35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
from resnet import build_resnet
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import cv2
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import torchvision.transforms as transforms
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backbone, _ = build_resnet(name="resnet50", pretrained=True)
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# 读取测试图像
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image = cv2.imread('E:/DeepLearning/PersonSearch/COAT/COAT/main/backbone/test.jpg') # 替换为您的测试图像文件路径
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image = cv2.resize(image, (224, 224))
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# 使用 OpenCV 显示原始图像
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cv2.imshow("Original Image", image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 创建转换以将图像缩放到 224x224 大小并转换为张量
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transform = transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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])
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# 应用转换并将图像转换为张量
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image_tensor = transform(image)
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image_tensor = image_tensor.unsqueeze(0) # 添加批次维度，将形状变为 [1, 3, 224, 224]
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# 使用模型进行推理
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output = backbone(image_tensor)
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# 输入的shape是[1,1024,7,7]
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#print(output)
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print(output['feat_res4'].shape)
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# 在这里，您可以处理模型的输出，进行后续的操作或分析
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