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onnx

import onnx_toolmodel_path = 'resnet50_224x224.onnx' #本地模型onnx_tool.model_profile(model_path)

模型

from torchprofile import profile_macs
from mirnet_v2_g3_conv13_noskff_arch import *
from thop import profileif __name__ == '__main__':model_temp = MIRNet_v2(inp_channels=4,out_channels=4,n_feat=32,n_RRG=4,n_MRB=1)model_temp.eval()inp_frames = torch.randn(1, 4, 2568//2, 1440//2)#inp_frames = torch.randn(1, 4, 1920//2, 1080//2)inp_frames = torch.randn(1, 4, 128, 128)macs = profile_macs(model_temp, (inp_frames))print(macs // 1e9) # 计算量在 587 G 左右flops, params = profile(model_temp, inputs = (inp_frames, ))print('MACs = ' + str(flops/1000**3) + 'G' + '(PMRID)')print('Params = ' + str(params/1000**2) + 'M' )