diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 173375185aae835a5e72269a392e2cb9eba151a8..c4e19b562ea1ba5392e5241985812bbabca28f5c 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -1 +1 @@
-{"cells":[{"cell_type":"markdown","id":"fbb8c8df","metadata":{"id":"fbb8c8df"},"source":["In this TD, you must modify this notebook to answer the questions. To do this,\n","\n","1. Fork this repository\n","2. Clone your forked repository on your local computer\n","3. Answer the questions\n","4. Commit and push regularly\n","\n","The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account."]},{"cell_type":"markdown","id":"3d167a29","metadata":{"id":"3d167a29"},"source":["Install and test PyTorch from  https://pytorch.org/get-started/locally."]},{"cell_type":"markdown","id":"7edf7168","metadata":{"id":"7edf7168"},"source":["# TD2: Deep learning"]},{"cell_type":"code","execution_count":null,"id":"330a42f5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"330a42f5","executionInfo":{"status":"error","timestamp":1701269008471,"user_tz":-60,"elapsed":5144,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"dcc4fa02-5623-4f54-a522-30f292347319"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"]},{"output_type":"stream","name":"stderr","text":["UsageError: Line magic function `%wget` not found.\n"]}],"source":["%pip install torch torchvision"]},{"cell_type":"markdown","id":"0882a636","metadata":{"id":"0882a636"},"source":["\n","To test run the following code"]},{"cell_type":"code","execution_count":null,"id":"b1950f0a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b1950f0a","executionInfo":{"status":"ok","timestamp":1701263807541,"user_tz":-60,"elapsed":1764,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"438e92af-9461-45dc-c8ba-baf4f39df465"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[ 0.8001, -3.1996,  0.8401, -0.4590,  0.0535,  1.3531,  0.6940, -0.5002,\n","         -2.4893, -0.2943],\n","        [-1.4480,  0.6830, -0.0291, -0.8080,  0.6988,  0.0612, -0.7034,  0.5975,\n","         -0.2097,  0.0544],\n","        [-0.5039,  0.3342, -0.5135,  0.5781, -0.2265,  0.1315,  1.6636, -0.1691,\n","         -0.0637,  0.4066],\n","        [ 1.3856,  1.4038,  0.5262, -0.3644, -1.2894,  0.7763,  0.3176, -0.5977,\n","         -0.8109, -0.2260],\n","        [-0.9714,  1.4755,  0.4159,  0.5655, -1.2068,  0.1483,  0.4998,  0.7127,\n","         -0.3208, -0.1878],\n","        [ 1.1300,  0.1293, -2.0233,  0.2644, -1.6500,  0.0594, -1.6955,  0.9623,\n","         -2.0099,  1.4013],\n","        [ 0.1372,  0.5833, -0.2481,  0.5644, -1.0033,  0.4947, -0.4332, -0.6983,\n","          0.2427,  1.1333],\n","        [ 0.5237, -0.4540,  0.3905, -1.3676,  0.1535, -0.8654,  1.1654, -0.3680,\n","          0.5602,  0.5605],\n","        [ 0.7205,  1.1636, -0.5012,  1.2403,  0.3021, -0.6127, -0.9504,  1.1685,\n","          0.0837, -0.5870],\n","        [ 0.1246, -1.0345, -0.2654, -0.4910, -0.0198, -0.2514, -0.0920, -0.6426,\n","          1.0792,  0.5414],\n","        [-0.0181,  0.5058,  0.5459,  0.4973,  0.3238, -0.8191,  1.1362,  0.5654,\n","         -1.7322, -0.6207],\n","        [-1.0556,  2.2030,  0.2627,  1.0543, -0.2510, -0.0635, -2.5471,  1.0420,\n","          1.0652,  0.3995],\n","        [ 0.8785, -0.0858,  0.3532, -0.0389,  1.1755, -1.7593,  0.5965,  0.0882,\n","          1.1826,  0.7950],\n","        [-1.6628, -0.1029, -0.0121, -1.1714,  0.3778,  1.4698, -0.0620,  0.2037,\n","         -0.8209, -0.5627]])\n","AlexNet(\n","  (features): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n","    (1): ReLU(inplace=True)\n","    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n","    (4): ReLU(inplace=True)\n","    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (7): ReLU(inplace=True)\n","    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (9): ReLU(inplace=True)\n","    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (11): ReLU(inplace=True)\n","    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n","  (classifier): Sequential(\n","    (0): Dropout(p=0.5, inplace=False)\n","    (1): Linear(in_features=9216, out_features=4096, bias=True)\n","    (2): ReLU(inplace=True)\n","    (3): Dropout(p=0.5, inplace=False)\n","    (4): Linear(in_features=4096, out_features=4096, bias=True)\n","    (5): ReLU(inplace=True)\n","    (6): Linear(in_features=4096, out_features=1000, bias=True)\n","  )\n",")\n"]}],"source":["import torch\n","\n","N, D = 14, 10\n","x = torch.randn(N, D).type(torch.FloatTensor)\n","print(x)\n","\n","from torchvision import models\n","\n","alexnet = models.alexnet()\n","print(alexnet)"]},{"cell_type":"markdown","id":"23f266da","metadata":{"id":"23f266da"},"source":["## Exercise 1: CNN on CIFAR10\n","\n","The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n","\n","Have a look at the following documentation to be familiar with PyTorch.\n","\n","https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n","\n","https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html"]},{"cell_type":"markdown","id":"4ba1c82d","metadata":{"id":"4ba1c82d"},"source":["You can test if GPU is available on your machine and thus train on it to speed up the process"]},{"cell_type":"code","execution_count":null,"id":"6e18f2fd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6e18f2fd","executionInfo":{"status":"ok","timestamp":1701263818107,"user_tz":-60,"elapsed":322,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"500c08da-ef25-4e2b-d7d2-c2f0dbd5c8cc"},"outputs":[{"output_type":"stream","name":"stdout","text":["CUDA is available!  Training on GPU ...\n"]}],"source":["import torch\n","\n","# check if CUDA is available\n","train_on_gpu = torch.cuda.is_available()\n","\n","if not train_on_gpu:\n","    print(\"CUDA is not available.  Training on CPU ...\")\n","else:\n","    print(\"CUDA is available!  Training on GPU ...\")"]},{"cell_type":"markdown","id":"5cf214eb","metadata":{"id":"5cf214eb"},"source":["Next we load the CIFAR10 dataset"]},{"cell_type":"code","execution_count":null,"id":"462666a2","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"462666a2","executionInfo":{"status":"ok","timestamp":1701263841822,"user_tz":-60,"elapsed":1805,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"2fe87fb5-7604-4935-9f4f-eecd8b3fe6b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Files already downloaded and verified\n","Files already downloaded and verified\n"]}],"source":["import numpy as np\n","from torchvision import datasets, transforms\n","from torch.utils.data.sampler import SubsetRandomSampler\n","\n","# number of subprocesses to use for data loading\n","num_workers = 0\n","# how many samples per batch to load\n","batch_size = 20\n","# percentage of training set to use as validation\n","valid_size = 0.2\n","\n","# convert data to a normalized torch.FloatTensor\n","transform = transforms.Compose(\n","    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",")\n","\n","# choose the training and test datasets\n","train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n","test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n","\n","# obtain training indices that will be used for validation\n","num_train = len(train_data)\n","indices = list(range(num_train))\n","np.random.shuffle(indices)\n","split = int(np.floor(valid_size * num_train))\n","train_idx, valid_idx = indices[split:], indices[:split]\n","\n","# define samplers for obtaining training and validation batches\n","train_sampler = SubsetRandomSampler(train_idx)\n","valid_sampler = SubsetRandomSampler(valid_idx)\n","\n","# prepare data loaders (combine dataset and sampler)\n","train_loader = torch.utils.data.DataLoader(\n","    train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",")\n","valid_loader = torch.utils.data.DataLoader(\n","    train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",")\n","test_loader = torch.utils.data.DataLoader(\n","    test_data, batch_size=batch_size, num_workers=num_workers\n",")\n","\n","# specify the image classes\n","classes = [\n","    \"airplane\",\n","    \"automobile\",\n","    \"bird\",\n","    \"cat\",\n","    \"deer\",\n","    \"dog\",\n","    \"frog\",\n","    \"horse\",\n","    \"ship\",\n","    \"truck\",\n","]"]},{"cell_type":"markdown","id":"58ec3903","metadata":{"id":"58ec3903"},"source":["CNN definition (this one is an example)"]},{"cell_type":"code","execution_count":null,"id":"317bf070","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"317bf070","executionInfo":{"status":"ok","timestamp":1701263851707,"user_tz":-60,"elapsed":6668,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"cecb36bc-27dd-4aae-ebc5-009a122e169b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Net(\n","  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n","  (fc1): Linear(in_features=400, out_features=120, bias=True)\n","  (fc2): Linear(in_features=120, out_features=84, bias=True)\n","  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",")\n"]}],"source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","# define the CNN architecture\n","\n","\n","class Net(nn.Module):\n","    def __init__(self):\n","        super(Net, self).__init__()\n","        self.conv1 = nn.Conv2d(3, 6, 5)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.conv2 = nn.Conv2d(6, 16, 5)\n","        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n","        self.fc2 = nn.Linear(120, 84)\n","        self.fc3 = nn.Linear(84, 10)\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = x.view(-1, 16 * 5 * 5)\n","        x = F.relu(self.fc1(x))\n","        x = F.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x\n","\n","\n","# create a complete CNN\n","model = Net()\n","print(model)\n","# move tensors to GPU if CUDA is available\n","if train_on_gpu:\n","    model.cuda()"]},{"cell_type":"markdown","id":"a2dc4974","metadata":{"id":"a2dc4974"},"source":["Loss function and training using SGD (Stochastic Gradient Descent) optimizer"]},{"cell_type":"code","execution_count":null,"id":"4b53f229","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4b53f229","executionInfo":{"status":"ok","timestamp":1701264436194,"user_tz":-60,"elapsed":569994,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"42ece0d7-d233-4f08-9935-40d2fcef166e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 44.612249 \tValidation Loss: 40.298942\n","Validation loss decreased (inf --> 40.298942).  Saving model ...\n","Epoch: 1 \tTraining Loss: 36.004778 \tValidation Loss: 33.401573\n","Validation loss decreased (40.298942 --> 33.401573).  Saving model ...\n","Epoch: 2 \tTraining Loss: 30.990529 \tValidation Loss: 29.245610\n","Validation loss decreased (33.401573 --> 29.245610).  Saving model ...\n","Epoch: 3 \tTraining Loss: 28.325317 \tValidation Loss: 26.954483\n","Validation loss decreased (29.245610 --> 26.954483).  Saving model ...\n","Epoch: 4 \tTraining Loss: 26.341247 \tValidation Loss: 26.349700\n","Validation loss decreased (26.954483 --> 26.349700).  Saving model ...\n","Epoch: 5 \tTraining Loss: 24.861439 \tValidation Loss: 24.664094\n","Validation loss decreased (26.349700 --> 24.664094).  Saving model ...\n","Epoch: 6 \tTraining Loss: 23.654918 \tValidation Loss: 23.904583\n","Validation loss decreased (24.664094 --> 23.904583).  Saving model ...\n","Epoch: 7 \tTraining Loss: 22.659880 \tValidation Loss: 24.153002\n","Epoch: 8 \tTraining Loss: 21.813652 \tValidation Loss: 22.728200\n","Validation loss decreased (23.904583 --> 22.728200).  Saving model ...\n","Epoch: 9 \tTraining Loss: 21.028281 \tValidation Loss: 22.683762\n","Validation loss decreased (22.728200 --> 22.683762).  Saving model ...\n","Epoch: 10 \tTraining Loss: 20.283682 \tValidation Loss: 22.527626\n","Validation loss decreased (22.683762 --> 22.527626).  Saving model ...\n","Epoch: 11 \tTraining Loss: 19.596292 \tValidation Loss: 22.082355\n","Validation loss decreased (22.527626 --> 22.082355).  Saving model ...\n","Epoch: 12 \tTraining Loss: 18.990277 \tValidation Loss: 22.173975\n","Epoch: 13 \tTraining Loss: 18.311255 \tValidation Loss: 21.511513\n","Validation loss decreased (22.082355 --> 21.511513).  Saving model ...\n","Epoch: 14 \tTraining Loss: 17.729348 \tValidation Loss: 21.373887\n","Validation loss decreased (21.511513 --> 21.373887).  Saving model ...\n","Epoch: 15 \tTraining Loss: 17.143107 \tValidation Loss: 21.404075\n","Epoch: 16 \tTraining Loss: 16.578313 \tValidation Loss: 22.213146\n","Epoch: 17 \tTraining Loss: 16.067654 \tValidation Loss: 21.753317\n","Epoch: 18 \tTraining Loss: 15.572635 \tValidation Loss: 23.228977\n","Epoch: 19 \tTraining Loss: 15.036218 \tValidation Loss: 22.608370\n","Epoch: 20 \tTraining Loss: 14.528461 \tValidation Loss: 22.057556\n","Epoch: 21 \tTraining Loss: 13.953359 \tValidation Loss: 23.037234\n","Epoch: 22 \tTraining Loss: 13.521695 \tValidation Loss: 23.248760\n","Epoch: 23 \tTraining Loss: 13.053585 \tValidation Loss: 23.488736\n","Epoch: 24 \tTraining Loss: 12.579523 \tValidation Loss: 23.827478\n","Epoch: 25 \tTraining Loss: 12.141763 \tValidation Loss: 24.365644\n","Epoch: 26 \tTraining Loss: 11.630654 \tValidation Loss: 24.792256\n","Epoch: 27 \tTraining Loss: 11.330323 \tValidation Loss: 25.310450\n","Epoch: 28 \tTraining Loss: 10.781678 \tValidation Loss: 25.629191\n","Epoch: 29 \tTraining Loss: 10.492249 \tValidation Loss: 26.488761\n"]}],"source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()  # specify loss function\n","optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n","\n","n_epochs = 30  # number of epochs to train the model\n","train_loss_list = []  # list to store loss to visualize\n","valid_loss_min = np.Inf  # track change in validation loss\n","\n","for epoch in range(n_epochs):\n","    # Keep track of training and validation loss\n","    train_loss = 0.0\n","    valid_loss = 0.0\n","\n","    # Train the model\n","    model.train()\n","    for data, target in train_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Clear the gradients of all optimized variables\n","        optimizer.zero_grad()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Backward pass: compute gradient of the loss with respect to model parameters\n","        loss.backward()\n","        # Perform a single optimization step (parameter update)\n","        optimizer.step()\n","        # Update training loss\n","        train_loss += loss.item() * data.size(0)\n","\n","    # Validate the model\n","    model.eval()\n","    for data, target in valid_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Update average validation loss\n","        valid_loss += loss.item() * data.size(0)\n","\n","    # Calculate average losses\n","    train_loss = train_loss / len(train_loader)\n","    valid_loss = valid_loss / len(valid_loader)\n","    train_loss_list.append(train_loss)\n","\n","    # Print training/validation statistics\n","    print(\n","        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n","            epoch, train_loss, valid_loss\n","        )\n","    )\n","\n","    # Save model if validation loss has decreased\n","    if valid_loss <= valid_loss_min:\n","        print(\n","            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n","                valid_loss_min, valid_loss\n","            )\n","        )\n","        torch.save(model.state_dict(), \"model_cifar.pt\")\n","        valid_loss_min = valid_loss"]},{"cell_type":"markdown","id":"13e1df74","metadata":{"id":"13e1df74"},"source":["Does overfit occur? If so, do an early stopping."]},{"cell_type":"code","execution_count":null,"id":"d39df818","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"d39df818","executionInfo":{"status":"ok","timestamp":1701264448133,"user_tz":-60,"elapsed":525,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d0c485cd-28cb-41c0-da4f-777701671915"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(n_epochs), train_loss_list)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Performance of Model 1\")\n","plt.show()"]},{"cell_type":"markdown","id":"11df8fd4","metadata":{"id":"11df8fd4"},"source":["Now loading the model with the lowest validation loss value\n"]},{"cell_type":"code","execution_count":null,"id":"e93efdfc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e93efdfc","executionInfo":{"status":"ok","timestamp":1701264460982,"user_tz":-60,"elapsed":3907,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"75f4d1f4-3dce-4323-8d8c-2112a97a81ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 21.447881\n","\n","Test Accuracy of airplane: 69% (699/1000)\n","Test Accuracy of automobile: 77% (776/1000)\n","Test Accuracy of  bird: 51% (511/1000)\n","Test Accuracy of   cat: 46% (460/1000)\n","Test Accuracy of  deer: 46% (460/1000)\n","Test Accuracy of   dog: 45% (459/1000)\n","Test Accuracy of  frog: 77% (774/1000)\n","Test Accuracy of horse: 66% (663/1000)\n","Test Accuracy of  ship: 79% (792/1000)\n","Test Accuracy of truck: 69% (699/1000)\n","\n","Test Accuracy (Overall): 62% (6293/10000)\n"]}],"source":["model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n","\n","# track test loss\n","test_loss = 0.0\n","class_correct = list(0.0 for i in range(10))\n","class_total = list(0.0 for i in range(10))\n","\n","model.eval()\n","# iterate over test data\n","for data, target in test_loader:\n","    # move tensors to GPU if CUDA is available\n","    if train_on_gpu:\n","        data, target = data.cuda(), target.cuda()\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = model(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = (\n","        np.squeeze(correct_tensor.numpy())\n","        if not train_on_gpu\n","        else np.squeeze(correct_tensor.cpu().numpy())\n","    )\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct[label] += correct[i].item()\n","        class_total[label] += 1\n","\n","# average test loss\n","test_loss = test_loss / len(test_loader)\n","print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")"]},{"cell_type":"markdown","id":"944991a2","metadata":{"id":"944991a2"},"source":["Build a new network with the following structure.\n","\n","- It has 3 convolutional layers of kernel size 3 and padding of 1.\n","- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n","- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n","- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n","- The first fully connected layer will have an output size of 512.\n","- The second fully connected layer will have an output size of 64.\n","\n","Compare the results obtained with this new network to those obtained previously."]},{"cell_type":"code","source":["# define the new CNN architecture\n","\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class Net_new(nn.Module):\n","    def __init__(self):\n","        super(Net_new, self).__init__()\n","        self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # Padding to prevent the output's dimension from changing\n","        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n","        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.fc1 = nn.Linear(64 * 4 * 4, 512) # Input size = nb of channels output on the last layer * pixel size of image (each MaxPool split by two)\n","        self.fc2 = nn.Linear(512, 64)\n","        self.fc3 = nn.Linear(64, 10)\n","        self.dropout = nn.Dropout()\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = self.pool(F.relu(self.conv3(x)))\n","        x = x.view(-1, 64 * 4 * 4)\n","        x = F.relu(self.fc1(x))\n","        x = self.dropout(x) # Helpful in preventing neuron co-adaptation\n","        x = F.relu(self.fc2(x))\n","        x = self.dropout(x)\n","        x = F.relu(self.fc3(x))\n","        return x\n","\n","# create a complete CNN\n","model_new = Net_new()\n","print(model_new)\n","# move tensors to GPU if CUDA is available\n","if train_on_gpu:\n","  model_new.cuda()"],"metadata":{"id":"gcRCs-iUEnaH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701264472424,"user_tz":-60,"elapsed":4,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"761a457c-f623-455a-97a3-5bc7bea1a48b"},"id":"gcRCs-iUEnaH","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Net_new(\n","  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n","  (fc2): Linear(in_features=512, out_features=64, bias=True)\n","  (fc3): Linear(in_features=64, out_features=10, bias=True)\n","  (dropout): Dropout(p=0.5, inplace=False)\n",")\n"]}]},{"cell_type":"code","source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()  # specify loss function\n","optimizer_new = optim.SGD(model_new.parameters(), lr=0.01)  # specify optimizer\n","\n","n_epochs = 30  # number of epochs to train the model\n","train_loss_list_new = []  # list to store loss to visualize\n","valid_loss_min = np.Inf  # track change in validation loss\n","\n","for epoch in range(n_epochs):\n","    # Keep track of training and validation loss\n","    train_loss = 0.0\n","    valid_loss = 0.0\n","\n","    # Train the model\n","    model_new.train()\n","    for data, target in train_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Clear the gradients of all optimized variables\n","        optimizer_new.zero_grad()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model_new(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Backward pass: compute gradient of the loss with respect to model parameters\n","        loss.backward()\n","        # Perform a single optimization step (parameter update)\n","        optimizer_new.step()\n","        # Update training loss\n","        train_loss += loss.item() * data.size(0)\n","\n","    # Validate the model\n","    model_new.eval()\n","    for data, target in valid_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model_new(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Update average validation loss\n","        valid_loss += loss.item() * data.size(0)\n","\n","    # Calculate average losses\n","    train_loss = train_loss / len(train_loader)\n","    valid_loss = valid_loss / len(valid_loader)\n","    train_loss_list_new.append(train_loss)\n","\n","    # Print training/validation statistics\n","    print(\n","        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n","            epoch, train_loss, valid_loss\n","        )\n","    )\n","\n","    # Save model if validation loss has decreased\n","    if valid_loss <= valid_loss_min:\n","        print(\n","            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n","                valid_loss_min, valid_loss\n","            )\n","        )\n","        torch.save(model_new.state_dict(), \"model_new_cifar.pt\")\n","        valid_loss_min = valid_loss"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1mux8ZZi2vd7","executionInfo":{"status":"ok","timestamp":1701265066443,"user_tz":-60,"elapsed":582783,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b2bf2851-3aff-48fe-aa83-9c7c0c6124f7"},"id":"1mux8ZZi2vd7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 46.035736 \tValidation Loss: 45.976020\n","Validation loss decreased (inf --> 45.976020).  