def call(self, inputs): output = K.local_conv2d(inputs, self.kernel, self.kernel_size, self.strides, (self.output_row, self.output_col), self.data_format) if self.use_bias: output = K.bias_add(output, self.bias, data_format=self.data_format) output = self.activation(output) return output
def call(self, inputs): frep_parts = tf.split(inputs, self.splitaxis, -1) convs = [] for i, frep_part in enumerate(frep_parts): individual_channels = tf.split(frep_part, frep_part.shape[-1], -1) for ind_ch in individual_channels: conv = K.local_conv2d(ind_ch, self.kernels[i], self.kernel_size, self.strides, (self.output_row, self.output_col), self.data_format) convs.append(conv) outputs = tf.concat(convs, -1) if self.use_bias: outputs = K.bias_add(outputs, self.bias, data_format=self.data_format) outputs = self.activation(outputs) return outputs