpmAR71a4qvXugJJRfhEhHJBj/8sPbwww+tWJPGWqwIC4ROrUD0uRv4cPc5qUsiIiKSLQYWCXVp7oRFz3QDAHwefQk7jkvwVjkiIiIrwMAisaEBzTHpkTYAgBnfHsfp69kSV0RERCQ/DCwy8MbADujn64aCYgMmro/F7Tx5vxeeiIiovjGwyIBSIeCz0d3R0tUeV28X4NVN8SgxyP8tZkREDYENvDdFcrXxM2RgkQlnew1WjguEvUaJAxczsPCns1KXRETUoKnVagBAfn6+xJVYv9KfYenPtDp4p1sZ6eDhiA+f88fkDfH48vdEdG7uiOHdW0hdFhFRg6RUKuHs7Iz09HQAgL29PQSBt5+whCiKyM/PR3p6OpydnaFUKqvdFwOLzAzq6olXH2uHpfsuYub3J9CuqQO6tnCSuiwiogbJw+POx6eUhhaqHmdnZ9PPsrp44zgZMhpFvLwuFlFn0+HlpMP2qX3h1lgrdVlERA2WwWBAcTHvSl4darW6wpkVS16/GVhkKruwGMOWHcCfN/LQq7UrNrwUDLWSS46IiMh28E63NsBRp8bKsCA01qpwJPEW5u84LXVJREREkmFgkbF2zRpjycgAAMC6mCvY8keStAURERFJhIFF5kI6uSP8ifYAgNnbTiE+6bbEFREREdU/BhYr8Opj7RDa2R16gxGvrI9Denah1CURERHVKwYWK6BQCPhwRAB8mzVGek4RXvk6DkUlBqnLIiIiqjcMLFaisVaFVeOC4KhTIT4pE3N/PMXbRRMRUYPBwGJFfNwa4dPR3aEQgM1/JGPDYS7CJSKihoGBxco86tcMbwzsAACYt/0UjiTekrgiIiKiusfAYoUmPdIGf+/miRKjiH9uiENKVoHUJREREdUpBhYrJAgCFj/bDR09HXEzV49J6+NQWMxFuEREZLsYWKyUvUaFlWGBcLFX4/jVLLy19QQX4RIRkc1iYLFi3q72WPp8DygVAn6Iv4b/HrgsdUlERER1goHFyj3czg1vDe4IAHhv1xkcvHhT4oqIiIhqHwOLDfjHwz54untzGIwipmyMR/KtfKlLIiIiqlUMLDZAEAS8/3RXdG3uhNv5xZi4Pg4Fei7CJSIi28HAYiN0aiVWhAWiSSMNzqRk443vj3MRLhER2QwGFhvi5WyHL8YGQqUQ8L9j17Hy1z+lLomIiKhWMLDYmF6tXTH3qc4AgEWRZ7H//A2JKyIiIqo5BhYbNDa4JUb19IZRBKZujMflm3lSl0RERFQjDCw2SBAEvD20M7q3dEZ2YQkmro9FblGJ1GURERFVGwOLjdKqlFg+NhDNHLQ4n5aL179JgNHIRbhERGSdGFhsmLujDsvDAqFRKvDzqTQs23dR6pKIiIiqhYHFxvVo6YJ3ht5ZhPvR3vPYezpN4oqIiIgsx8DSAIzq1RJhD7WCKAL/3pKAi+m5UpdERERkEQaWBmL23zuhl48rcoruLMLNLiyWuiQiIqIqY2BpIDQqBZaN6QFPJx3+vJGHf2/mIlwiIrIeDCwNSFMHLVaGBUGrUiDqbDqW7D0vdUlERERVwsDSwHRt4YQFT3cFAHz6y0VEnkyRuCIiIqIHY2BpgJ7u0QIv9m0NAAj/5hjOpeZIXBEREVHlGFgaqFmDOqBP2ybI1xvw8rpYZObrpS6JiIioQgwsDZRKqcDS53ughYsdkm7lY+qmozBwES4REclUtQLLsmXL4OPjA51Oh+DgYBw5cqTCtqtWrUK/fv3g4uICFxcXhISElGkviiLmzJkDT09P2NnZISQkBBcuXKhOaWQB10YarAwLgk6twG8XbmLxz2elLomIiKhcFgeWLVu2IDw8HHPnzkV8fDz8/f0RGhqK9PT0cttHR0dj9OjR2LdvH2JiYuDt7Y0BAwbg2rVrpjaLFy/Gp59+iuXLl+Pw4cNo1KgRQkNDUVhYWP2RUZV08nLEB8/6AwBW7P8T249dl7giIiKisgRRFC26DhAcHIyePXti6dKlAACj0Qhvb29MnToVM2fOfODxBoMBLi4uWLp0KcaNGwdRFOHl5YXXX38d06dPBwBkZWXB3d0da9aswahRox7YZ3Z2NpycnJCVlQVHR0dLhkN3LfzpLJbvvwSdWoHvJ/dBZy8nqUsiIiIbZ8nrt0UzLHq9HnFxcQgJCbnXgUKBkJAQxMTEVKmP/Px8FBcXw9XVFQCQmJiI1NRUsz6dnJwQHBxcYZ9FRUXIzs4226hmZoT64ZH2TVFYbMTEdXG4lcdFuEREJB8WBZabN2/CYDDA3d3dbL+7uztSU1Or1Mebb74JLy8vU0ApPc6SPhcsWAAnJyfT5u3tbckwqBxKhYDPRnWHTxN7XMsswJQN8SgxGKUui4iICEA9v0to4cKF2Lx5M7Zu3QqdTlftfmbNmoWsrCzTlpycXItVNlxO9mqsHBeERholYv7MwPu7uAiXiIjkwaLA4ubmBqVSibS0NLP9aWlp8PDwqPTYiIgILFy4ELt370a3bt1M+0uPs6RPrVYLR0dHs41qR3t3B3w4IgAAsPpAIr6PuyptQURERLAwsGg0GgQGBiIqKsq0z2g0IioqCr17967wuMWLF2P+/PmIjIxEUFCQ2XOtW7eGh4eHWZ/Z2dk4fPhwpX1S3RnYxQP/etwXADBr6wkcS86UtiAiImrwLL4kFB4ejlWrVmHt2rU4c+YMJk+ejLy8PEyYMAEAMG7cOMyaNcvUftGiRZg9ezZWr14NHx8fpKamIjU1Fbm5uQAAQRAwbdo0vPvuu9i+fTtOnDiBcePGwcvLC8OGDaudUZLFpj3ui5COzaAvMWLS+jjcyCmSuiQiImrAVJYeMHLkSNy4cQNz5sxBamoqAgICEBkZaVo0m5SUBIXiXg764osvoNfr8eyzz5r1M3fuXMybNw8A8MYbbyAvLw8TJ05EZmYm+vbti8jIyBqtc6GaUSgEfDwyAMOWHcClG3n454Y4bHjpIWhUvDkyERHVP4vvwyJHvA9L3bl0IxfDlh5ATlEJxj7UEu8O6yp1SUREZCPq7D4s1PC0bdoYn4wOgCAAXx9KwqYjSVKXREREDRADCz3Q3zq44/Un2gMA5vx4EnFXbklcERERNTQMLFQlUx5rh0FdPFBsEPHK1/FIy+bnPBERUf1hYKEqEQQBEc/5w8/dATdyijBpfRyKSgxSl0VERA0EAwtVWSOtCivHBcLJTo2E5EzM3nYSNrBmm4iIrAADC1mkVZNG+Gx0dygE4JvYq1h/6IrUJRERUQPAwEIWe6R9U8wc1AEA8M7/TuPQnxkSV0RERLaOgYWq5eV+bfCUvxdKjCKmbIjHtcwCqUsiIiIbxsBC1SIIAhY90w2dPB2RkafHpPWxKCzmIlwiIqobDCxUbXYaJVaOC4RrIw1OXsvGrB9OcBEuERHVCQYWqpEWLvZY+nx3KBUCth69hq9+T5S6JCIiskEMLFRjfdq64f+e7AgAeH/XGfx+4abEFRERka1hYKFaMb6PD54NbAGjCLy6KR7Jt/KlLomIiGwIAwvVCkEQ8O6wLvBv4YTM/GK8vC4W+foSqcsiIiIbwcBCtUanVmJ5WCDcGmtxNjUHM749zkW4RERUKxhYqFZ5Otlh+dgeUCsF7DyRgi/2X5K6JCIisgEMLFTrgnxcMe+pzgCAD34+h33n0iWuiIiIrB0DC9WJMcGtMLpXS4gi8K9NR5F4M0/qkoiIyIoxsFCdmfdUJwS2ckFOYQleXheL3CIuwiUiouphYKE6o1Up8cWYHnB31OJiei7CtyTAaOQiXCIishwDC9WpZo46LB8bCI1Sgd2n0/DZLxelLomIiKwQAwvVue4tXfDu8C4AgI/3nsee02kSV0RERNaGgYXqxYggb4zv4wMA+PeWBFxMz5G2ICIisioMLFRv/vNkRwS3dkVuUQleXheHrIJiqUsiIiIrwcBC9UatVODzMT3Q3NkOiTfzMG3zURi4CJeIiKqAgYXqVZPGWqwIC4RWpcC+czfw0Z5zUpdERERWgIGF6l2X5k5Y9Ew3AMCyfZew83iKxBUREZHcMbCQJIZ1b46X+7UGAEz/9hjOpGRLXBEREckZAwtJ5s2BHdC3nRsKig2YuD4Wmfl6qUsiIiKZYmAhyaiUCnw2uju8Xe2QfKsAUzcdRYnBKHVZREQkQwwsJCmXRhqsDAuCnVqJ3y7cxOKfuQiXiIjKYmAhyXX0dMSHI/wBACt//RM/JlyTuCIiIpIbBhaShcFdPTHlsbYAgDe+O46T17IkroiIiOSEgYVkI/wJPzzm1xRFJUZMWh+Hm7lFUpdEREQywcBCsqFUCFgyqjtauzXCtcwCTNkQj2IuwiUiIjCwkMw42amxalwgGmtVOJx4C+/tPCN1SUREJAMMLCQ77Zo54KO7i3DXHLyMb2KTJa6IiIikxsBCsjSgswemhfgCAP5v60kcTbotcUVERCQlBhaSrX/9zRdPdHKH3mDEK1/HIT2nUOqSiIhIIgwsJFsKhYCPRvijXbPGSMsuwuSv46Ev4SJcIqKGiIGFZM1Bp8aqcUFw0KkQd+U25v3vlNQlERGRBBhYSPZauzXCp6O7QxCAjYeTsOHwFalLIiKiesbAQlbhMb9mmBHqBwCYt/0UYi/fkrgiIiKqTwwsZDUm92+LJ7t6otgg4pWv45GSVSB1SUREVE8YWMhqCIKAD57rhg4eDriZW4RX1sehsNggdVlERFQPGFjIqthrVFgZFgRnezWOXc3Cf7aehCiKUpdFRER1jIGFrE7LJvZYOroHFALwffxVrD14WeqSiIiojjGwkFXq6+uGtwZ3BADM33kGMZcyJK6IiIjqEgMLWa0X+7bGsAAvGIwipmyMx9Xb+VKXREREdYSBhayWIAhY+Ew3dGnuiFt5ekxaH4cCPRfhEhHZIgYWsmo6tRIrwoLQpJEGp65nY+YPx7kIl4jIBjGwkNVr7myHz8f0gEoh4MeE6/jyt0SpSyIiolrGwEI2IbhNE8wZ0gkAsOCnM/j1/A2JKyIiotrEwEI2I+yhVhgR1AJGEZi66SiuZORJXRIREdUSBhayGYIg4J2hXRDg7YysgmJMXBeHvKISqcsiIqJaUK3AsmzZMvj4+ECn0yE4OBhHjhypsO2pU6fwzDPPwMfHB4IgYMmSJWXazJs3D4IgmG0dOnSoTmnUwOnUSiwfG4imDlqcS8vB9G+PcREuEZENsDiwbNmyBeHh4Zg7dy7i4+Ph7++P0NBQpKenl9s+Pz8fbdq0wcKFC+Hh4VFhv507d0ZKSopp+/333y0tjQgA4OGkw/KxPaBWCvjpZCo+j74kdUlERFRDFgeWjz76CC+//DImTJiATp06Yfny5bC3t8fq1avLbd+zZ0988MEHGDVqFLRabYX9qlQqeHh4mDY3NzdLSyMyCWzlineGdgEAROw+h1/OpklcERER1YRFgUWv1yMuLg4hISH3OlAoEBISgpiYmBoVcuHCBXh5eaFNmzYYM2YMkpKSKmxbVFSE7Oxss43or0b3aokxwS0hisBrmxJw6Uau1CUREVE1WRRYbt68CYPBAHd3d7P97u7uSE1NrXYRwcHBWLNmDSIjI/HFF18gMTER/fr1Q05OTrntFyxYACcnJ9Pm7e1d7e9Ntm3ukM7o6eOCnKISTFwXi5zCYqlLIiKiapDFu4QGDRqE5557Dt26dUNoaCh27dqFzMxMfPPNN+W2nzVrFrKyskxbcnJyPVdM1kKjUuDzMYHwcNTh0o08/HvLMRiNXIRLRGRtLAosbm5uUCqVSEszXw+QlpZW6YJaSzk7O6N9+/a4ePFiuc9rtVo4OjqabUQVaeqgxYqwQGhUCuw9k4YlURekLomIiCxkUWDRaDQIDAxEVFSUaZ/RaERUVBR69+5da0Xl5ubi0qVL8PT0rLU+qWHz93bGguFdAQCfRl1A5MnqX8IkIqL6Z/ElofDwcKxatQpr167FmTNnMHnyZOTl5WHChAkAgHHjxmHWrFmm9nq9HgkJCUhISIBer8e1a9eQkJBgNnsyffp07N+/H5cvX8bBgwcxfPhwKJVKjB49uhaGSHTHM4EtMOFhHwDA698k4Hxa+WukiIhIflSWHjBy5EjcuHEDc+bMQWpqKgICAhAZGWlaiJuUlASF4l4Oun79Orp37276OiIiAhEREejfvz+io6MBAFevXsXo0aORkZGBpk2bom/fvjh06BCaNm1aw+ERmXtrcEecTclBzJ8ZmLguFj9O6Qsne7XUZRER0QMIog3cBjQ7OxtOTk7IysriehZ6oIzcIjy19ACuZRagf/umWD2+J5QKQeqyiIgaHEtev2XxLiGi+tSk8Z1FuDq1AvvP30DE7nNSl0RERA/AwEINUpfmTlj0TDcAwBfRl7Dj+HWJKyIiosowsFCDNTSgOSb1bwMAmPHtcZy+zjsmExHJFQMLNWhvhHZAP183FBQbMHF9LG7n6aUuiYiIysHAQg2aUiHgs9Hd0aqJPa7eLsCrm+JRYjBKXRYREf0FAws1eM72GqwMC4K9RokDFzOw4KezUpdERER/wcBCBMDPwwEfjfAHAHz1eyJ+iL8qcUVERHQ/BhaiuwZ28cTUv7UDAMz84QSOX82UtiAiIjJhYCG6z79D2uPxDs2gLzFi0vo43MgpkrokIiICAwuRGYVCwMejAtCmaSOkZBViyoZ4FHMRLhGR5BhYiP7CUafGyrAgNNaqcOTyLczfcVrqkoiIGjwGFqJytGvWGEtGBkAQgHUxV7DljySpSyIiatAYWIgqENLJHeEh7QEAs7edQnzSbYkrIiJquBhYiCox5bF2GNjZA3qDEa+sj0N6dqHUJRERNUgMLESVUCgERIzwR3v3xkjPKcKkr+NQVGKQuiwiogaHgYXoARprVVgZFgRHnQpHkzIxZ9spiKIodVlERA0KAwtRFfi4NcJnz/eAQgC2xCbj68NchEtEVJ8YWIiqqH/7pnhjYAcAwNvbT+FI4i2JKyIiajgYWIgsMOmRNvh7N0+UGEX8c0McrmcWSF0SEVGDwMBCZAFBELD42W7o6OmIm7l6TFofh8JiLsIlIqprDCxEFrLXqLAyLBAu9mqcuJaFt7ae4CJcIqI6xsBCVA3ervZY9nwPKBUCfoi/hv8euCx1SURENo2Bhaia+rRzw38GdwQAvLfrDA5evClxRUREtouBhagGJjzsg6d7NIfBKGLKxngk38qXuiQiIpvEwEJUA4Ig4P3hXdGthRNu5xdj4vo45OtLpC6LiMjmMLAQ1ZBOrcTysYFwa6zBmZRsvPHdcS7CJSKqZQwsRLXAy9kOn48JhEohYMfxFKz49U+pSyIisikMLES1pFdrV8x9qjMAYFHkWUSfS5e4IiIi28HAQlSLxga3xKie3hBF4F+bjuLyzTypSyIisgkMLES1SBAEvD20M7q3dEZ2YQkmro9FbhEX4RIR1RQDC1Et06ruLMJt5qDF+bRcvP5NAoxGLsIlIqoJBhaiOuDuqMPysEBolAr8fCoNy/ZdlLokIiKrxsBCVEd6tHTBu8O6AAA+2nsee0+nSVwREZH1YmAhqkMjenpjXO9WEEVg2pYEXEzPlbokIiKrxMBCVMdm/70TerV2RW5RCSaui0V2YbHUJRERWR0GFqI6plYq8PmYHvB00uHPm3mYtpmLcImILMXAQlQP3BprsTIsCFqVAr+cTcfHe89LXRIRkVVhYCGqJ11bOGHB010BAJ/9chE/nUiRuCIiIuvBwEJUj57u0QIv9m0NAHj922M4l5ojcUVERNaBgYWons0a1AEPt2uCfL0BL6+LRWa+XuqSiIhkj4GFqJ6plAosHd0DLVzskHQrH1M3HYWBi3CJiCrFwEIkAZdGGqwMC4KdWonfLtzE4p/PSl0SEZGsMbAQSaSTlyM+eK4bAGDF/j/xY8I1iSsiIpIvBhYiCf29mxcmP9oWAPDm98dx8lqWxBUREckTAwuRxKYP8EP/9k1RWGzEpPVxyMgtkrokIiLZYWAhkphSIeDTUd3h08Qe1zIL8OrGoyg2GKUui4hIVhhYiGTAyV6NleOC0EijRMyfGXh/1xmpSyIikhUGFiKZaO/ugA9HBAAA/nvgMr6LuyptQUREMsLAQiQjA7t44F+P+wIA3tp6AseSM6UtiIhIJhhYiGRm2uO+COnoDn3JnUW4N3K4CJeIiIGFSGYUCgEfj/RH26aNkJpdiH9uiIO+hItwiahhY2AhkiEHnRqrxgXBQavCH5dv450dp6QuiYhIUgwsRDLVpmljfDI6AIIAfH0oCZuOJEldEhGRZBhYiGTsbx3cMX2AHwBgzo8nEXfllsQVERFJo1qBZdmyZfDx8YFOp0NwcDCOHDlSYdtTp07hmWeegY+PDwRBwJIlS2rcJ1FD8s9H22JwVw8UG0S88nU8UrMKpS6JiKjeWRxYtmzZgvDwcMydOxfx8fHw9/dHaGgo0tPTy22fn5+PNm3aYOHChfDw8KiVPokaEkEQ8MGz/vBzd8CNnCJM+joOhcUGqcsiIqpXgiiKoiUHBAcHo2fPnli6dCkAwGg0wtvbG1OnTsXMmTMrPdbHxwfTpk3DtGnTaq1PAMjOzoaTkxOysrLg6OhoyXCIrMaVjDw8tfQAsgqKMSKoBRY90w2CIEhdFhFRtVny+m3RDIter0dcXBxCQkLudaBQICQkBDExMdUqtjp9FhUVITs722wjsnWtmjTCZ6O7QyEA38RexfpDV6QuiYio3lgUWG7evAmDwQB3d3ez/e7u7khNTa1WAdXpc8GCBXBycjJt3t7e1freRNbmkfZNMXNQBwDAO/87jUN/ZkhcERFR/bDKdwnNmjULWVlZpi05OVnqkojqzcv92mBogBdKjCKmbIjHtcwCqUsiIqpzFgUWNzc3KJVKpKWlme1PS0urcEFtXfSp1Wrh6OhothE1FIIgYOHT3dDZyxEZeXpMWh/LRbhEZPMsCiwajQaBgYGIiooy7TMajYiKikLv3r2rVUBd9Elk6+w0SqwIC4RrIw1OXsvGzO+Pw8L180REVsXiS0Lh4eFYtWoV1q5dizNnzmDy5MnIy8vDhAkTAADjxo3DrFmzTO31ej0SEhKQkJAAvV6Pa9euISEhARcvXqxyn0RUVgsXeyx7vgeUCgHbEq7jq98TpS6JiKjOqCw9YOTIkbhx4wbmzJmD1NRUBAQEIDIy0rRoNikpCQrFvRx0/fp1dO/e3fR1REQEIiIi0L9/f0RHR1epTyIqX++2TTD7yY6Y97/TeH/XGXTwcERfXzepyyIiqnUW34dFjngfFmrIRFHEjO+O47u4q3C2V2P7lL5o2cRe6rKIiB6ozu7DQkTyIwgC3h3WBf4tnJCZX4yJ62ORry+RuiwiolrFwEJkA3RqJZaHBcKtsRZnU3Mw41suwiUi28LAQmQjPJ3ssHxsD6iVAnaeSMEX+y9JXRIRUa1hYCGyIUE+rnj7qS4AgA9+Pod95/gBokRkGxhYiGzM88Et8XxwS4gi8K9NR5F4M0/qkoiIaoyBhcgGzRvSGUGtXJBTWIKX18Uit4iLcInIujGwENkgjUqBz8f2gLujFhfTc/HvLQkwGrkIl4isFwMLkY1q5qDDirAgaJQK7Dmdhk9/uSB1SURE1cbAQmTDAryd8e7wO4twl+y9gN2nUiWuiIioehhYiGzciCBvjO/jAwD495YEXEjLkbYgIqJqYGAhagD+82RHBLd2RZ7egInr45BVUCx1SUREFmFgIWoA1EoFPh/TA82d7ZB4Mw+vbT4KAxfhEpEVYWAhaiCaNNZiRVggdGoFos/dwEd7zkldEhFRlTGwEDUgXZo7YdEz3QAAy/Zdws7jKRJXRERUNQwsRA3M0IDmmPhIGwDA9G+P4UxKtsQVERE9GAMLUQP0Rqgf+vm6oaDYgInrY3E7Ty91SURElWJgIWqAVEoFPhvdHS1d7ZF8qwBTNx1FicEodVlERBViYCFqoJztNVg5LhB2aiV+v3gTiyLPSl0SEVGFGFiIGrAOHo74cIQ/AGDVb4nYdvSaxBUREZWPgYWogRvc1RNTHmsLAHjz++M4eS1L4oqIiMpiYCEihD/hh8f8mqKoxIiJ62JxM7dI6pKIiMwwsBARlAoBS0Z1Rxu3RrieVYgpG+JRzEW4RCQjDCxEBABwslNj5bhANNaqcDjxFt7beUbqkoiITBhYiMikXTMHfDwyAACw5uBlfBObLG1BRER3MbAQkZknOrnj3yHtAQD/t/UkjibdlrgiIiIGFiIqx9S/tcOATu7QG4x45es