Memory problem

[LostStars3]

When I was using faster rcnn or reppoints, I found that using imgs_per_gpu equal to 1 got a larger memory usage than using imgs_per_gpu equal to 2 memory.Also it often an OOM error is reported during the case that imgs_per_gpu equal to 1.

Environment OS: [e.g., Ubuntu 16.04.6] PyTorch version [e.g., 1.1.0] GPU model [one GPU :1080Ti]

[LostStars3]

`# model settings fp16 = dict(loss_scale=512.) model = dict( type='FasterRCNN', pretrained='/home/ai/PJC/detection/mmdetection/pth/resnext101_32x4d-a5af3160.pth', backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', dcn=dict( modulated=False, groups=32, deformable_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True) ), neck=dict( type='NASFPN', 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=[15,30,45,60,90], 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=2, 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='SmoothL1Loss', beta=1.0, 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=1000, max_num=1000, nms_thr=0.3, 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='OHEMSampler', num=32, pos_fraction=0.33, 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=2000, 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=0.7), max_per_img=100)

soft-nms is also supported for rcnn testing

# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)

)

dataset settings

dataset_type = 'VOCDataset' data_root = '/home/ai/PJC/detection/mmdetection/data/VOCdevkit7/' #1-CBIS 2-INbreast 410 3-cut 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', to_float32=True), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion'), dict( type='MinIoURandomCrop', min_ious=(0.5, 0.7, 0.9), min_crop_size=0.5), dict( type='Resize',

img_scale=[(1333, 800)],

    img_scale=[(1200, 800),(1000, 800)],
    keep_ratio=True,
    multiscale_mode='range'),
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', to_float32=True), 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=data_root + 'VOC2007/ImageSets/CV1/trainval.txt', img_prefix=data_root + 'VOC2007/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'VOC2007/ImageSets/CV1/test.txt', img_prefix=data_root + 'VOC2007/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'VOC2007/ImageSets/CV1/test.txt', img_prefix=data_root + 'VOC2007/', pipeline=test_pipeline))

optimizer

optimizer = dict(type='SGD', lr=0.01, 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=None, warmup_iters=0, warmup_ratio=None, step=[15, 30,45,60]) 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 = 100 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/reppoints_moment_x101_dcn_fpn_2x_mt' load_from = None resume_from = None workflow = [('train', 1)] ` Here is my config file

[DRosemei]

@LostStars3 Recently I got stucked when implementing NASFPN, could you share your nasfpn implementation?Thx:)

[zht3344]

@LostStars3 hello,could you share your nasfpn implementation? I want to use it in cascade RCNN ，thx