Improve LRN doc (onnx#965)

Author: houseroad

docs/Changelog.md

@@ -1924,9 +1924,16 @@ This version of the operator has been available since version 1 of the default O
 
 ### <a name="LRN-1"></a>**LRN-1**</a>
 
-  Local Response Normalization. It normalizes over local input regions.
-  Each input value is divided by
-  (bias+(alpha/size)*sum(xi^2 for every xi in the local region))^beta.
+  Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+  It normalizes over local input regions.
+  The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+  of shape (N x C x D1 x D2, ..., Dk), its region is
+  {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1)}.
+  
+  square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+  where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1).
+  
+  Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
 
 #### Version
 
@@ -1935,12 +1942,12 @@ This version of the operator has been available since version 1 of the default O
 #### Attributes
 
 <dl>
-<dt><tt>alpha</tt> : float (required)</dt>
-<dd>Scaling parameter</dd>
-<dt><tt>beta</tt> : float (required)</dt>
-<dd>The exponent</dd>
+<dt><tt>alpha</tt> : float</dt>
+<dd>Scaling parameter, default is 1e-4f.</dd>
+<dt><tt>beta</tt> : float</dt>
+<dd>The exponent, default is 0.75f</dd>
 <dt><tt>bias</tt> : float</dt>
-<dd>Default to 1.f</dd>
+<dd>Default to 1.0f</dd>
 <dt><tt>size</tt> : int (required)</dt>
 <dd>The number of channels to sum over</dd>
 </dl>
@@ -1949,14 +1956,14 @@ This version of the operator has been available since version 1 of the default O
 
 <dl>
 <dt><tt>X</tt> : T</dt>
-<dd>Input tensor</dd>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
 </dl>
 
 #### Outputs
 
 <dl>
 <dt><tt>Y</tt> : T</dt>
-<dd>Output tensor</dd>
+<dd>Output tensor, which has the shape and type as input tensor</dd>
 </dl>
 
 #### Type Constraints

docs/Operators.md

@@ -3462,9 +3462,16 @@ Other versions of this operator: <a href="Changelog.md#InstanceNormalization-1">
 
 ### <a name="LRN"></a><a name="lrn">**LRN**</a>
 
-  Local Response Normalization. It normalizes over local input regions.
-  Each input value is divided by
-  (bias+(alpha/size)*sum(xi^2 for every xi in the local region))^beta.
+  Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+  It normalizes over local input regions.
+  The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+  of shape (N x C x D1 x D2, ..., Dk), its region is
+  {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1)}.
+  
+  square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+  where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1).
+  
+  Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
 
 #### Version
 
@@ -3473,12 +3480,12 @@ This version of the operator has been available since version 1 of the default O
 #### Attributes
 
 <dl>
-<dt><tt>alpha</tt> : float (required)</dt>
-<dd>Scaling parameter</dd>
-<dt><tt>beta</tt> : float (required)</dt>
-<dd>The exponent</dd>
+<dt><tt>alpha</tt> : float</dt>
+<dd>Scaling parameter, default is 1e-4f.</dd>
+<dt><tt>beta</tt> : float</dt>
+<dd>The exponent, default is 0.75f</dd>
 <dt><tt>bias</tt> : float</dt>
-<dd>Default to 1.f</dd>
+<dd>Default to 1.0f</dd>
 <dt><tt>size</tt> : int (required)</dt>
 <dd>The number of channels to sum over</dd>
 </dl>
@@ -3487,14 +3494,14 @@ This version of the operator has been available since version 1 of the default O
 
 <dl>
 <dt><tt>X</tt> : T</dt>
-<dd>Input tensor</dd>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
 </dl>
 
 #### Outputs
 
 <dl>
 <dt><tt>Y</tt> : T</dt>
-<dd>Output tensor</dd>
+<dd>Output tensor, which has the shape and type as input tensor</dd>
 </dl>
 
 #### Type Constraints

onnx/defs/nn/defs.cc

@@ -953,19 +953,40 @@ Flattens the input tensor into a 2D matrix. If input tensor has shape
 
 ONNX_OPERATOR_SCHEMA(LRN)
     .Attr("size", "The number of channels to sum over", AttributeProto::INT)
-    .Attr("alpha", "Scaling parameter", AttributeProto::FLOAT)
-    .Attr("beta", "The exponent", AttributeProto::FLOAT)
-    .Attr("bias", "Default to 1.f", AttributeProto::FLOAT, 1.0f)
-    .Input(0, "X", "Input tensor", "T")
-    .Output(0, "Y", "Output tensor", "T")
+    .Attr("alpha", "Scaling parameter, default is 1e-4f.", AttributeProto::FLOAT, 0.0001f)
+    .Attr("beta", "The exponent, default is 0.75f", AttributeProto::FLOAT, 0.75f)
+    .Attr("bias", "Default to 1.0f", AttributeProto::FLOAT, 1.0f)
+    .Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the batch "
+        "size. Optionally, if dimension denotation is "
+        "in effect, the operation expects the input "
+        "data tensor to arrive with the dimension denotation "
+        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T")
+    .Output(0, "Y", "Output tensor, which has the shape and type as input tensor", "T")
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
         "Constrain input and output "
         " types to float tensors.")
     .SetDoc(R"DOC(
-Local Response Normalization. It normalizes over local input regions.
-Each input value is divided by
-(bias+(alpha/size)*sum(xi^2 for every xi in the local region))^beta.
+Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+It normalizes over local input regions.
+The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+of shape (N x C x D1 x D2, ..., Dk), its region is
+{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1)}.
+
+square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2) - 1).
+
+Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
 )DOC")
     .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);