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[Feature] Support EfficientDet in projects #9810
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projects/EfficientDet/configs/efficientdet_effb0_bifpn_8xb16-crop512-300e_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,175 @@ | ||
| _base_ = [ | ||
| 'mmdet::_base_/datasets/coco_detection.py', | ||
| 'mmdet::_base_/schedules/schedule_1x.py', | ||
| 'mmdet::_base_/default_runtime.py' | ||
| ] | ||
| custom_imports = dict( | ||
| imports=['projects.EfficientDet.efficientdet'], allow_failed_imports=False) | ||
|
|
||
| image_size = 512 | ||
| batch_augments = [ | ||
| dict(type='BatchFixedSizePad', size=(image_size, image_size)) | ||
| ] | ||
| dataset_type = 'CocoDataset' | ||
| evalute_type = 'CocoMetric' | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True, eps=1e-3, momentum=0.01) | ||
| checkpoint = 'https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth' # noqa | ||
| model = dict( | ||
| type='EfficientDet', | ||
| data_preprocessor=dict( | ||
| type='DetDataPreprocessor', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| bgr_to_rgb=True, | ||
| pad_size_divisor=image_size, | ||
| batch_augments=batch_augments), | ||
| backbone=dict( | ||
| type='EfficientNet', | ||
| arch='b0', | ||
| drop_path_rate=0.2, | ||
| out_indices=(3, 4, 5), | ||
| frozen_stages=0, | ||
| conv_cfg=dict(type='Conv2dSamePadding'), | ||
| norm_cfg=norm_cfg, | ||
| norm_eval=False, | ||
| init_cfg=dict( | ||
| type='Pretrained', prefix='backbone', checkpoint=checkpoint)), | ||
| neck=dict( | ||
| type='BiFPN', | ||
| num_stages=3, | ||
| in_channels=[40, 112, 320], | ||
| out_channels=64, | ||
| start_level=0, | ||
| norm_cfg=norm_cfg), | ||
| bbox_head=dict( | ||
| type='EfficientDetSepBNHead', | ||
| num_classes=80, | ||
| num_ins=5, | ||
| in_channels=64, | ||
| feat_channels=64, | ||
| stacked_convs=3, | ||
| norm_cfg=norm_cfg, | ||
| anchor_generator=dict( | ||
| type='AnchorGenerator', | ||
| octave_base_scale=4, | ||
| scales_per_octave=3, | ||
| ratios=[1.0, 0.5, 2.0], | ||
| strides=[8, 16, 32, 64, 128], | ||
| center_offset=0.5), | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[.0, .0, .0, .0], | ||
| target_stds=[1.0, 1.0, 1.0, 1.0]), | ||
| loss_cls=dict( | ||
| type='FocalLoss', | ||
| use_sigmoid=True, | ||
| gamma=1.5, | ||
| alpha=0.25, | ||
| loss_weight=1.0), | ||
| loss_bbox=dict(type='HuberLoss', beta=0.1, loss_weight=50)), | ||
| # training and testing settings | ||
| train_cfg=dict( | ||
| assigner=dict( | ||
| type='MaxIoUAssigner', | ||
| pos_iou_thr=0.5, | ||
| neg_iou_thr=0.5, | ||
| min_pos_iou=0, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| type='PseudoSampler'), # Focal loss should use PseudoSampler | ||
| allowed_border=-1, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| test_cfg=dict( | ||
| nms_pre=1000, | ||
| min_bbox_size=0, | ||
| score_thr=0.05, | ||
| nms=dict( | ||
| type='soft_nms', | ||
| iou_threshold=0.3, | ||
| sigma=0.5, | ||
| min_score=1e-3, | ||
| method='gaussian'), | ||
| max_per_img=100)) | ||
|
|
||
| # dataset settings | ||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| file_client_args={{_base_.file_client_args}}), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='RandomResize', | ||
| scale=(image_size, image_size), | ||
| ratio_range=(0.1, 2.0), | ||
| keep_ratio=True), | ||
| dict(type='RandomCrop', crop_size=(image_size, image_size)), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='PackDetInputs') | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| file_client_args={{_base_.file_client_args}}), | ||
| dict(type='Resize', scale=(image_size, image_size), keep_ratio=True), | ||
| 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=16, | ||
| num_workers=8, | ||
| dataset=dict(type=dataset_type, pipeline=train_pipeline)) | ||
| val_dataloader = dict(dataset=dict(type=dataset_type, pipeline=test_pipeline)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict(type=evalute_type) | ||
| test_evaluator = val_evaluator | ||
|
|
||
| optim_wrapper = dict( | ||
| optimizer=dict(lr=0.16, weight_decay=4e-5), | ||
| paramwise_cfg=dict( | ||
| norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True), | ||
| clip_grad=dict(max_norm=10, norm_type=2)) | ||
|
|
||
| # learning policy | ||
| max_epochs = 300 | ||
| param_scheduler = [ | ||
| dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=917), | ||
| dict( | ||
| type='CosineAnnealingLR', | ||
| eta_min=0.0, | ||
| begin=1, | ||
| T_max=299, | ||
| end=300, | ||
| by_epoch=True, | ||
| convert_to_iter_based=True) | ||
| ] | ||
| train_cfg = dict(max_epochs=max_epochs, val_interval=1) | ||
|
|
||
| vis_backends = [ | ||
| dict(type='LocalVisBackend'), | ||
| dict(type='TensorboardVisBackend') | ||
| ] | ||
| visualizer = dict( | ||
| type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer') | ||
|
|
||
| default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=15)) | ||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0002, | ||
| update_buffers=True, | ||
| priority=49) | ||
| ] | ||
| # cudnn_benchmark=True can accelerate fix-size training | ||
| env_cfg = dict(cudnn_benchmark=True) | ||
|
|
||
| # NOTE: `auto_scale_lr` is for automatically scaling LR, | ||
| # USER SHOULD NOT CHANGE ITS VALUES. | ||
| # base_batch_size = (8 GPUs) x (16 samples per GPU) | ||
| auto_scale_lr = dict(base_batch_size=128) |
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