diff --git a/mindspore/ops/_grad/__init__.py b/mindspore/ops/_grad/__init__.py
index 9cf4104e5a1..c2c837b89c2 100644
--- a/mindspore/ops/_grad/__init__.py
+++ b/mindspore/ops/_grad/__init__.py
@@ -15,7 +15,7 @@
 
 """grad impl."""
 from . import grad_array_ops, grad_comm_ops, grad_debug_ops, grad_implementations, \
-               grad_math_ops, grad_nn_ops, grad_other_ops
+               grad_math_ops, grad_nn_ops, grad_other_ops, grad_quant_ops
 from .grad_base import get_bprop_fn
 
 __all__ = ['get_bprop_fn']
diff --git a/mindspore/ops/operations/_quant_ops.py b/mindspore/ops/operations/_quant_ops.py
index 4c7d64b581b..2d07549fd05 100644
--- a/mindspore/ops/operations/_quant_ops.py
+++ b/mindspore/ops/operations/_quant_ops.py
@@ -223,8 +223,8 @@ class BatchNormFold(PrimitiveWithInfer):
 
     Args:
         momentum (float): Momentum value should be [0, 1]. Default: 0.1.
-        epsilon (float): A small float number to avoid dividing by 0. 1e-12 if dtype in
-            float32 else 1e-3. Default: 1e-12.
+        epsilon (float): A small float number to avoid dividing by 0. 1e-5 if dtype in
+            float32 else 1e-3. Default: 1e-5.
         is_training (bool): In training mode set True, else set False. Default: True.
         freeze_bn (int): Delay in steps at which computation switches from regular batch
             norm to frozen mean and std. Default: 0.
@@ -247,7 +247,7 @@ class BatchNormFold(PrimitiveWithInfer):
     channel = 1
 
     @prim_attr_register
-    def __init__(self, momentum=0.1, epsilon=1e-12, is_training=True, freeze_bn=0):
+    def __init__(self, momentum=0.1, epsilon=1e-5, is_training=True, freeze_bn=0):
         """init batch norm fold layer"""
         self.momentum = validator.check_number_range('momentum', momentum, 0, 1, Rel.INC_BOTH, self.name)
         self.epsilon = validator.check_float_positive('epsilon', epsilon, self.name)
@@ -277,7 +277,7 @@ class BatchNormFoldGrad(PrimitiveWithInfer):
     channel = 1
 
     @prim_attr_register
-    def __init__(self, epsilon=1e-12, is_training=True, freeze_bn=0):
+    def __init__(self, epsilon=1e-5, is_training=True, freeze_bn=0):
         """init BatchNormGrad layer"""
         self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name)
         self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
diff --git a/mindspore/train/amp.py b/mindspore/train/amp.py
index 2e758b0e9dd..f83f9606ead 100644
--- a/mindspore/train/amp.py
+++ b/mindspore/train/amp.py
@@ -32,6 +32,7 @@ __all__ = ["build_train_network"]
 
 class OutputTo16(nn.Cell):
     "Wrap cell for amp. Cast network output back to float16"
+
     def __init__(self, op):
         super(OutputTo16, self).__init__(auto_prefix=False)
         self._op = op
@@ -53,7 +54,7 @@ def _do_keep_batchnorm_fp32(network):
             change = True
         else:
             _do_keep_batchnorm_fp32(subcell)
-    if  isinstance(network, nn.SequentialCell) and change:
+    if isinstance(network, nn.SequentialCell) and change:
         network.cell_list = list(network.cells())
 
 
@@ -72,7 +73,7 @@ def _check_kwargs(key_words):
     """Check kwargs."""
     for arg in key_words:
         if arg not in ['cast_model_type', 'keep_batchnorm_fp32', 'loss_scale_manager']:
-            raise  ValueError(f"Unsupported arg '{arg}'")
+            raise ValueError(f"Unsupported arg '{arg}'")
 
     if 'cast_model_type' in key_words:
         validator.check_type_name('cast_model_type', key_words['cast_model_type'],
diff --git a/scripts/get_shape_from_ir.sh b/scripts/get_shape_from_ir.sh
index 8e603add4ab..739ffca56e3 100755
--- a/scripts/get_shape_from_ir.sh
+++ b/scripts/get_shape_from_ir.sh
@@ -18,4 +18,16 @@ set -e
 
 # Usage : get_shape_from_ir.sh ir_file
 
-cat "$1" | perl -p -e 's/\n/NEWLINE/' | sed 's/NEWLINE      :/:/g' | sed 's/Tensor NEWLINEshape//g' | perl -p -e 's/NEWLINE/\n/g' | perl -p -e 's/<Array\[([\d\w]+)\]x\[[\w ]+\](\[[\d, ]*\])>/\2/g' | perl -p -e 's/<Tuple\[([\[\]\d\w\.\*]*)\]>/Tuple/g' | perl -p -e 's/ \%(\d+)\(.*= /\1\t/g' | perl -p -e 's/\(.*\)( \{.*\})*:/\t\1\t/g' | tr -d '()' | awk '/subgraph/{p=1;next}{if(p){print}}'| awk '/return/{p=1;next}{if(!p){print}}' | sed '/^$/d' | awk -F'\t' '{print $1"\t"$2"\t"$4"\t"$3}'
+cat "$1" | perl -p -e 's/\n/NEWLINE/' \
+         | sed 's/NEWLINE      :/:/g' \
+         | sed 's/Tensor NEWLINEshape//g' \
+         | perl -p -e 's/NEWLINE/\n/g' \
+         | perl -p -e 's/<Array\[([\d\w]+)\]x\[[\w ]+\](\[[\d, ]*\])>/\2/g' \
+         | perl -p -e 's/<Tuple\[([\[\]\d\w\.\*]*)\]>/Tuple/g' \
+         | perl -p -e 's/ \%(\d+)\(.*= /\1\t/g' \
+         | perl -p -e 's/\(.*\)( \{.*\})*:/\t\1\t/g' \
+         | tr -d '()' \
+         | awk '/subgraph/{p=1;next}{if(p){print}}'\
+         | awk '/return/{p=1;next}{if(!p){print}}' \
+         | sed '/^$/d' \
+         | awk -F'\t' '{print $1"\t"$2"\t"$4}'