161 lines
5.0 KiB
Python
161 lines
5.0 KiB
Python
_base_ = '../_base_/default_runtime.py'
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# model settings
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img_size = 550
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model = dict(
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type='YOLACT',
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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, # do not freeze stem
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=False, # update the statistics of bn
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zero_init_residual=False,
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style='pytorch',
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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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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start_level=1,
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add_extra_convs='on_input',
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num_outs=5,
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upsample_cfg=dict(mode='bilinear')),
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bbox_head=dict(
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type='YOLACTHead',
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num_classes=80,
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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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octave_base_scale=3,
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scales_per_octave=1,
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base_sizes=[8, 16, 32, 64, 128],
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ratios=[0.5, 1.0, 2.0],
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strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
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centers=[(550 * 0.5 / x, 550 * 0.5 / x)
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for x in [69, 35, 18, 9, 5]]),
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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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loss_cls=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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reduction='none',
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loss_weight=1.0),
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
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num_head_convs=1,
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num_protos=32,
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use_ohem=True),
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mask_head=dict(
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type='YOLACTProtonet',
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in_channels=256,
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num_protos=32,
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num_classes=80,
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max_masks_to_train=100,
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loss_mask_weight=6.125),
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segm_head=dict(
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type='YOLACTSegmHead',
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num_classes=80,
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in_channels=256,
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loss_segm=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
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# training and testing settings
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train_cfg=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=0.4,
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min_pos_iou=0.,
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ignore_iof_thr=-1,
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gt_max_assign_all=False),
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# smoothl1_beta=1.,
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allowed_border=-1,
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pos_weight=-1,
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neg_pos_ratio=3,
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debug=False),
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test_cfg=dict(
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nms_pre=1000,
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min_bbox_size=0,
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score_thr=0.05,
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iou_thr=0.5,
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top_k=200,
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max_per_img=100))
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# dataset settings
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], 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, with_mask=True),
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dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)),
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dict(
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type='Expand',
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mean=img_norm_cfg['mean'],
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to_rgb=img_norm_cfg['to_rgb'],
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ratio_range=(1, 4)),
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dict(
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type='MinIoURandomCrop',
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min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
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min_crop_size=0.3),
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dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(
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type='PhotoMetricDistortion',
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brightness_delta=32,
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contrast_range=(0.5, 1.5),
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saturation_range=(0.5, 1.5),
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hue_delta=18),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
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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=(img_size, img_size),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=False),
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dict(type='Normalize', **img_norm_cfg),
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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=8,
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
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optimizer_config = dict()
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# learning policy
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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=0.1,
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step=[20, 42, 49, 52])
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runner = dict(type='EpochBasedRunner', max_epochs=55)
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cudnn_benchmark = True
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evaluation = dict(metric=['bbox', 'segm'])
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