郑州酒店网站建设,抖音电商具体是做什么的,新网站怎么做,硬件设计包括哪些内容当我们加载了一个ONNX之后#xff0c;我们获得的就是一个ModelProto#xff0c;它包含了一些版本信息#xff0c;生产者信息和一个GraphProto。在GraphProto里面又包含了四个repeated数组#xff0c;它们分别是node(NodeProto类型)#xff0c;input(ValueInfoProto类型)我们获得的就是一个ModelProto它包含了一些版本信息生产者信息和一个GraphProto。在GraphProto里面又包含了四个repeated数组它们分别是node(NodeProto类型)input(ValueInfoProto类型)output(ValueInfoProto类型)和initializer(TensorProto类型)其中node中存放了模型中所有的计算节点input存放了模型的输入节点output存放了模型中所有的输出节点initializer存放了模型的所有权重参数。 这里用python将onnx中包含的有用的信息打印出来进行一个直观可视化。
整体流程
解析graph input解析graph output解析graph initializer模型的所有权重参数解析graph node打印op信息输入输出得到整个计算的graph
import onnx
import numpy as np
import logging
logging.basicConfig(levellogging.INFO)def onnx_datatype_to_npType(data_type):if data_type 1:return np.float32else:raise TypeError(dont support data type)def parser_initializer(initializer):name initializer.namelogging.info(finitializer name: {name})dims initializer.dimsshape [x for x in dims]logging.info(finitializer with shape:{shape})dtype initializer.data_typelogging.info(finitializer with type: {onnx_datatype_to_npType(dtype)} )# print tenth bufferweights np.frombuffer(initializer.raw_data, dtypeonnx_datatype_to_npType(dtype))logging.info(finitializer first 10 wights:{weights[:10]})def parser_tensor(tensor, usenormal):name tensor.namelogging.info(f{use} tensor name: {name})data_type tensor.type.tensor_type.elem_typelogging.info(f{use} tensor data type: {data_type})dims tensor.type.tensor_type.shape.dimshape []for i, dim in enumerate(dims):shape.append(dim.dim_value)logging.info(f{use} tensor with shape:{shape} )def parser_node(node):def attri_value(attri):if attri.type 1:return attri.ielif attri.type 7:return list(attri.ints)name node.namelogging.info(fnode name:{name})opType node.op_typelogging.info(fnode op type:{opType})inputs list(node.input)logging.info(fnode with {len(inputs)} inputs:{inputs})outputs list(node.output)logging.info(fnode with {len(outputs)} outputs:{outputs})attributes node.attributefor attri in attributes:name attri.namevalue attri_value(attri)logging.info(f{name} with value:{value})def parser_info(onnx_model):ir_version onnx_model.ir_versionproducer_name onnx_model.producer_nameproducer_version onnx_model.producer_versionfor info in [ir_version, producer_name, producer_version]:logging.info(onnx model with info:{}.format(info))def parser_inputs(onnx_graph):inputs onnx_graph.inputfor input in inputs:parser_tensor(input, input)def parser_outputs(onnx_graph):outputs onnx_graph.outputfor output in outputs:parser_tensor(output, output)def parser_graph_initializers(onnx_graph):initializers onnx_graph.initializerfor initializer in initializers:parser_initializer(initializer)def parser_graph_nodes(onnx_graph):nodes onnx_graph.nodefor node in nodes:parser_node(node)t 1def onnx_parser():model_path D:/project/public/yolov5-5.0/yolov5s-sim.onnxmodel onnx.load(model_path)# 0.parser_info(model)graph model.graph# 1.parser_inputs(graph)# 2. parser_outputs(graph)# 3.parser_graph_initializers(graph)# 4. parser_graph_nodes(graph)if __name__ __main__:onnx_parser()INFO:root:onnx model with info:7
INFO:root:onnx model with info:pytorch
INFO:root:onnx model with info:1.10
INFO:root:input tensor name: images
INFO:root:input tensor data type: 1
INFO:root:input tensor with shape:[1, 3, 640, 640]
INFO:root:output tensor name: output
INFO:root:output tensor data type: 1
INFO:root:output tensor with shape:[1, 3, 80, 80, 85]
INFO:root:output tensor name: 405
INFO:root:output tensor data type: 1
INFO:root:output tensor with shape:[1, 3, 40, 40, 85]
INFO:root:output tensor name: 419
INFO:root:output tensor data type: 1
INFO:root:output tensor with shape:[1, 3, 20, 20, 85]
INFO:root:initializer name: model.0.conv.conv.weight
INFO:root:initializer with shape:[32, 12, 3, 3]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-0.2730072 -1.4410826 -1.187087 -0.31312177 -0.94754034 -0.7239634 0.4315643 2.0547783 1.5080036 0.04422583]
