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_base_ = [ |
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'mmdet::_base_/datasets/coco_instance.py', |
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'mmdet::_base_/schedules/schedule_1x.py', |
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'mmdet::_base_/default_runtime.py' |
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] |
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custom_imports = dict( |
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imports=['projects.SparseInst.sparseinst'], allow_failed_imports=False) |
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model = dict( |
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type='SparseInst', |
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data_preprocessor=dict( |
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type='DetDataPreprocessor', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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bgr_to_rgb=True, |
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pad_mask=True, |
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pad_size_divisor=32), |
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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=(1, 2, 3), |
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frozen_stages=0, |
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norm_cfg=dict(type='BN', requires_grad=False), |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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encoder=dict( |
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type='InstanceContextEncoder', |
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in_channels=[512, 1024, 2048], |
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out_channels=256), |
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decoder=dict( |
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type='BaseIAMDecoder', |
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in_channels=256 + 2, |
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num_classes=80, |
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ins_dim=256, |
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ins_conv=4, |
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mask_dim=256, |
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mask_conv=4, |
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kernel_dim=128, |
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scale_factor=2.0, |
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output_iam=False, |
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num_masks=100), |
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criterion=dict( |
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type='SparseInstCriterion', |
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num_classes=80, |
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assigner=dict(type='SparseInstMatcher', alpha=0.8, beta=0.2), |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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alpha=0.25, |
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gamma=2.0, |
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reduction='sum', |
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loss_weight=2.0), |
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loss_obj=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=True, |
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reduction='mean', |
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loss_weight=1.0), |
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loss_mask=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=True, |
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reduction='mean', |
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loss_weight=5.0), |
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loss_dice=dict( |
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type='DiceLoss', |
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use_sigmoid=True, |
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reduction='sum', |
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eps=5e-5, |
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loss_weight=2.0), |
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), |
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test_cfg=dict(score_thr=0.005, mask_thr_binary=0.45)) |
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backend = 'pillow' |
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train_pipeline = [ |
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dict( |
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type='LoadImageFromFile', |
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backend_args={{_base_.backend_args}}, |
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imdecode_backend=backend), |
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dict( |
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type='LoadAnnotations', |
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with_bbox=True, |
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with_mask=True, |
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poly2mask=False), |
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dict( |
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type='RandomChoiceResize', |
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scales=[(416, 853), (448, 853), (480, 853), (512, 853), (544, 853), |
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(576, 853), (608, 853), (640, 853)], |
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keep_ratio=True, |
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backend=backend), |
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dict(type='RandomFlip', prob=0.5), |
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dict(type='PackDetInputs') |
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] |
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test_pipeline = [ |
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dict( |
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type='LoadImageFromFile', |
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backend_args={{_base_.backend_args}}, |
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imdecode_backend=backend), |
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dict(type='Resize', scale=(640, 853), keep_ratio=True, backend=backend), |
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dict( |
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type='PackDetInputs', |
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', |
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'scale_factor')) |
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] |
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train_dataloader = dict( |
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batch_size=8, |
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num_workers=8, |
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sampler=dict(type='InfiniteSampler'), |
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dataset=dict(pipeline=train_pipeline)) |
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test_dataloader = dict(dataset=dict(pipeline=test_pipeline)) |
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val_dataloader = test_dataloader |
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val_evaluator = dict(metric='segm') |
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test_evaluator = val_evaluator |
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict(_delete_=True, type='AdamW', lr=0.00005, weight_decay=0.05)) |
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train_cfg = dict( |
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_delete_=True, |
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type='IterBasedTrainLoop', |
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max_iters=270000, |
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val_interval=10000) |
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param_scheduler = [ |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=270000, |
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by_epoch=False, |
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milestones=[210000, 250000], |
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gamma=0.1) |
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] |
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default_hooks = dict( |
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checkpoint=dict(by_epoch=False, interval=10000, max_keep_ckpts=3)) |
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log_processor = dict(by_epoch=False) |
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auto_scale_lr = dict(base_batch_size=64, enable=True) |
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