[Feature Request] ORT web API to use WebGL texture as model input #15312
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feature request
request for unsupported feature or enhancement
platform:web
issues related to ONNX Runtime web; typically submitted using template
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@fs-eire - Can tou please take a look ? |
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feature request: IO binding (webGL) |
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@fs-eire any I was wondering if this issue is actively being worked on? I am also interested in contributing myself, but I'd need some guidance to dive into the project. If you'd be willing to help out that'd be great! :) |
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I am thinking about the API design for this feature. To generalize the requirement, I think it can be expand to:
I can start with a branch for the API part and share it out later |
fs-eire
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Jun 9, 2023
### Description <!-- Describe your changes. --> refactor tensor type in onnxruntime-common. ### Motivation and Context There major motivation is that I am doing a local change to address the API part of #15312. And I am doing a refactoring of onnxruntime-common anyway (#15772). The `tensor.ts` and `tensor-impl.ts` are too large, so I split contents into multiple files to make the type declarations clearer. The original target of this change is for API only ( ie. do not refactor any implementation.). However, there are a few type/implementation inconsistencies so I also made minimal changes to fix them. ### Changes - extract `TensorUtils` for non-template interfaces - extract `TensorFactory` for all overloads of `Tensor.fromImage()` - refactor options type that used for `Tensor.fromImage()` - fix JSDoc comments to make option descriptions consistent with actual type declarations - fix an inconsistency for `options.format` and `options.bitmapFormat`; change all `bitmapFormat` to `format` - extract `ConversionUtils` for `tensor.toDataURL()` and `tensor.toImageData()` - put implementations into multiple files from `tensor-impl.ts` - fix a bug that cause unittest fail. put comments for future fix.
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@erikvdplas I created a new PR for this issue. the PR is now in design reviewing. please feel free to comments on it |
fs-eire
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Aug 29, 2023
[//]: # (## Work In Progress. Feedbacks are welcome!) ### Description This PR adds a few properties, methods and factories to Tensor type to support IO-binding feature. This will allow user to create tensor from GPU/CPU bound data without a force transferring of data between CPU and GPU. This change is a way to resolve #15312 ### Change Summary 1. Add properties to `Tensor` type: a. `location`: indicating where the data is sitting. valid values are `cpu`, `cpu-pinned`, `texture`, `gpu-buffer`. b. `texture`: sit side to `data`, a readonly property of `WebGLTexture` type. available only when `location === 'texture'` c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer` type. available only when `location === 'gpu-buffer'` 2. Add methods to `Tensor` type (usually dealing with inference outputs): - async function `getData()` allows user to download data from GPU to CPU manually. - function `dispose()` allows user to release GPU resources manually. 3. Add factories for creating `Tensor` instances: a. `fromTexture()` to create a WebGL texture bound tensor data b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer ### Examples: create tensors from texture and pass to inference session as inputs ```js // when create session, specify we prefer 'image_output:0' to be stored on GPU as texture const session = await InferenceSession.create('./my_model.onnx', { executionProviders: [ 'webgl' ], preferredOutputLocation: { 'image_output:0': 'texture' } }); ... const myImageTexture = getTexture(); // user's function to get a texture const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format. const results = await session.run(myFeeds); const myOutputTexture = results['image_output:0'].texture; ```
kleiti
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Mar 22, 2024
[//]: # (## Work In Progress. Feedbacks are welcome!) ### Description This PR adds a few properties, methods and factories to Tensor type to support IO-binding feature. This will allow user to create tensor from GPU/CPU bound data without a force transferring of data between CPU and GPU. This change is a way to resolve microsoft#15312 ### Change Summary 1. Add properties to `Tensor` type: a. `location`: indicating where the data is sitting. valid values are `cpu`, `cpu-pinned`, `texture`, `gpu-buffer`. b. `texture`: sit side to `data`, a readonly property of `WebGLTexture` type. available only when `location === 'texture'` c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer` type. available only when `location === 'gpu-buffer'` 2. Add methods to `Tensor` type (usually dealing with inference outputs): - async function `getData()` allows user to download data from GPU to CPU manually. - function `dispose()` allows user to release GPU resources manually. 