forked from open-mmlab/mmsegmentation
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* use urls for human detection models * delete erroneous results in README
- Loading branch information
Showing
4 changed files
with
214 additions
and
15 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,186 @@ | ||
| checkpoint_config = dict(interval=1) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| # yapf:enable | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 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=500, | ||
| warmup_ratio=0.001, | ||
| step=[8, 11]) | ||
| total_epochs = 12 | ||
|
|
||
| model = dict( | ||
| type='FasterRCNN', | ||
| pretrained='torchvision://resnet50', | ||
| 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'), | ||
| 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='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='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=False, | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_bbox=dict(type='L1Loss', 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_across_levels=False, | ||
| nms_pre=2000, | ||
| nms_post=1000, | ||
| max_num=1000, | ||
| 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, | ||
| match_low_quality=False, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| type='RandomSampler', | ||
| num=512, | ||
| pos_fraction=0.25, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=True), | ||
| pos_weight=-1, | ||
| debug=False)) | ||
| 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_threshold=0.5), | ||
| max_per_img=100) | ||
| # soft-nms is also supported for rcnn testing | ||
| # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) | ||
| ) | ||
|
|
||
| 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), | ||
| 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']), | ||
| ] | ||
| 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( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + '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)) | ||
| evaluation = dict(interval=1, metric='bbox') |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters