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analysis.py
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analysis.py
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import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import pprint as pp
from timeit import default_timer
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.data import Data, DataLoader
from torchvision.transforms import GaussianBlur
from utilities import *
from util import record_data, to_cpu, to_np_array, make_dir
from BE_MPNN import HeteroGNS
import matplotlib.tri as tri
from torch_geometric.data import HeteroData
import random
from loguru import logger
import warnings
warnings.filterwarnings('ignore')
fix_seed = 2023
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
torch.set_num_threads(16)
torch.cuda.manual_seed_all(fix_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--dataset_type', default="64x64", type=str,
help='dataset type')
parser.add_argument('--epochs', default=1000, type=int,
help='Epochs')
parser.add_argument('--lr', default=0.00001, type=float,
help='learning rate')
parser.add_argument('--inspect_interval', default=100, type=int,
help='inspect interval')
parser.add_argument('--id', default="0", type=str,
help='ID')
parser.add_argument('--init_boudary_loc', default="regular", type=str,
help='choose from "random" or "regular" ')
parser.add_argument('--nd1', default=1.5, type=float,
help='sampling ratio')
parser.add_argument('--trans_layer', default=4, type=int,
help='Layer of Transformer')
parser.add_argument('--boundary_dim', default=128, type=int,
help='Layer of Transformer')
parser.add_argument('--act', default="relu", type=str,
help='activation choose from "relu","elu","leakyrelu","silu')
parser.add_argument('--nd2',default=10,type=int,
help='radius')
parser.add_argument('--nmlp_layers',default=2,type=int,
help='number of layers of GNS')
try:
is_jupyter = True
args = parser.parse_args([])
args.dataset_type = "4corners"
args.nd1 = 1
args.nd2 = 15
args.boundary_dim = 128
args.act = 'silu'
args.nmlp_layers = 3
args.lr=0.00005
args.trans_layer = 3
args.inspect_interval=50
except:
args = parser.parse_args()
pp.pprint(args.__dict__)
DATA_PATH = f"data/"
f_all = np.load(DATA_PATH + "RHS_N32_10.npy")[:9,:,:]
sol_all = np.load(DATA_PATH + "SOL_N32_10.npy")[:9,:,:]
bc_all=np.load(DATA_PATH + "BC_N32_10.npy")[:9,:,:]
f_all_test=np.load(DATA_PATH + "RHS_N32_10.npy")[9:,:,:]
sol_all_test = np.load(DATA_PATH + "SOL_N32_10.npy")[9:,:,:]
bc_all_test=np.load(DATA_PATH + "BC_N32_10.npy")[9:,:,:]
f_all=np.vstack([f_all,f_all_test])
sol_all=np.vstack([sol_all,sol_all_test])
bc_all=np.vstack([bc_all,bc_all_test])
ntrain = 9
ntest =1
gblur = GaussianBlur(kernel_size=5, sigma=5)
ms = [80,350,1000]
case = 2
m = ms[case]
r = 1
k = 1
nd = args.nd1
batch_size = 1
batch_size2 = 1
width = 64
ker_width = 256
depth = 4
edge_features = 7
node_features = 10
epochs = args.epochs
learning_rate = args.lr
scheduler_step = epochs
scheduler_gamma = 0.5
inspect_interval = args.inspect_interval
runtime = np.zeros(2, )
t1 = default_timer()
resolution = 32
s = resolution
n = s**2
delta=1/s
min_r=nd*delta
radius_train = args.nd2*delta
radius_test = args.nd2*delta
trans_layer = args.trans_layer
cells_state=f_all[:,:,3] # node type \in {0,1,2,3}
coord_all=f_all[:,:,0:2] # all node corrdinate
bc_euco=bc_all[:,:,0:2] # boundary corrdinate
bc_value=bc_all[:,:,2].reshape(-1,128,1)
bc_value=torch.tensor(bc_value)
bc_value_1=bc_value[0:900,:,:]
bc_euco=torch.tensor(bc_euco)
bcv_normalizer = GaussianNormalizer(bc_value_1)
bc_value = bcv_normalizer.encode(bc_value)
bc_euco= to_np_array(torch.cat([bc_euco,bc_value],dim=-1))
all_a = f_all[:,:,2]
all_a_smooth = to_np_array(gblur(torch.tensor(all_a.reshape(all_a.shape[0], resolution, resolution))).flatten(start_dim=1))