3. Add factories for creating `Tensor` instances: a. `fromTexture()` to create a WebGL texture bound tensor data b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer ### Examples: create tensors from texture and pass to inference session as inputs ```js // when create session, specify we prefer 'image_output:0' to be stored on GPU as texture const session = await InferenceSession.create('./my_model.onnx', { executionProviders: [ 'webgl' ], preferredOutputLocation: { 'image_output:0': 'texture' } }); ... const myImageTexture = getTexture(); // user's function to get a texture const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format. const results = await session.run(myFeeds); const myOutputTexture = results['image_output:0'].texture; ```
siweic0
pushed a commit
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that referenced
this issue
May 9, 2024
### Description <!-- Describe your changes. --> refactor tensor type in onnxruntime-common. ### Motivation and Context There major motivation is that I am doing a local change to address the API part of microsoft#15312. And I am doing a refactoring of onnxruntime-common anyway (microsoft#15772). The `tensor.ts` and `tensor-impl.ts` are too large, so I split contents into multiple files to make the type declarations clearer. The original target of this change is for API only ( ie. do not refactor any implementation.). However, there are a few type/implementation inconsistencies so I also made minimal changes to fix them. ### Changes - extract `TensorUtils` for non-template interfaces - extract `TensorFactory` for all overloads of `Tensor.fromImage()` - refactor options type that used for `Tensor.fromImage()` - fix JSDoc comments to make option descriptions consistent with actual type declarations - fix an inconsistency for `options.format` and `options.bitmapFormat`; change all `bitmapFormat` to `format` - extract `ConversionUtils` for `tensor.toDataURL()` and `tensor.toImageData()` - put implementations into multiple files from `tensor-impl.ts` - fix a bug that cause unittest fail. put comments for future fix.
siweic0
pushed a commit
to siweic0/onnxruntime-web
that referenced
this issue
May 9, 2024
[//]: # (## Work In Progress. Feedbacks are welcome!) ### Description This PR adds a few properties, methods and factories to Tensor type to support IO-binding feature. This will allow user to create tensor from GPU/CPU bound data without a force transferring of data between CPU and GPU. This change is a way to resolve microsoft#15312 ### Change Summary 1. Add properties to `Tensor` type: a. `location`: indicating where the data is sitting. valid values are `cpu`, `cpu-pinned`, `texture`, `gpu-buffer`. b. `texture`: sit side to `data`, a readonly property of `WebGLTexture` type. available only when `location === 'texture'` c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer` type. available only when `location === 'gpu-buffer'` 2. Add methods to `Tensor` type (usually dealing with inference outputs): - async function `getData()` allows user to download data from GPU to CPU manually. - function `dispose()` allows user to release GPU resources manually. 3. Add factories for creating `Tensor` instances: a. `fromTexture()` to create a WebGL texture bound tensor data b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer ### Examples: create tensors from texture and pass to inference session as inputs ```js // when create session, specify we prefer 'image_output:0' to be stored on GPU as texture const session = await InferenceSession.create('./my_model.onnx', { executionProviders: [ 'webgl' ], preferredOutputLocation: { 'image_output:0': 'texture' } }); ... const myImageTexture = getTexture(); // user's function to get a texture const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format. const results = await session.run(myFeeds); const myOutputTexture = results['image_output:0'].texture; ```
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Describe the feature request
I would like to be able to run models directly on WebGL textures as input. Right now, the only possibility is to first render such a WebGL texture to bitmap, then copying it to a Float32Array which can then be used to initialize an ORT Tensor object, which internally is again copied back to a WebGL texture when used with the WebGL execution backend. All this copying adds quite some overhead, which cannot be neglected, especially in real-time applications.
Describe scenario use case
It is not uncommon for image models to work on webcam/streams/video feeds or in general WebGL data. In the case of video feeds, they can efficiently be copied to a WebGL buffer using the
texImage2Dmethod in a WebGL rendering context (by directly the HTMLVideoElement as the source).If someone is willing to guide me through the process of correctly building the onnxruntime-web package from source locally, I could try to implement this feature myself... but maybe someone more skilled and familiar with the project could help out as well.
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