-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_smoke_h.py
executable file
·190 lines (166 loc) · 7.99 KB
/
test_smoke_h.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import os
from pathlib import Path
import logging
import pickle
from timeit import default_timer
from datetime import datetime, timedelta
import numpy as np
import matplotlib.pyplot as plt
from utils.data_factory import SmokeDataset, SmokeDatasetMemory
from utils.utilities3 import *
from utils.params import get_args, get_test_args
from utils.adam import Adam
from model_dict import get_model
from tqdm import tqdm
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
################################################################
# configs
################################################################
test_args = get_test_args()
ckpt_dir = test_args.ckpt_dir
dataset_nickname = test_args.dataset_nickname
model_name = test_args.model_name
time_str = test_args.time_str
milestone = test_args.milestone
T_out = test_args.T_out
args = get_args(cfg_file=Path(ckpt_dir)/dataset_nickname/model_name/time_str/'configs.txt')
test_save_path = os.path.join(args.run_save_path, f'test_{milestone}_{T_out}')
if not os.path.isdir(test_save_path):
os.makedirs(test_save_path)
LOG_FORMAT = "%(message)s"
logger = logging.getLogger('Loss logger')
logger.setLevel(logging.INFO)
f_handler = logging.FileHandler(os.path.join(test_save_path, args.log_save_name))
f_handler.setLevel(logging.INFO)
f_handler.setFormatter(logging.Formatter(LOG_FORMAT))
logger.addHandler(f_handler)
padding = [int(p) for p in args.padding.split(',')]
ntrain = args.ntrain
ntest = args.ntest
N = args.ntotal
args.in_channels = args.in_dim * args.in_var
args.out_channels = args.out_dim * args.out_var
r1 = args.z_down
r2 = args.h_down
r3 = args.w_down
s1 = int(((args.z - 1) / r1) + 1)
s2 = int(((args.h - 1) / r2) + 1)
s3 = int(((args.w - 1) / r3) + 1)
T_in = args.T_in
# T_out = args.T_out
patch_size = tuple(int(x) for x in args.patch_size.split(','))
batch_size = args.batch_size
learning_rate = args.learning_rate
epochs = args.epochs
step_size = args.step_size
gamma = args.gamma
model_save_path = args.model_save_path
model_save_name = args.model_save_name
################################################################
# models
################################################################
model = get_model(args, ckpt_dir=Path(ckpt_dir)/dataset_nickname/model_name/time_str)
state_dict = torch.load(Path(ckpt_dir)/dataset_nickname/model_name/time_str/ (model_save_name[:-3]+f'_{milestone}.pt'))
model.load_state_dict(state_dict)
################################################################
# load data and data normalization
################################################################
train_dataset = SmokeDatasetMemory(args, split='train')
test_dataset = SmokeDatasetMemory(args, split='test')
train_loader = train_dataset.loader()
test_loader = test_dataset.loader()
boundary = torch.ones(s1, s2, s3)
boundary[1:-1, 1:-1, 1:-1] = 0
domain = 1 - boundary.clone().detach()
if 'DeepLag' in args.model:
model.set_bdydom(boundary, domain)
if args.resample_strategy == 'uniform' or args.resample_strategy == 'learned':
model.num_samples = min(model.num_samples, s1*s2*s3//model.pixel_per_patch)
elif args.resample_strategy == 'boundary':
model.num_samples = min(model.num_samples, model.coo_boundary_ms[0].shape[0])
elif args.resample_strategy == 'domain':
model.num_samples = min(model.num_samples, model.coo_domain_ms[0].shape[0])
################################################################
# evaluation
################################################################
myloss = LpLoss(size_average=False, channel_wise=False)
