Questions about the details of configuration of RN50-CLIP

[JasonLin1998]

I cannot reach the mIoU of RN50-CLIP that was showed in the paper, though I used the configuration mentioned in the README. Could you please tell me what batch size was used and how many GPUs were used. More details of implement are very helpful. I've tried batch size of 16, but only got 38.85 of mIoU. Here is my configuration and log file is putted in the attachment.

''' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( type='EncoderDecoder', pretrained='pretrained/RN50.pt', backbone=dict( type='CLIPResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), dilations=(1, 1, 1, 1), strides=(1, 2, 2, 2), norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, style='pytorch', contract_dilation=True, layers=[3, 4, 6, 3]), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=4), decode_head=dict( type='FPNHead', in_channels=[256, 256, 256, 256], in_index=[0, 1, 2, 3], feature_strides=[4, 8, 16, 32], channels=256, dropout_ratio=0.1, num_classes=150, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), train_cfg=dict(), test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341))) dataset_type = 'ADE20KDataset' data_root = 'data/ade/ADEChallengeData2016' IMG_MEAN = [122.7709383, 116.7460125, 104.09373615000001] IMG_VAR = [68.5005327, 66.6321579, 70.32316304999999] img_norm_cfg = dict( mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/training', ann_dir='annotations/training', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[122.7709383, 116.7460125, 104.09373615000001], std=[68.5005327, 66.6321579, 70.32316304999999], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] cudnn_benchmark = True find_unused_parameters = True optimizer = dict( type='AdamW', lr=0.0001, weight_decay=0.0001, paramwise_cfg=dict( custom_keys=dict( backbone=dict(lr_mult=0.1), norm=dict(decay_mult=0.0)))) optimizer_config = dict() lr_config = dict( policy='poly', power=0.9, min_lr=1e-06, by_epoch=False, warmup='linear', warmup_iters=1500, warmup_ratio=1e-06) runner = dict(type='IterBasedRunner', max_iters=80000) checkpoint_config = dict(by_epoch=False, interval=8000) evaluation = dict(interval=8000, metric='mIoU') work_dir = './work_dirs/fpn_clipres50_test4k' gpu_ids = range(0, 1) '''

20220320_015954.log

[raoyongming]

Hi, thanks for your interest in our work.

You can use bash dist_train.sh configs/denseclip_fpn_res50_512x512_80k.py 8 to reproduce our results. As indicated in the command, we use 8 GPUs to conduct our experiments. The global batch size is 32.

[JasonLin1998]

Hi, thanks for your interest in our work.

You can use bash dist_train.sh configs/denseclip_fpn_res50_512x512_80k.py 8 to reproduce our results. As indicated in the command, we use 8 GPUs to conduct our experiments. The global batch size is 32.

Thanks for your reply. Specifically, I'm in trouble with running RN50-CLIP instead of RN50-DenseCLIP in segmentation. Could you please share more details?

[raoyongming]

We use the same batch size to fairly compare these two models. I think you can just replace the config name in the above command to train RN50-CLIP.

[JasonLin1998]

Thank you! Problem solved.