auto_channel_prune_search

Function Usage

This API is used to calculate the sparsity sensitivity (affecting accuracy) and sparsity gain (affecting performance) of each channel based on the user model. Then the search policy searches for the optimal layer-by-layer channel sparsity ratio based on the input to balance accuracy and performance. Finally, a configuration file is generated.

Constraints

None

Prototype

auto_channel_prune_search(model, config, input_data, output_cfg, sensitivity, search_alg)

Command-line options

Option	Input/Return	Meaning	Restriction
model	Input	PyTorch model to be sparsified.	A torch.nn.Module.
config	Input	Path of the auto channel pruning configuration file. Simplified configuration file generated based on AutoChannelPruneConfig in the basic_info.proto file. The basic_info.proto file is stored in Installation directory /amct_pytorch/proto/basic_info.proto. For details about the parameters in the basic_info.proto file and the example of the generated auto channel pruning search configuration file, see Simplified Configuration File for Auto Channel Pruning Search.	A string
input_data	Input	Input data (including labels) provided by the user	Data type: list[data,label] The data type of the list element is torch.tensor.
output_cfg	Input/Return	Path of the output channel pruning configuration file.	A string
sensitivity	Input	Sensitivity calculation method.	A string Subclass of SensitivityBase. The string is an existing method of amct. Currently, the value can be TaylorLossSensitivity. The subclass of SensitivityBase can be instantiated and defined by users.
search_alg	Input	Method of searching for channels to be sparsified.	A string Subclass of SearchChannelBase. The string is an existing method of amct. Currently, the value can be GreedySearch. The subclass of SearchChannelBase can be instantiated and defined by users.

Return Value

None

Outputs

Auto channel pruning configuration file.

This file needs to be transferred to the channel sparsity API for subsequent services.

Examples

import amct_pytorch as amct
Constructing the Input Data
input_data = torch.randn(input_shape)         
model.eval()        
output = model.forward(input_data)        
labels = torch.randn(output.size())        
data = [input_data,labels]

amct.auto_channel_prune_search(
     model=model,
     config='./tmp/sample.cfg',
     input_data=data,  
     output_cfg='./tmp/output.cfg', 
     sensitivity='TaylorLossSensitivity', 
     search_alg='GreedySearch')

Parent topic: Auto Channel Pruning Search APIs