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# encoding: utf-8
import os
from yolox.data import get_yolox_datadir
from yolox.exp import Exp as MyExp
class Exp(MyExp):
def __init__(self):
super(Exp, self).__init__()
self.num_classes = 20
self.depth = 0.33
self.width = 0.50
self.warmup_epochs = 1
# ---------- transform config ------------ #
self.mosaic_prob = 1.0
self.mixup_prob = 1.0
self.hsv_prob = 1.0
self.flip_prob = 0.5
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
def get_dataset(self, cache: bool, cache_type: str = "ram"):
from yolox.data import VOCDetection, TrainTransform
return VOCDetection(
data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"),
image_sets=[('2007', 'trainval'), ('2012', 'trainval')],
img_size=self.input_size,
preproc=TrainTransform(
max_labels=50,
flip_prob=self.flip_prob,
hsv_prob=self.hsv_prob),
cache=cache,
cache_type=cache_type,
)
def get_eval_dataset(self, **kwargs):
from yolox.data import VOCDetection, ValTransform
legacy = kwargs.get("legacy", False)
return VOCDetection(
data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"),
image_sets=[('2007', 'test')],
img_size=self.test_size,
preproc=ValTransform(legacy=legacy),
)
def get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False):
from yolox.evaluators import VOCEvaluator
return VOCEvaluator(
dataloader=self.get_eval_loader(batch_size, is_distributed,
testdev=testdev, legacy=legacy),
img_size=self.test_size,
confthre=self.test_conf,
nmsthre=self.nmsthre,
num_classes=self.num_classes,
)
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