Fix: Fix misusage of mmcls and the rep error

Author: MILK-BIOS

configs/classification/mobnetv2_0.35_rep_1bx16_300e_cifar10.py

@@ -16,7 +16,7 @@
     neck=dict(type='sscma.GlobalAveragePooling'),
     head=dict(
         type='sscma.LinearClsHead',
-        in_channels=32,
+        in_channels=16,
         loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
     ),
 )

configs/classification/mobnetv2_0.35_rep_1bx16_300e_cifar100.py

@@ -15,7 +15,7 @@
     neck=dict(type='sscma.GlobalAveragePooling'),
     head=dict(
         type='sscma.LinearClsHead',
-        in_channels=32,
+        in_channels=16,
         loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
     ),
 )

configs/classification/mobnetv2_0.35_rep_1bx16_300e_custom.py

@@ -14,7 +14,7 @@
     neck=dict(type='sscma.GlobalAveragePooling', dim=2),
     head=dict(
         type='sscma.LinearClsHead',
-        in_channels=32,
+        in_channels=16,
         loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
     ),
 )

configs/classification/mobnetv2_0.35_rep_1bx16_300e_mnist.py

@@ -44,7 +44,7 @@
     neck=dict(type='sscma.GlobalAveragePooling'),
     head=dict(
         type='sscma.LinearClsHead',
-        in_channels=32,
+        in_channels=16,
         loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
     ),
 )

configs/classification/mobnetv2_0.35_rep_1bx16_300e_vww.py

@@ -29,7 +29,7 @@
     neck=dict(type='sscma.GlobalAveragePooling'),
     head=dict(
         type='sscma.LinearClsHead',
-        in_channels=64,
+        in_channels=32,
         num_classes=num_classes,
         loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
         topk=(1, 5),

configs/classification/mobnetv2_1.0_1bx16_300e_custom.py

@@ -58,6 +58,7 @@
         data_prefix=train_data,
         pipeline=train_pipeline,
     ),
+    async_load=True,
 )
 
 val_dataloader = dict(
@@ -68,6 +69,7 @@
         data_prefix=val_data,
         pipeline=test_pipeline,
     ),
+    async_load=True,
 )
 
 test_dataloader = val_dataloader

configs/swift_yolo/swift_yolo_mb2_1xb16_300e_coco.py

@@ -129,8 +129,8 @@
         type='YOLOv5PAFPN',
         deepen_factor=deepen_factor,
         widen_factor=widen_factor,
-        in_channels=[32, 48, 112],
-        out_channels=[32, 48, 112],
+        in_channels=[32, 48, 160],
+        out_channels=[32, 48, 160],
         num_csp_blocks=1,
         norm_cfg=norm_cfg,
         act_cfg=dict(type='ReLU', inplace=True),
@@ -140,7 +140,7 @@
         head_module=dict(
             type='sscma.DetHead',
             num_classes=num_classes,
-            in_channels=[32, 48, 112],
+            in_channels=[32, 48, 160],
             widen_factor=widen_factor,
             featmap_strides=strides,
             num_base_priors=3,

sscma/engine/hooks/visualization_hook.py

@@ -9,7 +9,6 @@
 import mmcv
 import mmengine
 import mmengine.fileio as fileio
-from mmcls.structures import ClsDataSample
 from mmdet.engine.hooks import DetVisualizationHook
 from mmengine.fileio import join_path
 from mmengine.hooks import Hook