Saving model ...\n","Epoch: 1 \tTraining Loss: 45.187472 \tValidation Loss: 42.525370\n","Validation loss decreased (45.976020 --> 42.525370).  Saving model ...\n","Epoch: 2 \tTraining Loss: 40.269012 \tValidation Loss: 36.115885\n","Validation loss decreased (42.525370 --> 36.115885).  Saving model ...\n","Epoch: 3 \tTraining Loss: 35.383565 \tValidation Loss: 31.909517\n","Validation loss decreased (36.115885 --> 31.909517).  Saving model ...\n","Epoch: 4 \tTraining Loss: 32.746224 \tValidation Loss: 29.787075\n","Validation loss decreased (31.909517 --> 29.787075).  Saving model ...\n","Epoch: 5 \tTraining Loss: 30.653619 \tValidation Loss: 27.959262\n","Validation loss decreased (29.787075 --> 27.959262).  Saving model ...\n","Epoch: 6 \tTraining Loss: 28.983543 \tValidation Loss: 26.431537\n","Validation loss decreased (27.959262 --> 26.431537).  Saving model ...\n","Epoch: 7 \tTraining Loss: 27.683504 \tValidation Loss: 25.174931\n","Validation loss decreased (26.431537 --> 25.174931).  Saving model ...\n","Epoch: 8 \tTraining Loss: 26.336337 \tValidation Loss: 23.783314\n","Validation loss decreased (25.174931 --> 23.783314).  Saving model ...\n","Epoch: 9 \tTraining Loss: 24.991212 \tValidation Loss: 22.687754\n","Validation loss decreased (23.783314 --> 22.687754).  Saving model ...\n","Epoch: 10 \tTraining Loss: 23.787577 \tValidation Loss: 22.145078\n","Validation loss decreased (22.687754 --> 22.145078).  Saving model ...\n","Epoch: 11 \tTraining Loss: 22.818656 \tValidation Loss: 20.805044\n","Validation loss decreased (22.145078 --> 20.805044).  Saving model ...\n","Epoch: 12 \tTraining Loss: 21.811931 \tValidation Loss: 19.928644\n","Validation loss decreased (20.805044 --> 19.928644).  Saving model ...\n","Epoch: 13 \tTraining Loss: 20.853573 \tValidation Loss: 19.503793\n","Validation loss decreased (19.928644 --> 19.503793).  Saving model ...\n","Epoch: 14 \tTraining Loss: 20.078903 \tValidation Loss: 18.726372\n","Validation loss decreased (19.503793 --> 18.726372).  Saving model ...\n","Epoch: 15 \tTraining Loss: 19.173792 \tValidation Loss: 18.262609\n","Validation loss decreased (18.726372 --> 18.262609).  Saving model ...\n","Epoch: 16 \tTraining Loss: 18.500586 \tValidation Loss: 18.070592\n","Validation loss decreased (18.262609 --> 18.070592).  Saving model ...\n","Epoch: 17 \tTraining Loss: 17.639666 \tValidation Loss: 17.679802\n","Validation loss decreased (18.070592 --> 17.679802).  Saving model ...\n","Epoch: 18 \tTraining Loss: 17.082578 \tValidation Loss: 17.131204\n","Validation loss decreased (17.679802 --> 17.131204).  Saving model ...\n","Epoch: 19 \tTraining Loss: 16.418561 \tValidation Loss: 16.789966\n","Validation loss decreased (17.131204 --> 16.789966).  Saving model ...\n","Epoch: 20 \tTraining Loss: 15.737011 \tValidation Loss: 16.914572\n","Epoch: 21 \tTraining Loss: 15.217627 \tValidation Loss: 17.321893\n","Epoch: 22 \tTraining Loss: 14.692679 \tValidation Loss: 16.259236\n","Validation loss decreased (16.789966 --> 16.259236).  Saving model ...\n","Epoch: 23 \tTraining Loss: 14.104487 \tValidation Loss: 15.681182\n","Validation loss decreased (16.259236 --> 15.681182).  Saving model ...\n","Epoch: 24 \tTraining Loss: 13.509841 \tValidation Loss: 16.067594\n","Epoch: 25 \tTraining Loss: 13.031704 \tValidation Loss: 15.928080\n","Epoch: 26 \tTraining Loss: 12.543566 \tValidation Loss: 16.412866\n","Epoch: 27 \tTraining Loss: 12.077648 \tValidation Loss: 16.044644\n","Epoch: 28 \tTraining Loss: 11.713458 \tValidation Loss: 15.721017\n","Epoch: 29 \tTraining Loss: 11.205782 \tValidation Loss: 16.062376\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(n_epochs), train_loss_list_new)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Performance of the 3-layer Model\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"hEdjk_jV4mEm","executionInfo":{"status":"ok","timestamp":1701265110327,"user_tz":-60,"elapsed":342,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b713f1d6-4d88-42bc-f71c-7aba797301e5"},"id":"hEdjk_jV4mEm","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_new.load_state_dict(torch.load(\"./model_new_cifar.pt\"))\n","\n","# track test loss\n","test_loss = 0.0\n","class_correct = list(0.0 for i in range(10))\n","class_total = list(0.0 for i in range(10))\n","\n","model_new.eval()\n","# iterate over test data\n","for data, target in test_loader:\n","    # move tensors to GPU if CUDA is available\n","    if train_on_gpu:\n","        data, target = data.cuda(), target.cuda()\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = model_new(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = (\n","        np.squeeze(correct_tensor.numpy())\n","        if not train_on_gpu\n","        else np.squeeze(correct_tensor.cpu().numpy())\n","    )\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct[label] += correct[i].item()\n","        class_total[label] += 1\n","\n","# average test loss\n","test_loss = test_loss / len(test_loader)\n","print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9C9D34ZW43q7","executionInfo":{"status":"ok","timestamp":1701267205015,"user_tz":-60,"elapsed":4237,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9373df39-8c49-4700-8601-50c4b3a27548"},"id":"9C9D34ZW43q7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 15.725736\n","\n","Test Accuracy of airplane: 78% (784/1000)\n","Test Accuracy of automobile: 83% (838/1000)\n","Test Accuracy of  bird: 61% (615/1000)\n","Test Accuracy of   cat: 51% (513/1000)\n","Test Accuracy of  deer: 68% (680/1000)\n","Test Accuracy of   dog: 63% (635/1000)\n","Test Accuracy of  frog: 86% (860/1000)\n","Test Accuracy of horse: 76% (762/1000)\n","Test Accuracy of  ship: 84% (845/1000)\n","Test Accuracy of truck: 82% (821/1000)\n","\n","Test Accuracy (Overall): 73% (7353/10000)\n"]}]},{"cell_type":"markdown","source":["With our new model, we notice a substantial improvement in overall test accuracy.\n","\n","The result of the **original CNN** are :\n","\n","*   *Test loss* : 21.447881\n","*   *Test accuracy* : 62%\n","\n","The result of the our **new 3-layers CNN** are :\n","\n","*   *Test loss* : 15.725736\n","*   *Test accuracy* : 73%\n","\n","Despite the additional training period (~1min), the outcomes meet our expectations. Indeed, for each class, the accuracy is improved up to 10%."],"metadata":{"id":"uU_LD9l1mfvn"},"id":"uU_LD9l1mfvn"},{"cell_type":"markdown","id":"bc381cf4","metadata":{"id":"bc381cf4"},"source":["## Exercise 2: Quantization: try to compress the CNN to save space\n","\n","Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n","        \n","The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy\n","\n","\n","The size of the model is simply the size of the file."]},{"cell_type":"code","execution_count":9,"id":"ef623c26","metadata":{"id":"ef623c26","executionInfo":{"status":"ok","timestamp":1701423064227,"user_tz":-60,"elapsed":303,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}}},"outputs":[],"source":["import os\n","\n","def print_size_of_model(model, label=\"\"):\n","    torch.save(model.state_dict(), \"temp.p\")\n","    size = os.path.getsize(\"temp.p\")\n","    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n","    os.remove(\"temp.p\")\n","    return size\n","\n","#size_model = print_size_of_model(model_new, \"fp32\")"]},{"cell_type":"markdown","id":"05c4e9ad","metadata":{"id":"05c4e9ad"},"source":["Post training quantization example"]},{"cell_type":"code","execution_count":null,"id":"c4c65d4b","metadata":{"id":"c4c65d4b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701267155486,"user_tz":-60,"elapsed":623,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"6cbb1ee3-36ae-4d73-cf55-7c1eca65a778"},"outputs":[{"output_type":"stream","name":"stdout","text":["model:  int8  \t Size (KB): 659.934\n","The size of the original model has been divided by 3.53 compared to the Quantized model\n"]}],"source":["import torch.quantization\n","\n","\n","quantized_model = torch.quantization.quantize_dynamic(model_new, {torch.nn.Linear}, dtype=torch.qint8)\n","torch.save(quantized_model.state_dict(), \"quantized_model_cifar.pt\")\n","\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","\n","print(f\"The size of the original model has been divided by {size_model / size_quantized:.2f} compared to the Quantized model\")\n"]},{"cell_type":"markdown","id":"7b108e17","metadata":{"id":"7b108e17"},"source":["For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models."]},{"cell_type":"markdown","id":"a0a34b90","metadata":{"id":"a0a34b90"},"source":["Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"]},{"cell_type":"code","source":["quantized_model.load_state_dict(torch.load(\"./quantized_model_cifar.pt\",map_location=torch.device('cpu')))\n","\n","# track test loss\n","test_loss_quantized = 0.0\n","class_correct_quantized = list(0.0 for i in range(10))\n","class_total_quantized = list(0.0 for i in range(10))\n","\n","quantized_model.eval()\n","quantized_model.cpu()\n","\n","# iterate over test data\n","for data, target in test_loader:\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = quantized_model(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss_quantized += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = np.squeeze(correct_tensor.numpy()) #np.squeeze(correct_tensor.cpu().numpy()\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct_quantized[label] += correct[i].item()\n","        class_total_quantized[label] += 1\n","\n","# average test loss\n","test_loss_quantized = test_loss_quantized / len(test_loader)\n","loss_delta = test_loss_quantized - test_loss\n","print(\"Original Test Loss: {:.6f}\\n\".format(test_loss))\n","print(\"Quantized Test Loss: {:.6f}\\n\".format(test_loss_quantized))\n","print(\"Loss Delta: {:.6f}\\n\".format(loss_delta))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Initial model Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            ))\n","        print(\n","            \"Quantized model Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct_quantized[i] / class_total_quantized[i],\n","                np.sum(class_correct_quantized[i]),\n","                np.sum(class_total_quantized[i]),\n","            ))\n","        print(\n","            \"Difference in Instances Correctly classified of %5s: %2d \\n\"\n","            % (\n","                classes[i],\n","                class_correct_quantized[i]-class_correct[i],\n","\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nInitial model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")\n","print(\n","    \"\\nQuantized model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct_quantized) / np.sum(class_total_quantized),\n","        np.sum(class_correct_quantized),\n","        np.sum(class_total_quantized),\n","    )\n",")\n","print(\n","         \"\\nDifference in Instances Correctly classified (Overall) : %2d \\n\"\n","        % (\n","            np.sum(class_correct)-np.sum(class_correct_quantized),\n","            )\n","        )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"goj7b4vnAVvk","executionInfo":{"status":"ok","timestamp":1701267315881,"user_tz":-60,"elapsed":7778,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9a3e9626-e644-4a43-f7f1-500aeb7bfea9"},"id":"goj7b4vnAVvk","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original Test Loss: 15.725736\n","\n","Quantized Test Loss: 15.745716\n","\n","Loss Delta: 0.019980\n","\n","Initial model Test Accuracy of airplane: 78% (784/1000)\n","Quantized model Test Accuracy of airplane: 78% (786/1000)\n","Difference in Instances Correctly classified of airplane:  2 \n","\n","Initial model Test Accuracy of automobile: 83% (838/1000)\n","Quantized model Test Accuracy of automobile: 83% (837/1000)\n","Difference in Instances Correctly classified of automobile: -1 \n","\n","Initial model Test Accuracy of  bird: 61% (615/1000)\n","Quantized model Test Accuracy of  bird: 61% (611/1000)\n","Difference in Instances Correctly classified of  bird: -4 \n","\n","Initial model Test Accuracy of   cat: 51% (513/1000)\n","Quantized model Test Accuracy of   cat: 51% (512/1000)\n","Difference in Instances Correctly classified of   cat: -1 \n","\n","Initial model Test Accuracy of  deer: 68% (680/1000)\n","Quantized model Test Accuracy of  deer: 68% (681/1000)\n","Difference in Instances Correctly classified of  deer:  1 \n","\n","Initial model Test Accuracy of   dog: 63% (635/1000)\n","Quantized model Test Accuracy of   dog: 63% (636/1000)\n","Difference in Instances Correctly classified of   dog:  1 \n","\n","Initial model Test Accuracy of  frog: 86% (860/1000)\n","Quantized model Test Accuracy of  frog: 86% (861/1000)\n","Difference in Instances Correctly classified of  frog:  1 \n","\n","Initial model Test Accuracy of horse: 76% (762/1000)\n","Quantized model Test Accuracy of horse: 76% (764/1000)\n","Difference in Instances Correctly classified of horse:  2 \n","\n","Initial model Test Accuracy of  ship: 84% (845/1000)\n","Quantized model Test Accuracy of  ship: 84% (842/1000)\n","Difference in Instances Correctly classified of  ship: -3 \n","\n","Initial model Test Accuracy of truck: 82% (821/1000)\n","Quantized model Test Accuracy of truck: 82% (820/1000)\n","Difference in Instances Correctly classified of truck: -1 \n","\n","\n","Initial model Test Accuracy (Overall): 73% (7353/10000)\n","\n","Quantized model Test Accuracy (Overall): 73% (7350/10000)\n","\n","Difference in Instances Correctly classified (Overall) :  3 \n","\n"]}]},{"cell_type":"markdown","source":["The quantization of our model has minimal impact on classification accuracy, with the Test Loss and number of correctly classified instances remaining consistent across both models. This quantization is a beneficial method for saving space and memory, as the quantized file is 3.53 times smaller."],"metadata":{"id":"Eo8W3YvhnzVZ"},"id":"Eo8W3YvhnzVZ"},{"cell_type":"markdown","id":"201470f9","metadata":{"id":"201470f9"},"source":["## Exercise 3: working with pre-trained models.\n","\n","PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n","We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"]},{"cell_type":"code","execution_count":null,"id":"b4d13080","metadata":{"id":"b4d13080","colab":{"base_uri":"https://localhost:8080/","height":416},"executionInfo":{"status":"ok","timestamp":1701267795377,"user_tz":-60,"elapsed":2449,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"152af00a-3cb4-4faf-a3fb-36466c75d661"},"outputs":[{"output_type":"stream","name":"stdout","text":["Predicted class for resnet50 is: Alpine ibex\n","Predicted class for googlenet is: hartebeest\n","Predicted class for resnet_quantized is: Alpine ibex\n","Predicted class for googlenet_quantized is: hartebeest\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import json\n","from PIL import Image\n","\n","\n","# Choose an image to pass through the model\n","test_image_1 = \"dog.png\"\n","test_image_2 = \"ours.jpg\"\n","test_image_3 = \"cerf.jpg\"\n","\n","\n","# Configure matplotlib for pretty inline plots\n","#%matplotlib inline\n","#%config InlineBackend.figure_format = 'retina'\n","\n","# Prepare the labels\n","with open(\"imagenet-simple-labels.json\") as f:\n","    labels = json.load(f)\n","\n","# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n","data_transform = transforms.Compose(\n","    [\n","        transforms.Resize((224, 224)),\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","    ]\n",")\n","# Load the image\n","\n","#image = Image.open(test_image_1)\n","#image = Image.open(test_image_2)\n","image = Image.open(test_image_3)\n","\n","plt.imshow(image), plt.xticks([]), plt.yticks([])\n","\n","# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n","# image = data_transform(image).unsqueeze(0).cuda()\n","image = data_transform(image).unsqueeze(0)\n","\n","# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n","model = models.resnet50(pretrained=True)\n","googlenet_model = models.googlenet(pretrained=True)\n","resnet_quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n","googlenet_quantized_model = torch.quantization.quantize_dynamic(googlenet_model, dtype=torch.qint8)\n","\n","# Send the model to the GPU\n","# model.cuda()\n","# Set layers such as dropout and batchnorm in evaluation mode\n","model.eval()\n","googlenet_model.eval()\n","resnet_quantized_model.eval()\n","googlenet_quantized_model.eval()\n","\n","# Get the 1000-dimensional model output\n","out_1 = model(image)\n","out_2 = googlenet_model(image)\n","out_3 = resnet_quantized_model(image)\n","out_4 = googlenet_quantized_model(image)\n","\n","# Find the predicted class\n","print(\"Predicted class for resnet50 is: {}\".format(labels[out_1.argmax()]))\n","print(\"Predicted class for googlenet is: {}\".format(labels[out_2.argmax()]))\n","print(\"Predicted class for resnet_quantized is: {}\".format(labels[out_3.argmax()]))\n","print(\"Predicted class for googlenet_quantized is: {}\".format(labels[out_4.argmax()]))"]},{"cell_type":"markdown","id":"184cfceb","metadata":{"id":"184cfceb"},"source":["Experiments:\n","\n","Study the code and the results obtained. Possibly add other images downloaded from the internet.\n","\n","What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n","\n","Experiment with other pre-trained CNN models.\n","\n","    \n"]},{"cell_type":"markdown","source":["We tried to experiment the Resnet50 and GoogleNet with three different image. The results are satisfying for the two first image \"dog.png\", and \"ours.jpg\", but not for the \"cerf.jpg\". Indeed, the predicted class for resnet50 model : Alpine ibex and the predicted class for googlenet is: hartebeest."],"metadata":{"id":"neFEGq8SuQ98"},"id":"neFEGq8SuQ98"},{"cell_type":"code","source":["#sizes of the model\n","\n","print(\"Size of the 3-layers model :\")\n","size_model = print_size_of_model(model_new, \"fp32\")\n","print(\"Size of the 3-layers quantized model :\")\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","print(\"The size of the model has been divided by %.2f compared to the Quantized model\" % (size_model / size_quantized))\n","\n","print(\"\\nSize of the Resnet model :\")\n","size_Resnet = print_size_of_model(model, \"fp32\")\n","print(\"Size of the Resnet quantized model :\")\n","size_Quantized_Resnet = print_size_of_model(resnet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Resnet / size_quantized))\n","\n","print(\"\\nSize of Googlenet model:\")\n","size_Googlenet = print_size_of_model(googlenet_model, \"fp32\")\n","print(\"Size of Googlenet quantized model:\")\n","size_Quantized_Googlenet = print_size_of_model(googlenet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Googlenet / size_quantized))\n"],"metadata":{"id":"fAyldGwPIv60","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701268313794,"user_tz":-60,"elapsed":913,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"ac73bd2c-9af2-4652-9e47-981aad0ccc64"},"id":"fAyldGwPIv60","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Size of the 3-layers model :\n","model:  fp32  \t Size (KB): 2331.074\n","Size of the 3-layers quantized model :\n","model:  int8  \t Size (KB): 659.806\n","The size of the model has been divided by 3.53 compared to the Quantized model\n","\n","Size of the Resnet model :\n","model:  fp32  \t Size (KB): 102523.238\n","Size of the Resnet quantized model :\n","model:  fp32  \t Size (KB): 96379.996\n","The size of the original model has been divided by 146.07 compared to the 3-layer Quantized model\n","\n","Size of Googlenet model:\n","model:  fp32  \t Size (KB): 26654.254\n","Size of Googlenet quantized model:\n","model:  fp32  \t Size (KB): 23583.076\n","The size of the original model has been divided by 35.74 compared to the 3-layer Quantized model\n"]}]},{"cell_type":"markdown","source":["Even after quantization, the pretrained models are far larger than our own trained model."],"metadata":{"id":"2VjjWHXHwGl4"},"id":"2VjjWHXHwGl4"},{"cell_type":"markdown","id":"5d57da4b","metadata":{"id":"5d57da4b"},"source":["## Exercise 4: Transfer Learning\n","    \n","    \n","For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n","Download and unzip in your working directory the dataset available at the address :\n","    \n","https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","    \n","Execute the following code in order to display some images of the dataset."]},{"cell_type":"code","source":["!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","!unzip hymenoptera_data.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oVfFXO3fjOvS","executionInfo":{"status":"ok","timestamp":1701422795599,"user_tz":-60,"elapsed":1282,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"c73c1d62-d678-410d-e335-0bd356f935ec"},"id":"oVfFXO3fjOvS","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["--2023-12-01 09:26:34--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","Resolving download.pytorch.org (download.pytorch.org)... 99.86.38.106, 99.86.38.72, 99.86.38.37, ...