cfgs.py

# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import

import numpy as np

from libs.configs._base_.models.faster_rcnn_r50_fpn import *
from libs.configs._base_.datasets.dota_detection import *
from libs.configs._base_.schedules.schedule_1x import *
from dataloader.pretrained_weights.pretrain_zoo import PretrainModelZoo

# schedule
BATCH_SIZE = 1
GPU_GROUP = "0,1,2,3"
NUM_GPU = len(GPU_GROUP.strip().split(','))
LR = 0.001 * BATCH_SIZE * NUM_GPU
SAVE_WEIGHTS_INTE = 32000
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 = 'DOTA1.5'
CLASS_NUM = 16

# model
# backbone
pretrain_zoo = PretrainModelZoo()
PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')

# loss
FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0
FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 2.0

KL_TAU = 1.0
KL_FUNC = 1   # 0: sqrt  1: log

VERSION = 'FPN_Res50D_DOTA1.5_1x_20210602'

"""
R2CNN + KLD
FLOPs: 1238001985;    Trainable params: 41778832
"""