-
Notifications
You must be signed in to change notification settings - Fork 1
/
IP_osr_patches.py
299 lines (233 loc) · 13.8 KB
/
IP_osr_patches.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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
import os
import argparse
import datetime
import time
import importlib
import scipy.io as sio
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
from models import network
from core import train, test
from core.full_test import full_test
from generate_pic import aa_and_each_accuracy, sampling1, sampling2, sampling3, load_dataset, generate_png, generate_iter
from generate_pic import generate_train_iter, generate_valida_iter, generate_test_iter, generate_all_iter, generate_full_iter, generate_iter,generate_train_known_iter,generate_test_known_iter,generate_test_unknown_iter,generate_fulltest_iter
import numpy as np
from sklearn import metrics, preprocessing
from sklearn.metrics import accuracy_score
from sklearn.metrics import cohen_kappa_score
parser = argparse.ArgumentParser("Training")
# Dataset
parser.add_argument('--dataset', choices=['SA', 'PU', 'IP'], default='IP', help='dataset to use')
parser.add_argument('--patches', type=int, default=5, help='number of patches')
parser.add_argument('--num', type=int, default=30, help='number of samples')
# optimization
parser.add_argument('--batch-size', type=int, default=200)
parser.add_argument('--lr', type=float, default=0.001, help="learning rate for model")
parser.add_argument('--experiment_num', type=int, default=10)
parser.add_argument('--max-epoch', type=int, default=100)
parser.add_argument('--stepsize', type=int, default=20)
parser.add_argument('--temp', type=float, default=1.0, help="temp")
parser.add_argument('--num-centers', type=int, default=1)
# model
parser.add_argument('--weight-pl', type=float, default=0.1, help="weight for center loss")
parser.add_argument('--beta', type=float, default=0.1, help="weight for entropy loss")
parser.add_argument('--model', type=str, default='SSMLP-RPL')
parser.add_argument('--layers', type=int, default=4)
parser.add_argument('--embed_dims', type=int, default=64)
parser.add_argument('--segment_dim', type=int, default=8)
# misc
parser.add_argument('--nz', type=int, default=100)
parser.add_argument('--ns', type=int, default=1)
parser.add_argument('--eval-freq', type=int, default=100)
parser.add_argument('--print-freq', type=int, default=100)
parser.add_argument('--gpu', type=str, default='0')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--use-cpu', action='store_true')
parser.add_argument('--save-dir', type=str, default='../log')
parser.add_argument('--loss', type=str, default='ARPLoss')
parser.add_argument('--eval', action='store_true', help="Eval", default=False)
parser.add_argument('--cs', action='store_true', help="Confusing Sample", default=False)
def get_accuracy(y_true, y_pred):
num_perclass = np.zeros(int(y_true.max() + 1))
num = np.zeros(int(y_true.max() + 1))
for i in range(len(y_true)):
num_perclass[int(y_true[i])] += 1
for i in range(len(y_pred)):
if y_pred[i] == y_true[i]:
num[int(y_pred[i])] += 1
for i in range(len(num)):
num[i] = num[i] / num_perclass[i]
acc = accuracy_score(y_true, y_pred)
kappa = cohen_kappa_score(y_true, y_pred)
ac = np.zeros(int(y_true.max() + 1 + 2))
ac[:int(y_true.max() + 1)] = num
ac[-1] = acc
ac[-2] = kappa
return ac # acc,num.mean(),kappa
if __name__ == '__main__':
args = parser.parse_args()
options = vars(args)
results = dict()
#path = 'D:/HSI_data/'
#path = '/media/lenovo/0A08DBEC08DBD533/syf/HSI_data/'
path = '/home/lenovo/data/syf/HSI_data/'
#path='/media/liubing/cc5992d7-d217-4ded-97b4-fd47f4fa55f4/syf/HSI_data/'
dataname=args.dataset
model_name='SSMLP-RPL'
data_hsi=sio.loadmat(path+'Indian_pines_corrected.mat')
data_hsi=data_hsi['indian_pines_corrected']
gt=sio.loadmat(path+'Indian_pines_gt.mat')
gt=gt['indian_pines_gt']
# training samples per class
SAMPLES_NUM = args.num
