Accelerating Inference In TF-TRT User Guide :: NVIDIA Deep Learning Frameworks Documentation

During the TensorFlow with TensorRT (TF-TRT) optimization, TensorRT performs several important transformations and optimizations to the neural network graph. First, layers with unused output are eliminated to avoid unnecessary computation. Next, where possible, convolution, bias, and ReLU layers are fused to form a single layer. Another transformation is horizontal layer fusion, or layer aggregation, along with the required division of aggregated layers to their respective output. Horizontal layer fusion improves performance by combining layers that take the same source tensor and apply the same operations with similar parameters. This guide provides instructions on how to accelerate inference in TF-TRT.

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@Nextremer_nb_o " We have observed that the default value 3 gives the best performance for most models." じゃないのか? t.co/19w1GBVXyL

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