[Fix] batch size for citys in cfg file name (#1977)

Author: MeowZheng

configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py

@@ -1,2 +1,2 @@
-_base_ = './deeplabv3_r50-d8_4xb4-40k_cityscapes-512x1024.py'
+_base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py

@@ -1,2 +1,2 @@
-_base_ = './deeplabv3_r50-d8_4xb4-40k_cityscapes-769x769.py'
+_base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py'
 model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py

@@ -1,2 +1,2 @@
-_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-512x1024.py'
+_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
 model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py

@@ -1,2 +1,2 @@
-_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py'
+_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
 model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py

@@ -1,4 +1,4 @@
-_base_ = './deeplabv3_r101-d8_4xb4-40k_cityscapes-512x1024.py'
+_base_ = './deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py'
 optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
 optim_wrapper = dict(
     _delete_=True,

configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py

@@ -1,4 +1,4 @@
-_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py'
+_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
 model = dict(
     pretrained='open-mmlab://resnet18_v1c',
     backbone=dict(depth=18),

configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py

@@ -1,2 +1,2 @@
-_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py'
+_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
 model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

configs/fastfcn/README.md

@@ -40,11 +40,11 @@ year={2019}
 | Method                    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
 | ------------------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | FastFCN + DeepLabV3       | R-50-D32 | 512x1024  |   80000 | 5.67     | 2.64           | 79.12 | 80.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json)                 |
-| FastFCN + DeepLabV3 (4x4) | R-50-D32 | 512x1024  |   80000 | 9.79     | -              | 79.52 | 80.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
+| FastFCN + DeepLabV3 (4x4) | R-50-D32 | 512x1024  |   80000 | 9.79     | -              | 79.52 | 80.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
 | FastFCN + PSPNet          | R-50-D32 | 512x1024  |   80000 | 5.67     | 4.40           | 79.26 | 80.86         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json)                     |
-| FastFCN + PSPNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 9.94     | -              | 78.76 | 80.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)     |
+| FastFCN + PSPNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 9.94     | -              | 78.76 | 80.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)     |
 | FastFCN + EncNet          | R-50-D32 | 512x1024  |   80000 | 8.15     | 4.77           | 77.97 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json)                     |
-| FastFCN + EncNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 15.45    | -              |  78.6 | 80.25         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
+| FastFCN + EncNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 15.45    | -              |  78.6 | 80.25         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
 
 ### ADE20K
 

configs/fastfcn/fastfcn.yml

@@ -36,7 +36,7 @@ Models:
       mIoU(ms+flip): 80.58
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
-- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024
+- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024
   In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
@@ -49,7 +49,7 @@ Models:
     Metrics:
       mIoU: 79.52
       mIoU(ms+flip): 80.91
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
 - Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
   In Collection: FastFCN
@@ -73,7 +73,7 @@ Models:
       mIoU(ms+flip): 80.86
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
-- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024
+- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
   In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
@@ -86,7 +86,7 @@ Models:
     Metrics:
       mIoU: 78.76
       mIoU(ms+flip): 80.03
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024.py
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
 - Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
   In Collection: FastFCN
@@ -110,7 +110,7 @@ Models:
       mIoU(ms+flip): 79.92
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
-- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024
+- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
   In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
@@ -123,7 +123,7 @@ Models:
     Metrics:
       mIoU: 78.6
       mIoU(ms+flip): 80.25
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
 - Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512
   In Collection: FastFCN

docs/en/user_guides/1_config.md

@@ -13,7 +13,7 @@ The configs that are composed by components from `_base_` are called _primitive_
 For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.
 
 For easy understanding, we recommend contributors to inherit from existing methods.
-For example, if some modification is made base on DeepLabV3, user may first inherit the basic DeepLabV3 structure by specifying `_base_ = ../deeplabv3/deeplabv3_r50-d8_4xb4-40k_cityscapes-512x1024.py`, then modify the necessary fields in the config files.
+For example, if some modification is made base on DeepLabV3, user may first inherit the basic DeepLabV3 structure by specifying `_base_ = ../deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py`, then modify the necessary fields in the config files.
 
 If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxxnet` under `configs`,