score = fused
deconv_final = self.deconv2d(inputdata=score, out_channel=64, kernel_size=16,
stride=8, use_bias=False, name='deconv_final')
score_final = self.conv2d(inputdata=deconv_final, out_channel=2,
kernel_size=1, use_bias=False, name='score_final')
ret['logits'] = score_final
ret['deconv'] = deconv_final
return ret
if __name__ == '__main__':
vgg_encoder = vgg_encoder.VGG16Encoder(phase=tf.constant('train', tf.string))
dense_encoder = dense_encoder.DenseEncoder(l=40, growthrate=12,
with_bc=True, phase='train', n=5)
decoder = FCNDecoder(phase='train')
in_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 256, 512, 3],
name='input')
vgg_encode_ret = vgg_encoder.encode(in_tensor, name='vgg_encoder')
dense_encode_ret = dense_encoder.encode(in_tensor, name='dense_encoder')
decode_ret = decoder.decode(vgg_encode_ret, name='decoder',
decode_layer_list=['pool5',
'pool4',
'pool3'])
score = fused
deconv_final = self.deconv2d(inputdata=score, out_channel=64, kernel_size=16,
stride=8, use_bias=False, name='deconv_final')
score_final = self.conv2d(inputdata=deconv_final, out_channel=2,
kernel_size=1, use_bias=False, name='score_final')
ret['logits'] = score_final
ret['deconv'] = deconv_final
return ret
if __name__ == '__main__':
vgg_encoder = vgg_encoder.VGG16Encoder(phase=tf.constant('train', tf.string))
dense_encoder = dense_encoder.DenseEncoder(l=40, growthrate=12,
with_bc=True, phase='train', n=5)
decoder = FCNDecoder(phase='train')
in_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 256, 512, 3],
name='input')
vgg_encode_ret = vgg_encoder.encode(in_tensor, name='vgg_encoder')
dense_encode_ret = dense_encoder.encode(in_tensor, name='dense_encoder')
decode_ret = decoder.decode(vgg_encode_ret, name='decoder',
decode_layer_list=['pool5',
'pool4',
'pool3'])