-
Notifications
You must be signed in to change notification settings - Fork 34
/
eval.py
34 lines (25 loc) · 1.21 KB
/
eval.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
import os.path
from torch.utils.data import DataLoader
from evaluation.eval import eval_one_result
import dataloaders.pascal as pascal
exp_root_dir = './'
method_names = []
method_names.append('run_0')
if __name__ == '__main__':
# Dataloader
dataset = pascal.VOCSegmentation(transform=None, retname=True)
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
# Iterate through all the different methods
for method in method_names:
for ii in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:
results_folder = os.path.join(exp_root_dir, method, 'Results')
filename = os.path.join(exp_root_dir, 'eval_results', method.replace('/', '-') + '.txt')
if not os.path.exists(os.path.join(exp_root_dir, 'eval_results')):
os.makedirs(os.path.join(exp_root_dir, 'eval_results'))
jaccards = eval_one_result(dataloader, results_folder, mask_thres=ii)
val = jaccards["all_jaccards"].mean()
# Show mean and store result
print(ii)
print("Result for {:<80}: {}".format(method, str.format("{0:.4f}", 100*val)))
with open(filename, 'w') as f:
f.write(str(val))