{
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{
"cell_type": "code",
"execution_count": 15,
"id": "b60d911e-b970-4ac1-ac19-d8e7a3b8652e",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"# Load the model onto the CPU\n",
"AlexNet_Model = torch.load('AlexNet_Model.pt', map_location=torch.device('cpu'))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2d154a7e-f3ba-4f52-8489-c45e9c736a9c",
"metadata": {},
"outputs": [],
"source": [
"import torchvision.transforms as transforms\n",
"transform = transforms.Compose([\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "390a39ff-b95c-48bd-8556-2e6cc8fe8d40",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: car.jpeg\n",
"Class: Car, Probability: 1.0\n",
"Class: Truck, Probability: 1.9733740683203216e-11\n",
"Class: Ship, Probability: 1.0144151279612226e-16\n",
"Class: Airplane, Probability: 8.654194920607349e-20\n",
"Class: Frog, Probability: 5.849379056607012e-28\n",
"Class: Deer, Probability: 2.1034692384348658e-29\n",
"Class: Bird, Probability: 8.280908192503261e-30\n",
"Class: Horse, Probability: 3.741535700249233e-30\n",
"Class: Cat, Probability: 4.912921745335171e-31\n",
"Class: Dog, Probability: 1.9247921025898383e-33\n"
]
}
],
"source": [
"\n",
"import torch\n",
"from PIL import Image\n",
"\n",
"classes = ('Airplane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck')\n",
"# Define a function to preprocess the image\n",
"def preprocess_image(image_path):\n",
" image = Image.open(image_path)\n",
" image = transform(image).unsqueeze(0)\n",
" return image\n",
"\n",
"# Define the image paths\n",
"image_paths = [\n",
" \"car.jpeg\"\n",
"]\n",
"\n",
"# Preprocess the images\n",
"preprocessed_images = [preprocess_image(image_path) for image_path in image_paths]\n",
"\n",
"# Make predictions\n",
"with torch.no_grad():\n",
" predictions = AlexNet_Model(torch.cat(preprocessed_images, dim=0))\n",
"\n",
"# Get the predicted probabilities for each class\n",
"predicted_probabilities = torch.nn.functional.softmax(predictions, dim=1)\n",
"\n",
"# Print the probabilities for each class sorted by probability\n",
"for image_path, probabilities in zip(image_paths, predicted_probabilities):\n",
" print(f\"Image: {image_path}\")\n",
" sorted_indices = torch.argsort(probabilities, descending=True)\n",
" for idx in sorted_indices:\n",
" class_name = classes[idx]\n",
" prob = probabilities[idx].item()\n",
" print(f\"Class: {class_name}, Probability: {prob}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e01098f6-0b9b-4991-a6d4-29f9fa146ca9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7868\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
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"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"[('Car', 1.0), ('Truck', 1.9733740683203216e-11), ('Ship', 1.0144151279612226e-16), ('Airplane', 8.654194920607349e-20), ('Frog', 5.849379056607012e-28), ('Deer', 2.1034692384348658e-29), ('Bird', 8.280908192503261e-30), ('Horse', 3.741535700249233e-30), ('Cat', 4.912921745335171e-31), ('Dog', 1.9247921025898383e-33)]\n",
"[('Airplane', 0.5250793695449829), ('Truck', 0.3982219994068146), ('Car', 0.05421096831560135), ('Ship', 0.022192474454641342), ('Dog', 8.419439836870879e-05), ('Cat', 7.965308031998575e-05), ('Frog', 7.490476127713919e-05), ('Horse', 2.9290411475813016e-05), ('Bird', 2.588589813967701e-05), ('Deer', 1.2184905244794209e-06)]\n"
]
}
],
"source": [
"import torch\n",
"from PIL import Image\n",
"import torchvision.transforms as transforms\n",
"import gradio as gr\n",
"import numpy as np\n",
"\n",
"# Load the model onto the CPU\n",
"AlexNet_Model = torch.load('AlexNet_Model.pt', map_location=torch.device('cpu'))\n",
"\n",
"transform = transforms.Compose([\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
"])\n",
"\n",
"classes = ('Airplane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck')\n",
"\n",
"# Define a function to preprocess the image\n",
"def preprocess_image(image):\n",
" if isinstance(image, np.ndarray):\n",
" image = Image.fromarray(image)\n",
" image = transform(image).unsqueeze(0)\n",
" return image\n",
"\n",
"# Define the prediction function\n",
"def predict(image):\n",
" image = preprocess_image(image)\n",
" with torch.no_grad():\n",
" predictions = AlexNet_Model(image)\n",
"\n",
" # Get the predicted probabilities for each class\n",
" predicted_probabilities = torch.nn.functional.softmax(predictions, dim=1)\n",
" results = []\n",
" for probabilities in (predicted_probabilities):\n",
" sorted_indices = torch.argsort(probabilities, descending=True)\n",
" for idx in sorted_indices:\n",
" class_name = classes[idx]\n",
" prob = probabilities[idx].item()\n",
" #print(f\"Class: {class_name}, Probability: {prob}\")\n",
" results.append((class_name, prob))\n",
"\n",
"\n",
" return {class_name: prob for class_name, prob in results}\n",
"\n",
"# Create Gradio interface\n",
"iface = gr.Interface(predict, \n",
" inputs=\"image\", \n",
" outputs=\"label\", \n",
" title=\"AlexNet Image Classifier\",\n",
" description=\"Classify images into one of 10 classes: Airplane, Car, Bird, Cat, Deer, Dog, Frog, Horse, Ship, or Truck.\")\n",
"iface.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f6dac97-96d4-45f2-b630-9ecb6b2ca159",
"metadata": {},
"outputs": [],
"source": []
}
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