INFO:root:initializer name: model.0.conv.conv.bias
INFO:root:initializer with shape:[32]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 1.4819448 -0.9827409 -0.7623507 -0.503065 -0.1677513 3.1156974 1.4849529 0.15412715 -0.4954783 2.8073668 ]
INFO:root:initializer name: model.1.conv.weight
INFO:root:initializer with shape:[64, 32, 3, 3]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-0.0130239 -0.00788062 0.0033419 -0.08140857 -0.1592819 -0.10095055 -0.01685373 -0.00378994 -0.01589627 0.01994706]
INFO:root:initializer name: model.1.conv.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 2.9810083 1.6583345 2.4536395 4.068509 -0.03429741 -0.2963567 1.7936802 0.32334638 2.3112206 0.9088864 ]
INFO:root:initializer name: model.2.cv1.conv.weight
INFO:root:initializer with shape:[32, 64, 1, 1]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 0.00402472 -0.02254777 0.01670638 0.07000323 -0.01463853 -0.01208 -0.00047498 -0.01327164 0.02764146 0.03544556]
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INFO:root:initializer name: model.23.cv2.conv.bias
INFO:root:initializer with shape:[256]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 0.33130765 0.50842535 0.04622685 0.27884385 0.35381323 -0.05325952 0.18430158 0.16491716 -0.4574555 0.21860392]
INFO:root:initializer name: model.23.cv3.conv.weight
INFO:root:initializer with shape:[512, 512, 1, 1]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 0.15170689 -0.05846716 -0.03708049 -0.00515915 0.04973555 -0.01793442 0.26971823 -0.08900855 -0.08623905 -0.01718434]
INFO:root:initializer name: model.23.cv3.conv.bias
INFO:root:initializer with shape:[512]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-0.07636432 -0.14711903 -0.1693097 1.2009852 0.00255883 1.4637253-0.29597348 2.088275 0.5806205 0.49393153]
INFO:root:initializer name: model.23.m.0.cv1.conv.weight
INFO:root:initializer with shape:[256, 256, 1, 1]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 1.8370461e-01 -3.6310073e-02 2.6413003e-02 -3.4686580e-02-4.4441203e-04 3.3812389e-02 -3.7558913e-02 -9.6223257e-02-5.3294320e-02 -5.8845425e-01]
INFO:root:initializer name: model.23.m.0.cv1.conv.bias
INFO:root:initializer with shape:[256]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 0.4050864 -0.03902826 0.31003702
INFO:root:initializer name: model.24.m.0.bias
INFO:root:initializer with shape:[255]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[ 0.13708496 0.06561279 0.81152344 0.62353516 -4.1992188 -1.0546875 -5.2734375 -3.4414062 -5.5234375 -5.71875 ]
INFO:root:initializer name: model.24.m.1.weight
INFO:root:initializer with shape:[255, 256, 1, 1]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-0.00010455 -0.00139713 0.00134754 0.01174927 -0.00017703 -0.00750351-0.00029159 -0.00182247 -0.02702332 0.04980469]
INFO:root:initializer name: model.24.m.1.bias
INFO:root:initializer with shape:[255]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-0.05148315 -0.05493164 0.6333008 0.05197144 -2.625 -1.3398438 -5.7851562 -4.765625 -5.7929688 -6.2773438 ]
INFO:root:initializer name: model.24.m.2.weight
INFO:root:initializer with shape:[255, 512, 1, 1]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-2.5444031e-03 -1.1138916e-01 9.1195107e-06 1.0973215e-04-1.5907288e-03 -3.3130646e-03 2.6941299e-04 -9.5486641e-051.4615059e-04 -7.8857422e-02]
INFO:root:initializer name: model.24.m.2.bias
INFO:root:initializer with shape:[255]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[-1.6265869e-02 -1.9702911e-03 -6.9091797e-02 1.9494629e-01-2.0878906e00 -1.6210938e00 -6.5625000e00 -5.4531250e00-6.3437500e00 -6.7070312e00]
INFO:root:initializer name: 420
INFO:root:initializer with shape:[4]
INFO:root:initializer with type: class numpy.float32
INFO:root:initializer first 10 wights:[1. 1. 2. 2.]
