chenbinghui1
Results isssue
Hai, I have trained supervised model just like steps you give (step 1-4), after training supervised baseline model on COCO dataset, I have run semi_dest.sh with corresponding file paths to determine performance of supervised model, and the performance is 12% ( I have used 10% as partially labelled data) but in Table 1 of your paper the result is "23.70 ± 0.22". How I solve this issue??
Secondly, I am training model on 1 GPU device. This is the only difference.
I am waiting for your positive response and guidance please. Thanks.
Hai, I have trained supervised model just like steps you give (step 1-4), after training supervised baseline model on COCO dataset, I have run semi_dest.sh with corresponding file paths to determine performance of supervised model, and the performance is 12% ( I have used 10% as partially labelled data) but in Table 1 of your paper the result is "23.70 ± 0.22". How I solve this issue??
Secondly, I am training model on 1 GPU device. This is the only difference.
I am waiting for your positive response and guidance please. Thanks.
Learning rate and running epochs might be adjusted for different GPU numbers.
I did not understand should I kept learning rate low or high, with one GPU device???? Because, I have result of 12% and your results is 23%. I have added code of config file below? Please tell me which parameters should I tune??? Secondly, does GPU devices matter in achieving same performance like you used 8 GPUs, but i have used 1 GPU.??
================================== Code
**# model settings model = dict( type='FCOS', 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=False), norm_eval=True, style='caffe', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron2/resnet50_caffe')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_output', # use P5 num_outs=5, relu_before_extra_convs=True), bbox_head=dict( type='FCOSHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, strides=[8, 16, 32, 64, 128], norm_on_bbox=True, centerness_on_reg=True, dcn_on_last_conv=False, center_sampling=True, conv_bias=True, loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=1.0), loss_centerness=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), # training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100) )
img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', 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']), ]) ]
dataset_type = 'CocoDataset'
recommend to use absolute path
data_root = '/gruntdata1/bhchen/factory/data/semicoco/' data_root="I:/DSL4/data/semicoco/" data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type=dataset_type, ann_file='I:/DSL4/DSL-main/data_list/coco_semi/semi_supervised/[email protected]', img_prefix=data_root + 'images/full/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file='I:/DSL4/DSL-main/data_list/coco_semi/semi_supervised/instances_val2017.json', img_prefix=data_root + 'valid_images/full/', pipeline=test_pipeline), ## This bellow is used for generating the initial pseudo-labels for unlabeled images when running tools/inference_unlabeled_coco_data.sh test=dict( type=dataset_type, ann_file='I:/DSL4/DSL-main/data_list/coco_semi/semi_supervised/[email protected]', img_prefix=data_root + 'images/full/', pipeline=test_pipeline) ## This bellow is used for testing model performances over COCO dataset, if you use toos/semi_dist_test.sh, please uncomment this bellow codes and comment the above #test=dict( # type = dataset_type, # ann_file = 'data_list/coco_semi/semi_supervised/instances_val2017.json', # img_prefix = data_root + 'valid_images/full/', # pipeline = test_pipeline, # ) )
learning policy
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001,paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict( #delete=True, grad_clip=None)
learning policy
lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3, # 0.01/0.02 data 150-180-200; 0.05 data 90-120-140; 0.1data 50-80-100; full data 16-22-24 step=[50, 80]) runner = dict(type='EpochBasedRunner', max_epochs=100) evaluation = dict(interval=1, metric='bbox')
checkpoint_config = dict(interval=5)
yapf:disable
log_config = dict( interval=10, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ])
yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)]**
@Maryam483 Sorry, I didn't test on 1 gpu. So I cannot tell you how to change the value. You can try by your self, good luck~
The semi-supervised model requires 158 days on my "1 GPU device" . Why??
You have used 8 GPUs but I have 1 GPU device.
I have "Titan XP (12GB VRAM) GPU with 16 GB RAM". I have added screenshot below.
@Maryam483 ; Please check (1) Whether moving model to GPU for computing
If yes, emm, you can change a task. Titan Xp might not be good for detection task.
I use 8 NVIDIA-V100 for training