[Question 4-6] Add sklearn confusion matrix
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@ -137,6 +137,13 @@
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"image_transform = transforms.Compose(\n",
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" [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
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"\n",
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"# image_transform = transforms.Compose([\n",
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"# # transforms.Resize(256),\n",
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"# # transforms.CenterCrop(224),\n",
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"# transforms.ToTensor(),\n",
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"# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
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"# ])\n",
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"\n",
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"test_data = torchvision.datasets.ImageFolder('data/EXCV10/val/', transform=image_transform)\n",
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"test_loader = DataLoader(test_data, batch_size=64, shuffle=False, num_workers=4, pin_memory=True)"
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]
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@ -208,7 +215,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test Accuracy: 75.8%\n"
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"Test Accuracy: 70.05%\n"
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]
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}
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],
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@ -232,37 +239,25 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_cnn(model, test_loader):\n",
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" \"\"\"\n",
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" Test the trained ResNet model on the test dataset.\n",
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"\n",
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" Args:\n",
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" model (nn.Module): The trained ResNet model.\n",
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" test_loader (DataLoader): Data loader for the test data.\n",
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" \n",
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" Returns:\n",
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" float: Test accuracy.\n",
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" list: Predicted labels.\n",
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" list: True labels.\n",
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" \"\"\"\n",
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" model.eval()\n",
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" correct = 0\n",
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"def test_cnn(model, test_loader, device='cpu'):\n",
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" model.to(device)\n",
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" model.eval() \n",
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" total = 0\n",
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" predicted_labels = []\n",
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" true_labels = []\n",
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" correct_num = 0\n",
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" all_predicted_labels = []\n",
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"\n",
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" with torch.no_grad():\n",
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" with torch.no_grad(): # No need to track gradients for testing\n",
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" for images, labels in test_loader:\n",
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" images, labels = images.to(device), labels.to(device)\n",
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" outputs = model(images)\n",
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" _, predicted = torch.max(outputs.data, 1)\n",
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" total += labels.size(0)\n",
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" correct += (predicted == labels).sum().item()\n",
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" predicted_labels.extend(predicted.tolist())\n",
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" true_labels.extend(labels.tolist())\n",
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" correct_num += (predicted == labels).sum().item() \n",
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" all_predicted_labels.append(predicted.cpu().numpy())\n",
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"\n",
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" accuracy = correct / total\n",
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"\n",
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" return predicted_labels, accuracy*100"
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" accuracy = (correct_num / total) * 100\n",
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" all_predicted_labels = np.concatenate(all_predicted_labels)\n",
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" return all_predicted_labels, accuracy"
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]
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},
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{
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@ -275,7 +270,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test Accuracy: 75.8%\n"
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"Test Accuracy: 70.05%\n"
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]
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}
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],
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@ -286,7 +281,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "17bc1948",
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"id": "985e4f91",
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"metadata": {},
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"source": [
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"### Test (Should output ALL PASS)"
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@ -295,15 +290,15 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "853c4db3",
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"id": "694097e2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test accuracy: 75.8\n",
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"Score 100%: 15.0\n",
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"Test accuracy: 70.05\n",
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"Score 90%: 13.5\n",
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"ALL PASS\n"
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]
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}
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@ -388,9 +383,32 @@
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"m1_confusion_matrix = m1_compute_confusion_matrix(true_labels, m1_predicted_labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "60591999",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import confusion_matrix\n",
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"\n",
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"def m2_compute_confusion_matrix(true, predictions):\n",
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" return confusion_matrix(true, predictions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "44b131f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"m2_confusion_matrix = m2_compute_confusion_matrix(true_labels, m1_predicted_labels)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "608265af",
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"id": "dd78bea6",
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"metadata": {},
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"source": [
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"### Put Students' implementations here"
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@ -398,43 +416,33 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 19,
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"id": "1dce952c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_confusion_matrix(true_labels, predicted_labels):\n",
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"\n",
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" # Ensure inputs are NumPy arrays\n",
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" true_labels = np.array(true_labels)\n",
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" predicted_labels = np.array(predicted_labels)\n",
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"\n",
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" # Determine the number of classes\n",
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" num_classes = len(np.unique(true_labels))\n",
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"\n",
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" # Initialize the confusion matrix with zeros\n",
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" cm = np.zeros((num_classes, num_classes))\n",
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"\n",
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" # Count occurrences of true-predicted label pairs\n",
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" for i in range(len(true_labels)):\n",
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" cm[true_labels[i]][predicted_labels[i]] += 1\n",
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"\n",
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" return cm"
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"def compute_confusion_matrix(true, predictions):\n",
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" unique_labels = np.unique(np.concatenate((true, predictions)))\n",
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" confusion_matrix = np.zeros((len(unique_labels), len(unique_labels)), dtype=np.int64)\n",
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" for i, true_label in enumerate(unique_labels):\n",
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" for j, predicted_label in enumerate(unique_labels):\n",
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" confusion_matrix[i, j] = np.sum((true == true_label) & (predictions == predicted_label))\n",
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" return confusion_matrix"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "21917014",
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"execution_count": 20,
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"id": "1945d637",
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"metadata": {},
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"outputs": [],
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"source": [
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"confusion_matrix = m1_compute_confusion_matrix(true_labels, m1_predicted_labels)"
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"confusion_matrix = compute_confusion_matrix(true_labels, predicted_labels)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "935956b7",
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"id": "3e1fd6eb",
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"metadata": {},
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"source": [
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"### Test (Should output ALL PASS)"
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@ -442,8 +450,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "b77da2e8",
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"execution_count": 21,
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"id": "6c87f7d6",
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"metadata": {},
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"outputs": [
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{
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@ -455,6 +463,7 @@
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}
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],
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"source": [
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"assert np.allclose(m1_confusion_matrix, m2_confusion_matrix)\n",
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"assert np.allclose(confusion_matrix, m1_confusion_matrix)\n",
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"\n",
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"print(\"ALL PASS\")"
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@ -463,7 +472,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "adc0a7c7",
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"id": "d9bb6316",
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"metadata": {},
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"outputs": [],
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"source": []
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