DL-Art-School/codes/models/steps/steps.py
2020-08-12 08:45:23 -06:00

29 lines
947 B
Python

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