/
eval.py
182 lines (159 loc) · 7.33 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
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
"""
For evaluate the network
Modified from
https://github.com/microsoft/Recursive-Cascaded-Networks/blob/master/eval.py
"""
import argparse
import os
import json
import re
import time
import numpy as np
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--checkpoint', type=str, default=None,
help='Specifies a previous checkpoint to load')
parser.add_argument('-r', '--rep', type=int, default=1,
help='Number of times of shared-weight cascading')
parser.add_argument('-g', '--gpu', type=str, default='0',
help='Specifies gpu device(s)')
parser.add_argument('-d', '--dataset', type=str, default=None,
help='Specifies a data config')
parser.add_argument('-v', '--val_subset', type=str, default=None)
parser.add_argument('--batch', type=int, default=4, help='Size of minibatch')
parser.add_argument('--fast_reconstruction', action='store_true')
parser.add_argument('--paired', action='store_true')
parser.add_argument('--data_args', type=str, default=None)
parser.add_argument('--net_args', type=str, default=None)
parser.add_argument('--name', type=str, default=None)
parser.add_argument('--image_size', type=int, default=128)
parser.add_argument('--start', type=int)
parser.add_argument('--end', type=int)
parser.add_argument('--saved_file', type=str)
parser.add_argument('--flow_multiplier', type=float, default=1.)
parser.add_argument('--eval_output_dir', type=str, default='evaluate')
parser.add_argument('--writing_flow', action='store_true')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import tensorflow as tf
import tflearn
tf.logging.set_verbosity(tf.logging.ERROR)
import network
import data_util.liver
import data_util.brain
def main():
if args.checkpoint is None:
print('Checkpoint must be specified!')
return
if ':' in args.checkpoint:
args.checkpoint, steps = args.checkpoint.split(':')
steps = int(steps)
else:
steps = None
args.checkpoint = find_checkpoint_step(args.checkpoint, steps)
print(args.checkpoint)
model_dir = os.path.dirname(args.checkpoint)
try:
with open(os.path.join(model_dir, 'args.json'), 'r') as f:
model_args = json.load(f)
print(model_args)
except Exception as e:
print(e)
model_args = {}
if args.dataset is None:
args.dataset = model_args['dataset']
if args.data_args is None:
args.data_args = model_args['data_args']
Framework = network.FrameworkUnsupervised
Framework.net_args['base_network'] = model_args['base_network']
Framework.net_args['n_cascades'] = model_args['n_cascades']
Framework.net_args['dataset'] = model_args['dataset']
Framework.net_args['flow_multiplier'] = args.flow_multiplier
Framework.net_args['rep'] = args.rep
Framework.net_args.update(eval('dict({})'.format(model_args['net_args'])))
if args.net_args is not None:
Framework.net_args.update(eval('dict({})'.format(args.net_args)))
with open(os.path.join(args.dataset), 'r') as f:
cfg = json.load(f)
image_size = cfg.get('image_size', [128, 128, 128])
image_type = cfg.get('image_type')
gpus = 0 if args.gpu == '-1' else len(args.gpu.split(','))
framework = Framework(devices=gpus, image_size=args.image_size,
segmentation_class_value=cfg.get(
'segmentation_class_value', None),
fast_reconstruction=args.fast_reconstruction,
validation=True)
print('Graph built')
Dataset = eval('data_util.{}.Dataset'.format(image_type))
ds = Dataset(args, args.dataset, image_size=args.image_size,
batch_size=args.batch, paired=args.paired,
**eval('dict({})'.format(args.data_args)))
session_config = tf.ConfigProto(allow_soft_placement=True)
session_config.gpu_options.allow_growth = True
sess = tf.Session(config=session_config)
tf.global_variables_initializer().run(session=sess)
saver = tf.train.Saver(tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES))
checkpoint = args.checkpoint
saver.restore(sess, checkpoint)
tflearn.is_training(False, session=sess)
val_subsets = [data_util.liver.Split.VALID]
if args.writing_flow:
val_subsets = [data_util.liver.Split.TRAIN]
if args.val_subset is not None:
val_subsets = args.val_subset.split(',')
tflearn.is_training(False, session=sess)
keys = ['pt_mask', 'landmark_dists', 'jaccs', 'dices', 'jacobian_det', 'real_flow']
if not os.path.exists(args.eval_output_dir):
os.mkdir(args.eval_output_dir)
path_prefix = os.path.join(args.eval_output_dir,
short_name(checkpoint) + "-" + str(args.flow_multiplier))
if args.rep > 1:
path_prefix = path_prefix + '-rep' + str(args.rep)
if args.name is not None:
path_prefix = path_prefix + '-' + args.name
for val_subset in val_subsets:
if args.val_subset is not None:
output_fname = path_prefix + '-' + str(val_subset) + '.txt'
else:
output_fname = path_prefix + '.txt'
with open(output_fname, 'w') as fo:
print("Validation subset {}".format(val_subset))
gen = ds.generator(val_subset, loop=False)
results = framework.validate(sess, gen, args, keys=keys,
summary=False, show_tqdm=True)
for i in range(len(results['jaccs'])):
print(results['id1'][i], results['id2'][i],
np.mean(results['dices'][i]), np.mean(results['jaccs'][i]),
np.mean(results['landmark_dists'][i]),
results['jacobian_det'][i], file=fo)
print('Summary', file=fo)
jaccs, dices, landmarks = results['jaccs'],\
results['dices'],\
results['landmark_dists']
jacobian_det = results['jacobian_det']
print("Dice score: {} ({})".format(np.mean(dices), np.std(
np.mean(dices, axis=-1))), file=fo)
print("Jacc score: {} ({})".format(np.mean(jaccs), np.std(
np.mean(jaccs, axis=-1))), file=fo)
print("Landmark distance: {} ({})".format(np.mean(landmarks), np.std(
np.mean(landmarks, axis=-1))), file=fo)
print("Jacobian determinant: {} ({})".format(np.mean(
jacobian_det), np.std(jacobian_det)), file=fo)
def short_name(checkpoint):
cpath, steps = os.path.split(checkpoint)
_, exp = os.path.split(cpath)
return exp + '-' + steps
def find_checkpoint_step(checkpoint_path, target_steps=None):
pattern = re.compile(r'model-(\d+).index')
checkpoints = []
for f in os.listdir(checkpoint_path):
m = pattern.match(f)
if m:
steps = int(m.group(1))
checkpoints.append((-steps if target_steps is None else abs(
target_steps - steps),
os.path.join(checkpoint_path, f.replace('.index', ''))))
return min(checkpoints, key=lambda x: x[0])[1]
if __name__ == '__main__':
main()