计算模型计算量MAC

onnx

import onnx_tool

model_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 profile


if __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' )