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[Improvement] Rename config files according to the config name standa…
…rd. (open-mmlab#508) * Update tnt config * Rename config files * Update docs * Update metafile in dev_scripts * Fix some files * Remove fp16 metafile and README. * Fix names in metafiles.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,6 @@ | ||
| _base_ = ['../resnet/resnet50_b32x8_imagenet.py'] | ||
| _base_ = '../resnet/resnet50_8xb32-fp16-dynamic_in1k.py' | ||
|
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| # fp16 settings | ||
| fp16 = dict(loss_scale='dynamic') | ||
| _deprecation_ = dict( | ||
| expected='../resnet/resnet50_8xb32-fp16-dynamic_in1k.py', | ||
| reference='https://github.com/open-mmlab/mmclassification/pull/508', | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,6 @@ | ||
| _base_ = ['../resnet/resnet50_b32x8_imagenet.py'] | ||
| _base_ = '../resnet/resnet50_8xb32-fp16_in1k.py' | ||
|
|
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| # fp16 settings | ||
| fp16 = dict(loss_scale=512.) | ||
| _deprecation_ = dict( | ||
| expected='../resnet/resnet50_8xb32-fp16_in1k.py', | ||
| reference='https://github.com/open-mmlab/mmclassification/pull/508', | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,6 @@ | ||
| _base_ = [ | ||
| '../_base_/models/mobilenet_v2_1x.py', | ||
| '../_base_/datasets/imagenet_bs32_pil_resize.py', | ||
| '../_base_/schedules/imagenet_bs256_epochstep.py', | ||
| '../_base_/default_runtime.py' | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| _base_ = [ | ||
| '../_base_/models/mobilenet_v2_1x.py', | ||
| '../_base_/datasets/imagenet_bs32_pil_resize.py', | ||
| '../_base_/schedules/imagenet_bs256_epochstep.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
| _base_ = 'mobilenet-v2_8xb32_in1k.py' | ||
|
|
||
| _deprecation_ = dict( | ||
| expected='mobilenet-v2_8xb32_in1k.py', | ||
| reference='https://github.com/open-mmlab/mmclassification/pull/508', | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,158 @@ | ||
| # Refer to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification | ||
| # ---------------------------- | ||
| # -[x] auto_augment='imagenet' | ||
| # -[x] batch_size=128 (per gpu) | ||
| # -[x] epochs=600 | ||
| # -[x] opt='rmsprop' | ||
| # -[x] lr=0.064 | ||
| # -[x] eps=0.0316 | ||
| # -[x] alpha=0.9 | ||
| # -[x] weight_decay=1e-05 | ||
| # -[x] momentum=0.9 | ||
| # -[x] lr_gamma=0.973 | ||
| # -[x] lr_step_size=2 | ||
| # -[x] nproc_per_node=8 | ||
| # -[x] random_erase=0.2 | ||
| # -[x] workers=16 (workers_per_gpu) | ||
| # - modify: RandomErasing use RE-M instead of RE-0 | ||
|
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| _base_ = [ | ||
| '../_base_/models/mobilenet-v3-large_8xb32_in1k.py', | ||
| '../_base_/datasets/imagenet_bs32_pil_resize.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
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| policies = [ | ||
| [ | ||
| dict(type='Posterize', bits=4, prob=0.4), | ||
| dict(type='Rotate', angle=30., prob=0.6) | ||
| ], | ||
| [ | ||
| dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), | ||
| dict(type='AutoContrast', prob=0.6) | ||
| ], | ||
| [dict(type='Equalize', prob=0.8), | ||
| dict(type='Equalize', prob=0.6)], | ||
| [ | ||
| dict(type='Posterize', bits=5, prob=0.6), | ||
| dict(type='Posterize', bits=5, prob=0.6) | ||
| ], | ||
| [ | ||
| dict(type='Equalize', prob=0.4), | ||
| dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) | ||
| ], | ||
| [ | ||
| dict(type='Equalize', prob=0.4), | ||
| dict(type='Rotate', angle=30 / 9 * 8, prob=0.8) | ||
| ], | ||
| [ | ||
| dict(type='Solarize', thr=256 / 9 * 6, prob=0.6), | ||