\n","Connecting to download.pytorch.org (download.pytorch.org)|99.86.38.106|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 47286322 (45M) [application/zip]\n","Saving to: ‘hymenoptera_data.zip’\n","\n","hymenoptera_data.zi 100%[===================>]  45.10M   152MB/s    in 0.3s    \n","\n","2023-12-01 09:26:34 (152 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n","\n","Archive:  hymenoptera_data.zip\n","   creating: hymenoptera_data/\n","   creating: hymenoptera_data/train/\n","   creating: hymenoptera_data/train/ants/\n","  inflating: hymenoptera_data/train/ants/0013035.jpg  \n","  inflating: hymenoptera_data/train/ants/1030023514_aad5c608f9.jpg  \n","  inflating: hymenoptera_data/train/ants/1095476100_3906d8afde.jpg  \n","  inflating: hymenoptera_data/train/ants/1099452230_d1949d3250.jpg  \n","  inflating: hymenoptera_data/train/ants/116570827_e9c126745d.jpg  \n","  inflating: hymenoptera_data/train/ants/1225872729_6f0856588f.jpg  \n","  inflating: hymenoptera_data/train/ants/1262877379_64fcada201.jpg  \n","  inflating: hymenoptera_data/train/ants/1269756697_0bce92cdab.jpg  \n","  inflating: hymenoptera_data/train/ants/1286984635_5119e80de1.jpg  \n","  inflating: hymenoptera_data/train/ants/132478121_2a430adea2.jpg  \n","  inflating: hymenoptera_data/train/ants/1360291657_dc248c5eea.jpg  \n","  inflating: hymenoptera_data/train/ants/1368913450_e146e2fb6d.jpg  \n","  inflating: hymenoptera_data/train/ants/1473187633_63ccaacea6.jpg  \n","  inflating: hymenoptera_data/train/ants/148715752_302c84f5a4.jpg  \n","  inflating: hymenoptera_data/train/ants/1489674356_09d48dde0a.jpg  \n","  inflating: hymenoptera_data/train/ants/149244013_c529578289.jpg  \n","  inflating: hymenoptera_data/train/ants/150801003_3390b73135.jpg  \n","  inflating: hymenoptera_data/train/ants/150801171_cd86f17ed8.jpg  \n","  inflating: hymenoptera_data/train/ants/154124431_65460430f2.jpg  \n","  inflating: hymenoptera_data/train/ants/162603798_40b51f1654.jpg  \n","  inflating: hymenoptera_data/train/ants/1660097129_384bf54490.jpg  \n","  inflating: hymenoptera_data/train/ants/167890289_dd5ba923f3.jpg  \n","  inflating: hymenoptera_data/train/ants/1693954099_46d4c20605.jpg  \n","  inflating: hymenoptera_data/train/ants/175998972.jpg  \n","  inflating: hymenoptera_data/train/ants/178538489_bec7649292.jpg  \n","  inflating: hymenoptera_data/train/ants/1804095607_0341701e1c.jpg  \n","  inflating: hymenoptera_data/train/ants/1808777855_2a895621d7.jpg  \n","  inflating: hymenoptera_data/train/ants/188552436_605cc9b36b.jpg  \n","  inflating: hymenoptera_data/train/ants/1917341202_d00a7f9af5.jpg  \n","  inflating: hymenoptera_data/train/ants/1924473702_daa9aacdbe.jpg  \n","  inflating: hymenoptera_data/train/ants/196057951_63bf063b92.jpg  \n","  inflating: hymenoptera_data/train/ants/196757565_326437f5fe.jpg  \n","  inflating: hymenoptera_data/train/ants/201558278_fe4caecc76.jpg  \n","  inflating: hymenoptera_data/train/ants/201790779_527f4c0168.jpg  \n","  inflating: hymenoptera_data/train/ants/2019439677_2db655d361.jpg  \n","  inflating: hymenoptera_data/train/ants/207947948_3ab29d7207.jpg  \n","  inflating: hymenoptera_data/train/ants/20935278_9190345f6b.jpg  \n","  inflating: hymenoptera_data/train/ants/224655713_3956f7d39a.jpg  \n","  inflating: hymenoptera_data/train/ants/2265824718_2c96f485da.jpg  \n","  inflating: hymenoptera_data/train/ants/2265825502_fff99cfd2d.jpg  \n","  inflating: hymenoptera_data/train/ants/226951206_d6bf946504.jpg  \n","  inflating: hymenoptera_data/train/ants/2278278459_6b99605e50.jpg  \n","  inflating: hymenoptera_data/train/ants/2288450226_a6e96e8fdf.jpg  \n","  inflating: hymenoptera_data/train/ants/2288481644_83ff7e4572.jpg  \n","  inflating: hymenoptera_data/train/ants/2292213964_ca51ce4bef.jpg  \n","  inflating: hymenoptera_data/train/ants/24335309_c5ea483bb8.jpg  \n","  inflating: hymenoptera_data/train/ants/245647475_9523dfd13e.jpg  \n","  inflating: hymenoptera_data/train/ants/255434217_1b2b3fe0a4.jpg  \n","  inflating: hymenoptera_data/train/ants/258217966_d9d90d18d3.jpg  \n","  inflating: hymenoptera_data/train/ants/275429470_b2d7d9290b.jpg  \n","  inflating: hymenoptera_data/train/ants/28847243_e79fe052cd.jpg  \n","  inflating: hymenoptera_data/train/ants/318052216_84dff3f98a.jpg  \n","  inflating: hymenoptera_data/train/ants/334167043_cbd1adaeb9.jpg  \n","  inflating: hymenoptera_data/train/ants/339670531_94b75ae47a.jpg  \n","  inflating: hymenoptera_data/train/ants/342438950_a3da61deab.jpg  \n","  inflating: hymenoptera_data/train/ants/36439863_0bec9f554f.jpg  \n","  inflating: hymenoptera_data/train/ants/374435068_7eee412ec4.jpg  \n","  inflating: hymenoptera_data/train/ants/382971067_0bfd33afe0.jpg  \n","  inflating: hymenoptera_data/train/ants/384191229_5779cf591b.jpg  \n","  inflating: hymenoptera_data/train/ants/386190770_672743c9a7.jpg  \n","  inflating: hymenoptera_data/train/ants/392382602_1b7bed32fa.jpg  \n","  inflating: hymenoptera_data/train/ants/403746349_71384f5b58.jpg  \n","  inflating: hymenoptera_data/train/ants/408393566_b5b694119b.jpg  \n","  inflating: hymenoptera_data/train/ants/424119020_6d57481dab.jpg  \n","  inflating: hymenoptera_data/train/ants/424873399_47658a91fb.jpg  \n","  inflating: hymenoptera_data/train/ants/450057712_771b3bfc91.jpg  \n","  inflating: hymenoptera_data/train/ants/45472593_bfd624f8dc.jpg  \n","  inflating: hymenoptera_data/train/ants/459694881_ac657d3187.jpg  \n","  inflating: hymenoptera_data/train/ants/460372577_f2f6a8c9fc.jpg  \n","  inflating: hymenoptera_data/train/ants/460874319_0a45ab4d05.jpg  \n","  inflating: hymenoptera_data/train/ants/466430434_4000737de9.jpg  \n","  inflating: hymenoptera_data/train/ants/470127037_513711fd21.jpg  \n","  inflating: hymenoptera_data/train/ants/474806473_ca6caab245.jpg  \n","  inflating: hymenoptera_data/train/ants/475961153_b8c13fd405.jpg  \n","  inflating: hymenoptera_data/train/ants/484293231_e53cfc0c89.jpg  \n","  inflating: hymenoptera_data/train/ants/49375974_e28ba6f17e.jpg  \n","  inflating: hymenoptera_data/train/ants/506249802_207cd979b4.jpg  \n","  inflating: hymenoptera_data/train/ants/506249836_717b73f540.jpg  \n","  inflating: hymenoptera_data/train/ants/512164029_c0a66b8498.jpg  \n","  inflating: hymenoptera_data/train/ants/512863248_43c8ce579b.jpg  \n","  inflating: hymenoptera_data/train/ants/518773929_734dbc5ff4.jpg  \n","  inflating: hymenoptera_data/train/ants/522163566_fec115ca66.jpg  \n","  inflating: hymenoptera_data/train/ants/522415432_2218f34bf8.jpg  \n","  inflating: hymenoptera_data/train/ants/531979952_bde12b3bc0.jpg  \n","  inflating: hymenoptera_data/train/ants/533848102_70a85ad6dd.jpg  \n","  inflating: hymenoptera_data/train/ants/535522953_308353a07c.jpg  \n","  inflating: hymenoptera_data/train/ants/540889389_48bb588b21.jpg  \n","  inflating: hymenoptera_data/train/ants/541630764_dbd285d63c.jpg  \n","  inflating: hymenoptera_data/train/ants/543417860_b14237f569.jpg  \n","  inflating: hymenoptera_data/train/ants/560966032_988f4d7bc4.jpg  \n","  inflating: hymenoptera_data/train/ants/5650366_e22b7e1065.jpg  \n","  inflating: hymenoptera_data/train/ants/6240329_72c01e663e.jpg  \n","  inflating: hymenoptera_data/train/ants/6240338_93729615ec.jpg  \n","  inflating: hymenoptera_data/train/ants/649026570_e58656104b.jpg  \n","  inflating: hymenoptera_data/train/ants/662541407_ff8db781e7.jpg  \n","  inflating: hymenoptera_data/train/ants/67270775_e9fdf77e9d.jpg  \n","  inflating: hymenoptera_data/train/ants/6743948_2b8c096dda.jpg  \n","  inflating: hymenoptera_data/train/ants/684133190_35b62c0c1d.jpg  \n","  inflating: hymenoptera_data/train/ants/69639610_95e0de17aa.jpg  \n","  inflating: hymenoptera_data/train/ants/707895295_009cf23188.jpg  \n","  inflating: hymenoptera_data/train/ants/7759525_1363d24e88.jpg  \n","  inflating: hymenoptera_data/train/ants/795000156_a9900a4a71.jpg  \n","  inflating: hymenoptera_data/train/ants/822537660_caf4ba5514.jpg  \n","  inflating: hymenoptera_data/train/ants/82852639_52b7f7f5e3.jpg  \n","  inflating: hymenoptera_data/train/ants/841049277_b28e58ad05.jpg  \n","  inflating: hymenoptera_data/train/ants/886401651_f878e888cd.jpg  \n","  inflating: hymenoptera_data/train/ants/892108839_f1aad4ca46.jpg  \n","  inflating: hymenoptera_data/train/ants/938946700_ca1c669085.jpg  \n","  inflating: hymenoptera_data/train/ants/957233405_25c1d1187b.jpg  \n","  inflating: hymenoptera_data/train/ants/9715481_b3cb4114ff.jpg  \n","  inflating: hymenoptera_data/train/ants/998118368_6ac1d91f81.jpg  \n","  inflating: hymenoptera_data/train/ants/ant photos.jpg  \n","  inflating: hymenoptera_data/train/ants/Ant_1.jpg  \n","  inflating: hymenoptera_data/train/ants/army-ants-red-picture.jpg  \n","  inflating: hymenoptera_data/train/ants/formica.jpeg  \n","  inflating: hymenoptera_data/train/ants/hormiga_co_por.jpg  \n","  inflating: hymenoptera_data/train/ants/imageNotFound.gif  \n","  inflating: hymenoptera_data/train/ants/kurokusa.jpg  \n","  inflating: hymenoptera_data/train/ants/MehdiabadiAnt2_600.jpg  \n","  inflating: hymenoptera_data/train/ants/Nepenthes_rafflesiana_ant.jpg  \n","  inflating: hymenoptera_data/train/ants/swiss-army-ant.jpg  \n","  inflating: hymenoptera_data/train/ants/termite-vs-ant.jpg  \n","  inflating: hymenoptera_data/train/ants/trap-jaw-ant-insect-bg.jpg  \n","  inflating: hymenoptera_data/train/ants/VietnameseAntMimicSpider.jpg  \n","   creating: hymenoptera_data/train/bees/\n","  inflating: hymenoptera_data/train/bees/1092977343_cb42b38d62.jpg  \n","  inflating: hymenoptera_data/train/bees/1093831624_fb5fbe2308.jpg  \n","  inflating: hymenoptera_data/train/bees/1097045929_1753d1c765.jpg  \n","  inflating: hymenoptera_data/train/bees/1232245714_f862fbe385.jpg  \n","  inflating: hymenoptera_data/train/bees/129236073_0985e91c7d.jpg  \n","  inflating: hymenoptera_data/train/bees/1295655112_7813f37d21.jpg  \n","  inflating: hymenoptera_data/train/bees/132511197_0b86ad0fff.jpg  \n","  inflating: hymenoptera_data/train/bees/132826773_dbbcb117b9.jpg  \n","  inflating: hymenoptera_data/train/bees/150013791_969d9a968b.jpg  \n","  inflating: hymenoptera_data/train/bees/1508176360_2972117c9d.jpg  \n","  inflating: hymenoptera_data/train/bees/154600396_53e1252e52.jpg  \n","  inflating: hymenoptera_data/train/bees/16838648_415acd9e3f.jpg  \n","  inflating: hymenoptera_data/train/bees/1691282715_0addfdf5e8.jpg  \n","  inflating: hymenoptera_data/train/bees/17209602_fe5a5a746f.jpg  \n","  inflating: hymenoptera_data/train/bees/174142798_e5ad6d76e0.jpg  \n","  inflating: hymenoptera_data/train/bees/1799726602_8580867f71.jpg  \n","  inflating: hymenoptera_data/train/bees/1807583459_4fe92b3133.jpg  \n","  inflating: hymenoptera_data/train/bees/196430254_46bd129ae7.jpg  \n","  inflating: hymenoptera_data/train/bees/196658222_3fffd79c67.jpg  \n","  inflating: hymenoptera_data/train/bees/198508668_97d818b6c4.jpg  \n","  inflating: hymenoptera_data/train/bees/2031225713_50ed499635.jpg  \n","  inflating: hymenoptera_data/train/bees/2037437624_2d7bce461f.jpg  \n","  inflating: hymenoptera_data/train/bees/2053200300_8911ef438a.jpg  \n","  inflating: hymenoptera_data/train/bees/205835650_e6f2614bee.jpg  \n","  inflating: hymenoptera_data/train/bees/208702903_42fb4d9748.jpg  \n","  inflating: hymenoptera_data/train/bees/21399619_3e61e5bb6f.jpg  \n","  inflating: hymenoptera_data/train/bees/2227611847_ec72d40403.jpg  \n","  inflating: hymenoptera_data/train/bees/2321139806_d73d899e66.jpg  \n","  inflating: hymenoptera_data/train/bees/2330918208_8074770c20.jpg  \n","  inflating: hymenoptera_data/train/bees/2345177635_caf07159b3.jpg  \n","  inflating: hymenoptera_data/train/bees/2358061370_9daabbd9ac.jpg  \n","  inflating: hymenoptera_data/train/bees/2364597044_3c3e3fc391.jpg  \n","  inflating: hymenoptera_data/train/bees/2384149906_2cd8b0b699.jpg  \n","  inflating: hymenoptera_data/train/bees/2397446847_04ef3cd3e1.jpg  \n","  inflating: hymenoptera_data/train/bees/2405441001_b06c36fa72.jpg  \n","  inflating: hymenoptera_data/train/bees/2445215254_51698ff797.jpg  \n","  inflating: hymenoptera_data/train/bees/2452236943_255bfd9e58.jpg  \n","  inflating: hymenoptera_data/train/bees/2467959963_a7831e9ff0.jpg  \n","  inflating: hymenoptera_data/train/bees/2470492904_837e97800d.jpg  \n","  inflating: hymenoptera_data/train/bees/2477324698_3d4b1b1cab.jpg  \n","  inflating: hymenoptera_data/train/bees/2477349551_e75c97cf4d.jpg  \n","  inflating: hymenoptera_data/train/bees/2486729079_62df0920be.jpg  \n","  inflating: hymenoptera_data/train/bees/2486746709_c43cec0e42.jpg  \n","  inflating: hymenoptera_data/train/bees/2493379287_4100e1dacc.jpg  \n","  inflating: hymenoptera_data/train/bees/2495722465_879acf9d85.jpg  \n","  inflating: hymenoptera_data/train/bees/2528444139_fa728b0f5b.jpg  \n","  inflating: hymenoptera_data/train/bees/2538361678_9da84b77e3.jpg  \n","  inflating: hymenoptera_data/train/bees/2551813042_8a070aeb2b.jpg  \n","  inflating: hymenoptera_data/train/bees/2580598377_a4caecdb54.jpg  \n","  inflating: hymenoptera_data/train/bees/2601176055_8464e6aa71.jpg  \n","  inflating: hymenoptera_data/train/bees/2610833167_79bf0bcae5.jpg  \n","  inflating: hymenoptera_data/train/bees/2610838525_fe8e3cae47.jpg  \n","  inflating: hymenoptera_data/train/bees/2617161745_fa3ebe85b4.jpg  \n","  inflating: hymenoptera_data/train/bees/2625499656_e3415e374d.jpg  \n","  inflating: hymenoptera_data/train/bees/2634617358_f32fd16bea.jpg  \n","  inflating: hymenoptera_data/train/bees/2638074627_6b3ae746a0.jpg  \n","  inflating: hymenoptera_data/train/bees/2645107662_b73a8595cc.jpg  \n","  inflating: hymenoptera_data/train/bees/2651621464_a2fa8722eb.jpg  \n","  inflating: hymenoptera_data/train/bees/2652877533_a564830cbf.jpg  \n","  inflating: hymenoptera_data/train/bees/266644509_d30bb16a1b.jpg  \n","  inflating: hymenoptera_data/train/bees/2683605182_9d2a0c66cf.jpg  \n","  inflating: hymenoptera_data/train/bees/2704348794_eb5d5178c2.jpg  \n","  inflating: hymenoptera_data/train/bees/2707440199_cd170bd512.jpg  \n","  inflating: hymenoptera_data/train/bees/2710368626_cb42882dc8.jpg  \n","  inflating: hymenoptera_data/train/bees/2722592222_258d473e17.jpg  \n","  inflating: hymenoptera_data/train/bees/2728759455_ce9bb8cd7a.jpg  \n","  inflating: hymenoptera_data/train/bees/2756397428_1d82a08807.jpg  \n","  inflating: hymenoptera_data/train/bees/2765347790_da6cf6cb40.jpg  \n","  inflating: hymenoptera_data/train/bees/2781170484_5d61835d63.jpg  \n","  inflating: hymenoptera_data/train/bees/279113587_b4843db199.jpg  \n","  inflating: hymenoptera_data/train/bees/2792000093_e8ae0718cf.jpg  \n","  inflating: hymenoptera_data/train/bees/2801728106_833798c909.jpg  \n","  inflating: hymenoptera_data/train/bees/2822388965_f6dca2a275.jpg  \n","  inflating: hymenoptera_data/train/bees/2861002136_52c7c6f708.jpg  \n","  inflating: hymenoptera_data/train/bees/2908916142_a7ac8b57a8.jpg  \n","  inflating: hymenoptera_data/train/bees/29494643_e3410f0d37.jpg  \n","  inflating: hymenoptera_data/train/bees/2959730355_416a18c63c.jpg  \n","  inflating: hymenoptera_data/train/bees/2962405283_22718d9617.jpg  \n","  inflating: hymenoptera_data/train/bees/3006264892_30e9cced70.jpg  \n","  inflating: hymenoptera_data/train/bees/3030189811_01d095b793.jpg  \n","  inflating: hymenoptera_data/train/bees/3030772428_8578335616.jpg  \n","  inflating: hymenoptera_data/train/bees/3044402684_3853071a87.jpg  \n","  inflating: hymenoptera_data/train/bees/3074585407_9854eb3153.jpg  \n","  inflating: hymenoptera_data/train/bees/3079610310_ac2d0ae7bc.jpg  \n","  inflating: hymenoptera_data/train/bees/3090975720_71f12e6de4.jpg  \n","  inflating: hymenoptera_data/train/bees/3100226504_c0d4f1e3f1.jpg  \n","  inflating: hymenoptera_data/train/bees/342758693_c56b89b6b6.jpg  \n","  inflating: hymenoptera_data/train/bees/354167719_22dca13752.jpg  \n","  inflating: hymenoptera_data/train/bees/359928878_b3b418c728.jpg  \n","  inflating: hymenoptera_data/train/bees/365759866_b15700c59b.jpg  \n","  inflating: hymenoptera_data/train/bees/36900412_92b81831ad.jpg  \n","  inflating: hymenoptera_data/train/bees/39672681_1302d204d1.jpg  \n","  inflating: hymenoptera_data/train/bees/39747887_42df2855ee.jpg  \n","  inflating: hymenoptera_data/train/bees/421515404_e87569fd8b.jpg  \n","  inflating: hymenoptera_data/train/bees/444532809_9e931e2279.jpg  \n","  inflating: hymenoptera_data/train/bees/446296270_d9e8b93ecf.jpg  \n","  inflating: hymenoptera_data/train/bees/452462677_7be43af8ff.jpg  \n","  inflating: hymenoptera_data/train/bees/452462695_40a4e5b559.jpg  \n","  inflating: hymenoptera_data/train/bees/457457145_5f86eb7e9c.jpg  \n","  inflating: hymenoptera_data/train/bees/465133211_80e0c27f60.jpg  \n","  inflating: hymenoptera_data/train/bees/469333327_358ba8fe8a.jpg  \n","  inflating: hymenoptera_data/train/bees/472288710_2abee16fa0.jpg  \n","  inflating: hymenoptera_data/train/bees/473618094_8ffdcab215.jpg  \n","  inflating: hymenoptera_data/train/bees/476347960_52edd72b06.jpg  \n","  inflating: hymenoptera_data/train/bees/478701318_bbd5e557b8.jpg  \n","  inflating: hymenoptera_data/train/bees/507288830_f46e8d4cb2.jpg  \n","  inflating: hymenoptera_data/train/bees/509247772_2db2d01374.jpg  \n","  inflating: hymenoptera_data/train/bees/513545352_fd3e7c7c5d.jpg  \n","  inflating: hymenoptera_data/train/bees/522104315_5d3cb2758e.jpg  \n","  inflating: hymenoptera_data/train/bees/537309131_532bfa59ea.jpg  \n","  inflating: hymenoptera_data/train/bees/586041248_3032e277a9.jpg  \n","  inflating: hymenoptera_data/train/bees/760526046_547e8b381f.jpg  \n","  inflating: hymenoptera_data/train/bees/760568592_45a52c847f.jpg  \n","  inflating: hymenoptera_data/train/bees/774440991_63a4aa0cbe.jpg  \n","  inflating: hymenoptera_data/train/bees/85112639_6e860b0469.jpg  \n","  inflating: hymenoptera_data/train/bees/873076652_eb098dab2d.jpg  \n","  inflating: hymenoptera_data/train/bees/90179376_abc234e5f4.jpg  \n","  inflating: hymenoptera_data/train/bees/92663402_37f379e57a.jpg  \n","  inflating: hymenoptera_data/train/bees/95238259_98470c5b10.jpg  \n","  inflating: hymenoptera_data/train/bees/969455125_58c797ef17.jpg  \n","  inflating: hymenoptera_data/train/bees/98391118_bdb1e80cce.jpg  \n","   creating: hymenoptera_data/val/\n","   creating: hymenoptera_data/val/ants/\n","  inflating: hymenoptera_data/val/ants/10308379_1b6c72e180.jpg  \n","  inflating: hymenoptera_data/val/ants/1053149811_f62a3410d3.jpg  \n","  inflating: hymenoptera_data/val/ants/1073564163_225a64f170.jpg  \n","  inflating: hymenoptera_data/val/ants/1119630822_cd325ea21a.jpg  \n","  inflating: hymenoptera_data/val/ants/1124525276_816a07c17f.jpg  \n","  inflating: hymenoptera_data/val/ants/11381045_b352a47d8c.jpg  \n","  inflating: hymenoptera_data/val/ants/119785936_dd428e40c3.jpg  \n","  inflating: hymenoptera_data/val/ants/1247887232_edcb61246c.jpg  \n","  inflating: hymenoptera_data/val/ants/1262751255_c56c042b7b.jpg  \n","  inflating: hymenoptera_data/val/ants/1337725712_2eb53cd742.jpg  \n","  inflating: hymenoptera_data/val/ants/1358854066_5ad8015f7f.jpg  \n","  inflating: hymenoptera_data/val/ants/1440002809_b268d9a66a.jpg  \n","  inflating: hymenoptera_data/val/ants/147542264_79506478c2.jpg  \n","  inflating: hymenoptera_data/val/ants/152286280_411648ec27.jpg  \n","  inflating: hymenoptera_data/val/ants/153320619_2aeb5fa0ee.jpg  \n","  inflating: hymenoptera_data/val/ants/153783656_85f9c3ac70.jpg  \n","  inflating: hymenoptera_data/val/ants/157401988_d0564a9d02.jpg  \n","  inflating: hymenoptera_data/val/ants/159515240_d5981e20d1.jpg  \n","  inflating: hymenoptera_data/val/ants/161076144_124db762d6.jpg  \n","  inflating: hymenoptera_data/val/ants/161292361_c16e0bf57a.jpg  \n","  inflating: hymenoptera_data/val/ants/170652283_ecdaff5d1a.jpg  \n","  inflating: hymenoptera_data/val/ants/17081114_79b9a27724.jpg  \n","  inflating: hymenoptera_data/val/ants/172772109_d0a8e15fb0.jpg  \n","  inflating: hymenoptera_data/val/ants/1743840368_b5ccda82b7.jpg  \n","  inflating: hymenoptera_data/val/ants/181942028_961261ef48.jpg  \n","  inflating: hymenoptera_data/val/ants/183260961_64ab754c97.jpg  \n","  inflating: hymenoptera_data/val/ants/2039585088_c6f47c592e.jpg  \n","  inflating: hymenoptera_data/val/ants/205398178_c395c5e460.jpg  \n","  inflating: hymenoptera_data/val/ants/208072188_f293096296.jpg  \n","  inflating: hymenoptera_data/val/ants/209615353_eeb38ba204.jpg  \n","  inflating: hymenoptera_data/val/ants/2104709400_8831b4fc6f.jpg  \n","  inflating: hymenoptera_data/val/ants/212100470_b485e7b7b9.jpg  \n","  inflating: hymenoptera_data/val/ants/2127908701_d49dc83c97.jpg  \n","  inflating: hymenoptera_data/val/ants/2191997003_379df31291.jpg  \n","  inflating: hymenoptera_data/val/ants/2211974567_ee4606b493.jpg  \n","  inflating: hymenoptera_data/val/ants/2219621907_47bc7cc6b0.jpg  \n","  inflating: hymenoptera_data/val/ants/2238242353_52c82441df.jpg  \n","  inflating: hymenoptera_data/val/ants/2255445811_dabcdf7258.jpg  \n","  inflating: hymenoptera_data/val/ants/239161491_86ac23b0a3.jpg  \n","  inflating: hymenoptera_data/val/ants/263615709_cfb28f6b8e.jpg  \n","  inflating: hymenoptera_data/val/ants/308196310_1db5ffa01b.jpg  \n","  inflating: hymenoptera_data/val/ants/319494379_648fb5a1c6.jpg  \n","  inflating: hymenoptera_data/val/ants/35558229_1fa4608a7a.jpg  \n","  inflating: hymenoptera_data/val/ants/412436937_4c2378efc2.jpg  \n","  inflating: hymenoptera_data/val/ants/436944325_d4925a38c7.jpg  \n","  inflating: hymenoptera_data/val/ants/445356866_6cb3289067.jpg  \n","  inflating: hymenoptera_data/val/ants/459442412_412fecf3fe.jpg  \n","  inflating: hymenoptera_data/val/ants/470127071_8b8ee2bd74.jpg  \n","  inflating: hymenoptera_data/val/ants/477437164_bc3e6e594a.jpg  \n","  inflating: hymenoptera_data/val/ants/488272201_c5aa281348.jpg  \n","  inflating: hymenoptera_data/val/ants/502717153_3e4865621a.jpg  \n","  inflating: hymenoptera_data/val/ants/518746016_bcc28f8b5b.jpg  \n","  inflating: hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg  \n","  inflating: hymenoptera_data/val/ants/562589509_7e55469b97.jpg  \n","  inflating: hymenoptera_data/val/ants/57264437_a19006872f.jpg  \n","  inflating: hymenoptera_data/val/ants/573151833_ebbc274b77.jpg  \n","  inflating: hymenoptera_data/val/ants/649407494_9b6bc4949f.jpg  \n","  inflating: hymenoptera_data/val/ants/751649788_78dd7d16ce.jpg  \n","  inflating: hymenoptera_data/val/ants/768870506_8f115d3d37.jpg  \n","  inflating: hymenoptera_data/val/ants/800px-Meat_eater_ant_qeen_excavating_hole.jpg  \n","  inflating: hymenoptera_data/val/ants/8124241_36b290d372.jpg  \n","  inflating: hymenoptera_data/val/ants/8398478_50ef10c47a.jpg  \n","  inflating: hymenoptera_data/val/ants/854534770_31f6156383.jpg  \n","  inflating: hymenoptera_data/val/ants/892676922_4ab37dce07.jpg  \n","  inflating: hymenoptera_data/val/ants/94999827_36895faade.jpg  \n","  inflating: hymenoptera_data/val/ants/Ant-1818.jpg  \n","  inflating: hymenoptera_data/val/ants/ants-devouring-remains-of-large-dead-insect-on-red-tile-in-Stellenbosch-South-Africa-closeup-1-DHD.jpg  \n","  inflating: hymenoptera_data/val/ants/desert_ant.jpg  \n","  inflating: hymenoptera_data/val/ants/F.pergan.28(f).jpg  \n","  inflating: hymenoptera_data/val/ants/Hormiga.jpg  \n","   creating: hymenoptera_data/val/bees/\n","  inflating: hymenoptera_data/val/bees/1032546534_06907fe3b3.jpg  \n","  inflating: hymenoptera_data/val/bees/10870992_eebeeb3a12.jpg  \n","  inflating: hymenoptera_data/val/bees/1181173278_23c36fac71.jpg  \n","  inflating: hymenoptera_data/val/bees/1297972485_33266a18d9.jpg  \n","  inflating: hymenoptera_data/val/bees/1328423762_f7a88a8451.jpg  \n","  inflating: hymenoptera_data/val/bees/1355974687_1341c1face.jpg  \n","  inflating: hymenoptera_data/val/bees/144098310_a4176fd54d.jpg  \n","  inflating: hymenoptera_data/val/bees/1486120850_490388f84b.jpg  \n","  inflating: hymenoptera_data/val/bees/149973093_da3c446268.jpg  \n","  inflating: