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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms.loading import LoadImageFromFile
from mmengine.dataset.sampler import DefaultSampler
from mmdet.datasets.coco import CocoDataset
from mmdet.datasets.samplers.batch_sampler import AspectRatioBatchSampler
from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import LoadAnnotations
from mmdet.datasets.transforms.transforms import RandomFlip, Resize
from mmdet.evaluation.metrics.coco_metric import CocoMetric
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadAnnotations, with_bbox=True, with_mask=True),
dict(type=Resize, scale=(1333, 800), keep_ratio=True),
dict(type=RandomFlip, prob=0.5),
dict(type=PackDetInputs)
]
test_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=Resize, scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type=LoadAnnotations, with_bbox=True, with_mask=True),
dict(
type=PackDetInputs,
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type=DefaultSampler, shuffle=True),
batch_sampler=dict(type=AspectRatioBatchSampler),
dataset=dict(
type=CocoDataset,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type=DefaultSampler, shuffle=False),
dataset=dict(
type=CocoDataset,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type=CocoMetric,
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
# inference on test dataset and
# format the output results for submission.
# test_dataloader = dict(
# batch_size=1,
# num_workers=2,
# persistent_workers=True,
# drop_last=False,
# sampler=dict(type=DefaultSampler, shuffle=False),
# dataset=dict(
# type=CocoDataset,
# data_root=data_root,
# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
# data_prefix=dict(img='test2017/'),
# test_mode=True,
# pipeline=test_pipeline))
# test_evaluator = dict(
# type=CocoMetric,
# metric=['bbox', 'segm'],
# format_only=True,
# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
# outfile_prefix='./work_dirs/coco_instance/test')
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