-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsp.py
176 lines (156 loc) · 5.76 KB
/
sp.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
import cv2
import random
import logging
import argparse
import numpy as np
from addict import Dict
from tensorboardX import SummaryWriter
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from util import dataset, transform, config
from util.util import AverageMeter
from model.spnet import SPNet
CONFIG = Dict(
# default settings
seed=42,
device='cpu',
# dataset
split=0,
train_h=473,
train_w=473,
# transform
scale_min=0.9,
rotate_min=-10,
rotate_max=10,
zoom_factor=8,
ignore_label=255,
padding_label=255
)
def get_parser():
parser = argparse.ArgumentParser(description='PyTorch Semantic Segmentation')
parser.add_argument('--config', type=str, default='config/ade20k/ade20k_pspnet50.yaml', help='config file')
parser.add_argument('opts', help='see config/ade20k/ade20k_pspnet50.yaml for all options', default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def get_logger():
logger_name = "main-logger"
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
fmt = "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s"
handler.setFormatter(logging.Formatter(fmt))
logger.addHandler(handler)
return logger
def init_seed(manual_seed=42):
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(manual_seed)
np.random.seed(manual_seed)
torch.manual_seed(manual_seed)
torch.cuda.manual_seed(manual_seed)
torch.cuda.manual_seed_all(manual_seed)
def main():
args = get_parser()
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.train_h - 1) % 8 == 0 and (args.train_w - 1) % 8 == 0
if args.manual_seed is not None:
init_seed(CONFIG.seed)
args.ngpus_per_node = len(args.train_gpu)
if len(args.train_gpu) == 1:
args.sync_bn = False
args.distributed = False
args.multiprocessing_distributed = False
# device = 'cpu' if torch.cuda.is_available() else 'cuda'
# model = SPNet(args).to(device)
model = SPNet(args).cuda()
global logger, writer
logger = get_logger()
writer = SummaryWriter(args.save_path)
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
logger.info(model)
print(args)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
assert args.split in [0, 1, 2, 3, 999]
train_transform = [
transform.RandScale([args.scale_min, args.scale_max]),
transform.RandRotate([args.rotate_min, args.rotate_max], padding=mean, ignore_label=args.padding_label),
transform.RandomGaussianBlur(),
transform.RandomHorizontalFlip(),
transform.Crop([args.train_h, args.train_w], crop_type='rand', padding=mean, ignore_label=args.padding_label),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)]
train_transform = transform.Compose(train_transform)
train_data = dataset.SemData_SP(
split=args.split,
shot=args.shot,
max_sp=args.max_sp,
data_root=args.data_root,
data_list=args.train_list,
transform=train_transform,
mode='train',
use_coco=args.use_coco,
use_split_coco=args.use_split_coco
)
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True)
# if args.evaluate:
# if args.resized_val:
# val_transform = transform.Compose([
# transform.Resize(size=args.val_size),
# transform.ToTensor(),
# transform.Normalize(mean=mean, std=std)])
# else:
# val_transform = transform.Compose([
# transform.test_Resize(size=args.val_size),
# transform.ToTensor(),
# transform.Normalize(mean=mean, std=std)])
# val_data = dataset.SemData_SP(
# split=args.split,
# shot=args.shot,
# max_sp=args.max_sp,
# data_root=args.data_root,
# data_list=args.val_list,
# transform=val_transform,
# mode='val',
# use_coco=args.use_coco,
# use_split_coco=args.use_split_coco
# )
# val_sampler = None
# val_loader = torch.utils.data.DataLoader(val_data, batch_size=args.batch_size_val, shuffle=False, num_workers=args.workers, pin_memory=True, sampler=val_sampler)
max_iou = 0.0
filename = 'SPNet.pth'
image, label, s_x, s_y, s_fg_seed, s_bg_seed, subcls_list = iter(train_loader).next()
s_x = s_x.cuda(non_blocking=True)
s_y = s_y.cuda(non_blocking=True)
image = image.cuda(non_blocking=True)
label = label.cuda(non_blocking=True)
s_fg_seed = s_fg_seed.cuda(non_blocking=True)
s_bg_seed = s_bg_seed.cuda(non_blocking=True)
cuda2cpu = lambda lst: [i.cpu() for i in lst]
pred = list(map(cuda2cpu, model(image, s_x, s_y, s_fg_seed, s_bg_seed, label)))
dump_dict = dict(
fg_centers=pred[0],
bg_centers=pred[1],
sp_feats=pred[2],
sp_masks=pred[3],
s_x=s_x.cpu(),
s_y=s_y.cpu(),
)
torch.save(dump_dict, 'fixed_pred_down.obj')
if __name__ == '__main__':
main()