Overview

Deploying a Semantic Segmentation Network such as U-Net with NVIDIA's TensorRT allows for optimized inference on NVIDIA hardware, offering reduced latency and improved throughput. This article discusses the steps and considerations involved in deploying a U-Net model using TensorRT, especially focusing on the lack of native upsampling support in TensorRT.

Understanding U-Net and TensorRT

U-Net

U-Net is a popular convolutional neural network architecture used for semantic segmentation tasks. Key features of U-Net include:

• Fully Convolutional Network: U-Net uses convolutional layers to extract features and transposed convolutions for upsampling, forming an encoder-decoder structure.
• Skip Connections: They help in capturing spatial information by connecting layers with similar resolutions.
• Application: Frequently used in medical image processing, satellite imaging, etc.

TensorRT

TensorRT is an SDK for high-performance inference on NVIDIA GPUs. It optimizes neural network models for latency, memory, power efficiency, and throughput. Important aspects include:

• Network Layer Fusion: Combines layers for reduced computation.
• Precision Calibration: Supports FP16 and INT8 precision for faster computations.
• Flexible APIs: Allows integration with diverse DNN frameworks.

Deploying U-Net with TensorRT

Preprocessing

1. Model Conversion: Convert the U-Net model, usually trained in libraries like PyTorch or TensorFlow, to an ONNX (Open Neural Network Exchange) format. Use framework-specific functionalities (`torch.onnx.export` for PyTorch) for this purpose.
2. Upsampling Consideration: As TensorRT doesn't support some upsampling operations natively, such layers must be preprocessed before conversion or handled through custom plugins.

Model Optimization

1. Import ONNX Model: Use TensorRT's ONNX parser to import the model.

• Unsupported Layers: Extensions via custom plugins become crucial when native support is absent, such as with certain upsampling methods. Proper testing and validation are necessary to ensure plugin functionality.
• Optimization vs. Accuracy Trade-off: Precision mode changes (FP16/INT8) need calibration datasets to maintain accuracy while improving performance.