### Description
onnxjs contains a `Resize` op input check which is outdated since opset
9. Currently `Resize` supports up to 4 inputs. This PR looses the input
check.
### Motivation and Context
Fixes#15636
### Description
This change upgrades a lot of dependencies. There are 2 motivations of
doing this change:
- fix the security issue reported by dependabot (protobufjs Prototype
Pollution vulnerability -
https://github.com/advisories/GHSA-h755-8qp9-cq85)
- resolve the requirement of using ONNX IR_VERSION 9 (#16638)
This requires:
- upgrade protobufjs to v7.2.4
- upgrade library 'onnx-proto' to consume latest ONNX release (v1.14.0).
Problems:
- protobufjs v7.2.4 depends on long.js v5, which does not work well with
typescript (commonjs).
- onnx-proto depends on this fix with a new release of long.js
- long.js is in maintenance and it takes longer than expected to put in
new changes
Solutions:
- use a patch script in `preprepare` to copy type declarations to make
long.js work with typescript (commonjs)
- generate onnx protobuf JS/TS files and put them under
js/web/lib/onnxjs/ort-schema/protobuf folder - remove 'onnx-proto' from
dependency.
- apply fixes to generated onnx.d.ts
**Description**:
1. add pytorch_half_pixel interpolation mode in resize-packed.ts
Changes: add the following case in createPackedResizeProgramInfo
function:
```
case 'pytorch_half_pixel':
getSourceFracIndex = `
vec4 getSourceFracIndex(ivec4 coords) {
vec4 fcoords = vec4(coords);
return vec4(
${outputWidth}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
${outputHeight}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
${outputWidth}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
${outputHeight}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
);
}
`;
break;
```
2. fix "unrecognized input '' for node: Resize_$num" error when inputs
like [input_tensor, None, scale_factor] (roiInput not given) are fed
into the resize layer.
Changes: change in input handling logic in upsample.ts & node scanning
logic in graph.ts
**Motivation and Context**
Before this fix, we aren't able to use webGL backend when the neural
network contains pytorch resize layers. This fix adds
'pytorch_half_pixel' interpolation mode support and makes it possible to
use webGL backend for more kind of computer vision networks.
This commit solves:
#10430
Co-authored-by: neo <neo@icode-lab.com>
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
* add p50 in test
* Support FusedConv in WebGL
* resolve comments
* add a comment for longToNumber change
Co-authored-by: Yulong Wang <yulongw@microsoft.com>
* Support opset-13 for squeeze, unsqueeze, maxpool, pad, cast, clip
* merge master and update a operators.md
* resolve comment. revise pool and cast kernel implementation.
* skip fusion when clip min and max is not in initializer
