[WIP] Add Mask2Former swin-b swin-l config files for instance segmentation

.github/workflows/stale.yml

@@ -0,0 +1,25 @@
+name: 'Close stale issues and PRs'
+
+on:
+  schedule:
+    # check issue and pull request once every day
+    - cron: '25 11 * * *'
+
+jobs:
+  invalid-stale-close:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v4
+        with:
+          stale-issue-message: 'This issue is marked as stale because it has been marked as invalid or awaiting response for 7 days without any further response. It will be closed in 5 days if the stale label is not removed or if there is no further response.'
+          stale-pr-message: 'This PR is marked as stale because there has been no activity in the past 45 days. It will be closed in 10 days if the stale label is not removed or if there is no further updates.'
+          close-issue-message: 'This issue is closed because it has been stale for 5 days. Please open a new issue if you have similar issues or you have any new updates now.'
+          close-pr-message: 'This PR is closed because it has been stale for 10 days. Please reopen this PR if you have any updates and want to keep contributing the code.'
+          # only issues/PRS with any of invalid and awaiting response labels are checked
+          any-of-labels: 'invalid, awaiting response'
+          days-before-issue-stale: 7
+          days-before-pr-stale: 45
+          days-before-issue-close: 5
+          days-before-pr-close: 10
+          # automatically remove the stale label when the issues or the pull reqquests are updated or commented
+          remove-stale-when-updated: true

configs/htc/README.md

@@ -1,6 +1,6 @@
 # HTC
 
-> [Hybrid Task Cascade for Instance Segmentation](ttps://arxiv.org/abs/1901.07518)
+> [Hybrid Task Cascade for Instance Segmentation](https://arxiv.org/abs/1901.07518)
 
 <!-- [ALGORITHM] -->
 

configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco.py

@@ -0,0 +1,5 @@
+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth'  # noqa
+
+model = dict(
+    backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))

configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco.py

@@ -0,0 +1,42 @@
+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth'  # noqa
+
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        pretrain_img_size=384,
+        embed_dims=128,
+        depths=depths,
+        num_heads=[4, 8, 16, 32],
+        window_size=12,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(in_channels=[128, 256, 512, 1024]))
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))

configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco.py

@@ -0,0 +1,26 @@
+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        embed_dims=192,
+        num_heads=[6, 12, 24, 48],
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(num_queries=200, in_channels=[192, 384, 768, 1536]))
+
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+
+lr_config = dict(step=[655556, 710184])
+
+max_iters = 737500
+runner = dict(type='IterBasedRunner', max_iters=max_iters)
+
+# Before 735001th iteration, we do evaluation every 5000 iterations.
+# After 735000th iteration, we do evaluation every 737500 iterations,
+# which means that we do evaluation at the end of training.
+interval = 5000
+dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
+evaluation = dict(
+    interval=interval,
+    dynamic_intervals=dynamic_intervals,
+    metric=['bbox', 'segm'])

requirements/docs.txt

@@ -1,4 +1,5 @@
 docutils==0.16.0
+markdown<3.4.0
 myst-parser
 -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
 sphinx==4.0.2

tools/deployment/pytorch2onnx.py

@@ -101,31 +101,17 @@ def pytorch2onnx(model,
 
     model.forward = origin_forward
 
-    # get the custom op path
-    ort_custom_op_path = ''
-    try:
-        from mmcv.ops import get_onnxruntime_op_path
-        ort_custom_op_path = get_onnxruntime_op_path()
-    except (ImportError, ModuleNotFoundError):
-        warnings.warn('If input model has custom op from mmcv, \
-            you may have to build mmcv with ONNXRuntime from source.')
-
     if do_simplify:
         import onnxsim
 
         from mmdet import digit_version
 
-        min_required_version = '0.3.0'
+        min_required_version = '0.4.0'
         assert digit_version(onnxsim.__version__) >= digit_version(
             min_required_version
-        ), f'Requires to install onnx-simplify>={min_required_version}'
+        ), f'Requires to install onnxsim>={min_required_version}'
 
-        input_dic = {'input': img_list[0].detach().cpu().numpy()}
-        model_opt, check_ok = onnxsim.simplify(
-            output_file,
-            input_data=input_dic,
-            custom_lib=ort_custom_op_path,
-            dynamic_input_shape=dynamic_export)
+        model_opt, check_ok = onnxsim.simplify(output_file)
         if check_ok:
             onnx.save(model_opt, output_file)
             print(f'Successfully simplified ONNX model: {output_file}')