-
Notifications
You must be signed in to change notification settings - Fork 40
/
test.py
261 lines (229 loc) · 10.5 KB
/
test.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
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_2012, KITTI_2015
from core.evaluation import eval_flow_avg, load_gt_flow_kitti
from core.evaluation import eval_depth
from core.visualize import Visualizer_debug
from core.networks import Model_depth_pose, Model_flow, Model_flowposenet
from core.evaluation import load_gt_flow_kitti, load_gt_mask
import torch
from tqdm import tqdm
import pdb
import cv2
import numpy as np
import yaml
def test_kitti_2012(cfg, model, gt_flows, noc_masks):
dataset = KITTI_2012(cfg.gt_2012_dir)
flow_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None,:,:,:]
K = K[None,:,:]
K_inv = K_inv[None,:,:]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, _, _, _, _, _ = model.inference(img1, img2, K, K_inv)
#pdb.set_trace()
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1,2,0)
flow_list.append(flow)
eval_flow_res = eval_flow_avg(gt_flows, noc_masks, flow_list, cfg, write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2012]')
print(eval_flow_res)
return eval_flow_res
def test_kitti_2015(cfg, model, gt_flows, noc_masks, gt_masks, depth_save_dir=None):
dataset = KITTI_2015(cfg.gt_2015_dir)
visualizer = Visualizer_debug(depth_save_dir)
pred_flow_list = []
pred_disp_list = []
img_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None,:,:,:]
K = K[None,:,:]
K_inv = K_inv[None,:,:]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]
img_list.append(img1)
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, disp1, disp2, Rt, _, _ = model.inference(img1, img2, K, K_inv)
disp = disp1[0].detach().cpu().numpy()
disp = disp.transpose(1,2,0)
pred_disp_list.append(disp)
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1,2,0)
pred_flow_list.append(flow)
#pdb.set_trace()
eval_flow_res = eval_flow_avg(gt_flows, noc_masks, pred_flow_list, cfg, moving_masks=gt_masks, write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2015]')
print(eval_flow_res)
## depth evaluation
return eval_flow_res
def disp2depth(disp, min_depth=0.001, max_depth=80.0):
min_disp = 1 / max_depth
max_disp = 1 / min_depth
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def resize_depths(gt_depth_list, pred_disp_list):
gt_disp_list = []
pred_depth_list = []
pred_disp_resized = []
for i in range(len(pred_disp_list)):
h, w = gt_depth_list[i].shape
pred_disp = cv2.resize(pred_disp_list[i], (w,h))
pred_depth = 1.0 / (pred_disp + 1e-4)
pred_depth_list.append(pred_depth)
pred_disp_resized.append(pred_disp)
return pred_depth_list, pred_disp_resized
def test_eigen_depth(cfg, model):
print('Evaluate depth using eigen split. Using model in ' + cfg.model_dir)
filenames = open('./data/eigen/test_files.txt').readlines()
pred_disp_list = []
for i in range(len(filenames)):
path1, idx, _ = filenames[i].strip().split(' ')
img = cv2.imread(os.path.join(os.path.join(cfg.raw_base_dir, path1), 'image_02/data/'+str(idx)+'.png'))
#img_resize = cv2.resize(img, (832,256))
img_resize = cv2.resize(img, (cfg.img_hw[1], cfg.img_hw[0]))
img_input = torch.from_numpy(img_resize / 255.0).float().cuda().unsqueeze(0).permute(0,3,1,2)
disp = model.infer_depth(img_input)
disp = disp[0].detach().cpu().numpy()
disp = disp.transpose(1,2,0)
pred_disp_list.append(disp)
#print(i)
gt_depths = np.load('./data/eigen/gt_depths.npz', allow_pickle=True)['data']
pred_depths, pred_disp_resized = resize_depths(gt_depths, pred_disp_list)
eval_depth_res = eval_depth(gt_depths, pred_depths)
abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res
sys.stderr.write(
"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".
format('abs_rel', 'sq_rel', 'rms', 'log_rms',
'a1', 'a2', 'a3'))
sys.stderr.write(
"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \n".
