def prepare_model_stuff(*,
model,
criterion_params=None,
optimizer_params=None,
scheduler_params=None):
fp16 = isinstance(model, Fp16Wrap)
criterion_params = criterion_params or {}
criterion = Registry.get_criterion(**criterion_params)
optimizer_params = optimizer_params or {}
optimizer = Registry.get_optimizer(model,
**optimizer_params,
fp16=fp16)
scheduler_params = scheduler_params or {}
scheduler = Registry.get_scheduler(optimizer, **scheduler_params)
return criterion, optimizer, scheduler
def get_criterion(self, stage: str) -> _Criterion:
criterion_params = (self.stages_config[stage].get(
"criterion_params", {}))
criterion = Registry.get_criterion(**criterion_params)
return criterion
def __init__(self,
actor,
critic,
gamma,
n_step,
actor_optimizer_params,
critic_optimizer_params,
actor_grad_clip_params=None,
critic_grad_clip_params=None,
actor_loss_params=None,
critic_loss_params=None,
actor_scheduler_params=None,
critic_scheduler_params=None,
resume=None,
load_optimizer=True,
actor_tau=1.0,
critic_tau=1.0,
min_action=-1.0,
max_action=1.0,
**kwargs):
# hack to prevent cycle dependencies
from catalyst.contrib.registry import Registry
self._device = UtilsFactory.prepare_device()
self.actor = actor.to(self._device)
self.critic = critic.to(self._device)
self.target_actor = copy.deepcopy(actor).to(self._device)
self.target_critic = copy.deepcopy(critic).to(self._device)
self.actor_optimizer = Registry.get_optimizer(self.actor,
**actor_optimizer_params)
self.critic_optimizer = Registry.get_optimizer(
self.critic, **critic_optimizer_params)
self.actor_optimizer_params = actor_optimizer_params
self.critic_optimizer_params = critic_optimizer_params
actor_scheduler_params = actor_scheduler_params or {}
critic_scheduler_params = critic_scheduler_params or {}
self.actor_scheduler = Registry.get_scheduler(self.actor_optimizer,
**actor_scheduler_params)
self.critic_scheduler = Registry.get_scheduler(
self.critic_optimizer, **critic_scheduler_params)
self.actor_scheduler_params = actor_scheduler_params
self.critic_scheduler_params = critic_scheduler_params
self.n_step = n_step
self.gamma = gamma
actor_grad_clip_params = actor_grad_clip_params or {}
critic_grad_clip_params = critic_grad_clip_params or {}
self.actor_grad_clip_fn = Registry.get_grad_clip_fn(
**actor_grad_clip_params)
self.critic_grad_clip_fn = Registry.get_grad_clip_fn(
**critic_grad_clip_params)
self.actor_grad_clip_params = actor_grad_clip_params
self.critic_grad_clip_params = critic_grad_clip_params
self.actor_criterion = Registry.get_criterion(
**(actor_loss_params or {}))
self.critic_criterion = Registry.get_criterion(
**(critic_loss_params or {}))
self.actor_loss_params = actor_loss_params
self.critic_loss_params = critic_loss_params
self.actor_tau = actor_tau
self.critic_tau = critic_tau
self.min_action = min_action
self.max_action = max_action
self._init(**kwargs)
if resume is not None:
self.load_checkpoint(resume, load_optimizer=load_optimizer)
