我在Keras中编写OctConv卷积层，扩展了Keras的层，编写的代码如下。
import keras.backend as Kfrom keras.layers import Layer, UpSampling2D, Add, Concatenate, Conv2D, Conv2DTransposeclass OCTCONV_LAYER(Layer): def __init__(self, filters=16, kernel_size=(3, 3), strides=(2, 2), dilation_rate=(1, 1), padding='same', alpha_in=0.6, alpha_out=0.6, **kwargs ): self.filters = filters self.kernel_size = kernel_size self.strides = strides self.padding = padding self.alpha_in = alpha_in self.alpha_out = alpha_out if dilation_rate[0] > 1: self.strides = (1, 1) self.dilation_rate = dilation_rate super(OCTCONV_LAYER, self).__init__(**kwargs) def build(self, input_shape): print('INPUT_SHAPE : {}'.format(input_shape)) op_channels = self.filters low_op_channels = int(op_channels*self.alpha_out) high_op_channels = op_channels-low_op_channels inp_channels = input_shape[-1] low_inp_channels = int(inp_channels*self.alpha_in) high_inp_channels = inp_channels-low_inp_channels self.h_2_l = self.add_weight( name='hl', shape=self.kernel_size + (high_inp_channels, low_op_channels), initializer='he_normal' ) self.h_2_h = self.add_weight( name='hh', shape=self.kernel_size + (high_inp_channels, high_op_channels), initializer='he_normal' ) self.l_2_h = self.add_weight( name='lh', shape=self.kernel_size + (low_inp_channels, high_op_channels), initializer='he_normal' ) self.l_2_l = self.add_weight( name='ll', shape=self.kernel_size + (low_inp_channels, low_op_channels), initializer='he_normal' ) print('High 2 low : {}'.format(self.h_2_l.shape)) print('High 2 high : {}'.format(self.h_2_h.shape)) print('Low 2 high : {}'.format(self.l_2_h.shape)) print('Low 2 low : {}'.format(self.l_2_l.shape)) super(OCTCONV_LAYER, self).build(input_shape) def call(self, x): inp_channels = int(x.shape[-1]) low_inp_channels = int(inp_channels*self.alpha_in) high_inp_channels = inp_channels-low_inp_channels high_inp = x[:,:,:, :high_inp_channels] print('High input shape : {}'.format(high_inp.shape)) low_inp = x[:,:,:, high_inp_channels:] low_inp = K.pool2d(low_inp, (2, 2), strides=(2, 2), pool_mode='avg') print('Low input shape : {}'.format(low_inp.shape)) out_high_high = K.conv2d(high_inp, self.h_2_h, strides=(2, 2), padding='same') print('OUT HIGH HIGH shape : {}'.format(out_high_high.shape)) out_low_high = UpSampling2D((2, 2))(K.conv2d(low_inp, self.l_2_h, strides=(2, 2), padding='same')) print('OUT LOW HIGH shape : {}'.format(out_low_high.shape)) out_low_low = K.conv2d(low_inp, self.l_2_l, strides=(2, 2), padding='same') print('OUT LOW LOW shape : {}'.format(out_low_low.shape)) out_high_low = K.pool2d(high_inp, (2, 2), strides=(2, 2), pool_mode='avg') out_high_low = K.conv2d(out_high_low, self.h_2_l, strides=(2, 2), padding='same') print('OUT HIGH LOW shape : {}'.format(out_high_low.shape)) out_high = Add()([out_high_high, out_low_high]) print('OUT HIGH shape : {}'.format(out_high.shape)) out_low = Add()([out_low_low, out_high_low]) out_low = UpSampling2D((2, 2))(out_low) print('OUT LOW shape : {}'.format(out_low.shape)) out_final = K.concatenate([out_high, out_low], axis=-1) print('OUT SHAPE : {}'.format(out_final.shape)) out_final._keras_shape = self.compute_output_shape(out_final.shape) return out_final def compute_output_shape(self, inp_shape): return inp_shape为了创建层，我使用以下代码来创建模型
from keras.layers import Inputinp = Input(shape=(224, 224, 3))x = OCTCONV_LAYER(filters=16)(inp)x = OCTCONV_LAYER()(x)...控制台上的输出是，
如您所见，最后一层的输入形状与输入层相同，而第一个OctConv层的输出形状不是输入形状。代码有什么问题吗？我是不是遗漏了什么？
回答：
您的代码可能没问题。似乎存在与K.conv2d相关的问题，如这个回答中观察到的那样。一个解决方法是从tensorflow导入keras（例如，将所有keras导入更改为tensorflow.keras）。
然后您需要进行以下更改：
low_inp_channels = int(int(inp_channels) * self.alpha_in)high_inp_channels = int(inp_channels) - low_inp_channels在这种情况下，第二个OCTCONV_LAYER的输入维度与第一个OCTCONV_LAYER的输出维度相对应。经过之前的更改，结果是：
第一层
INPUT_SHAPE : (?, 224, 224, 3)High 2 low : (3, 3, 2, 9)High 2 high : (3, 3, 2, 7)Low 2 high : (3, 3, 1, 7)Low 2 low : (3, 3, 1, 9)High input shape : (?, 224, 224, 2)Low input shape : (?, 112, 112, 1)OUT HIGH HIGH shape : (?, 112, 112, 7)OUT LOW HIGH shape : (?, 112, 112, 7)OUT LOW LOW shape : (?, 56, 56, 9)OUT HIGH LOW shape : (?, 56, 56, 9)OUT HIGH shape : (?, 112, 112, 7)OUT LOW shape : (?, 112, 112, 9)OUT SHAPE : (?, 112, 112, 16)第二层
INPUT_SHAPE : (?, 112, 112, 16)High 2 low : (3, 3, 7, 9)High 2 high : (3, 3, 7, 7)Low 2 high : (3, 3, 9, 7)Low 2 low : (3, 3, 9, 9)High input shape : (?, 112, 112, 7)Low input shape : (?, 56, 56, 9)OUT HIGH HIGH shape : (?, 56, 56, 7)OUT LOW HIGH shape : (?, 56, 56, 7)OUT LOW LOW shape : (?, 28, 28, 9)OUT HIGH LOW shape : (?, 28, 28, 9)OUT HIGH shape : (?, 56, 56, 7)OUT LOW shape : (?, 56, 56, 9)OUT SHAPE : (?, 56, 56, 16)