{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# ResNet-50 in PyTorch","metadata":{}},{"cell_type":"markdown","source":"This notebook is an implementation of the coursework from the [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) in CNNs - Week 2. \n\nThe objective is replicate the identity and convolutional blocks to then implement the ResNet-50 in `Pytorch` instead of `Tensorflow` as seen in the coursework.\n\n* Adapted from: Deep Learning Specialization by Andrew Ng, Coursera\n  \n  Source: Course Assignment - [Convolutional Neural Networks], Week 2\n  \n  URL: https://www.coursera.org/specializations/deep-learning\n\n  Retrieved: October 2025\n  ","metadata":{}},{"cell_type":"markdown","source":"## Import Libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:23:40.288116Z","iopub.execute_input":"2025-10-15T18:23:40.288384Z","iopub.status.idle":"2025-10-15T18:23:44.267618Z","shell.execute_reply.started":"2025-10-15T18:23:40.288362Z","shell.execute_reply":"2025-10-15T18:23:44.266503Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 1. Identity Block","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:28492b25-c5ef-467c-8090-2e5213356e37.png)\n<caption><center> </u><font color='black'> <b>Identity block structure</center></caption>\n\n* The identity block is composed of:\n\n  * Main Path:\n    1. CONV2D - BatchNorm - ReLU\n    2. CONV2D - BatchNorm - ReLU\n    3. CONV2D - BatchNorm\n\n  * Shortcut:\n \n    The output of the 3rd layer of the main path is added to the original input.","metadata":{},"attachments":{"05f6870e-031d-4574-8507-b7ea303b91db.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Set up the random uniform initializer for the conv layers that works similar\n# to TensorFlow's random_uniform initializer\ndef random_uniform_initializer(tensor, a=-0.05, b=0.05, seed=0):\n    torch.manual_seed(seed)\n    return nn.init.uniform_(tensor, a=a, b=b)\n    \n# Indentity Block Class:\nclass IdentityBlock(nn.Module):\n    def __init__(self, in_channels, f, filters, initializer=random_uniform_initializer):\n        super(IdentityBlock, self).__init__()\n        F1, F2, F3 = filters\n\n        self.conv1 = nn.Conv2d(in_channels, F1, kernel_size=1, stride=1, padding=0)\n        self.bn1 = nn.BatchNorm2d(F1)\n        self.relu1 = nn.ReLU(inplace=True)\n\n        self.conv2 = nn.Conv2d(F1, F2, kernel_size=f, stride=1, padding=f//2) # 'same' padding\n        self.bn2 = nn.BatchNorm2d(F2)\n        self.relu2 = nn.ReLU(inplace=True)\n\n        self.conv3 = nn.Conv2d(F2, F3, kernel_size=1, stride=1, padding=0)\n        self.bn3 = nn.BatchNorm2d(F3)\n        self.relu_output = nn.ReLU(inplace=True)\n\n        # Apply random uniform initializer to all conv layers\n        initializer(self.conv1.weight)\n        initializer(self.conv2.weight)\n        initializer(self.conv3.weight)\n\n    def forward(self, X):\n        X_shortcut = X\n\n        # First component of main path\n        X = self.conv1(X)\n        X = self.bn1(X)\n        X = self.relu1(X)\n\n        # Second component of main path\n        X = self.conv2(X)\n        X = self.bn2(X)\n        X = self.relu2(X)\n\n        # Third component of main path\n        X = self.conv3(X)\n        X = self.bn3(X)\n\n        # Add shortcut and final activation\n        X = X + X_shortcut\n        X = self.relu_output(X)\n\n        return X","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:23:49.643318Z","iopub.execute_input":"2025-10-15T18:23:49.644069Z","iopub.status.idle":"2025-10-15T18:23:49.652612Z","shell.execute_reply.started":"2025-10-15T18:23:49.644040Z","shell.execute_reply":"2025-10-15T18:23:49.651765Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 2. Convolutional Block","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:661f6f6b-37ae-4dd6-956c-bedb4ed27df3.png)\n<caption><center> </u><font color='black'> <b>Convolutional block structure</center></caption>\n\n* The identity block is composed of:\n\n  * Main Path:\n    1. CONV2D - BatchNorm - ReLU\n    2. CONV2D - BatchNorm - ReLU\n    3. CONV2D - BatchNorm\n\n  * Shortcut:\n \n    CONV2D - BatchNorm","metadata":{},"attachments":{"661f6f6b-37ae-4dd6-956c-bedb4ed27df3.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Set up glorot uniform initializer for the conv layers \ndef glorot_uniform_initializer(tensor, seed=0):\n    torch.manual_seed(seed)\n    nn.init.xavier_uniform_(tensor)\n    \n# Convolutional Block Class:\nclass ConvBlock(nn.Module):\n    def __init__(self, in_channels, f, filters, s=2, initializer=glorot_uniform_initializer):\n        super(ConvBlock, self).