modified built-in function problem in the network

model_zoo/official/cv/inceptionv3/src/loss.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """define loss function for network."""
-from mindspore.nn.loss.loss import _Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -21,7 +20,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(nn.Cell):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
     def __init__(self, smooth_factor=0, num_classes=1000, factor=0.4):
         super(CrossEntropy, self).__init__()
@@ -43,7 +42,7 @@ class CrossEntropy(_Loss):
         return loss_logit + self.factor*loss_aux
 
 
-class CrossEntropy_Val(_Loss):
+class CrossEntropy_Val(nn.Cell):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits, will be used in inference process"""
     def __init__(self, smooth_factor=0, num_classes=1000):
         super(CrossEntropy_Val, self).__init__()

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -24,7 +24,7 @@ import mindspore.nn as nn
 from mindspore import context
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import ModelCheckpoint, RunContext
-from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
+from mindspore.train.callback import CheckpointConfig
 from mindspore import amp
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.common import set_seed
@@ -113,6 +113,16 @@ def parallel_init(args):
     context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=degree)
 
 
+class InternalCallbackParam(dict):
+    """Internal callback object's parameters."""
+
+    def __getattr__(self, key):
+        return self[key]
+
+    def __setattr__(self, key, value):
+        self[key] = value
+
+
 def modelarts_pre_process():
     '''modelarts pre process function.'''
     def unzip(zip_file, save_dir):
@@ -231,7 +241,7 @@ def run_train():
         ckpt_cb = ModelCheckpoint(config=ckpt_config,
                                   directory=save_ckpt_path,
                                   prefix='{}'.format(config.rank))
-        cb_params = _InternalCallbackParam()
+        cb_params = InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
         cb_params.cur_epoch_num = 1

model_zoo/official/cv/yolov3_darknet53_quant/train.py

@@ -25,7 +25,7 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import ModelCheckpoint, RunContext
-from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
+from mindspore.train.callback import CheckpointConfig
 import mindspore as ms
 from mindspore.compression.quant import QuantizationAwareTraining
 from mindspore.common import set_seed
@@ -149,6 +149,16 @@ def build_quant_network(network):
     return network
 
 
+class InternalCallbackParam(dict):
+    """Internal callback object's parameters."""
+
+    def __getattr__(self, key):
+        return self[key]
+
+    def __setattr__(self, key, value):
+        self[key] = value
+
+
 def train():
     """Train function."""
     args = parse_args()
@@ -215,7 +225,7 @@ def train():
                                        keep_checkpoint_max=ckpt_max_num)
         save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
         ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank))
-        cb_params = _InternalCallbackParam()
+        cb_params = InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
         cb_params.cur_epoch_num = 1

model_zoo/official/recommend/deepfm/src/deepfm.py

@@ -28,8 +28,8 @@ from mindspore.nn.metrics import Metric
 from mindspore import nn, Tensor, ParameterTuple, Parameter
 from mindspore.common.initializer import Uniform, initializer
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_gradients_mean
-from mindspore.context import ParallelMode
+from mindspore.context import ParallelMode, get_auto_parallel_context
+from mindspore.communication.management import get_group_size
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 
 from src.callback import EvalCallBack, LossCallBack
@@ -276,12 +276,12 @@ class TrainStepWrap(nn.Cell):
 
         self.reducer_flag = False
         self.grad_reducer = None
-        parallel_mode = _get_parallel_mode()
+        parallel_mode = get_auto_parallel_context("parallel_mode")
         if parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL):
             self.reducer_flag = True
         if self.reducer_flag:
-            mean = _get_gradients_mean()
-            degree = _get_device_num()
+            mean = get_auto_parallel_context("gradients_mean")
+            degree = get_group_size()
             self.grad_reducer = DistributedGradReducer(self.optimizer.parameters, mean, degree)
 
     def construct(self, batch_ids, batch_wts, label):