_base_ = ['configs/base.py'] CLASSES = [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] norm_cfg = dict(type='SyncBN', requires_grad=True) # model settings model = dict( type='MaskRCNN', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(1, 2, 3, 4), frozen_stages=-1, norm_cfg=norm_cfg, norm_eval=False), # mmdet ResNet # backbone=dict( # type='ResNet', # depth=50, # num_stages=4, # out_indices=(0, 1, 2, 3), # frozen_stages=-1, # norm_cfg=norm_cfg, # norm_eval=False, # style='pytorch', # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', norm_cfg=norm_cfg, 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='L1Loss', loss_weight=1.0)), roi_head=dict( type='StandardRoIHead', 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='Shared4Conv1FCBBoxHead', norm_cfg=norm_cfg, 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=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='L1Loss', 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='FCNMaskHead', norm_cfg=norm_cfg, 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, match_low_quality=True, 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=-1, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=2000, max_per_img=1000, 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, match_low_quality=True, 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=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100, mask_thr_binary=0.5))) mmlab_modules = [ dict(type='mmdet', name='MaskRCNN', module='model'), # dict(type=MMDET, name='ResNet', module='backbone'), # comment out, use EasyCV ResNet dict(type='mmdet', name='FPN', module='neck'), dict(type='mmdet', name='RPNHead', module='head'), dict(type='mmdet', name='StandardRoIHead', module='head'), ] # dataset settings 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='MMResize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='MMRandomFlip', flip_ratio=0.5), dict(type='MMNormalize', **img_norm_cfg), dict(type='MMPad', size_divisor=32), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'], meta_keys=('filename', 'ori_filename', 'ori_shape', 'ori_img_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg')) ] test_pipeline = [ dict( type='MMMultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='MMResize', keep_ratio=True), dict(type='MMRandomFlip'), dict(type='MMNormalize', **img_norm_cfg), dict(type='MMPad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict( type='Collect', keys=['img'], meta_keys=('filename', 'ori_filename', 'ori_shape', 'ori_img_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg')), ]) ] train_dataset = dict( type='DetDataset', data_source=dict( type='DetSourceCoco', ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True, with_mask=True) ], classes=CLASSES, filter_empty_gt=True, iscrowd=False, ), pipeline=train_pipeline) val_dataset = dict( type='DetDataset', imgs_per_gpu=1, data_source=dict( type='DetSourceCoco', ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True) ], classes=CLASSES, test_mode=True, iscrowd=True), pipeline=test_pipeline) data = dict( imgs_per_gpu=2, workers_per_gpu=2, train=train_dataset, val=val_dataset) checkpoint_config = dict(interval=1) # optimizer optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.001, step=[8, 11]) total_epochs = 12 # evaluation eval_config = dict(interval=1, gpu_collect=False) eval_pipelines = [ dict( mode='test', evaluators=[ dict(type='CocoDetectionEvaluator', classes=CLASSES), dict(type='CocoMaskEvaluator', classes=CLASSES) ], ) ]