You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
183 lines
6.5 KiB
183 lines
6.5 KiB
11 months ago
|
from torch import nn
|
||
|
|
from torch.autograd import Function
|
||
|
|
import torch
|
||
|
|
import numpy as np
|
||
|
|
import time
|
||
|
|
#import feconv_cuda
|
||
|
|
|
||
|
|
|
||
|
|
def data_pre(batchsize = 512):
|
||
|
|
print(f'**** batchsize = {batchsize} ****')
|
||
|
|
# U = torch.rand((batchsize,18,40,40,40),dtype = torch.float)
|
||
|
|
U = torch.ones((batchsize,18,40,40,40),dtype = torch.float)
|
||
|
|
# rho = torch.rand((batchsize,1,40,40,40),dtype = torch.float)
|
||
|
|
rho = torch.ones((batchsize,1,40,40,40),dtype = torch.float)
|
||
|
|
rho[rho<=0.5] = 0
|
||
|
|
rho[rho >0.5] = 1
|
||
|
|
print('discre rho:', torch.abs((rho-1)*rho).max())
|
||
|
|
|
||
|
|
resolution = 40
|
||
|
|
E = 1; nu = 0.3
|
||
|
|
from ConstMatricesForHomogenization import ISOelasticitytensor,LocalKeFe
|
||
|
|
D0 = ISOelasticitytensor(E, nu)
|
||
|
|
Ke,Fe,intB = LocalKeFe(resolution,D0)
|
||
|
|
h = 1.0/resolution
|
||
|
|
nele = resolution**3
|
||
|
|
I = np.eye(6)
|
||
|
|
datashape = resolution
|
||
|
|
# Ke2 = np.loadtxt("3D/k.txt", delimiter=',', dtype=np.float64) /2
|
||
|
|
# Fe2 = np.loadtxt("3D/f.txt", delimiter=',', dtype=np.float64) /4
|
||
|
|
# Ke = torch.from_numpy(Ke).to(device)
|
||
|
|
# Fe = torch.from_numpy(Fe).to(device)
|
||
|
|
|
||
|
|
from PeriodicMesh3D import PeriodicMesh3D,edofMatrix
|
||
|
|
eleidx,MESH,V = PeriodicMesh3D(resolution)
|
||
|
|
# mesh = np.loadtxt("3D/40mesh.txt", delimiter='\t', dtype=np.int)
|
||
|
|
edofMat = edofMatrix(MESH)
|
||
|
|
|
||
|
|
return U,rho,Ke,edofMat
|
||
|
|
def originalMethod_check(output_img,input,Ke, edofMat):
|
||
|
|
|
||
|
|
size = input.shape[0]
|
||
|
|
# 3d rho的顺序?
|
||
|
|
pp = input.view(size, -1, 1, 1)#.to(device)
|
||
|
|
K = pp * Ke # [bs, 8000, 24, 24]
|
||
|
|
# F = pp * Fe # [bs, 8000, 24, 6]
|
||
|
|
|
||
|
|
ref18 = output_img.contiguous().view(size,18,-1)
|
||
|
|
# map0 = ref18[:,0::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
# map1 = ref18[:,1::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
# map2 = ref18[:,2::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
# map3 = ref18[:,3::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
# map4 = ref18[:,4::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
# map5 = ref18[:,5::6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
|
||
|
|
map0 = ref18[:,0:3].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
map1 = ref18[:,3:6].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
map2 = ref18[:,6:9].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
map3 = ref18[:,9:12].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
map4 = ref18[:,12:15].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
map5 = ref18[:,15:18].permute((0,2,1)).contiguous().view(size,-1,1)
|
||
|
|
|
||
|
|
# ref18 = output_img.permute((0,2,3,4,1)).contiguous().view(size,-1,18)
|
||
|
|
# map0 = ref18[:,:,0:3].contiguous().view(size,-1,1)
|
||
|
|
# map1 = ref18[:,:,3:6].contiguous().view(size,-1,1)
|
||
|
|
# map2 = ref18[:,:,6:9].contiguous().view(size,-1,1)
|
||
|
|
# map3 = ref18[:,:,9:12].contiguous().view(size,-1,1)
|
||
|
|
# map4 = ref18[:,:,12:15].contiguous().view(size,-1,1)
|
||
|
|
# map5 = ref18[:,:,15:18].contiguous().view(size,-1,1)
|
||
|
|
ref_map = torch.cat([map0,map1,map2,map3,map4,map5], 2)# [bs,3*40**3,6]
|
||
|
|
# ref_map[:,0:3,:] = 0
|
||
|
|
|
||
|
|
# U = torch.zeros([size, 8000, 24, 6]).to(device)
|
||
|
|
U = ref_map[:, edofMat, :]
|
||
|
|
|
||
|
|
UT = U.permute([0, 1, 3, 2])
|
||
|
|
# losst1 = torch.matmul(torch.matmul(UT, K), U).sum()
|
||
|
|
# print(losst1)
|
||
|
|
|
||
|
|
# FU = (U * F).sum()
|
||
|
|
|
||
|
|
# losst1 =
|
||
|
|
UKU = torch.matmul(torch.matmul(UT, K), U)
