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from omegaconf import OmegaConf
import detectron2.data.transforms as T
from detectron2.config import LazyCall as L
from detectron2.data import (
DatasetMapper,
build_detection_test_loader,
build_detection_train_loader,
get_detection_dataset_dicts,
)
from detectron2.evaluation import COCOEvaluator
dataloader = OmegaConf.create()
dataloader.train = L(build_detection_train_loader)(
dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
mapper=L(DatasetMapper)(
is_train=True,
augmentations=[
L(T.RandomApply)(
tfm_or_aug=L(T.AugmentationList)(
augs=[
L(T.ResizeShortestEdge)(
short_edge_length=[400, 500, 600], sample_style="choice"
),
L(T.RandomCrop)(crop_type="absolute_range", crop_size=(384, 600)),
]
),
prob=0.5,
),
L(T.ResizeShortestEdge)(
short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800),
sample_style="choice",
max_size=1333,
),
L(T.RandomFlip)(horizontal=True),
],
image_format="RGB",
use_instance_mask=True,
),
total_batch_size=16,
num_workers=4,
)
dataloader.test = L(build_detection_test_loader)(
dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
mapper=L(DatasetMapper)(
is_train=False,
augmentations=[
L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333),
],
image_format="${...train.mapper.image_format}",
),
num_workers=4,
)
dataloader.evaluator = L(COCOEvaluator)(
dataset_name="${..test.dataset.names}",
)
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