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Model Loading
Figure 4-3 Model loading process
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- Before model loading, convert the third-party network (for example, Caffe ResNet-50) into an offline model (.om file) that adapts to Ascend AI Processors with the ATC. For details, see ATC Tool Instructions.
- In fixed-batch scenarios with batchSize >1, set batchSize in the input_shape parameter during model conversion.
- In dynamic batch size scenarios, set the batchSize choices in the dynamic_batch_size parameter during model conversion.
- In dynamic image size scenarios, set the dynamic_image_size choices for model conversion.
- In dynamic AIPP scenarios, set aipp_mode to dynamic in the configuration file of the insert_op_conf parameter during model conversion.
- A model can be loaded by calling the following APIs. A model ID is returned after the model is successfully loaded.
- acl.mdl.load_from_file: loads offline model data from a file. The memory is managed by the system.
- acl.mdl.load_from_mem: loads offline model data from the memory. The memory is managed by the system.
- acl.mdl.load_from_file_with_mem: loads offline model data from a file. The memory (including working memory for storing input and output data of the model, and weight memory for storing weight data) is managed by the user.
- acl.mdl.load_from_mem_with_mem: loads offline model data from the memory. The memory (including working memory and weight memory) is managed by the user.
