-
Notifications
You must be signed in to change notification settings - Fork 37
/
Copy pathrun.py
218 lines (190 loc) · 10 KB
/
run.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
import argparse
import os
import torch
import utils.evaluator as eu
from quicknat import QuickNat
from settings import Settings
from solver import Solver
from utils.data_utils import get_imdb_dataset
from utils.log_utils import LogWriter
import logging
import shutil
torch.set_default_tensor_type('torch.FloatTensor')
def load_data(data_params):
print("Loading dataset")
train_data, test_data = get_imdb_dataset(data_params)
print("Train size: %i" % len(train_data))
print("Test size: %i" % len(test_data))
return train_data, test_data
def train(train_params, common_params, data_params, net_params):
train_data, test_data = load_data(data_params)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=train_params['train_batch_size'], shuffle=True,
num_workers=4, pin_memory=True)
val_loader = torch.utils.data.DataLoader(test_data, batch_size=train_params['val_batch_size'], shuffle=False,
num_workers=4, pin_memory=True)
net_params_ = net_params.copy()
empty_model = QuickNat(net_params_)
if train_params['use_pre_trained']:
quicknat_model = torch.load(train_params['pre_trained_path'])
else:
quicknat_model = QuickNat(net_params)
solver = Solver(quicknat_model,
device=common_params['device'],
num_class=net_params['num_class'],
optim_args={"lr": train_params['learning_rate'],
"betas": train_params['optim_betas'],
"eps": train_params['optim_eps'],
"weight_decay": train_params['optim_weight_decay']},
model_name=common_params['model_name'],
exp_name=train_params['exp_name'],
labels=data_params['labels'],
log_nth=train_params['log_nth'],
num_epochs=train_params['num_epochs'],
lr_scheduler_step_size=train_params['lr_scheduler_step_size'],
lr_scheduler_gamma=train_params['lr_scheduler_gamma'],
use_last_checkpoint=train_params['use_last_checkpoint'],
log_dir=common_params['log_dir'],
exp_dir=common_params['exp_dir'])
solver.train(train_loader, val_loader)
final_model_path = os.path.join(common_params['save_model_dir'], train_params['final_model_file'])
# quicknat_model.save(final_model_path)
solver.model = empty_model
solver.save_best_model(final_model_path)
print("final model saved @ " + str(final_model_path))
def evaluate(eval_params, net_params, data_params, common_params, train_params):
eval_model_path = eval_params['eval_model_path']
num_classes = net_params['num_class']
labels = data_params['labels']
data_dir = eval_params['data_dir']
label_dir = eval_params['label_dir']
volumes_txt_file = eval_params['volumes_txt_file']
remap_config = eval_params['remap_config']
device = common_params['device']
log_dir = common_params['log_dir']
exp_dir = common_params['exp_dir']
exp_name = train_params['exp_name']
save_predictions_dir = eval_params['save_predictions_dir']
prediction_path = os.path.join(exp_dir, exp_name, save_predictions_dir)
orientation = eval_params['orientation']
data_id = eval_params['data_id']
logWriter = LogWriter(num_classes, log_dir, exp_name, labels=labels)
avg_dice_score, class_dist = eu.evaluate_dice_score(eval_model_path,
num_classes,
data_dir,
label_dir,
volumes_txt_file,
remap_config,
orientation,
prediction_path,
data_id,
device,
logWriter)
logWriter.close()
def evaluate_bulk(eval_bulk):
data_dir = eval_bulk['data_dir']
prediction_path = eval_bulk['save_predictions_dir']
volumes_txt_file = eval_bulk['volumes_txt_file']
device = eval_bulk['device']
label_names = ["vol_ID", "Background", "Left WM", "Left Cortex", "Left Lateral ventricle", "Left Inf LatVentricle",
"Left Cerebellum WM", "Left Cerebellum Cortex", "Left Thalamus", "Left Caudate", "Left Putamen",
"Left Pallidum", "3rd Ventricle", "4th Ventricle", "Brain Stem", "Left Hippocampus", "Left Amygdala",
