Network module and distribution method and apparatus, electronic device, and storage medium

An electronic device, comprising:

• a processor;
• and
• a memory configured to store processor-executable instructions;
• wherein when the processor-executable instructions are executed, the processor is configured to:
• distribute, when multiple feature maps exist in an image processing model, to each feature map a neuron through which the feature map passes;
• filtrate neurons according to importance of the neurons of multiple convolution layers in the image processing model to obtain a first result;
• wherein the multiple convolution layers are located at a same depth of the image processing model, and
• the multiple convolution layers are respectively configured to process the feature maps of different scales;
• the first result comprises multiple neurons;
• collect statistics on a scale of the feature map corresponding to each neuron according to a position attribute of the each neuron in the first result to obtain a distribution relationship;
• wherein the position attribute indicates a convolution layer to which the each neuron belongs;
• the distribution relationship indicates a correspondence between the each feature map and the neuron through which the feature map passes;
• and
• distribute, according to the distribution relationship, to the each feature map the neuron through which the feature map passes.

A non-transitory computer-readable storage medium, having computer program instructions thereon, wherein when the computer program instructions are executed by a processor, the processor is configured to perform:

• distributing, when multiple feature maps exist in an image processing model, to each feature map a neuron through which the feature map passes;
• filtrating, according to importance of neurons of multiple convolution layers in an image processing model, the neurons to obtain a first result;
• wherein the multiple convolution layers are located at a same depth of the image processing model, and
• the multiple convolution layers are respectively configured to process the feature maps of different scales;
• the first result comprises multiple neurons;
• collecting, according to a position attribute of the each neuron in the first result, statistics on a scale of the feature map corresponding to each neuron to obtain a distribution relationship;
• wherein the position attribute indicates a convolution layer to which the each neuron belongs;
• the distribution relationship indicates a correspondence between the each feature map and the neuron through which the feature map passes;
• and
• distributing, according to the distribution relationship, to the each feature map the neuron through which the feature map passes.