Release MMSegmentation v1.1.0

Author: xiexinch

.circleci/test.yml

@@ -61,10 +61,11 @@ jobs:
           command: |
             pip install git+https://github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
-            mim install 'mmcv>=2.0.0rc4'
-            pip install mmcls==1.0.0rc6
-            pip install git+https://github.com/open-mmlab/mmdetection.git@main
+            mim install mmcv>=2.0.0
+            pip install mmpretrain>=1.0.0rc7
+            pip install mmdet>=3.0.0
             pip install -r requirements/tests.txt -r requirements/optional.txt
+            python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - run:
           name: Build and install
           command: |
@@ -96,21 +97,21 @@ jobs:
           name: Clone Repos
           command: |
             git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
-            git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
       - run:
           name: Build Docker image
           command: |
             docker build .circleci/docker -t mmseg:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
-            docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmclassification:/mmclassification -v /home/circleci/mmdetection:/mmdetection -w /mmseg --name mmseg mmseg:gpu
+            docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmpretrain:/mmpretrain -v /home/circleci/mmdetection:/mmdetection -w /mmseg --name mmseg mmseg:gpu
       - run:
           name: Install mmseg dependencies
           command: |
             docker exec mmseg pip install -e /mmengine
             docker exec mmseg pip install -U openmim
-            docker exec mmseg mim install 'mmcv>=2.0.0rc4'
-            docker exec mmseg pip install mmcls==1.0.0rc6
-            docker exec mmseg pip install -e /mmdetection
+            docker exec mmseg mim install mmcv>=2.0.0
+            docker exec mmseg pip install mmpretrain>=1.0.0rc7
+            docker exec mmseg mim install mmdet>=3.0.0
             docker exec mmseg pip install -r requirements/tests.txt -r requirements/optional.txt
+            docker exec mmseg python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - run:
           name: Build and install
           command: |
@@ -144,8 +145,8 @@ workflows:
                 - main
       - build_cpu:
           name: minimum_version_cpu
-          torch: 1.6.0
-          torchvision: 0.7.0
+          torch: 1.8.1
+          torchvision: 0.9.1
           python: "3.7"
           requires:
             - lint
@@ -176,10 +177,10 @@ workflows:
     jobs:
       - build_cuda:
           name: minimum_version_gpu
-          torch: 1.6.0
+          torch: 1.8.1
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          cuda: "10.1"
+          cuda: "10.2"
           filters:
             branches:
               only:

.dev/batch_test_list.py renamed to .dev_scripts/batch_test_list.py

@@ -0,0 +1,56 @@
+ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py
+apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py
+beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py
+bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
+bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
+ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py
+convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py
+danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
+deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
+dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py
+dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py
+emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py
+erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
+fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
+fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py
+fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py
+gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
+icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
+isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py
+mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
+mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py
+mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py
+mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py
+mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
+maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
+maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
+maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
+maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
+mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py
+mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py
+nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py
+ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py
+pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py
+point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py
+poolformer/fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py
+psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py
+pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
+resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
+segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
+segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
+segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
+segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
+sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py
+setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
+stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py
+swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
+twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py
+unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
+upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py
+vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py

.dev/batch_train_list.txt renamed to .dev_scripts/batch_train_list.txt

@@ -0,0 +1,10 @@
+third_part_libs = [
+    'pip install mmengine',
+    'pip install mmcv>=2.0.0',
+    'pip install mmcls==1.0.0rc6',
+    'pip install mmdet==3.0.0',
+    'pip install -r requirements.txt',
+    'pip install timm',
+]
+
+default_floating_range = 0.5

.dev/benchmark_evaluation.sh renamed to .dev_scripts/benchmark_evaluation.sh

@@ -0,0 +1,25 @@
+bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
+bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
+deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
+deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py
+deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
+deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py
+hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
+hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py
+knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
+knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
+mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
+mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
+maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
+mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
+ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py
+pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py
+pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
+pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py
+segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
+segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
+segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
+swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
+twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
+unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
+upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py