Convolutional neural network pruning processing method, data processing method, and device

A data processing method, comprising:

• obtaining input data related to a target task;
• pruning a trained target convolutional neural network based on the input data to obtain a pruned sub-network, wherein the trained target convolutional neural network is obtained through training by using an objective loss function, the objective loss function is obtained based on a first sub-loss function and a second sub-loss function, the first sub-loss function is determined based on the target task, the second sub-loss function is determined based on a channel importance function and a dynamic weight, the first sub-loss function represents a difference between a training sample input to the target convolutional neural network and an output prediction result, when a value of the first sub-loss function does not reach a first threshold, a value of the dynamic weight is obtained based on the value of the first sub-loss function, the value of the dynamic weight is in an inverse correlation with the value of the first sub-loss function, the objective loss function is used to perform sparsening on the target convolutional neural network during training of the target convolutional neural network, and the sparsening is pruning a convolution kernel of the target convolutional neural network based on a value of the channel importance function;
• and
• processing the input data by using the sub-network to obtain output data.

A training device, comprising:

• a first determining module, configured to determine a first sub-loss function based on a target task, wherein the first sub-loss function represents a difference between a training sample input to a target convolutional neural network and an output prediction result, the target convolutional neural network is a convolutional neural network used for training, the target convolutional neural network has different network parameters and loss functions in training processes for different training samples, and the training sample is a sample in a training set;
• a second determining module, configured to obtain a second sub-loss function based on a channel importance function and a dynamic weight, wherein when a value of the first sub-loss function does not reach a first threshold, a value of the dynamic weight is obtained based on the value of the first sub-loss function, and the value of the dynamic weight is in an inverse correlation with the value of the first sub-loss function;
• a third determining module, configured to obtain an objective loss function based on the first sub-loss function and the second sub-loss function;
• and
• a training module, configured to perform sparse training on the target convolutional neural network by using the objective loss function to obtain a trained target convolutional neural network, wherein the sparse training is pruning a convolution kernel of the target convolutional neural network based on a value of the channel importance function.