Sample image tensor dimensions: (channels=3, height=100, width=100)

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import cv2
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import numpy as np
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import torch
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# Sample image tensor dimensions: (channels=3, height=100, width=100)
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image_tensor = torch.rand(3, 100, 100)  # Replace with your actual tensor
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# Convert PyTorch tensor to NumPy array
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image_np = image_tensor.permute(1, 2, 0).cpu().numpy()  # Channels last for OpenCV
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# Define the target size
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target_height = 64
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target_width = 64
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# Resize the image using OpenCV
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resized_image_np = cv2.resize(image_np, (target_width, target_height))
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# Convert NumPy array back to PyTorch tensor
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resized_image_tensor = torch.from_numpy(resized_image_np).permute(2, 0, 1).float()
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# Print the shapes to verify
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print("Original tensor shape:", image_tensor.shape)
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print("Resized tensor shape:", resized_image_tensor.shape)

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import torch
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import torch.nn.functional as F
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# 假设你有一个480x480的RGB图片，可以表示为一个3维的torch.Tensor
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# 假设img是你的原始图像，大小为(1, 3, 480, 480)
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# (1, 3, 480, 480)表示(batch_size, channels, height, width)
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# 生成一个480x480的假图片，这里用随机数代替
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img = torch.rand(1, 3, 480, 480)
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# 缩放成128x128的图像
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scaled_img = F.interpolate(img, size=(128, 128), mode='bilinear', align_corners=False)
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# 输出缩放后图像的大小
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print("缩放后图像大小:", scaled_img.size())  # 输出: torch.Size([1, 3, 128, 128])