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Running
on
Zero
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_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com/)
# to downloaded dsdl dataset.
dataset_type = 'DSDLDetDataset'
data_root = 'data/VOC07-det'
img_prefix = 'original'
train_ann = 'dsdl/set-train/train.yaml'
val_ann = 'dsdl/set-test/test.yaml'
specific_key_path = dict(ignore_flag='./objects/*/difficult')
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1000, 600), 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=(1000, 600), keep_ratio=True),
# avoid bboxes being resized
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'instances'))
]
train_dataloader = dict(
dataset=dict(
type=dataset_type,
specific_key_path=specific_key_path,
data_root=data_root,
ann_file=train_ann,
data_prefix=dict(img_path=img_prefix),
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,
specific_key_path=specific_key_path,
data_root=data_root,
ann_file=val_ann,
data_prefix=dict(img_path=img_prefix),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
# Pascal VOC2007 uses `11points` as default evaluate mode, while PASCAL
# VOC2012 defaults to use 'area'.
val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')
# val_evaluator = dict(type='CocoMetric', metric='bbox')
test_evaluator = val_evaluator
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 12
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=3)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[9],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
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