-
Notifications
You must be signed in to change notification settings - Fork 11
/
predict.py
162 lines (137 loc) · 7.63 KB
/
predict.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
import network
import os
import argparse
import numpy as np
from datasets import Cityscapes
from torchvision import transforms as T
import torch
import torch.nn as nn
from PIL import Image
from utils import ext_transforms as et
from collections import namedtuple
CityscapesClass = namedtuple('CityscapesClass', ['name', 'id', 'train_id', 'category', 'category_id',
'has_instances', 'ignore_in_eval', 'color'])
classes = [
CityscapesClass('unlabeled', 0, 255, 'void', 0, False, True, (0, 0, 0)),
CityscapesClass('ego vehicle', 1, 255, 'void', 0, False, True, (0, 0, 0)),
CityscapesClass('rectification border', 2, 255, 'void', 0, False, True, (0, 0, 0)),
CityscapesClass('out of roi', 3, 255, 'void', 0, False, True, (0, 0, 0)),
CityscapesClass('static', 4, 255, 'void', 0, False, True, (0, 0, 0)),
CityscapesClass('dynamic', 5, 255, 'void', 0, False, True, (111, 74, 0)),
CityscapesClass('ground', 6, 255, 'void', 0, False, True, (81, 0, 81)),
CityscapesClass('road', 7, 0, 'flat', 1, False, False, (128, 64, 128)),
CityscapesClass('sidewalk', 8, 1, 'flat', 1, False, False, (244, 35, 232)),
CityscapesClass('parking', 9, 255, 'flat', 1, False, True, (250, 170, 160)),
CityscapesClass('rail track', 10, 255, 'flat', 1, False, True, (230, 150, 140)),
CityscapesClass('building', 11, 2, 'construction', 2, False, False, (70, 70, 70)),
CityscapesClass('wall', 12, 3, 'construction', 2, False, False, (102, 102, 156)),
CityscapesClass('fence', 13, 4, 'construction', 2, False, False, (190, 153, 153)),
CityscapesClass('guard rail', 14, 255, 'construction', 2, False, True, (180, 165, 180)),
CityscapesClass('bridge', 15, 255, 'construction', 2, False, True, (150, 100, 100)),
CityscapesClass('tunnel', 16, 255, 'construction', 2, False, True, (150, 120, 90)),
CityscapesClass('pole', 17, 5, 'object', 3, False, False, (153, 153, 153)),
CityscapesClass('polegroup', 18, 255, 'object', 3, False, True, (153, 153, 153)),
CityscapesClass('traffic light', 19, 6, 'object', 3, False, False, (250, 170, 30)),
CityscapesClass('traffic sign', 20, 7, 'object', 3, False, False, (220, 220, 0)),
CityscapesClass('vegetation', 21, 8, 'nature', 4, False, False, (107, 142, 35)),
CityscapesClass('terrain', 22, 9, 'nature', 4, False, False, (152, 251, 152)),
CityscapesClass('sky', 23, 10, 'sky', 5, False, False, (70, 130, 180)),
CityscapesClass('person', 24, 11, 'human', 6, True, False, (220, 20, 60)),
CityscapesClass('rider', 25, 12, 'human', 6, True, False, (255, 0, 0)),
CityscapesClass('car', 26, 13, 'vehicle', 7, True, False, (0, 0, 142)),
CityscapesClass('truck', 27, 14, 'vehicle', 7, True, False, (0, 0, 70)),
CityscapesClass('bus', 28, 15, 'vehicle', 7, True, False, (0, 60, 100)),
CityscapesClass('caravan', 29, 255, 'vehicle', 7, True, True, (0, 0, 90)),
CityscapesClass('trailer', 30, 255, 'vehicle', 7, True, True, (0, 0, 110)),
CityscapesClass('train', 31, 16, 'vehicle', 7, True, False, (0, 80, 100)),
CityscapesClass('motorcycle', 32, 17, 'vehicle', 7, True, False, (0, 0, 230)),
CityscapesClass('bicycle', 33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
CityscapesClass('license plate', -1, 255, 'vehicle', 7, False, True, (0, 0, 142)),
]
id_to_train_id = np.array([c.train_id for c in classes])
def encode_target(target):
return id_to_train_id[np.array(target)]
def get_argparser():
parser = argparse.ArgumentParser()
available_models = sorted(name for name in network.modeling.__dict__ if name.islower() and \
not (name.startswith("__") or name.startswith('_')) and callable(
network.modeling.__dict__[name])
)
parser.add_argument("--model", type=str, default='deeplabv3plus_resnet_clip',
choices=available_models, help='model name')
parser.add_argument("--save_val_results_to", default=None,
help="save segmentation results to the specified dir")
parser.add_argument("--crop_val", action='store_true', default=False,
help='crop validation (default: False)')
parser.add_argument("--crop_size", type=int, default=513)
parser.add_argument("--ckpt", default=None, type=str,
help="resume from checkpoint")
parser.add_argument("--gpu_id", type=str, default='0',
help="GPU ID")
return parser
def mask_transform(mask):
target = np.array(mask).astype('int32')
target[target == 255] = -1
return torch.from_numpy(target).long()
def main():
opts = get_argparser().parse_args()
opts.num_classes = 19
decode_fn = Cityscapes.decode_target
os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Device: %s" % device)
model = network.modeling.__dict__[opts.model](num_classes=19, BB= 'RN50')
model.backbone.attnpool = nn.Identity()
if opts.ckpt is not None and os.path.isfile(opts.ckpt):
checkpoint = torch.load(opts.ckpt, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint["model_state"])
model.to(device)
print("Resume model from %s" % opts.ckpt)
del checkpoint
else:
print("[!] Retrain")
model = nn.DataParallel(model)
model.to(device)
if opts.crop_val:
transform = T.Compose([
T.Resize(opts.crop_size),
T.CenterCrop(opts.crop_size),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if opts.save_val_results_to is not None:
os.makedirs(opts.save_val_results_to, exist_ok=True)
val_transform = et.ExtCompose([
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
img_list = []
path = 'predict_test/'
model = model.eval()
for filename in os.listdir(path):
f = os.path.join(path, filename)
# checking if it is a file
if os.path.isfile(f):
img_list.append(f)
with torch.no_grad():
for i in range(len(img_list)):
print(i)
img = Image.open(img_list[i])
img = transform(img)
img = img.unsqueeze(0).to(device)
out,feat = model(img)
pred = out.detach().max(1)[1].cpu().numpy()[0] # HW
colorized_preds = decode_fn(pred).astype('uint8')
colorized_preds = Image.fromarray(colorized_preds)
if opts.save_val_results_to:
colorized_preds.save(os.path.join(opts.save_val_results_to,img_list[i].split('/')[-1]))
if __name__ == '__main__':
main()