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libcoral changes

Grouper release

  • API changes for PipelinedModelRunner:

  • Push() and Pop() now return either absl::OkStatus or absl::InternalError, instead of True/False.

  • Added tensor name to PipelineTensor.

  • GetInputTensorNames() and GetInputTensor() moved to tflite_utils.h.

  • GetDetectionResults() now supports SSD models with different orders in the output tensor.

  • Updated profiling-based partitioner:

  • Renamed executable to partition_with_profiling.

  • Added support for SSD models, and other models with large CPU segments and/or graph branches. (This also requires v16 of the Edge TPU Compiler.)

  • Automatically enables the search_delegate option (add in v16 of the Edge TPU Compiler), so that if the compiler first fails to compile the model, it continues to search for a portion of the graph that can compile.

  • Added flags:

    • delegate_search_step: Same as the delegate_search_step option added in v16 of the Edge TPU Compiler.
    • partition_search_step: Similar to the delegate_search_step option, but applied to the delegate search for each segment (rather than the entire pipelined graph).
    • initial_lower_bound_ns and initial_upper_bound_ns: The known smallest/largest latency among your model's segments. These are otherwise calculated in the tool by benchmarking the heuristic-based segments from the Edge TPU Compiler.

  • Added split_fc tool to pre-process tflite models with fully-connected layers that are too big for the Edge TPU Compiler. For example, the compiler is unable to compile a layer that has 100,000 output classes due to the size of the weights matrix applied to the fully-connected layer (this operation would be cut from the Edge TPU delegate and instead execute on the CPU). So the split_fc tool divides the weights into smaller blocks using block-wise matrix multiplication (you can control the ratio of the split operation). The revised .tflite file output by split_fc can then be passed to the Edge TPU Compiler.

  • Added append_recurrent_links tool, which helps you create recurrent networks for the Edge TPU with a hidden saved state. Without this tool, creating a recurrent network that can compile for the Edge TPU typically requires that your model output the saved state and then you must pass back the saved state into your model manually. By instead passing such a model to the append_recurrent_links tool, you can make that saved state hidden again so your application code can focus on the final output.

Frogfish release

  • Initial libcoral release