all_a_reshape = all_a_smooth.reshape(-1, resolution, resolution)
all_a_gradx = np.concatenate([
all_a_reshape[:,1:2] - all_a_reshape[:,0:1],
(all_a_reshape[:,2:] - all_a_reshape[:,:-2]) / 2,
all_a_reshape[:,-1:] - all_a_reshape[:,-2:-1],
], 1)
all_a_gradx = all_a_gradx.reshape(-1, n)
all_a_grady = np.concatenate([
all_a_reshape[:,:,1:2] - all_a_reshape[:,:,0:1],
(all_a_reshape[:,:,2:] - all_a_reshape[:,:,:-2]) / 2,
all_a_reshape[:,:,-1:] - all_a_reshape[:,:,-2:-1],
], 2)
all_a_grady = all_a_grady.reshape(-1, n)
all_u = sol_all[:,:,0]
train_a = torch.FloatTensor(all_a[:ntrain]) # [num_train, 4096]
train_a_smooth = torch.FloatTensor(all_a_smooth[:ntrain]) # [num_train, 4096]
train_a_gradx = torch.FloatTensor(all_a_gradx[:ntrain]) # [num_train, 4096]
train_a_grady = torch.FloatTensor(all_a_grady[:ntrain]) # [num_train, 4096]
train_u = torch.FloatTensor(all_u[:ntrain]) # [num_train, 4096]
test_a = torch.FloatTensor(all_a[ntrain:])
test_a_smooth = torch.FloatTensor(all_a_smooth[ntrain:])
test_a_gradx = torch.FloatTensor(all_a_gradx[ntrain:])
test_a_grady = torch.FloatTensor(all_a_grady[ntrain:])
test_u = torch.FloatTensor(all_u[ntrain:])
bc_euco_train=bc_euco[:ntrain,:,:]
bc_euco_test=bc_euco[ntrain:,:,:]
#* normalization
indomain_a = np.array([])
indomain_u = np.array([])
for j in range(ntrain):
outdomain_idx=np.array([],dtype=int)
indomain_idx=np.array([],dtype=int)
for p in range(f_all.shape[1]):
if (cells_state[j][p]!=0):
outdomain_idx=np.append(outdomain_idx,int(p))
indomain_idx = list(set([i for i in range(resolution*resolution)]) - set(list(outdomain_idx)))
indomain_u = np.append(indomain_u,sol_all[j][indomain_idx])
indomain_a = np.append(indomain_a,f_all[j][indomain_idx][:,2])
indomain_u=torch.tensor(indomain_u)
indomain_a=torch.tensor(indomain_a)
a_normalizer = GaussianNormalizer(indomain_a)
train_a = a_normalizer.encode(train_a)
test_a = a_normalizer.encode(test_a)
as_normalizer = GaussianNormalizer(train_a_smooth)
train_a_smooth = as_normalizer.encode(train_a_smooth)
test_a_smooth = as_normalizer.encode(test_a_smooth)
agx_normalizer = GaussianNormalizer(train_a_gradx)
train_a_gradx = agx_normalizer.encode(train_a_gradx)
test_a_gradx = agx_normalizer.encode(test_a_gradx)
agy_normalizer = GaussianNormalizer(train_a_grady)
train_a_grady = agy_normalizer.encode(train_a_grady)
test_a_grady = agy_normalizer.encode(test_a_grady)
u_normalizer = GaussianNormalizer(x=indomain_u)
train_u = u_normalizer.encode(train_u)
grid_input=f_all[0,:,0:2]
meshgenerator = RandomMeshGenerator([[0,1],[0,1]],[s,s], sample_size=m, grid_input = grid_input)
data_test = []
for j in range(ntest):
mesh_idx_temp=[p for p in range(resolution**2)]
outdomain_idx=np.array([])
for p in range(f_all.shape[1]):
if (cells_state[j+ntrain][p]!=0):
outdomain_idx=np.append(outdomain_idx,p)
del_idx=[]
for p in range(len(mesh_idx_temp)):
if mesh_idx_temp[p] in outdomain_idx:
del_idx.append(mesh_idx_temp[p])
for p in range(len(del_idx)):
mesh_idx_temp.remove(del_idx[p])
dist2bd_x=np.array([0,0])[np.newaxis,:]
dist2bd_y=np.array([0,0])[np.newaxis,:]
for p in range(len(mesh_idx_temp)):
indomain_x = coord_all[j+ntrain][mesh_idx_temp[p]][0]
indomain_y = coord_all[j+ntrain][mesh_idx_temp[p]][1]
horizon_bd_y = np.where(bc_euco_test[j,:,0].round(4) == indomain_x.round(4))[0]
dist2bd_y_temp = np.array(
[np.abs(bc_euco_test[j,horizon_bd_y[0],1] - indomain_y),
np.abs(bc_euco_test[j,horizon_bd_y[1],1] - indomain_y)
]
)
dist2bd_y = np.vstack([dist2bd_y,dist2bd_y_temp[np.newaxis,:]])
horizon_bd_x = np.where(bc_euco_test[j,:,1].round(4) == indomain_y.round(4))[0]
dist2bd_x_temp = np.array(
[np.abs(bc_euco_test[j,horizon_bd_x[0],0] - indomain_x),
np.abs(bc_euco_test[j,horizon_bd_x[1],0] - indomain_x)
]
)
dist2bd_x = np.vstack([dist2bd_x,dist2bd_x_temp[np.newaxis,:]])