hymenoptera_data/val/bees/151594775_ee7dc17b60.jpg  \n","  inflating: hymenoptera_data/val/bees/151603988_2c6f7d14c7.jpg  \n","  inflating: hymenoptera_data/val/bees/1519368889_4270261ee3.jpg  \n","  inflating: hymenoptera_data/val/bees/152789693_220b003452.jpg  \n","  inflating: hymenoptera_data/val/bees/177677657_a38c97e572.jpg  \n","  inflating: hymenoptera_data/val/bees/1799729694_0c40101071.jpg  \n","  inflating: hymenoptera_data/val/bees/181171681_c5a1a82ded.jpg  \n","  inflating: hymenoptera_data/val/bees/187130242_4593a4c610.jpg  \n","  inflating: hymenoptera_data/val/bees/203868383_0fcbb48278.jpg  \n","  inflating: hymenoptera_data/val/bees/2060668999_e11edb10d0.jpg  \n","  inflating: hymenoptera_data/val/bees/2086294791_6f3789d8a6.jpg  \n","  inflating: hymenoptera_data/val/bees/2103637821_8d26ee6b90.jpg  \n","  inflating: hymenoptera_data/val/bees/2104135106_a65eede1de.jpg  \n","  inflating: hymenoptera_data/val/bees/215512424_687e1e0821.jpg  \n","  inflating: hymenoptera_data/val/bees/2173503984_9c6aaaa7e2.jpg  \n","  inflating: hymenoptera_data/val/bees/220376539_20567395d8.jpg  \n","  inflating: hymenoptera_data/val/bees/224841383_d050f5f510.jpg  \n","  inflating: hymenoptera_data/val/bees/2321144482_f3785ba7b2.jpg  \n","  inflating: hymenoptera_data/val/bees/238161922_55fa9a76ae.jpg  \n","  inflating: hymenoptera_data/val/bees/2407809945_fb525ef54d.jpg  \n","  inflating: hymenoptera_data/val/bees/2415414155_1916f03b42.jpg  \n","  inflating: hymenoptera_data/val/bees/2438480600_40a1249879.jpg  \n","  inflating: hymenoptera_data/val/bees/2444778727_4b781ac424.jpg  \n","  inflating: hymenoptera_data/val/bees/2457841282_7867f16639.jpg  \n","  inflating: hymenoptera_data/val/bees/2470492902_3572c90f75.jpg  \n","  inflating: hymenoptera_data/val/bees/2478216347_535c8fe6d7.jpg  \n","  inflating: hymenoptera_data/val/bees/2501530886_e20952b97d.jpg  \n","  inflating: hymenoptera_data/val/bees/2506114833_90a41c5267.jpg  \n","  inflating: hymenoptera_data/val/bees/2509402554_31821cb0b6.jpg  \n","  inflating: hymenoptera_data/val/bees/2525379273_dcb26a516d.jpg  \n","  inflating: hymenoptera_data/val/bees/26589803_5ba7000313.jpg  \n","  inflating: hymenoptera_data/val/bees/2668391343_45e272cd07.jpg  \n","  inflating: hymenoptera_data/val/bees/2670536155_c170f49cd0.jpg  \n","  inflating: hymenoptera_data/val/bees/2685605303_9eed79d59d.jpg  \n","  inflating: hymenoptera_data/val/bees/2702408468_d9ed795f4f.jpg  \n","  inflating: hymenoptera_data/val/bees/2709775832_85b4b50a57.jpg  \n","  inflating: hymenoptera_data/val/bees/2717418782_bd83307d9f.jpg  \n","  inflating: hymenoptera_data/val/bees/272986700_d4d4bf8c4b.jpg  \n","  inflating: hymenoptera_data/val/bees/2741763055_9a7bb00802.jpg  \n","  inflating: hymenoptera_data/val/bees/2745389517_250a397f31.jpg  \n","  inflating: hymenoptera_data/val/bees/2751836205_6f7b5eff30.jpg  \n","  inflating: hymenoptera_data/val/bees/2782079948_8d4e94a826.jpg  \n","  inflating: hymenoptera_data/val/bees/2809496124_5f25b5946a.jpg  \n","  inflating: hymenoptera_data/val/bees/2815838190_0a9889d995.jpg  \n","  inflating: hymenoptera_data/val/bees/2841437312_789699c740.jpg  \n","  inflating: hymenoptera_data/val/bees/2883093452_7e3a1eb53f.jpg  \n","  inflating: hymenoptera_data/val/bees/290082189_f66cb80bfc.jpg  \n","  inflating: hymenoptera_data/val/bees/296565463_d07a7bed96.jpg  \n","  inflating: hymenoptera_data/val/bees/3077452620_548c79fda0.jpg  \n","  inflating: hymenoptera_data/val/bees/348291597_ee836fbb1a.jpg  \n","  inflating: hymenoptera_data/val/bees/350436573_41f4ecb6c8.jpg  \n","  inflating: hymenoptera_data/val/bees/353266603_d3eac7e9a0.jpg  \n","  inflating: hymenoptera_data/val/bees/372228424_16da1f8884.jpg  \n","  inflating: hymenoptera_data/val/bees/400262091_701c00031c.jpg  \n","  inflating: hymenoptera_data/val/bees/416144384_961c326481.jpg  \n","  inflating: hymenoptera_data/val/bees/44105569_16720a960c.jpg  \n","  inflating: hymenoptera_data/val/bees/456097971_860949c4fc.jpg  \n","  inflating: hymenoptera_data/val/bees/464594019_1b24a28bb1.jpg  \n","  inflating: hymenoptera_data/val/bees/485743562_d8cc6b8f73.jpg  \n","  inflating: hymenoptera_data/val/bees/540976476_844950623f.jpg  \n","  inflating: hymenoptera_data/val/bees/54736755_c057723f64.jpg  \n","  inflating: hymenoptera_data/val/bees/57459255_752774f1b2.jpg  \n","  inflating: hymenoptera_data/val/bees/576452297_897023f002.jpg  \n","  inflating: hymenoptera_data/val/bees/586474709_ae436da045.jpg  \n","  inflating: hymenoptera_data/val/bees/590318879_68cf112861.jpg  \n","  inflating: hymenoptera_data/val/bees/59798110_2b6a3c8031.jpg  \n","  inflating: hymenoptera_data/val/bees/603709866_a97c7cfc72.jpg  \n","  inflating: hymenoptera_data/val/bees/603711658_4c8cd2201e.jpg  \n","  inflating: hymenoptera_data/val/bees/65038344_52a45d090d.jpg  \n","  inflating: hymenoptera_data/val/bees/6a00d8341c630a53ef00e553d0beb18834-800wi.jpg  \n","  inflating: hymenoptera_data/val/bees/72100438_73de9f17af.jpg  \n","  inflating: hymenoptera_data/val/bees/759745145_e8bc776ec8.jpg  \n","  inflating: hymenoptera_data/val/bees/936182217_c4caa5222d.jpg  \n","  inflating: hymenoptera_data/val/bees/abeja.jpg  \n"]}]},{"cell_type":"code","execution_count":3,"id":"be2d31f5","metadata":{"id":"be2d31f5","colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"status":"ok","timestamp":1701422800097,"user_tz":-60,"elapsed":1144,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"5e3a2d09-e1fa-429a-a7f3-83b363bbed6a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import os\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","import torchvision\n","from torchvision import datasets, transforms\n","\n","# Data augmentation and normalization for training\n","# Just normalization for validation\n","data_transforms = {\n","    \"train\": transforms.Compose(\n","        [\n","            transforms.RandomResizedCrop(\n","                224\n","            ),  # ImageNet models were trained on 224x224 images\n","            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n","            transforms.ToTensor(),  # convert it to a PyTorch tensor\n","            transforms.Normalize(\n","                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n","            ),  # ImageNet models expect this norm\n","        ]\n","    ),\n","    \"val\": transforms.Compose(\n","        [\n","            transforms.Resize(256),\n","            transforms.CenterCrop(224),\n","            transforms.ToTensor(),\n","            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","        ]\n","    ),\n","}\n","\n","data_dir = \"hymenoptera_data\"\n","# Create train and validation datasets and loaders\n","image_datasets = {\n","    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n","    for x in [\"train\", \"val\"]\n","}\n","dataloaders = {\n","    x: torch.utils.data.DataLoader(\n","        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n","    )\n","    for x in [\"train\", \"val\"]\n","}\n","dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n","class_names = image_datasets[\"train\"].classes\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Helper function for displaying images\n","def imshow(inp, title=None):\n","    \"\"\"Imshow for Tensor.\"\"\"\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","\n","    # Un-normalize the images\n","    inp = std * inp + mean\n","    # Clip just in case\n","    inp = np.clip(inp, 0, 1)\n","    plt.imshow(inp)\n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.001)  # pause a bit so that plots are updated\n","    plt.show()\n","\n","\n","# Get a batch of training data\n","inputs, classes = next(iter(dataloaders[\"train\"]))\n","\n","# Make a grid from batch\n","out = torchvision.utils.make_grid(inputs)\n","\n","imshow(out, title=[class_names[x] for x in classes])\n","\n"]},{"cell_type":"markdown","id":"bbd48800","metadata":{"id":"bbd48800"},"source":["Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."]},{"cell_type":"code","execution_count":4,"id":"572d824c","metadata":{"id":"572d824c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701422847189,"user_tz":-60,"elapsed":45558,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"f534dbab-7494-4b0e-f8ef-ed09c4202ab5"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n","  warnings.warn(_create_warning_msg(\n","/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n","Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n","100%|██████████| 44.7M/44.7M [00:00<00:00, 145MB/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n","  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"]},{"output_type":"stream","name":"stdout","text":["train Loss: 0.6333 Acc: 0.6680\n","val Loss: 0.2039 Acc: 0.9346\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.4067 Acc: 0.8115\n","val Loss: 0.2551 Acc: 0.9150\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.4257 Acc: 0.8238\n","val Loss: 0.3287 Acc: 0.8627\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.4683 Acc: 0.7992\n","val Loss: 0.3911 Acc: 0.8301\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.3521 Acc: 0.8484\n","val Loss: 0.2014 Acc: 0.9216\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.5724 Acc: 0.7746\n","val Loss: 0.1985 Acc: 0.9412\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.4140 Acc: 0.8197\n","val Loss: 0.1855 Acc: 0.9477\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.2967 Acc: 0.8730\n","val Loss: 0.1749 Acc: 0.9542\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.4109 Acc: 0.8115\n","val Loss: 0.1694 Acc: 0.9542\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.3838 Acc: 0.8115\n","val Loss: 0.2089 Acc: 0.9412\n","\n","Training complete in 0m 39s\n","Best val Acc: 0.954248\n"]}],"source":["import copy\n","import os\n","import time\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torchvision\n","from torch.optim import lr_scheduler\n","from torchvision import datasets, transforms\n","\n","# Data augmentation and normalization for training\n","# Just normalization for validation\n","data_transforms = {\n","    \"train\": transforms.Compose(\n","        [\n","            transforms.RandomResizedCrop(\n","                224\n","            ),  # ImageNet models were trained on 224x224 images\n","            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n","            transforms.ToTensor(),  # convert it to a PyTorch tensor\n","            transforms.Normalize(\n","                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n","            ),  # ImageNet models expect this norm\n","        ]\n","    ),\n","    \"val\": transforms.Compose(\n","        [\n","            transforms.Resize(256),\n","            transforms.CenterCrop(224),\n","            transforms.ToTensor(),\n","            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","        ]\n","    ),\n","}\n","\n","data_dir = \"hymenoptera_data\"\n","# Create train and validation datasets and loaders\n","image_datasets = {\n","    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n","    for x in [\"train\", \"val\"]\n","}\n","dataloaders = {\n","    x: torch.utils.data.DataLoader(\n","        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n","    )\n","    for x in [\"train\", \"val\"]\n","}\n","dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n","class_names = image_datasets[\"train\"].classes\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Helper function for displaying images\n","def imshow(inp, title=None):\n","    \"\"\"Imshow for Tensor.\"\"\"\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","\n","    # Un-normalize the images\n","    inp = std * inp + mean\n","    # Clip just in case\n","    inp = np.clip(inp, 0, 1)\n","    plt.imshow(inp)\n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.001)  # pause a bit so that plots are updated\n","    plt.show()\n","\n","\n","# Get a batch of training data\n","# inputs, classes = next(iter(dataloaders['train']))\n","\n","# Make a grid from batch\n","# out = torchvision.utils.make_grid(inputs)\n","\n","# imshow(out, title=[class_names[x] for x in classes])\n","# training\n","\n","\n","def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n","    since = time.time()\n","\n","    best_model_wts = copy.deepcopy(model.state_dict())\n","    best_acc = 0.0\n","\n","    epoch_time = []  # we'll keep track of the time needed for each epoch\n","\n","    for epoch in range(num_epochs):\n","        epoch_start = time.time()\n","        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n","        print(\"-\" * 10)\n","\n","        # Each epoch has a training and validation phase\n","        for phase in [\"train\", \"val\"]:\n","            if phase == \"train\":\n","                scheduler.step()\n","                model.train()  # Set model to training mode\n","            else:\n","                model.eval()  # Set model to evaluate mode\n","\n","            running_loss = 0.0\n","            running_corrects = 0\n","\n","            # Iterate over data.\n","            for inputs, labels in dataloaders[phase]:\n","                inputs = inputs.to(device)\n","                labels = labels.to(device)\n","\n","                # zero the parameter gradients\n","                optimizer.zero_grad()\n","\n","                # Forward\n","                # Track history if only in training phase\n","                with torch.set_grad_enabled(phase == \"train\"):\n","                    outputs = model(inputs)\n","                    _, preds = torch.max(outputs, 1)\n","                    loss = criterion(outputs, labels)\n","\n","                    # backward + optimize only if in training phase\n","                    if phase == \"train\":\n","                        loss.backward()\n","                        optimizer.step()\n","\n","                # Statistics\n","                running_loss += loss.item() * inputs.size(0)\n","                running_corrects += torch.sum(preds == labels.data)\n","\n","            epoch_loss = running_loss / dataset_sizes[phase]\n","            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n","\n","            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n","\n","            # Deep copy the model\n","            if phase == \"val\" and epoch_acc > best_acc:\n","                best_acc = epoch_acc\n","                best_model_wts = copy.deepcopy(model.state_dict())\n","\n","        # Add the epoch time\n","        t_epoch = time.time() - epoch_start\n","        epoch_time.append(t_epoch)\n","        print()\n","\n","    time_elapsed = time.time() - since\n","    print(\n","        \"Training complete in {:.0f}m {:.0f}s\".format(\n","            time_elapsed // 60, time_elapsed % 60\n","        )\n","    )\n","    print(\"Best val Acc: {:4f}\".format(best_acc))\n","\n","    # Load best model weights\n","    model.load_state_dict(best_model_wts)\n","    return model, epoch_time\n","\n","\n","# Download a pre-trained ResNet18 model and freeze its weights\n","model = torchvision.models.resnet18(pretrained=True)\n","for param in model.parameters():\n","    param.requires_grad = False\n","\n","# Replace the final fully connected layer\n","# Parameters of newly constructed modules have requires_grad=True by default\n","num_ftrs = model.fc.in_features\n","model.fc = nn.Linear(num_ftrs, 2)\n","# Send the model to the GPU\n","model = model.to(device)\n","# Set the loss function\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that only the parameters of the final layer are being optimized\n","optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n","model, epoch_time = train_model(\n","    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"ac-bvTMY-LkN"},"source":["Experiments:\n","Study the code and the results obtained.\n","\n","Modify the code and add an \"eval_model\" function to allow\n","the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n","\n","Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n","\n","Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."],"id":"ac-bvTMY-LkN"},{"cell_type":"code","source":["# Function to evaluate the accuracy of the model on a test folder of images from the internet\n","def eval_mode(model):\n","\n","    # track test loss\n","    test_loss = 0.0\n","    class_correct = list(0.0 for i in range(10))\n","    class_total = list(0.0 for i in range(10))\n","\n","    model.eval()\n","    # iterate over test data\n","    for data, target in test_loader:\n","        # forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # calculate the batch loss\n","        loss = criterion(output, target)\n","        # update test loss\n","        test_loss += loss.item() * data.size(0)\n","        # convert output probabilities to predicted class\n","        _, pred = torch.max(output, 1)\n","        # compare predictions to true label\n","        correct_tensor = pred.eq(target.data.view_as(pred))\n","        correct = (\n","            np.squeeze(correct_tensor.numpy())\n","            if not train_on_gpu\n","            else np.squeeze(correct_tensor.cpu().numpy())\n","        )\n","        # calculate test accuracy for each object class\n","        for i in range(batch_size):\n","            label = target.data[i]\n","            class_correct[label] += correct[i].item()\n","            class_total[label] += 1\n","\n","    # average test loss\n","    test_loss = test_loss / len(test_loader)\n","    print(f\"Test Loss: {test_loss:.6f}\\n\")\n","\n","    for i in range(10):\n","        if class_total[i] > 0:\n","            accuracy = 100 * class_correct[i] / class_total[i]\n","            print(f\"Test Accuracy of {classes[i]}: {accuracy:.2f}% \"\n","                  f\"({int(np.sum(class_correct[i]))}/{int(np.sum(class_total[i]))})\")\n","        else:\n","            print(f\"Test Accuracy of {classes[i]}: N/A (no training examples)\")\n","\n","    overall_accuracy = 100.0 * np.sum(class_correct) / np.sum(class_total)\n","    print(f\"\\nTest Accuracy (Overall): {overall_accuracy:.2f}% \"\n","          f\"({int(np.sum(class_correct))}/{int(np.sum(class_total))})\")"],"metadata":{"id":"9wj4N6we8DIQ","executionInfo":{"status":"ok","timestamp":1701422847190,"user_tz":-60,"elapsed":22,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}}},"id":"9wj4N6we8DIQ","execution_count":5,"outputs":[]},{"cell_type":"code","source":["# Get a pre-trained ResNet18 model\n","new_resNet18 = torchvision.models.resnet18(pretrained=True)\n","for param in new_resNet18.parameters():\n","    param.requires_grad = False\n","\n","new_resNet18.parameters = new_resNet18.parameters\n","\n","# First classification layer\n","in_features = new_resNet18.fc.in_features\n","out_features = 16\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","\n","# Second classification layer where we use a \"relu\" activation function for this middle layer\n","new_resNet18.fc2 = nn.Linear(out_features, 2)\n","def new_forward(self, x):\n","    x = self.forward(x)\n","    x = F.relu(self.fc2(self.drop(x)))\n","    return x\n","\n","# Set the loss function\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that only the parameters of the final layer are being optimized\n","optimizer_conv = optim.SGD(new_resNet18.fc.parameters(), lr=0.001, momentum=0.9)\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n","val_loss, train_loss, val_accuracy, train_accuracy = [], [], [], []\n","new_resNet18, epoch_time = train_model(\n","    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",")"],"metadata":{"id":"CU1Ot6rt8FdD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701423029527,"user_tz":-60,"elapsed":34613,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"2fb10135-1a41-47e9-b8fa-d13e61c38ac0"},"id":"CU1Ot6rt8FdD","execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n","train Loss: 0.2825 Acc: 0.8852\n","val Loss: 0.1934 Acc: 0.9412\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.3601 Acc: 0.8607\n","val Loss: 0.1822 Acc: 0.9477\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.4400 Acc: 0.8074\n","val Loss: 0.2581 Acc: 0.9150\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.3524 Acc: 0.8361\n","val Loss: 0.1873 Acc: 0.9346\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.3934 Acc: 0.8402\n","val Loss: 0.1931 Acc: 0.9412\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.2766 Acc: 0.8770\n","val Loss: 0.2002 Acc: 0.9412\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.3498 Acc: 0.8525\n","val Loss: 0.2010 Acc: 0.9412\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.3339 Acc: 0.8607\n","val Loss: 0.1672 Acc: 0.9477\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.3057 Acc: 0.8361\n","val Loss: 0.2184 Acc: 0.9412\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.4405 Acc: 0.8320\n","val Loss: 0.1864 Acc: 0.9412\n","\n","Training complete in 0m 34s\n","Best val Acc: 0.947712\n"]}]},{"cell_type":"code","source":["import torchvision.models as models\n","new_resNet18_quantized = torch.quantization.quantize_dynamic(new_resNet18, dtype=torch.qint8)\n","\n","size_resNet18 = print_size_of_model(new_resNet18, \"fp32\")\n","size_resNet18_quantized = print_size_of_model(new_resNet18_quantized, \"fp32\")\n","\n","print(\n","    f\"\\nThe size of the ResNet18 model is {size_resNet18 / 1000000:.2f} MB. \\nThe size of the Quantized ResNet18 model is {size_resNet18_quantized / 1000000:.2f} MB, which is {size_resNet18 / size_resNet18_quantized:.0f} times smaller than the ResNet18 model\"\n",")"],"metadata":{"id":"UTpZmkFJ8P11","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701423224431,"user_tz":-60,"elapsed":607,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d2e59a7c-390d-48b6-a086-e94944c47c28"},"id":"UTpZmkFJ8P11","execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["model:  fp32  \t Size (KB): 44782.148\n","model:  fp32  \t Size (KB): 44779.834\n","\n","The size of the ResNet18 model is 44.78 MB. \n","The size of the Quantized ResNet18 model is 44.78 MB, which is 1 times smaller than the ResNet18 model\n"]}]},{"cell_type":"markdown","id":"04a263f0","metadata":{"id":"04a263f0"},"source":["## Optional\n","    \n","Try this at home!!\n","\n","\n","Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n","\n","The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n","\n"]},{"cell_type":"markdown","id":"fe954ce4","metadata":{"id":"fe954ce4"},"source":["## Author\n","\n","Alberto BOSIO - Ph. D."]}],"metadata":{"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":"3.11.5"},"vscode":{"interpreter":{"hash":"9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"}},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