experiment_num=args.experiment_num
ROWS, COLUMNS, BAND = data_hsi.shape
data = data_hsi.reshape(np.prod(data_hsi.shape[:2]), np.prod(data_hsi.shape[2:]))
gt2 = gt.reshape(np.prod(gt.shape[:2]), )
CLASSES_NUM = gt.max()
print('The class numbers of the HSI data is:', CLASSES_NUM)
known = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] # define the known classes
print('The known class of the HSI data is:', known)
unknown = list(set(list(range(0, gt.max()))) - set(known))
print('The unknown class of the HSI data is:', unknown)
print('-----Importing Setting Parameters-----')
patch_list=[7]
for i_patch in range(len(patch_list)):
patches=patch_list[i_patch]
PATCH_LENGTH = int((patches-1)/2)
# number of training samples per class
# lr, num_epochs, batch_size = 0.0001, 200, 32
img_rows = 2 * PATCH_LENGTH + 1
img_cols = 2 * PATCH_LENGTH + 1
INPUT_DIMENSION = data_hsi.shape[2]
FULL_SIZE = data_hsi.shape[0] * data_hsi.shape[1]
ALL_SIZE = data_hsi.shape[0] * data_hsi.shape[1]
data = preprocessing.scale(data)
whole_data = data.reshape(data_hsi.shape[0], data_hsi.shape[1], data_hsi.shape[2])
padded_data = np.lib.pad(whole_data, ((PATCH_LENGTH, PATCH_LENGTH), (PATCH_LENGTH, PATCH_LENGTH), (0, 0)),'symmetric')
Experiment_result = np.zeros([CLASSES_NUM + 7, experiment_num + 2])
for iter_num in range(experiment_num):
np.random.seed(iter_num+123456)
train_indices, test_indices = sampling1(SAMPLES_NUM, gt2, options['dataset'])
full_indices = sampling3(gt2, 1)
TRAIN_SIZE = len(train_indices)
VAL_SIZE = int(TRAIN_SIZE)
TEST_SIZE = len(test_indices)
if unknown == None:
#full_iter = generate_full_iter(whole_data, PATCH_LENGTH, padded_data, INPUT_DIMENSION, args.batch_size, gt2, FULL_SIZE,full_indices)
train_iter = generate_train_iter(TRAIN_SIZE, train_indices, whole_data, PATCH_LENGTH, padded_data, INPUT_DIMENSION,args.batch_size, gt2)
test_iter = generate_test_iter(TEST_SIZE, test_indices, VAL_SIZE, whole_data, PATCH_LENGTH, padded_data,INPUT_DIMENSION, args.batch_size, gt2)
known_test_iter = None
unknown_test_iter = None
else:
full_iter = generate_full_iter(whole_data, PATCH_LENGTH, padded_data, INPUT_DIMENSION, args.batch_size, gt2,FULL_SIZE, full_indices)
test_iter = generate_fulltest_iter(TEST_SIZE, test_indices, VAL_SIZE, whole_data, PATCH_LENGTH, padded_data,INPUT_DIMENSION, args.batch_size, gt2, known, CLASSES_NUM-1)
train_iter = generate_train_known_iter(TRAIN_SIZE, train_indices, whole_data, PATCH_LENGTH, padded_data,INPUT_DIMENSION, args.batch_size, gt2, known, augmentation=True)
known_test_iter = generate_test_known_iter(TEST_SIZE, test_indices, VAL_SIZE, whole_data, PATCH_LENGTH, padded_data,INPUT_DIMENSION, args.batch_size, gt2, known)
unknown_test_iter = generate_test_unknown_iter(TEST_SIZE, test_indices, VAL_SIZE, whole_data, PATCH_LENGTH, padded_data,INPUT_DIMENSION, args.batch_size, gt2, unknown, CLASSES_NUM-1)
options.update(
{
'BAND':BAND,
'known': known,
'unknown': unknown,
'full_iter' : full_iter,
'test_iter' : test_iter,
'train_iter': train_iter,
'known_test_iter' : known_test_iter,
'unknown_test_iter' : unknown_test_iter,
}
)
torch.manual_seed(options['seed'])
os.environ['CUDA_VISIBLE_DEVICES'] = options['gpu']
use_gpu = torch.cuda.is_available()
if options['use_cpu']: use_gpu = False
if use_gpu:
print("Currently using GPU: {}".format(options['gpu']))
cudnn.benchmark = True
torch.cuda.manual_seed_all(options['seed'])
else:
print("Currently using CPU")
# Dataset
print("{} Preparation".format(options['dataset']))
if options['unknown'] == None:
trainloader, testloader, outloader = options['train_iter'], options['test_iter'], None
else:
trainloader, full_testloader, full_loader, testloader, outloader = options['train_iter'], options['test_iter'], \
options['full_iter'], options[
'known_test_iter'], options[
'unknown_test_iter']
# unknow=options['unknown'][0]
# Model
print("Creating model: {}".format(options['model']))
