from mmcv.runner import OptimizerHook


class DistOptimizerHook(OptimizerHook):

    def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1):
        self.grad_clip = grad_clip
        self.coalesce = coalesce
        self.bucket_size_mb = bucket_size_mb
        self.update_interval = update_interval

    def before_run(self, runner):
        runner.optimizer.zero_grad()

    def after_train_iter(self, runner):
        runner.outputs['loss'] /= self.update_interval
        runner.outputs['loss'].backward()
        if self.every_n_iters(runner, self.update_interval):
            if self.grad_clip is not None:
                self.clip_grads(runner.model.parameters())
            runner.optimizer.step()
            runner.optimizer.zero_grad()