https://github.com/gpuopen-librariesandsdks/radeonml
https://github.com/gpuopen-librariesandsdks/radeonml
Last synced: 4 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/gpuopen-librariesandsdks/radeonml
- Owner: GPUOpen-LibrariesAndSDKs
- License: other
- Created: 2020-03-06T09:30:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-09-21T16:19:21.000Z (almost 3 years ago)
- Last Synced: 2025-07-20T08:34:05.735Z (11 months ago)
- Language: C
- Homepage: https://gpuopen.com/radeon-prorender-suite/
- Size: 88.4 MB
- Stars: 85
- Watchers: 15
- Forks: 11
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# RadeonML: AMD Inference Library
## 1. C/C++ API
* [RadeonML.h](include/rml/RadeonML.h) - main C API
* [RadeonML.hpp](include/rml/RadeonML.hpp) - main and graph manipulation C++ API
* [RadeonML_cl.h](include/rml/RadeonML_cl.h) - OpenCL interoperation C API
* [RadeonML_cl.hpp](include/rml/RadeonML_cl.hpp) - OpenCL interoperation C++ API
* [RadeonML_d3d12.h](include/rml/RadeonML_d3d12.h) - Direct3D 12 interoperation C API
* [RadeonML_d3d12.hpp](include/rml/RadeonML_d3d12.hpp) - Direct3D 12 interoperation C++ API
* [RadeonML_dml.h](include/rml/RadeonML_dml.h) - DirectML interoperation C API
* [RadeonML_dml.hpp](include/rml/RadeonML_dml.hpp) - DirectML interoperation C++ API
* [RadeonML_graph.h](include/rml/RadeonML_graph.h) - graph manipulation C API
* [RadeonML_miopen.h](include/rml/RadeonML_miopen.h) - MIOpen interoperation C API
* [RadeonML_miopen.hpp](include/rml/RadeonML_miopen.hpp) - MIOpen interoperation C++ API
* [RadeonML_mtl.h](include/rml/RadeonML_mtl.h) - Metal interoperation C API
* [RadeonML_mtl.hpp](include/rml/RadeonML_mtl.hpp) - Metal interoperation C++ API
## 2. Code examples
* [Loading a model from a file (C)](samples/load_model.c)
* [Loading a model from a file (C++)](samples/load_model.cpp)
* [graph operation example (C++)](samples/graph_ops.cpp)
* [graph operation example (C)](samples/graph_ops.c)
### 2.1. List of supported models for load_model sample
* vgg16.onnx, vgg19.onnx
* Supported backends: DirectML
* Input format: NCHW (Nx3x224x224)
* vgg16_opset11.onnx, vgg19_opset11.onnx
* Supported backends: DirectML
* Input format: NHWC (Nx224x224x3)
* mobilenet_v1_1.0_224.onnx, mobilenet_v2_1.0_224.onnx
* Supported backends: DirectML
* Input format: NCHW (Nx3x224x224)
* mobilenet_v1_opset11.onnx, mobilenet_v2_opset11.onnx
* Supported backends: DirectML
* Input format: NHWC (Nx224x224x3)
* resnet18v1.onnx, resnet34v1.onnx, resnet50v1.onnx, resnet101v1.onnx, resnet152v1.onnx
* resnet18v2.onnx, resnet34v2.onnx, resnet50v2.onnx, resnet101v2.onnx, resnet152v2.onnx
* Supported backends: DirectML
* Input format: NCHW (Nx3x224x224)
* inception_v1_opset8.onnx, inception_v2_opset8.onnx
* Supported backends: DirectML
* Input format: NCHW (Nx3x224x224)
* inception_v1_opset11.onnx, inception_v2_opset11.onnx
* Supported backends: DirectML
* Input format: NHWC (Nx224x224x3)
* inception_v3_opset11.onnx, inception_v4_opset11.onnx
* Supported backends: DirectML
* Input format: NHWC (Nx299x299x3)
* denoise_c3_ldr.onnx, denoise_c3_hdr.onnx
* Supported backends: DirectML, MIOpen, MPS
* Input format: NHWC (NxHxWx3)
* denoise_c9_ldr.onnx, denoise_c9_hdr.onnx
* Supported backends: DirectML, MIOpen, MPS
* Input format: NHWC (NxHxWx9)
* upscale2x_c3_rt.onnx, upscale2x_c3_esrgan_small.onnx
* Supported backends: DirectML, MIOpen, MPS
* Input format: NHWC
To inference supported models just substitute 'path/model', 'path/input' and 'path/output' with correct paths in load_model sample.
Additional information about supported models: https://github.com/onnx/models
## 3. System requirements
* Windows 10 19H1 or later (for DirectML backend)
* Windows 7 uses the MIOpen backend
* Ubuntu 18.04
* CentOS/RHEL 7.6, 7.7
* OSX Mojave and Catalina
## 3.1 Features supported
* ONNX support (opset 6-11)
* TensforFlow frozen graph pb files
* FP32 and FP16 for ONNX
For more information, see documentation at this link
https://radeon-pro.github.io/RadeonProRenderDocs/rml/about.html
## 3.2 Features supported by OS
* Windows DirectML supports our denoisers, upscalers and common models like resnet, VGG etc..
* Miopen backend for Windows and Linux only supports our denoisers and upscalers. When creating a RML context if DML is not supported we will fallback automatically to MIOpen
* MPS backend only supports our denoisers
* Model supported by the different backend
| | DIRECTML | MIOPEN | MPS |
| ------------- | ------------- |------------- |------------- |
| Inception V1 | Yes | Yes | No |
| Inception V2 | Yes | Yes | No |
| Inception V3 | Yes | Yes | No |
| Inception V4 | Yes | Yes | No |
| MobileNet V1 | Yes | Yes | No |
| MobileNet V2 | Yes | Yes | No |
| ResNet V1 50 | Yes | No | No |
| ResNet V2 50 | Yes | No | No |
| VGG 16 | Yes | No | No |
| VGG 19 | Yes | No | No |
| UNet(denoiser) | Yes | Yes | Yes |
| ESRGAN | Yes | Yes | Yes|
| RTUnet | Yes | Yes | Yes |
Others models may work as they will have similar operators, but we haven't checked them
## 3.3 DirectML and Directx12 interop
* A device and command queue can be passed when creating a RML context. We support both async compute queue(D3D12_COMMAND_LIST_TYPE_COMPUTE) and the default command queue(D3D12_COMMAND_LIST_TYPE_DIRECT).
Compute queue are preferred as it will run asynchronously with any graphics job.
If no queue are passed, RML will create a compute queue
## 4. Building and running the samples
You will need CMake 3.10 at least to build the sample.
The input must contain contiguous data of a tensor with specified dimensions.
The input (.bin files) in the repo don't necessarily represent real data at the moment, but just show to format the data
## 5. Future
We aim at providing the same level of feature for every back end and will provide updates monthly for that