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import numpy as np from tvm import te import onnx import numpy as np import tvm import tvm.relay as relay from tvm import rpc from tvm.contrib import utils x = np.zeros((1,3, 640, 640)) from tvm.contrib import graph_executor # target = tvm.target.Target('llvm -mtriple=riscv64-unknown-linux-gnu -mcpu=generic-rv64 -mfloat-abi=hard') #target = "llvm -mtriple=riscv64-unknown-linux-gnu -mcpu=generic-rv64" -mcpu=sifive-u54 -mcpu=sifive-u54 -device=arm_cpu target = 'llvm -mtriple=riscv64-unknown-linux-gnu -mcpu=generic-rv64 -device=arm_cpu' input_name = "input.1" shape_dict = {input_name: x.shape} #导入onnx模型 onnx_model = onnx.load('resnet18.onnx') mod, params = relay.frontend.from_onnx(onnx_model, shape_dict) # 这里利用TVM构建出优化后模型的信息 # with relay.build_config(opt_level=3): # lib = relay.build_module.build(sym, target, params=params) with tvm.transform.PassContext(opt_level=3): graph, lib, params = relay.build(mod, target=target, params=params) lib.save("model.ll") #这个relay.build_module.build与relay.build一样； dtype = 'float32' from tvm.contrib import graph_runtime # 下面的函数导出我们需要的动态链接库，地址可自定义 print("Output model files") libpath = "model1.so" #with tvm.transform.PassContext(opt_level=3): # graph, lib, params = relay.build(mod, target=target, params=params) #lib.save("model.ll") from tvm.contrib import cc ##################### Test code start ############################## # import subprocess # def c_compile(file_name, objects, **kwargs): # cmd = "/usr/bin/riscv64-linux-gnu-gcc" # options = ["-march=rv64g", "-mabi=lp64", "-o", file_name] + objects # subprocess.check_call(cmd + " " + " ".join(options), shell=True) # lib.export_library(libpath, fcompile=c_compile) ##################### Test code end ############################## lib.export_library(libpath, cc="/usr/bin/riscv64-linux-gnu-gcc") #lib.export_library("model.so", cc="riscv64-unknown-linux-gnu-g++", options=["-march=rv64gc", "-mabi=lp64d", "-static-libstdc++"])