4pGcXSl0SETVwDCxEVIZCIeCjkQHwbdYYadlFeOXrOBSVGKQui4gaMAYWIipXY60KK8cFwUGnQnxSJuZtPy11SUTUgDGwEFGFWrs1wqeju0MQgE1HkrDh8BWpSyKiBoqBhYgq9ZhfM8wI9QMAzNt+Cn9cviVxRUTUEDGwENEDTe7fFk9280SxQcTkr+ORklUgdUlE1MAwsBDRAwmCgA+e7YYOHg64mVuEV9bHobCYi3CJqP4wsBBRldhrVFg1LgjO9mocu5qF/2w9CVEUpS6LiBoIBhYiqjJvV3sse74HFALwffxVrD14WeqSiKiBYGAhIos83M4Nbw3uCACYv/MMDl66KXFFRNQQMLAQkcVe7Nsaw7s3h8EoYsqGeCTfype6JCKycQwsRGQxQRCw4Omu6NLcEbfzizFpfRwK9FyES0R1h4GFiKpFp1ZiRVgQmjTS4HRKNt78/jgX4RJRnWFgIaJqa+5sh8/H9IBKIWD7setY9dufUpdERDaKgYWIaiS4TRPMGdIJALDwp7P49fwNiSsiIlvEwEJENRb2UCuMDPKGUQSmbjqKKxl5UpdERDaGgYWIakwQBLwzrDO6t3RGVkExJq6LQ15RidRlEZENYWAholqhVSmxfGwgmjpocS4tB9O/PcZFuERUaxhYiKjWuDvqsHxsINRKAT+dTMWyfRelLomIbAQDCxHVqsBWLpg/tAsA4MM95xF1Jk3iiojIFjCwEFGtG9WrJcY+1BKiCEzbnIBLN3KlLomIrBwDCxHViTl/74yePi7IKSrBy+tikV1YLHVJRGTFGFiIqE5oVAp8PiYQHo46/HkjD+FbEmA0chEuEVUPAwsR1ZmmDlqsCAuERqXA3jPpWBJ1QeqSiMhKMbAQUZ3y93bGguFdAQCfRl1A5MlUiSsiImvEwEJEde6ZwBb4x8OtAQCvf5OA82k5EldERNaGgYWI6sVbgzugT9smyNMbMHFdLLLyuQiXiKqOgYWI6oVKqcDS53ugubMdLmfk41+bj8LARbhEVEUMLERUb1wbabByXCB0agX2n7+BD34+J3VJRGQlGFiIqF519nLC4mf9AQDL91/C/45dl7giIrIG1Qosy5Ytg4+PD3Q6HYKDg3HkyJFK23/77bfo0KEDdDodunbtil27dpk9P378eAiCYLYNHDiwOqURkRV4yt8Lk/q3AQDM+O4YTl/PlrgiIpI7iwPLli1bEB4ejrlz5yI+Ph7+/v4IDQ1Fenp6ue0PHjyI0aNH48UXX8TRo0cxbNgwDBs2DCdPnjRrN3DgQKSkpJi2TZs2VW9ERGQV3gjtgH6+bigsNmLi+ljcytNLXRIRyZggWvj578HBwejZsyeWLl0KADAajfD29sbUqVMxc+bMMu1HjhyJvLw87Nixw7TvoYceQkBAAJYvXw7gzgxLZmYmtm3bVq1BZGdnw8nJCVlZWXB0dKxWH0RU/zLz9Ri67ACuZOSjT9smWPePXlApeaWaqKGw5PXbov8y6PV6xMXFISQk5F4HCgVCQkIQExNT7jExMTFm7QEgNDS0TPvo6Gg0a9YMfn5+mDx5MjIyMiqso6ioCNnZ2WYbEVkfZ3sNVoYFwV6jxMFLGVjw01mpSyIimbIosNy8eRMGgwHu7u5m+93d3ZGaWv7dK1NTUx/YfuDAgVi3bh2ioqKwaNEi7N+/H4MGDYLBYCi3zwULFsDJycm0eXt7WzIMIpIRPw8HfDTiziLcr35PxA/xVyWuiIjkSBZzr6NGjcJTTz2Frl27YtiwYdixYwf++OMPREdHl9t+1qxZyMrKMm3Jycn1WzAR1aqBXTzxr7+1AwDM/OEEjl/NlLYgIpIdiwKLm5sblEol0tLSzPanpaXBw8Oj3GM8PDwsag8Abdq0gZubGy5evFju81qtFo6OjmYbEVm3aSHt8XiHZtCXGDFpfRxu5BRJXRIRyYhFgUWj0SAwMBBRUVGmfUajEVFRUejdu3e5x/Tu3dusPQDs2bOnwvYAcPXqVWRkZMDT09OS8ojIiikUAj4eFYA2TRshJasQUzbEQ19ilLosIpIJiy8JhYeHY9WqVVi7di3OnDmDyZMnIy8vDxMmTAAAjBs3DrNmzTK1f+211xAZGYkPP/wQZ8+exbx58xAbG4tXX30VAJCbm4sZM2bg0KFDuHz5MqKiojB06FC0a9cOoaGhtTRMIrIGjjo1Vo0LgoNWhSOXb2H+jtNSl0REMmFxYBk5ciQiIiIwZ84cBAQEICEhAZGRkaaFtUlJSUhJSTG179OnDzZu3IiVK1fC398f3333HbZt24YuXboAAJRKJY4fP46nnnoK7du3x4svvojAwED89ttv0Gq1tTRMIrIWbZs2xpJRARAEYP2hK9h8JEnqkohIBiy+D4sc8T4sRLbns6gL+HDPeWiUCmya+BACW7lIXRIR1TJLXr9V9VQTEZFFpjzWDqeuZyPyVComfx2HMcGtYKdRwE6jgr1aCXuNEjqN8u5jldlzdholtCoFBEGQehhEVEs4w0JEspVbVIKnPz+A82m5Fh+rEAA7tRJ2d8OMvVoFO82doGN3N9Tce6yC/d2vdXfDkL3m7rHq0sdKs8caJQMRUU1xhoWIbEJjrQpfvxSMr2OuICNPjwK9Afl6AwqKDXceF5cgX29Aod6A/OI7z5W+s8goAnl6A/L05d+AsqaUCqGc4FP6+G44urvv/sem2SB1eSFJZepHo5LFbbKIZIOBhYhkrZmDDuED/Krc3mAUUVBsQL6+BAV3w02+3vCXsFNiHnzuboV3jyttf//zpY/1BqPp++QWlSC3qKROxq1SCH+Z1VHBTq24G3gsnCm6f3bpbls1P7OJrAwDCxHZFKVCQGOtCo21dfOftxKDEfnFd2d1/jrjoy8pN+TceWwehMwfl5gCU7HhzlX6EqOInMIS5BTWTSBSK4W7YUhV5nKXeUi6t988JKnMAtNfn1MqeLmMahcDCxGRBVRKBRyVCjjq1HXSf7HBWCbM1Gim6O5zBXcvmxmM4t3vI6LYUILsOgpEGqWizKxOuTNF5Vw2K7OwWqM0rUEq7YuBqOFhYCEikhG1UgEnOwWc7Go/EImiiGKDaLb+p+yMz33774Yc88dlQ9L9M0x38xD0BiP0BUZkFRTX+jgAQKtSlF0bVNGaokoWT9uXM4ukUymhYCCSHQYWIqIGQhAEaFQCNCoFnFA3gaioxHh3hqfsZS+LZopKL7sVl5i1LX1fa1GJEUUlRmSibgKRrnQWqNKF1Yoygaf0Mttf1xHdv7Bap+Y7zKqDgYWIiGqFIAjQqe+8ODvb137/pYHor0Gn/BmfytcMlb/O6N47ygqLjSgs1tf+IO560IxPVWaK7Ct4zlbvQcTAQkREVuH+QFQXRFFEYbHRbBH0vUBUggK9sdyF1XcCkPGBC6sLi+99mGdB8d2AlFf747h3D6LSYKO67yaLNVtY7WSnliwMMbAQERHhTiAqfZFvUgf9G40iCkvKXhKrbManSjNFd48pquN7ECkE4NL7g2u1T0swsBAREdUDhUK4u76lbl56S+9BdG+R9H03Vrxv0XT5i6lL35ZvLH9htd4AjcSXmhhYiIiIbEBd34Oo9C3xUuGtDomIiOiBpL73DQMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJHgMLERERyR4DCxEREckeAwsRERHJnkrqAmQv5Rig0gEqrfm/Si2gYN4jIiKqDwwslTGUACseqfh5hbr8MGP6V1P580ptJcf+tY9ynlOoAEGov58HERGRRBhYKmPQAw6eQEkhUFJ051/ReO95YzGgLwb0OdLUJyiqEHoeEIiUFYWq+4+vJHBxlomIiOoBA0tlNPbA62fN9xlKzAOM2eMiwFBk/nWZtvoKnqtiHwb9vVpEI1BScGeTSp3PMt3/PGeZiIgaKgYWSylVgLIxoG0szfc3Gu+ElspCT6Whqei+rYLg9aD+5TTLBOEBgekBgajSwMRZJiIiuWBgsTYKBaDQAWqddDXU9SzTAwPZfbNMEDnLxFkmImoAGFjIcpxlamCzTFUIXJxlIqI6xsBC1oezTFY2y1SVwFTFwKW2u/vY7s75Lw1VnGEisnkMLETVIfdZJkNls0s2OMtkCjalQeZusFHb3Qs1pfvvDzplApAFxymU0oyVqIFiYCGyRg1tlqm44O7+AqC48M6/9wem0j6RVX/jV6jKCTrlhJ6KZoWqE5aUGs4mUYPFwEJE1SP1LJOhuPwgYwo4d0NMefvLHFd43/7yjrv7+P5LccaSOzNK9TqrJNRgVuivYekvbStrwzVKJAMMLERknZTqO1t9MhruzQCVG24Kyw9AZfZVMWSVHgfxbgEiUJx/Z8Pt+hu3UnNfAKqnS2/1fW5J9hhYiIiqSqG8c0NJjX39fU9RvDOzY/GskKWzSX/pw1hyrwaD/s5WVI+X3ARlFYJOLYcllZaX3GSMgYWISM4E4d47peqTaY2ShbNCNbn0VlJ47/uLBkCfe2erT7WxINuSgMQF3FVWrcCybNkyfPDBB0hNTYW/vz8+++wz9OrVq8L23377LWbPno3Lly/D19cXixYtwuDBg03Pi6KIuXPnYtWqVcjMzMTDDz+ML774Ar6+vtUpj4iIakqKNUpG453F2rU+m/SAPspbwF2YWX/jVqjLCTp1HJaUaqubTbI4sGzZsgXh4eFYvnw5goODsWTJEoSGhuLcuXNo1qxZmfYHDx7E6NGjsWDBAvz973/Hxo0bMWzYMMTHx6NLly4AgMWLF+PTTz/F2rVr0bp1a8yePRuhoaE4ffo0dDpdzUdJRETyp1AACrs7L7D1RRTvXP6q9oLsal56M1vAXQwUFQNF9TdsCIpKgs79++8POjpgwLuSBR1BFEXxwc3uCQ4ORs+ePbF06VIAgNFohLe3N6ZOnYqZM2eWaT9y5Ejk5eVhx44dpn0PPfQQAgICsHz5coiiCC8vL7z++uuYPn06ACArKwvu7u5Ys2YNRo0aVabPoqIiFBXdO7PZ2dnw9vZGVlYWHB0dLRkOERFR/TMaarYgu7qX3mpCqQVmp9fO+O/Kzs6Gk5NTlV6/LZph0ev1iIuLw6xZs0z7FAoFQkJCEBMTU+4xMTExCA8PN9sXGhqKbdu2AQASExORmpqKkJAQ0/NOTk4IDg5GTExMuYFlwYIFePvtty0pnYiISD4USkDT6M5WX8wWcFdjNsmy+Y1aZ1FguXnzJgwGA9zd3c32u7u74+zZs+Uek5qaWm771NRU0/Ol+ypq81ezZs0yC0GlMyxERERUAakWcNcSq3yXkFarhVZrnT9wIiIispxFty90c3ODUqlEWlqa2f60tDR4eHiUe4yHh0el7Uv/taRPIiIialgsCiwajQaBgYGIiooy7TMajYiKikLv3r3LPaZ3795m7QFgz549pvatW7eGh4eHWZvs7GwcPny4wj6JiIioYbH4klB4eDheeOEFBAUFoVevXliyZAny8vIwYcIEAMC4cePQvHlzLFiwAADw2muvoX///vjwww/x5JNPYvPmzYiNjcXKlSsBAIIgYNq0aXj33Xfh6+treluzl5cXhg0bVnsjJSIiIqtlcWAZOXIkbty4gTlz5iA1NRUBAQGIjIw0LZpNSkqC4r4PyurTpw82btyI//u//8Nbb70FX19fbNu2zXQPFgB44403kJeXh4kTJyIzMxN9+/ZFZGQk78FCREREAKpxHxY5suR93ERERCQPlrx+8zPDiYiISPYYWIiIiEj2GFiIiIhI9hhYiIiISPYYWIiIiEj2GFiIiIhI9hhYiIiISPas8sMP/6r0VjLZ2dkSV0JERERVVfq6XZVbwtlEYMnJyQEAeHt7S1wJERERWSonJwdOTk6VtrGJO90ajUZcv34dDg4OEAShVvvOzs6Gt7c3kpOTbfIuurY+PsD2x8jxWT9bH6Otjw+w/THW1fhEUUROTg68vLzMPtanPDYxw6JQKNCiRYs6/R6Ojo42+UtYytbHB9j+GDk+62frY7T18QG2P8a6GN+DZlZKcdEtERERyR4DCxEREckeA8sDaLVazJ07F1qtVupS6oStjw+w/TFyfNbP1sdo6+MDbH+MchifTSy6JSIiItvGGRYiIiKSPQYWIiIikj0GFiIiIpI9BhYiIiKSPQYWIiIikr0GGViWLVsGHx8f6HQ6BAcH48iRI5W2//bbb9GhQwfodDp07doVu3btMnteFEXMmTMHnp6esLOzQ0hICC5cuFCXQ6iUJeNbtWoV+vXrBxcXF7i4uCAkJKRM+/Hjx0MQBLNt4MCBdT2MClkyvjVr1pSpXafTmbWR2/kDLBvjo48+WmaMgiDgySefNLWR0zn89ddfMWTIEHh5eUEQBGzbtu2Bx0RHR6NHjx7QarVo164d1qxZU6aNpX/XdcXS8f3www944okn0LRpUzg6OqJ37974+eefzdrMmzevzPnr0KFDHY6iYpaOLzo6utzfz9TUVLN2cjl/gOVjLO/vSxAEdO7c2dRGTudwwYIF6NmzJxwcHNCsWTMMGzYM586de+BxUr8WNrjAsmXLFoSHh2Pu3LmIj4+Hv78/QkNDkZ6eXm77gwcPYvTo0XjxxRdx9OhRDBs2DMOGDcPJkydNbRYvXoxPP/0Uy5cvx+HDh9GoUSOEhoaisLCwvoZlYun4oqOjMXr0aOzbtw8xMTHw9vbGgAEDcO3aNbN2AwcOREpKimnbtGlTfQynDEvHB9y5lfT9tV+5csXseTmdP8DyMf7www9m4zt58iSUSiWee+45s3ZyOYd5eXnw9/fHsmXLqtQ+MTERTz75JB577DEkJCRg2rRpeOmll8xe1Kvze1FXLB3fr7/+iieeeAK7du1CXFwcHnvsMQwZMgRHjx41a9e5c2ez8/f777/XRfkPZOn4Sp07d86s/mbNmpmek9P5Aywf4yeffGI2tuTkZLi6upb5G5TLOdy/fz+mTJmCQ4cOYc+ePSguLsaAAQOQl5dX4TGyeC0UG5hevXqJU6ZMMX1tMBhELy8vccGCBeW2HzFihPjkk0+a7QsODhYnTZokiqIoGo1G0cPDQ/zggw9Mz2dmZoparVbctGlTHYygcpaO769KSkpEBwcHce3ataZ9L7zwgjh06NDaLrVaLB3ff//7X9HJyanC/uR2/kSx5ufw448/Fh0cHMTc3FzTPjmdw/sBELdu3VppmzfeeEPs3Lmz2b6RI0eKoaGhpq9r+jOrK1UZX3k6deokvv3226av586dK/r7+9deYbWkKuPbt2+fCEC8fft2hW3kev5EsXrncOvWraIgCOLly5dN++R6DkVRFNPT00UA4v79+ytsI4fXwgY1w6LX6xEXF4eQkBDTPoVCgZCQEMTExJR7TExMjFl7AAgNDTW1T0xMRGpqqlkbJycnBAcHV9hnXanO+P4qPz8fxcXFcHV1NdsfHR2NZs2awc/PD5MnT0ZGRkat1l4V1R1fbm4uWrVqBW9vbwwdOhSnTp0yPSen8wfUzjn86quvMGrUKDRq1MhsvxzOYXU86G+wNn5mcmI0GpGTk1Pmb/DChQvw8vJCmzZtMGbMGCQlJUlUYfUEBATA09MTTzzxBA4cOGDab2vnD7jzNxgSEoJWrVqZ7ZfrOczKygKAMr9z95PDa2GDCiw3b96EwWCAu7u72X53d/cy11NLpaamVtq+9F9L+qwr1RnfX7355pvw8vIy+6UbOHAg1q1bh6ioKCxatAj79+/HoEGDYDAYarX+B6nO+Pz8/LB69Wr8+OOP+Prrr2E0GtGnTx9cvXoVgLzOH1Dzc3jkyBGcPHkSL730ktl+uZzD6qjobzA7OxsFBQW18nsvJxEREcjNzcWIESNM+4KDg7FmzRpERkbiiy++QGJiIvr164ecnBwJK60aT09PLF++HN9//z2+//57eHt749FHH0V8fDyA2vnvlpxcv34dP/30U5m/QbmeQ6PRiGnTpuHhhx9Gly5dKmwnh9dCVa30QjZh4cKF2Lx5M6Kjo80Wpo4aNcr0uGvXrujWrRvatm2L6OhoPP7441KUWmW9e/dG7969TV/36dMHHTt2xIoVKzB//nwJK6sbX331Fbp27YpevXqZ7bfmc9iQbNy4EW+//TZ+/PFHszUegwYNMj3u1q0bgoOD0apVK3zzzTd48cUXpSi1yvz8/ODn52f6uk+fPrh06RI+/vhjrF+/XsLK6sbatWvh7OyMYcOGme2X6zmcMmUKTp48Kdl6Gks0qBkWNzc3KJVKpKWlme1PS0uDh4dHucd4eHhU2r70X0v6rCvVGV+piIgILFy4ELt370a3bt0qbdumTRu4ubnh4sWLNa7ZEjUZXym1Wo3u3bubapfT+QNqNsa8vDxs3ry5Sv/xk+ocVkdFf4OOjo6ws7Orld8LOdi8eTNeeuklfPPNN2Wm3v/K2dkZ7du3t4rzV55evXqZareV8wfceZfM6tWrERYWBo1GU2lbOZzDV199FTt27MC+ffvQokWLStvK4bWwQQUWjUaDwMBAREVFmfYZjUZERUWZ/V/4/Xr37m3WHgD27Nljat+6dWt4eHiYtcnOzsbhw4cr7LOuVGd8wJ2V3fPnz0dkZCSCgoIe+H2uXr2KjIwMeHp61krdVVXd8d3PYDDgxIkTptrldP6Amo3x22+/RVFREcaOHfvA7yPVOayOB/0N1sbvhdQ2bdqECRMmYNOmTWZvR69Ibm4uLl26ZBXnrzwJCQmm2m3h/JXav38/Ll68WKX/aZDyHIqiiFdffRVbt27FL7/8gtatWz/wGFm8FtbK0l0rsnnzZlGr1Ypr1qwRT58+LU6cOFF0dnYWU1NTRVEUxbCwMHHmzJmm9gcOHBBVKpUYEREhnjlzRpw7d66oVqvFEydOmNosXLhQdHZ2Fn/88Ufx+PHj4tChQ8XWrVuLBQUFsh/fwoULRY1GI3733XdiSkqKacvJyRFFURRzcnLE6dOnizExMWJiYqK4d+9esUePHqKvr69YWFgo+/G9/fbb4s8//yxeunRJjIuLE0eNGiXqdDrx1KlTpjZyOn+iaPkYS/Xt21ccOXJkmf1yO4c5OTni0aNHxaNHj4oAxI8++kg8evSoeOXKFVEURXHmzJliWFiYqf2ff/4p2tvbizNmzBDPnDkjLlu2TFQqlWJkZKSpzYN+ZnIe34YNG0SVSiUuW7bM7G8wMzPT1Ob1118Xo6OjxcTERPHAgQNiSEiI6ObmJqanp8t+fB9//LG4bds28cKFC+KJEyfE1157TVQoFOLevXtNbeR0/kTR8jGWGjt2rBgcHFxun3I6h5MnTxadnJzE6Ohos9+5/Px8Uxs5vhY2uMAiiqL42WefiS1bthQ1Go3Yq1cv8dChQ6bn+vfvL77wwgtm7b/55huxffv2okajETt37izu3LnT7Hmj0SjOnj1bdHd3F7Varfj444+L586dq4+hlMuS8bVq1UoEUGabO3euKIqimJ+fLw4YMEBs2rSpqFarxVatWokvv/yyZP8hEUXLxjdt2jRTW3d3d3Hw4MFifHy8WX9yO3+iaPnv6NmzZ0UA4u7du8v0JbdzWPo2179upWN64YUXxP79+5c5JiAgQNRoNGKbNm3E//73v2X6rexnVp8sHV///v0rbS+Kd97G7enpKWo0GrF58+biyJEjxYsXL9bvwO6ydHyLFi0S27ZtK+p0OtHV1VV89NFHxV9++aVMv3I5f6JYvd/RzMxM0c7OTly5cmW5fcrpHJY3NgBmf1dyfC0U7hZPREREJFsNag0LERERWScGFiIiIpI9BhYiIiKSPQYWIiIikj0GFiIiIpI9BhYiIiKSPQYWIiIikj0GFiIiIpI9BhYiIiKSPQYWIiIikj0GFiIiIpK9/weC3ZtuEcPuhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title(\"Loss\")\n",
    "plt.plot(train_losses, label=\"train\")\n",
    "plt.plot(val_losses, label=\"val\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "alphabet_dict = {0: 'A',1: 'B',2: 'C',3: 'D',4: 'E',5: 'F',6: 'G',7: 'H',8: 'I',9: 'K',10: 'L',11: 'M',\n",