| dict(type='Equalize', prob=0.6) | ||
| ], | ||
| [dict(type='Posterize', bits=6, prob=0.8), | ||
| dict(type='Equalize', prob=1.)], | ||
| [ | ||
| dict(type='Rotate', angle=10., prob=0.2), | ||
| dict(type='Solarize', thr=256 / 9, prob=0.6) | ||
| ], | ||
| [ | ||
| dict(type='Equalize', prob=0.6), | ||
| dict(type='Posterize', bits=5, prob=0.4) | ||
| ], | ||
| [ | ||
| dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), | ||
| dict(type='ColorTransform', magnitude=0., prob=0.4) | ||
| ], | ||
| [ | ||
| dict(type='Rotate', angle=30., prob=0.4), | ||
| dict(type='Equalize', prob=0.6) | ||
| ], | ||
| [dict(type='Equalize', prob=0.0), | ||
| dict(type='Equalize', prob=0.8)], | ||
| [dict(type='Invert', prob=0.6), | ||
| dict(type='Equalize', prob=1.)], | ||
| [ | ||
| dict(type='ColorTransform', magnitude=0.4, prob=0.6), | ||
| dict(type='Contrast', magnitude=0.8, prob=1.) | ||
| ], | ||
| [ | ||
| dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), | ||
| dict(type='ColorTransform', magnitude=0.2, prob=1.) | ||
| ], | ||
| [ | ||
| dict(type='ColorTransform', magnitude=0.8, prob=0.8), | ||
| dict(type='Solarize', thr=256 / 9 * 2, prob=0.8) | ||
| ], | ||
| [ | ||
| dict(type='Sharpness', magnitude=0.7, prob=0.4), | ||
| dict(type='Invert', prob=0.6) | ||
| ], | ||
| [ | ||
| dict( | ||
| type='Shear', | ||
| magnitude=0.3 / 9 * 5, | ||
| prob=0.6, | ||
| direction='horizontal'), | ||
| dict(type='Equalize', prob=1.) | ||
| ], | ||
| [ | ||
| dict(type='ColorTransform', magnitude=0., prob=0.4), | ||
| dict(type='Equalize', prob=0.6) | ||
| ], | ||
| [ | ||
| dict(type='Equalize', prob=0.4), | ||
| dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) | ||
| ], | ||
| [ | ||
| dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), | ||
| dict(type='AutoContrast', prob=0.6) | ||
| ], | ||
| [dict(type='Invert', prob=0.6), | ||
| dict(type='Equalize', prob=1.)], | ||
| [ | ||
| dict(type='ColorTransform', magnitude=0.4, prob=0.6), | ||
| dict(type='Contrast', magnitude=0.8, prob=1.) | ||
| ], | ||
| [dict(type='Equalize', prob=0.8), | ||
| dict(type='Equalize', prob=0.6)], | ||
| ] | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='RandomResizedCrop', size=224, backend='pillow'), | ||
| dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), | ||
| dict(type='AutoAugment', policies=policies), | ||
| dict( | ||
| type='RandomErasing', | ||
| erase_prob=0.2, | ||
| mode='const', | ||
| min_area_ratio=0.02, | ||
| max_area_ratio=1 / 3, | ||
| fill_color=img_norm_cfg['mean']), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='ToTensor', keys=['gt_label']), | ||
| dict(type='Collect', keys=['img', 'gt_label']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| samples_per_gpu=128, | ||
| workers_per_gpu=4, | ||
| train=dict(pipeline=train_pipeline)) | ||
| evaluation = dict(interval=10, metric='accuracy') | ||
|
|
||
| # optimizer | ||
| optimizer = dict( | ||
| type='RMSprop', | ||
| lr=0.064, | ||
| alpha=0.9, | ||
| momentum=0.9, | ||
| eps=0.0316, | ||
| weight_decay=1e-5) | ||
| optimizer_config = dict(grad_clip=None) | ||
| # learning policy | ||
| lr_config = dict(policy='step', step=2, gamma=0.973, by_epoch=True) | ||
| runner = dict(type='EpochBasedRunner', max_epochs=600) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| _base_ = [ | ||
| '../_base_/models/mobilenet-v3-small_8xb16_cifar.py', | ||
| '../_base_/datasets/cifar10_bs16.py', | ||
| '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| lr_config = dict(policy='step', step=[120, 170]) | ||
| runner = dict(type='EpochBasedRunner', max_epochs=200) |
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