ConvolutionNd层（Convolution 层）1

import numpy as np

from cuda import cudart

import tensorrt as trt

nIn, cIn, hIn, wIn = 1, 1, 6, 9 #

cOut, hW, wW = 1, 3, 3 #

data = np.tile(np.arange(1, 1 + hW * wW, dtype=np.float32).reshape(hW, wW), (cIn, hIn // hW, wIn //

wW)).reshape(1, cIn, hIn, wIn) #

weight = np.power(10, range(4, -5, -1), dtype=np.float32).reshape(cOut, hW, wW) #

bias = np.zeros(cOut, dtype=np.float32) #

inputT0 = network.add_input('inputT0', trt.DataType.FLOAT, (nIn, cIn, hIn, wIn))

#---------------------------------------------------------- --------------------#

convolutionLayer = network.add_convolution_nd(inputT0, cOut, (hW, wW), weight, bias)

#---------------------------------------------------------- --------------------#

network.mark_output(convolutionLayer.get_output(0))

engineString = builder.build_serialized_network(network, config)

engine = trt.Runtime(logger).deserialize_cuda_engine(engineString)

context = engine.create_execution_context()

_, stream = cudart.cudaStreamCreate()

inputH0 = np.ascontiguousarray(data.reshape(-1))