优化算子使用

xt/model/dqn/dqn_cnn.py

@@ -107,7 +107,7 @@ def construct(self, x):
 
 @_grad_scale.register("Tensor", "Tensor")
 def tensor_grad_scale(scale, grad):
- return grad * ms.ops.cast(ms.ops.Reciprocal()(scale), ms.ops.dtype(grad))
+ return grad * ops.cast(ops.Reciprocal()(scale), ops.dtype(grad))
 
 
 class MyTrainOneStepCell(ms.nn.TrainOneStepWithLossScaleCell):
@@ -123,11 +123,11 @@ def construct(self, state, label):
  loss = self.network(state, label)
  scaling_sens = self.scale_sense
  status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
- scaling_sens_filled = ms.ops.ones_like(loss) * ms.ops.cast(scaling_sens, ms.ops.dtype(loss))
+ scaling_sens_filled = ops.ones_like(loss) * ops.cast(scaling_sens, ops.dtype(loss))
  grads = self.grad(self.network, weights)(state, label, scaling_sens_filled)
- grads = self.hyper_map(ms.ops.partial(_grad_scale, scaling_sens), grads)
+ grads = self.hyper_map(ops.partial(_grad_scale, scaling_sens), grads)
  if self.grad_clip:
- grads = ms.ops.clip_by_global_norm(grads, self.clipnorm)
+ grads = ops.clip_by_global_norm(grads, self.clipnorm)
  grads = self.grad_reducer(grads)
  cond = self.get_overflow_status(status, grads)
  overflow = self.process_loss_scale(cond)