!10067 fix bugs of op AvgPool2d, Conv2dBnFoldQuantOneConv, ApplyAdadelta, ApplyAdagradV2, ApplyPowerSign and so on

From: @lihongkang1
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/layer/normalization.py

@@ -383,8 +383,10 @@ class BatchNorm2d(_BatchNorm):
         >>> print(output)
         [[[[171.99915   46.999763 ]
            [116.99941  191.99904  ]]
+
           [[ 66.999664 250.99875  ]
            [194.99902  102.99948  ]]
+
           [[  8.999955 210.99895  ]
            [ 20.999895 241.9988   ]]]]
     """

mindspore/nn/layer/pooling.py

@@ -111,11 +111,8 @@ class MaxPool2d(_PoolNd):
         >>> pool = nn.MaxPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         >>> output = pool(x)
-        >>> print(output)
-        [[[[7. 8.]
-           [9. 9.]]
-          [[7. 8.]
-           [8. 8.]]]]
+        >>> print(output.shape)
+        (1, 2, 2, 2)
     """
 
     def __init__(self, kernel_size=1, stride=1, pad_mode="valid", data_format="NCHW"):
@@ -256,11 +253,8 @@ class AvgPool2d(_PoolNd):
         >>> pool = nn.AvgPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         >>> output = pool(x)
-        >>> print(output)
-        [[[[4.888889  4.4444447]
-           [4.111111  3.4444444]]
-          [[4.2222223 4.5555553]
-           [3.2222223 4.5555553]]]]
+        >>> print(output.shape)
+        (1, 2, 2, 2)
     """
 
     def __init__(self,

mindspore/nn/layer/quant.py

@@ -375,7 +375,7 @@ class Conv2dBnFoldQuantOneConv(Cell):
     Examples:
         >>> qconfig = compression.quant.create_quant_config()
         >>> conv2d_bnfold = nn.Conv2dBnFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
-        >>>                                      quant_config=qconfig)
+        ...                                      quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
         >>> result = conv2d_bnfold(input)
         >>> output = result.shape

mindspore/nn/loss/loss.py

@@ -249,12 +249,13 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
 
     Examples:
         >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
+        >>> np.random.seed(0)
         >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32)
         >>> labels_np = np.ones([1,]).astype(np.int32)
         >>> labels = Tensor(labels_np)
         >>> output = loss(logits, labels)
         >>> print(output)
-        [5.6924148]
+        [7.868383]
     """
     def __init__(self,
                  sparse=False,

mindspore/ops/operations/other_ops.py

@@ -318,11 +318,8 @@ class IOU(PrimitiveWithInfer):
         >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> output = iou(anchor_boxes, gt_boxes)
-        >>> print(output)
-        [[65500. 65500. 65500.]
-         [   -0.    -0.    -0.]
-         [   -0.    -0.    -0.]]
-
+        >>> print(output.shape)
+        (3, 3)
     """
 
     @prim_attr_register