[onert-micro] Introduce OMTraining entities #13145
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This pr introduces OMTrainingInterpreter, OMTrainingContext and OMTrainingRuntimeModule entities. ONE-DCO-1.0-Signed-off-by: Artem Balyshev <a.balyshev@samsung.com>
| uint32_t num_of_train_layers = 0; | ||
| OMTrainOptimizer optimizer = SGD; | ||
| OMLoss loss = MSE; | ||
| float learning_rate = 0.f; |
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how about default value other than 0
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For learning_rate, right? Yes, I agree, I will use 0.001
| // Close file | ||
| out_file.close(); | ||
| #else | ||
| assert(fasle && "Not supported"); |
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| assert(fasle && "Not supported"); | |
| assert(false && "Not supported"); |
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Thank you, fixed it
| { | ||
| OMStatus status = Ok; | ||
| uint32_t batch_size = config.training_context.batch_size; | ||
| config.training_context.num_step += 1; |
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Only problem, where to reset this value to 0. For example we now start new epoch and that is why we need to reset this value again to 0.
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AFAIK, this num_step is used only for ADAM. Then it does not need to be reset at new epoch.
| /* | ||
| * OMMetrics - enum to store metrics supported by training with onert-micro | ||
| */ | ||
| enum OMMetrics |
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@BalyshevArtem Note that nnfw api does not expose Metric Evaluation. That is, nnfw api will expose loss evaluation function.
| return UnknownError; | ||
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| // Write data | ||
| out_file.write(config.model_ptr, config.model_size); |
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Maybe it would be better to catch and to process some exceptions of the write function and in case of arising ones to return Error status (the same can be apply for saveCheckpoint). Just check ios_base::badbit state.
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IN next pr will add this, thank you
This pr introduces OMTrainingInterpreter, OMTrainingContext and OMTrainingRuntimeModule entities.
for issue #12873
from draft: #13107
ONE-DCO-1.0-Signed-off-by: Artem Balyshev a.balyshev@samsung.com