[Feature] Support Resnet strikes back (open-mmlab#1390)

* [Feature] Support Resnet strikes back

* fix url

* [Feature] Add multi machine `dist_train`. (open-mmlab#1383)

* Add training startup documentation

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* modify R-50b rsb

Co-authored-by: FangjianLin <93248678+linfangjian01@users.noreply.github.com>

configs/pspnet/pspnet.yml

@@ -148,6 +148,28 @@ Models:
       mIoU(ms+flip): 79.79
   Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
+- Name: pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes
+  In Collection: PSPNet
+  Metadata:
+    backbone: R-50b-D8 rsb
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 261.78
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.47
+      mIoU(ms+flip): 79.45
+  Config: configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth
 - Name: pspnet_r101-d8_512x1024_80k_cityscapes
   In Collection: PSPNet
   Metadata:
@@ -365,6 +387,72 @@ Models:
       mIoU(ms+flip): 80.04
   Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
+- Name: pspnet_r50-d32_512x1024_80k_cityscapes
+  In Collection: PSPNet
+  Metadata:
+    backbone: R-50-D32
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 65.75
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 3.0
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.88
+      mIoU(ms+flip): 76.85
+  Config: configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth
+- Name: pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes
+  In Collection: PSPNet
+  Metadata:
+    backbone: R-50b-D32 rsb
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 62.19
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 3.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.09
+      mIoU(ms+flip): 77.18
+  Config: configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth
+- Name: pspnet_r50b-d32_512x1024_80k_cityscapes
+  In Collection: PSPNet
+  Metadata:
+    backbone: R-50b-D32
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 64.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 2.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.61
+      mIoU(ms+flip): 75.51
+  Config: configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth
 - Name: pspnet_r50-d8_512x512_80k_ade20k
   In Collection: PSPNet
   Metadata:

configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(backbone=dict(dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))

configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py

@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    pretrained=None,
+    backbone=dict(
+        type='ResNet',
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 2, 2)))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0005, weight_decay=0.05)
+optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[60000, 72000],
+    by_epoch=False)

configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py

@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    pretrained=None,
+    backbone=dict(
+        type='ResNet',
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0005, weight_decay=0.05)
+optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[60000, 72000],
+    by_epoch=False)

configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    pretrained='torchvision://resnet50',
+    backbone=dict(type='ResNet', dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))