dist2bd_y = torch.tensor(dist2bd_y[1:]).float()
dist2bd_x = torch.tensor(dist2bd_x[1:]).float() # [num, 2]
idx = meshgenerator.poisson_disk_sample(mesh_idx_temp)
grid = meshgenerator.get_grid()
xx=to_np_array(grid[:,0])
yy=to_np_array(grid[:,1])
triang = tri.Triangulation(xx, yy)
tri_edge = triang.edges
edge_index = meshgenerator.ball_connectivity_dist_0(radius_train,ns=10,tri_edge=tri_edge)
edge_attr = meshgenerator.attributes_dist_neo7(theta=test_a[j,:])
test_x = torch.cat([grid, test_a[j, idx].reshape(-1, 1),
test_a_smooth[j, idx].reshape(-1, 1), test_a_gradx[j, idx].reshape(-1, 1),
test_a_grady[j, idx].reshape(-1, 1),dist2bd_x,dist2bd_y
], dim=1)
test_x_2 = torch.cat([grid, torch.zeros([grid.shape[0],4]), dist2bd_x,dist2bd_y
], dim=1)
bd_coord_input = torch.tensor(bc_euco_test[j])
bd_coord_input_1=bd_coord_input.clone()
bd_coord_input_1[:,2]=0
data=HeteroData()
data['G1'].x=test_x #node features ▲u=f
data['G1'].boundary=bd_coord_input_1 #boundary value=0
data['G1'].edge_features=edge_attr
data['G1'].sample_idx=idx
data['G1'].edge_index=edge_index
data['G2'].x=test_x_2 ##node features ▲u=0
data['G2'].boundary=bd_coord_input #boundary value=g(x)
data['G2'].edge_features=edge_attr
data['G2'].sample_idx=idx
data['G2'].edge_index=edge_index
data['G1+2'].y=test_u[j, idx]
data_test.append(data)
test_loader = DataLoader(data_test, batch_size=batch_size2, shuffle=False)
if args.act == 'leakyrelu':
activation = nn.LeakyReLU
elif args.act == 'elu':
activation = nn.ELU
elif args.act == 'relu':
activation = nn.ReLU
else:
activation = nn.SiLU
model = HeteroGNS(nnode_in_features = node_features, nnode_out_features = 1, nedge_in_features = edge_features, nmlp_layers=args.nmlp_layers,
activation = activation,boundary_dim = args.boundary_dim,trans_layer = trans_layer).to(device)
filename_model='Non-zero_Resolution_32_poisson_dataset_4corners_ntrain900_kerwidth256_m01000_radius0.46875_Transformer_layer3_Rollingregular_nd11_nd215_nheads2_bddim128_actsilulr5e-05new_nmlp_layers3_TRANS_ns10_nstep10_bcnorm_0corner'
filename = filename_model + '_Test_NOcorners'
myloss = LpLoss(size_average=False)
u_normalizer.cuda(device)
data_record = pickle.load(open(f"./{filename_model}", "rb"))
model.load_state_dict(data_record["state_dict"][-1])
pdb.set_trace()
analysis_record = {}
model.eval()
out_all=np.array([])
label_all=np.array([])
a_ori_all=np.array([])
with torch.no_grad():
for ii, data in enumerate(test_loader):
data = data.to(device)
out_indomain = model(data) #out_indomain tensor [indomain点个数，1]
data_all = torch.zeros((resolution*resolution, 10)).to(device) #data_all.shape=[1024,6]
out = torch.zeros((resolution*resolution,1)).to(device)
label = torch.zeros((resolution*resolution)).to(device) #label.shape=[1024]
out[data['G1'].sample_idx] = out_indomain #data.sample_idx: tensor 一维 #out.shape=[1,1024]
label[data['G1'].sample_idx] = data['G1+2'].y
data_all[data['G1'].sample_idx,:] = data['G1'].x
out = u_normalizer.decode(out.view(batch_size2,-1))
out_tem = torch.zeros((1,resolution*resolution)).to(device)
out_tem[0][data['G1'].sample_idx] = out[0][data['G1'].sample_idx]
a_ori = a_normalizer.decode(data_all[:,2].view(1,-1))
a_ori_tem = torch.zeros((1,resolution*resolution)).to(device) #[1，1024]
a_ori_tem[0][data['G1'].sample_idx] = a_ori[0][data['G1'].sample_idx]
l2_item = myloss(out_tem, label.view(batch_size2, -1)).item()
mae_item = nn.L1Loss()(out_tem, label.view(batch_size2, -1)).item()
record_data(analysis_record, [l2_item, mae_item], ["L2", "MAE"])
out_all=np.append(out_all,to_np_array(out_tem))
label_all=np.append(label_all,to_np_array(label))
a_ori_all=np.append(a_ori_all,to_np_array(a_ori_tem))
print(np.mean(analysis_record['L2']))
print(np.std(analysis_record['L2']))
print(np.mean(analysis_record['MAE']))
print(np.std(analysis_record['MAE']))