+{"cells":[{"cell_type":"markdown","id":"fbb8c8df","metadata":{"id":"fbb8c8df"},"source":["In this TD, you must modify this notebook to answer the questions. To do this,\n","\n","1. Fork this repository\n","2. Clone your forked repository on your local computer\n","3. Answer the questions\n","4. Commit and push regularly\n","\n","The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account."]},{"cell_type":"markdown","id":"3d167a29","metadata":{"id":"3d167a29"},"source":["Install and test PyTorch from  https://pytorch.org/get-started/locally."]},{"cell_type":"markdown","id":"7edf7168","metadata":{"id":"7edf7168"},"source":["# TD2: Deep learning"]},{"cell_type":"code","execution_count":null,"id":"330a42f5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"330a42f5","executionInfo":{"status":"error","timestamp":1701269008471,"user_tz":-60,"elapsed":5144,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"dcc4fa02-5623-4f54-a522-30f292347319"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"]},{"output_type":"stream","name":"stderr","text":["UsageError: Line magic function `%wget` not found.\n"]}],"source":["%pip install torch torchvision"]},{"cell_type":"markdown","id":"0882a636","metadata":{"id":"0882a636"},"source":["\n","To test run the following code"]},{"cell_type":"code","execution_count":null,"id":"b1950f0a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b1950f0a","executionInfo":{"status":"ok","timestamp":1701263807541,"user_tz":-60,"elapsed":1764,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"438e92af-9461-45dc-c8ba-baf4f39df465"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[ 0.8001, -3.1996,  0.8401, -0.4590,  0.0535,  1.3531,  0.6940, -0.5002,\n","         -2.4893, -0.2943],\n","        [-1.4480,  0.6830, -0.0291, -0.8080,  0.6988,  0.0612, -0.7034,  0.5975,\n","         -0.2097,  0.0544],\n","        [-0.5039,  0.3342, -0.5135,  0.5781, -0.2265,  0.1315,  1.6636, -0.1691,\n","         -0.0637,  0.4066],\n","        [ 1.3856,  1.4038,  0.5262, -0.3644, -1.2894,  0.7763,  0.3176, -0.5977,\n","         -0.8109, -0.2260],\n","        [-0.9714,  1.4755,  0.4159,  0.5655, -1.2068,  0.1483,  0.4998,  0.7127,\n","         -0.3208, -0.1878],\n","        [ 1.1300,  0.1293, -2.0233,  0.2644, -1.6500,  0.0594, -1.6955,  0.9623,\n","         -2.0099,  1.4013],\n","        [ 0.1372,  0.5833, -0.2481,  0.5644, -1.0033,  0.4947, -0.4332, -0.6983,\n","          0.2427,  1.1333],\n","        [ 0.5237, -0.4540,  0.3905, -1.3676,  0.1535, -0.8654,  1.1654, -0.3680,\n","          0.5602,  0.5605],\n","        [ 0.7205,  1.1636, -0.5012,  1.2403,  0.3021, -0.6127, -0.9504,  1.1685,\n","          0.0837, -0.5870],\n","        [ 0.1246, -1.0345, -0.2654, -0.4910, -0.0198, -0.2514, -0.0920, -0.6426,\n","          1.0792,  0.5414],\n","        [-0.0181,  0.5058,  0.5459,  0.4973,  0.3238, -0.8191,  1.1362,  0.5654,\n","         -1.7322, -0.6207],\n","        [-1.0556,  2.2030,  0.2627,  1.0543, -0.2510, -0.0635, -2.5471,  1.0420,\n","          1.0652,  0.3995],\n","        [ 0.8785, -0.0858,  0.3532, -0.0389,  1.1755, -1.7593,  0.5965,  0.0882,\n","          1.1826,  0.7950],\n","        [-1.6628, -0.1029, -0.0121, -1.1714,  0.3778,  1.4698, -0.0620,  0.2037,\n","         -0.8209, -0.5627]])\n","AlexNet(\n","  (features): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n","    (1): ReLU(inplace=True)\n","    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n","    (4): ReLU(inplace=True)\n","    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (7): ReLU(inplace=True)\n","    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (9): ReLU(inplace=True)\n","    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (11): ReLU(inplace=True)\n","    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n","  (classifier): Sequential(\n","    (0): Dropout(p=0.5, inplace=False)\n","    (1): Linear(in_features=9216, out_features=4096, bias=True)\n","    (2): ReLU(inplace=True)\n","    (3): Dropout(p=0.5, inplace=False)\n","    (4): Linear(in_features=4096, out_features=4096, bias=True)\n","    (5): ReLU(inplace=True)\n","    (6): Linear(in_features=4096, out_features=1000, bias=True)\n","  )\n",")\n"]}],"source":["import torch\n","\n","N, D = 14, 10\n","x = torch.randn(N, D).type(torch.FloatTensor)\n","print(x)\n","\n","from torchvision import models\n","\n","alexnet = models.alexnet()\n","print(alexnet)"]},{"cell_type":"markdown","id":"23f266da","metadata":{"id":"23f266da"},"source":["## Exercise 1: CNN on CIFAR10\n","\n","The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n","\n","Have a look at the following documentation to be familiar with PyTorch.\n","\n","https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n","\n","https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html"]},{"cell_type":"markdown","id":"4ba1c82d","metadata":{"id":"4ba1c82d"},"source":["You can test if GPU is available on your machine and thus train on it to speed up the process"]},{"cell_type":"code","execution_count":1,"id":"6e18f2fd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6e18f2fd","executionInfo":{"status":"ok","timestamp":1701424609863,"user_tz":-60,"elapsed":3277,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"e31874ac-bdb1-4425-dbfb-ea1d264e3ee1"},"outputs":[{"output_type":"stream","name":"stdout","text":["CUDA is available!  Training on GPU ...\n"]}],"source":["import torch\n","\n","# check if CUDA is available\n","train_on_gpu = torch.cuda.is_available()\n","\n","if not train_on_gpu:\n","    print(\"CUDA is not available.  Training on CPU ...\")\n","else:\n","    print(\"CUDA is available!  Training on GPU ...\")"]},{"cell_type":"markdown","id":"5cf214eb","metadata":{"id":"5cf214eb"},"source":["Next we load the CIFAR10 dataset"]},{"cell_type":"code","execution_count":2,"id":"462666a2","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"462666a2","executionInfo":{"status":"ok","timestamp":1701424623219,"user_tz":-60,"elapsed":12089,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"ad99dba0-2635-408f-94dd-15757f1f0fad"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 170498071/170498071 [00:04<00:00, 41433127.54it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting data/cifar-10-python.tar.gz to data\n","Files already downloaded and verified\n"]}],"source":["import numpy as np\n","from torchvision import datasets, transforms\n","from torch.utils.data.sampler import SubsetRandomSampler\n","\n","# number of subprocesses to use for data loading\n","num_workers = 0\n","# how many samples per batch to load\n","batch_size = 20\n","# percentage of training set to use as validation\n","valid_size = 0.2\n","\n","# convert data to a normalized torch.FloatTensor\n","transform = transforms.Compose(\n","    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",")\n","\n","# choose the training and test datasets\n","train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n","test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n","\n","# obtain training indices that will be used for validation\n","num_train = len(train_data)\n","indices = list(range(num_train))\n","np.random.shuffle(indices)\n","split = int(np.floor(valid_size * num_train))\n","train_idx, valid_idx = indices[split:], indices[:split]\n","\n","# define samplers for obtaining training and validation batches\n","train_sampler = SubsetRandomSampler(train_idx)\n","valid_sampler = SubsetRandomSampler(valid_idx)\n","\n","# prepare data loaders (combine dataset and sampler)\n","train_loader = torch.utils.data.DataLoader(\n","    train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",")\n","valid_loader = torch.utils.data.DataLoader(\n","    train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",")\n","test_loader = torch.utils.data.DataLoader(\n","    test_data, batch_size=batch_size, num_workers=num_workers\n",")\n","\n","# specify the image classes\n","classes = [\n","    \"airplane\",\n","    \"automobile\",\n","    \"bird\",\n","    \"cat\",\n","    \"deer\",\n","    \"dog\",\n","    \"frog\",\n","    \"horse\",\n","    \"ship\",\n","    \"truck\",\n","]"]},{"cell_type":"markdown","id":"58ec3903","metadata":{"id":"58ec3903"},"source":["CNN definition (this one is an example)"]},{"cell_type":"code","execution_count":null,"id":"317bf070","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"317bf070","executionInfo":{"status":"ok","timestamp":1701263851707,"user_tz":-60,"elapsed":6668,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"cecb36bc-27dd-4aae-ebc5-009a122e169b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Net(\n","  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n","  (fc1): Linear(in_features=400, out_features=120, bias=True)\n","  (fc2): Linear(in_features=120, out_features=84, bias=True)\n","  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",")\n"]}],"source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","# define the CNN architecture\n","\n","\n","class Net(nn.Module):\n","    def __init__(self):\n","        super(Net, self).__init__()\n","        self.conv1 = nn.Conv2d(3, 6, 5)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.conv2 = nn.Conv2d(6, 16, 5)\n","        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n","        self.fc2 = nn.Linear(120, 84)\n","        self.fc3 = nn.Linear(84, 10)\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = x.view(-1, 16 * 5 * 5)\n","        x = F.relu(self.fc1(x))\n","        x = F.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x\n","\n","\n","# create a complete CNN\n","model = Net()\n","print(model)\n","# move tensors to GPU if CUDA is available\n","if train_on_gpu:\n","    model.cuda()"]},{"cell_type":"markdown","id":"a2dc4974","metadata":{"id":"a2dc4974"},"source":["Loss function and training using SGD (Stochastic Gradient Descent) optimizer"]},{"cell_type":"code","execution_count":null,"id":"4b53f229","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4b53f229","executionInfo":{"status":"ok","timestamp":1701264436194,"user_tz":-60,"elapsed":569994,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"42ece0d7-d233-4f08-9935-40d2fcef166e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 44.612249 \tValidation Loss: 40.298942\n","Validation loss decreased (inf --> 40.298942).  Saving model ...\n","Epoch: 1 \tTraining Loss: 36.004778 \tValidation Loss: 33.401573\n","Validation loss decreased (40.298942 --> 33.401573).  Saving model ...\n","Epoch: 2 \tTraining Loss: 30.990529 \tValidation Loss: 29.245610\n","Validation loss decreased (33.401573 --> 29.245610).  Saving model ...\n","Epoch: 3 \tTraining Loss: 28.325317 \tValidation Loss: 26.954483\n","Validation loss decreased (29.245610 --> 26.954483).  Saving model ...\n","Epoch: 4 \tTraining Loss: 26.341247 \tValidation Loss: 26.349700\n","Validation loss decreased (26.954483 --> 26.349700).  Saving model ...\n","Epoch: 5 \tTraining Loss: 24.861439 \tValidation Loss: 24.664094\n","Validation loss decreased (26.349700 --> 24.664094).  Saving model ...\n","Epoch: 6 \tTraining Loss: 23.654918 \tValidation Loss: 23.904583\n","Validation loss decreased (24.664094 --> 23.904583).  Saving model ...\n","Epoch: 7 \tTraining Loss: 22.659880 \tValidation Loss: 24.153002\n","Epoch: 8 \tTraining Loss: 21.813652 \tValidation Loss: 22.728200\n","Validation loss decreased (23.904583 --> 22.728200).  Saving model ...\n","Epoch: 9 \tTraining Loss: 21.028281 \tValidation Loss: 22.683762\n","Validation loss decreased (22.728200 --> 22.683762).  Saving model ...\n","Epoch: 10 \tTraining Loss: 20.283682 \tValidation Loss: 22.527626\n","Validation loss decreased (22.683762 --> 22.527626).  Saving model ...\n","Epoch: 11 \tTraining Loss: 19.596292 \tValidation Loss: 22.082355\n","Validation loss decreased (22.527626 --> 22.082355).  Saving model ...\n","Epoch: 12 \tTraining Loss: 18.990277 \tValidation Loss: 22.173975\n","Epoch: 13 \tTraining Loss: 18.311255 \tValidation Loss: 21.511513\n","Validation loss decreased (22.082355 --> 21.511513).  Saving model ...\n","Epoch: 14 \tTraining Loss: 17.729348 \tValidation Loss: 21.373887\n","Validation loss decreased (21.511513 --> 21.373887).  Saving model ...\n","Epoch: 15 \tTraining Loss: 17.143107 \tValidation Loss: 21.404075\n","Epoch: 16 \tTraining Loss: 16.578313 \tValidation Loss: 22.213146\n","Epoch: 17 \tTraining Loss: 16.067654 \tValidation Loss: 21.753317\n","Epoch: 18 \tTraining Loss: 15.572635 \tValidation Loss: 23.228977\n","Epoch: 19 \tTraining Loss: 15.036218 \tValidation Loss: 22.608370\n","Epoch: 20 \tTraining Loss: 14.528461 \tValidation Loss: 22.057556\n","Epoch: 21 \tTraining Loss: 13.953359 \tValidation Loss: 23.037234\n","Epoch: 22 \tTraining Loss: 13.521695 \tValidation Loss: 23.248760\n","Epoch: 23 \tTraining Loss: 13.053585 \tValidation Loss: 23.488736\n","Epoch: 24 \tTraining Loss: 12.579523 \tValidation Loss: 23.827478\n","Epoch: 25 \tTraining Loss: 12.141763 \tValidation Loss: 24.365644\n","Epoch: 26 \tTraining Loss: 11.630654 \tValidation Loss: 24.792256\n","Epoch: 27 \tTraining Loss: 11.330323 \tValidation Loss: 25.310450\n","Epoch: 28 \tTraining Loss: 10.781678 \tValidation Loss: 25.629191\n","Epoch: 29 \tTraining Loss: 10.492249 \tValidation Loss: 26.488761\n"]}],"source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()  # specify loss function\n","optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n","\n","n_epochs = 30  # number of epochs to train the model\n","train_loss_list = []  # list to store loss to visualize\n","valid_loss_min = np.Inf  # track change in validation loss\n","\n","for epoch in range(n_epochs):\n","    # Keep track of training and validation loss\n","    train_loss = 0.0\n","    valid_loss = 0.0\n","\n","    # Train the model\n","    model.train()\n","    for data, target in train_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Clear the gradients of all optimized variables\n","        optimizer.zero_grad()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Backward pass: compute gradient of the loss with respect to model parameters\n","        loss.backward()\n","        # Perform a single optimization step (parameter update)\n","        optimizer.step()\n","        # Update training loss\n","        train_loss += loss.item() * data.size(0)\n","\n","    # Validate the model\n","    model.eval()\n","    for data, target in valid_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Update average validation loss\n","        valid_loss += loss.item() * data.size(0)\n","\n","    # Calculate average losses\n","    train_loss = train_loss / len(train_loader)\n","    valid_loss = valid_loss / len(valid_loader)\n","    train_loss_list.append(train_loss)\n","\n","    # Print training/validation statistics\n","    print(\n","        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n","            epoch, train_loss, valid_loss\n","        )\n","    )\n","\n","    # Save model if validation loss has decreased\n","    if valid_loss <= valid_loss_min:\n","        print(\n","            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n","                valid_loss_min, valid_loss\n","            )\n","        )\n","        torch.save(model.state_dict(), \"model_cifar.pt\")\n","        valid_loss_min = valid_loss"]},{"cell_type":"markdown","id":"13e1df74","metadata":{"id":"13e1df74"},"source":["Does overfit occur? If so, do an early stopping."]},{"cell_type":"code","execution_count":null,"id":"d39df818","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"d39df818","executionInfo":{"status":"ok","timestamp":1701264448133,"user_tz":-60,"elapsed":525,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d0c485cd-28cb-41c0-da4f-777701671915"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(n_epochs), train_loss_list)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Performance of Model 1\")\n","plt.show()"]},{"cell_type":"markdown","id":"11df8fd4","metadata":{"id":"11df8fd4"},"source":["Now loading the model with the lowest validation loss value\n"]},{"cell_type":"code","execution_count":null,"id":"e93efdfc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e93efdfc","executionInfo":{"status":"ok","timestamp":1701264460982,"user_tz":-60,"elapsed":3907,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"75f4d1f4-3dce-4323-8d8c-2112a97a81ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 21.447881\n","\n","Test Accuracy of airplane: 69% (699/1000)\n","Test Accuracy of automobile: 77% (776/1000)\n","Test Accuracy of  bird: 51% (511/1000)\n","Test Accuracy of   cat: 46% (460/1000)\n","Test Accuracy of  deer: 46% (460/1000)\n","Test Accuracy of   dog: 45% (459/1000)\n","Test Accuracy of  frog: 77% (774/1000)\n","Test Accuracy of horse: 66% (663/1000)\n","Test Accuracy of  ship: 79% (792/1000)\n","Test Accuracy of truck: 69% (699/1000)\n","\n","Test Accuracy (Overall): 62% (6293/10000)\n"]}],"source":["model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n","\n","# track test loss\n","test_loss = 0.0\n","class_correct = list(0.0 for i in range(10))\n","class_total = list(0.0 for i in range(10))\n","\n","model.eval()\n","# iterate over test data\n","for data, target in test_loader:\n","    # move tensors to GPU if CUDA is available\n","    if train_on_gpu:\n","        data, target = data.cuda(), target.cuda()\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = model(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = (\n","        np.squeeze(correct_tensor.numpy())\n","        if not train_on_gpu\n","        else np.squeeze(correct_tensor.cpu().numpy())\n","    )\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct[label] += correct[i].item()\n","        class_total[label] += 1\n","\n","# average test loss\n","test_loss = test_loss / len(test_loader)\n","print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")"]},{"cell_type":"markdown","id":"944991a2","metadata":{"id":"944991a2"},"source":["Build a new network with the following structure.\n","\n","- It has 3 convolutional layers of kernel size 3 and padding of 1.\n","- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n","- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n","- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n","- The first fully connected layer will have an output size of 512.\n","- The second fully connected layer will have an output size of 64.\n","\n","Compare the results obtained with this new network to those obtained previously."]},{"cell_type":"code","source":["# define the new CNN architecture\n","\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class Net_new(nn.Module):\n","    def __init__(self):\n","        super(Net_new, self).__init__()\n","        self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # Padding to prevent the output's dimension from changing\n","        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n","        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.fc1 = nn.Linear(64 * 4 * 4, 512) # Input size = nb of channels output on the last layer * pixel size of image (each MaxPool split by two)\n","        self.fc2 = nn.Linear(512, 64)\n","        self.fc3 = nn.Linear(64, 10)\n","        self.dropout = nn.Dropout()\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = self.pool(F.relu(self.conv3(x)))\n","        x = x.view(-1, 64 * 4 * 4)\n","        x = F.relu(self.fc1(x))\n","        x = self.dropout(x) # Helpful in preventing neuron co-adaptation\n","        x = F.relu(self.fc2(x))\n","        x = self.dropout(x)\n","        x = F.relu(self.fc3(x))\n","        return x\n","\n","# create a complete CNN\n","model_new = Net_new()\n","print(model_new)\n","# move tensors to GPU if CUDA is available\n","if train_on_gpu:\n","  model_new.cuda()"],"metadata":{"id":"gcRCs-iUEnaH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701264472424,"user_tz":-60,"elapsed":4,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"761a457c-f623-455a-97a3-5bc7bea1a48b"},"id":"gcRCs-iUEnaH","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Net_new(\n","  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n","  (fc2): Linear(in_features=512, out_features=64, bias=True)\n","  (fc3): Linear(in_features=64, out_features=10, bias=True)\n","  (dropout): Dropout(p=0.5, inplace=False)\n",")\n"]}]},{"cell_type":"code","source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()  # specify loss function\n","optimizer_new = optim.SGD(model_new.parameters(), lr=0.01)  # specify optimizer\n","\n","n_epochs = 30  # number of epochs to train the model\n","train_loss_list_new = []  # list to store loss to visualize\n","valid_loss_min = np.Inf  # track change in validation loss\n","\n","for epoch in range(n_epochs):\n","    # Keep track of training and validation loss\n","    train_loss = 0.0\n","    valid_loss = 0.0\n","\n","    # Train the model\n","    model_new.train()\n","    for data, target in train_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Clear the gradients of all optimized variables\n","        optimizer_new.zero_grad()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model_new(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Backward pass: compute gradient of the loss with respect to model parameters\n","        loss.backward()\n","        # Perform a single optimization step (parameter update)\n","        optimizer_new.step()\n","        # Update training loss\n","        train_loss += loss.item() * data.size(0)\n","\n","    # Validate the model\n","    model_new.eval()\n","    for data, target in valid_loader:\n","        # Move tensors to GPU if CUDA is available\n","        if train_on_gpu:\n","            data, target = data.cuda(), target.cuda()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model_new(data)\n","        # Calculate the batch loss\n","        loss = criterion(output, target)\n","        # Update average validation loss\n","        valid_loss += loss.item() * data.size(0)\n","\n","    # Calculate average losses\n","    train_loss = train_loss / len(train_loader)\n","    valid_loss = valid_loss / len(valid_loader)\n","    train_loss_list_new.append(train_loss)\n","\n","    # Print training/validation statistics\n","    print(\n","        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n","            epoch, train_loss, valid_loss\n","        )\n","    )\n","\n","    # Save model if validation loss has decreased\n","    if valid_loss <= valid_loss_min:\n","        print(\n","            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n","                valid_loss_min, valid_loss\n","            )\n","        )\n","        torch.save(model_new.state_dict(), \"model_new_cifar.pt\")\n","        valid_loss_min = valid_loss"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1mux8ZZi2vd7","executionInfo":{"status":"ok","timestamp":1701265066443,"user_tz":-60,"elapsed":582783,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b2bf2851-3aff-48fe-aa83-9c7c0c6124f7"},"id":"1mux8ZZi2vd7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 46.035736 \tValidation Loss: 45.976020\n","Validation loss decreased (inf --> 45.976020).  