论文SCA-CNN的tensorflow代码实现（保存下来）：
论文：SCA-CNN: Spatial and Channel-Wise Attention 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