mirror of
https://github.com/lordmathis/CUDANet.git
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39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import sys
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import torch
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import torchvision
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from PIL import Image
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from torchvision import transforms
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sys.path.append('../../tools') # Ugly hack
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from utils import export_model_weights, print_model_parameters
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def predict(model, image_path):
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input_image = Image.open(image_path)
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preprocess = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(227),
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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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input_tensor = preprocess(input_image)
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input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
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# move the input and model to GPU for speed if available
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if torch.cuda.is_available():
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input_batch = input_batch.to('cuda')
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model.to('cuda')
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with torch.no_grad():
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output = model(input_batch)
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print(torch.argmax(output))
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if __name__ == "__main__":
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alexnet = torchvision.models.alexnet(weights=torchvision.models.AlexNet_Weights.DEFAULT)
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print_model_parameters(alexnet) # print layer names and number of parameters
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export_model_weights(alexnet, 'alexnet_weights.bin')
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# predict(alexnet, 'cat.jpg')
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