forked from alibaba/EasyCV
-
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.
update moby pretrained model to deit small (alibaba#10)
* add auto publish config * [bugfix]: fix extract.py for benchmarks * update moby pretrained model to deit small
- Loading branch information
Showing
3 changed files
with
177 additions
and
2 deletions.
There are no files selected for viewing
66 changes: 66 additions & 0 deletions
66
benchmarks/selfsup/classification/imagenet/moby_deit_small_p16_8xb2048_30e_feature.py
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 +1,66 @@ | ||
| _base_ = 'configs/base.py' | ||
|
|
||
| # oss config only works when using oss | ||
| # sync local models and logs to oss | ||
| oss_sync_config = dict(other_file_list=['**/events.out.tfevents*', '**/*log*']) | ||
| oss_io_config = dict( | ||
| ak_id='your oss ak id', | ||
| ak_secret='your oss ak secret', | ||
| hosts='your oss hosts', | ||
| buckets=['your oss buckets']) | ||
|
|
||
| # model settings | ||
| # 1920: merge 4 layers of features, open models/backbones/vit_transfomer_dynamic.py:311: self.forward_return_n_last_blocks | ||
| # 384: default | ||
| feature_num = 1920 | ||
| model = dict( | ||
| type='Classification', | ||
| pretrained=None, | ||
| with_sobel=False, | ||
| backbone=dict(type='BenchMarkMLP', feature_num=feature_num), | ||
| head=dict( | ||
| type='ClsHead', | ||
| with_avg_pool=True, | ||
| in_channels=feature_num, | ||
| num_classes=1000)) | ||
| # dataset settings | ||
| data_source_cfg = dict(type='SSLSourceImageNetFeature') | ||
|
|
||
| root_path = 'linear_eval/imagenet_features/' | ||
| dataset_type = 'ClsDataset' | ||
| train_pipeline = [] | ||
| test_pipeline = [] | ||
|
|
||
| data = dict( | ||
| imgs_per_gpu=2048, # total 2048*8=256, 8GPU linear cls | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| data_source=dict( | ||
| root_path=root_path, training=True, **data_source_cfg), | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| data_source=dict( | ||
| root_path=root_path, training=False, **data_source_cfg), | ||
| pipeline=test_pipeline)) | ||
|
|
||
| # additional hooks | ||
|
|
||
| eval_config = dict(interval=5, gpu_collect=True) | ||
| eval_pipelines = [ | ||
| dict( | ||
| mode='test', | ||
| data=data['val'], | ||
| evaluators=[dict(type='ClsEvaluator', topk=(1, 5))]) | ||
| ] | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='AdamW', lr=0.001, weight_decay=4e-5) | ||
|
|
||
| # learning policy | ||
| lr_config = dict(policy='CosineAnnealing', min_lr=0.0, by_epoch=False) | ||
|
|
||
| checkpoint_config = dict(interval=5) | ||
| # runtime settings | ||
| total_epochs = 30 |
109 changes: 109 additions & 0 deletions
109
configs/selfsup/moby/moby_deit_small_p16_4xb128_300e_tfrecord.py
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 +1,109 @@ | ||
| _base_ = '../../base.py' | ||
|
|
||
| # open oss config when using oss | ||
| # sync local models and logs to oss | ||
| # oss_sync_config = dict(other_file_list=['**/events.out.tfevents*', '**/*log*']) | ||
| # oss_io_config = dict( | ||
| # ak_id='your oss ak id', | ||
| # ak_secret='your oss ak secret', | ||
| # hosts='your oss hosts', | ||
| # buckets=['your oss buckets']) | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='MoBY', | ||
| train_preprocess=['randomGrayScale', 'gaussianBlur'], | ||
| queue_len=4096, | ||
| momentum=0.99, | ||
| pretrained=None, | ||
| backbone=dict( | ||
| type='PytorchImageModelWrapper', | ||
| # model_name='pit_xs_distilled_224', | ||
| # model_name='swin_small_patch4_window7_224', # bad 16G memory will down | ||
| # model_name='swin_tiny_patch4_window7_224', # good 768 | ||
| # model_name='swin_base_patch4_window7_224_in22k', # bad 16G memory will down | ||
| # model_name='vit_deit_tiny_distilled_patch16_224', # good 192, | ||
|
|
||
| # model_name = 'vit_deit_small_distilled_patch16_224', # good 384, | ||
| model_name='dynamic_deit_small_p16', # 384 | ||
| # model_name='xcit_small_12_p16', # 384 | ||
| # model_name='shuffletrans_tiny_p4_w7_224', #768 | ||
| # model_name = 'resnet50', # 2048 | ||
| num_classes=0, | ||
| pretrained=False, | ||
| ), | ||
| neck=dict( | ||
| type='MoBYMLP', | ||
| in_channels=384, | ||
| hid_channels=4096, | ||
| out_channels=256, | ||
| num_layers=2), | ||
| head=dict( | ||
| type='MoBYMLP', | ||
| in_channels=256, | ||
| hid_channels=4096, | ||
| out_channels=256, | ||
| num_layers=2)) | ||
|
|
||
| dataset_type = 'DaliTFRecordMultiViewDataset' | ||
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
| mean = [x * 255 for x in img_norm_cfg['mean']] | ||
| std = [x * 255 for x in img_norm_cfg['std']] | ||
| train_pipeline = [ | ||
| dict(type='DaliImageDecoder'), | ||
| dict(type='DaliRandomResizedCrop', size=224, random_area=(0.2, 1.0)), | ||
| dict( | ||
| type='DaliColorTwist', | ||
| prob=0.8, | ||
| brightness=0.4, | ||
| contrast=0.4, | ||
| saturation=0.4, | ||
| hue=0.4), | ||
| dict( | ||
| type='DaliCropMirrorNormalize', | ||
| crop=[224, 224], | ||
| mean=mean, | ||
| std=std, | ||
| crop_pos_x=[0.0, 1.0], | ||
| crop_pos_y=[0.0, 1.0], | ||
| prob=0.5) | ||
| ] | ||
|
|
||
| data = dict( | ||
| imgs_per_gpu=128, # total 128*4 | ||
| workers_per_gpu=4, | ||
| train=dict( | ||
| type='DaliTFRecordMultiViewDataset', | ||
| data_source=dict( | ||
| type='ClsSourceImageNetTFRecord', | ||
| file_pattern='data/imagenet_tfrecord/train-*', | ||
| # root='data/imagenet_tfrecord/', # pick one of `file_pattern` and `root&list_file` | ||
| # list_file='data/imagenet_tfrecord/train_list.txt' | ||
| ), | ||
| num_views=[1, 1], | ||
| pipelines=[train_pipeline, train_pipeline], | ||
| )) | ||
|
|
||
| # optimizer | ||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.001, | ||
| weight_decay=0.05, | ||
| trans_weight_decay_set=['backbone']) # 0.001 for 512 | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='CosineAnnealing', | ||
| min_lr=5e-6, | ||
| warmup='linear', | ||
| warmup_iters=5, | ||
| warmup_ratio=0.0001, | ||
| warmup_by_epoch=True) | ||
|
|
||
| checkpoint_config = dict(interval=10) | ||
|
|
||
| # runtime settings | ||
| total_epochs = 300 | ||
|
|
||
| # export config | ||
| export = dict(export_neck=False) | ||
| checkpoint_sync_export = True |
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