correct conv ops api equations

docs/api/api_python/nn/mindspore.nn.Conv2d.rst

@@ -45,28 +45,28 @@ mindspore.nn.Conv2d
 
        .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
+                H_{out} ＝ \left \lceil{\frac{H_{in}}{\text{stride[0]}}} \right \rceil \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
+                W_{out} ＝ \left \lceil{\frac{W_{in}}{\text{stride[1]}}} \right \rceil \\
             \end{array}
 
        pad_mode为"valid"时：
 
        .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
+                H_{out} ＝ \left \lceil{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
-                {\text{stride[0]}} + 1} \right \rfloor \\
+                {\text{stride[0]}}} \right \rceil \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
+                W_{out} ＝ \left \lceil{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
-                {\text{stride[1]}} + 1} \right \rfloor \\
+                {\text{stride[1]}}} \right \rceil \\
             \end{array}
 
        pad_mode为"pad"时：
 
        .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
+                H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{kernel_size[0]} - 1) \times
-                \text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
+                \text{dilation[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
+                W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{kernel_size[1]} - 1) \times
-                \text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
+                \text{dilation[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
             \end{array}
 
    异常：

docs/api/api_python/ops/mindspore.ops.Conv2D.rst

@@ -12,7 +12,7 @@ mindspore.ops.Conv2D
 
     其中， :math:`ccor` 为 `cross-correlation <https://en.wikipedia.org/wiki/Cross-correlation>`_ ， :math:`C_{in}` 为输入通道数， :math:`j` 的范围从 :math:`0` 到 :math:`C_{out} - 1` ， :math:`W_{ij}` 对应第 :math:`j` 个过滤器的第 :math:`i` 个通道， :math:`out_{j}` 对应输出的第 :math:`j` 个通道。 :math:`W_{ij}` 为卷积核的切片，其shape为 :math:`(\text{kernel_size[0]},\text{kernel_size[1]})` ，其中 :math:`\text{kernel_size[0]}` 和 :math:`\text{kernel_size[1]}` 是卷积核的高度和宽度。完整卷积核的shape为 :math:`(C_{out}, C_{in} / \text{group}, \text{kernel_size[0]}, \text{kernel_size[1]})` ，其中 `group` 是在通道上分割输入 `x` 的组数。
 
-    如果 `pad_mode` 设置为"valid"，则输出高度和宽度将分别为 :math:`\left \lfloor{1 + \frac{H_{in} + \text{padding[0]} + \text{padding[1]} - \text{kernel_size[0]} - (\text{kernel_size[0]} - 1) \times (\text{dilation[0]} - 1) }{\text{stride[0]}}} \right \rfloor` 和 :math:`\left \lfloor{1 + \frac{W_{in} + \text{padding[2]} + \text{padding[3]} - \text{kernel_size[1]} - (\text{kernel_size[1]} - 1) \times (\text{dilation[1]} - 1) }{\text{stride[1]}}} \right \rfloor` 。
+    如果 `pad_mode` 设置为"pad"，则输出高度和宽度将分别为 :math:`\left \lfloor{1 + \frac{H_{in} + \text{padding[0]} + \text{padding[1]} - \text{kernel_size[0]} - (\text{kernel_size[0]} - 1) \times (\text{dilation[0]} - 1) }{\text{stride[0]}}} \right \rfloor` 和 :math:`\left \lfloor{1 + \frac{W_{in} + \text{padding[2]} + \text{padding[3]} - \text{kernel_size[1]} - (\text{kernel_size[1]} - 1) \times (\text{dilation[1]} - 1) }{\text{stride[1]}}} \right \rfloor` 。
     其中， :math:`dialtion` 为卷积核元素之间的间距， :math:`stride` 为移动步长， :math:`padding` 为添加到输入两侧的零填充。
 
     请参考论文 `Gradient Based Learning Applied to Document Recognition <http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_ 。更详细的介绍，参见：http://cs231n.github.io/convolutional-networks/。

mindspore/python/mindspore/nn/layer/conv.py

@@ -195,28 +195,28 @@ class Conv2d(_Conv):
 
         .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
+                H_{out} ＝ \left \lceil{\frac{H_{in}}{\text{stride[0]}}} \right \rceil \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
+                W_{out} ＝ \left \lceil{\frac{W_{in}}{\text{stride[1]}}} \right \rceil \\
             \end{array}
 
         pad_mode is 'valid':
 
         .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
+                H_{out} ＝ \left \lceil{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
-                {\text{stride[0]}} + 1} \right \rfloor \\
+                {\text{stride[0]}}} \right \rceil \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
+                W_{out} ＝ \left \lceil{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
-                {\text{stride[1]}} + 1} \right \rfloor \\
+                {\text{stride[1]}}} \right \rceil \\
             \end{array}
 
         pad_mode is 'pad':
 
         .. math::
             \begin{array}{ll} \\
-                H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
+                H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{kernel_size[0]} - 1) \times
-                \text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
+                \text{dilation[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
-                W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
+                W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{kernel_size[1]} - 1) \times
-                \text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
+                \text{dilation[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
             \end{array}
 
     Raises:
@@ -1223,19 +1223,19 @@ class Conv1dTranspose(_Conv):
         pad_mode is 'same':
 
         .. math::
-            L_{out} ＝ \left \lfloor{\frac{L_{in}}{\text{stride}} + 1} \right \rfloor
+            L_{out} ＝ \left \lceil{\frac{L_{in}}{\text{stride}}} \right \rceil
 
         pad_mode is 'valid':
 
         .. math::
-            L_{out} ＝ \left \lfloor{\frac{L_{in} - \text{dilation} \times (\text{kernel_size} - 1) }
+            L_{out} ＝ \left \lceil{\frac{L_{in} - \text{dilation} \times (\text{kernel_size} - 1) }
-            {\text{stride}} + 1} \right \rfloor
+            {\text{stride}}} \right \rceil
 
         pad_mode is 'pad':
 
         .. math::
-            L_{out} ＝ \left \lfloor{\frac{L_{in} + 2 \times padding - (\text{dilation} - 1) \times
+            L_{out} ＝ \left \lfloor{\frac{L_{in} + 2 \times padding - (\text{kernel_size} - 1) \times
-            \text{kernel_size} - 1 }{\text{stride}} + 1} \right \rfloor
+            \text{dilation} - 1 }{\text{stride}} + 1} \right \rfloor
 
     Raises:
         TypeError: If `in_channels`, `out_channels`, `kernel_size`, `stride`, `padding` or `dilation` is not an int.

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -1280,7 +1280,7 @@ class Conv2D(Primitive):
     :math:`(C_{out}, C_{in} / \text{group}, \text{kernel_size[0]}, \text{kernel_size[1]})`,
     where group is the group number to split the input in the channel dimension.
 
-    If the 'pad_mode' is set to be "valid", the output height and width will be
+    If the 'pad_mode' is set to be "pad", the output height and width will be
     :math:`\left \lfloor{1 + \frac{H_{in} + \text{padding[0]} + \text{padding[1]} - \text{kernel_size[0]} -
     (\text{kernel_size[0]} - 1) \times (\text{dilation[0]} - 1) }{\text{stride[0]}}} \right \rfloor` and
     :math:`\left \lfloor{1 + \frac{W_{in} + \text{padding[2]} + \text{padding[3]} - \text{kernel_size[1]} -