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| # model settings | |
| model = dict( | |
| type='CascadeRCNN', | |
| num_stages=3, | |
| pretrained='open-mmlab://msra/hrnetv2_w32', | |
| backbone=dict( | |
| type='HRNet', | |
| extra=dict( | |
| stage1=dict( | |
| num_modules=1, | |
| num_branches=1, | |
| block='BOTTLENECK', | |
| num_blocks=(4, ), | |
| num_channels=(64, )), | |
| stage2=dict( | |
| num_modules=1, | |
| num_branches=2, | |
| block='BASIC', | |
| num_blocks=(4, 4), | |
| num_channels=(32, 64)), | |
| stage3=dict( | |
| num_modules=4, | |
| num_branches=3, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4), | |
| num_channels=(32, 64, 128)), | |
| stage4=dict( | |
| num_modules=3, | |
| num_branches=4, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4, 4), | |
| num_channels=(32, 64, 128, 256)))), | |
| neck=dict(type='HRFPN', in_channels=[32, 64, 128, 256], out_channels=256), | |
| 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='FCNMaskHead', | |
| 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))) | |
| # 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=0.05, | |
| nms=dict(type='nms', iou_thr=0.5), | |
| max_per_img=100, | |
| mask_thr_binary=0.5)) | |
| # dataset settings | |
| dataset_type = 'CocoDataset' | |
| data_root = '/content/drive/My Drive/Mmdetection/' | |
| 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), | |
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| 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=2, | |
| workers_per_gpu=2, | |
| train=dict( | |
| type=dataset_type, | |
| ann_file='/content/drive/My Drive/chunk.json', | |
| img_prefix='/content/drive/My Drive/chunk_images/', | |
| pipeline=train_pipeline), | |
| val=dict( | |
| type=dataset_type, | |
| ann_file=data_root + 'VOC2007/test.json', | |
| img_prefix=data_root + 'VOC2007/Test/', | |
| pipeline=test_pipeline), | |
| test=dict( | |
| type=dataset_type, | |
| ann_file=data_root + 'VOC2007/test.json', | |
| img_prefix=data_root + 'VOC2007/Test/', | |
| pipeline=test_pipeline)) | |
| # evaluation = dict(interval=1, metric=['bbox']) | |
| # optimizer | |
| optimizer = dict(type='SGD', lr=0.0012, 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,create_symlink=False) | |
| # yapf:disable | |
| log_config = dict( | |
| interval=50, | |
| hooks=[ | |
| dict(type='TextLoggerHook'), | |
| # dict(type='TensorboardLoggerHook') | |
| ]) | |
| # yapf:enable | |
| # runtime settings | |
| total_epochs = 36 | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| work_dir = '/content/drive/My Drive/Mmdetection/new_chunk_cascade_mask_rcnn_hrnetv2p_w32_20e' | |
| load_from = None | |
| resume_from = '/content/drive/My Drive/Mmdetection/new_chunk_cascade_mask_rcnn_hrnetv2p_w32_20e/epoch_30.pth' | |
| workflow = [('train', 1)] | |