def cascade_feature(self, _input, block_num):
atrous_layer = []
out_feature = int(_input.get_shape()[-1])
output = _input
#output_1x1 = _conv3d(_input, kernel_size=1, stride=1, output_feature=out_feature, use_bias=True, name='pyramid_conv_1')
for i in range(1, self.atrou_num + 1):
#dilate_rate = int(np.power(2,4-block_num)*i)
#dilate_rate = int(np.power(2,4-block_num)) # 8 8 8 // 4 4 4 // 2 2 2
dilate_rate = int(np.power(2, i)) # 2 4 8 // 2 4 8 // 2 4 8
output = atrous_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=out_feature,
dilation_rate=dilate_rate,
is_training=self.is_training,
name='atrous_conv%d' % i)
atrous_layer.append(output)
output = tf.concat([atrous_layer[0], atrous_layer[1], atrous_layer[2]],
axis=-1)
# output = self.Squeeze_excitation_layer(output, int(output.get_shape()[-1]), self.reduction_ratio,
# layer_name='SE%d' % block_num)
output = _conv2d(output,
kernel_size=1,
stride=1,
output_feature=out_feature,
use_bias=True,
name='pyramid_conv_1x1')
return output
def cascade_feature(self, _input, block_num):
atrous_layer = []
out_feature = int(_input.get_shape()[-1])
output = _input
#output_1x1 = _conv3d(_input, kernel_size=1, stride=1, output_feature=out_feature, use_bias=True, name='pyramid_conv_1')
for i in range(1, self.atrou_num + 1):
dilate_rate = int(np.power(2, 4 - block_num) * i)
#print(dilate_rate)
# dilate rate : 24 16 8 / 12 8 4 / 6 4 2
output = atrous_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=out_feature,
dilation_rate=dilate_rate,
is_training=self.is_training,
name='atrous_conv%d' % i)
atrous_layer.append(output)
output = tf.concat([atrous_layer[0], atrous_layer[1], atrous_layer[2]],
axis=-1)
print('atrous conv shape:', output)
output = _conv3d(output,
kernel_size=1,
stride=1,
output_feature=out_feature,
use_bias=True,
name='pyramid_conv_1x1')
return output
