-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain_lth_generate_cavs.py
183 lines (160 loc) · 5.81 KB
/
main_lth_generate_cavs.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
import argparse
import os
import sys
import yaml
import utils
from lth_pruning import lth_generate_cavs
sys.path.append(os.path.abspath("/ocean/projects/asc170022p/shg121/PhD/Project_Pruning"))
def run_mnist(args):
print("Generate CAVs of BB using Pruning by LTH for MNIST")
main_dir = args.main_dir
# run config
with open(os.path.join(main_dir, args.config)) as config_file:
config = yaml.safe_load(config_file)
_bb_layers = config["bb_layers_for_concepts"]
_img_size = config["img_size"]
_seed = config["seed"]
_dataset_name = config["dataset_name"]
_data_root = config["data_root"]
_json_root = config["json_root"]
_model_arch = config["model_arch"]
_pretrained = config["pretrained"]
_transfer_learning = config["transfer_learning"]
_lr = config["lr"]
_logs = config["logs"]
_num_classes = config["num_classes"]
_epochs = config["epochs"]
_device = utils.get_device()
_prune_type = config["prune_type"]
_prune_iterations = config["prune_iterations"]
_prune_percent = config["prune_percent"]
_start_iter = config["start_iter"]
_end_iter = config["end_iter"]
_resample = config["resample"]
_epsilon = config["epsilon"]
_concept_names = config["concept_names"]
_cav_flattening_type = config["cav_flattening_type"]
lth_generate_cavs.generate_cavs_with_Pruning(
_seed,
_prune_type,
_dataset_name,
_start_iter,
_prune_iterations,
_logs,
_model_arch,
_bb_layers,
_concept_names,
_cav_flattening_type
)
def run_cub(args):
print("Generate CAVs of BB using Pruning by LTH for CUB")
main_dir = args.main_dir
# run config
with open(os.path.join(main_dir, args.config)) as config_file:
config = yaml.safe_load(config_file)
_bb_layers = config["bb_layers_for_concepts"]
_img_size = config["img_size"]
_seed = config["seed"]
_dataset_name = config["dataset_name"]
_data_root = config["data_root"]
_json_root = config["json_root"]
_model_arch = config["model_arch"]
_pretrained = config["pretrained"]
_transfer_learning = config["transfer_learning"]
_lr = config["lr"]
_logs = config["logs"]
_num_classes = config["num_classes"]
_device = utils.get_device()
_prune_type = config["prune_type"]
_prune_iterations = config["prune_iterations"]
_prune_percent = config["prune_percent"]
_start_iter = config["start_iter"]
_end_iter = config["end_iter"]
_resample = config["resample"]
_epsilon = config["epsilon"]
_concept_names = config["concept_names"]
_cav_flattening_type = config["cav_flattening_type"]
lth_generate_cavs.generate_cavs_with_Pruning(
_seed,
_prune_type,
_dataset_name,
_start_iter,
_prune_iterations,
_logs,
_model_arch,
_bb_layers,
_concept_names,
_cav_flattening_type
)
def run_derma(args):
print("Generate CAVs of BB using Pruning by LTH for HAM10k")
# parse arguments
main_dir = args.main_dir
# run config
with open(os.path.join(main_dir, args.config)) as config_file:
config = yaml.safe_load(config_file)
_bb_layers = config["bb_layers_for_concepts"]
_img_size = config["img_size"]
_seed = config["seed"]
_dataset_name = config["dataset_name"]
_data_root = config["data_root"]
_json_root = config["json_root"]
_model_arch = config["model_arch"]
_pretrained = config["pretrained"]
_transfer_learning = config["transfer_learning"]
_lr = config["lr"]
_logs = config["logs"]
_num_classes = config["num_classes"]
_device = utils.get_device()
_prune_type = config["prune_type"]
_prune_iterations = config["prune_iterations"]
_prune_percent = config["prune_percent"]
_start_iter = config["start_iter"]
_end_iter = config["end_iter"]
_resample = config["resample"]
_epsilon = config["epsilon"]
_concept_names = config["concept_names"]
_cav_flattening_type = config["cav_flattening_type"]
_bb_dir = config["bb_dir"]
_derm7_folder = config["derm7_folder"]
_derm7_meta = config["derm7_meta"]
_C = config["C"]
_model_name = config["model_name"]
_derm7_train_idx = config["derm7_train_idx"]
_derm7_val_idx = config["derm7_val_idx"]
_n_samples = config["n_samples"]
lth_generate_cavs.generate_cavs_with_Pruning_derma(
_seed,
_prune_type,
_dataset_name,
_start_iter,
_prune_iterations,
_logs,
_model_arch,
_derm7_folder,
_derm7_meta,
_C,
_bb_dir,
_model_name,
_derm7_train_idx,
_derm7_val_idx,
_concept_names,
_n_samples,
_cav_flattening_type
)
if __name__ == '__main__':
print("Train BB using pruning by LTH")
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", "-c", default="mnist")
parser.add_argument(
"--main_dir", "-m", default="/ocean/projects/asc170022p/shg121/PhD/Project_Pruning/")
args = parser.parse_args()
if args.dataset == "cub":
args.config = "config/BB_cub.yaml"
run_cub(args)
elif args.dataset == "mnist":
args.config = "config/BB_mnist.yaml"
run_mnist(args)
elif args.dataset == "HAM10k":
args.config = "config/BB_derma.yaml"
run_derma(args)