!2649 Add group params check method and fix print comment

Merge pull request !2649 from ghzl/fix-group-params-type-r0.5
This commit is contained in:
mindspore-ci-bot 2020-06-28 20:47:40 +08:00 committed by Gitee
commit b5d8134682
2 changed files with 24 additions and 9 deletions

View File

@ -219,8 +219,32 @@ class Optimizer(Cell):
raise TypeError("Learning rate should be float, Tensor or Iterable.")
return lr
def _check_group_params(self, parameters):
"""Check group params."""
parse_keys = ['params', 'lr', 'weight_decay', 'order_params']
for group_param in parameters:
invalid_key = list(filter(lambda x: x not in parse_keys, group_param.keys()))
if invalid_key:
raise KeyError(f'The key "{invalid_key}" cannot be recognized in group params.')
if 'order_params' in group_param.keys():
if len(group_param.keys()) > 1:
raise ValueError("The order params dict in group parameters should "
"only include the 'order_params' key.")
if not isinstance(group_param['order_params'], Iterable):
raise TypeError("The value of 'order_params' should be an Iterable type.")
continue
if not group_param['params']:
raise ValueError("Optimizer got an empty group parameter list.")
for param in group_param['params']:
if not isinstance(param, Parameter):
raise TypeError("The group param should be an iterator of Parameter type.")
def _parse_group_params(self, parameters, learning_rate):
"""Parse group params."""
self._check_group_params(parameters)
if self.dynamic_lr:
dynamic_lr_length = learning_rate.size()
else:
@ -250,9 +274,6 @@ class Optimizer(Cell):
if dynamic_lr_length not in (lr_length, 0):
raise ValueError("The dynamic learning rate in group should be the same size.")
if not group_param['params']:
raise ValueError("Optimizer got an empty group parameter list.")
dynamic_lr_length = lr_length
self.dynamic_lr_length = dynamic_lr_length

View File

@ -309,12 +309,6 @@ class Print(PrimitiveWithInfer):
Output tensor or string to stdout.
Note:
The print operation cannot support the following cases currently.
1. The type of tensor is float64 or bool.
2. The data of tensor is a scalar type.
In pynative mode, please use python print function.
Inputs: