[Question 4-6] Add a good model
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@ -16,6 +16,7 @@
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from ca_utils import ResNet, BasicBlock"
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]
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},
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@ -96,7 +97,8 @@
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}
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],
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"source": [
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"checkpoint = torch.load(\"data/weights_resnet.pth\")\n",
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"checkpoint = torch.load(\"data/weights_resnet.pth\", map_location=torch.device('cpu'))\n",
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"\n",
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"model.load_state_dict(checkpoint)\n",
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"model.eval()"
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]
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@ -118,7 +120,9 @@
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"source": [
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"import torchvision\n",
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"from torch.utils.data import DataLoader\n",
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"from torchvision import transforms"
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"from torchvision import transforms\n",
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"\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
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]
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},
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{
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@ -163,23 +167,32 @@
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"outputs": [],
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"source": [
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"def m1_test_cnn(model, test_loader):\n",
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" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\n",
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" model.to(device)\n",
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" model.eval()\n",
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"\n",
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" correct = 0\n",
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" total = 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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" for images, labels in test_loader:\n",
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"\n",
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" # Make predictions\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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"\n",
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" _, predicted = torch.max(outputs.data, 1)\n",
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"\n",
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" # Save results\n",
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" total += labels.size(0)\n",
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" correct += (predicted == labels).sum().item()\n",
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" \n",
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" all_predicted_labels.append(predicted.cpu().numpy())\n",
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"\n",
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" accuracy = 100 * correct / total\n",
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" all_predicted_labels = np.concatenate(all_predicted_labels)\n",
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"\n",
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" return all_predicted_labels, accuracy"
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]
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},
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@ -187,13 +200,15 @@
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"cell_type": "code",
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"execution_count": 8,
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"id": "3fcb0a3a",
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"metadata": {},
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"metadata": {
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"scrolled": false
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},
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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: 67.85%\n"
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"Test Accuracy: 75.8%\n"
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]
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}
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],
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@ -218,9 +233,36 @@
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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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" all_predicted_labels, accuracy = 0, 0\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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" return all_predicted_labels, accuracy"
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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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" total = 0\n",
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" predicted_labels = []\n",
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" true_labels = []\n",
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"\n",
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" with torch.no_grad():\n",
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" for images, labels in test_loader:\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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"\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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]
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},
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{
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@ -233,7 +275,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test Accuracy: 0%\n"
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"Test Accuracy: 75.8%\n"
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]
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}
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],
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@ -242,6 +284,53 @@
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"print(f'Test Accuracy: {test_accuracy}%')"
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]
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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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"metadata": {},
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"source": [
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"### Test (Should output ALL PASS)"
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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": 11,
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"id": "853c4db3",
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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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"ALL PASS\n"
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]
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}
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],
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"source": [
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"assert np.allclose(predicted_labels, m1_predicted_labels)\n",
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"assert np.allclose(test_accuracy, m1_test_accuracy)\n",
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"\n",
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"print(\"Test accuracy: \", test_accuracy)\n",
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"\n",
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"if (test_accuracy >= 75):\n",
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" print(\"Score 100%:\", 15 * 1.0)\n",
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"elif (test_accuracy >= 70):\n",
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" print(\"Score 90%:\", 15 * 0.90)\n",
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"elif (test_accuracy >= 65):\n",
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" print(\"Score 80%:\", 15 * 0.80)\n",
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"elif (test_accuracy >= 60):\n",
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" print(\"Score 70%:\", 15 * 0.70)\n",
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"elif (test_accuracy >= 55):\n",
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" print(\"Score 60%:\", 15 * 0.60)\n",
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"elif (test_accuracy >= 50):\n",
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" print(\"Score 50%:\", 15 * 0.50)\n",
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"else:\n",
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" print(\"Accuracy less than 50%\")\n",
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"print(\"ALL PASS\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cef7dc17",
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@ -252,13 +341,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"id": "0990f3b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"true_labels = []\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\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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@ -269,12 +357,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 13,
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"id": "8da35032",
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_confusion_matrix(true, predictions):\n",
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"def m1_compute_confusion_matrix(true, predictions):\n",
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" unique_labels = np.unique(np.concatenate((true, predictions)))\n",
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"\n",
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" confusion_mat = np.zeros((len(unique_labels), len(unique_labels)), dtype=np.int64)\n",
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@ -295,35 +383,87 @@
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"execution_count": 14,
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"id": "16b6f9e7",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "markdown",
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"id": "608265af",
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"metadata": {},
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"source": [
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"### Put Students' implementations here"
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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": 15,
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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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]
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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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"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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]
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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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"metadata": {},
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"source": [
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"### Test (Should output ALL PASS)"
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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": "b77da2e8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[168, 2, 5, 0, 6, 0, 2, 0, 6, 11],\n",
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" [ 13, 108, 2, 18, 29, 16, 1, 13, 0, 0],\n",
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" [ 1, 1, 162, 0, 1, 0, 7, 0, 0, 28],\n",
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" [ 0, 7, 2, 74, 46, 40, 2, 26, 1, 2],\n",
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" [ 1, 2, 2, 6, 166, 9, 6, 8, 0, 0],\n",
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" [ 1, 3, 0, 11, 37, 108, 27, 12, 1, 0],\n",
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" [ 1, 0, 1, 0, 40, 30, 121, 4, 0, 3],\n",
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" [ 1, 7, 1, 6, 32, 24, 2, 127, 0, 0],\n",
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" [ 11, 1, 2, 2, 1, 1, 0, 0, 153, 29],\n",
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" [ 4, 0, 13, 2, 5, 0, 1, 0, 5, 170]], dtype=int64)"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ALL PASS\n"
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]
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}
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],
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"source": [
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"compute_confusion_matrix(true_labels, m1_predicted_labels)"
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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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1dce952c",
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"id": "adc0a7c7",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -333,7 +473,7 @@
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"kernelspec": {
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"display_name": "what",
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"language": "python",
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"name": "python3"
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"name": "what"
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},
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"language_info": {
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"codemirror_mode": {
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