-
Notifications
You must be signed in to change notification settings - Fork 177
/
bts_eval.py
335 lines (247 loc) · 12.6 KB
/
bts_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
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
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
from __future__ import absolute_import, division, print_function
import os
import argparse
import time
import numpy as np
import cv2
import sys
from bts_dataloader import *
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
def convert_arg_line_to_args(arg_line):
for arg in arg_line.split():
if not arg.strip():
continue
yield arg
parser = argparse.ArgumentParser(description='BTS TensorFlow implementation.', fromfile_prefix_chars='@')
parser.convert_arg_line_to_args = convert_arg_line_to_args
parser.add_argument('--model_name', type=str, help='model name', default='bts_v0_0_1')
parser.add_argument('--encoder', type=str, help='type of encoder, desenet121_bts or densenet161_bts', default='densenet161_bts')
parser.add_argument('--data_path', type=str, help='path to the data', required=True)
parser.add_argument('--gt_path', type=str, help='path to the groundtruth data', required=False)
parser.add_argument('--filenames_file', type=str, help='path to the filenames text file', required=True)
parser.add_argument('--input_height', type=int, help='input height', default=480)
parser.add_argument('--input_width', type=int, help='input width', default=640)
parser.add_argument('--max_depth', type=float, help='maximum depth in estimation', default=80)
parser.add_argument('--output_directory', type=str, help='output directory for summary, if empty outputs to checkpoint folder', default='')
parser.add_argument('--checkpoint_path', type=str, help='path to a specific checkpoint to load', default='')
parser.add_argument('--dataset', type=str, help='dataset to train on, make3d or nyudepthv2', default='nyu')
parser.add_argument('--eigen_crop', help='if set, crops according to Eigen NIPS14', action='store_true')
parser.add_argument('--garg_crop', help='if set, crops according to Garg ECCV16', action='store_true')
parser.add_argument('--min_depth_eval', type=float, help='minimum depth for evaluation', default=1e-3)
parser.add_argument('--max_depth_eval', type=float, help='maximum depth for evaluation', default=80)
parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
if sys.argv.__len__() == 2:
arg_filename_with_prefix = '@' + sys.argv[1]
args = parser.parse_args([arg_filename_with_prefix])
else:
args = parser.parse_args()
model_dir = os.path.dirname(args.checkpoint_path)
sys.path.append(model_dir)
for key, val in vars(__import__(args.model_name)).items():
if key.startswith('__') and key.endswith('__'):
continue
vars()[key] = val
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
d1 = (thresh < 1.25).mean()
d2 = (thresh < 1.25 ** 2).mean()
d3 = (thresh < 1.25 ** 3).mean()
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred)**2) / gt)
err = np.log(pred) - np.log(gt)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
err = np.abs(np.log10(pred) - np.log10(gt))
log10 = np.mean(err)
return silog, log10, abs_rel, sq_rel, rmse, rmse_log, d1, d2, d3
def get_num_lines(file_path):
f = open(file_path, 'r')
lines = f.readlines()
f.close()
return len(lines)
def test(params):
global gt_depths, is_missing, missing_ids
gt_depths = []
is_missing = []
missing_ids = set()
write_summary = False
if os.path.exists(args.checkpoint_path + '.meta'):
steps = [str(args.checkpoint_path).split('/')[-1].split('-')[-1]]
else:
with open(args.checkpoint_path + '/checkpoint') as file:
lines = file.readlines()[1:]
steps = set()
for line in lines:
step = line.split()[1].split('/')[-1].split('-')[-1].replace('\"', '')
steps.add('{:06d}'.format(int(step)))
lines = []
if os.path.exists(args.checkpoint_path + '/evaluated_checkpoints'):
with open(args.checkpoint_path + '/evaluated_checkpoints') as file:
lines = file.readlines()
for line in lines:
if line.rstrip() in steps:
steps.remove(line.rstrip())
steps = sorted(steps)
if args.output_directory != '':
summary_path = os.path.join(args.output_directory, args.model_name)
else:
summary_path = os.path.join(args.checkpoint_path, 'eval')
write_summary = True
if len(steps) == 0:
print('No new model to evaluate. Abort.')
return
time_modified = os.path.getmtime(args.checkpoint_path + 'checkpoint')
time_diff = time.time() - time_modified
if time_diff < 60:
print('Model file might not be mature due to short time_diff: %s' % str(time_diff))
print('Aborting')
return
else:
print('time_diff: %s' % str(time_diff))
dataloader = BtsDataloader(args.data_path, args.gt_path, args.filenames_file, params, 'test',
do_kb_crop=args.do_kb_crop)
dataloader_iter = dataloader.loader.make_initializable_iterator()
iter_init_op = dataloader_iter.initializer
image, focal = dataloader_iter.get_next()
model = BtsModel(params, 'test', image, None, focal=focal, bn_training=False)
if write_summary:
summary_writer = tf.summary.FileWriter(summary_path)
# SESSION
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# INIT
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)
# SAVER
train_saver = tf.train.Saver()
with tf.device('/cpu:0'):
for step in steps:
if os.path.exists(args.checkpoint_path + '.meta'):
restore_path = args.checkpoint_path
else:
restore_path = os.path.join(args.checkpoint_path, 'model-' + str(int(step)))
# RESTORE
train_saver.restore(sess, restore_path)
num_test_samples = get_num_lines(args.filenames_file)
with open(args.filenames_file) as f:
lines = f.readlines()
print('now testing {} files for step {}'.format(num_test_samples, step))
sess.run(iter_init_op)
pred_depths = []
start_time = time.time()
for s in range(num_test_samples):
depth = sess.run([model.depth_est])
pred_depths.append(depth[0].squeeze())
elapsed_time = time.time() - start_time
print('Elapesed time: %s' % str(elapsed_time))
print('Done.')
