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_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_preprocessor,
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, 2, 2, 2, 2, 4],
norm_cfg=dict(type='BN', requires_grad=True)),
neck=None,
bbox_head=dict(
type='CentripetalHead',
num_classes=80,
in_channels=256,
num_feat_levels=2,
corner_emb_channels=0,
loss_heatmap=dict(
type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1),
loss_guiding_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
loss_centripetal_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1)),
# training and testing settings
train_cfg=None,
test_cfg=dict(
corner_topk=100,
local_maximum_kernel=3,
distance_threshold=0.5,
score_thr=0.05,
max_per_img=100,
nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
# data settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
# The cropped images are padded into squares during training,
# but may be smaller than crop_size.
type='RandomCenterCropPad',
crop_size=(511, 511),
ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
test_mode=False,
test_pad_mode=None,
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb']),
dict(type='Resize', scale=(511, 511), keep_ratio=False),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs'),
]
test_pipeline = [
dict(
type='LoadImageFromFile',
to_float32=True,
backend_args=_base_.backend_args),
# don't need Resize
dict(
type='RandomCenterCropPad',
crop_size=None,
ratios=None,
border=None,
test_mode=True,
test_pad_mode=['logical_or', 127],
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb']),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'border'))
]
train_dataloader = dict(
batch_size=6,
num_workers=3,
batch_sampler=None,
dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=0.0005),
clip_grad=dict(max_norm=35, norm_type=2))
max_epochs = 210
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 3,
by_epoch=False,
begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[190],
gamma=0.1)
]
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (16 GPUs) x (6 samples per GPU)
auto_scale_lr = dict(base_batch_size=96)
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(
nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'),
max_per_img=100))
tta_pipeline = [
dict(
type='LoadImageFromFile',
to_float32=True,
backend_args=_base_.backend_args),
dict(
type='TestTimeAug',
transforms=[
[
# ``RandomFlip`` must be placed before ``RandomCenterCropPad``,
# otherwise bounding box coordinates after flipping cannot be
# recovered correctly.
dict(type='RandomFlip', prob=1.),
dict(type='RandomFlip', prob=0.)
],
[
dict(
type='RandomCenterCropPad',
crop_size=None,
ratios=None,
border=None,
test_mode=True,
test_pad_mode=['logical_or', 127],
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb'])
],
[dict(type='LoadAnnotations', with_bbox=True)],
[
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'flip', 'flip_direction', 'border'))
]
])
]
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