# -*- 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 = 27000 * 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 = 'HRSC2016'
IMG_SHORT_SIDE_LEN = 512
IMG_MAX_LENGTH = 512
CLASS_NUM = 1

# data augmentation
IMG_ROTATE = True
RGB2GRAY = True
VERTICAL_FLIP = True
HORIZONTAL_FLIP = True
IMAGE_PYRAMID = False

# 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

# sample
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]

# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 2.0
USE_IOU_FACTOR = False

GWD_TAU = 2.0
GWD_FUNC = tf.sqrt

VERSION = 'RetinaNet_HRSC2016_R3Det_GWD_2x_20201228'

"""
r3det+gwd (only refine stage) + sqrt tau=2
cls : ship|| Recall: 0.9641693811074918 || Precison: 0.3954575818303273|| AP: 0.8943491430116616
F1:0.9233921715798948 P:0.944633730834753 R:0.9030944625407166
mAP is : 0.8943491430116616

89.43	89.32	88.89	87.45	78.3	65.88	45.47	15.02	0.92	0.06	56.07
"""

