-
Notifications
You must be signed in to change notification settings - Fork 181
/
Copy pathcfgs_res50_dota2.0_r3det_dcl_v2.py
83 lines (67 loc) · 2.16 KB
/
cfgs_res50_dota2.0_r3det_dcl_v2.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
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from alpharotate.utils.pretrain_zoo import PretrainModelZoo
from configs._base_.models.retinanet_r50_fpn import *
from configs._base_.datasets.dota_detection import *
from configs._base_.schedules.schedule_1x import *
# schedule
BATCH_SIZE = 1 # r3det only support 1
GPU_GROUP = '0,1,2'
NUM_GPU = len(GPU_GROUP.strip().split(','))
SAVE_WEIGHTS_INTE = 40000 * 2
DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
# dataset
DATASET_NAME = 'DOTA2.0'
CLASS_NUM = 18
# model
pretrain_zoo = PretrainModelZoo()
PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
# bbox head
NUM_REFINE_STAGE = 1
ANGLE_RANGE = 180
# sample
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0
ANGLE_WEIGHT = 0.5
USE_IOU_FACTOR = True
# DCL
OMEGA = 180 / 256.
ANGLE_MODE = 0 # {0: BCL, 1: GCL}
VERSION = 'RetinaNet_DOTA2.0_R3Det_DCL_B_2x_20210501'
"""
FLOPs: 1038098744; Trainable params: 37937921
This is your evaluation result for task 1:
mAP: 0.48714130776739434
ap of each class:
plane:0.7880116046903097,
baseball-diamond:0.4692647978217154,
bridge:0.38858081752752943,
ground-track-field:0.5987126479221437,
small-vehicle:0.41932698755109493,
large-vehicle:0.5009640328109685,
ship:0.5767508546587997,
tennis-court:0.7559193172453369,
basketball-court:0.561313088594416,
storage-tank:0.5843369292867531,
soccer-ball-field:0.41987150042788446,
roundabout:0.5008947819556756,
harbor:0.4142153419620439,
swimming-pool:0.5404425029378608,
helicopter:0.5613433762889186,
container-crane:0.14421137326896488,
airport:0.4891753744365273,
helipad:0.05520821042615445
The submitted information is :
Description: RetinaNet_DOTA2.0_R3Det_DCL_B_2x_20210501_104w
Username: sjtu-deter
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""