Introduction to TNN
TNN is a high-performance and lightweight neural network inference framework developed by Tencent Youtu Lab. It is designed to be efficient, cross-platform, and adaptable with several key features including model compression and code tailoring. The framework's goal is to optimize neural network processing, especially on mobile devices, by building on the capabilities of existing frameworks such as Rapidnet and ncnn. It also integrates the performance and scalability advantages of leading open-source frameworks to enhance support for X86 and NV GPUs.
Widely adopted in various applications like mobile QQ, Weishi, and Pitu, TNN serves as the foundational acceleration framework for Tencent Cloud AI, providing significant performance improvements for numerous business implementations. The project is open-source and invites community collaboration to continue improving its capabilities.
Effect Examples
TNN supports a variety of applications, showcasing its versatility:
- Face Detection (Blazeface): Quickly detects faces in images and videos.
- Face Alignment: Enhances facial feature detection using advanced algorithms from Tencent Youtu Lab.
- Hair Segmentation: Accurately segments hair for applications in image processing.
- Pose Estimation: Provides detailed skeletal data from Tencent Guangliu and Blazepose models.
- Chinese OCR: Recognizes text, including vertical and rotated text, using a lightweight implementation of the chineseocr_lite project.
- Object Detection (YOLOv5s and MobilenetV2-SSD): Identifies and localizes objects within an image.
- Reading Comprehension: Processes and understands text context using BERT models.
These demos demonstrate TNN's ability to deliver high performance across different platforms and devices.
Quick Start
Using TNN is straightforward and can be accomplished in three steps:
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Convert the Model: Take a trained model from frameworks like TensorFlow, Pytorch, or Caffe and convert it into a TNN-compatible model using the available tools.
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Compile the TNN Engine: Depending on the target platform (Android, iOS, Linux, etc.), compile the TNN engine. Various acceleration solutions are available, including ARM, OpenCL, Metal, NPU, and CUDA.
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Deploy and Infer: Once compiled, use the TNN engine within your application to perform inference. TNN provides numerous demos to simplify integration into different platform environments.
Technical Solutions
TNN excels in several technical areas:
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Computation Optimization: Utilizes well-designed backend operators optimized for different hardware architectures to maximize computational efficiency. Techniques such as Winograd, Tile-GEMM, and direct convolution are applied to optimize performance.
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Low Precision Computation: Supports INT8/FP16 computation to reduce model size and memory usage while leveraging hardware-specific instructions for faster processing.
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Memory Optimization: Implements a memory pooling system to share memory between models, reducing overall memory costs significantly.
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Development Environment: Compatible with major operating systems and hardware platforms, facilitating seamless implementation across different technologies. TNN also supports multiple training frameworks via ONNX, expanding its compatibility and performance capabilities.
Development and Community Involvement
Hands-on tools and tutorials are available to help developers create, compile, and deploy models using TNN efficiently. The project welcomes contributions, whether through enhancing its core functionality, adding new operators, or integrating additional hardware support.
Roadmap and Acknowledgements
TNN continues to develop, guided by a roadmap that considers new features and improvements based on user feedback and technological advancements. The framework acknowledges contributions from several open-source projects that have influenced its development, ensuring it remains on the cutting edge.
To join the community or contribute to discussions about TNN, Tencent provides various communication channels and encourages both individual and collaborative efforts to further advance this innovative inference framework.