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Merge pull request #425 from zolkis/stage-resample2d-algorithm
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Add the resample2d() algorithm
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@anssiko
anssiko authored Jun 29, 2023
2 parents 3aad28e + 81b35b0 commit 0e8223a
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Expand Up @@ -4639,55 +4639,7 @@ partial interface MLGraphBuilder {
</pre>
</div>

#### The {{MLGraphBuilder/relu(input)}} method #### {#api-mlgraphbuilder-relu-input}
<div>
**Arguments:**
- *input*: an {{MLOperand}}. The input tensor.

**Returns:**
- an {{MLOperand}}. The output tensor of the same shape as *x*.
</div>

<details open>
<summary>
The {{MLGraphBuilder/relu(input)}} steps are:
</summary>
<div algorithm=relu-input class=algorithm-steps>
1. If |input| is not an instance of {{MLOperand}}, then throw a "{{TypeError}}" {{DOMException}} and stop.
1. If any of the following sub-steps fail, throw an "{{OperationError}}" {{DOMException}} and stop.
1. Let |output| be the result of invoking the <a>copy MLOperand</a> steps given |input|.
1. Make a request to the underlying platform to:
1. Let |opImpl| be an [=implementation-defined=] platform operator for the ReLU operation.
1. Store a reference of |opImpl| in |output|.{{MLOperand/[[operator]]}}.
1. Create an [=implementation-defined=] platform operand |outputImpl| to represent the output, given |output| and |opImpl|.
1. Store a reference to |outputImpl| in |output|.{{MLOperand/[[operand]]}}.
1. Connect |input|.{{MLOperand/[[operand]]}} as input to |opImpl|.
1. Connect |output|.{{MLOperand/[[operand]]}} as output to |opImpl|.
1. Return |output|.
</div>
</details>

#### The {{MLGraphBuilder/relu()}} method #### {#api-mlgraphbuilder-relu}
<div>
**Arguments:**
- None.

**Returns:**
- an {{MLActivation}}. The activation function representing the relu operation.
</div>

<details open>
<summary>
The {{MLGraphBuilder/relu()}} method steps are:
</summary>
<div algorithm=relu class=algorithm-steps>
1. Let |op| be the result of invoking the <a>create MLActivation</a> steps with `"relu"`.
1. If that throws an error, re-throw the error and abort these steps.
1. Return |op|.
</div>
</details>

### The resample2d() method ### {#api-mlgraphbuilder-resample2d}
### The resample2d() method ### {#api-mlgraphbuilder-resample2d-method}
Resample the tensor values from the source to the destination spatial dimensions according to the scaling factors.
<script type=idl>
enum MLInterpolationMode {
Expand All @@ -4706,19 +4658,99 @@ partial interface MLGraphBuilder {
MLOperand resample2d(MLOperand input, optional MLResample2dOptions options = {});
};
</script>
<div algorithm=resample2d>
<div>
**Arguments:**
- *input*: an {{MLOperand}}. The input 4-D tensor.
- *options*: an optional {{MLResample2dOptions}}. The optional parameters of the operation.
- *mode*: an {{MLInterpolationMode}}. The interpolation algorithm used to fill the output tensor values.
If not set, it is assumed to be the *Nearest Neighbor* interpolation.
- *scales*: a sequence of {{float}} of length 2. Each value represents the scaling factor used to scale in each spatial dimensions of input, [scale_height, scale_width]. If not set, the values are assumed to be [1.0, 1.0].
- *sizes*: a sequence of {{unsigned long}} of length 2. The target sizes for each spatial dimensions of input, [size_height, size_width]. When the target sizes are specified, the *options.scales* argument is ignored as the scaling factor values are derived from the target sizes of each spatial dimension of input.
- *axes*: a sequence of {{unsigned long}} of length 2. The two consecutive dimensions of the input tensor to which the interpolation algorithm applies. The valid values in the sequence are [0, 1], [1, 2] or [2, 3]. When not specified, the sequence is assumed to be [2, 3].

**Returns:** an {{MLOperand}}. The output 4-D tensor.
</div>

{{MLResample2dOptions}} has the following members:
<dl dfn-type=dict-member dfn-for=MLResample2dOptions>
: <dfn>mode</dfn>
::
An {{MLInterpolationMode}} [=string=].
Specifies the interpolation algorithm used to fill the output tensor values.
The default value is `"nearest-neighbor"`, standing for *Nearest Neighbor* interpolation.

: <dfn>scales</dfn>
::
A sequence of {{float}} of length 2.
Specifies the scaling factor in each spatial dimensions of the input: [scale_height, scale_width].
The default value is [1.0, 1.0].

: <dfn>sizes</dfn>
::
A sequence of {{unsigned long}} of length 2.
Specifies the target sizes for each spatial dimensions of the input: [size_height, size_width]. When the target sizes are specified, the {{MLResample2dOptions/scales}} argument is ignored, since the scaling factor values are derived from the target sizes of each spatial dimension of the input.

