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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,117 @@ | ||
| _base_ = [ | ||
| '../_base_/datasets/rsg.py', '../_base_/schedules/schedule_1x.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| angle_version = 'le135' | ||
| model = dict( | ||
| type='RotatedRetinaNetCrop', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| zero_init_residual=False, | ||
| norm_cfg=dict(type='BN', requires_grad=True), | ||
| norm_eval=True, | ||
| style='pytorch', | ||
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=256, | ||
| start_level=1, | ||
| add_extra_convs='on_input', | ||
| num_outs=5), | ||
| bbox_head=dict( | ||
| type='RDCFLHead', | ||
| num_classes=48, | ||
| in_channels=256, | ||
| stacked_convs=4, | ||
| feat_channels=256, | ||
| assign_by_circumhbbox=None, | ||
| dcn_assign = True, | ||
| dilation_rate = 4, | ||
| anchor_generator=dict( | ||
| type='RotatedAnchorGenerator', | ||
| octave_base_scale=4, | ||
| scales_per_octave=1, | ||
| ratios=[1.0], | ||
| strides=[8, 16, 32, 64, 128]), | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHAOBBoxCoder', | ||
| angle_range=angle_version, | ||
| norm_factor=1, | ||
| edge_swap=False, | ||
| proj_xy=True, | ||
| target_means=(.0, .0, .0, .0, .0), | ||
| target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)), | ||
| loss_cls=dict( | ||
| type='FocalLoss', | ||
| use_sigmoid=True, | ||
| gamma=2.0, | ||
| alpha=0.25, | ||
| loss_weight=1.0), | ||
| reg_decoded_bbox=True, | ||
| loss_bbox=dict( | ||
| type='RotatedIoULoss', | ||
| loss_weight=1.0)), | ||
| train_cfg=dict( | ||
| assigner=dict( | ||
| type='C2FAssigner', | ||
| ignore_iof_thr=-1, | ||
| gpu_assign_thr= 1024, | ||
| iou_calculator=dict(type='RBboxMetrics2D'), | ||
| assign_metric='gjsd', | ||
| topk=16, | ||
| topq=12, | ||
| constraint='dgmm', | ||
| gauss_thr=0.6), | ||
| allowed_border=-1, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| test_cfg=dict( | ||
| nms_pre=2000, | ||
| min_bbox_size=0, | ||
| score_thr=0.05, | ||
| nms=dict(iou_thr=0.4), | ||
| max_per_img=2000)) | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='RResize', img_scale=(1024, 1024)), | ||
| dict( | ||
| type='RRandomFlip', | ||
| flip_ratio=[0.25, 0.25, 0.25], | ||
| direction=['horizontal', 'vertical', 'diagonal'], | ||
| version=angle_version), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict(pipeline=train_pipeline, version=angle_version), | ||
| val=dict(version=angle_version), | ||
| test=dict(version=angle_version)) | ||
|
|
||
| optimizer = dict( | ||
| _delete_=True, | ||
| type='AdamW', | ||
| lr=0.0001, | ||
| betas=(0.9, 0.999), | ||
| weight_decay=0.05, | ||
| paramwise_cfg=dict( | ||
| custom_keys=dict( | ||
| absolute_pos_embed=dict(decay_mult=0.0), | ||
| relative_position_bias_table=dict(decay_mult=0.0), | ||
| norm=dict(decay_mult=0.0)))) | ||
|
|
||
| checkpoint_config = dict(interval=1, max_keep_ckpts=1) | ||
| evaluation = dict(interval=6, metric='mAP') |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| _base_ = [ | ||
| '../_base_/datasets/rsg.py', '../_base_/schedules/schedule_1x.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
| angle_version = 'le90' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='H2RBoxV2PDetectorCrop', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| zero_init_residual=False, | ||
| norm_cfg=dict(type='BN', requires_grad=True), | ||
| norm_eval=True, | ||
| style='pytorch', | ||
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=256, | ||
| start_level=1, | ||
| add_extra_convs='on_output', # use P5 | ||
| num_outs=5, | ||
| relu_before_extra_convs=True), | ||
| bbox_head=dict( | ||
| type='H2RBoxV2PHead', | ||
| num_classes=48, | ||
| in_channels=256, | ||
| stacked_convs=4, | ||
| feat_channels=256, | ||
| strides=[8, 16, 32, 64, 128], | ||
| center_sampling=True, | ||
| center_sample_radius=1.5, | ||
| norm_on_bbox=True, | ||
| centerness_on_reg=True, | ||
| square_cls=[4, 44], | ||
| # resize_cls=[1], | ||
| scale_angle=False, | ||
| bbox_coder=dict( | ||
| type='DistanceAnglePointCoder', angle_version=angle_version), | ||
| loss_cls=dict( | ||
| type='FocalLoss', | ||
| use_sigmoid=True, | ||
| gamma=2.0, | ||
| alpha=0.25, | ||
| loss_weight=1.0), | ||
| loss_bbox=dict(type='IoULoss', loss_weight=1.0), | ||
| loss_centerness=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | ||
| loss_ss_symmetry=dict( | ||
| type='SmoothL1Loss', loss_weight=0.2, beta=0.1)), | ||
| # training and testing settings | ||
| train_cfg=None, | ||
| test_cfg=dict( | ||
| nms_pre=2000, | ||
| min_bbox_size=0, | ||
| score_thr=0.05, | ||
| nms=dict(iou_thr=0.1), | ||
| max_per_img=2000)) | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='RResize', img_scale=(1024, 1024)), | ||
| dict( | ||
| type='RRandomFlip', | ||
| flip_ratio=[0.25, 0.25, 0.25], | ||
| direction=['horizontal', 'vertical', 'diagonal'], | ||
| version=angle_version), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| train=dict(pipeline=train_pipeline, version=angle_version), | ||
| val=dict(version=angle_version), | ||
| test=dict(version=angle_version)) | ||
|
|
||
| optimizer = dict( | ||
| _delete_=True, | ||
| type='AdamW', | ||
| lr=0.0001, | ||
| betas=(0.9, 0.999), | ||
| weight_decay=0.05) | ||
|
|
||
| checkpoint_config = dict(interval=1, max_keep_ckpts=1) | ||
| evaluation = dict(interval=6, metric='mAP') | ||
|
|
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