33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
from pvt_v2 import pvt_v2_b2
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import cv2
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import torchvision.transforms as transforms
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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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model = pvt_v2_b2(pretrained = "E:/DeepLearning/PersonSearch/COAT/COAT/main/backbone/pvt_v2_b2.pth")
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# 使用模型进行推理
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output = model(image_tensor)
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print(output[0].shape)
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print(output[1].shape)
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print(output[2].shape)
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print(output[3].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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