# model settings
model = dict(type='HybridTaskCascade',
             num_stages=3,
             pretrained='open-mmlab://resnext101_64x4d',
             interleaved=True,
             mask_info_flow=True,
             backbone=dict(type='ResNeXt',
                           depth=101,
                           groups=64,
                           base_width=4,
                           num_stages=4,
                           out_indices=(0, 1, 2, 3),
                           frozen_stages=1,
                           style='pytorch',
                           dcn=dict(modulated=False,
                                    groups=64,
                                    deformable_groups=1,
                                    fallback_on_stride=False),
                           stage_with_dcn=(False, True, True, True)),
             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_scales=[8],
                           anchor_ratios=[0.5, 1.0, 2.0],
                           anchor_strides=[4, 8, 16, 32, 64],
                           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)),
             bbox_roi_extractor=dict(type='SingleRoIExtractor',
                                     roi_layer=dict(type='RoIAlign',
                                                    out_size=7,
                                                    sample_num=2),
                                     out_channels=256,
                                     featmap_strides=[4, 8, 16, 32]),
             bbox_head=[
                 dict(type='SharedFCBBoxHead',
                      num_fcs=2,
                      in_channels=256,
                      fc_out_channels=1024,
                      roi_feat_size=7,
                      num_classes=81,
                      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='SharedFCBBoxHead',
                      num_fcs=2,
                      in_channels=256,
                      fc_out_channels=1024,
                      roi_feat_size=7,
                      num_classes=81,
                      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='SharedFCBBoxHead',
                      num_fcs=2,
                      in_channels=256,
                      fc_out_channels=1024,
                      roi_feat_size=7,
                      num_classes=81,
                      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',
                                                    out_size=14,
                                                    sample_num=2),
                                     out_channels=256,
                                     featmap_strides=[4, 8, 16, 32]),
             mask_head=dict(type='HTCMaskHead',
                            num_convs=4,
                            in_channels=256,
                            conv_out_channels=256,
                            num_classes=81,
                            loss_mask=dict(type='CrossEntropyLoss',
                                           use_mask=True,
                                           loss_weight=1.0)),
             semantic_roi_extractor=dict(type='SingleRoIExtractor',
                                         roi_layer=dict(type='RoIAlign',
                                                        out_size=14,
                                                        sample_num=2),
                                         out_channels=256,
                                         featmap_strides=[8]),
             semantic_head=dict(type='FusedSemanticHead',
                                num_ins=5,
                                fusion_level=1,
                                num_convs=4,
                                in_channels=256,
                                conv_out_channels=256,
                                num_classes=183,
                                ignore_label=255,
                                loss_weight=0.2))
# 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_across_levels=False,
                                   nms_pre=2000,
                                   nms_post=2000,
                                   max_num=2000,
                                   nms_thr=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)
                 ],
                 stage_loss_weights=[1, 0.5, 0.25])
test_cfg = dict(rpn=dict(nms_across_levels=False,
                         nms_pre=1000,
                         nms_post=1000,
                         max_num=1000,
                         nms_thr=0.7,
                         min_bbox_size=0),
                rcnn=dict(score_thr=0.001,
                          nms=dict(type='nms', iou_thr=0.5),
                          max_per_img=100,
                          mask_thr_binary=0.5),
                keep_all_stages=False)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True,
         with_seg=True),
    dict(type='Resize',
         img_scale=[(1600, 400), (1600, 1400)],
         multiscale_mode='range',
         keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='SegResizeFlipPadRescale', scale_factor=1 / 8),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect',
         keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks',
               'gt_semantic_seg']),
]
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(
    imgs_per_gpu=1,
    workers_per_gpu=1,
    train=dict(type=dataset_type,
               ann_file=data_root + 'annotations/instances_train2017.json',
               img_prefix=data_root + 'train2017/',
               seg_prefix=data_root + 'stuffthingmaps/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=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(policy='step',
                 warmup='linear',
                 warmup_iters=500,
                 warmup_ratio=1.0 / 3,
                 step=[16, 19])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 20
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e'
load_from = None
resume_from = None
workflow = [('train', 1)]
