Meta2032流动性质押挖矿开发源码版丨Meta2032流动性质押挖矿系统开发（逻辑及功能）

　　The public chain is the core carrier of Web3.0,supporting interoperability,empowerment,and credit mechanisms,as well as various applications.The Web3.0 public chain circuit mainly includes Layer1,Layer2,and Layer0.

　　Blockchain technology can thus empower enterprises in many ways:providing reliable shared data and building trust between parties;Eliminate data silos,which integrate data into a system through centralized ledgers that are shared in a network and support licensee access;Give data a high degree of security

　　The evolution of new data oriented information technologies such as blockchain,artificial intelligence,digital twins,human-computer interaction,and the Internet of Things is not accidental,but rather a technological preparation for the evolution from Web 2.0 to Web 3.0.From a technical perspective,MetaUniverse is a trusted digital value interaction network based on the support of the Web3.0 technical system and operating mechanism,and is a new Web3.0 digital ecosystem centered on blockchain.Metaverse is a new scenario,new industry,and new ecology supported by the Web3.0 technology system with blockchain as its core,which will spawn a large number of innovative business models in the digital environment and form a new paradigm for digital space.

　　import io

　　import torch

　　import torch.onnx

　　from models.C3AEModel import PlainC3AENetCBAM

　　device=torch.device("cuda:0"if torch.cuda.is_available()else"cpu")

　　def test():

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　　model=PlainC3AENetCBAM()

　　pthfile=r'/home/joy/Projects/models/emotion/PlainC3AENet.pth'

　　loaded_model=torch.load(pthfile,map_location='cpu')

　　#try:

　　#loaded_model.eval()

　　#except AttributeError as error:

　　#print(error)

　　model.load_state_dict(loaded_model['state_dict'])

　　#model=model.to(device)

　　#data type nchw

　　dummy_input1=torch.randn(1,3,64,64)

　　#dummy_input2=torch.randn(1,3,64,64)

　　#dummy_input3=torch.randn(1,3,64,64)

　　input_names=["actual_input_1"]

　　output_names=["output1"]

　　#torch.onnx.export(model,(dummy_input1,dummy_input2,dummy_input3),"C3AE.onnx",verbose=True,input_names=input_names,output_names=output_names)

　　torch.onnx.export(model,dummy_input1,"C3AE_emotion.onnx",verbose=True,input_names=input_names,output_names=output_names)

　　if __name__=="__main__":

　　test()