set_dl_model_layer_weights — 设置深度学习模型层的权重(或值)。
set_dl_model_layer_weights(Weights : : DLModelHandle, LayerName, WeightsType : )
The operator set_dl_model_layer_weights sets for the model
DLModelHandle the given Weights in the specified
LayerName.
The parameter WeightsType determines which type of layer values
are set.
Which values can be set, please refer to the get_dl_model_layer_weights
documentation.
The operator set_dl_model_layer_weights is only applicable to self-created
networks. For networks delivered by HALCON, the operator does have no impact.
Weights (输入对象) (multichannel-)image(-array) → object (real)
Input weights.
DLModelHandle (输入控制) dl_model → (handle)
Handle of the deep learning model.
LayerName (输入控制) string → (string)
Name of the layer, whose weights are to be set.
WeightsType (输入控制) string → (string)
Selected type of layer values to be set.
默认值: 'weights'
值列表: 'batchnorm_mean', 'batchnorm_mean_avg', 'batchnorm_variance', 'batchnorm_variance_avg', 'bias', 'weights'
* Create weights for a convolution layer.
gen_image_const (Weights, 'real', 1, 1)
paint_region (Weights, Weights, Weights, 1, 'fill')
gen_empty_obj (WeightsArray)
for Index := 0 to 10 by 1
concat_obj (WeightsArray, Weights, WeightsArray)
endfor
*
* Input image with rows consisting of 1s to 10s.
gen_image_const (Image, 'real', 10, 10)
for Index := 0 to 9 by 1
gen_rectangle1 (Rectangle, Index, 0, Index, 9)
paint_region (Rectangle, Image, Image, Index + 1, 'fill')
endfor
*
* Create a small model network.
create_dl_layer_input ('image', [10, 2, 1], [], [], ImageNode)
create_dl_layer_convolution (ImageNode, 'conv', 1, 1, 2, 11, 1, 'none', \
'none', [], [], ConvNode)
create_dl_layer_zoom_factor (ConvNode, 'zoom', 2, 2, 'bilinear', 'true', [],\
[], ZoomNode)
create_dl_model (ZoomNode, DLModelHandle)
set_dl_model_param (DLModelHandle, 'runtime', 'cpu')
*
* Set the weights to the convolution layer.
set_dl_model_layer_weights (WeightsArray, DLModelHandle, 'conv', 'weights')
create_dl_model,
get_dl_model_layer_weights
基础。该算子采用动态许可机制(详见《安装指南》)。所需模块取决于算子的具体使用场景: 深度学习训练