229 lines
6.8 KiB
Python
229 lines
6.8 KiB
Python
_base_ = [
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'../_base_/datasets/coco_detection.py',
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'../_base_/schedules/schedule_1x.py',
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'../_base_/default_runtime.py'
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]
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# model settings
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model = dict(
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type='FasterRCNN',
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pretrained='torchvision://resnet50',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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style='pytorch'),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=5),
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rpn_head=dict(
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type='OlnRPNHead',
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in_channels=256,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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# Use single anchor per location.
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scales=[8],
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ratios=[1.0],
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strides=[4, 8, 16, 32, 64]),
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bbox_coder=dict(
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type='TBLRBBoxCoder',
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normalizer=1.0,),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.0),
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reg_decoded_bbox=True,
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loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0),
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objectness_type='Centerness',
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loss_objectness=dict(type='L1Loss', loss_weight=1.0),
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),
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roi_head=dict(
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type='OlnRoIHead',
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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='Shared2FCBBoxScoreHead',
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=1,
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0., 0., 0., 0.],
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target_stds=[0.1, 0.1, 0.2, 0.2]),
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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=0.0,
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),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0),
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bbox_score_type='BoxIoU', # 'BoxIoU' or 'Centerness'
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loss_bbox_score=dict(type='L1Loss', loss_weight=1.0),
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)),
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# model training and testing settings
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train_cfg=dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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# Objectness assigner and sampler
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objectness_assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.3,
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neg_iou_thr=0.1,
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min_pos_iou=0.3,
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ignore_iof_thr=-1),
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objectness_sampler=dict(
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type='RandomSampler',
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num=256,
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# Ratio 0 for negative samples.
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pos_fraction=1.,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=0,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(
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nms_across_levels=False,
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nms_pre=2000,
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nms_post=2000,
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max_num=2000,
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# <<<
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nms_thr=0.9,
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# >>>
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min_bbox_size=0),
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rcnn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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min_pos_iou=0.5,
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match_low_quality=False,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rpn=dict(
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nms_across_levels=False,
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nms_pre=2000,
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nms_post=2000,
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max_num=2000,
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nms_thr=0.7,
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min_bbox_size=0),
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rcnn=dict(
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score_thr=0.0,
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nms=dict(type='nms', iou_threshold=0.7),
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# max_per_img should be greater enough than k of AR@k evaluation
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# because the cross-dataset AR evaluation does not count those
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# proposals on the 'seen' classes into the budget (k), to avoid
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# evaluating recall on seen-class objects.
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max_per_img=1500,
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)
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))
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# Dataset
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dataset_type = 'CocoSplitDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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# <
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is_class_agnostic=True,
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train_class='voc',
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eval_class='nonvoc',
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# >
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type=dataset_type,
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pipeline=train_pipeline,
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),
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val=dict(
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# <
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is_class_agnostic=True,
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train_class='voc',
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eval_class='nonvoc',
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# >
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type=dataset_type,
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pipeline=test_pipeline),
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test=dict(
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# <
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is_class_agnostic=True,
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train_class='voc',
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eval_class='nonvoc',
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# >
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type=dataset_type,
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pipeline=test_pipeline))
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lr_config = dict(
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policy='step',
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warmup='linear',
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warmup_iters=500,
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warmup_ratio=0.001,
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step=[6, 7])
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total_epochs = 8
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checkpoint_config = dict(interval=2)
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# yapf:disable
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log_config = dict(
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interval=10,
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hooks=[
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dict(type='TextLoggerHook'),
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dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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work_dir='./work_dirs/oln_box_rpnnms09/' |