Remove useless files

mmdet/models/backbones/__init__.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .convnext import ConvNeXt, ConvNeXtFlow
+from .convnext import ConvNeXt
 from .csp_darknet import CSPDarknet
 from .darknet import Darknet
 from .detectors_resnet import DetectoRS_ResNet
@@ -13,18 +13,15 @@
 from .res2net import Res2Net
 from .resnest import ResNeSt
 from .resnet import ResNet, ResNetV1d
-from .resnet_flow import ResNetFlow
 from .resnext import ResNeXt
 from .ssd_vgg import SSDVGG
 from .swin import SwinTransformer
-from .swin_flow import SwinTransformerFlow
 from .trident_resnet import TridentResNet
 
 __all__ = [
     'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet',
     'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet',
     'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet', 'CSPDarknet',
-    'SwinTransformer', 'PyramidVisionTransformer',
+    'SwinTransformer', 'PyramidVisionTransformer', "ConvNeXt",
     'PyramidVisionTransformerV2', 'EfficientNet',
-    'ResNetFlow', 'SwinTransformerFlow', 'ConvNeXt', 'ConvNeXtFlow',
 ]

mmdet/models/backbones/convnext.py

@@ -322,43 +322,3 @@ def _freeze_stages(self):
     def train(self, mode=True):
         super(ConvNeXt, self).train(mode)
         self._freeze_stages()
-
-
-@BACKBONES.register_module()
-class ConvNeXtFlow(ConvNeXt):
-    """ConvNeXtFlow backbone."""
-
-    def __init__(self, extra_freeze=None, **kwargs):
-        self.extra_freeze = extra_freeze
-        super(ConvNeXtFlow, self).__init__(**kwargs)
-
-    def _freeze_stages(self):
-        super()._freeze_stages()
-        if self.extra_freeze is not None:
-            for i in self.extra_freeze:
-                downsample_layer = self.downsample_layers[i]
-                stage = self.stages[i]
-                downsample_layer.eval()
-                stage.eval()
-                for param in chain(downsample_layer.parameters(),
-                                stage.parameters()):
-                    param.requires_grad = False
-
-    def forward(self, x):
-        outs = []
-        for i, stage in enumerate(self.stages):
-            if i not in self.out_indices:
-                break
-            x = self.downsample_layers[i](x)
-            x = stage(x)
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                if self.gap_before_final_norm:
-                    gap = x.mean([-2, -1], keepdim=True)
-                    outs.append(norm_layer(gap).flatten(1))
-                else:
-                    # The output of LayerNorm2d may be discontiguous, which
-                    # may cause some problem in the downstream tasks
-                    outs.append(norm_layer(x).contiguous())
-
-        return tuple(outs)