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- _base_ = '../_base_/default_runtime.py'
-
- # model settings
- img_size = 550
- model = dict(
- type='YOLACT',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1, # do not freeze stem
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False, # update the statistics of bn
- zero_init_residual=False,
- 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,
- upsample_cfg=dict(mode='bilinear')),
- bbox_head=dict(
- type='YOLACTHead',
- num_classes=80,
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=3,
- scales_per_octave=1,
- base_sizes=[8, 16, 32, 64, 128],
- ratios=[0.5, 1.0, 2.0],
- strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
- centers=[(550 * 0.5 / x, 550 * 0.5 / x)
- for x in [69, 35, 18, 9, 5]]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- reduction='none',
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
- num_head_convs=1,
- num_protos=32,
- use_ohem=True),
- mask_head=dict(
- type='YOLACTProtonet',
- in_channels=256,
- num_protos=32,
- num_classes=80,
- max_masks_to_train=100,
- loss_mask_weight=6.125),
- segm_head=dict(
- type='YOLACTSegmHead',
- num_classes=80,
- in_channels=256,
- loss_segm=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.4,
- min_pos_iou=0.,
- ignore_iof_thr=-1,
- gt_max_assign_all=False),
- # smoothl1_beta=1.,
- allowed_border=-1,
- pos_weight=-1,
- neg_pos_ratio=3,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- iou_thr=0.5,
- top_k=200,
- max_per_img=100))
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- img_norm_cfg = dict(
- mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
- train_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)),
- dict(
- type='PhotoMetricDistortion',
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18),
- dict(
- type='Expand',
- mean=img_norm_cfg['mean'],
- to_rgb=img_norm_cfg['to_rgb'],
- ratio_range=(1, 4)),
- dict(
- type='MinIoURandomCrop',
- min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
- min_crop_size=0.3),
- dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(img_size, img_size),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
- ]
- data = dict(
- samples_per_gpu=8,
- workers_per_gpu=4,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
- # optimizer
- optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
- optimizer_config = dict()
- # learning policy
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.1,
- step=[20, 42, 49, 52])
- runner = dict(type='EpochBasedRunner', max_epochs=55)
- cudnn_benchmark = True
- evaluation = dict(metric=['bbox', 'segm'])
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