论文SCA-CNN的tensorflow代码实现（保存下来）：
论文：in Convolutional Networks for Image Captioning
""" Attention Model: WARNING: Use BatchNorm layer otherwise no accuracy gain. Lower layer with SpatialAttention, high layer with ChannelWiseAttention. In Visual155, Accuracy at 1, from 75.39% to 75.72%(↑0.33%). """ import tensorflow as tf def spatial_attention(feature_map, K=1024, weight_decay=0.00004, scope="", reuse=None): """This method is used to add spatial attention to model. Parameters --------------- @feature_map: Which visual feature map as branch to use. @K: Map `H*W` units to K units. Now unused. @reuse: reuse variables if use multi gpus. Return --------------- @attended_fm: Feature map with Spatial Attention. """ with tf.variable_scope(scope, 'SpatialAttention', reuse=reuse): # Tensorflow's tensor is in BHWC format. H for row split while W for column split. _, H, W, C = tuple([int(x) for x in feature_map.get_shape()]) w_s = tf.get_variable("SpatialAttention_w_s", [C, 1], dtype=tf.float32, initializer=tf.initializers.orthogonal, regularizer=tf.contrib.layers.l2_regularizer(weight_decay)) b_s = tf.get_variable("SpatialAttention_b_s", [1], dtype=tf.float32, initializer=tf.initializers.zeros) spatial_attention_fm = tf.matmul(tf.reshape(feature_map, [-1, C]), w_s) + b_s spatial_attention_fm = tf.nn.sigmoid(tf.reshape(spatial_attention_fm, [-1, W * H])) # spatial_attention_fm = tf.clip_by_value(tf.nn.relu(tf.reshape(spatial_attention_fm, # [-1, W * H])), # clip_value_min = 0, # clip_value_max = 1) attention = tf.reshape(tf.concat([spatial_attention_fm] * C, axis=1), [-1, H, W, C]) attended_fm = attention * feature_map return attended_fm def channel_wise_attention(feature_map, K=1024, weight_decay=0.00004, scope='', reuse=None): """This method is used to add spatial attention to model. Parameters --------------- @feature_map: Which visual feature map as branch to use. @K: Map `H*W` units to K units. Now unused. @reuse: reuse variables if use multi gpus. Return --------------- @attended_fm: Feature map with Channel-Wise Attention. """ with tf.variable_scope(scope, 'ChannelWiseAttention', reuse=reuse): # Tensorflow's tensor is in BHWC format. H for row split while W for column split. _, H, W, C = tuple([int(x) for x in feature_map.get_shape()]) w_s = tf.get_variable("ChannelWiseAttention_w_s", [C, C], dtype=tf.float32, initializer=tf.initializers.orthogonal, regularizer=tf.contrib.layers.l2_regularizer(weight_decay)) b_s = tf.get_variable("ChannelWiseAttention_b_s", [C], dtype=tf.float32, initializer=tf.initializers.zeros) transpose_feature_map = tf.transpose(tf.reduce_mean(feature_map, [1, 2], keep_dims=True), perm=[0, 3, 1, 2]) channel_wise_attention_fm = tf.matmul(tf.reshape(transpose_feature_map, [-1, C]), w_s) + b_s channel_wise_attention_fm = tf.nn.sigmoid(channel_wise_attention_fm) # channel_wise_attention_fm = tf.clip_by_value(tf.nn.relu(channel_wise_attention_fm), # clip_value_min = 0, # clip_value_max = 1) attention = tf.reshape(tf.concat([channel_wise_attention_fm] * (H * W), axis=1), [-1, H, W, C]) attended_fm = attention * feature_map return attended_fm