小编Aay*_*ahu的帖子

Processed_image() 函数返回一个 cv2.Umat 类型值，该值将从 3 维重塑(h, ch, w)为 4 维，(h, ch, w, 1)因此i需要将其转换为 numpy 数组，或者如果可能的话，还可以帮助我直接 rehshapecv2.umat 类型变量直接重塑并转换为pytorch 张量，可以分配给 reshape_image_tensor。

img_w=640
img_h=640
img_ch=3
umat_img = cv2.UMat(img)
display_one(umat_img, "RESPONSE")    #function created by me to display image
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
with torch.no_grad():
    processed_img = preprocess_image(umat_img, model_image_size = (img_h, img_ch, img_w))
    #___________write YOUR CODE here________
    reshaped_images_tensor = torch.from_numpy(processed_img.reshape(img_h, img_ch, img_w, 1)).float().to(device)      #images_tensor.reshape(img_h, img_ch, img_w, 1)
    outputs = model(reshaped_images_tensor)
    _, predicted = torch.max(outputs, 1) …