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on
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Running
on
Zero
File size: 2,799 Bytes
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_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py',
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com/)
# to downloaded dsdl dataset.
dataset_type = 'DSDLDetDataset'
data_root = 'data/OpenImages'
train_ann = 'dsdl/set-train/train.yaml'
val_ann = 'dsdl/set-val/val.yaml'
specific_key_path = dict(
image_level_labels='./image_labels/*/label',
Label='./objects/*/label',
is_group_of='./objects/*/isgroupof',
)
backend_args = dict(
backend='petrel',
path_mapping=dict({'data/': 's3://open_dataset_original/'}))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1024, 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=(1024, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'instances', 'image_level_labels'))
]
train_dataloader = dict(
sampler=dict(type='ClassAwareSampler', num_sample_class=1),
dataset=dict(
type=dataset_type,
with_imagelevel_label=True,
with_hierarchy=True,
specific_key_path=specific_key_path,
data_root=data_root,
ann_file=train_ann,
filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),
pipeline=train_pipeline))
val_dataloader = dict(
dataset=dict(
type=dataset_type,
with_imagelevel_label=True,
with_hierarchy=True,
specific_key_path=specific_key_path,
data_root=data_root,
ann_file=val_ann,
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
default_hooks = dict(logger=dict(type='LoggerHook', interval=1000), )
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=3, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[1, 2],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
val_evaluator = dict(
type='OpenImagesMetric',
iou_thrs=0.5,
ioa_thrs=0.5,
use_group_of=True,
get_supercategory=True)
test_evaluator = val_evaluator
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