Method and apparatus for data calculation in neural network model, and image processing method and apparatus

An apparatus for data calculation in a neural network model, comprising

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first weight data reading code configured to cause the at least one processor to read weight data associated with a data processing layer in a neural network model into a GroupShared variable of a thread group of a graphics processing unit (GPU)
• data division code configured to cause the at least one processor to divide input data of the data processing layer into a plurality of groups of input data based on the number of threads in a depth dimension of the thread group:
• second weight data reading code configured to cause the at least one processor to read, for each group of input data, respective group weight data corresponding to each group of input data from the GroupShared variable;
• and
• data calculation code configured to cause the at least one processor to enable each thread in the thread group to perform data processing by using a group of input data read by the second weight data reading code and respective weight data corresponding to the group of input data, to obtain a calculation result corresponding to the group of input data, wherein
• a data amount of the weight data corresponding to each group of input data after division for data processing is:
• Kw″Kh*C/Tz, wherein
• Tz is the number of threads in the depth dimension of the thread group, Kw is a width of a convolution kernel, Kh is a height of the convolution kernel, C is the number of input channels of the convolution layer, and C/Tz represents the number of input channels of convolution processed by each thread during a data processing.

A non-transitory computer-readable storage medium, storing a computer program that when executed by at least one processor causes the at least one processor to:

• read weight data associated with a data processing layer in a neural network model into a GroupShared variable of a thread group of a graphics processing unit (GPU);
• divide input data of the data processing layer into a plurality of groups of input data based on the number of threads in a depth dimension of the thread group;
• read, for each group of input data, respective group weight data corresponding to each group of input data from the GroupShared variable;
• and
• perform, by each thread in the thread group, the data processing by using a group of read input data and respective weight data corresponding to the group of input data, to obtain a calculation result corresponding to the group of input data, wherein
• a data amount of the weight data corresponding to each group of input data after division for data processing is:
• Kw*Kh*C/Tz, wherein
• Tz is the number of threads in the depth dimension of the thread group, Kw is a width of a convolution kernel, Kh is a height of the convolution kernel, C is the number of input channels of the convolution layer, and C/Tz represents the number of input channels of convolution processed by each thread during a data processing.