Missed args for skresnext w/ refactoring.

timm/models/layers/selective_kernel.py

@@ -49,7 +49,7 @@ class SelectiveKernelAttn(nn.Module):
 class SelectiveKernel(nn.Module):
 
     def __init__(self, in_channels, out_channels=None, kernel_size=None, stride=1, dilation=1, groups=1,
-                 rd_ratio=1./16, rd_channels=None, min_rd_channels=16, rd_divisor=8, keep_3x3=True, split_input=True,
+                 rd_ratio=1./16, rd_channels=None, min_rd_channels=32, rd_divisor=8, keep_3x3=True, split_input=True,
                  drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
         """ Selective Kernel Convolution Module
 

timm/models/sknet.py

@@ -207,8 +207,9 @@ def skresnext50_32x4d(pretrained=False, **kwargs):
     """Constructs a Select Kernel ResNeXt50-32x4d model. This should be equivalent to
     the SKNet-50 model in the Select Kernel Paper
     """
+    sk_kwargs = dict(min_rd_channels=32, rd_ratio=1/16, split_input=False)
     model_args = dict(
         block=SelectiveKernelBottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
-        zero_init_last_bn=False, **kwargs)
+        block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False, **kwargs)
     return _create_skresnet('skresnext50_32x4d', pretrained, **model_args)