mirror of https://github.com/open-mmlab/mmpose
243 lines
8.3 KiB
Python
243 lines
8.3 KiB
Python
model = dict(
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type='MaskRCNN',
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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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num_outs=5),
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rpn_head=dict(
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type='RPNHead',
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in_channels=256,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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scales=[8],
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ratios=[0.5, 1.0, 2.0],
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strides=[4, 8, 16, 32, 64]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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roi_head=dict(
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type='StandardRoIHead',
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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=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='Shared2FCBBoxHead',
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=80,
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[0.1, 0.1, 0.2, 0.2]),
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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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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=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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mask_head=dict(
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type='FCNMaskHead',
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num_convs=4,
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in_channels=256,
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conv_out_channels=256,
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num_classes=80,
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loss_mask=dict(
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type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
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train_cfg=dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(
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nms_pre=2000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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min_pos_iou=0.5,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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mask_size=28,
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rpn=dict(
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nms_pre=1000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100,
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mask_thr_binary=0.5)))
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type='CocoDataset',
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ann_file='data/coco/annotations/instances_train2017.json',
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img_prefix='data/coco/train2017/',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(
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type='Collect',
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keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
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]),
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val=dict(
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type='CocoDataset',
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ann_file='data/coco/annotations/instances_val2017.json',
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img_prefix='data/coco/val2017/',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]),
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test=dict(
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type='CocoDataset',
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ann_file='data/coco/annotations/instances_val2017.json',
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img_prefix='data/coco/val2017/',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]))
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evaluation = dict(metric=['bbox', 'segm'])
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optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
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optimizer_config = dict(grad_clip=None)
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lr_config = dict(
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policy='step',
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warmup='linear',
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warmup_iters=500,
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warmup_ratio=0.001,
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step=[16, 22])
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runner = dict(type='EpochBasedRunner', max_epochs=24)
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checkpoint_config = dict(interval=1)
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
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custom_hooks = [dict(type='NumClassCheckHook')]
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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