: <dfn>axes</dfn>
::
A sequence of {{unsigned long}} of length 2.
Specifies the two consecutive dimensions of the input tensor to which the interpolation algorithm applies. The valid values in the sequence are [0, 1], [1, 2] or [2, 3].
The default value is [2, 3].
</dl>

<details open>
<summary>
To <dfn for="MLGraphBuilder">check resample options</dfn> given |options|, run the following steps:
</summary>
<div algorithm=check-resample-options class=algorithm-steps>
1. If |options| is `undefined`, let |options| be a new {{MLResample2dOptions}} object.
1. If |options|.{{MLResample2dOptions/mode}} [=map/exists=]:
1. If its value is not one of `"nearest-neighbor"` or `"linear"`, return `null`.
1. Otherwise, set |options|.{{MLResample2dOptions/mode}} to `"nearest-neighbor"`.
1. If |options|.{{MLResample2dOptions/scales}} [=map/exists=]:
1. If its size is not `2`, or if any of its values is not greater than `0`, return `null`.
1. Otherwise, set |options|.{{MLResample2dOptions/scales}} to `[1.0, 1.0]`.
1. If |options|.{{MLResample2dOptions/sizes}} [=map/exists=]: if its size is not `2`, or if any of its values is not greater than `0`, return `null`.
1. If |options|.{{MLResample2dOptions/axes}} [=map/exists=]:
1. If its value is not one of `[0, 1], [1, 2], [2, 3]`, return `null`.
1. Otherwise, set |options|.{{MLResample2dOptions/axes}} to `[2, 3]`.
1. Return |options|.
</div>
</details>

<details open>
<summary>
To <dfn for="MLGraphBuilder">resample output sizes</dfn> given |input| and |options|, run the following steps:
</summary>
<div algorithm=resample-output-sizes class=algorithm-steps>
1. Let |desc| be an {{MLOperandDescriptor}} initialized to |input|.{{MLOperand/[[descriptor]]}}.
1. If |options|.{{MLResample2dOptions/sizes}} [=map/exists=], then set |desc|.{{MLOperand/[[descriptor]]}}.{{MLOperandDescriptor/dimensions}} to |options|.{{MLResample2dOptions/sizes}} and return |desc|.
1. For |index| between `0` and the rank of |desc|.{{MLOperand/[[descriptor]]}}.{{MLOperandDescriptor/dimensions}}:
1. Let |inputSize| be the size of |input|.{{MLOperand/[[descriptor]]}}.{{MLOperandDescriptor/dimensions}}[|index|].
1. Let |outputSize| be |inputSize| multiplied by |options|.{{MLResample2dOptions/scales}}.
1. If that fails or |outputSize| is not a positive [=number=], then throw a "{{DataError}}" {{DOMException}} and stop.
1. Set |desc|.{{MLOperandDescriptor/dimensions}}[|index|] to |outputSize|.
1. Return |desc|.
</div>
</details>

<details open>
<summary>
The {{MLGraphBuilder/resample2d(input, options)}} steps are:
</summary>
<div algorithm=resample2d class=algorithm-steps>
1. Check if the input is a 4-dimensional tensor: if the size of |input|.{{MLOperand/[[descriptor]]}}.{{MLOperandDescriptor/dimensions}} is not `4`, throw a "{{DataError}}" {{DOMException}} and stop.
1. Let |options| be the result of running the <a>check resample options</a> steps given |options|.
1. If that returns `null`, then throw a "{{DataError}}" {{DOMException}} and stop.
1. Let |desc| be the result of running the <a>resample output sizes</a> steps given |options|.
1. If that throws an error, re-throw the error and stop.
1. If any of the following sub-steps fail, throw an "{{OperationError}}" {{DOMException}} and stop.
1. Let |output| be the result of invoking the <a>create MLOperand</a> steps given [=this=] and |desc|.
1. Make a request to the underlying platform to:
1. Let |opImpl| be an [=implementation-defined=] platform operator for the resample 2D operation, given |options|.
1. Store a reference of |opImpl| in |output|.{{MLOperand/[[operator]]}}.
1. Create an [=implementation-defined=] platform operand |outputImpl| to represent the output, given |output| and |opImpl|.
1. Store a reference to |outputImpl| in |output|.{{MLOperand/[[operand]]}}.
1. Connect |input|.{{MLOperand/[[operand]]}} as input to |opImpl|.
1. Connect |output|.{{MLOperand/[[operand]]}} as output to |opImpl|.
1. Return |output|.
</div>
</details>

### The reshape() method ### {#api-mlgraphbuilder-reshape-method}
Alter the shape of a tensor to a new shape. Reshape does not copy or change the content of the tensor. It just changes the tensor's logical dimensions for the subsequent operations.
<script type=idl>
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