Additional inst_seg configs

configs/mask2former/mask2former_r101_lsj_8x2_50e_coco_ins.py

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+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco_ins.py']
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco_ins.py

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+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco_ins.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_ins.py

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+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_ins.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_ins.py

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+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco_ins.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=['PQ', 'bbox', 'segm'])

configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_ins.py

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+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_ins.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'  # noqa
+
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        depths=depths, init_cfg=dict(type='Pretrained',
+                                     checkpoint=pretrained)))
+
+# 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))