from keras import backend as K
from keras.layers import InputSpec
import tensorflow as tf
from keras.engine.topology import Layer
# -------------------------------------------------------------------------------------
# Attention Layer from Self-Attention Generative Adversarial Networks
# Paper: https://arxiv.org/abs/1805.08318
# Author of the layer: Hao Chen
# Source: https://stackoverflow.com/questions/50819931/self-attention-gan-in-keras
# -------------------------------------------------------------------------------------
class Attention(Layer):
def __init__(self, ch, **kwargs):
super(Attention, self).__init__(**kwargs)
self.channels = ch
self.filters_f_g = self.channels // 8
self.filters_h = self.channels
def build(self, input_shape):
kernel_shape_f_g = (1, 1) + (self.channels, self.filters_f_g)
kernel_shape_h = (1, 1) + (self.channels, self.filters_h)
# Create a trainable weight variable for this layer:
self.gamma = self.add_weight(name='gamma', shape=[1], initializer='zeros', trainable=True)
self.kernel_f = self.add_weight(shape=kernel_shape_f_g,
initializer='glorot_uniform',
name='kernel_f')
self.kernel_g = self.add_weight(shape=kernel_shape_f_g,
initializer='glorot_uniform',
name='kernel_g')
self.kernel_h = self.add_weight(shape=kernel_shape_h,
initializer='glorot_uniform',
name='kernel_h')
self.bias_f = self.add_weight(shape=(self.filters_f_g,),
initializer='zeros',
name='bias_F')
self.bias_g = self.add_weight(shape=(self.filters_f_g,),
initializer='zeros',
name='bias_g')
self.bias_h = self.add_weight(shape=(self.filters_h,),
initializer='zeros',
name='bias_h')
super(Attention, self).build(input_shape)
# Set input spec.
self.input_spec = InputSpec(ndim=4,
axes={3: input_shape[-1]})
self.built = True
def call(self, x):
def hw_flatten(x):
return K.reshape(x, shape=[K.shape(x)[0], K.shape(x)[1]*K.shape(x)[2], K.shape(x)[-1]])
f = K.conv2d(x,
kernel=self.kernel_f,
strides=(1, 1), padding='same') # [bs, h, w, c']
f = K.bias_add(f, self.bias_f)
g = K.conv2d(x,
kernel=self.kernel_g,
strides=(1, 1), padding='same') # [bs, h, w, c']
g = K.bias_add(g, self.bias_g)
h = K.conv2d(x,
kernel=self.kernel_h,
strides=(1, 1), padding='same') # [bs, h, w, c]
h = K.bias_add(h, self.bias_h)
s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # # [bs, N, N]
beta = K.softmax(s, axis=-1) # attention map
o = K.batch_dot(beta, hw_flatten(h)) # [bs, N, C]
o = K.reshape(o, shape=K.shape(x)) # [bs, h, w, C]
x = self.gamma * o + x
return x
def compute_output_shape(self, input_shape):
return input_shape
