| import torch
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| import gradio as gr
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| import torchvision.transforms as transforms
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| from PIL import Image
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| import torch.nn as nn
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| import torch.nn.functional as F
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|
|
| transform_test = transforms.Compose([
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| transforms.Resize(256),
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| transforms.CenterCrop(224),
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| transforms.ToTensor(),
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| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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| ])
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|
|
| class_names = [
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| 'Auto Rickshaws', 'Bikes', 'Cars', 'Motorcycles',
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| 'Planes', 'Ships', 'Trains'
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| ]
|
| class VehicleClassifier(nn.Module):
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| def __init__(self):
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| super(VehicleClassifier, self).__init__()
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|
|
|
|
| self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
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| self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
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| self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
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| self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
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|
|
|
|
| self.pool = nn.MaxPool2d(2, 2)
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|
|
|
|
| self.fc1 = nn.Linear(256 * 14 * 14, 512)
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| self.fc2 = nn.Linear(512, 256)
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| self.fc3 = nn.Linear(256, 7)
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|
|
| self.dropout = nn.Dropout(0.5)
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|
|
| def forward(self, x):
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|
|
| x = self.pool(F.relu(self.conv1(x)))
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| x = self.pool(F.relu(self.conv2(x)))
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| x = self.pool(F.relu(self.conv3(x)))
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| x = self.pool(F.relu(self.conv4(x)))
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|
|
|
|
| x = x.view(-1, 256 * 14 * 14)
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| x = F.relu(self.fc1(x))
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| x = self.dropout(x)
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| x = F.relu(self.fc2(x))
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| x = self.dropout(x)
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| x = self.fc3(x)
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| return x
|
| model = VehicleClassifier().to('cpu')
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| model.load_state_dict(torch.load('vehicle_classifier.pth', map_location=torch.device('cpu')))
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| model.eval()
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|
|
| def predict(image):
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| try:
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| image = Image.open(image).convert('RGB')
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| image = transform_test(image).unsqueeze(0)
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|
|
| print(f"Image shape after transformation: {image.shape}")
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|
|
| with torch.no_grad():
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| outputs = model(image)
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| print(f"Model output: {outputs}")
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| _, predicted = torch.max(outputs, 1)
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|
|
| prediction = class_names[predicted.item()]
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| print(f"Predicted class: {prediction}")
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|
|
| return prediction
|
| except Exception as e:
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| print(f"Error during prediction: {e}")
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| traceback.print_exc()
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| return "An error occurred during prediction."
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|
|
|
|
| interface = gr.Interface(
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| fn=predict,
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| inputs=gr.Image(type='filepath'),
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| outputs=gr.Label(num_top_classes=1),
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| title="Vehicle Classification",
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| description="Upload an image of a vehicle, and the model will predict its type."
|
| )
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|
|
|
|
| interface.launch(share=True)
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|
|