Saving model ...\n","Epoch: 1 \tTraining Loss: 45.187472 \tValidation Loss: 42.525370\n","Validation loss decreased (45.976020 --> 42.525370).  Saving model ...\n","Epoch: 2 \tTraining Loss: 40.269012 \tValidation Loss: 36.115885\n","Validation loss decreased (42.525370 --> 36.115885).  Saving model ...\n","Epoch: 3 \tTraining Loss: 35.383565 \tValidation Loss: 31.909517\n","Validation loss decreased (36.115885 --> 31.909517).  Saving model ...\n","Epoch: 4 \tTraining Loss: 32.746224 \tValidation Loss: 29.787075\n","Validation loss decreased (31.909517 --> 29.787075).  Saving model ...\n","Epoch: 5 \tTraining Loss: 30.653619 \tValidation Loss: 27.959262\n","Validation loss decreased (29.787075 --> 27.959262).  Saving model ...\n","Epoch: 6 \tTraining Loss: 28.983543 \tValidation Loss: 26.431537\n","Validation loss decreased (27.959262 --> 26.431537).  Saving model ...\n","Epoch: 7 \tTraining Loss: 27.683504 \tValidation Loss: 25.174931\n","Validation loss decreased (26.431537 --> 25.174931).  Saving model ...\n","Epoch: 8 \tTraining Loss: 26.336337 \tValidation Loss: 23.783314\n","Validation loss decreased (25.174931 --> 23.783314).  Saving model ...\n","Epoch: 9 \tTraining Loss: 24.991212 \tValidation Loss: 22.687754\n","Validation loss decreased (23.783314 --> 22.687754).  Saving model ...\n","Epoch: 10 \tTraining Loss: 23.787577 \tValidation Loss: 22.145078\n","Validation loss decreased (22.687754 --> 22.145078).  Saving model ...\n","Epoch: 11 \tTraining Loss: 22.818656 \tValidation Loss: 20.805044\n","Validation loss decreased (22.145078 --> 20.805044).  Saving model ...\n","Epoch: 12 \tTraining Loss: 21.811931 \tValidation Loss: 19.928644\n","Validation loss decreased (20.805044 --> 19.928644).  Saving model ...\n","Epoch: 13 \tTraining Loss: 20.853573 \tValidation Loss: 19.503793\n","Validation loss decreased (19.928644 --> 19.503793).  Saving model ...\n","Epoch: 14 \tTraining Loss: 20.078903 \tValidation Loss: 18.726372\n","Validation loss decreased (19.503793 --> 18.726372).  Saving model ...\n","Epoch: 15 \tTraining Loss: 19.173792 \tValidation Loss: 18.262609\n","Validation loss decreased (18.726372 --> 18.262609).  Saving model ...\n","Epoch: 16 \tTraining Loss: 18.500586 \tValidation Loss: 18.070592\n","Validation loss decreased (18.262609 --> 18.070592).  Saving model ...\n","Epoch: 17 \tTraining Loss: 17.639666 \tValidation Loss: 17.679802\n","Validation loss decreased (18.070592 --> 17.679802).  Saving model ...\n","Epoch: 18 \tTraining Loss: 17.082578 \tValidation Loss: 17.131204\n","Validation loss decreased (17.679802 --> 17.131204).  Saving model ...\n","Epoch: 19 \tTraining Loss: 16.418561 \tValidation Loss: 16.789966\n","Validation loss decreased (17.131204 --> 16.789966).  Saving model ...\n","Epoch: 20 \tTraining Loss: 15.737011 \tValidation Loss: 16.914572\n","Epoch: 21 \tTraining Loss: 15.217627 \tValidation Loss: 17.321893\n","Epoch: 22 \tTraining Loss: 14.692679 \tValidation Loss: 16.259236\n","Validation loss decreased (16.789966 --> 16.259236).  Saving model ...\n","Epoch: 23 \tTraining Loss: 14.104487 \tValidation Loss: 15.681182\n","Validation loss decreased (16.259236 --> 15.681182).  Saving model ...\n","Epoch: 24 \tTraining Loss: 13.509841 \tValidation Loss: 16.067594\n","Epoch: 25 \tTraining Loss: 13.031704 \tValidation Loss: 15.928080\n","Epoch: 26 \tTraining Loss: 12.543566 \tValidation Loss: 16.412866\n","Epoch: 27 \tTraining Loss: 12.077648 \tValidation Loss: 16.044644\n","Epoch: 28 \tTraining Loss: 11.713458 \tValidation Loss: 15.721017\n","Epoch: 29 \tTraining Loss: 11.205782 \tValidation Loss: 16.062376\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(n_epochs), train_loss_list_new)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Performance of the 3-layer Model\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"hEdjk_jV4mEm","executionInfo":{"status":"ok","timestamp":1701265110327,"user_tz":-60,"elapsed":342,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b713f1d6-4d88-42bc-f71c-7aba797301e5"},"id":"hEdjk_jV4mEm","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_new.load_state_dict(torch.load(\"./model_new_cifar.pt\"))\n","\n","# track test loss\n","test_loss = 0.0\n","class_correct = list(0.0 for i in range(10))\n","class_total = list(0.0 for i in range(10))\n","\n","model_new.eval()\n","# iterate over test data\n","for data, target in test_loader:\n","    # move tensors to GPU if CUDA is available\n","    if train_on_gpu:\n","        data, target = data.cuda(), target.cuda()\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = model_new(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = (\n","        np.squeeze(correct_tensor.numpy())\n","        if not train_on_gpu\n","        else np.squeeze(correct_tensor.cpu().numpy())\n","    )\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct[label] += correct[i].item()\n","        class_total[label] += 1\n","\n","# average test loss\n","test_loss = test_loss / len(test_loader)\n","print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9C9D34ZW43q7","executionInfo":{"status":"ok","timestamp":1701267205015,"user_tz":-60,"elapsed":4237,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9373df39-8c49-4700-8601-50c4b3a27548"},"id":"9C9D34ZW43q7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 15.725736\n","\n","Test Accuracy of airplane: 78% (784/1000)\n","Test Accuracy of automobile: 83% (838/1000)\n","Test Accuracy of  bird: 61% (615/1000)\n","Test Accuracy of   cat: 51% (513/1000)\n","Test Accuracy of  deer: 68% (680/1000)\n","Test Accuracy of   dog: 63% (635/1000)\n","Test Accuracy of  frog: 86% (860/1000)\n","Test Accuracy of horse: 76% (762/1000)\n","Test Accuracy of  ship: 84% (845/1000)\n","Test Accuracy of truck: 82% (821/1000)\n","\n","Test Accuracy (Overall): 73% (7353/10000)\n"]}]},{"cell_type":"markdown","source":["With our new model, we notice a substantial improvement in overall test accuracy.\n","\n","The result of the **original CNN** are :\n","\n","*   *Test loss* : 21.447881\n","*   *Test accuracy* : 62%\n","\n","The result of the our **new 3-layers CNN** are :\n","\n","*   *Test loss* : 15.725736\n","*   *Test accuracy* : 73%\n","\n","Despite the additional training period (~1min), the outcomes meet our expectations. Indeed, for each class, the accuracy is improved up to 10%."],"metadata":{"id":"uU_LD9l1mfvn"},"id":"uU_LD9l1mfvn"},{"cell_type":"markdown","id":"bc381cf4","metadata":{"id":"bc381cf4"},"source":["## Exercise 2: Quantization: try to compress the CNN to save space\n","\n","Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n","        \n","The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy\n","\n","\n","The size of the model is simply the size of the file."]},{"cell_type":"code","execution_count":null,"id":"ef623c26","metadata":{"id":"ef623c26"},"outputs":[],"source":["import os\n","\n","def print_size_of_model(model, label=\"\"):\n","    torch.save(model.state_dict(), \"temp.p\")\n","    size = os.path.getsize(\"temp.p\")\n","    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n","    os.remove(\"temp.p\")\n","    return size\n","\n","#size_model = print_size_of_model(model_new, \"fp32\")"]},{"cell_type":"markdown","id":"05c4e9ad","metadata":{"id":"05c4e9ad"},"source":["Post training quantization example"]},{"cell_type":"code","execution_count":null,"id":"c4c65d4b","metadata":{"id":"c4c65d4b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701267155486,"user_tz":-60,"elapsed":623,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"6cbb1ee3-36ae-4d73-cf55-7c1eca65a778"},"outputs":[{"output_type":"stream","name":"stdout","text":["model:  int8  \t Size (KB): 659.934\n","The size of the original model has been divided by 3.53 compared to the Quantized model\n"]}],"source":["import torch.quantization\n","\n","\n","quantized_model = torch.quantization.quantize_dynamic(model_new, {torch.nn.Linear}, dtype=torch.qint8)\n","torch.save(quantized_model.state_dict(), \"quantized_model_cifar.pt\")\n","\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","\n","print(f\"The size of the original model has been divided by {size_model / size_quantized:.2f} compared to the Quantized model\")\n"]},{"cell_type":"markdown","id":"7b108e17","metadata":{"id":"7b108e17"},"source":["For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models."]},{"cell_type":"markdown","id":"a0a34b90","metadata":{"id":"a0a34b90"},"source":["Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"]},{"cell_type":"code","source":["quantized_model.load_state_dict(torch.load(\"./quantized_model_cifar.pt\",map_location=torch.device('cpu')))\n","\n","# track test loss\n","test_loss_quantized = 0.0\n","class_correct_quantized = list(0.0 for i in range(10))\n","class_total_quantized = list(0.0 for i in range(10))\n","\n","quantized_model.eval()\n","quantized_model.cpu()\n","\n","# iterate over test data\n","for data, target in test_loader:\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = quantized_model(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss_quantized += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = np.squeeze(correct_tensor.numpy()) #np.squeeze(correct_tensor.cpu().numpy()\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct_quantized[label] += correct[i].item()\n","        class_total_quantized[label] += 1\n","\n","# average test loss\n","test_loss_quantized = test_loss_quantized / len(test_loader)\n","loss_delta = test_loss_quantized - test_loss\n","print(\"Original Test Loss: {:.6f}\\n\".format(test_loss))\n","print(\"Quantized Test Loss: {:.6f}\\n\".format(test_loss_quantized))\n","print(\"Loss Delta: {:.6f}\\n\".format(loss_delta))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Initial model Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            ))\n","        print(\n","            \"Quantized model Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct_quantized[i] / class_total_quantized[i],\n","                np.sum(class_correct_quantized[i]),\n","                np.sum(class_total_quantized[i]),\n","            ))\n","        print(\n","            \"Difference in Instances Correctly classified of %5s: %2d \\n\"\n","            % (\n","                classes[i],\n","                class_correct_quantized[i]-class_correct[i],\n","\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nInitial model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")\n","print(\n","    \"\\nQuantized model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct_quantized) / np.sum(class_total_quantized),\n","        np.sum(class_correct_quantized),\n","        np.sum(class_total_quantized),\n","    )\n",")\n","print(\n","         \"\\nDifference in Instances Correctly classified (Overall) : %2d \\n\"\n","        % (\n","            np.sum(class_correct)-np.sum(class_correct_quantized),\n","            )\n","        )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"goj7b4vnAVvk","executionInfo":{"status":"ok","timestamp":1701267315881,"user_tz":-60,"elapsed":7778,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9a3e9626-e644-4a43-f7f1-500aeb7bfea9"},"id":"goj7b4vnAVvk","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original Test Loss: 15.725736\n","\n","Quantized Test Loss: 15.745716\n","\n","Loss Delta: 0.019980\n","\n","Initial model Test Accuracy of airplane: 78% (784/1000)\n","Quantized model Test Accuracy of airplane: 78% (786/1000)\n","Difference in Instances Correctly classified of airplane:  2 \n","\n","Initial model Test Accuracy of automobile: 83% (838/1000)\n","Quantized model Test Accuracy of automobile: 83% (837/1000)\n","Difference in Instances Correctly classified of automobile: -1 \n","\n","Initial model Test Accuracy of  bird: 61% (615/1000)\n","Quantized model Test Accuracy of  bird: 61% (611/1000)\n","Difference in Instances Correctly classified of  bird: -4 \n","\n","Initial model Test Accuracy of   cat: 51% (513/1000)\n","Quantized model Test Accuracy of   cat: 51% (512/1000)\n","Difference in Instances Correctly classified of   cat: -1 \n","\n","Initial model Test Accuracy of  deer: 68% (680/1000)\n","Quantized model Test Accuracy of  deer: 68% (681/1000)\n","Difference in Instances Correctly classified of  deer:  1 \n","\n","Initial model Test Accuracy of   dog: 63% (635/1000)\n","Quantized model Test Accuracy of   dog: 63% (636/1000)\n","Difference in Instances Correctly classified of   dog:  1 \n","\n","Initial model Test Accuracy of  frog: 86% (860/1000)\n","Quantized model Test Accuracy of  frog: 86% (861/1000)\n","Difference in Instances Correctly classified of  frog:  1 \n","\n","Initial model Test Accuracy of horse: 76% (762/1000)\n","Quantized model Test Accuracy of horse: 76% (764/1000)\n","Difference in Instances Correctly classified of horse:  2 \n","\n","Initial model Test Accuracy of  ship: 84% (845/1000)\n","Quantized model Test Accuracy of  ship: 84% (842/1000)\n","Difference in Instances Correctly classified of  ship: -3 \n","\n","Initial model Test Accuracy of truck: 82% (821/1000)\n","Quantized model Test Accuracy of truck: 82% (820/1000)\n","Difference in Instances Correctly classified of truck: -1 \n","\n","\n","Initial model Test Accuracy (Overall): 73% (7353/10000)\n","\n","Quantized model Test Accuracy (Overall): 73% (7350/10000)\n","\n","Difference in Instances Correctly classified (Overall) :  3 \n","\n"]}]},{"cell_type":"markdown","source":["The quantization of our model has minimal impact on classification accuracy, with the Test Loss and number of correctly classified instances remaining consistent across both models. This quantization is a beneficial method for saving space and memory, as the quantized file is 3.53 times smaller."],"metadata":{"id":"Eo8W3YvhnzVZ"},"id":"Eo8W3YvhnzVZ"},{"cell_type":"markdown","id":"201470f9","metadata":{"id":"201470f9"},"source":["## Exercise 3: working with pre-trained models.\n","\n","PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n","We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"]},{"cell_type":"code","execution_count":null,"id":"b4d13080","metadata":{"id":"b4d13080","colab":{"base_uri":"https://localhost:8080/","height":416},"executionInfo":{"status":"ok","timestamp":1701267795377,"user_tz":-60,"elapsed":2449,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"152af00a-3cb4-4faf-a3fb-36466c75d661"},"outputs":[{"output_type":"stream","name":"stdout","text":["Predicted class for resnet50 is: Alpine ibex\n","Predicted class for googlenet is: hartebeest\n","Predicted class for resnet_quantized is: Alpine ibex\n","Predicted class for googlenet_quantized is: hartebeest\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import json\n","from PIL import Image\n","\n","\n","# Choose an image to pass through the model\n","test_image_1 = \"dog.png\"\n","test_image_2 = \"ours.jpg\"\n","test_image_3 = \"cerf.jpg\"\n","\n","\n","# Configure matplotlib for pretty inline plots\n","#%matplotlib inline\n","#%config InlineBackend.figure_format = 'retina'\n","\n","# Prepare the labels\n","with open(\"imagenet-simple-labels.json\") as f:\n","    labels = json.load(f)\n","\n","# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n","data_transform = transforms.Compose(\n","    [\n","        transforms.Resize((224, 224)),\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","    ]\n",")\n","# Load the image\n","\n","#image = Image.open(test_image_1)\n","#image = Image.open(test_image_2)\n","image = Image.open(test_image_3)\n","\n","plt.imshow(image), plt.xticks([]), plt.yticks([])\n","\n","# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n","# image = data_transform(image).unsqueeze(0).cuda()\n","image = data_transform(image).unsqueeze(0)\n","\n","# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n","model = models.resnet50(pretrained=True)\n","googlenet_model = models.googlenet(pretrained=True)\n","resnet_quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n","googlenet_quantized_model = torch.quantization.quantize_dynamic(googlenet_model, dtype=torch.qint8)\n","\n","# Send the model to the GPU\n","# model.cuda()\n","# Set layers such as dropout and batchnorm in evaluation mode\n","model.eval()\n","googlenet_model.eval()\n","resnet_quantized_model.eval()\n","googlenet_quantized_model.eval()\n","\n","# Get the 1000-dimensional model output\n","out_1 = model(image)\n","out_2 = googlenet_model(image)\n","out_3 = resnet_quantized_model(image)\n","out_4 = googlenet_quantized_model(image)\n","\n","# Find the predicted class\n","print(\"Predicted class for resnet50 is: {}\".format(labels[out_1.argmax()]))\n","print(\"Predicted class for googlenet is: {}\".format(labels[out_2.argmax()]))\n","print(\"Predicted class for resnet_quantized is: {}\".format(labels[out_3.argmax()]))\n","print(\"Predicted class for googlenet_quantized is: {}\".format(labels[out_4.argmax()]))"]},{"cell_type":"markdown","id":"184cfceb","metadata":{"id":"184cfceb"},"source":["Experiments:\n","\n","Study the code and the results obtained. Possibly add other images downloaded from the internet.\n","\n","What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n","\n","Experiment with other pre-trained CNN models.\n","\n","    \n"]},{"cell_type":"markdown","source":["We tried to experiment the Resnet50 and GoogleNet with three different image. The results are satisfying for the two first image \"dog.png\", and \"ours.jpg\", but not for the \"cerf.jpg\". Indeed, the predicted class for resnet50 model : Alpine ibex and the predicted class for googlenet is: hartebeest."],"metadata":{"id":"neFEGq8SuQ98"},"id":"neFEGq8SuQ98"},{"cell_type":"code","source":["#sizes of the model\n","\n","print(\"Size of the 3-layers model :\")\n","size_model = print_size_of_model(model_new, \"fp32\")\n","print(\"Size of the 3-layers quantized model :\")\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","print(\"The size of the model has been divided by %.2f compared to the Quantized model\" % (size_model / size_quantized))\n","\n","print(\"\\nSize of the Resnet model :\")\n","size_Resnet = print_size_of_model(model, \"fp32\")\n","print(\"Size of the Resnet quantized model :\")\n","size_Quantized_Resnet = print_size_of_model(resnet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Resnet / size_quantized))\n","\n","print(\"\\nSize of Googlenet model:\")\n","size_Googlenet = print_size_of_model(googlenet_model, \"fp32\")\n","print(\"Size of Googlenet quantized model:\")\n","size_Quantized_Googlenet = print_size_of_model(googlenet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Googlenet / size_quantized))\n"],"metadata":{"id":"fAyldGwPIv60","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701268313794,"user_tz":-60,"elapsed":913,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"ac73bd2c-9af2-4652-9e47-981aad0ccc64"},"id":"fAyldGwPIv60","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Size of the 3-layers model :\n","model:  fp32  \t Size (KB): 2331.074\n","Size of the 3-layers quantized model :\n","model:  int8  \t Size (KB): 659.806\n","The size of the model has been divided by 3.53 compared to the Quantized model\n","\n","Size of the Resnet model :\n","model:  fp32  \t Size (KB): 102523.238\n","Size of the Resnet quantized model :\n","model:  fp32  \t Size (KB): 96379.996\n","The size of the original model has been divided by 146.07 compared to the 3-layer Quantized model\n","\n","Size of Googlenet model:\n","model:  fp32  \t Size (KB): 26654.254\n","Size of Googlenet quantized model:\n","model:  fp32  \t Size (KB): 23583.076\n","The size of the original model has been divided by 35.74 compared to the 3-layer Quantized model\n"]}]},{"cell_type":"markdown","source":["Even after quantization, the pretrained models are far larger than our own trained model."],"metadata":{"id":"2VjjWHXHwGl4"},"id":"2VjjWHXHwGl4"},{"cell_type":"markdown","id":"5d57da4b","metadata":{"id":"5d57da4b"},"source":["## Exercise 4: Transfer Learning\n","    \n","    \n","For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n","Download and unzip in your working directory the dataset available at the address :\n","    \n","https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","    \n","Execute the following code in order to display some images of the dataset."]},{"cell_type":"markdown","source":["L'exercice 4 n'a pas été terminé"],"metadata":{"id":"pqlgLQ7J0QuQ"},"id":"pqlgLQ7J0QuQ"},{"cell_type":"code","source":["!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","!unzip hymenoptera_data.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oVfFXO3fjOvS","executionInfo":{"status":"ok","timestamp":1701424629804,"user_tz":-60,"elapsed":1634,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"c5089342-b148-4d83-ded8-001954737407"},"id":"oVfFXO3fjOvS","execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["--2023-12-01 09:57:08--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","Resolving download.pytorch.org (download.pytorch.org)... 18.164.154.30, 18.164.154.37, 18.164.154.123, ...