-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathtest.py
297 lines (215 loc) · 9.73 KB
/
test.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
import argparse
from solver import trainer,tester
import time
def get_args():
parser = argparse.ArgumentParser('parameters')
parser.add_argument('--dataset_name', type=str, default="XMU", help='dataset name (default: XMU)')
parser.add_argument('--framework', type=str, default="Classification_Network", help='dataset name (default: Classification_Network)')
parser.add_argument('--learning-rate', type=float, default=1e-1, help='learning rate, (default: 1e-1)')
parser.add_argument('--batch_size', type=int, default=128, help='batch size, (default: 128)')
parser.add_argument('--dataset-mode', type=str, default="vehicle_logo", help='dataset, (default: CIFAR100)')
parser.add_argument('--filename', type=str, default="vehicle_logo_1", help='dataset, (default: CIFAR100)')
parser.add_argument('--debug', type=bool, default=False, help='use the val dataset(default: False)')
parser.add_argument('--use_val', type=bool, default=False, help='use the val dataset(default: True)')
parser.add_argument('--mask_path', type=str, default=None, help='mask root for train')
parser.add_argument('--train_Root', type=str, default="./data/vehicle_logo", help='dataset root for train')
parser.add_argument('--train_dir', type=str, default="./data/vehicle_logo/car_trainData",
help='dataset root for train')
parser.add_argument('--val_dir', type=str, default="./data/vehicle_logo/car_valData", help='dataset,root for val')
parser.add_argument('--test_dir', type=str, default="./data/vehicle_logo/car_test", help='dataset, root for test')
parser.add_argument('--num_classes', type=int, default=63, help='batch size, (default: 100)')
parser.add_argument('--num_workers', type=int, default=4, help='num_workers')
parser.add_argument('--modelName', type=str, default="DenseNet", help='train_argu_dir for train student dir ')
parser.add_argument('--size', type=str, default="64,64", help='size of the input images ')
parser.add_argument('--crop', type=str, default="64,64", help='crop size of the input images ')
parser.add_argument('--gpus', type=str, default="0", help=' gpu options, eg. 0, 1 ')
parser.add_argument('--ccml_loss_weight', type=float, default=0.01, help='ccml_loss_weight, (default: 0.01)')
args = parser.parse_args()
return args
def test_HFUT_V1():
args = get_args()
args.debug = False
args.framework = "Classification_Network"
args.dataset_mode = "vehicle_logo"
args.num_classes = 80
args.train_Root = "/root/workspace/Data/HFUT-VL1_classify"
args.modelName = "ReDenseNet_nv22"
args.batch_size = 128
args.size = "64,64"
args.crop = "64,64"
args.filename = "20210620" + "_" + args.modelName + "HFUT-VL1_" + args.size + "_" + args.crop + "_batch_" + str(
args.batch_size)
tester(args)
def test_HFUT_V2():
args = get_args()
args.debug = False
args.framework = "Classification_Network"
args.dataset_mode = "vehicle_logo"
args.num_classes = 80
args.train_Root = "/root/workspace/Data/HFUT-VL2_classify"
args.modelName = "ReDenseNet_nv22"
args.batch_size = 128
#height, width
args.size = "112,112"
#height, width
args.crop = "112,112"
args.filename = "20210620" + "_" + args.modelName + "HFUT-VL2_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)
tester(args)
def test_XMU():
args = get_args()
args.framework = "Classification_Network"
args.dataset_mode = "vehicle_logo"
args.debug = False
args.num_classes = 10
args.train_Root = "/root/workspace/Data/XMU_data_7_3_split"
args.modelName = "ReDenseNet_nv22"
args.size = "70,70"
args.crop = "70,70"
args.batch_size = 128
args.filename = "20210620" + "_" + args.modelName + "XMU_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)
tester(args)
def test_CompCar():
args = get_args()
args.debug = False
args.framework = "Classification_Network"
args.dataset_mode = "vehicle_logo"
args.num_classes = 68
args.train_Root = "/root/workspace/Data/sv_data_by_logo_0811/"
args.modelName = "ReDenseNet_nv22"
args.batch_size = 32
args.size = "256,256"
args.crop = "224,224"
args.filename = "20210620"+ "_" + args.modelName + "_sv_data_by_logo_" + args.size + "_" + args.crop + "_batch_" + str(
args.batch_size) + "_bce"
tester(args)
def test_VLD_45B():
args = get_args()
args.framework = "Classification_Network"
args.dataset_mode = "vehicle_logo"