|
||
|
|
UKU0 = UKU[:,:,0,0].sum()
|
||
|
|
UKU1 = UKU[:,:,1,1].sum()
|
||
|
|
UKU2 = UKU[:,:,2,2].sum()
|
||
|
|
UKU3 = UKU[:,:,3,3].sum()
|
||
|
|
UKU4 = UKU[:,:,4,4].sum()
|
||
|
|
UKU5 = UKU[:,:,5,5].sum()
|
||
|
|
|
||
|
|
losst1 = UKU0+UKU1+UKU2+UKU3+UKU4+UKU5
|
||
|
|
print(losst1)
|
||
|
|
return losst1
|
||
|
|
|
||
|
|
def feconv_check(U,rho,Ke):
|
||
|
|
from feconv import FECONV
|
||
|
|
from feconv import FEconvFunction
|
||
|
|
|
||
|
|
print('FECONV imported')
|
||
|
|
|
||
|
|
# batchsize = 512
|
||
|
|
# print(f'**** batchsize = {batchsize} ****')
|
||
|
|
from periodicU import periodicU
|
||
|
|
U = periodicU(U)
|
||
|
|
|
||
|
|
from getTypeH8 import typeH8
|
||
|
|
H8types = typeH8(rho)
|
||
|
|
H8types = H8types.int()
|
||
|
|
|
||
|
|
from arrangeIndex import arrangeIndex
|
||
|
|
nodIdx = arrangeIndex()
|
||
|
|
|
||
|
|
# Ke = np.eye(24).astype(np.float32)
|
||
|
|
from symbolicExec_vec import getFilters
|
||
|
|
filters = getFilters(Ke)
|
||
|
|
filters = filters.astype(np.float32)
|
||
|
|
|
||
|
|
device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
|
||
|
|
print("DEVICE : ", device)
|
||
|
|
|
||
|
|
nodIdx = nodIdx.astype(np.int32)
|
||
|
|
U = U.to(device)
|
||
|
|
# H8types = torch.from_numpy(H8types).to(device)
|
||
|
|
H8types = H8types.to(device)
|
||
|
|
nodIdx = torch.from_numpy(nodIdx).to(device)
|
||
|
|
filters = torch.from_numpy(filters).to(device)
|
||
|
|
filters = filters*1e6
|
||
|
|
print("INPUT info.:----------------------------------")
|
||
|
|
print('* U :',U.cpu().numpy().shape,U.dtype,U.sum().cpu().numpy())
|
||
|
|
print('* H8types :',H8types.cpu().numpy().shape,H8types.dtype,H8types.sum().cpu().numpy(),H8types.min().cpu().numpy(),H8types.max().cpu().numpy())
|
||
|
|
print('* nodIdx :',nodIdx.cpu().numpy().shape,nodIdx.dtype,nodIdx.sum().cpu().numpy(),nodIdx.min().cpu().numpy(),nodIdx.max().cpu().numpy())
|
||
|
|
print('* filters :',filters.cpu().numpy().shape,filters.dtype,filters.sum().cpu().numpy())
|
||
|
|
|
||
|
|
steps = 10
|
||
|
|
convOP = FECONV().to(device)
|
||
|
|
|
||
|
|
start = time.perf_counter()
|
||
|
|
# for i in range(steps):
|
||
|
|
KU = convOP(U,H8types,nodIdx,filters)
|
||
|
|
# KU = FEconvFunction.apply(U,H8types,nodIdx,filters)
|
||
|
|
uku = (KU*U).sum((2,3,4))
|
||
|
|
uku1 = uku.view(-1,6,3).sum((2))
|
||
|
|
elapsed = time.perf_counter() - start
|
||
|
|
|
||
|
|
print(f"elapsed in {elapsed} s")
|
||
|
|
print("OUTPUT info.:---------------------------------")
|
||
|
|
print('* KU :',KU.shape,KU.dtype,KU.device)
|
||
|
|
print('* U :',U.shape,U.dtype,U.device)
|
||
|
|
#uku2 = uku.view(-1,3,6).sum((1))
|
||
|
|
print('* UKU :',uku1.shape,uku1.dtype,uku1.device)
|
||
|
|
print(uku1.cpu().numpy()[0,0])
|
||
|
|
sum0 = uku[0,:3].sum()
|
||
|
|
print(uku1.shape, uku1[0,0]-sum0)
|
||
|
|
print(uku1[:2])
|
||
|
|
|
||
|
|
def oricheck(U,rho,Ke,edofMat):
|
||
|
|
device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
|
||
|
|
print("DEVICE : ", device)
|
||
|
|
edofMat = torch.from_numpy(edofMat).to(device).long()
|
||
|
|
Ke = torch.from_numpy(Ke).to(device)
|
||
|
|
rho = rho.to(device)
|
||
|
|
U = U.to(device)
|
||
|
|
|
||
|
|
start = time.perf_counter()
|
||
|
|
originalMethod_check(U,rho,Ke, edofMat)
|
||
|
|
elapsed = time.perf_counter() - start
|
||
|
|
print(f"elapsed in {elapsed} s")
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
print('modify mark 1')
|
||
|
|
U,rho,Ke,edofMat = data_pre(batchsize = 32)
|
||
|
|
feconv_check(U,rho,Ke)
|
||
|
|
|
||
|
|
# device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
|
||
|
|
# from periodicU import periodicU
|
||
|
|
# U = periodicU(U)
|
||
|
|
# U = U.to(device)
|
||
|
|
# tmp = U*U
|
||
|
|
# print('tmp: ',tmp.shape,tmp.device)
|
||
|
|
|
||
|
|
|
||
|
|
#oricheck(U,rho,Ke,edofMat)
|
||
|
|
|