44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import torch
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from PIL import Image
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from torchvision import transforms
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import torchvision.models as models
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# load torchcript模型并进行单张图片推理
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# preprocess input
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filename = R'./dog.jpg'
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input_image = Image.open(filename)
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preprocess = 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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input_tensor = preprocess(input_image)
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input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model,[3,224,224]
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print('input size =',input_tensor.shape)
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# load model
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pt_path = '../2_compile/fmodel/ShuffleNetv2_224x224.pt'
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model = torch.load(pt_path,map_location='cpu')
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print('Model Load Done')
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model.eval()
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output = model(input_batch)
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print('output size =',output[0].shape)
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probabilities = torch.nn.functional.softmax(output[0], dim=0)
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# Read the categories
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with open("imagenet_classes.txt", "r") as f:
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categories = [s.strip() for s in f.readlines()]
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# Show top categories per image
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top5_prob, top5_catid = torch.topk(probabilities, 5)
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print('--------Classification results----------')
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for i in range(top5_prob.size(0)):
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print(categories[top5_catid[i]], top5_prob[i].item())
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