TensorRT acceleration in Computer Vision

Introduction

TensorRT acceleration helps your AI models run faster and use less power, making them better for real-time tasks like video or image recognition.

You want to speed up your AI model to work in real-time on videos or cameras.

You need to run AI models on devices with limited power, like drones or robots.

You want to reduce the delay when your AI model makes predictions.

You are deploying AI models in production and want to save computing costs.

You want to optimize deep learning models trained in frameworks like TensorFlow or PyTorch.

Syntax

Computer Vision

import tensorrt as trt

# Create a logger
logger = trt.Logger(trt.Logger.WARNING)

# Create builder and network
builder = trt.Builder(logger)
network = builder.create_network()

# Parse your model (e.g., ONNX) and build engine
parser = trt.OnnxParser(network, logger)
with open('model.onnx', 'rb') as model_file:
    parser.parse(model_file.read())

engine = builder.build_cuda_engine(network)

TensorRT works by converting your trained model into a fast engine optimized for your hardware.

You usually start by loading a model in ONNX format, which is a common AI model format.

Examples

Basic example to load an ONNX model and build a TensorRT engine.

Computer Vision

import tensorrt as trt

logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)
network = builder.create_network()
parser = trt.OnnxParser(network, logger)

with open('model.onnx', 'rb') as f:
    parser.parse(f.read())

engine = builder.build_cuda_engine(network)

Set batch size and workspace memory to control optimization and memory use.

Computer Vision

builder.max_batch_size = 1
builder.max_workspace_size = 1 << 30  # 1GB
engine = builder.build_cuda_engine(network)

Sample Model

This program loads an ONNX model, builds a TensorRT engine, runs a dummy input through it, and prints the top 5 predicted classes.

Computer Vision

import tensorrt as trt
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit

# Logger for TensorRT
logger = trt.Logger(trt.Logger.WARNING)

# Build TensorRT engine from ONNX model
builder = trt.Builder(logger)
network = builder.create_network()
parser = trt.OnnxParser(network, logger)

with open('model.onnx', 'rb') as model_file:
    if not parser.parse(model_file.read()):
        print('Failed to parse ONNX model')
        for error in range(parser.num_errors):
            print(parser.get_error(error))
        exit(1)

builder.max_batch_size = 1
builder.max_workspace_size = 1 << 30  # 1GB
engine = builder.build_cuda_engine(network)

# Create execution context
context = engine.create_execution_context()

# Prepare dummy input data
input_shape = (1, 3, 224, 224)  # Example input shape
input_data = np.random.random(input_shape).astype(np.float32)

# Allocate device memory
d_input = cuda.mem_alloc(input_data.nbytes)
output_shape = (1, 1000)  # Example output shape for classification
output_data = np.empty(output_shape, dtype=np.float32)
d_output = cuda.mem_alloc(output_data.nbytes)

# Create CUDA stream
stream = cuda.Stream()

# Transfer input data to device
cuda.memcpy_htod_async(d_input, input_data, stream)

# Execute model
context.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)

# Transfer predictions back
cuda.memcpy_dtoh_async(output_data, d_output, stream)

# Synchronize stream
stream.synchronize()

# Print top 5 predictions
top5 = output_data[0].argsort()[-5:][::-1]
print('Top 5 predicted class indices:', top5)

Important Notes

TensorRT requires NVIDIA GPUs and CUDA installed to work.

You need to convert your model to ONNX format before using TensorRT.

The output depends on the model and input; here we use random data for demonstration.

Summary

TensorRT speeds up AI models by optimizing them for NVIDIA GPUs.

It works best with models in ONNX format.

You can use TensorRT to make AI run faster and save power on real devices.