OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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139 lines
4.8 KiB
139 lines
4.8 KiB
3 years ago
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_base_ = [
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'../_base_/datasets/coco_instance.py',
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
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]
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num_stages = 6
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num_proposals = 100
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model = dict(
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type='QueryInst',
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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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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=0,
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add_extra_convs='on_input',
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num_outs=4),
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rpn_head=dict(
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type='EmbeddingRPNHead',
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num_proposals=num_proposals,
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proposal_feature_channel=256),
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roi_head=dict(
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type='SparseRoIHead',
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num_stages=num_stages,
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stage_loss_weights=[1] * num_stages,
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proposal_feature_channel=256,
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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=2),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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mask_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=[
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dict(
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type='DIIHead',
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num_classes=80,
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num_ffn_fcs=2,
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num_heads=8,
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num_cls_fcs=1,
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num_reg_fcs=3,
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feedforward_channels=2048,
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in_channels=256,
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dropout=0.0,
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ffn_act_cfg=dict(type='ReLU', inplace=True),
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dynamic_conv_cfg=dict(
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type='DynamicConv',
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in_channels=256,
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feat_channels=64,
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out_channels=256,
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input_feat_shape=7,
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act_cfg=dict(type='ReLU', inplace=True),
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norm_cfg=dict(type='LN')),
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loss_bbox=dict(type='L1Loss', loss_weight=5.0),
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loss_iou=dict(type='GIoULoss', loss_weight=2.0),
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loss_cls=dict(
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type='FocalLoss',
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use_sigmoid=True,
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gamma=2.0,
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alpha=0.25,
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loss_weight=2.0),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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clip_border=False,
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target_means=[0., 0., 0., 0.],
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target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
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],
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mask_head=[
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dict(
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type='DynamicMaskHead',
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dynamic_conv_cfg=dict(
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type='DynamicConv',
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in_channels=256,
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feat_channels=64,
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out_channels=256,
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input_feat_shape=14,
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with_proj=False,
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act_cfg=dict(type='ReLU', inplace=True),
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norm_cfg=dict(type='LN')),
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num_convs=4,
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num_classes=80,
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roi_feat_size=14,
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in_channels=256,
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conv_kernel_size=3,
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conv_out_channels=256,
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class_agnostic=False,
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norm_cfg=dict(type='BN'),
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upsample_cfg=dict(type='deconv', scale_factor=2),
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loss_mask=dict(
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type='DiceLoss',
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loss_weight=8.0,
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use_sigmoid=True,
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activate=False,
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eps=1e-5)) for _ in range(num_stages)
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]),
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# training and testing settings
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train_cfg=dict(
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rpn=None,
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rcnn=[
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dict(
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assigner=dict(
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type='HungarianAssigner',
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cls_cost=dict(type='FocalLossCost', weight=2.0),
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reg_cost=dict(type='BBoxL1Cost', weight=5.0),
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iou_cost=dict(type='IoUCost', iou_mode='giou',
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weight=2.0)),
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sampler=dict(type='PseudoSampler'),
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pos_weight=1,
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mask_size=28,
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) for _ in range(num_stages)
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]),
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test_cfg=dict(
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rpn=None, rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
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# optimizer
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optimizer = dict(
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_delete_=True,
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type='AdamW',
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lr=0.0001,
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weight_decay=0.0001,
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paramwise_cfg=dict(
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custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
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optimizer_config = dict(
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_delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
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# learning policy
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lr_config = dict(policy='step', step=[8, 11], warmup_iters=1000)
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runner = dict(type='EpochBasedRunner', max_epochs=12)
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