Pytorch加权张量

debugcn 发表于 Dev

达尔邦

我正在将一些复杂的TF2代码移植到Pytorch。由于TF2不能区分Tensor和numpy数组，因此它很简单。但是，我感觉回到了TF1时代，当时我遇到几个错误，说“您不能在Pytorch中混合Tensor和numpy数组！”。这是原始的TF2代码：

def get_weighted_imgs(points, centers, imgs):
  weights = np.array([[tf.norm(p - c) for c in centers] for p in points], dtype=np.float32)
  weighted_imgs = np.array([[w * img for w, img in zip(weight, imgs)] for weight in weights])

  weights = tf.expand_dims(1 / tf.reduce_sum(weights, axis=1), axis=-1)
  weighted_imgs = tf.reshape(tf.reduce_sum(weighted_imgs, axis=1), [len(weights), 64*64*3])

  return weights * weighted_imgs

还有我有问题的Pytorch代码：

def get_weighted_imgs(points, centers, imgs):
  weights = torch.Tensor([[torch.norm(p - c) for c in centers] for p in points])
  weighted_imgs = torch.Tensor([[w * img for w, img in zip(weight, imgs)] for weight in weights])

  weights = torch.unsqueeze(1 / torch.sum(weights, dim=1), dim=-1)
  weighted_imgs = torch.sum(weighted_imgs, dim=1).view([len(weights), 64*64*3])

  return weights * weighted_imgs

def reproducible():
  points = torch.Tensor(np.random.random((128, 5)))
  centers = torch.Tensor(np.random.random((10, 5)))
  imgs = torch.Tensor(np.random.random((10, 64, 64, 3)))

  weighted_imgs = get_weighted_imgs(points, centers, imgs)

我可以保证张量/数组的尺寸顺序或形状没有问题。我收到的错误消息是

ValueError: only one element tensors can be converted to Python scalars

来自

weighted_imgs = torch.Tensor([[w * img for w, img in zip(weight, imgs)] for weight in weights])

有人可以帮我解决这个问题吗？这将不胜感激。

吉尔·平斯基

也许这会为您提供帮助，但是我不确定权重和weighted_imgs之间的最终乘积，因为它们即使在重塑后也可能不具有相同的形状。我不确定我是否正确理解您的逻辑：

import torch
def get_weighted_imgs(points, centers, imgs):
  weights = torch.Tensor([[torch.norm(p - c) for c in centers] for p in points])
  
  imgs = imgs.unsqueeze(0).repeat(weights.shape[0],1,1,1,1)
  dims_to_rep = list(imgs.shape[-3:])
  weights = weights.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1,1,*dims_to_rep)
  weights /= torch.sum(weights[...,0:1,0:1,0:1],dim=1, keepdim=True)
  weighted_imgs =  torch.sum(imgs * weights, dim=1).view(weights.shape[0], -1)
  
  return weighted_imgs #weights.view(weighted_imgs.shape[0],-1) *\
         #weighted_imgs # Shapes are torch.Size([128, 122880]) and torch.Size([128, 12288])

def reproducible():
  points = torch.Tensor(np.random.random((128, 5)))
  centers = torch.Tensor(np.random.random((10, 5)))
  imgs = torch.Tensor(np.random.random((10, 64, 64, 3)))

  weighted_imgs = get_weighted_imgs(points, centers, imgs)
#Test:
reproducible()

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