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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_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 torch.nn.modules.activation import SiLU | |
| 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.dense_heads.rtmdet_ins_head import RTMDetInsSepBNHead | |
| from mmdet.models.layers.ema import ExpMomentumEMA | |
| from mmdet.models.losses.dice_loss import DiceLoss | |
| from mmdet.models.losses.gfocal_loss import QualityFocalLoss | |
| from mmdet.models.losses.iou_loss import GIoULoss | |
| from mmdet.models.task_modules.coders.distance_point_bbox_coder import \ | |
| DistancePointBBoxCoder | |
| from mmdet.models.task_modules.prior_generators.point_generator import \ | |
| MlvlPointGenerator | |
| model.merge( | |
| dict( | |
| bbox_head=dict( | |
| _delete_=True, | |
| type=RTMDetInsSepBNHead, | |
| num_classes=80, | |
| in_channels=256, | |
| stacked_convs=2, | |
| share_conv=True, | |
| pred_kernel_size=1, | |
| feat_channels=256, | |
| act_cfg=dict(type=SiLU, inplace=True), | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| anchor_generator=dict( | |
| type=MlvlPointGenerator, offset=0, strides=[8, 16, 32]), | |
| bbox_coder=dict(type=DistancePointBBoxCoder), | |
| loss_cls=dict( | |
| type=QualityFocalLoss, | |
| use_sigmoid=True, | |
| beta=2.0, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type=GIoULoss, loss_weight=2.0), | |
| loss_mask=dict( | |
| type=DiceLoss, loss_weight=2.0, eps=5e-6, reduction='mean')), | |
| test_cfg=dict( | |
| nms_pre=1000, | |
| min_bbox_size=0, | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.6), | |
| max_per_img=100, | |
| mask_thr_binary=0.5), | |
| )) | |
| 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=(0.1, 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_dataloader.update( | |
| dict(pin_memory=True, dataset=dict(pipeline=train_pipeline))) | |
| 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=(0.1, 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) | |
| ] | |
| 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) | |
| ] | |
| val_evaluator.update(dict(metric=['bbox', 'segm'])) | |
| test_evaluator = val_evaluator | |