2D attention实现

2D attention是指在处理二维数据（如图像）时，使用注意力机制来自动地选择相关的特征。下面是一个简单的2D attention实现例子：

import tensorflow as tf

def two_d_attention(inputs, num_heads):
    # inputs: [batch_size, height, width, channels]
    # num_heads: number of attention heads
    
    # Flatten the spatial dimensions
    flattened = tf.reshape(inputs, [-1, inputs.shape[1] * inputs.shape[2], inputs.shape[3]])
    
    # Compute the query, key, and value vectors
    query = tf.layers.dense(flattened, units=inputs.shape[3])
    key = tf.layers.dense(flattened, units=inputs.shape[3])
    value = tf.layers.dense(flattened, units=inputs.shape[3])
    
    # Split the query, key, and value vectors into multiple heads
    query = tf.concat(tf.split(query, num_heads, axis=2), axis=0)
    key = tf.concat(tf.split(key, num_heads, axis=2), axis=0)
    value = tf.concat(tf.split(value, num_heads, axis=2), axis=0)
    
    # Compute the dot product of the query and key vectors
    dots = tf.matmul(query, key, transpose_b=True)
    dots /= tf.sqrt(tf.cast(inputs.shape[3], tf.float32))
    
    # Apply softmax to obtain the attention weights
    weights = tf.nn.softmax(dots, axis=-1)
    
    # Apply the attention weights to the value vectors
    attended = tf.matmul(weights, value)
    
    # Concatenate the multiple heads and reshape to the original spatial dimensions
    concatenated = tf.concat(tf.split(attended, num_heads, axis=0), axis=2)
    reshaped = tf.reshape(concatenated, [-1, inputs.shape[1], inputs.shape[2], inputs.shape[3]])
    
    # Add a residual connection and layer normalization
    output = tf.layers.dense(inputs + reshaped, units=inputs.shape[3]])
    output = tf.contrib.layers.layer_norm(output)
    
    return output

这个实现使用了一个简单的多头注意力机制，它将输入张量展平为一个矩阵，然后对其进行线性变换（通过query、key和value向量），将其划分为多个头，并计算每个头的注意力权重。然后将注意力权重应用于value向量，并将结果连接在一起，最后通过残差连接和层规范化来获得最终输出。