forked from mrq/DL-Art-School
29 lines
947 B
Python
29 lines
947 B
Python
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def create_step(opt_step):
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pass
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# Defines the expected API for a step
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class base_step:
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# Returns all optimizers used in this step.
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def get_optimizers(self):
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pass
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# Returns optimizers which are opting in for default LR scheduling.
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def get_optimizers_with_default_scheduler(self):
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pass
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# Returns the names of the networks this step will train. Other networks will be frozen.
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def get_networks_trained(self):
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pass
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# Performs all forward and backward passes for this step given an input state. All input states are lists or
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# chunked tensors. Use grad_accum_step to derefernce these steps. Return the state with any variables the step
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# exports (which may be used by subsequent steps)
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def do_forward_backward(self, state, grad_accum_step):
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return state
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# Performs the optimizer step after all gradient accumulation is completed.
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def do_step(self):
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pass
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