\n","Connecting to download.pytorch.org (download.pytorch.org)|18.164.154.30|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 47286322 (45M) [application/zip]\n","Saving to: ‘hymenoptera_data.zip’\n","\n","hymenoptera_data.zi 100%[===================>]  45.10M  92.6MB/s    in 0.5s    \n","\n","2023-12-01 09:57:08 (92.6 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n","\n","Archive:  hymenoptera_data.zip\n","   creating: hymenoptera_data/\n","   creating: hymenoptera_data/train/\n","   creating: hymenoptera_data/train/ants/\n","  inflating: hymenoptera_data/train/ants/0013035.jpg  \n","  inflating: hymenoptera_data/train/ants/1030023514_aad5c608f9.jpg  \n","  inflating: hymenoptera_data/train/ants/1095476100_3906d8afde.jpg  \n","  inflating: hymenoptera_data/train/ants/1099452230_d1949d3250.jpg  \n","  inflating: hymenoptera_data/train/ants/116570827_e9c126745d.jpg  \n","  inflating: hymenoptera_data/train/ants/1225872729_6f0856588f.jpg  \n","  inflating: hymenoptera_data/train/ants/1262877379_64fcada201.jpg  \n","  inflating: hymenoptera_data/train/ants/1269756697_0bce92cdab.jpg  \n","  inflating: hymenoptera_data/train/ants/1286984635_5119e80de1.jpg  \n","  inflating: hymenoptera_data/train/ants/132478121_2a430adea2.jpg  \n","  inflating: hymenoptera_data/train/ants/1360291657_dc248c5eea.jpg  \n","  inflating: hymenoptera_data/train/ants/1368913450_e146e2fb6d.jpg  \n","  inflating: hymenoptera_data/train/ants/1473187633_63ccaacea6.jpg  \n","  inflating: hymenoptera_data/train/ants/148715752_302c84f5a4.jpg  \n","  inflating: hymenoptera_data/train/ants/1489674356_09d48dde0a.jpg  \n","  inflating: hymenoptera_data/train/ants/149244013_c529578289.jpg  \n","  inflating: hymenoptera_data/train/ants/150801003_3390b73135.jpg  \n","  inflating: hymenoptera_data/train/ants/150801171_cd86f17ed8.jpg  \n","  inflating: hymenoptera_data/train/ants/154124431_65460430f2.jpg  \n","  inflating: hymenoptera_data/train/ants/162603798_40b51f1654.jpg  \n","  inflating: hymenoptera_data/train/ants/1660097129_384bf54490.jpg  \n","  inflating: hymenoptera_data/train/ants/167890289_dd5ba923f3.jpg  \n","  inflating: hymenoptera_data/train/ants/1693954099_46d4c20605.jpg  \n","  inflating: hymenoptera_data/train/ants/175998972.jpg  \n","  inflating: hymenoptera_data/train/ants/178538489_bec7649292.jpg  \n","  inflating: hymenoptera_data/train/ants/1804095607_0341701e1c.jpg  \n","  inflating: hymenoptera_data/train/ants/1808777855_2a895621d7.jpg  \n","  inflating: hymenoptera_data/train/ants/188552436_605cc9b36b.jpg  \n","  inflating: hymenoptera_data/train/ants/1917341202_d00a7f9af5.jpg  \n","  inflating: hymenoptera_data/train/ants/1924473702_daa9aacdbe.jpg  \n","  inflating: hymenoptera_data/train/ants/196057951_63bf063b92.jpg  \n","  inflating: hymenoptera_data/train/ants/196757565_326437f5fe.jpg  \n","  inflating: hymenoptera_data/train/ants/201558278_fe4caecc76.jpg  \n","  inflating: hymenoptera_data/train/ants/201790779_527f4c0168.jpg  \n","  inflating: hymenoptera_data/train/ants/2019439677_2db655d361.jpg  \n","  inflating: hymenoptera_data/train/ants/207947948_3ab29d7207.jpg  \n","  inflating: hymenoptera_data/train/ants/20935278_9190345f6b.jpg  \n","  inflating: hymenoptera_data/train/ants/224655713_3956f7d39a.jpg  \n","  inflating: hymenoptera_data/train/ants/2265824718_2c96f485da.jpg  \n","  inflating: hymenoptera_data/train/ants/2265825502_fff99cfd2d.jpg  \n","  inflating: hymenoptera_data/train/ants/226951206_d6bf946504.jpg  \n","  inflating: hymenoptera_data/train/ants/2278278459_6b99605e50.jpg  \n","  inflating: hymenoptera_data/train/ants/2288450226_a6e96e8fdf.jpg  \n","  inflating: hymenoptera_data/train/ants/2288481644_83ff7e4572.jpg  \n","  inflating: hymenoptera_data/train/ants/2292213964_ca51ce4bef.jpg  \n","  inflating: hymenoptera_data/train/ants/24335309_c5ea483bb8.jpg  \n","  inflating: hymenoptera_data/train/ants/245647475_9523dfd13e.jpg  \n","  inflating: hymenoptera_data/train/ants/255434217_1b2b3fe0a4.jpg  \n","  inflating: hymenoptera_data/train/ants/258217966_d9d90d18d3.jpg  \n","  inflating: hymenoptera_data/train/ants/275429470_b2d7d9290b.jpg  \n","  inflating: hymenoptera_data/train/ants/28847243_e79fe052cd.jpg  \n","  inflating: hymenoptera_data/train/ants/318052216_84dff3f98a.jpg  \n","  inflating: hymenoptera_data/train/ants/334167043_cbd1adaeb9.jpg  \n","  inflating: hymenoptera_data/train/ants/339670531_94b75ae47a.jpg  \n","  inflating: hymenoptera_data/train/ants/342438950_a3da61deab.jpg  \n","  inflating: hymenoptera_data/train/ants/36439863_0bec9f554f.jpg  \n","  inflating: hymenoptera_data/train/ants/374435068_7eee412ec4.jpg  \n","  inflating: hymenoptera_data/train/ants/382971067_0bfd33afe0.jpg  \n","  inflating: hymenoptera_data/train/ants/384191229_5779cf591b.jpg  \n","  inflating: hymenoptera_data/train/ants/386190770_672743c9a7.jpg  \n","  inflating: hymenoptera_data/train/ants/392382602_1b7bed32fa.jpg  \n","  inflating: hymenoptera_data/train/ants/403746349_71384f5b58.jpg  \n","  inflating: hymenoptera_data/train/ants/408393566_b5b694119b.jpg  \n","  inflating: hymenoptera_data/train/ants/424119020_6d57481dab.jpg  \n","  inflating: hymenoptera_data/train/ants/424873399_47658a91fb.jpg  \n","  inflating: hymenoptera_data/train/ants/450057712_771b3bfc91.jpg  \n","  inflating: hymenoptera_data/train/ants/45472593_bfd624f8dc.jpg  \n","  inflating: hymenoptera_data/train/ants/459694881_ac657d3187.jpg  \n","  inflating: hymenoptera_data/train/ants/460372577_f2f6a8c9fc.jpg  \n","  inflating: hymenoptera_data/train/ants/460874319_0a45ab4d05.jpg  \n","  inflating: hymenoptera_data/train/ants/466430434_4000737de9.jpg  \n","  inflating: hymenoptera_data/train/ants/470127037_513711fd21.jpg  \n","  inflating: hymenoptera_data/train/ants/474806473_ca6caab245.jpg  \n","  inflating: hymenoptera_data/train/ants/475961153_b8c13fd405.jpg  \n","  inflating: hymenoptera_data/train/ants/484293231_e53cfc0c89.jpg  \n","  inflating: hymenoptera_data/train/ants/49375974_e28ba6f17e.jpg  \n","  inflating: hymenoptera_data/train/ants/506249802_207cd979b4.jpg  \n","  inflating: hymenoptera_data/train/ants/506249836_717b73f540.jpg  \n","  inflating: hymenoptera_data/train/ants/512164029_c0a66b8498.jpg  \n","  inflating: hymenoptera_data/train/ants/512863248_43c8ce579b.jpg  \n","  inflating: hymenoptera_data/train/ants/518773929_734dbc5ff4.jpg  \n","  inflating: hymenoptera_data/train/ants/522163566_fec115ca66.jpg  \n","  inflating: hymenoptera_data/train/ants/522415432_2218f34bf8.jpg  \n","  inflating: hymenoptera_data/train/ants/531979952_bde12b3bc0.jpg  \n","  inflating: hymenoptera_data/train/ants/533848102_70a85ad6dd.jpg  \n","  inflating: hymenoptera_data/train/ants/535522953_308353a07c.jpg  \n","  inflating: hymenoptera_data/train/ants/540889389_48bb588b21.jpg  \n","  inflating: hymenoptera_data/train/ants/541630764_dbd285d63c.jpg  \n","  inflating: hymenoptera_data/train/ants/543417860_b14237f569.jpg  \n","  inflating: hymenoptera_data/train/ants/560966032_988f4d7bc4.jpg  \n","  inflating: hymenoptera_data/train/ants/5650366_e22b7e1065.jpg  \n","  inflating: hymenoptera_data/train/ants/6240329_72c01e663e.jpg  \n","  inflating: hymenoptera_data/train/ants/6240338_93729615ec.jpg  \n","  inflating: hymenoptera_data/train/ants/649026570_e58656104b.jpg  \n","  inflating: hymenoptera_data/train/ants/662541407_ff8db781e7.jpg  \n","  inflating: hymenoptera_data/train/ants/67270775_e9fdf77e9d.jpg  \n","  inflating: hymenoptera_data/train/ants/6743948_2b8c096dda.jpg  \n","  inflating: hymenoptera_data/train/ants/684133190_35b62c0c1d.jpg  \n","  inflating: hymenoptera_data/train/ants/69639610_95e0de17aa.jpg  \n","  inflating: hymenoptera_data/train/ants/707895295_009cf23188.jpg  \n","  inflating: hymenoptera_data/train/ants/7759525_1363d24e88.jpg  \n","  inflating: hymenoptera_data/train/ants/795000156_a9900a4a71.jpg  \n","  inflating: hymenoptera_data/train/ants/822537660_caf4ba5514.jpg  \n","  inflating: hymenoptera_data/train/ants/82852639_52b7f7f5e3.jpg  \n","  inflating: hymenoptera_data/train/ants/841049277_b28e58ad05.jpg  \n","  inflating: hymenoptera_data/train/ants/886401651_f878e888cd.jpg  \n","  inflating: hymenoptera_data/train/ants/892108839_f1aad4ca46.jpg  \n","  inflating: hymenoptera_data/train/ants/938946700_ca1c669085.jpg  \n","  inflating: hymenoptera_data/train/ants/957233405_25c1d1187b.jpg  \n","  inflating: hymenoptera_data/train/ants/9715481_b3cb4114ff.jpg  \n","  inflating: hymenoptera_data/train/ants/998118368_6ac1d91f81.jpg  \n","  inflating: hymenoptera_data/train/ants/ant photos.jpg  \n","  inflating: hymenoptera_data/train/ants/Ant_1.jpg  \n","  inflating: hymenoptera_data/train/ants/army-ants-red-picture.jpg  \n","  inflating: hymenoptera_data/train/ants/formica.jpeg  \n","  inflating: hymenoptera_data/train/ants/hormiga_co_por.jpg  \n","  inflating: hymenoptera_data/train/ants/imageNotFound.gif  \n","  inflating: hymenoptera_data/train/ants/kurokusa.jpg  \n","  inflating: hymenoptera_data/train/ants/MehdiabadiAnt2_600.jpg  \n","  inflating: hymenoptera_data/train/ants/Nepenthes_rafflesiana_ant.jpg  \n","  inflating: hymenoptera_data/train/ants/swiss-army-ant.jpg  \n","  inflating: hymenoptera_data/train/ants/termite-vs-ant.jpg  \n","  inflating: hymenoptera_data/train/ants/trap-jaw-ant-insect-bg.jpg  \n","  inflating: hymenoptera_data/train/ants/VietnameseAntMimicSpider.jpg  \n","   creating: hymenoptera_data/train/bees/\n","  inflating: hymenoptera_data/train/bees/1092977343_cb42b38d62.jpg  \n","  inflating: hymenoptera_data/train/bees/1093831624_fb5fbe2308.jpg  \n","  inflating: hymenoptera_data/train/bees/1097045929_1753d1c765.jpg  \n","  inflating: hymenoptera_data/train/bees/1232245714_f862fbe385.jpg  \n","  inflating: hymenoptera_data/train/bees/129236073_0985e91c7d.jpg  \n","  inflating: hymenoptera_data/train/bees/1295655112_7813f37d21.jpg  \n","  inflating: hymenoptera_data/train/bees/132511197_0b86ad0fff.jpg  \n","  inflating: hymenoptera_data/train/bees/132826773_dbbcb117b9.jpg  \n","  inflating: hymenoptera_data/train/bees/150013791_969d9a968b.jpg  \n","  inflating: hymenoptera_data/train/bees/1508176360_2972117c9d.jpg  \n","  inflating: hymenoptera_data/train/bees/154600396_53e1252e52.jpg  \n","  inflating: hymenoptera_data/train/bees/16838648_415acd9e3f.jpg  \n","  inflating: hymenoptera_data/train/bees/1691282715_0addfdf5e8.jpg  \n","  inflating: hymenoptera_data/train/bees/17209602_fe5a5a746f.jpg  \n","  inflating: hymenoptera_data/train/bees/174142798_e5ad6d76e0.jpg  \n","  inflating: hymenoptera_data/train/bees/1799726602_8580867f71.jpg  \n","  inflating: hymenoptera_data/train/bees/1807583459_4fe92b3133.jpg  \n","  inflating: hymenoptera_data/train/bees/196430254_46bd129ae7.jpg  \n","  inflating: hymenoptera_data/train/bees/196658222_3fffd79c67.jpg  \n","  inflating: hymenoptera_data/train/bees/198508668_97d818b6c4.jpg  \n","  inflating: hymenoptera_data/train/bees/2031225713_50ed499635.jpg  \n","  inflating: hymenoptera_data/train/bees/2037437624_2d7bce461f.jpg  \n","  inflating: hymenoptera_data/train/bees/2053200300_8911ef438a.jpg  \n","  inflating: hymenoptera_data/train/bees/205835650_e6f2614bee.jpg  \n","  inflating: hymenoptera_data/train/bees/208702903_42fb4d9748.jpg  \n","  inflating: hymenoptera_data/train/bees/21399619_3e61e5bb6f.jpg  \n","  inflating: hymenoptera_data/train/bees/2227611847_ec72d40403.jpg  \n","  inflating: hymenoptera_data/train/bees/2321139806_d73d899e66.jpg  \n","  inflating: hymenoptera_data/train/bees/2330918208_8074770c20.jpg  \n","  inflating: hymenoptera_data/train/bees/2345177635_caf07159b3.jpg  \n","  inflating: hymenoptera_data/train/bees/2358061370_9daabbd9ac.jpg  \n","  inflating: hymenoptera_data/train/bees/2364597044_3c3e3fc391.jpg  \n","  inflating: hymenoptera_data/train/bees/2384149906_2cd8b0b699.jpg  \n","  inflating: hymenoptera_data/train/bees/2397446847_04ef3cd3e1.jpg  \n","  inflating: hymenoptera_data/train/bees/2405441001_b06c36fa72.jpg  \n","  inflating: hymenoptera_data/train/bees/2445215254_51698ff797.jpg  \n","  inflating: hymenoptera_data/train/bees/2452236943_255bfd9e58.jpg  \n","  inflating: hymenoptera_data/train/bees/2467959963_a7831e9ff0.jpg  \n","  inflating: hymenoptera_data/train/bees/2470492904_837e97800d.jpg  \n","  inflating: hymenoptera_data/train/bees/2477324698_3d4b1b1cab.jpg  \n","  inflating: hymenoptera_data/train/bees/2477349551_e75c97cf4d.jpg  \n","  inflating: hymenoptera_data/train/bees/2486729079_62df0920be.jpg  \n","  inflating: hymenoptera_data/train/bees/2486746709_c43cec0e42.jpg  \n","  inflating: hymenoptera_data/train/bees/2493379287_4100e1dacc.jpg  \n","  inflating: hymenoptera_data/train/bees/2495722465_879acf9d85.jpg  \n","  inflating: hymenoptera_data/train/bees/2528444139_fa728b0f5b.jpg  \n","  inflating: hymenoptera_data/train/bees/2538361678_9da84b77e3.jpg  \n","  inflating: hymenoptera_data/train/bees/2551813042_8a070aeb2b.jpg  \n","  inflating: hymenoptera_data/train/bees/2580598377_a4caecdb54.jpg  \n","  inflating: hymenoptera_data/train/bees/2601176055_8464e6aa71.jpg  \n","  inflating: hymenoptera_data/train/bees/2610833167_79bf0bcae5.jpg  \n","  inflating: hymenoptera_data/train/bees/2610838525_fe8e3cae47.jpg  \n","  inflating: hymenoptera_data/train/bees/2617161745_fa3ebe85b4.jpg  \n","  inflating: hymenoptera_data/train/bees/2625499656_e3415e374d.jpg  \n","  inflating: hymenoptera_data/train/bees/2634617358_f32fd16bea.jpg  \n","  inflating: hymenoptera_data/train/bees/2638074627_6b3ae746a0.jpg  \n","  inflating: hymenoptera_data/train/bees/2645107662_b73a8595cc.jpg  \n","  inflating: hymenoptera_data/train/bees/2651621464_a2fa8722eb.jpg  \n","  inflating: hymenoptera_data/train/bees/2652877533_a564830cbf.jpg  \n","  inflating: hymenoptera_data/train/bees/266644509_d30bb16a1b.jpg  \n","  inflating: hymenoptera_data/train/bees/2683605182_9d2a0c66cf.jpg  \n","  inflating: hymenoptera_data/train/bees/2704348794_eb5d5178c2.jpg  \n","  inflating: hymenoptera_data/train/bees/2707440199_cd170bd512.jpg  \n","  inflating: hymenoptera_data/train/bees/2710368626_cb42882dc8.jpg  \n","  inflating: hymenoptera_data/train/bees/2722592222_258d473e17.jpg  \n","  inflating: hymenoptera_data/train/bees/2728759455_ce9bb8cd7a.jpg  \n","  inflating: hymenoptera_data/train/bees/2756397428_1d82a08807.jpg  \n","  inflating: hymenoptera_data/train/bees/2765347790_da6cf6cb40.jpg  \n","  inflating: hymenoptera_data/train/bees/2781170484_5d61835d63.jpg  \n","  inflating: hymenoptera_data/train/bees/279113587_b4843db199.jpg  \n","  inflating: hymenoptera_data/train/bees/2792000093_e8ae0718cf.jpg  \n","  inflating: hymenoptera_data/train/bees/2801728106_833798c909.jpg  \n","  inflating: hymenoptera_data/train/bees/2822388965_f6dca2a275.jpg  \n","  inflating: hymenoptera_data/train/bees/2861002136_52c7c6f708.jpg  \n","  inflating: hymenoptera_data/train/bees/2908916142_a7ac8b57a8.jpg  \n","  inflating: hymenoptera_data/train/bees/29494643_e3410f0d37.jpg  \n","  inflating: hymenoptera_data/train/bees/2959730355_416a18c63c.jpg  \n","  inflating: hymenoptera_data/train/bees/2962405283_22718d9617.jpg  \n","  inflating: hymenoptera_data/train/bees/3006264892_30e9cced70.jpg  \n","  inflating: hymenoptera_data/train/bees/3030189811_01d095b793.jpg  \n","  inflating: hymenoptera_data/train/bees/3030772428_8578335616.jpg  \n","  inflating: hymenoptera_data/train/bees/3044402684_3853071a87.jpg  \n","  inflating: hymenoptera_data/train/bees/3074585407_9854eb3153.jpg  \n","  inflating: hymenoptera_data/train/bees/3079610310_ac2d0ae7bc.jpg  \n","  inflating: hymenoptera_data/train/bees/3090975720_71f12e6de4.jpg  \n","  inflating: hymenoptera_data/train/bees/3100226504_c0d4f1e3f1.jpg  \n","  inflating: hymenoptera_data/train/bees/342758693_c56b89b6b6.jpg  \n","  inflating: hymenoptera_data/train/bees/354167719_22dca13752.jpg  \n","  inflating: hymenoptera_data/train/bees/359928878_b3b418c728.jpg  \n","  inflating: hymenoptera_data/train/bees/365759866_b15700c59b.jpg  \n","  inflating: hymenoptera_data/train/bees/36900412_92b81831ad.jpg  \n","  inflating: hymenoptera_data/train/bees/39672681_1302d204d1.jpg  \n","  inflating: hymenoptera_data/train/bees/39747887_42df2855ee.jpg  \n","  inflating: hymenoptera_data/train/bees/421515404_e87569fd8b.jpg  \n","  inflating: hymenoptera_data/train/bees/444532809_9e931e2279.jpg  \n","  inflating: hymenoptera_data/train/bees/446296270_d9e8b93ecf.jpg  \n","  inflating: hymenoptera_data/train/bees/452462677_7be43af8ff.jpg  \n","  inflating: hymenoptera_data/train/bees/452462695_40a4e5b559.jpg  \n","  inflating: hymenoptera_data/train/bees/457457145_5f86eb7e9c.jpg  \n","  inflating: hymenoptera_data/train/bees/465133211_80e0c27f60.jpg  \n","  inflating: hymenoptera_data/train/bees/469333327_358ba8fe8a.jpg  \n","  inflating: hymenoptera_data/train/bees/472288710_2abee16fa0.jpg  \n","  inflating: hymenoptera_data/train/bees/473618094_8ffdcab215.jpg  \n","  inflating: hymenoptera_data/train/bees/476347960_52edd72b06.jpg  \n","  inflating: hymenoptera_data/train/bees/478701318_bbd5e557b8.jpg  \n","  inflating: hymenoptera_data/train/bees/507288830_f46e8d4cb2.jpg  \n","  inflating: hymenoptera_data/train/bees/509247772_2db2d01374.jpg  \n","  inflating: hymenoptera_data/train/bees/513545352_fd3e7c7c5d.jpg  \n","  inflating: hymenoptera_data/train/bees/522104315_5d3cb2758e.jpg  \n","  inflating: hymenoptera_data/train/bees/537309131_532bfa59ea.jpg  \n","  inflating: hymenoptera_data/train/bees/586041248_3032e277a9.jpg  \n","  inflating: hymenoptera_data/train/bees/760526046_547e8b381f.jpg  \n","  inflating: hymenoptera_data/train/bees/760568592_45a52c847f.jpg  \n","  inflating: hymenoptera_data/train/bees/774440991_63a4aa0cbe.jpg  \n","  inflating: hymenoptera_data/train/bees/85112639_6e860b0469.jpg  \n","  inflating: hymenoptera_data/train/bees/873076652_eb098dab2d.jpg  \n","  inflating: hymenoptera_data/train/bees/90179376_abc234e5f4.jpg  \n","  inflating: hymenoptera_data/train/bees/92663402_37f379e57a.jpg  \n","  inflating: hymenoptera_data/train/bees/95238259_98470c5b10.jpg  \n","  inflating: hymenoptera_data/train/bees/969455125_58c797ef17.jpg  \n","  inflating: hymenoptera_data/train/bees/98391118_bdb1e80cce.jpg  \n","   creating: hymenoptera_data/val/\n","   creating: hymenoptera_data/val/ants/\n","  inflating: hymenoptera_data/val/ants/10308379_1b6c72e180.jpg  \n","  inflating: hymenoptera_data/val/ants/1053149811_f62a3410d3.jpg  \n","  inflating: hymenoptera_data/val/ants/1073564163_225a64f170.jpg  \n","  inflating: hymenoptera_data/val/ants/1119630822_cd325ea21a.jpg  \n","  inflating: hymenoptera_data/val/ants/1124525276_816a07c17f.jpg  \n","  inflating: hymenoptera_data/val/ants/11381045_b352a47d8c.jpg  \n","  inflating: hymenoptera_data/val/ants/119785936_dd428e40c3.jpg  \n","  inflating: hymenoptera_data/val/ants/1247887232_edcb61246c.jpg  \n","  inflating: hymenoptera_data/val/ants/1262751255_c56c042b7b.jpg  \n","  inflating: hymenoptera_data/val/ants/1337725712_2eb53cd742.jpg  \n","  inflating: hymenoptera_data/val/ants/1358854066_5ad8015f7f.jpg  \n","  inflating: hymenoptera_data/val/ants/1440002809_b268d9a66a.jpg  \n","  inflating: hymenoptera_data/val/ants/147542264_79506478c2.jpg  \n","  inflating: hymenoptera_data/val/ants/152286280_411648ec27.jpg  \n","  inflating: hymenoptera_data/val/ants/153320619_2aeb5fa0ee.jpg  \n","  inflating: hymenoptera_data/val/ants/153783656_85f9c3ac70.jpg  \n","  inflating: hymenoptera_data/val/ants/157401988_d0564a9d02.jpg  \n","  inflating: hymenoptera_data/val/ants/159515240_d5981e20d1.jpg  \n","  inflating: hymenoptera_data/val/ants/161076144_124db762d6.jpg  \n","  inflating: hymenoptera_data/val/ants/161292361_c16e0bf57a.jpg  \n","  inflating: hymenoptera_data/val/ants/170652283_ecdaff5d1a.jpg  \n","  inflating: hymenoptera_data/val/ants/17081114_79b9a27724.jpg  \n","  inflating: hymenoptera_data/val/ants/172772109_d0a8e15fb0.jpg  \n","  inflating: hymenoptera_data/val/ants/1743840368_b5ccda82b7.jpg  \n","  inflating: hymenoptera_data/val/ants/181942028_961261ef48.jpg  \n","  inflating: hymenoptera_data/val/ants/183260961_64ab754c97.jpg  \n","  inflating: hymenoptera_data/val/ants/2039585088_c6f47c592e.jpg  \n","  inflating: hymenoptera_data/val/ants/205398178_c395c5e460.jpg  \n","  inflating: hymenoptera_data/val/ants/208072188_f293096296.jpg  \n","  inflating: hymenoptera_data/val/ants/209615353_eeb38ba204.jpg  \n","  inflating: hymenoptera_data/val/ants/2104709400_8831b4fc6f.jpg  \n","  inflating: hymenoptera_data/val/ants/212100470_b485e7b7b9.jpg  \n","  inflating: hymenoptera_data/val/ants/2127908701_d49dc83c97.jpg  \n","  inflating: hymenoptera_data/val/ants/2191997003_379df31291.jpg  \n","  inflating: hymenoptera_data/val/ants/2211974567_ee4606b493.jpg  \n","  inflating: hymenoptera_data/val/ants/2219621907_47bc7cc6b0.jpg  \n","  inflating: hymenoptera_data/val/ants/2238242353_52c82441df.jpg  \n","  inflating: hymenoptera_data/val/ants/2255445811_dabcdf7258.jpg  \n","  inflating: hymenoptera_data/val/ants/239161491_86ac23b0a3.jpg  \n","  inflating: hymenoptera_data/val/ants/263615709_cfb28f6b8e.jpg  \n","  inflating: hymenoptera_data/val/ants/308196310_1db5ffa01b.jpg  \n","  inflating: hymenoptera_data/val/ants/319494379_648fb5a1c6.jpg  \n","  inflating: hymenoptera_data/val/ants/35558229_1fa4608a7a.jpg  \n","  inflating: hymenoptera_data/val/ants/412436937_4c2378efc2.jpg  \n","  inflating: hymenoptera_data/val/ants/436944325_d4925a38c7.jpg  \n","  inflating: hymenoptera_data/val/ants/445356866_6cb3289067.jpg  \n","  inflating: hymenoptera_data/val/ants/459442412_412fecf3fe.jpg  \n","  inflating: hymenoptera_data/val/ants/470127071_8b8ee2bd74.jpg  \n","  inflating: hymenoptera_data/val/ants/477437164_bc3e6e594a.jpg  \n","  inflating: hymenoptera_data/val/ants/488272201_c5aa281348.jpg  \n","  inflating: hymenoptera_data/val/ants/502717153_3e4865621a.jpg  \n","  inflating: hymenoptera_data/val/ants/518746016_bcc28f8b5b.jpg  \n","  inflating: hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg  \n","  inflating: hymenoptera_data/val/ants/562589509_7e55469b97.jpg  \n","  inflating: hymenoptera_data/val/ants/57264437_a19006872f.jpg  \n","  inflating: hymenoptera_data/val/ants/573151833_ebbc274b77.jpg  \n","  inflating: hymenoptera_data/val/ants/649407494_9b6bc4949f.jpg  \n","  inflating: hymenoptera_data/val/ants/751649788_78dd7d16ce.jpg  \n","  inflating: hymenoptera_data/val/ants/768870506_8f115d3d37.jpg  \n","  inflating: hymenoptera_data/val/ants/800px-Meat_eater_ant_qeen_excavating_hole.jpg  \n","  inflating: hymenoptera_data/val/ants/8124241_36b290d372.jpg  \n","  inflating: hymenoptera_data/val/ants/8398478_50ef10c47a.jpg  \n","  inflating: hymenoptera_data/val/ants/854534770_31f6156383.jpg  \n","  inflating: hymenoptera_data/val/ants/892676922_4ab37dce07.jpg  \n","  inflating: hymenoptera_data/val/ants/94999827_36895faade.jpg  \n","  inflating: hymenoptera_data/val/ants/Ant-1818.jpg  \n","  inflating: hymenoptera_data/val/ants/ants-devouring-remains-of-large-dead-insect-on-red-tile-in-Stellenbosch-South-Africa-closeup-1-DHD.jpg  \n","  inflating: hymenoptera_data/val/ants/desert_ant.jpg  \n","  inflating: hymenoptera_data/val/ants/F.pergan.28(f).jpg  \n","  inflating: hymenoptera_data/val/ants/Hormiga.jpg  \n","   creating: hymenoptera_data/val/bees/\n","  inflating: hymenoptera_data/val/bees/1032546534_06907fe3b3.jpg  \n","  inflating: hymenoptera_data/val/bees/10870992_eebeeb3a12.jpg  \n","  inflating: hymenoptera_data/val/bees/1181173278_23c36fac71.jpg  \n","  inflating: hymenoptera_data/val/bees/1297972485_33266a18d9.jpg  \n","  inflating: hymenoptera_data/val/bees/1328423762_f7a88a8451.jpg  \n","  inflating: hymenoptera_data/val/bees/1355974687_1341c1face.jpg  \n","  inflating: hymenoptera_data/val/bees/144098310_a4176fd54d.jpg  \n","  