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yolox_dota_s_obb_kld.py
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yolox_dota_s_obb_kld.py
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# encoding: utf-8
import os
import torch
import torch.distributed as dist
from yolox.data import get_yolox_datadir
from yolox.exp import ExpOBB_KLD as MyExp
class Exp(MyExp):
def __init__(self):
super(Exp, self).__init__()
self.input_size = (1024, 1024) #add
self.random_size = (28, 36)
self.num_classes = 16
self.depth = 0.33
self.width = 0.50
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
# --------------- transform config ----------------- #
self.degrees = 0.0
self.translate = 0.1
self.scale = (0.1, 2)
self.mscale = (0.8, 1.6)
self.shear = 2.0
self.perspective = 0.0
self.enable_mixup = False
# -------------- training config --------------------- #
self.warmup_epochs = 5
self.max_epoch = 50
self.warmup_lr = 0
self.basic_lr_per_img = 0.0025 / 16.0
self.scheduler = "yoloxwarmcos"
self.no_aug_epochs = 15
self.min_lr_ratio = 0.05
self.ema = True
self.weight_decay = 5e-4
self.momentum = 0.9
self.save_interval = 1
self.print_interval = 5
self.eval_interval = 50
# ----------------- testing config ------------------ #
self.test_size = (1024, 1024)
self.test_conf = 0.01
self.nmsthre = 0.3 #default 0.65
def get_data_loader(self, batch_size, is_distributed, no_aug=False): # no_aug=False
from yolox.data import (
DOTAOBBDetection,
TrainTransformOBB,
YoloBatchSampler,
DataLoader,
InfiniteSampler,
MosaicDetectionOBB,
)
dataset = DOTAOBBDetection(
#data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"), #delete
data_dir = '/home/lyy/gxw/DOTA_OBB_1_5',
#image_sets=[('2007', 'trainval'), ('2012', 'trainval')],
image_sets=[('2012', 'train')],
img_size=self.input_size,
preproc=TrainTransformOBB(
rgb_means=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
max_labels=150,
),
)
dataset = MosaicDetectionOBB(
dataset,
mosaic=not no_aug,
img_size=self.input_size,
preproc=TrainTransformOBB(
rgb_means=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
max_labels=400,
),
degrees=self.degrees,
translate=self.translate,
scale=self.scale,
shear=self.shear,
perspective=self.perspective,
enable_mixup=self.enable_mixup,
)
self.dataset = dataset
if is_distributed:
batch_size = batch_size // dist.get_world_size()
sampler = InfiniteSampler(
len(self.dataset), seed=self.seed if self.seed else 0
)
batch_sampler = YoloBatchSampler(
sampler=sampler,
batch_size=batch_size,
drop_last=False,
input_dimension=self.input_size,
mosaic=not no_aug,
)
dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
dataloader_kwargs["batch_sampler"] = batch_sampler
train_loader = DataLoader(self.dataset, **dataloader_kwargs)
return train_loader
def get_eval_loader(self, batch_size, is_distributed, testdev=False):
from yolox.data import DOTAOBBDetection, ValTransformOBB
valdataset = DOTAOBBDetection(
#data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"),
data_dir='/home/lyy/gxw/DOTA_OBB_1_5', #add
image_sets=[('2012', 'val')],
img_size=self.test_size,
preproc=ValTransformOBB(
rgb_means=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
),
)
if is_distributed:
batch_size = batch_size // dist.get_world_size()
sampler = torch.utils.data.distributed.DistributedSampler(
valdataset, shuffle=False
)
else:
sampler = torch.utils.data.SequentialSampler(valdataset)
dataloader_kwargs = {
"num_workers": self.data_num_workers,
"pin_memory": True,
"sampler": sampler,
}
dataloader_kwargs["batch_size"] = batch_size
val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)
return val_loader
def get_evaluator(self, batch_size, is_distributed, testdev=False):
from yolox.evaluators import DOTAEvaluator
val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)
evaluator = DOTAEvaluator(
dataloader=val_loader,
img_size=self.test_size,
confthre=self.test_conf,
nmsthre=self.nmsthre,
num_classes=self.num_classes,
)
return evaluator