args.train_Root = "/root/workspace/Workspace/Data/VLD-45-B_class_30000"
args.debug = False
args.num_classes = 45
args.modelName = "ReDenseNet_nv22"
args.batch_size = 8
args.size = "512,512"
args.crop = "512,512"
args.num_workers = 8
args.filename = "20210620" + "_" + args.modelName + "VLD_45B_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)
tester(args)
return
def ccml_test_HFUT_V1():
args = get_args()
args.framework = "CCML_Network"
args.dataset_mode = "CCML_vehicle_logo"
args.num_classes = 80
args.train_Root = "/root/workspace/Data/HFUT-VL1_classify"
args.modelName = "pm_ReDenseNet_nv22"
args.batch_size = 128
args.size = "64,64"
args.crop = "64,64"
args.debug = True
args.mask_path = "/root/workspace/Data/HFUT-VL1_classify/all_mask"
args.filename = "20210620" + "_" + args.modelName + "HFUT-VL1_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)+ "_bce_" + str(args.ccml_loss_weight)
tester(args)
def ccml_test_HFUT_V2():
args = get_args()
args.framework = "CCML_Network"
args.dataset_mode = "CCML_vehicle_logo"
args.num_classes = 80
args.train_Root = "/root/workspace/Data/HFUT-VL2_classify"
args.modelName = "pm_ReDenseNet_nv22"
args.batch_size = 128
args.size = "112,112"
args.crop = "112,112"
args.debug = True
args.mask_path = "/root/workspace/Data/HFUT-VL2_classify/all_mask"
args.filename = "20210620" + "_" + args.modelName + "HFUT-VL2_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)+ "_bce_" + str(args.ccml_loss_weight)
tester(args)
def ccml_test_VLD_45B():
args = get_args()
args.framework = "CCML_Network"
args.dataset_mode = "CCML_vehicle_logo"
args.num_classes = 45
# args.filename = "pmdcl_FLD_HFUT-VL1_64_64_batch_128_bce_2"
args.modelName = "pm_ReDenseNet_nv22"
args.batch_size = 8
args.num_workers = 8
args.size = "512,512"
args.crop = "512,512"
args.debug = False
args.train_Root = "/root/workspace/Workspace/Data/VLD-45-B_class_30000"
args.mask_path = "/root/workspace/Workspace/Data/VLD-45-B_class_30000/512_all_mask"
args.filename = "20210620" + "_" + args.modelName + "VLD_45B_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)+ "_bce_" + str(args.ccml_loss_weight)
tester(args)
return
def ccml_test_XMU():
args = get_args()
args.framework = "CCML_Network"
args.dataset_mode = "CCML_vehicle_logo"
args.num_classes = 10
args.train_Root = "/root/workspace/Data/XMU_data_7_3_split"
args.modelName = "pm_ReDenseNet_nv22"
args.batch_size = 128
args.size = "70,70"
args.crop = "70,70"
args.debug = False
args.mask_path = "/root/workspace/Data/XMU_data_7_3_split/all_mask"
args.filename = "20210620" + "_" + args.modelName + "_XMU_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)+ "_bce_" + str(args.ccml_loss_weight)
tester(args)
def ccml_test_CompCar():
args = get_args()
#frame work name
args.framework = "CCML_Network"
#
args.dataset_mode = "CCML_vehicle_logo"
args.num_classes = 68
#data root for training
args.train_Root = "/root/workspace/Workspace/Data/sv_data_by_logo_0811/"
args.modelName = "pm_ReDenseNet_nv22"
args.batch_size =32
args.size = "256,256"
args.crop = "224,224"
args.debug = True
#root for masks
args.mask_path = "/root/workspace/Workspace/Data/sv_data_by_logo_0811/all_mask"
args.filename = "20210620" + "_" + args.modelName + "_sv_data_by_logo_" + args.size + "_" + args.crop + "_batch_" + str(args.batch_size)+ "_bce_" + str(args.ccml_loss_weight)
tester(args)
def main():
args = get_args()
if args.dataset_name == "XMU":
if args.framework == "Classification_Network":
test_XMU()
elif args.framework == "CCML_Network":
ccml_test_XMU()
elif args.dataset_name == "HFUT_VL1":
if args.framework == "Classification_Network":
test_HFUT_V1()
elif args.framework == "CCML_Network":
ccml_test_HFUT_V1()
elif args.dataset_name == "HFUT_VL2":
if args.framework == "Classification_Network":
test_HFUT_V2()
elif args.framework == "CCML_Network":
ccml_test_HFUT_V2()
elif args.dataset_name == "CompCars":
if args.framework == "Classification_Network":
test_CompCar()
elif args.framework == "CCML_Network":
ccml_test_CompCar()
elif args.dataset_name == "VLD-45":
if args.framework == "Classification_Network":
test_VLD_45B()
elif args.framework == "CCML_Network":
ccml_test_VLD_45B()
if __name__ == '__main__':
main()
# test_XMU()
# test_HFUT_V1()
# test_HFUT_V2()
# test_VLD_45B()
# test_CompCar()
# ccml_test_XMU()
# ccml_test_HFUT_V1()
# ccml_test_HFUT_V2()
# ccml_test_VLD_45B()
# ccml_test_CompCar()