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Interpolate operation specification refactoring. (#5928)

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* Interpolate  spec refactored against explicit type indication.

* Chnaged types names.
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Jozef Daniecki 2021-06-02 07:22:20 +02:00 committed by GitHub
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docs/ops/image/Interpolate_4.md
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**Inputs** **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** **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`. * **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** **Detailed description**
Calculations are performed according to the following rules. Calculations are performed according to the following rules.