    "    12: 'N',13: 'O',14: 'P',15: 'Q',16: 'R',17: 'S',18: 'T',19: 'U',20: 'V',21: 'W',22: 'X',23: 'Y'}\n",
    "\n",
    "figure = plt.figure(figsize=(12, 12))\n",
    "cols, rows = 3, 4\n",
    "\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(test_dataset), size=(1,)).item() #Generating a random number from the dataset\n",
    "    img, label = test_dataset[sample_idx]\n",
    "    img, label = img, label #Moving img Tensor to GPU to load it on the network\n",
    "    pred = CNN_Model(img[None, ...]).argmax(1) #I've extended the tensor because the network expect a tensor of size 4 ([number of images, Channel, Width, Hight]) and the image sample would only have the last 3\n",
    "\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(f\"Pred: {str(alphabet_dict[pred.item()])}, Label: {str(alphabet_dict[label])}\", fontdict={\"fontsize\": 14, \"color\": (\"green\" if str(pred.item()) == str(label) else \"red\")})\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.cpu().squeeze()) #Had to move the tensor to the cpu before squeezing it to visualize it.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2400/2400 [00:15<00:00, 153.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 2400 images the model has predicted 0 wrong images\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "alphabet_dict = {0: 'A',1: 'B',2: 'C',3: 'D',4: 'E',5: 'F',6: 'G',7: 'H',8: 'I',9: 'K',10: 'L',11: 'M',\n",
    "    12: 'N',13: 'O',14: 'P',15: 'Q',16: 'R',17: 'S',18: 'T',19: 'U',20: 'V',21: 'W',22: 'X',23: 'Y'}\n",
    "\n",
    "y_pred = []\n",
    "y_true = []\n",
    "wrong_preds = []\n",
    "\n",
    "CNN_Model.to(\"cpu\")\n",
    "\n",
    "# iterate over test data\n",
    "for img, label in tqdm(test_dataset):\n",
    "        output =CNN_Model(img[None, ...]).argmax(1)\n",
    "        y_pred.append(output.item()) # Save Prediction\n",
    "        y_true.append(label) # Save Truth\n",
    "        if label != output:\n",
    "            wrong_preds.append((img,label))\n",
    "\n",
    "# images that the model predicted wrong\n",
    "print(f\"Out of {len(test_dataset)} images the model has predicted {len(wrong_preds)} wrong images\")\n",
    "\n",
    "# visualize the mistakes if they exist\n",
    "if len(wrong_preds)>0:\n",
    "    # Build confusion matrix\n",
    "    cf_matrix = confusion_matrix(y_true, y_pred)\n",
    "    df_cm = pd.DataFrame(cf_matrix/np.sum(cf_matrix) *10) #instead of numbers to get a precentage\n",
    "    plt.figure(figsize = (12,7))\n",
    "    sn.heatmap(df_cm, annot=True)\n",
    "\n",
    "    # Visualizing random images that the model predicted wrong\n",
    "    figure = plt.figure(figsize=(12, 12))\n",
    "    cols, rows = 3, 4\n",
    "\n",
    "    for i in range(1, cols * rows + 1):\n",
    "        sample_idx = torch.randint(len(wrong_preds), size=(1,)).item() \n",
    "        img, label = wrong_preds[sample_idx]\n",
    "\n",
    "        pred = CNN_Model(img[None, ...]).argmax(1)\n",
    "\n",
    "        figure.add_subplot(rows, cols, i)\n",
    "        plt.title(f\"Pred: {str(alphabet_dict[pred.item()])}, Label: {str(alphabet_dict[label])}\", fontdict={\"fontsize\": 14, \"color\": (\"green\" if str(pred.item()) == str(label) else \"red\")})\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.cpu().squeeze()) \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============== Diagnostic Run torch.onnx.export version 2.0.0+cpu ==============\n",