__init__()\n        F1, F2, F3 = filters\n\n        self.conv1 = nn.Conv2d(in_channels, F1, kernel_size=1, stride=s, padding=0)\n        self.bn1 = nn.BatchNorm2d(F1)\n        self.relu1 = nn.ReLU(inplace=True)\n\n        self.conv2 = nn.Conv2d(F1, F2, kernel_size=f, stride=1, padding=f//2) # 'same' padding\n        self.bn2 = nn.BatchNorm2d(F2)\n        self.relu2 = nn.ReLU(inplace=True)\n\n        self.conv3 = nn.Conv2d(F2, F3, kernel_size=1, stride=1, padding=0)\n        self.bn3 = nn.BatchNorm2d(F3)\n\n        self.conv_shortcut = nn.Conv2d(in_channels, F3, kernel_size=1, stride=s, padding=0)\n        self.bn_shortcut = nn.BatchNorm2d(F3)\n        self.relu_output = nn.ReLU(inplace=True)\n\n        # Apply glorot uniform initializer to all conv layers\n        initializer(self.conv1.weight)\n        initializer(self.conv2.weight)\n        initializer(self.conv3.weight)\n        initializer(self.conv_shortcut.weight)\n\n    def forward(self, X):\n        X_shortcut = X\n\n        # First component of main path\n        X = self.conv1(X)\n        X = self.bn1(X)\n        X = self.relu1(X)\n        \n        # Second component of main path\n        X = self.conv2(X)\n        X = self.bn2(X)\n        X = self.relu2(X)\n\n        # Third component of main path\n        X = self.conv3(X)\n        X = self.bn3(X)\n\n        # Shortcut convolution and final activation\n        X_shortcut = self.conv_shortcut(X_shortcut)\n        X_shortcut = self.bn_shortcut(X_shortcut)\n        X = X + X_shortcut\n        X = self.relu_output(X)\n\n        return X","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:23:53.636627Z","iopub.execute_input":"2025-10-15T18:23:53.636942Z","iopub.status.idle":"2025-10-15T18:23:53.645636Z","shell.execute_reply.started":"2025-10-15T18:23:53.636923Z","shell.execute_reply":"2025-10-15T18:23:53.644945Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 3. RestNet-50 Model","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:2f828237-daa5-4f28-8581-5f719b1d5b31.png)\n\n<caption><center> </u><font color='black'> <b>ResNet-50 structure</center></caption>\n","metadata":{},"attachments":{"2f828237-daa5-4f28-8581-5f719b1d5b31.png":{"image/png":"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"}}},{"cell_type":"code","source":"class ResNet50(nn.Module):\n    def __init__(self, input_shape=(3, 64, 64), classes=6, initializer=glorot_uniform_initializer):\n        super(ResNet50, self).__init__()\n        # Stage 1\n        self.pad = nn.ConstantPad2d(3, 0)  # ZeroPadding2D((3, 3))\n        self.conv1 = nn.Conv2d(input_shape[0], 64, kernel_size=7, stride=2, padding=0)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu1 = nn.ReLU(inplace=True)\n        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)\n        \n        # Stage 2\n        self.stage2_0 = ConvBlock(64, f=3, filters=[64, 64, 256], s=1)\n        self.stage2_1 = IdentityBlock(256, f=3, filters=[64, 64, 256])\n        self.stage2_2 = IdentityBlock(256, f=3, filters=[64, 64, 256])\n\n        # Stage 3\n        self.stage3_0 = ConvBlock(256, f=3, filters=[128, 128, 512], s=2)\n        self.stage3_1 = IdentityBlock(512, f=3, filters=[128, 128, 512])\n        self.stage3_2 = IdentityBlock(512, f=3, filters=[128, 128, 512])\n        self.stage3_3 = IdentityBlock(512, f=3, filters=[128, 128, 512])\n\n        # Stage 4\n        self.stage4_0 = ConvBlock(512, f=3, filters=[256, 256, 1024], s=2)\n        self.stage4_1 = IdentityBlock(1024, f=3, filters=[256, 256, 1024])\n        self.stage4_2 = IdentityBlock(1024, f=3, filters=[256, 256, 1024])\n        self.stage4_3 = IdentityBlock(1024, f=3, filters=[256, 256, 1024])\n        self.stage4_4 = IdentityBlock(1024, f=3, filters=[256, 256, 1024])\n        self.stage4_5 = IdentityBlock(1024, f=3, filters=[256, 256, 1024])\n\n        # Stage 5\n        self.stage5_0 = ConvBlock(1024, f=3, filters=[512, 512, 2048], s=2)\n        self.stage5_1 = IdentityBlock(2048, f=3, filters=[512, 512, 2048])\n        self.stage5_2 = IdentityBlock(2048, f=3, filters=[512, 512, 2048])\n\n        self.avgpool = nn.AvgPool2d(kernel_size=2)\n        self.flatten = nn.Flatten()\n        self.fc = nn.Linear(2048 * 1 * 1, classes)\n\n        # Apply glorot uniform initializer to