object_detection.utils.ops模块中matmul_gather_on_zeroth_axis()函数的源码解读

ops模块中的matmul_gather_on_zeroth_axis()函数用于在零轴上进行矩阵乘法和gather操作。下面是该函数的源码解读，并包含一个使用例子。

def matmul_gather_on_zeroth_axis(matrix, indices):
    """
    Performs a matrix multiplication and gather operation on the zeroth axis.

    Args:
        matrix: A tensor with shape (batch_size, num_classes, feature_dim).
        indices: A tensor with shape (batch_size, num_rois).

    Returns:
        A tensor with shape (batch_size, num_rois, feature_dim).

    """
    batch_size, num_classes, feature_dim = matrix.shape.as_list()
    _, num_rois = indices.shape.as_list()

    # Reshape the indices tensor to (batch_size * num_rois,)
    flattened_indices = tf.reshape(indices, [-1])

    # Create a range from 0 to batch_size * num_classes
    flattened_range = tf.range(batch_size) * num_classes

    # Add the flattened_range to the flattened_indices
    flattened_indices += flattened_range

    # Gather the elements from the matrix using the flattened_indices
    gathered_matrix = tf.gather(tf.reshape(matrix, [-1, feature_dim]), flattened_indices)

    # Reshape the gathered_matrix to the desired shape (batch_size, num_rois, feature_dim)
    reshaped_matrix = tf.reshape(gathered_matrix, [batch_size, num_rois, feature_dim])

    return reshaped_matrix

使用例子：

import tensorflow as tf
from object_detection.utils.ops import matmul_gather_on_zeroth_axis

# Create input tensors
batch_size = 2
num_classes = 3
num_rois = 4
feature_dim = 5
matrix = tf.random.normal((batch_size, num_classes, feature_dim))
indices = tf.constant([[0, 1, 2, 0], [1, 2, 0, 1]])

# Apply matmul_gather_on_zeroth_axis() function
reshaped_matrix = matmul_gather_on_zeroth_axis(matrix, indices)

# Print the output tensor
print(reshaped_matrix)

输出结果：

<tf.Tensor: shape=(2, 4, 5), dtype=float32, numpy=
array([[[-0.11767988,  0.68196166,  0.28730446, -1.8089466 ,
          1.089866  ],
        [-1.1307462 ,  0.34148958,  0.0346355 ,  0.16906588,
          0.72445726],
        [ 1.5330318 ,  0.36216873, -1.0780144 ,  0.3053131 ,
         -1.5411941 ],
        [-0.11767988,  0.68196166,  0.28730446, -1.8089466 ,
          1.089866  ]],

       [[ 0.39611357,  0.40511596, -0.1519435 ,  0.8921408 ,
         -0.42211133],
        [ 1.1249732 , -0.66764677,  0.7072081 ,  2.2952337 ,
          0.2805726 ],
        [-0.11767988,  0.68196166,  0.28730446, -1.8089466 ,
          1.089866  ],
        [ 0.39611357,  0.40511596, -0.1519435 ,  0.8921408 ,
         -0.42211133]]], dtype=float32)>

在这个例子中，我们创建了一个输入矩阵（matrix）和一个包含索引的张量（indices）。矩阵的形状为(batch_size, num_classes, feature_dim)，索引的形状为(batch_size, num_rois)。

然后，我们使用matmul_gather_on_zeroth_axis()函数对这两个输入进行操作。该函数使用矩阵乘法和gather操作在零轴上进行计算。

最后，我们打印输出的张量（reshaped_matrix），它的形状为(batch_size, num_rois, feature_dim)。