[Feature] Support K-Net

configs/knet/knet_s3_deeplabv3_r50-d8_4x2_512x512_80k_adamw_ade20k.py

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+_base_ = [
+    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+num_stages = 3
+conv_kernel_size = 1
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='IterativeDecodeHead',
+        num_stages=num_stages,
+        kernel_update_head=[
+            dict(
+                type='KernelUpdateHead',
+                num_classes=150,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_mask_fcs=1,
+                feedforward_channels=2048,
+                in_channels=512,
+                out_channels=512,
+                dropout=0.0,
+                conv_kernel_size=conv_kernel_size,
+                mask_upsample_stride=2,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                with_ffn=True,
+                feat_transform_cfg=dict(
+                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
+                kernel_updator_cfg=dict(
+                    type='KernelUpdator',
+                    in_channels=256,
+                    feat_channels=256,
+                    out_channels=256,
+                    input_feat_shape=3,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
+        ],
+        kernel_generate_head=dict(
+            type='ASPPHead',
+            in_channels=2048,
+            in_index=3,
+            channels=512,
+            dilations=(1, 12, 24, 36),
+            dropout_ratio=0.1,
+            num_classes=150,
+            norm_cfg=norm_cfg,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0005)
+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)
+# In K-Net implementation we use batch size 2 per GPU as default
+data = dict(samples_per_gpu=2, workers_per_gpu=2)

configs/knet/knet_s3_fcn_r50-d8_4x2_512x512_80k_adamw_ade20k.py

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+_base_ = [
+    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+num_stages = 3
+conv_kernel_size = 1
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='IterativeDecodeHead',
+        num_stages=num_stages,
+        kernel_update_head=[
+            dict(
+                type='KernelUpdateHead',
+                num_classes=150,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_mask_fcs=1,
+                feedforward_channels=2048,
+                in_channels=512,
+                out_channels=512,
+                dropout=0.0,
+                conv_kernel_size=conv_kernel_size,
+                mask_upsample_stride=2,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                with_ffn=True,
+                feat_transform_cfg=dict(
+                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
+                kernel_updator_cfg=dict(
+                    type='KernelUpdator',
+                    in_channels=256,
+                    feat_channels=256,
+                    out_channels=256,
+                    input_feat_shape=3,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
+        ],
+        kernel_generate_head=dict(
+            type='FCNHead',
+            in_channels=2048,
+            in_index=3,
+            channels=512,
+            num_convs=2,
+            concat_input=True,
+            dropout_ratio=0.1,
+            num_classes=150,
+            norm_cfg=norm_cfg,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0005)
+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)
+# In K-Net implementation we use batch size 2 per GPU as default
+data = dict(samples_per_gpu=2, workers_per_gpu=2)

configs/knet/knet_s3_pspnet_r50-d8_4x2_512x512_80k_adamw_ade20k.py

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+_base_ = [
+    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+num_stages = 3
+conv_kernel_size = 1
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='IterativeDecodeHead',
+        num_stages=num_stages,
+        kernel_update_head=[
+            dict(
+                type='KernelUpdateHead',
+                num_classes=150,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_mask_fcs=1,
+                feedforward_channels=2048,
+                in_channels=512,
+                out_channels=512,
+                dropout=0.0,
+                conv_kernel_size=conv_kernel_size,
+                mask_upsample_stride=2,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                with_ffn=True,
+                feat_transform_cfg=dict(
+                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
+                kernel_updator_cfg=dict(
+                    type='KernelUpdator',
+                    in_channels=256,
+                    feat_channels=256,
+                    out_channels=256,
+                    input_feat_shape=3,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
+        ],
+        kernel_generate_head=dict(
+            type='PSPHead',
+            in_channels=2048,
+            in_index=3,
+            channels=512,
+            pool_scales=(1, 2, 3, 6),
+            dropout_ratio=0.1,
+            num_classes=150,
+            norm_cfg=norm_cfg,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0005)
+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)
+# In K-Net implementation we use batch size 2 per GPU as default
+data = dict(samples_per_gpu=2, workers_per_gpu=2)

configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_80k_adamw_ade20k.py

@@ -0,0 +1,95 @@
+_base_ = [
+    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+num_stages = 3
+conv_kernel_size = 1
+
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 1, 1),
+        strides=(1, 2, 2, 2),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='IterativeDecodeHead',
+        num_stages=num_stages,
+        kernel_update_head=[
+            dict(
+                type='KernelUpdateHead',
+                num_classes=150,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_mask_fcs=1,
+                feedforward_channels=2048,
+                in_channels=256,
+                out_channels=256,
+                dropout=0.0,
+                conv_kernel_size=conv_kernel_size,
+                mask_upsample_stride=2,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                with_ffn=True,
+                feat_transform_cfg=dict(
+                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
+                kernel_updator_cfg=dict(
+                    type='KernelUpdator',
+                    in_channels=256,
+                    feat_channels=256,
+                    out_channels=256,
+                    input_feat_shape=3,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
+        ],
+        kernel_generate_head=dict(
+            type='UPerHead',
+            in_channels=[256, 512, 1024, 2048],
+            in_index=[0, 1, 2, 3],
+            pool_scales=(1, 2, 3, 6),
+            channels=256,
+            dropout_ratio=0.1,
+            num_classes=150,
+            norm_cfg=norm_cfg,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0005)
+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)
+# In K-Net implementation we use batch size 2 per GPU as default
+data = dict(samples_per_gpu=2, workers_per_gpu=2)