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cfgs_res50_dota_refine_dcl_v1.py
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cfgs_res50_dota_refine_dcl_v1.py
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# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import os
import tensorflow as tf
import math
"""
FLOPs: 1077428423; Trainable params: 39030491
This is your result for task 1:
mAP: 0.7062510123429832
ap of each class:
plane:0.889222387873444,
baseball-diamond:0.7905891725560461,
bridge:0.46287665099875247,
ground-track-field:0.6421018066297935,
small-vehicle:0.7502680853730467,
large-vehicle:0.7427829844868099,
ship:0.8514223056634352,
tennis-court:0.9069448505931067,
basketball-court:0.7836368107658089,
storage-tank:0.8112671079136692,
occer-ball-field:0.5588421346212779,
roundabout:0.6036215829993754,
harbor:0.5755000514699725,
swimming-pool:0.6769034488658684,
helicopter:0.5477858043343405
The submitted information is :
Description: RetinaNet_DOTA_Refine_DCL_B_2x_20201023_70.2w
Username: SJTU-Det
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""
# ------------------------------------------------
VERSION = 'RetinaNet_DOTA_Refine_DCL_B_2x_20201023'
NET_NAME = 'resnet50_v1d' # 'MobilenetV2'
ADD_BOX_IN_TENSORBOARD = True
# ---------------------------------------- System_config
ROOT_PATH = os.path.abspath('../')
print(20*"++--")
print(ROOT_PATH)
GPU_GROUP = "0,1,2"
NUM_GPU = len(GPU_GROUP.strip().split(','))
SHOW_TRAIN_INFO_INTE = 20
SMRY_ITER = 200
SAVE_WEIGHTS_INTE = 27000 * 2
SUMMARY_PATH = ROOT_PATH + '/output/summary'
TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result'
if NET_NAME.startswith("resnet"):
weights_name = NET_NAME
elif NET_NAME.startswith("MobilenetV2"):
weights_name = "mobilenet/mobilenet_v2_1.0_224"
else:
raise Exception('net name must in [resnet_v1_101, resnet_v1_50, MobilenetV2]')
PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt'
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/'
# ------------------------------------------ Train config
RESTORE_FROM_RPN = False
FIXED_BLOCKS = 1 # allow 0~3
FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone
USE_07_METRIC = True
MUTILPY_BIAS_GRADIENT = 2.0 # if None, will not multipy
GRADIENT_CLIPPING_BY_NORM = 10.0 # if None, will not clip
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0
ANGLE_WEIGHT = 0.5
USE_IOU_FACTOR = True
REG_LOSS_MODE = None
ALPHA = 1.0
BETA = 1.0
BATCH_SIZE = 1
EPSILON = 1e-5
MOMENTUM = 0.9
LR = 5e-4
DECAY_STEP = [SAVE_WEIGHTS_INTE*12, SAVE_WEIGHTS_INTE*16, SAVE_WEIGHTS_INTE*20]
MAX_ITERATION = SAVE_WEIGHTS_INTE*20
WARM_SETP = int(1.0 / 4.0 * SAVE_WEIGHTS_INTE)
# -------------------------------------------- Data_preprocess_config
DATASET_NAME = 'DOTA' # 'pascal', 'coco'
PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR
PIXEL_MEAN_ = [0.485, 0.456, 0.406]
PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR
IMG_SHORT_SIDE_LEN = 800
IMG_MAX_LENGTH = 800
CLASS_NUM = 15
OMEGA = 180 / 256.
ANGLE_MODE = 0
IMG_ROTATE = False
RGB2GRAY = False
VERTICAL_FLIP = False
HORIZONTAL_FLIP = True
IMAGE_PYRAMID = False
# --------------------------------------------- Network_config
SUBNETS_WEIGHTS_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01, seed=None)
SUBNETS_BIAS_INITIALIZER = tf.constant_initializer(value=0.0)
PROBABILITY = 0.01
FINAL_CONV_BIAS_INITIALIZER = tf.constant_initializer(value=-math.log((1.0 - PROBABILITY) / PROBABILITY))
WEIGHT_DECAY = 1e-4
USE_GN = False
FPN_CHANNEL = 256
# ---------------------------------------------Anchor config
LEVEL = ['P3', 'P4', 'P5', 'P6', 'P7']
BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512]
ANCHOR_STRIDE = [8, 16, 32, 64, 128]
ANCHOR_SCALES = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]
ANCHOR_RATIOS = [1, 1 / 2, 2., 1 / 3., 3., 5., 1 / 5.]
ANCHOR_ANGLES = [-90, -75, -60, -45, -30, -15]
ANCHOR_SCALE_FACTORS = None
USE_CENTER_OFFSET = True
METHOD = 'H'
USE_ANGLE_COND = False
ANGLE_RANGE = 180 # 90 or 180
# --------------------------------------------RPN config
SHARE_NET = True
USE_P5 = True
IOU_POSITIVE_THRESHOLD = 0.5
IOU_NEGATIVE_THRESHOLD = 0.4
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
NMS = True
NMS_IOU_THRESHOLD = 0.1
MAXIMUM_DETECTIONS = 100
FILTERED_SCORE = 0.05
VIS_SCORE = 0.4