大部分深度学习框架在训练神经网络时网络中的张量(Tensor)都是FP32精度,一旦网络训练完成,在部署推理的过程中由于不需要反向传播,完全可以适当降低数据精度,比如降为FP16或INT8的精度。更低的数据精度将会使得内存占用和延迟更低,模型体积更小。下表为不同精度的动态范围: INT8只有256个不同的数值,使用INT8来表示...
torch.set_default_tensor_type('torch.FloatTensor') # torch.set_default_tensor_type('torch.cuda.FloatTensor') class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = torch.nn.Conv2d(1, 32, (5, 5), padding=(2, 2), bias=True) self.conv2 =...
TensorOrWeights.hpp 10.0 GA Update Apr 26, 2024 WeightsContext.cpp ONNX-TensorRT 10.8-GA Release (#1012) Jan 31, 2025 WeightsContext.hpp ONNX-TensorRT 10.1 GA release (#975) Jun 18, 2024 bfloat16.cpp TRT 9.0 EA - OSS (#930) ...
a = helper.make_tensor_value_info('a', TensorProto.FLOAT, [10, 10]) x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 10]) b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [10, 10]) output = helper.make_tensor_value_info('output', TensorProto.FLOAT, ...
from onnx import helper,AttributeProto, TensorProto, GraphProto X=helper.make_tensor_value_info('X',TensorProto.FLOAT,[1,3,32,32]) #n,ci,h,w W = helper.make_tensor_value_info('W', TensorProto.FLOAT, [64,3,3,3,]) #co,ci,kh,kw ...
importonnxfrom onnximporthelperfrom onnximportTensorProtoa = helper.make_tensor_value_info('a', TensorProto.FLOAT, [10,10])x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10,10])b = helper.make_tensor_value_info('b', TensorPr...
Added support for FLOAT4E2M1 types for quantized networks Added support for dynamic axes and improved performance of CumSum operations Fixed the import of local functions when their input tensor names aliased one from an outside scope Added support for Pow ops with integer-typed exponent values...
add_input = helper.make_tensor_value_info('add_input',TensorProto.FLOAT,[1]) output = helper.make_tensor_value_info('output',TensorProto.FLOAT,[1,32,512,512]) # makenode resize_node = helper.make_node("Resize",['input','roi','scales'],['conv_input'...
engine_name = “resnet50.plan”onnx_path ="/path/to/onnx/result/file/"batch_size =1model = ModelProto()withopen(onnx_path,"rb")asf: model.ParseFromString(f.read()) d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value ...
self.output_tensors[tensor_name]# 调用onnx的helper.make_tensor_value_info构建onnx张量,此时并未填充权重output_tensor = helper.make_tensor_value_info( tensor_name, TensorProto.FLOAT, output_dims) outputs.append(output_tensor) inputs = [self.input_tensor] ...