Stage 1 and FC layer\n        initializer(self.conv1.weight)\n        initializer(self.fc.weight)\n\n    def forward(self, x):\n        # Stage 1\n        x = self.pad(x)\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu1(x)\n        x = self.maxpool1(x)\n\n        # Stage 2\n        x = self.stage2_0(x)\n        x = self.stage2_1(x)\n        x = self.stage2_2(x)\n\n        # Stage 3\n        x = self.stage3_0(x)\n        x = self.stage3_1(x)\n        x = self.stage3_2(x)\n        x = self.stage3_3(x)\n\n        # Stage 4\n        x = self.stage4_0(x)\n        x = self.stage4_1(x)\n        x = self.stage4_2(x)\n        x = self.stage4_3(x)\n        x = self.stage4_4(x)\n        x = self.stage4_5(x)\n\n        # Stage 5\n        x = self.stage5_0(x)\n        x = self.stage5_1(x)\n        x = self.stage5_2(x)\n\n        x = self.avgpool(x)\n        x = self.flatten(x)\n        x = self.fc(x)\n        # x = F.softmax(x, dim=1) this is commented because i'll use nn.CrossEntropyLoss() later\n        return x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:23:56.723282Z","iopub.execute_input":"2025-10-15T18:23:56.723562Z","iopub.status.idle":"2025-10-15T18:23:56.736117Z","shell.execute_reply.started":"2025-10-15T18:23:56.723542Z","shell.execute_reply":"2025-10-15T18:23:56.735383Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 4. Train the model","metadata":{}},{"cell_type":"markdown","source":"To train/test the model I'll use the [Dogs vs. Cats Dataset](https://www.kaggle.com/competitions/dogs-vs-cats), that contains 25,000 labeled images of cats and dogs","metadata":{}},{"cell_type":"markdown","source":"### Data Preprocessing","metadata":{}},{"cell_type":"markdown","source":"Extract the zip file that contains the pictures for training:","metadata":{}},{"cell_type":"code","source":"import os\nimport zipfile\n\n# Extract the zip file of training\nwith zipfile.ZipFile('/kaggle/input/dogs-vs-cats/train.zip', 'r') as zip_ref:\n    zip_ref.extractall('/kaggle/working/train')\n\nwith zipfile.ZipFile('/kaggle/input/dogs-vs-cats/test1.zip', 'r') as zip_ref:\n    zip_ref.extractall('/kaggle/working/test')\n\nprint(len(os.listdir('/kaggle/working/train')))\nprint(os.listdir('/kaggle/working/train')[:10])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:24:00.651459Z","iopub.execute_input":"2025-10-15T18:24:00.652093Z","iopub.status.idle":"2025-10-15T18:24:13.489523Z","shell.execute_reply.started":"2025-10-15T18:24:00.652068Z","shell.execute_reply":"2025-10-15T18:24:13.488828Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Set up the root direction:","metadata":{}},{"cell_type":"code","source":"root_dir_train = '/kaggle/working/train/train'\nroot_dir_test = '/kaggle/working/test/test1'\nprint(len(os.listdir(root_dir_train)))           # Should be 25000\nprint(os.listdir(root_dir_train)[:10])\nprint(len(os.listdir(root_dir_test)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:24:15.912704Z","iopub.execute_input":"2025-10-15T18:24:15.913028Z","iopub.status.idle":"2025-10-15T18:24:15.954266Z","shell.execute_reply.started":"2025-10-15T18:24:15.912980Z","shell.execute_reply":"2025-10-15T18:24:15.953399Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\n\nclass CatsDogsDataset(Dataset):\n    def __init__(self, root_dir, transform=None):\n        self.root_dir = root_dir\n        self.files = [f for f in os.listdir(root_dir) if f.endswith('.jpg')]\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.files)\n\n    def __getitem__(self, idx):\n        img_name = self.files[idx]\n        label = 0 if img_name.startswith('cat') else 1  # 0: cat, 1: dog\n        img_path = os.path.join(self.root_dir, img_name)\n        image = Image.open(img_path).convert('RGB')\n        if self.transform:\n            image = self.transform(image)\n        return image, label\n\n# Transforms (match ResNet50 input size and normalization)\ntransform = transforms.Compose([\n    transforms.Resize((64, 64)),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406],\n                         [0.229, 0.224, 0.225])\n])\n\ndataset_train = CatsDogsDataset(root_dir_train, transform=transform)\ndataset_test = CatsDogsDataset(root_dir_test, transform=transform)\n\ntrain_loader = DataLoader(dataset_train, batch_size=32, shuffle=True, num_workers=2)\nval_loader = DataLoader(dataset_test, batch_size=32, shuffle=False, num_workers=2)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:24:17.816361Z","iopub.execute_input":"2025-10-15T18:24:17.816976Z","iopub.status.idle":"2025-10-15T18:24:21.180726Z","shell.execute_reply.started":"2025-10-15T18:24:17.816952Z","shell.execute_reply":"2025-10-15T18:24:21.180156Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Training and Validation Loops","metadata":{}},{"cell_type":"code","source":"model = ResNet50()\n# Optimizer and Loss Function\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = model.to(device)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=0.0015)\ncriterion = nn.CrossEntropyLoss()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:24:24.877357Z","iopub.execute_input":"2025-10-15T18:24:24.878121Z","iopub.status.idle":"2025-10-15T18:24:25.568023Z","shell.execute_reply.started":"2025-10-15T18:24:24.878097Z","shell.execute_reply":"2025-10-15T18:24:25.567399Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"n_epochs = 10\ntrain_losses, val_losses = [], []\ntrain_accuracies, val_accuracies = [], []\n\nfor epoch in range(n_epochs):\n    # Training\n    model.train()\n    train_loss, train_correct, train_total = 0.0, 0, 0\n    for images, labels in train_loader:\n        images, labels = images.to(device), labels.to(device)\n        optimizer.zero_grad()                     # Clear previous gradients\n        outputs = model(images)                   # Forward pass\n        loss = criterion(outputs, labels)         # Compute Loss\n        loss.backward()                           # Backward pass\n        optimizer.step()                          # Update Parameters\n        \n        train_loss += loss.item() * images.size(0)\n        _, preds = torch.max(outputs, 1)\n        train_correct += (preds == labels).sum().item()\n        train_total += labels.size(0)\n    train_loss /= train_total\n    train_acc = train_correct / train_total\n\n    # Validation\n    model.eval()\n    val_loss, val_correct, val_total = 0.0, 0, 0\n    with torch.no_grad():\n        for images, labels in val_loader:\n            images, labels = images.to(device), labels.to(device)\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n            val_loss += loss.item() * images.size(0)\n            _, preds = torch.max(outputs, 1)\n            val_correct += (preds == labels).sum().item()\n            val_total += labels.size(0)\n    val_loss /= val_total\n    val_acc = val_correct / val_total\n\n    # Store metrics\n    train_losses.append(train_loss)\n    val_losses.append(val_loss)\n    train_accuracies.append(train_acc)\n    val_accuracies.append(val_acc)\n\n    print(f\"Epoch {epoch+1}/{n_epochs} | Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:24:29.015264Z","iopub.execute_input":"2025-10-15T18:24:29.015533Z","iopub.status.idle":"2025-10-15T18:34:45.072929Z","shell.execute_reply.started":"2025-10-15T18:24:29.015513Z","shell.execute_reply":"2025-10-15T18:34:45.072148Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Plotting Loss/Accuracy:\ndf_metrics = pd.DataFrame({\n    'train_loss': train_losses,\n    'val_loss': val_losses,\n    'train_acc': train_accuracies,\n    'val_acc': val_accuracies\n})\n\n# Loss\ndf_metrics[['train_loss', 'val_loss']].plot(title='Model Loss', figsize=(12,8)).set(xlabel='Epoch', ylabel='Loss')\nplt.show()\n\n# Accuracy\ndf_metrics[['train_acc', 'val_acc']].plot(title='Model Accuracy', figsize=(12,8)).set(xlabel='Epoch', ylabel='Accuracy')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-15T18:35:14.214699Z","iopub.execute_input":"2025-10-15T18:35:14.215403Z","iopub.status.idle":"2025-10-15T18:35:14.700733Z","shell.execute_reply.started":"2025-10-15T18:35:14.215375Z","shell.execute_reply":"2025-10-15T18:35:14.700063Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Conclusions","metadata":{}},{"cell_type":"markdown","source":"As we can see, the performance of the model is pretty poor and with a lot of fluctuation, but the pourpose of this little application was to get a functional ResNet in Pytorch.\n\n* To power up the performance we could run the ResNet for a longer time, as this result came from running only for 10 epochs (roughly 10 minutes, I wanted to save the free Kaggle's GPU for bigger projects).\n* Other possible thing to do would be tweak the hyperparameters and/or layers structure, as I exactly implemented the same worked in [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning), from **Keras** to **Torch**.","metadata":{}}]}