inflating: hymenoptera_data/val/bees/1486120850_490388f84b.jpg  \n","  inflating: hymenoptera_data/val/bees/149973093_da3c446268.jpg  \n","  inflating: hymenoptera_data/val/bees/151594775_ee7dc17b60.jpg  \n","  inflating: hymenoptera_data/val/bees/151603988_2c6f7d14c7.jpg  \n","  inflating: hymenoptera_data/val/bees/1519368889_4270261ee3.jpg  \n","  inflating: hymenoptera_data/val/bees/152789693_220b003452.jpg  \n","  inflating: hymenoptera_data/val/bees/177677657_a38c97e572.jpg  \n","  inflating: hymenoptera_data/val/bees/1799729694_0c40101071.jpg  \n","  inflating: hymenoptera_data/val/bees/181171681_c5a1a82ded.jpg  \n","  inflating: hymenoptera_data/val/bees/187130242_4593a4c610.jpg  \n","  inflating: hymenoptera_data/val/bees/203868383_0fcbb48278.jpg  \n","  inflating: hymenoptera_data/val/bees/2060668999_e11edb10d0.jpg  \n","  inflating: hymenoptera_data/val/bees/2086294791_6f3789d8a6.jpg  \n","  inflating: hymenoptera_data/val/bees/2103637821_8d26ee6b90.jpg  \n","  inflating: hymenoptera_data/val/bees/2104135106_a65eede1de.jpg  \n","  inflating: hymenoptera_data/val/bees/215512424_687e1e0821.jpg  \n","  inflating: hymenoptera_data/val/bees/2173503984_9c6aaaa7e2.jpg  \n","  inflating: hymenoptera_data/val/bees/220376539_20567395d8.jpg  \n","  inflating: hymenoptera_data/val/bees/224841383_d050f5f510.jpg  \n","  inflating: hymenoptera_data/val/bees/2321144482_f3785ba7b2.jpg  \n","  inflating: hymenoptera_data/val/bees/238161922_55fa9a76ae.jpg  \n","  inflating: hymenoptera_data/val/bees/2407809945_fb525ef54d.jpg  \n","  inflating: hymenoptera_data/val/bees/2415414155_1916f03b42.jpg  \n","  inflating: hymenoptera_data/val/bees/2438480600_40a1249879.jpg  \n","  inflating: hymenoptera_data/val/bees/2444778727_4b781ac424.jpg  \n","  inflating: hymenoptera_data/val/bees/2457841282_7867f16639.jpg  \n","  inflating: hymenoptera_data/val/bees/2470492902_3572c90f75.jpg  \n","  inflating: hymenoptera_data/val/bees/2478216347_535c8fe6d7.jpg  \n","  inflating: hymenoptera_data/val/bees/2501530886_e20952b97d.jpg  \n","  inflating: hymenoptera_data/val/bees/2506114833_90a41c5267.jpg  \n","  inflating: hymenoptera_data/val/bees/2509402554_31821cb0b6.jpg  \n","  inflating: hymenoptera_data/val/bees/2525379273_dcb26a516d.jpg  \n","  inflating: hymenoptera_data/val/bees/26589803_5ba7000313.jpg  \n","  inflating: hymenoptera_data/val/bees/2668391343_45e272cd07.jpg  \n","  inflating: hymenoptera_data/val/bees/2670536155_c170f49cd0.jpg  \n","  inflating: hymenoptera_data/val/bees/2685605303_9eed79d59d.jpg  \n","  inflating: hymenoptera_data/val/bees/2702408468_d9ed795f4f.jpg  \n","  inflating: hymenoptera_data/val/bees/2709775832_85b4b50a57.jpg  \n","  inflating: hymenoptera_data/val/bees/2717418782_bd83307d9f.jpg  \n","  inflating: hymenoptera_data/val/bees/272986700_d4d4bf8c4b.jpg  \n","  inflating: hymenoptera_data/val/bees/2741763055_9a7bb00802.jpg  \n","  inflating: hymenoptera_data/val/bees/2745389517_250a397f31.jpg  \n","  inflating: hymenoptera_data/val/bees/2751836205_6f7b5eff30.jpg  \n","  inflating: hymenoptera_data/val/bees/2782079948_8d4e94a826.jpg  \n","  inflating: hymenoptera_data/val/bees/2809496124_5f25b5946a.jpg  \n","  inflating: hymenoptera_data/val/bees/2815838190_0a9889d995.jpg  \n","  inflating: hymenoptera_data/val/bees/2841437312_789699c740.jpg  \n","  inflating: hymenoptera_data/val/bees/2883093452_7e3a1eb53f.jpg  \n","  inflating: hymenoptera_data/val/bees/290082189_f66cb80bfc.jpg  \n","  inflating: hymenoptera_data/val/bees/296565463_d07a7bed96.jpg  \n","  inflating: hymenoptera_data/val/bees/3077452620_548c79fda0.jpg  \n","  inflating: hymenoptera_data/val/bees/348291597_ee836fbb1a.jpg  \n","  inflating: hymenoptera_data/val/bees/350436573_41f4ecb6c8.jpg  \n","  inflating: hymenoptera_data/val/bees/353266603_d3eac7e9a0.jpg  \n","  inflating: hymenoptera_data/val/bees/372228424_16da1f8884.jpg  \n","  inflating: hymenoptera_data/val/bees/400262091_701c00031c.jpg  \n","  inflating: hymenoptera_data/val/bees/416144384_961c326481.jpg  \n","  inflating: hymenoptera_data/val/bees/44105569_16720a960c.jpg  \n","  inflating: hymenoptera_data/val/bees/456097971_860949c4fc.jpg  \n","  inflating: hymenoptera_data/val/bees/464594019_1b24a28bb1.jpg  \n","  inflating: hymenoptera_data/val/bees/485743562_d8cc6b8f73.jpg  \n","  inflating: hymenoptera_data/val/bees/540976476_844950623f.jpg  \n","  inflating: hymenoptera_data/val/bees/54736755_c057723f64.jpg  \n","  inflating: hymenoptera_data/val/bees/57459255_752774f1b2.jpg  \n","  inflating: hymenoptera_data/val/bees/576452297_897023f002.jpg  \n","  inflating: hymenoptera_data/val/bees/586474709_ae436da045.jpg  \n","  inflating: hymenoptera_data/val/bees/590318879_68cf112861.jpg  \n","  inflating: hymenoptera_data/val/bees/59798110_2b6a3c8031.jpg  \n","  inflating: hymenoptera_data/val/bees/603709866_a97c7cfc72.jpg  \n","  inflating: hymenoptera_data/val/bees/603711658_4c8cd2201e.jpg  \n","  inflating: hymenoptera_data/val/bees/65038344_52a45d090d.jpg  \n","  inflating: hymenoptera_data/val/bees/6a00d8341c630a53ef00e553d0beb18834-800wi.jpg  \n","  inflating: hymenoptera_data/val/bees/72100438_73de9f17af.jpg  \n","  inflating: hymenoptera_data/val/bees/759745145_e8bc776ec8.jpg  \n","  inflating: hymenoptera_data/val/bees/936182217_c4caa5222d.jpg  \n","  inflating: hymenoptera_data/val/bees/abeja.jpg  \n"]}]},{"cell_type":"code","execution_count":4,"id":"be2d31f5","metadata":{"id":"be2d31f5","colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"status":"ok","timestamp":1701424632438,"user_tz":-60,"elapsed":928,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"e2508843-671e-48d3-8be7-8fb7970cc1e9"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import os\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","import torchvision\n","from torchvision import datasets, transforms\n","\n","# Data augmentation and normalization for training\n","# Just normalization for validation\n","data_transforms = {\n","    \"train\": transforms.Compose(\n","        [\n","            transforms.RandomResizedCrop(\n","                224\n","            ),  # ImageNet models were trained on 224x224 images\n","            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n","            transforms.ToTensor(),  # convert it to a PyTorch tensor\n","            transforms.Normalize(\n","                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n","            ),  # ImageNet models expect this norm\n","        ]\n","    ),\n","    \"val\": transforms.Compose(\n","        [\n","            transforms.Resize(256),\n","            transforms.CenterCrop(224),\n","            transforms.ToTensor(),\n","            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","        ]\n","    ),\n","}\n","\n","data_dir = \"hymenoptera_data\"\n","# Create train and validation datasets and loaders\n","image_datasets = {\n","    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n","    for x in [\"train\", \"val\"]\n","}\n","dataloaders = {\n","    x: torch.utils.data.DataLoader(\n","        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n","    )\n","    for x in [\"train\", \"val\"]\n","}\n","dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n","class_names = image_datasets[\"train\"].classes\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Helper function for displaying images\n","def imshow(inp, title=None):\n","    \"\"\"Imshow for Tensor.\"\"\"\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","\n","    # Un-normalize the images\n","    inp = std * inp + mean\n","    # Clip just in case\n","    inp = np.clip(inp, 0, 1)\n","    plt.imshow(inp)\n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.001)  # pause a bit so that plots are updated\n","    plt.show()\n","\n","\n","# Get a batch of training data\n","inputs, classes = next(iter(dataloaders[\"train\"]))\n","\n","# Make a grid from batch\n","out = torchvision.utils.make_grid(inputs)\n","\n","imshow(out, title=[class_names[x] for x in classes])\n","\n"]},{"cell_type":"markdown","id":"bbd48800","metadata":{"id":"bbd48800"},"source":["Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."]},{"cell_type":"code","execution_count":5,"id":"572d824c","metadata":{"id":"572d824c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701424685991,"user_tz":-60,"elapsed":48393,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"0623d86b-e03a-42dc-e7e0-08b08042f020"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n","  warnings.warn(_create_warning_msg(\n","/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n","Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n","100%|██████████| 44.7M/44.7M [00:00<00:00, 119MB/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n","  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"]},{"output_type":"stream","name":"stdout","text":["train Loss: 0.6195 Acc: 0.7131\n","val Loss: 0.2378 Acc: 0.9281\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.5774 Acc: 0.7623\n","val Loss: 0.2003 Acc: 0.9281\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.5762 Acc: 0.7541\n","val Loss: 0.2884 Acc: 0.8824\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.4840 Acc: 0.7787\n","val Loss: 0.1974 Acc: 0.9477\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.5062 Acc: 0.7910\n","val Loss: 0.2426 Acc: 0.9281\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.4300 Acc: 0.8197\n","val Loss: 0.3305 Acc: 0.8758\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.2380 Acc: 0.8852\n","val Loss: 0.1955 Acc: 0.9346\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.3416 Acc: 0.8525\n","val Loss: 0.1956 Acc: 0.9477\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.4384 Acc: 0.7828\n","val Loss: 0.2050 Acc: 0.9216\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.2982 Acc: 0.8811\n","val Loss: 0.2003 Acc: 0.9216\n","\n","Training complete in 0m 42s\n","Best val Acc: 0.947712\n"]}],"source":["import copy\n","import os\n","import time\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torchvision\n","from torch.optim import lr_scheduler\n","from torchvision import datasets, transforms\n","\n","# Data augmentation and normalization for training\n","# Just normalization for validation\n","data_transforms = {\n","    \"train\": transforms.Compose(\n","        [\n","            transforms.RandomResizedCrop(\n","                224\n","            ),  # ImageNet models were trained on 224x224 images\n","            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n","            transforms.ToTensor(),  # convert it to a PyTorch tensor\n","            transforms.Normalize(\n","                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n","            ),  # ImageNet models expect this norm\n","        ]\n","    ),\n","    \"val\": transforms.Compose(\n","        [\n","            transforms.Resize(256),\n","            transforms.CenterCrop(224),\n","            transforms.ToTensor(),\n","            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n","        ]\n","    ),\n","}\n","\n","data_dir = \"hymenoptera_data\"\n","# Create train and validation datasets and loaders\n","image_datasets = {\n","    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n","    for x in [\"train\", \"val\"]\n","}\n","dataloaders = {\n","    x: torch.utils.data.DataLoader(\n","        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n","    )\n","    for x in [\"train\", \"val\"]\n","}\n","dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n","class_names = image_datasets[\"train\"].classes\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Helper function for displaying images\n","def imshow(inp, title=None):\n","    \"\"\"Imshow for Tensor.\"\"\"\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","\n","    # Un-normalize the images\n","    inp = std * inp + mean\n","    # Clip just in case\n","    inp = np.clip(inp, 0, 1)\n","    plt.imshow(inp)\n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.001)  # pause a bit so that plots are updated\n","    plt.show()\n","\n","\n","# Get a batch of training data\n","# inputs, classes = next(iter(dataloaders['train']))\n","\n","# Make a grid from batch\n","# out = torchvision.utils.make_grid(inputs)\n","\n","# imshow(out, title=[class_names[x] for x in classes])\n","# training\n","\n","\n","def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n","    since = time.time()\n","\n","    best_model_wts = copy.deepcopy(model.state_dict())\n","    best_acc = 0.0\n","\n","    epoch_time = []  # we'll keep track of the time needed for each epoch\n","\n","    for epoch in range(num_epochs):\n","        epoch_start = time.time()\n","        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n","        print(\"-\" * 10)\n","\n","        # Each epoch has a training and validation phase\n","        for phase in [\"train\", \"val\"]:\n","            if phase == \"train\":\n","                scheduler.step()\n","                model.train()  # Set model to training mode\n","            else:\n","                model.eval()  # Set model to evaluate mode\n","\n","            running_loss = 0.0\n","            running_corrects = 0\n","\n","            # Iterate over data.\n","            for inputs, labels in dataloaders[phase]:\n","                inputs = inputs.to(device)\n","                labels = labels.to(device)\n","\n","                # zero the parameter gradients\n","                optimizer.zero_grad()\n","\n","                # Forward\n","                # Track history if only in training phase\n","                with torch.set_grad_enabled(phase == \"train\"):\n","                    outputs = model(inputs)\n","                    _, preds = torch.max(outputs, 1)\n","                    loss = criterion(outputs, labels)\n","\n","                    # backward + optimize only if in training phase\n","                    if phase == \"train\":\n","                        loss.backward()\n","                        optimizer.step()\n","\n","                # Statistics\n","                running_loss += loss.item() * inputs.size(0)\n","                running_corrects += torch.sum(preds == labels.data)\n","\n","            epoch_loss = running_loss / dataset_sizes[phase]\n","            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n","\n","            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n","\n","            # Deep copy the model\n","            if phase == \"val\" and epoch_acc > best_acc:\n","                best_acc = epoch_acc\n","                best_model_wts = copy.deepcopy(model.state_dict())\n","\n","        # Add the epoch time\n","        t_epoch = time.time() - epoch_start\n","        epoch_time.append(t_epoch)\n","        print()\n","\n","    time_elapsed = time.time() - since\n","    print(\n","        \"Training complete in {:.0f}m {:.0f}s\".format(\n","            time_elapsed // 60, time_elapsed % 60\n","        )\n","    )\n","    print(\"Best val Acc: {:4f}\".format(best_acc))\n","\n","    # Load best model weights\n","    model.load_state_dict(best_model_wts)\n","    return model, epoch_time\n","\n","\n","# Download a pre-trained ResNet18 model and freeze its weights\n","model = torchvision.models.resnet18(pretrained=True)\n","for param in model.parameters():\n","    param.requires_grad = False\n","\n","# Replace the final fully connected layer\n","# Parameters of newly constructed modules have requires_grad=True by default\n","num_ftrs = model.fc.in_features\n","model.fc = nn.Linear(num_ftrs, 2)\n","# Send the model to the GPU\n","model = model.to(device)\n","# Set the loss function\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that only the parameters of the final layer are being optimized\n","optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n","model, epoch_time = train_model(\n","    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"ac-bvTMY-LkN"},"source":["Experiments:\n","Study the code and the results obtained.\n","\n","Modify the code and add an \"eval_model\" function to allow\n","the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n","\n","Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n","\n","Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."],"id":"ac-bvTMY-LkN"},{"cell_type":"code","source":["# Function to evaluate the accuracy of the model on a test folder of images from the internet\n","def eval_mode(model):\n","\n","    # track test loss\n","    test_loss = 0.0\n","    class_correct = list(0.0 for i in range(10))\n","    class_total = list(0.0 for i in range(10))\n","\n","    model.eval()\n","    # iterate over test data\n","    for data, target in test_loader:\n","        # forward pass: compute predicted outputs by passing inputs to the model\n","        output = model(data)\n","        # calculate the batch loss\n","        loss = criterion(output, target)\n","        # update test loss\n","        test_loss += loss.item() * data.size(0)\n","        # convert output probabilities to predicted class\n","        _, pred = torch.max(output, 1)\n","        # compare predictions to true label\n","        correct_tensor = pred.eq(target.data.view_as(pred))\n","        correct = (\n","            np.squeeze(correct_tensor.numpy())\n","            if not train_on_gpu\n","            else np.squeeze(correct_tensor.cpu().numpy())\n","        )\n","        # calculate test accuracy for each object class\n","        for i in range(batch_size):\n","            label = target.data[i]\n","            class_correct[label] += correct[i].item()\n","            class_total[label] += 1\n","\n","    # average test loss\n","    test_loss = test_loss / len(test_loader)\n","    print(f\"Test Loss: {test_loss:.6f}\\n\")\n","\n","    for i in range(10):\n","        if class_total[i] > 0:\n","            accuracy = 100 * class_correct[i] / class_total[i]\n","            print(f\"Test Accuracy of {classes[i]}: {accuracy:.2f}% \"\n","                  f\"({int(np.sum(class_correct[i]))}/{int(np.sum(class_total[i]))})\")\n","        else:\n","            print(f\"Test Accuracy of {classes[i]}: N/A (no training examples)\")\n","\n","    overall_accuracy = 100.0 * np.sum(class_correct) / np.sum(class_total)\n","    print(f\"\\nTest Accuracy (Overall): {overall_accuracy:.2f}% \"\n","          f\"({int(np.sum(class_correct))}/{int(np.sum(class_total))})\")"],"metadata":{"id":"9wj4N6we8DIQ"},"id":"9wj4N6we8DIQ","execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Get a pre-trained ResNet18 model\n","new_resNet18 = torchvision.models.resnet18(pretrained=True)\n","for param in new_resNet18.parameters():\n","    param.requires_grad = False\n","\n","new_resNet18.parameters = new_resNet18.parameters\n","\n","# First classification layer\n","in_features = new_resNet18.fc.in_features\n","out_features = 16\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","\n","# Second classification layer where we use a \"relu\" activation function for this middle layer\n","new_resNet18.fc2 = nn.Linear(out_features, 2)\n","\n","# Set the loss function\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that only the parameters of the final layer are being optimized\n","optimizer_conv = optim.SGD(new_resNet18.fc.parameters(), lr=0.001, momentum=0.9)\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n","val_loss, train_loss, val_accuracy, train_accuracy = [], [], [], []\n","new_resNet18, epoch_time = train_model(\n","    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",")"],"metadata":{"id":"CU1Ot6rt8FdD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701423029527,"user_tz":-60,"elapsed":34613,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"2fb10135-1a41-47e9-b8fa-d13e61c38ac0"},"id":"CU1Ot6rt8FdD","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n","train Loss: 0.2825 Acc: 0.8852\n","val Loss: 0.1934 Acc: 0.9412\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.3601 Acc: 0.8607\n","val Loss: 0.1822 Acc: 0.9477\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.4400 Acc: 0.8074\n","val Loss: 0.2581 Acc: 0.9150\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.3524 Acc: 0.8361\n","val Loss: 0.1873 Acc: 0.9346\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.3934 Acc: 0.8402\n","val Loss: 0.1931 Acc: 0.9412\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.2766 Acc: 0.8770\n","val Loss: 0.2002 Acc: 0.9412\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.3498 Acc: 0.8525\n","val Loss: 0.2010 Acc: 0.9412\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.3339 Acc: 0.8607\n","val Loss: 0.1672 Acc: 0.9477\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.3057 Acc: 0.8361\n","val Loss: 0.2184 Acc: 0.9412\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.4405 Acc: 0.8320\n","val Loss: 0.1864 Acc: 0.9412\n","\n","Training complete in 0m 34s\n","Best val Acc: 0.947712\n"]}]},{"cell_type":"code","source":["import torchvision.models as models\n","new_resNet18_quantized = torch.quantization.quantize_dynamic(new_resNet18, dtype=torch.qint8)\n","\n","size_resNet18 = print_size_of_model(new_resNet18, \"fp32\")\n","size_resNet18_quantized = print_size_of_model(new_resNet18_quantized, \"fp32\")\n","\n","print(\n","    f\"\\nThe size of the ResNet18 model is {size_resNet18 / 1000000:.2f} MB. \\nThe size of the Quantized ResNet18 model is {size_resNet18_quantized / 1000000:.2f} MB, which is {size_resNet18 / size_resNet18_quantized:.0f} times smaller than the ResNet18 model\"\n",")"],"metadata":{"id":"UTpZmkFJ8P11","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701423224431,"user_tz":-60,"elapsed":607,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d2e59a7c-390d-48b6-a086-e94944c47c28"},"id":"UTpZmkFJ8P11","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["model:  fp32  \t Size (KB): 44782.148\n","model:  fp32  \t Size (KB): 44779.834\n","\n","The size of the ResNet18 model is 44.78 MB. \n","The size of the Quantized ResNet18 model is 44.78 MB, which is 1 times smaller than the ResNet18 model\n"]}]},{"cell_type":"markdown","id":"04a263f0","metadata":{"id":"04a263f0"},"source":["## Optional\n","    \n","Try this at home!!\n","\n","\n","Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n","\n","The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n","\n"]},{"cell_type":"markdown","id":"fe954ce4","metadata":{"id":"fe954ce4"},"source":["## Author\n","\n","Alberto BOSIO - Ph. D."]}],"metadata":{"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":"3.11.5"},"vscode":{"interpreter":{"hash":"9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"}},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file