Maxpool spec update

docs/ops/pooling/MaxPool_1.md

@@ -6,7 +6,79 @@
 
 **Short description**: [Reference](http://caffe.berkeleyvision.org/tutorial/layers/pooling.html)
 
-**Detailed description**: [Reference](http://cs231n.github.io/convolutional-networks/#pool)
+**Detailed description**: [Reference](https://deeplizard.com/learn/video/ZjM_XQa5s6s). Input shape can be either 3D, 4D or 5D. Pooling operation is performed with the respect to input shape from the third dimension to the last dimension. If paddings are used then during the pooling calculation their value is 0.  
+        `H_out = (H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1`  
+        `W_out = (H + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1`  
+        `D_out = (H + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1`  
+
+Example 1 shows how *MaxPool* operates with `auto_pad = explicit`
+
+```
+input = [[1, 2, 3],
+         [4, 5, 6],
+         [7, 8, 9]]
+strides = [1, 1]
+pads_begin = [1, 1]
+pads_end = [1, 1]
+kernel = [2, 2]
+rounding_type = "floor"
+auto_pad = "explicit"
+output = [[1, 2, 3, 3],
+          [4, 5, 6, 6],
+          [7, 8, 9, 9],
+          [7, 8, 9, 9]]
+```
+
+Example 2 shows how *MaxPool* operates with `auto_pad = valid`
+
+```
+input = [[1, 2, 3],
+         [4, 5, 6],
+         [7, 8, 9]]
+strides = [1, 1]
+pads_begin = [1, 1]
+pads_end = [1, 1]
+kernel = [2, 2]
+rounding_type = "floor"
+auto_pad = "valid"
+output = [[5, 6],
+          [8, 9]]
+```
+
+
+Example 3 shows how *MaxPool* operates with `auto_pad = same_upper`
+
+```
+input = [[1, 2, 3],
+         [4, 5, 6],
+         [7, 8, 9]]
+strides = [1, 1]
+pads_begin = [1, 1]
+pads_end = [1, 1]
+kernel = [2, 2]
+rounding_type = "floor"
+auto_pad = "same_upper"
+output = [[1, 2, 3],
+          [4, 5, 6]
+          [7, 8, 9]]
+```
+
+Example 4 shows how *MaxPool* operates with `auto_pad = same_lower`
+
+```
+input = [[1, 2, 3],
+         [4, 5, 6],
+         [7, 8, 9]]
+strides = [1, 1]
+pads_begin = [1, 1]
+pads_end = [1, 1]
+kernel = [2, 2]
+rounding_type = "floor"
+auto_pad = "same_lower"
+output = [[5, 6, 6],
+          [8, 9, 9]
+          [8, 9, 9]]
+```
 
 **Attributes**: *Pooling* attributes are specified in the `data` node, which is a child of the layer node.
 
@@ -46,7 +118,7 @@
 
 * *rounding_type*
 
-  * **Description**: *rounding_type* is a type of rounding to be applied.
+  * **Description**: *rounding_type* is a type of rounding to be used to compute output shape.
   * **Range of values**:
     * *ceil*
     * *floor*
@@ -67,7 +139,10 @@
 
 **Inputs**:
 
-*   **1**: 4D or 5D input tensor. Required.
+*   **1**: 3D, 4D or 5D input tensor. Required.
+
+**Outputs**:
+  * **1**: Input shape can be either [N,C,H], [N,C,H,W] or [N,C,H,W,D]. Then the corresponding output shape will be [N,C,H_out], [N,C,H_out,W_out] or [N,C,H_out,W_out,D_out]
 
 **Mathematical Formulation**
 
@@ -79,8 +154,62 @@
 
 ```xml
 <layer ... type="MaxPool" ... >
-        <data auto_pad="same_upper" kernel="3,3" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
-        <input> ... </input>
-        <output> ... </output>
+        <data auto_pad="same_upper" kernel="2,2" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
+        <input> 
+          <port id="0">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>32</dim>
+            <dim>32</dim>
+          </port>
+        </input>
+        <output>
+          <port id="1">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>32</dim>
+            <dim>32</dim>
+          </port>
+    </output>
+</layer>
+
+<layer ... type="MaxPool" ... >
+        <data auto_pad="explicit" kernel="2,2" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
+        <input> 
+          <port id="0">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>32</dim>
+            <dim>32</dim>
+          </port>
+        </input>
+        <output>
+          <port id="1">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>17</dim>
+            <dim>17</dim>
+          </port>
+    </output>
+</layer>
+
+<layer ... type="MaxPool" ... >
+        <data auto_pad="valid" kernel="2,2" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
+        <input> 
+          <port id="0">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>32</dim>
+            <dim>32</dim>
+          </port>
+        </input>
+        <output>
+          <port id="1">
+            <dim>1</dim>
+            <dim>3</dim>
+            <dim>16</dim>
+            <dim>16</dim>
+          </port>
+    </output>
 </layer>
 ```