-
Notifications
You must be signed in to change notification settings - Fork 7
/
test_video_seg.py
executable file
·140 lines (109 loc) · 5.66 KB
/
test_video_seg.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
import numpy as np
from tqdm import tqdm, trange
import os
import argparse
from glob import glob
import torch
from torch import utils
from torch.nn import functional as F
from torchvision.transforms import functional as TF
from torchvision.transforms import InterpolationMode
from video_module.dataset import Video_DS
from video_module.model import AFB_URR, FeatureBank
from test_image_seg import test_waterseg
import myutils
torch.set_grad_enabled(False)
def get_args():
parser = argparse.ArgumentParser(description='V-FloodNet: Water Video Segmentation')
parser.add_argument('--gpu', type=int, default=0,
help='GPU card id.')
parser.add_argument('--budget', type=int, default='250000',
help='Max number of features that feature bank can store. Default: 300000')
parser.add_argument('--viz', action='store_true', default=True,
help='Visualize data.')
parser.add_argument('--model-path', type=str, default='records/video_seg_checkpoint_20200212-001734.pth',
help='Path to the checkpoint (default: none)')
parser.add_argument('--update-rate', type=float, default=0.1,
help='Update Rate. Impact of merging new features.')
parser.add_argument('--merge-thres', type=float, default=0.95,
help='Merging Rate. If similarity higher than this, then merge, else append.')
parser.add_argument('--test-path', type=str, required=True,
help='Video Path')
parser.add_argument('--test-name', type=str, required=True,
help='Video Name')
return parser.parse_args()
def main(args, device):
model = AFB_URR(device, update_bank=True, load_imagenet_params=False)
model = model.to(device)
model.eval()
downsample_size = 480
if os.path.isfile(args.model_path):
checkpoint = torch.load(args.model_path)
end_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'], strict=False)
train_loss = checkpoint['loss']
seed = checkpoint['seed']
print(myutils.gct(),
f'Loaded checkpoint {args.model_path}. (end_epoch: {end_epoch}, train_loss: {train_loss}, seed: {seed})')
else:
print(myutils.gct(), f'No checkpoint found at {args.model_path}')
raise IOError
img_list = sorted(glob(os.path.join(args.test_path, '*.jpg')) + glob(os.path.join(args.test_path, '*.png')))
first_frame = myutils.load_image_in_PIL(img_list[0])
first_name = os.path.basename(img_list[0])[:-4]
out_dir = './output/segs'
mask_dir = os.path.join(out_dir, args.test_name, 'mask')
mask_path = os.path.join(mask_dir, first_name + '.png')
if not os.path.exists(mask_path):
image_model_path = './records/link_efficientb4_model.pth'
test_waterseg(image_model_path, img_list[0], args.test_name, out_dir, device)
first_mask = myutils.load_image_in_PIL(mask_path, 'P')
seq_dataset = Video_DS(img_list, first_frame, first_mask)
seq_loader = utils.data.DataLoader(seq_dataset, batch_size=1, shuffle=False, num_workers=1)
seg_dir = os.path.join(out_dir, args.test_name, 'mask')
os.makedirs(seg_dir, exist_ok=True)
if args.viz:
overlay_dir = os.path.join(out_dir, args.test_name, 'overlay')
os.makedirs(overlay_dir, exist_ok=True)
obj_n = seq_dataset.obj_n
fb = FeatureBank(obj_n, args.budget, device, update_rate=args.update_rate, thres_close=args.merge_thres)
ori_first_frame = seq_dataset.first_frame.unsqueeze(0).to(device)
ori_first_mask = seq_dataset.first_mask.unsqueeze(0).to(device)
first_frame = TF.resize(ori_first_frame, downsample_size, InterpolationMode.BICUBIC)
first_mask = TF.resize(ori_first_mask, downsample_size, InterpolationMode.NEAREST)
pred = torch.argmax(ori_first_mask[0], dim=0).cpu().numpy().astype(np.uint8)
seg_path = os.path.join(seg_dir, f'{first_name}.png')
myutils.save_seg_mask(pred, seg_path, myutils.color_palette)
if args.viz:
overlay_path = os.path.join(overlay_dir, f'{first_name}.png')
myutils.save_overlay(ori_first_frame[0], pred, overlay_path, myutils.color_palette)
with torch.no_grad():
k4_list, v4_list = model.memorize(first_frame, first_mask)
fb.init_bank(k4_list, v4_list)
for idx, (frame, frame_name) in enumerate(tqdm(seq_loader)):
ori_frame = frame.to(device)
ori_size = ori_frame.shape[-2:]
frame = TF.resize(ori_frame, downsample_size, InterpolationMode.BICUBIC)
score, _ = model.segment(frame, fb)
pred_mask = F.softmax(score, dim=1)
k4_list, v4_list = model.memorize(frame, pred_mask)
fb.update(k4_list, v4_list, idx + 1)
pred = TF.resize(pred_mask, ori_size, InterpolationMode.BICUBIC)
pred = torch.argmax(pred[0], dim=0).cpu().numpy().astype(np.uint8)
pred = myutils.postprocessing_pred(pred)
seg_path = os.path.join(seg_dir, f'{frame_name[0]}.png')
myutils.save_seg_mask(pred, seg_path, myutils.color_palette)
if args.viz:
overlay_path = os.path.join(overlay_dir, f'{frame_name[0]}.png')
myutils.save_overlay(ori_frame[0], pred, overlay_path, myutils.color_palette)
fb.print_peak_mem()
if __name__ == '__main__':
args = get_args()
print(myutils.gct(), 'Args =', args)
if args.gpu >= 0 and torch.cuda.is_available():
device = torch.device('cuda', args.gpu)
else:
raise ValueError('CUDA is required. --gpu must be >= 0.')
assert os.path.isdir(args.test_path)
main(args, device)
print(myutils.gct(), 'Test video segmentation done.')