[MMSIG#357] Add new configs for panoptic_fpn (#11109)

mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py

@@ -0,0 +1,13 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+
+with read_base():
+    from .panoptic_fpn_r50_fpn_1x_coco import *
+
+model.update(
+    dict(
+        backbone=dict(
+            depth=101,
+            init_cfg=dict(
+                type=PretrainedInit, checkpoint='torchvision://resnet101'))))

mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_ms_3x_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+
+with read_base():
+    from .panoptic_fpn_r50_fpn_ms_3x_coco import *
+
+model.update(
+    dict(
+        backbone=dict(
+            depth=101,
+            init_cfg=dict(
+                type=PretrainedInit, checkpoint='torchvision://resnet101'))))

mmdet/configs/panoptic_fpn/panoptic_fpn_r50_fpn_ms_3x_coco.py

@@ -0,0 +1,45 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR
+
+with read_base():
+    from .panoptic_fpn_r50_fpn_1x_coco import *
+
+from mmcv.transforms import RandomResize
+from mmcv.transforms.loading import LoadImageFromFile
+
+from mmdet.datasets.transforms.formatting import PackDetInputs
+from mmdet.datasets.transforms.loading import LoadPanopticAnnotations
+from mmdet.datasets.transforms.transforms import RandomFlip
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(
+        type=LoadPanopticAnnotations,
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type=RandomResize, scale=[(1333, 640), (1333, 800)], keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=PackDetInputs)
+]
+
+train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
+
+# TODO: Use RepeatDataset to speed up training
+# training schedule for 3x
+train_cfg.update(dict(max_epochs=36, val_interval=3))
+
+# learning rate
+param_scheduler = [
+    dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=36,
+        by_epoch=True,
+        milestones=[24, 33],
+        gamma=0.1)
+]