Spaces:
Runtime error
Runtime error
File size: 3,497 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
# 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_ins_l_8xb32_300e_coco import *
from mmcv.transforms.loading import LoadImageFromFile
from mmcv.transforms.processing import RandomResize
from mmengine.hooks.ema_hook import EMAHook
from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import (FilterAnnotations,
LoadAnnotations)
from mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic,
Pad, RandomCrop, RandomFlip,
Resize, YOLOXHSVRandomAug)
from mmdet.engine.hooks.pipeline_switch_hook import PipelineSwitchHook
from mmdet.models.layers.ema import ExpMomentumEMA
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model.update(
dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(
in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1),
bbox_head=dict(in_channels=128, feat_channels=128)))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(type=CachedMosaic, img_scale=(640, 640), pad_val=114.0),
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),
recompute_bbox=True,
allow_negative_crop=True),
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=20,
pad_val=(114, 114, 114)),
dict(type=FilterAnnotations, min_gt_bbox_wh=(1, 1)),
dict(type=PackDetInputs)
]
train_pipeline_stage2 = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(
type=RandomResize,
scale=(640, 640),
ratio_range=(0.5, 2.0),
resize_type=Resize,
keep_ratio=True),
dict(
type=RandomCrop,
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type=FilterAnnotations, min_gt_bbox_wh=(1, 1)),
dict(type=YOLOXHSVRandomAug),
dict(type=RandomFlip, prob=0.5),
dict(type=Pad, size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type=PackDetInputs)
]
train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
custom_hooks = [
dict(
type=EMAHook,
ema_type=ExpMomentumEMA,
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type=PipelineSwitchHook,
switch_epoch=280,
switch_pipeline=train_pipeline_stage2)
]
|