def generator_att(inp):
global reuse_genatt, bn_training, blknum
blknum = 0
with tf.variable_scope('gen_att', reuse=reuse_genatt):
mod = Model(inp)
mod.set_bn_training(bn_training)
mod.convLayer(5,
32,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #64
block(mod, 64, 1)
mod.convLayer(3,
128,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #32
block(mod, 128, 1)
# block(mod,256,1)
# block(mod,256,1)
block(mod, 256, 1)
mod.SelfAttention(64, residual=True)
# block(mod,512,1)
block(mod, 256, 1)
# block(mod,256,1)
block(mod, 128, 1)
mod.deconvLayer(3,
64,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) # 64
block(mod, 64, 1)
feat = mod.deconvLayer(5,
64,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #128
A = mod.convLayer(5, 1, activation=M.PARAM_SIGMOID) #output_attention
mod.set_current_layer(feat)
C = mod.convLayer(5, 3, activation=M.PARAM_TANH)
reuse_genatt = True
return A, C
def generator(inp):
global reuse_gen, bn_training
with tf.variable_scope('generator', reuse=reuse_gen):
mod = Model(inp)
mod.set_bn_training(bn_training)
mod.reshape([-1, 2, 2, 512])
mod.deconvLayer(3,
256,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #8
mod.deconvLayer(3,
128,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #16
mod.SelfAttention(32)
mod.deconvLayer(5,
64,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #32
mod.deconvLayer(5,
32,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #64
mod.deconvLayer(5,
16,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #128
mod.deconvLayer(5, 3, activation=M.PARAM_TANH) #output
reuse_gen = True
return mod.get_current_layer()
def generator_att(inp):
global reuse_genatt, bn_training
with tf.variable_scope('gen_att', reuse=reuse_genatt):
mod = Model(inp)
mod.set_bn_training(bn_training)
mod.deconvLayer(3,
512,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #4
mod.deconvLayer(3,
256,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #8
mod.deconvLayer(3,
128,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #16
mod.SelfAttention(32)
mod.deconvLayer(5,
64,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #32
mod.deconvLayer(5,
32,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #64
feat = mod.deconvLayer(5,
16,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True) #128
A = mod.convLayer(5, 3, activation=M.PARAM_SIGMOID) #output_attention
mod.set_current_layer(feat)
C = mod.convLayer(5, 3, activation=M.PARAM_TANH)
reuse_genatt = True
return A, C