官方文档地址 https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#define-loops

非顺序结构，其内容确实有点乱，而且没有完整可运行的样例。

可以有多个IIteratorLayer, IRecurrenceLayer, and ILoopOutputLayer 层，最多有2个ITripLimitLayers层。

这里贴2个可运行的代码样例，分别是2种ITripLimitLayers层（TripLimit::kCOUNT 、 TripLimit::kWHILE），简单可运行的代码以帮助入门：

trt.TripLimit.COUNT

loop.add_trip_limit(trip_limit.get_output(0), trt.TripLimit.COUNT)

trt.TripLimit.WHILE

实现类似于for(i = 0; i<3；i++)

i_init = network.add_constant(shape=(), weights=trt.Weights(np.array([0], dtype=np.dtype("i"))))
i_one = network.add_constant(shape=(), weights=trt.Weights(np.array([1], dtype=np.dtype("i"))))
i_stop = network.add_constant(shape=(), weights=trt.Weights(np.array([num_iterations], dtype=np.dtype("i"))))
iRec = loop.add_recurrence(i_init.get_output(0))
iContinue = network.add_elementwise(iRec.get_output(0), i_stop.get_output(0), op=trt.ElementWiseOperation.LESS)
loop.add_trip_limit(iContinue.get_output(0), trt.TripLimit.WHILE)
iNext = network.add_elementwise(iRec.get_output(0), i_one.get_output(0), op=trt.ElementWiseOperation.SUM)
iRec.set_input(1, iNext.get_output(0))

可运行的完整样例

import numpy as np
import tensorrt as trt
from tensorrt import INetworkDefinition
from trt_inference import TRTInference


logger = trt.Logger(trt.Logger.WARNING)
# class MyLogger(trt.ILogger):
#     def __init__(self):
#        trt.ILogger.__init__(self)

#     def log(self, severity, msg):
#         pass # Your custom logging implementation here
# logger = MyLogger()

builder = trt.Builder(logger)
network = builder.create_network(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION)


num_iterations = 3
trip_limit = network.add_constant(shape=(), weights=trt.Weights(np.array([num_iterations], dtype=np.dtype("i"))))
accumaltor_value = network.add_input("input1", dtype=trt.float32, shape=(2, 3))
accumaltor_added_value = network.add_input("input2", dtype=trt.float32, shape=(2, 3))
loop = network.add_loop()
# setting the ITripLimit layer to stop after `num_iterations` iterations
loop.add_trip_limit(trip_limit.get_output(0), trt.TripLimit.COUNT)
# initialzing the IRecurrenceLayer with a init value
rec = loop.add_recurrence(accumaltor_value)
# eltwise inputs are 'accumaltor_added_value', and the IRecurrenceLayer output.
eltwise = network.add_elementwise(accumaltor_added_value, rec.get_output(0), op=trt.ElementWiseOperation.SUM)
# wiring the IRecurrenceLayer with the output of eltwise.
# The IRecurrenceLayer output would now be `accumaltor_value` for the first iteration, and the eltwise output for any other iteration
rec.set_input(1, eltwise.get_output(0))
# marking the IRecurrenceLayer output as the Loop output
loop_out = loop.add_loop_output(rec.get_output(0), trt.LoopOutput.LAST_VALUE)
# marking the Loop output as the network output
network.mark_output(loop_out.get_output(0))


inputs = {}
outputs = {}
expected = {}

inputs[accumaltor_value.name] = np.array(
    [
        [2.7, -4.9, 23.34],
        [8.9, 10.3, -19.8],
    ])
inputs[accumaltor_added_value.name] = np.array(
    [
        [1.1, 2.2, 3.3],
        [-5.7, 1.3, 4.6],
    ])

outputs[loop_out.get_output(0).name] = eltwise.get_input(0).shape
expected[loop_out.get_output(0).name] = inputs[accumaltor_value.name] + inputs[accumaltor_added_value.name] * num_iterations
print("Expected:", expected)

builder_config = builder.create_builder_config()
builder_config.set_flag(trt.BuilderFlag.VERSION_COMPATIBLE)
builder_config.set_flag(trt.BuilderFlag.EXCLUDE_LEAN_RUNTIME)
plan = builder.build_serialized_network(network, builder_config)

# v10_runtime = trt.Runtime(logger)
# v8_shim_runtime = v10_runtime.load_runtime('/home/mark.yj/TensorRT-8.6.1.6/bin/trtexec')
# engine = v10_runtime.deserialize_cuda_engine(plan)
trtInfer = TRTInference(plan)
r = trtInfer.infer(inputs, outputs)
print("Prediction:", r)