segnet keras 实现

segnet 是早期的一个图像分割网络，虽然现在相比于deeplab 等一些大牛的网络结构的准确度有一定的的下降。但是这是segnet，在早期是有一个很好的图像分割思路。

其中segnet 和其他网络最大的区别是使用了一个池化索引的方法，进行了上采样。

这里使用了keras 进行了实现，其中最主要的就是池化索引和上采样这个操作。其他网络的结构用keras 是很容易实现的。

from keras import backend as K
from keras.layers import Layer


class MaxPoolingWithArgmax2D(Layer):

    def __init__(
            self,
            pool_size=(2, 2),
            strides=(2, 2),
            padding='same',
            **kwargs):
        super(MaxPoolingWithArgmax2D, self).__init__(**kwargs)
        self.padding = padding
        self.pool_size = pool_size
        self.strides = strides

    def call(self, inputs, **kwargs):
        padding = self.padding
        pool_size = self.pool_size
        strides = self.strides
        if K.backend() == 'tensorflow':
            ksize = [1, pool_size[0], pool_size[1], 1]
            padding = padding.upper()
            strides = [1, strides[0], strides[1], 1]
            output, argmax = K.tf.nn.max_pool_with_argmax(
                    inputs,
                    ksize=ksize,
                    strides=strides,
                    padding=padding)
        else:
            errmsg = '{} backend is not supported for layer {}'.format(
                    K.backend(), type(self).__name__)
            raise NotImplementedError(errmsg)
        argmax = K.cast(argmax, K.floatx())
        return [output, argmax]

    def compute_output_shape(self, input_shape):
        ratio = (1, 2, 2, 1)
        output_shape = [
                dim//ratio[idx]
                if dim is not None else None
                for idx, dim in enumerate(input_shape)]
        output_shape = tuple(output_shape)
        return [output_shape, output_shape]

    def compute_mask(self, inputs, mask=None):
        return 2 * [None]


class MaxUnpooling2D(Layer):
    def __init__(self, size=(2, 2), **kwargs):
        super(MaxUnpooling2D, self).__init__(**kwargs)
        self.size = size

    def call(self, inputs, output_shape=None):
        updates, mask = inputs[0], inputs[1]
        with K.tf.variable_scope(self.name):
            mask = K.cast(mask, 'int32')
            input_shape = K.tf.shape(updates, out_type='int32')
            #  calculation new shape
            if output_shape is None:
                output_shape = (
                        input_shape[0],
                        input_shape[1]*self.size[0],
                        input_shape[2]*self.size[1],
                        input_shape[3])
            self.output_shape1 = output_shape

            # calculation indices for batch, height, width and feature maps
            one_like_mask = K.ones_like(mask, dtype='int32')
            batch_shape = K.concatenate(
                    [[input_shape[0]], [1], [1], [1]],
                    axis=0)
            batch_range = K.reshape(
                    K.tf.range(output_shape[0], dtype='int32'),
                    shape=batch_shape)
            b = one_like_mask * batch_range
            y = mask // (output_shape[2] * output_shape[3])
            x = (mask // output_shape[3]) % output_shape[2]
            feature_range = K.tf.range(output_shape[3], dtype='int32')
            f = one_like_mask * feature_range

            # transpose indices & reshape update values to one dimension
            updates_size = K.tf.size(updates)
            indices = K.transpose(K.reshape(
                K.stack([b, y, x, f]),
                [4, updates_size]))
            values = K.reshape(updates, [updates_size])
            ret = K.tf.scatter_nd(indices, values, output_shape)
            return ret

    def compute_output_shape(self, input_shape):
        mask_shape = input_shape[1]
        return (
                mask_shape[0],
                mask_shape[1]*self.size[0],
                mask_shape[2]*self.size[1],
                mask_shape[3]
                )

完整代码参考segnet keas实现