PYTHON

tensorflow 1.x spp implementation

def spatial_pyramid_pool(previous_conv, num_sample, previous_conv_size, out_pool_size):
  """
  previous_conv: a tensor vector of previous convolution layer
  num_sample: an int number of image in the batch
  previous_conv_size: an int vector [height, width] of the matrix features size of previous convolution layer
  out_pool_size: a int vector of expected output size of max pooling layer
  
  returns: a tensor vector with shape [1 x n] is the concentration of multi-level pooling
  """
  for i in range(len(out_pool_size)):
    h_strd = h_size = math.ceil(float(previous_conv_size[0]) / out_pool_size[i])
    w_strd = w_size = math.ceil(float(previous_conv_size[1]) / out_pool_size[i])
    pad_h = int(out_pool_size[i] * h_size - previous_conv_size[0])
    pad_w = int(out_pool_size[i] * w_size - previous_conv_size[1])
    new_previous_conv = tf.pad(previous_conv, tf.constant([[0, 0], [0, pad_h], [0, pad_w], [0, 0]]))
    max_pool = tf.nn.max_pool(new_previous_conv,
                   ksize=[1,h_size, h_size, 1],
                   strides=[1,h_strd, w_strd,1],
                   padding='SAME')
    if (i == 0):
      spp = tf.reshape(max_pool, [num_sample, -1])
    else:
      spp = tf.concat(axis=1, values=[spp, tf.reshape(max_pool, [num_sample, -1])])
  
  return spp