)
meshgridExample::xtorch.tensor([1,2,3])ytorch.tensor([4,5,6])grid_x,grid_ytorch.meshgrid(x,y)grid_x tensor([[1,1,1],[2,2,2],[3,3,3]])grid_y tensor([[4,5,6],[4,5,6],[4,5,6]])tensorflow:xnp.array([1,2,3])ynp.array([4,5,6])grid_x,grid_ytf.meshgrid(x,y)sesstf.Session()print(sess.run(grid_x))print(sess.run(grid_y))results:[[123][123][123]][[444][555][666]]做代碼重構(gòu)的時(shí)候發(fā)現(xiàn)tensorflow和pytorch的meshgrid的實(shí)現(xiàn)是不一致的,要保持一致需要后續(xù)進(jìn)行transpose和reshape操作,以此簡單記錄. 【Pytorch】PyTorch: meshgrid的用法(與tensorflow的差異)
meshgridExample::xtorch.tensor([1,2,3])ytorch.tensor([4,5,6])grid_x,grid_ytorch.meshgrid(x,y)grid_x tensor([[1,1,1],[2,2,2],[3,3,3]])grid_y tensor([[4,5,6],[4,5,6],[4,5,6]])tensorflow:xnp.array([1,2,3])ynp.array([4,5,6])grid_x,grid_ytf.meshgrid(x,y)sesstf.Session()print(sess.run(grid_x))print(sess.run(grid_y))results:[[123][123][123]][[444][555][666]]做代碼重構(gòu)的時(shí)候發(fā)現(xiàn)tensorflow和pytorch的meshgrid的實(shí)現(xiàn)是不一致的,要保持一致需要后續(xù)進(jìn)行transpose和reshape操作,以此簡單記錄.