      "verbose: False, log level: Level.ERROR\n",
      "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import os\n",
    "\n",
    "CNN_Model = CNN_Model.to('cpu') #Loading the model back to cpu to avoid Problems\n",
    "CNN_Model.eval()\n",
    "\n",
    "dummy_input = torch.randn(1,1,28,28) #Saving a dummy input since it's required for Onnx Format where as the first elem. is the patch size\n",
    "input_names = [ \"actual_input\" ] \n",
    "output_names = [ \"output\" ]\n",
    "\n",
    "model_path = \"Models/\"\n",
    "os.makedirs(model_path,exist_ok=True) #Create the file if the file doesn't exist\n",
    "\n",
    "torch.onnx.export(CNN_Model, \n",
    "                dummy_input,\n",
    "                model_path + \"CNN_Model_Sign_Language_best.onnx\",\n",
    "                input_names=input_names,\n",
    "                output_names=output_names,\n",
    "                opset_version = 11,\n",
    "                export_params=True,\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\VIRGA\\anaconda3\\envs\\test\\lib\\site-packages\\onnxruntime\\capi\\onnxruntime_inference_collection.py:54: UserWarning: Specified provider 'CUDAExecutionProvider' is not in available provider names.Available providers: 'CPUExecutionProvider'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import onnx, onnxruntime\n",
    "import torch\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "alphabet_dict = {0: 'A',1: 'B',2: 'C',3: 'D',4: 'E',5: 'F',6: 'G',7: 'H',8: 'I',9: 'K',10: 'L',11: 'M',\n",
    "    12: 'N',13: 'O',14: 'P',15: 'Q',16: 'R',17: 'S',18: 'T',19: 'U',20: 'V',21: 'W',22: 'X',23: 'Y'}\n",
    "\n",
    "model_path = 'Models/CNN_Model_Sign_Language_best.onnx'\n",
    "onnx_model = onnx.load(model_path)\n",
    "onnx.checker.check_model(onnx_model)\n",
    "\n",
    "#to preprocess an external image\n",
    "resize = transforms.Compose(\n",
    "                [ transforms.Resize((28,28)),\n",
    "                transforms.Grayscale(num_output_channels=1),\n",
    "                transforms.ToTensor()])        \n",
    "\n",
    "EP_list = ['CUDAExecutionProvider', 'CPUExecutionProvider']\n",
    "\n",
    "ort_session = onnxruntime.InferenceSession(model_path, providers=EP_list)\n",
    "\n",
    "def to_numpy(x):\n",
    "    return x.detach().cpu().numpy() if x.requires_grad else x.cpu().numpy()\n",
    "\n",
    "# compute ONNX Runtime output prediction\n",
    "def predict_class(img):\n",
    "    ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)}\n",
    "    ort_outs = ort_session.run(None, ort_inputs)\n",
    "    pred = list(ort_outs[0][0]).index(max(ort_outs[0][0]))\n",
    "\n",
    "    return pred\n",
    "\n",
    "figure = plt.figure(figsize=(12, 12))\n",
    "cols, rows = 3, 4\n",
    "\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(test_dataset), size=(1,)).item() \n",
    "    img, label = test_dataset[sample_idx]\n",
    "    pred = predict_class(img[None, ...])\n",
    "\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(f\"Pred: {str(alphabet_dict[pred])}, Label: {str(alphabet_dict[label])}\", fontdict={\"fontsize\": 14, \"color\": (\"green\" if str(pred) == str(label) else \"red\")})\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.cpu().squeeze()) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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