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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_ins_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 (FilterAnnotations, | |
| 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))) | |
| 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, | |
| 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=FilterAnnotations, min_gt_bbox_wh=(1, 1)), | |
| dict(type=PackDetInputs) | |
| ] | |
| train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline))) | |