"CSF (Cranial)", "Left Accumbens", "Left Ventral DC", "Right WM", "Right Cortex",
"Right Lateral Ventricle", "Right Inf LatVentricle", "Right Cerebellum WM",
"Right Cerebellum Cortex", "Right Thalamus", "Right Caudate", "Right Putamen", "Right Pallidum",
"Right Hippocampus", "Right Amygdala", "Right Accumbens", "Right Ventral DC"]
batch_size = eval_bulk['batch_size']
need_unc = eval_bulk['estimate_uncertainty']
mc_samples = eval_bulk['mc_samples']
dir_struct = eval_bulk['directory_struct']
if 'exit_on_error' in eval_bulk.keys():
exit_on_error = eval_bulk['exit_on_error']
else:
exit_on_error = False
if eval_bulk['view_agg'] == 'True':
coronal_model_path = eval_bulk['coronal_model_path']
axial_model_path = eval_bulk['axial_model_path']
eu.evaluate2view(coronal_model_path,
axial_model_path,
volumes_txt_file,
data_dir, device,
prediction_path,
batch_size,
label_names,
dir_struct,
need_unc,
mc_samples,
exit_on_error=exit_on_error)
else:
coronal_model_path = eval_bulk['coronal_model_path']
eu.evaluate(coronal_model_path,
volumes_txt_file,
data_dir,
device,
prediction_path,
batch_size,
"COR",
label_names,
dir_struct,
need_unc,
mc_samples,
exit_on_error=exit_on_error)
def compute_vol(eval_bulk):
prediction_path = eval_bulk['save_predictions_dir']
label_names = ["vol_ID", "Background", "Left WM", "Left Cortex", "Left Lateral ventricle", "Left Inf LatVentricle",
"Left Cerebellum WM", "Left Cerebellum Cortex", "Left Thalamus", "Left Caudate", "Left Putamen",
"Left Pallidum", "3rd Ventricle", "4th Ventricle", "Brain Stem", "Left Hippocampus", "Left Amygdala",
"CSF (Cranial)", "Left Accumbens", "Left Ventral DC", "Right WM", "Right Cortex",
"Right Lateral Ventricle", "Right Inf LatVentricle", "Right Cerebellum WM",
"Right Cerebellum Cortex", "Right Thalamus", "Right Caudate", "Right Putamen", "Right Pallidum",
"Right Hippocampus", "Right Amygdala", "Right Accumbens", "Right Ventral DC"]
volumes_txt_file = eval_bulk['volumes_txt_file']
eu.compute_vol_bulk(prediction_path, "Linear", label_names, volumes_txt_file)
def delete_contents(folder):
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', '-m', required=True, help='run mode, valid values are train and eval')
parser.add_argument('--setting_path', '-sp', required=False, help='optional path to settings_eval.ini')
args = parser.parse_args()
settings = Settings('settings.ini')
common_params, data_params, net_params, train_params, eval_params = settings['COMMON'], settings['DATA'], \
settings[
'NETWORK'], settings['TRAINING'], \
settings['EVAL']
if args.mode == 'train':
train(train_params, common_params, data_params, net_params)
elif args.mode == 'eval':
evaluate(eval_params, net_params, data_params, common_params, train_params)
elif args.mode == 'eval_bulk':
logging.basicConfig(filename='error.log')
if args.setting_path is not None:
settings_eval = Settings(args.setting_path)
else:
settings_eval = Settings('settings_eval.ini')
evaluate_bulk(settings_eval['EVAL_BULK'])
elif args.mode == 'clear':
shutil.rmtree(os.path.join(common_params['exp_dir'], train_params['exp_name']))
print("Cleared current experiment directory successfully!!")
shutil.rmtree(os.path.join(common_params['log_dir'], train_params['exp_name']))
print("Cleared current log directory successfully!!")
elif args.mode == 'clear-all':
delete_contents(common_params['exp_dir'])
print("Cleared experiments directory successfully!!")
delete_contents(common_params['log_dir'])
print("Cleared logs directory successfully!!")
elif args.mode == 'compute_vol':
if args.setting_path is not None:
settings_eval = Settings(args.setting_path)
else:
settings_eval = Settings('settings_eval.ini')
compute_vol(settings_eval['EVAL_BULK'])
else:
raise ValueError('Invalid value for mode. only support values are train, eval and clear')