(self, num_output, kernel_size, stride=(1), pad=(0,),
                   weight_filler_type='xavier', bias_filler_type='constant',
                   bias_term=True, dilation=None,groups=None)
add a conv_param layer if you spec the layer type "Convolution"
        Args:
            num_output: a int
            kernel_size: int list
            stride: a int list
            weight_filler_type: the weight filer type
            bias_filler_type: the bias filler type
        Returns:
add a conv_param layer if you spec the layer type "Convolution"
        Args:
            num_output: a int
            kernel_size: int list
            stride: a int list
            weight_filler_type: the weight filer type
            bias_filler_type: the bias filler type
        Returns:
add a conv_param layer if you spec the layer type Convolution Args : num_output : a int kernel_size : int list stride : a int list weight_filler_type : the weight filer type bias_filler_type : the bias filler type Returns :
def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),
                   weight_filler_type='xavier', bias_filler_type='constant',
                   bias_term=True, dilation=None,groups=None):
        """
        add a conv_param layer if you spec the layer type "Convolution"
        Args:
            num_output: a int
            kernel_size: int list
            stride: a int list
            weight_filler_type: the weight filer type
            bias_filler_type: the bias filler type
        Returns:
        """
        if self.type not in ['Convolution','Deconvolution']:
            raise TypeError('the layer type must be Convolution or Deconvolution if you want set conv param')
        conv_param=pb.ConvolutionParameter()
        conv_param.num_output=num_output
        conv_param.kernel_size.extend(pair_reduce(kernel_size))
        conv_param.stride.extend(pair_reduce(stride))
        conv_param.pad.extend(pair_reduce(pad))
        conv_param.bias_term=bias_term
        conv_param.weight_filler.type=weight_filler_type
        if bias_term:
            conv_param.bias_filler.type = bias_filler_type
        if dilation:
            conv_param.dilation.extend(pair_reduce(dilation))
        if groups:
            conv_param.group=groups
        self.param.convolution_param.CopyFrom(conv_param)
def conv_param ( self , num_output , kernel_size , stride = ( 1 ) , pad = ( 0 , ) , weight_filler_type = 'xavier' , bias_filler_type = 'constant' , bias_term = True , dilation = None , groups = None ) : if self . type not in [ 'Convolution' , 'Deconvolution' ] : raise TypeError ( 'the layer type must be Convolution or Deconvolution if you want set conv param' ) conv_param = pb . ConvolutionParameter ( ) conv_param . num_output = num_output conv_param . kernel_size . extend ( pair_reduce ( kernel_size ) ) conv_param . stride . extend ( pair_reduce ( stride ) ) conv_param . pad . extend ( pair_reduce ( pad ) ) conv_param . bias_term = bias_term conv_param . weight_filler . type = weight_filler_type if bias_term : conv_param . bias_filler . type = bias_filler_type if dilation : conv_param . dilation . extend ( pair_reduce ( dilation ) ) if groups : conv_param . group = groups self . param . convolution_param . CopyFrom ( conv_param )