set_shape("token_type_ids", min = (batch_size, 1), opt = (batch_size, max_seq_length//2), max = (batch_size, max_seq_length)) config.add_optimization_profile(profile) TensorRT 将根据 min, opt 和 max 设置的值进行优化。 TensorRT Engine 加载 加载TensorRT Engine 模型至少需要用到 ...
set_shape(inputTensor.name, [1, 1, 1], [3, 4, 5], [6, 7, 8]) config.add_optimization_profile(profile) identityLayer = network.add_identity(inputTensor) network.mark_output(identityLayer.get_output(0)) engineString = builder.build_serialized_network(network, config) with open(trtFile...
profile = builder.create_optimization_profile() # 动态输入时候需要 分别为最小输入、常规输入、最大输入 # 有几个输入就要写几个profile.set_shape 名字和转onnx的时候要对应 # tensorrt6以后的版本是支持动态输入的,需要给每个动态输入绑定一个profile,用于指定最小值,常规值和最大值,如果超出这个范围会报异常。
# INPUT0可以接收[1,2] -> [max_batch,2]的维度 profile.set_shape("INPUT0", [1,2], [1,2], [max_batch,2]) profile.set_shape("INPUT1", [1,2], [1,2], [max_batch,2]) config=builder.create_builder_config() config.add_optimization_profile(profile) # tensorrt8.x # config.set...
(engineString)## 运行期engine = trt.Runtime(logger).deserialize_cuda_engine(engineString)context = engine.create_execution_context()dataShape = [3, 4, 5]context.set_binding_shape(0, dataShape)data = np.arange(np.prod(dataShape), dtype=np.float32).reshape(*dataShape)bufferH = [np.as...
有get_shape/get_binding_shape/set_shape_input/set_binding_shape等方法 其他作用:我也不知道剩下函数干什么用的get_strides/set_optimization_profile_async 2. 推理 2.1 相关API详解 推理的整体流程是 模型解析与优化,即将 ONNX/UFF/CAFFE 等形式转换为 Engine。本节不考虑这个。
If the values have not been set yet, an empty list is returned.set_shape(self: tensorrt.tensorrt.IOptimizationProfile, input: str, min: tensorrt.tensorrt.Dims, opt: tensorrt.tensorrt.Dims, max: tensorrt.tensorrt.Dims) → None Set the minimum/optimum/maximum dimensions for a dynamic input ...
profile.set_shape("input_1", (1,3,512,512), (1,3,1024,1024), (1,3,1600,1600)) config.add_optimization_profile(profile) #加上一个sigmoid层 previous_output = network.get_output(0) network.unmark_output(previous_output) sigmoid_layer=network.add_activation(previous_output,trt.ActivationTyp...
If the network has any dynamic input tensors, the appropriate calls to IOptimizationProfile.set_shape() must be made. Likewise, if there are any shape input tensors, the appropriate calls to IOptimizationProfile.set_shape_input() are required. See IOptimizationProfileis_network_supported(self...
您可以使用INetworkDefinition::setWeightsName()在构建时命名权重 – ONNX 解析器使用此 API 将权重与 ONNX 模型中使用的名称相关联。然后,您可以使用setNamedWeights更新权重: Weights newWeights = ...; refitter->setNamedWeights("MyWeights", newWeights); ...