format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))
return eval_depth_res
def resize_disp(pred_disp_list, gt_depths):
pred_depths = []
h, w = gt_depths[0].shape[0], gt_depths[0].shape[1]
for i in range(len(pred_disp_list)):
disp = pred_disp_list[i]
resize_disp = cv2.resize(disp, (w,h))
depth = 1.0 / resize_disp
pred_depths.append(depth)
return pred_depths
import h5py
import scipy.io as sio
def load_nyu_test_data(data_dir):
data = h5py.File(os.path.join(data_dir, 'nyu_depth_v2_labeled.mat'), 'r')
splits = sio.loadmat(os.path.join(data_dir, 'splits.mat'))
test = np.array(splits['testNdxs']).squeeze(1)
images = np.transpose(data['images'], [0,1,3,2])
depths = np.transpose(data['depths'], [0,2,1])
images = images[test-1]
depths = depths[test-1]
return images, depths
def test_nyu(cfg, model, test_images, test_gt_depths):
leng = test_images.shape[0]
print('Test nyu depth on '+str(leng)+' images. Using depth model in '+cfg.model_dir)
pred_disp_list = []
crop_imgs = []
crop_gt_depths = []
for i in range(leng):
img = test_images[i]
img_crop = img[:,45:472,41:602]
crop_imgs.append(img_crop)
gt_depth_crop = test_gt_depths[i][45:472,41:602]
crop_gt_depths.append(gt_depth_crop)
#img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (576,448)), [2,0,1])
img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (cfg.img_hw[1],cfg.img_hw[0])), [2,0,1])
img_t = torch.from_numpy(img).float().cuda().unsqueeze(0) / 255.0
disp = model.infer_depth(img_t)
disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])
pred_disp_list.append(disp)
pred_depths = resize_disp(pred_disp_list, crop_gt_depths)
eval_depth_res = eval_depth(crop_gt_depths, pred_depths, nyu=True)
abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res
sys.stderr.write(
"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".
format('abs_rel', 'sq_rel', 'rms', 'log10',
'a1', 'a2', 'a3'))
sys.stderr.write(
"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \n".
format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))
return eval_depth_res
def test_single_image(img_path, model, training_hw, save_dir='./'):
img = cv2.imread(img_path)
h, w = img.shape[0:2]
img_resized = cv2.resize(img, (training_hw[1], training_hw[0]))
img_t = torch.from_numpy(np.transpose(img_resized, [2,0,1])).float().cuda().unsqueeze(0) / 255.0
disp = model.infer_depth(img_t)
disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])
disp_resized = cv2.resize(disp, (w,h))
depth = 1.0 / (1e-6 + disp_resized)
visualizer = Visualizer_debug(dump_dir=save_dir)
visualizer.save_disp_color_img(disp_resized, name='demo')
print('Depth prediction saved in ' + save_dir)
if __name__ == '__main__':
import argparse
arg_parser = argparse.ArgumentParser(
description="TrianFlow testing."
)
arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')
arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')
arg_parser.add_argument('--mode', type=str, default='depth', help='mode for testing.')
arg_parser.add_argument('--task', type=str, default='kitti_depth', help='To test on which task, kitti_depth or kitti_flow or nyuv2 or demo')
arg_parser.add_argument('--image_path', type=str, default=None, help='Set this only when task==demo. Depth demo for single image.')
arg_parser.add_argument('--pretrained_model', type=str, default=None, help='directory for loading flow pretrained models')
arg_parser.add_argument('--result_dir', type=str, default=None, help='directory for saving predictions')
args = arg_parser.parse_args()
if not os.path.exists(args.config_file):
raise ValueError('config file not found.')
with open(args.config_file, 'r') as f:
cfg = yaml.safe_load(f)
cfg['img_hw'] = (cfg['img_hw'][0], cfg['img_hw'][1])
#cfg['log_dump_dir'] = os.path.join(args.model_dir, 'log.pkl')
cfg['model_dir'] = args.result_dir
# copy attr into cfg
for attr in dir(args):
if attr[:2] != '__':
cfg[attr] = getattr(args, attr)
class pObject(object):
def __init__(self):
pass
cfg_new = pObject()
for attr in list(cfg.keys()):
setattr(cfg_new, attr, cfg[attr])
if args.mode == 'flow':
model = Model_flow(cfg_new)
elif args.mode == 'depth' or args.mode == 'flow_3stage':
model = Model_depth_pose(cfg_new)
elif args.mode == 'flowposenet':
model = Model_flowposenet(cfg_new)
if args.task == 'demo':
model = Model_depth_pose(cfg_new)
model.cuda()
weights = torch.load(args.pretrained_model)
model.load_state_dict(weights['model_state_dict'])
model.eval()
print('Model Loaded.')
if args.task == 'kitti_depth':
depth_res = test_eigen_depth(cfg_new, model)
elif args.task == 'kitti_flow':
gt_flows_2015, noc_masks_2015 = load_gt_flow_kitti(cfg_new.gt_2015_dir, 'kitti_2015')
gt_masks_2015 = load_gt_mask(cfg_new.gt_2015_dir)
flow_res = test_kitti_2015(cfg_new, model, gt_flows_2015, noc_masks_2015, gt_masks_2015)
elif args.task == 'nyuv2':
test_images, test_gt_depths = load_nyu_test_data(cfg_new.nyu_test_dir)
depth_res = test_nyu(cfg_new, model, test_images, test_gt_depths)
elif args.task == 'demo':
test_single_image(args.image_path, model, training_hw=cfg['img_hw'], save_dir=args.result_dir)