layers_export.py 文件源码

def deconvolution(layer, layer_in, layerId):
    out = {}
    padding = get_padding(layer)
    k_h, k_w = layer['params']['kernel_h'], layer['params']['kernel_w']
    s_h, s_w = layer['params']['stride_h'], layer['params']['stride_w']
    d_h, d_w = layer['params']['dilation_h'], layer['params']['dilation_w']
    if (layer['params']['weight_filler'] in fillerMap):
        kernel_initializer = fillerMap[layer['params']['weight_filler']]
    else:
        kernel_initializer = layer['params']['weight_filler']
    if (layer['params']['bias_filler'] in fillerMap):
        bias_initializer = fillerMap[layer['params']['bias_filler']]
    else:
        bias_initializer = layer['params']['bias_filler']
    filters = layer['params']['num_output']
    if (padding == 'custom'):
        p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w']
        out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in)
        padding = 'valid'
        layer_in = [out[layerId + 'Pad']]
    kernel_regularizer = regularizerMap[layer['params']['kernel_regularizer']]
    bias_regularizer = regularizerMap[layer['params']['bias_regularizer']]
    activity_regularizer = regularizerMap[layer['params']['activity_regularizer']]
    kernel_constraint = constraintMap[layer['params']['kernel_constraint']]
    bias_constraint = constraintMap[layer['params']['bias_constraint']]
    use_bias = layer['params']['use_bias']
    out[layerId] = Conv2DTranspose(filters, [k_h, k_w], strides=(s_h, s_w), padding=padding,
                                   dilation_rate=(d_h, d_w), kernel_initializer=kernel_initializer,
                                   bias_initializer=bias_initializer,
                                   kernel_regularizer=kernel_regularizer,
                                   bias_regularizer=bias_regularizer,
                                   activity_regularizer=activity_regularizer, use_bias=use_bias,
                                   bias_constraint=bias_constraint,
                                   kernel_constraint=kernel_constraint)(*layer_in)
    return out