From 9970f5b2483f187c9977517e63f857c46dc7ea04 Mon Sep 17 00:00:00 2001 From: Jozef Daniecki Date: Wed, 2 Jun 2021 07:22:20 +0200 Subject: [PATCH] Interpolate operation specification refactoring. (#5928) * Interpolate spec refactored against explicit type indication. * Chnaged types names. --- docs/ops/image/Interpolate_4.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/docs/ops/image/Interpolate_4.md b/docs/ops/image/Interpolate_4.md index 163cfa01dbf..773802dab93 100644 --- a/docs/ops/image/Interpolate_4.md +++ b/docs/ops/image/Interpolate_4.md @@ -89,18 +89,24 @@ **Inputs** -* **1**: `data` - Input tensor with data for interpolation. Type of elements is any supported floating point type or `int8` type. Required. +* **1**: `data` - tensor of type `T` with data for interpolation. **Required.** -* **2**: `sizes` - 1D tensor describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. Required. +* **2**: `sizes` - 1D tensor of type `T_SIZE` describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.** -* **3**: `scales` - 1D tensor describing scales for spatial axes. Type of elements is any supported floating point type. Number and order of elements match the number and order of indices in `axes` input. Required. +* **3**: `scales` - 1D tensor of type `T_SCALES` describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.** -* **4**: `axes` - 1D tensor specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. Optional with default value `[0,...,rank(data) - 1]`. +* **4**: `axes` - 1D tensor of type `T_AXES` specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`. **Outputs** * **1**: Resulting interpolated tensor with elements of the same type as input `data` tensor. The shape of the output matches input `data` shape except spatial dimensions mentioned in `axes` attribute. For other dimensions shape matches sizes from `sizes` in order specified in `axes`. +**Types** +* *T*: any supported numeric type. +* *T_SIZE*: any supported integer type. +* *T_SCALES*: any supported floating point type. +* *T_AXES*: any supported integer type. + **Detailed description** Calculations are performed according to the following rules.