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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
_base_ = './rtmdet_x_8xb32-300e_coco.py'
model = dict(
backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)),
neck=dict(in_channels=[320, 640, 960, 1280]),
bbox_head=dict(
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32, 64])))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='CachedMosaic', img_scale=(1280, 1280), pad_val=114.0),
dict(
type='RandomResize',
scale=(2560, 2560),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(1280, 1280)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(1280, 1280),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='PackDetInputs')
]
train_pipeline_stage2 = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(1280, 1280)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8, num_workers=20, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(
batch_size=5, num_workers=20, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.004 * 32 / 256
optim_wrapper = dict(optimizer=dict(lr=base_lr))
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
by_epoch=False,
begin=0,
end=1000),
dict(
# use cosine lr from 150 to 300 epoch
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=max_epochs // 2,
end=max_epochs,
T_max=max_epochs // 2,
by_epoch=True,
convert_to_iter_based=True),
]
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_pipeline=train_pipeline_stage2)
]
img_scales = [(1280, 1280), (640, 640), (1920, 1920)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
dict(type='Resize', scale=s, keep_ratio=True)
for s in img_scales
],
[
# ``RandomFlip`` must be placed before ``Pad``, otherwise
# bounding box coordinates after flipping cannot be
# recovered correctly.
dict(type='RandomFlip', prob=1.),
dict(type='RandomFlip', prob=0.)
],
[
dict(
type='Pad',
size=(1920, 1920),
pad_val=dict(img=(114, 114, 114))),
],
[dict(type='LoadAnnotations', with_bbox=True)],
[
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction'))
]
])
]
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