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from mmcv import TransformBroadcaster, LoadImageFromFile, RandomResize
from mmdet.datasets.transforms import Resize, RandomFlip, RandomCrop
from mmengine.dataset import DefaultSampler
from seg.datasets.pipeliens.loading import LoadVideoSegAnnotations, ResizeOri
from seg.datasets.pipeliens.formatting import PackVidSegInputs
from seg.datasets.pipeliens.frame_sampling import VideoClipSample
from seg.datasets.samplers.batch_sampler import VideoSegAspectRatioBatchSampler
from seg.datasets.vipseg import VIPSegDataset
from seg.evaluation.metrics.vip_seg_metric import VIPSegMetric
dataset_type = VIPSegDataset
data_root = 'data/VIPSeg'
backend_args = None
image_size = (1280, 736)
# dataset settings
train_pipeline = [
dict(
type=VideoClipSample,
num_selected=2,
interval=2),
dict(
type=TransformBroadcaster,
share_random_params=True,
transforms=[
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadVideoSegAnnotations, with_bbox=True, with_label=True, with_mask=True, with_seg=True),
dict(
type=RandomResize,
resize_type=Resize,
scale=image_size,
ratio_range=(.8, 2.),
keep_ratio=True,
),
dict(
type=RandomCrop,
crop_size=image_size,
crop_type='absolute',
recompute_bbox=True,
allow_negative_crop=True),
dict(type=RandomFlip, prob=0.5),
]),
dict(type=PackVidSegInputs)
]
test_pipeline = [
dict(
type=TransformBroadcaster,
transforms=[
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadVideoSegAnnotations, with_bbox=True, with_label=True, with_mask=True, with_seg=True),
dict(type=Resize, scale=image_size, keep_ratio=True),
dict(type=ResizeOri),
]),
dict(type=PackVidSegInputs)
]
# dataloader
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type=DefaultSampler, shuffle=True),
batch_sampler=dict(type=VideoSegAspectRatioBatchSampler),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='train.txt',
data_prefix=dict(img='imgs/', seg='panomasks/'),
# check whether it is necessary.
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='val.txt',
data_prefix=dict(img='imgs/', seg='panomasks/'),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(
type=VIPSegMetric,
metric=['VPQ@1', 'VPQ@2', 'VPQ@4', 'VPQ@6'],
)
test_evaluator = val_evaluator
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