mseloss = nn.MSELoss()
step = 1
min_test_l2_full = 114514
t1 = default_timer()
test_l2_step = 0
test_l2_full = 0
test_vor_step = 0
test_vor_full = 0
with torch.no_grad():
for batch_idx, (xx, yy) in enumerate(tqdm(test_loader)):
loss = 0
vor_loss = 0
xx = xx.to(device) # B Z H W T*C
yy = yy.to(device) # B Z H W T*C
if 'DeepLag' in args.model:
h_x_q, h_coo_q, h_coo_offset_q = [], [], []
for i in range(model.num_layers):
if args.resample_strategy == 'uniform':
num_samples = model.num_samples // (8**i)
coo_q = torch.cat([
torch.randint(0,model.img_z_layers[i]-1,(batch_size,num_samples,1)),
torch.randint(0,model.img_h_layers[i]-1,(batch_size,num_samples,1)),
torch.randint(0,model.img_w_layers[i]-1,(batch_size,num_samples,1))
], dim=-1).to(torch.float32) # b k 3
elif args.resample_strategy == 'boundary':
num_samples = min(model.num_samples//(8**i), model.coo_boundary_ms[i].shape[0])
idx_coo_sample = torch.multinomial(1./torch.ones(model.coo_boundary_ms[i].shape[0]), num_samples, replacement=False) # k
coo_q = model.coo_boundary_ms[i][idx_coo_sample][None, ...].repeat(batch_size,1,1).to(torch.float32) # b k 3
elif args.resample_strategy == 'domain':
num_samples = min(model.num_samples//(8**i), model.coo_domain_ms[i].shape[0])
idx_coo_sample = torch.multinomial(1./torch.ones(model.coo_domain_ms[i].shape[0]), num_samples, replacement=False) # k
coo_q = model.coo_domain_ms[i][idx_coo_sample][None, ...].repeat(batch_size,1,1).to(torch.float32) # b k 3
elif args.resample_strategy == 'learned':
num_samples = model.num_samples // (8**i)
coo_q = None
num_chan = args.d_model*(2**i) if i < model.num_layers-1 else args.d_model*(2**(i-1))
h_x_q.append(torch.zeros(batch_size, num_samples, num_chan).to(device))
h_coo_q.append(coo_q.to(device) if args.resample_strategy != 'learned' else None)
h_coo_offset_q.append(torch.zeros(batch_size, num_samples, 3).to(device))
for t in range(0, T_out, step):
y = yy[..., t*args.out_var : (t + step)*args.out_var] # B Z H W C_out=V_out
if 'DeepLag' in args.model:
im, h_x_q, h_coo_q, h_coo_offset_q, coo_offset_zxys = model(xx, h_x_q, h_coo_q, h_coo_offset_q) # B Z H W C_out=V_out
else:
im = model(xx)
loss += myloss(im.reshape(batch_size, -1), y.reshape(batch_size, -1))
vor_loss += mseloss(vorticity_3d(im[..., -3], im[..., -2], im[..., -1]), vorticity_3d(-y[..., -3], y[..., -2], y[..., -1]))
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
xx = torch.cat((xx[..., step*args.in_var:], im), dim=-1)
test_l2_step += loss.item()
test_l2_full += myloss(pred.reshape(batch_size, -1), yy.reshape(batch_size, -1)).item()
test_vor_step += vor_loss.item()
test_vor_full += mseloss(vorticity_3d(pred[..., 1::args.in_var], pred[..., 2::args.in_var], pred[..., 3::args.in_var]), vorticity_3d(yy[..., 1::args.in_var], yy[..., 2::args.in_var], yy[..., 3::args.in_var])).item()
t2 = default_timer()
if test_l2_full / ntest < min_test_l2_full:
print(t2 - t1,
'test_rel_l2:',
test_l2_step / ntest / (T_out / step),
test_l2_full / ntest,
'test_vor:',
test_vor_step / ntest / (T_out / step),
test_vor_full / ntest)
logger.info(f'{t2 - t1} ' + \
f'test_rel_l2: {test_l2_step / ntest / (T_out / step)} {test_l2_full / ntest} ' + \
f'test_vor: {test_vor_step / ntest / (T_out / step)} {test_vor_full / ntest}')
pd = pred[-1, :, :, :, -4:].detach().cpu().numpy()
gt = yy[-1, :, :, :, -4:].detach().cpu().numpy()
vars = ['field', 'ux', 'uy', 'uz']
for i in range(4):
visual_zoy(pd[...,i], os.path.join(test_save_path, f'{milestone}_{vars[i]}_pred.png'))
visual_zoy(gt[...,i], os.path.join(test_save_path, f'{milestone}_{vars[i]}_gt.png'))
visual_zoy(np.abs(gt-pd)[...,i], os.path.join(test_save_path, f'{milestone}_{vars[i]}_err.png'))
else:
raise Exception('Abnormal loss!')