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# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .rtmdet_s_8xb32_300e_coco import *
from mmcv.transforms.loading import LoadImageFromFile
from mmcv.transforms.processing import RandomResize
from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import LoadAnnotations
from mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic,
Pad, RandomCrop, RandomFlip,
Resize, YOLOXHSVRandomAug)
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model.update(
dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(
in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1),
bbox_head=dict(in_channels=96, feat_channels=96, exp_on_reg=False)))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadAnnotations, with_bbox=True),
dict(
type=CachedMosaic,
img_scale=(640, 640),
pad_val=114.0,
max_cached_images=20,
random_pop=False),
dict(
type=RandomResize,
scale=(1280, 1280),
ratio_range=(0.5, 2.0),
resize_type=Resize,
keep_ratio=True),
dict(type=RandomCrop, crop_size=(640, 640)),
dict(type=YOLOXHSVRandomAug),
dict(type=RandomFlip, prob=0.5),
dict(type=Pad, size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type=CachedMixUp,
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=10,
random_pop=False,
pad_val=(114, 114, 114),
prob=0.5),
dict(type=PackDetInputs)
]
train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
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