[Improvement] Rename config files according to the config name standard.

.dev_scripts/benchmark_regression/bench_train.yml

@@ -6,7 +6,7 @@ Models:
           Top 1 Accuracy: 73.85
           Top 5 Accuracy: 91.53
     Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
-    Config: configs/resnet/resnet34_b32x8_imagenet.py
+    Config: configs/resnet/resnet34_8xb32_in1k.py
     Gpus: 8
 
   - Name: vgg11bn
@@ -16,7 +16,7 @@ Models:
           Top 1 Accuracy: 70.75
           Top 5 Accuracy: 90.12
     Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
-    Config: configs/vgg/vgg11bn_b32x8_imagenet.py
+    Config: configs/vgg/vgg11bn_8xb32_in1k.py
     Gpus: 8
 
   - Name: seresnet50
@@ -26,7 +26,7 @@ Models:
           Top 1 Accuracy: 77.74
           Top 5 Accuracy: 93.84
     Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
-    Config: configs/seresnet/seresnet50_b32x8_imagenet.py
+    Config: configs/seresnet/seresnet50_8xb32_in1k.py
     Gpus: 8
 
   - Name: resnext50
@@ -36,7 +36,7 @@ Models:
           Top 1 Accuracy: 77.92
           Top 5 Accuracy: 93.74
     Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
-    Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
+    Config: configs/resnext/resnext50-32x4d_8xb32_in1k.py
     Gpus: 8
 
   - Name: mobilenet
@@ -46,7 +46,7 @@ Models:
           Top 1 Accuracy: 71.86
           Top 5 Accuracy: 90.42
     Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
-    Config: configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
+    Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
     Gpus: 8
     Months:
       - 1
@@ -61,7 +61,7 @@ Models:
           Top 1 Accuracy: 68.13
           Top 5 Accuracy: 87.81
     Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
-    Config: configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
+    Config: configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py
     Gpus: 16
     Months:
       - 2
@@ -76,7 +76,7 @@ Models:
         Top 1 Accuracy: 81.18
         Top 5 Accuracy: 95.61
     Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
-    Config: configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py
+    Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py
     Gpus: 16
     Months:
       - 3

configs/fp16/README.md

@@ -17,4 +17,4 @@
 
 |         Model         | Params(M) | Flops(G) | Mem (GB) | Top-1 (%) | Top-5 (%) | Config | Download |
 |:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:| :---------:|:--------:|
-| ResNet-50             | 25.56     | 4.12     |    1.9    |76.30 | 93.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/fp16/resnet50_b32x8_fp16_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) | [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) |
+| ResNet-50             | 25.56     | 4.12     |    1.9    |76.30 | 93.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/fp16/resnet50_8xb32-fp16_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) | [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) |

configs/fp16/metafile.yml

@@ -32,4 +32,4 @@ Models:
           Top 1 Accuracy: 76.30
           Top 5 Accuracy: 93.07
     Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth
-    Config: configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py
+    Config: configs/fp16/resnet50_8xb32-fp16-dynamic_in1k.py

configs/fp16/resnet50_8xb32-fp16-dynamic_in1k.py

@@ -0,0 +1,4 @@
+_base_ = ['../resnet/resnet50_8xb32_in1k.py']
+
+# fp16 settings
+fp16 = dict(loss_scale='dynamic')

configs/fp16/resnet50_8xb32-fp16_in1k.py

@@ -0,0 +1,4 @@
+_base_ = ['../resnet/resnet50_8xb32_in1k.py']
+
+# fp16 settings
+fp16 = dict(loss_scale=512.)

configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py

@@ -1,4 +1,6 @@
-_base_ = ['../resnet/resnet50_b32x8_imagenet.py']
+_base_ = 'resnet50_8xb32-fp16-dynamic_in1k.py'
 
-# fp16 settings
-fp16 = dict(loss_scale='dynamic')
+_deprecation_ = dict(
+    expected='resnet50_8xb32-fp16-dynamic_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)

configs/fp16/resnet50_b32x8_fp16_imagenet.py

@@ -1,4 +1,6 @@
-_base_ = ['../resnet/resnet50_b32x8_imagenet.py']
+_base_ = 'resnet50_8xb32-fp16_in1k.py'
 
-# fp16 settings
-fp16 = dict(loss_scale=512.)
+_deprecation_ = dict(
+    expected='resnet50_8xb32-fp16_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)

configs/mobilenet_v2/README.md

@@ -24,4 +24,4 @@
 
 |         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
 |:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
-| MobileNet V2          | 3.5       | 0.319    | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
+| MobileNet V2          | 3.5       | 0.319    | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |

configs/mobilenet_v2/metafile.yml

@@ -31,4 +31,4 @@ Models:
           Top 5 Accuracy: 90.42
         Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
-    Config: configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
+    Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py

configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py

@@ -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'
+]

configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py

@@ -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',
+)

configs/mobilenet_v3/metafile.yml

@@ -26,7 +26,7 @@ Models:
           Top 5 Accuracy: 87.41
         Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth
-    Config: configs/mobilenet_v3/mobilenet_v3_small_imagenet.py
+    Config: configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py
   - Name: mobilenet_v3_large_imagenet
     Metadata:
       FLOPs: 230000000
@@ -39,4 +39,4 @@ Models:
           Top 5 Accuracy: 91.34
         Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth
-    Config: configs/mobilenet_v3/mobilenet_v3_large_imagenet.py
+    Config: configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py

configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py

@@ -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
+
+_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)
+
+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)

configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py

@@ -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)