if len(gt_depths) == 0:
for t_id in range(num_test_samples):
gt_depth_path = os.path.join(args.gt_path, lines[t_id].split()[1])
depth = cv2.imread(gt_depth_path, -1)
if depth is None:
print('Missing: %s ' % gt_depth_path)
missing_ids.add(t_id)
continue
if args.dataset == 'nyu':
depth = depth.astype(np.float32) / 1000.0
else:
depth = depth.astype(np.float32) / 256.0
gt_depths.append(depth)
print('Computing errors')
silog, log10, abs_rel, sq_rel, rms, log_rms, d1, d2, d3 = eval(pred_depths, int(step))
if write_summary:
summary = tf.Summary()
summary.value.add(tag='silog', simple_value=silog.mean())
summary.value.add(tag='abs_rel', simple_value=abs_rel.mean())
summary.value.add(tag='log10', simple_value=log10.mean())
summary.value.add(tag='sq_rel', simple_value=sq_rel.mean())
summary.value.add(tag='rms', simple_value=rms.mean())
summary.value.add(tag='log_rms', simple_value=log_rms.mean())
summary.value.add(tag='d1', simple_value=d1.mean())
summary.value.add(tag='d2', simple_value=d2.mean())
summary.value.add(tag='d3', simple_value=d3.mean())
summary_writer.add_summary(summary, global_step=step)
summary_writer.flush()
with open(os.path.dirname(args.checkpoint_path) + '/evaluated_checkpoints', 'a') as file:
file.write(step + '\n')
print('Evaluation done')
def eval(pred_depths, step):
num_samples = get_num_lines(args.filenames_file)
pred_depths_valid = []
for t_id in range(num_samples):
if t_id in missing_ids:
continue
pred_depths_valid.append(pred_depths[t_id])
num_samples = num_samples - len(missing_ids)
silog = np.zeros(num_samples, np.float32)
log10 = np.zeros(num_samples, np.float32)
rms = np.zeros(num_samples, np.float32)
log_rms = np.zeros(num_samples, np.float32)
abs_rel = np.zeros(num_samples, np.float32)
sq_rel = np.zeros(num_samples, np.float32)
d1 = np.zeros(num_samples, np.float32)
d2 = np.zeros(num_samples, np.float32)
d3 = np.zeros(num_samples, np.float32)
for i in range(num_samples):
gt_depth = gt_depths[i]
pred_depth = pred_depths_valid[i]
if args.do_kb_crop:
height, width = gt_depth.shape
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
pred_depth_uncropped = np.zeros((height, width), dtype=np.float32)
pred_depth_uncropped[top_margin:top_margin + 352, left_margin:left_margin + 1216] = pred_depth
pred_depth = pred_depth_uncropped
pred_depth[pred_depth < args.min_depth_eval] = args.min_depth_eval
pred_depth[pred_depth > args.max_depth_eval] = args.max_depth_eval
pred_depth[np.isinf(pred_depth)] = args.max_depth_eval
valid_mask = np.logical_and(gt_depth > args.min_depth_eval, gt_depth < args.max_depth_eval)
if args.garg_crop or args.eigen_crop:
gt_height, gt_width = gt_depth.shape
eval_mask = np.zeros(valid_mask.shape)
if args.garg_crop:
eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
elif args.eigen_crop:
if args.dataset == 'kitti':
eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
else:
eval_mask[45:471, 41:601] = 1
valid_mask = np.logical_and(valid_mask, eval_mask)
silog[i], log10[i], abs_rel[i], sq_rel[i], rms[i], log_rms[i], d1[i], d2[i], d3[i] = compute_errors(gt_depth[valid_mask], pred_depth[valid_mask])
print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format('silog', 'abs_rel', 'log10', 'rms', 'sq_rel', 'log_rms', 'd1', 'd2', 'd3'))
print("{:7.4f}, {:7.4f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}".format(
silog.mean(), abs_rel.mean(), log10.mean(), rms.mean(), sq_rel.mean(), log_rms.mean(), d1.mean(), d2.mean(), d3.mean()))
return silog, log10, abs_rel, sq_rel, rms, log_rms, d1, d2, d3
def main(_):
params = bts_parameters(
encoder=args.encoder,
height=args.input_height,
width=args.input_width,
batch_size=None,
dataset=args.dataset,
max_depth=args.max_depth,
num_gpus=None,
num_threads=None,
num_epochs=None)
test(params)
if __name__ == '__main__':
tf.app.run()