options['num_classes'] = len(options['known'])
unknow = options['num_classes']
net = network.SSMLP(patches, options['BAND'], options['num_classes'], layers=options['layers'], embed_dims=options['embed_dims'],segment_dim=options['segment_dim'])
feat_dim = options['embed_dims']
# feat_dim = 128
# Loss
options.update(
{
'feat_dim': feat_dim,
'use_gpu': use_gpu
}
)
Loss = importlib.import_module('loss.' + options['loss'])
criterion = getattr(Loss, options['loss'])(**options)
if use_gpu:
net = nn.DataParallel(net).cuda()
criterion = criterion.cuda()
params_list = [{'params': net.parameters()},
{'params': criterion.parameters()}]
# optimizer = torch.optim.SGD(params_list, lr=options['lr'], momentum=0.9, weight_decay=1e-4)
optimizer = torch.optim.Adam(params_list, lr=options['lr'])
if options['stepsize'] > 0:
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[30, 60, 90, 120])
start_time = time.time()
for epoch in range(options['max_epoch']):
print("==> Epoch {}/{}".format(epoch + 1, options['max_epoch']))
_, logits_min, dis_min, loss_r = train(net, criterion, optimizer, trainloader, epoch=epoch, **options)
#if options['eval_freq'] > 0 and (epoch + 1) % options['eval_freq'] == 0 or (epoch + 1) == options['max_epoch']:
# print("==> Test", options['loss'])
#results, pred, label = test(net, criterion, full_testloader, testloader, outloader, logits_min, dis_min,
# loss_r, unknow, epoch=epoch, **options)
#print("Acc (%): {:.3f}\t AUROC (%): {:.3f}\t OSCR (%): {:.3f}\t".format(results['ACC'], results['AUROC'],
# results['OSCR']))
# save_networks(net, model_path, file_name, criterion=criterion)
if options['stepsize'] > 0: scheduler.step()
train_time2=time.time()
tes_time1=time.time()
results, pred, label = test(net, criterion, full_testloader, testloader, outloader, logits_min, dis_min,loss_r, unknow, epoch=epoch, **options)
print("Acc (%): {:.3f}\t AUROC (%): {:.3f}\t OSCR (%): {:.3f}\t".format(results['ACC'], results['AUROC'],results['OSCR']))
tes_time2=time.time()
#pred_global = full_test(net, criterion, full_testloader, full_loader, testloader, outloader, logits_min, dis_min,loss_r, unknow, epoch=epoch, **options)
#generate_png(gt, pred_global, dataname, ROWS, COLUMNS, SAMPLES_NUM)
ac = get_accuracy(label, pred)
# ac2 = get_accuracy(_labels, _pred)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Finished. Total elapsed time (h:m:s): {}".format(elapsed))
Experiment_result[0, iter_num] = ac[-1] * 100 # OA
Experiment_result[1, iter_num] = np.mean(ac[:-2]) * 100 # AA
Experiment_result[2, iter_num] = ac[-2] * 100 # Kappa
Experiment_result[3, iter_num] = results['ACC'] #Closed-set OA
Experiment_result[4, iter_num] = results['OSCR'] #OSCR
Experiment_result[5, iter_num] = train_time2 - start_time
Experiment_result[6, iter_num] = tes_time2 - tes_time1
Experiment_result[7:, iter_num] = ac[:-2] * 100
print('########### Experiment {}，Model assessment Finished！ ###########'.format(iter_num))
########## mean value & standard deviation #############
Experiment_result[:, -2] = np.mean(Experiment_result[:, 0:-2], axis=1) # 计算均值
Experiment_result[:, -1] = np.std(Experiment_result[:, 0:-2], axis=1) # 计算平均差
day = datetime.datetime.now()
day_str = day.strftime('%m_%d_%H_%M')
f = open('./record/' + dataname + '/' + str(day_str) + '_' + dataname + '_' + model_name + '_' +str(SAMPLES_NUM)+ '_num_patch='+str(patches)+'_layers='+str(options['layers'])+'_embed_dims='+str(options['embed_dims'])+'_segment_dim='+str(options['segment_dim'])+'.txt','w')
for i in range(Experiment_result.shape[0]):
f.write(str(i + 1) + ':' + str(round(Experiment_result[i, -2], 2)) + '+/-' + str(
round(Experiment_result[i, -1], 2)) + '\n')
for i in range(Experiment_result.shape[1] - 2):
f.write('Experiment_num' + str(i) + '_open_OA:' + str(Experiment_result[0, i]) + '\n')
for i in range(Experiment_result.shape[1] - 2):
f.write('Experiment_num' + str(i) + '_closed_OA:' + str(Experiment_result[3, i]) + '\n')
f.close()