Remove impl

Author: syt123450

src/ops/pool.ts

@@ -509,52 +509,13 @@ function withSpaceToBatchBasePaddings(
  *    - For more info, see this guide:
  *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
  *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
- * @param dimRoundingMode The rounding mode used when computing output
- *     dimensions if pad is a number. If none is provided, it will not round
- *     and error if the output is of fractional size.
- * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- */
-/** @doc {heading: 'Operations', subheading: 'Convolution'} */
-function avgPool3d_(
-    x: Tensor5D|TensorLike, filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Tensor5D {
-  return avgPool3dImpl_(
-      x, filterSize, strides, 1, pad, dimRoundingMode, dataFormat);
-}
-
-/**
- * Computes the 3D average pooling.
- *
- * @param x The input tensor, of rank 5 of shape
- *     `[batch, depth, height, width, inChannels]`.
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     If `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideDepth == strideHeight == strideWidth`.
  * @param dilations The dilation rates:
  *     `[dilationDepth, dilationHeight, dilationWidth]`
  *     in which we sample input values across the depth, height and width
  *     dimensions in dilated pooling.
  *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,
  *     then `dilationDepth == dilationHeight == dilationWidth`.
  *     If it is greater than 1, then all values of `strides` must be 1.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
- *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
  * @param dimRoundingMode The rounding mode used when computing output
  *     dimensions if pad is a number. If none is provided, it will not round
  *     and error if the output is of fractional size.
@@ -563,14 +524,15 @@ function avgPool3d_(
  *     default format "NDHWC", the data is stored in the order of: [batch,
  *     depth, height, width, channels]. Only "NDHWC" is currently supported.
  */
-function avgPool3dImpl_(
+/** @doc {heading: 'Operations', subheading: 'Convolution'} */
+function avgPool3d_(
     x: Tensor5D|TensorLike, filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number,
-    dilations: [number, number, number]|number, pad: 'valid'|'same'|number,
+    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
+    dilations: [number, number, number]|number,
     dimRoundingMode?: 'floor'|'round'|'ceil',
     dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Tensor5D {
   const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');
-
+  
   if (dilations == null) {
     dilations = [1, 1, 1];
   }
@@ -591,22 +553,22 @@ function avgPool3dImpl_(
         () => `Error in avgPool3d: pad must be an integer when using, ` +
             `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
   }
-
+  
   const convInfo = conv_util.computePool3DInfo(
       $x.shape, filterSize, strides, dilations, pad, dimRoundingMode,
       dataFormat);
-
+  
   const grad = (dy: Tensor5D) => {
     return {
       x: () => avgPool3dBackprop(
           dy, $x, filterSize, strides, dilations, pad, dimRoundingMode)
     };
   };
-
+  
   let res = ENGINE.runKernel(
       backend => backend.avgPool3d($x, convInfo), {x: $x}, grad);
   res = res.cast($x.dtype);
-
+  
   return res;
 }
 
@@ -706,52 +668,13 @@ function avgPool3dBackprop(
  *    - For more info, see this guide:
  *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
  *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
- * @param dimRoundingMode The rounding mode used when computing output
- *     dimensions if pad is a number. If none is provided, it will not round
- *     and error if the output is of fractional size.
- * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- */
-/** @doc {heading: 'Operations', subheading: 'Convolution'} */
-function maxPool3d_(
-    x: Tensor5D|TensorLike, filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Tensor5D {
-  return maxPool3dImpl_(
-      x, filterSize, strides, 1, pad, dimRoundingMode, dataFormat);
-}
-
-/**
- * Computes the 3D max pooling.
- *
- * @param x The input tensor, of rank 5 of shape
- *     `[batch, depth, height, width, inChannels]`.
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     If `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideDepth == strideHeight == strideWidth`.
  * @param dilations The dilation rates:
  *     `[dilationDepth, dilationHeight, dilationWidth]`
  *     in which we sample input values across the depth, height and width
  *     dimensions in dilated pooling.
  *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,
  *     then `dilationDepth == dilationHeight == dilationWidth`.
  *     If it is greater than 1, then all values of `strides` must be 1.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
- *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
  * @param dimRoundingMode The rounding mode used when computing output
  *     dimensions if pad is a number. If none is provided, it will not round
  *     and error if the output is of fractional size.
@@ -760,14 +683,15 @@ function maxPool3d_(
  *     default format "NDHWC", the data is stored in the order of: [batch,
  *     depth, height, width, channels]. Only "NDHWC" is currently supported.
  */
-function maxPool3dImpl_(
+/** @doc {heading: 'Operations', subheading: 'Convolution'} */
+function maxPool3d_(
     x: Tensor5D|TensorLike, filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number,
-    dilations: [number, number, number]|number, pad: 'valid'|'same'|number,
+    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
+    dilations: [number, number, number]|number,
     dimRoundingMode?: 'floor'|'round'|'ceil',
     dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Tensor5D {
   const $x = convertToTensor(x, 'x', 'maxPool3d');
-
+  
   if (dilations == null) {
     dilations = [1, 1, 1];
   }
@@ -788,11 +712,11 @@ function maxPool3dImpl_(
         () => `Error in maxPool3d: pad must be an integer when using, ` +
             `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
   }
-
+  
   const convInfo = conv_util.computePool3DInfo(
       $x.shape, filterSize, strides, dilations, pad, dimRoundingMode,
       dataFormat);
-
+  
   const grad = (dy: Tensor5D, saved: Tensor[]) => {
     const [$x, y] = saved;
     return {
@@ -801,13 +725,13 @@ function maxPool3dImpl_(
           pad, dimRoundingMode)
     };
   };
-
+  
   const res = ENGINE.runKernel((backend, save) => {
     const y = backend.maxPool3d($x, convInfo);
     save([$x, y]);
     return y;
   }, {x: $x}, grad);
-
+  
   return res;
 }