[Feature] add Mask2former to mmdet

configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py

@@ -0,0 +1,253 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+num_things_classes = 80
+num_stuff_classes = 53
+num_classes = num_things_classes + num_stuff_classes
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    panoptic_head=dict(
+        type='Mask2FormerHead',
+        in_channels=[256, 512, 1024, 2048],  # pass to pixel_decoder inside
+        strides=[4, 8, 16, 32],
+        feat_channels=256,
+        out_channels=256,
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        num_queries=100,
+        num_transformer_feat_level=3,
+        pixel_decoder=dict(
+            type='MSDeformAttnPixelDecoder',
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=256,
+                        feedforward_channels=1024,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=128, normalize=True),
+            init_cfg=None),
+        enforce_decoder_input_project=False,
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=9,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    attn_drop=0.0,
+                    proj_drop=0.0,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.0,
+                    dropout_layer=None,
+                    add_identity=True),
+                feedforward_channels=2048,
+                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=2.0,
+            reduction='mean',
+            class_weight=[1.0] * num_classes + [0.1]),
+        loss_mask=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='mean',
+            loss_weight=5.0),
+        loss_dice=dict(
+            type='DiceLoss',
+            use_sigmoid=True,
+            activate=True,
+            reduction='mean',
+            naive_dice=True,
+            eps=1.0,
+            loss_weight=5.0)),
+    panoptic_fusion_head=dict(
+        type='MaskFormerFusionHead',
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_panoptic=None,
+        init_cfg=None),
+    train_cfg=dict(
+        num_points=12544,
+        oversample_ratio=3.0,
+        importance_sample_ratio=0.75,
+        assigner=dict(
+            type='MaskHungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=2.0),
+            mask_cost=dict(
+                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
+            dice_cost=dict(
+                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
+        sampler=dict(type='MaskPseudoSampler')),
+    test_cfg=dict(
+        panoptic_on=True,
+        # For now, the dataset does not support
+        # evaluating semantic segmentation metric.
+        semantic_on=False,
+        instance_on=True,
+        # max_per_image is for instance segmentation.
+        max_per_image=100,
+        iou_thr=0.8,
+        # In Mask2Former's panoptic postprocessing,
+        # it will filter mask area where score is less than 0.5 .
+        filter_low_score=True),
+    init_cfg=None)
+
+# dataset settings
+image_size = (1024, 1024)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='LoadPanopticAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # large scale jittering
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_size=image_size,
+        crop_type='absolute',
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=image_size),
+    dict(type='DefaultFormatBundle', img_to_float=True),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data_root = 'data/coco/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(
+        pipeline=test_pipeline,
+        ins_ann_file=data_root + 'annotations/instances_val2017.json',
+    ),
+    test=dict(
+        pipeline=test_pipeline,
+        ins_ann_file=data_root + 'annotations/instances_val2017.json',
+    ))
+
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(
+        custom_keys={
+            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+            'query_embed': embed_multi,
+            'query_feat': embed_multi,
+            'level_embed': embed_multi,
+        },
+        norm_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    gamma=0.1,
+    by_epoch=False,
+    step=[327778, 355092],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_ratio=1.0,  # no warmup
+    warmup_iters=10)
+
+max_iters = 368750
+runner = dict(type='IterBasedRunner', max_iters=max_iters)
+
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False)
+    ])
+interval = 200000
+workflow = [('train', interval)]
+checkpoint_config = dict(
+    by_epoch=False, interval=interval, save_last=True, max_keep_ckpts=3)
+
+# Before 365001th iteration, we do evaluation every 200000 iterations.
+# After 365000th iteration, we do evaluation every 368750 iterations,
+# which means do evaluation at the end of training.
+# In all, we do evaluation at the 200000th iteration and the
+# last iteratoin.
+dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
+evaluation = dict(
+    interval=interval, dynamic_intervals=dynamic_intervals, metric='PQ')

configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py

@@ -0,0 +1,62 @@
+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+
+depths = [2, 2, 6, 2]
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=depths,
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        frozen_stages=-1,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(
+        type='Mask2FormerHead', in_channels=[96, 192, 384, 768]),
+    init_cfg=None)
+
+# 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(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))

mmdet/core/bbox/match_costs/__init__.py

@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .builder import build_match_cost
-from .match_cost import (BBoxL1Cost, ClassificationCost, DiceCost,
-                         FocalLossCost, IoUCost)
+from .match_cost import (BBoxL1Cost, ClassificationCost, CrossEntropyLossCost,
+                         DiceCost, FocalLossCost, IoUCost)
 
 __all__ = [
     'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
-    'FocalLossCost', 'DiceCost'
+    'FocalLossCost', 'DiceCost', 'CrossEntropyLossCost'
 ]