mmdetection-2.26.0/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py

_base_ = [
 '../_base_/datasets/coco_instance.py',
 '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
 type='HybridTaskCascade',
 backbone=dict(
 type='ResNet',
 depth=50,
 num_stages=4,
 out_indices=(0, 1, 2, 3),
 frozen_stages=1,
 norm_cfg=dict(type='BN', requires_grad=True),
 norm_eval=True,
 style='pytorch',
 init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
 neck=dict(
 type='FPN',
 in_channels=[256, 512, 1024, 2048],
 out_channels=256,
 num_outs=5),
 rpn_head=dict(
 type='RPNHead',
 in_channels=256,
 feat_channels=256,
 anchor_generator=dict(
 type='AnchorGenerator',
 scales=[8],
 ratios=[0.5, 1.0, 2.0],
 strides=[4, 8, 16, 32, 64]),
 bbox_coder=dict(
 type='DeltaXYWHBBoxCoder',
 target_means=[.0, .0, .0, .0],
 target_stds=[1.0, 1.0, 1.0, 1.0]),
 loss_cls=dict(
 type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
 loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
 roi_head=dict(
 type='HybridTaskCascadeRoIHead',
 interleaved=True,
 mask_info_flow=True,
 num_stages=3,
 stage_loss_weights=[1, 0.5, 0.25],
 bbox_roi_extractor=dict(
 type='SingleRoIExtractor',
 roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
 out_channels=256,
 featmap_strides=[4, 8, 16, 32]),
 bbox_head=[
 dict(
 type='Shared2FCBBoxHead',
 in_channels=256,
 fc_out_channels=1024,
 roi_feat_size=7,
 num_classes=80,
 bbox_coder=dict(
 type='DeltaXYWHBBoxCoder',
 target_means=[0., 0., 0., 0.],
 target_stds=[0.1, 0.1, 0.2, 0.2]),
 reg_class_agnostic=True,
 loss_cls=dict(
 type='CrossEntropyLoss',
 use_sigmoid=False,
 loss_weight=1.0),
 loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
 loss_weight=1.0)),
 dict(
 type='Shared2FCBBoxHead',
 in_channels=256,
 fc_out_channels=1024,
 roi_feat_size=7,
 num_classes=80,
 bbox_coder=dict(
 type='DeltaXYWHBBoxCoder',
 target_means=[0., 0., 0., 0.],
 target_stds=[0.05, 0.05, 0.1, 0.1]),
 reg_class_agnostic=True,
 loss_cls=dict(
 type='CrossEntropyLoss',
 use_sigmoid=False,
 loss_weight=1.0),
 loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
 loss_weight=1.0)),
 dict(
 type='Shared2FCBBoxHead',
 in_channels=256,
 fc_out_channels=1024,
 roi_feat_size=7,
 num_classes=80,
 bbox_coder=dict(
 type='DeltaXYWHBBoxCoder',
 target_means=[0., 0., 0., 0.],
 target_stds=[0.033, 0.033, 0.067, 0.067]),
 reg_class_agnostic=True,
 loss_cls=dict(
 type='CrossEntropyLoss',
 use_sigmoid=False,
 loss_weight=1.0),
 loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
 ],
 mask_roi_extractor=dict(
 type='SingleRoIExtractor',
 roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
 out_channels=256,
 featmap_strides=[4, 8, 16, 32]),
 mask_head=[
 dict(
 type='HTCMaskHead',
 with_conv_res=False,
 num_convs=4,
 in_channels=256,
 conv_out_channels=256,
 num_classes=80,
 loss_mask=dict(
 type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
 dict(
 type='HTCMaskHead',
 num_convs=4,
 in_channels=256,
 conv_out_channels=256,
 num_classes=80,
 loss_mask=dict(
 type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
 dict(
 type='HTCMaskHead',
 num_convs=4,
 in_channels=256,
 conv_out_channels=256,
 num_classes=80,
 loss_mask=dict(
 type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
 ]),
 # model training and testing settings
 train_cfg=dict(
 rpn=dict(
 assigner=dict(
 type='MaxIoUAssigner',
 pos_iou_thr=0.7,
 neg_iou_thr=0.3,
 min_pos_iou=0.3,
 ignore_iof_thr=-1),
 sampler=dict(
 type='RandomSampler',
 num=256,
 pos_fraction=0.5,
 neg_pos_ub=-1,
 add_gt_as_proposals=False),
 allowed_border=0,
 pos_weight=-1,
 debug=False),
 rpn_proposal=dict(
 nms_pre=2000,
 max_per_img=2000,
 nms=dict(type='nms', iou_threshold=0.7),
 min_bbox_size=0),
 rcnn=[
 dict(
 assigner=dict(
 type='MaxIoUAssigner',
 pos_iou_thr=0.5,
 neg_iou_thr=0.5,
 min_pos_iou=0.5,
 ignore_iof_thr=-1),
 sampler=dict(
 type='RandomSampler',
 num=512,
 pos_fraction=0.25,
 neg_pos_ub=-1,
 add_gt_as_proposals=True),
 mask_size=28,
 pos_weight=-1,
 debug=False),
 dict(
 assigner=dict(
 type='MaxIoUAssigner',
 pos_iou_thr=0.6,
 neg_iou_thr=0.6,
 min_pos_iou=0.6,
 ignore_iof_thr=-1),
 sampler=dict(
 type='RandomSampler',
 num=512,
 pos_fraction=0.25,
 neg_pos_ub=-1,
 add_gt_as_proposals=True),
 mask_size=28,
 pos_weight=-1,
 debug=False),
 dict(
 assigner=dict(
 type='MaxIoUAssigner',
 pos_iou_thr=0.7,
 neg_iou_thr=0.7,
 min_pos_iou=0.7,
 ignore_iof_thr=-1),
 sampler=dict(
 type='RandomSampler',
 num=512,
 pos_fraction=0.25,
 neg_pos_ub=-1,
 add_gt_as_proposals=True),
 mask_size=28,
 pos_weight=-1,
 debug=False)
 ]),
 test_cfg=dict(
 rpn=dict(
 nms_pre=1000,
 max_per_img=1000,
 nms=dict(type='nms', iou_threshold=0.7),
 min_bbox_size=0),
 rcnn=dict(
 score_thr=0.001,
 nms=dict(type='nms', iou_threshold=0.5),
 max_per_img=100,
 mask_thr_binary=0.5)))
img_norm_cfg = dict(
 mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
test_pipeline = [
 dict(type='LoadImageFromFile'),
 dict(
 type='MultiScaleFlipAug',
 img_scale=(1333, 800),
 flip=False,
 transforms=[
 dict(type='Resize', keep_ratio=True),
 dict(type='RandomFlip', flip_ratio=0.5),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='Pad', size_divisor=32),
 dict(type='ImageToTensor', keys=['img']),
 dict(type='Collect', keys=['img']),
 ])
]
data = dict(
 val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))