随机3d图像切片tensorflow数据，非类型形状的深度

我需要做的是随机切割一些3D二进制掩模的切片（固定大小）。 数据存储在tensorflow数据集中（tf.data）。必须是这种数据类型才能使用缓存来提高速度

到目前为止，我的源代码：

import tensorflow as tf      #version 2.2.0


mask.shape # (512,512,None,1), where (width, height, depth, channel), depth is NOT FIXED and depends on the image and therefore unknown

slice_number = 10
positive = tf.where(tf.equal(masks[:, :, :-slice_number,:],1))[:, 2] #slices with non zero values

# now we need to select slice id from positive mask slices randomly,
# which failes since the shape is always None due to the fact that image depth is unknown.

pos_id = random.randint(0, positive.shape[0])
mask = mask[:, :, positive[pos_id]:positive[pos_id] + slice_number]

我如何得到这个形状？任何想法都将受到高度赞赏

---

提前谢谢

假设您希望从深度未知的张量维度中随机切片固定的

切片大小

，下面演示如何执行此操作：

import tensorflow as tf

@tf.function
def random_slice(slice_size):
  # For demonstration purposes, generate your mask with random depth
  random_depth = tf.random.uniform(shape=[], dtype=tf.int32,
    minval=20, maxval=50)
  mask = tf.ones([512, 512, random_depth, 1], dtype=tf.int32)
  print(mask) # Mask with unknown depth: Tensor("ones:0", shape=(512, 512, None, 1), dtype=int32)
  depth = tf.shape(mask)[2]
  print(depth) # Unknown depth: Tensor("strided_slice:0", shape=(), dtype=int32)
  depth_begin = tf.random.uniform(shape=[], dtype=tf.int32,
    minval=0, maxval=depth-slice_size)
  print(depth_begin) # Random begin of slice based on unknown depth: Tensor("random_uniform_1:0", shape=(), dtype=int32)
  mask_sliced = tf.slice(mask,
    begin=[0, 0, depth_begin, 0],
    size=[512, 512, slice_size, 1])
  print(mask_sliced) # Random slice with known dimensions: Tensor("Slice:0", shape=(512, 512, 10, 1), dtype=int32)
  return mask_sliced

mask_sliced = random_slice(slice_size=10)
print(mask_sliced) # Resolved random slice