From c7b40414bb250cbc0c96b3484ae8e89b01c3c4c8 Mon Sep 17 00:00:00 2001 From: Aman Arora Date: Mon, 3 May 2021 01:49:02 +0000 Subject: [PATCH] Make sure reduction ratio in resnetrs is 0.25 --- timm/models/resnet.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/timm/models/resnet.py b/timm/models/resnet.py index 24d1bf3b..2412100c 100644 --- a/timm/models/resnet.py +++ b/timm/models/resnet.py @@ -334,7 +334,7 @@ class Bottleneck(nn.Module): def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + attn_layer=None, aa_layer=None, drop_block=None, drop_path=None, **kwargs): super(Bottleneck, self).__init__() width = int(math.floor(planes * (base_width / 64)) * cardinality) @@ -357,7 +357,7 @@ class Bottleneck(nn.Module): self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False) self.bn3 = norm_layer(outplanes) - self.se = create_attn(attn_layer, outplanes) + self.se = create_attn(attn_layer, outplanes, **kwargs) self.act3 = act_layer(inplace=True) self.downsample = downsample @@ -1093,7 +1093,7 @@ def ecaresnet50d(pretrained=False, **kwargs): def resnetrs50(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs50', pretrained, **model_args) @@ -1101,7 +1101,7 @@ def resnetrs50(pretrained=False, **kwargs): def resnetrs101(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs101', pretrained, **model_args) @@ -1109,7 +1109,7 @@ def resnetrs101(pretrained=False, **kwargs): def resnetrs152(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs152', pretrained, **model_args) @@ -1117,7 +1117,7 @@ def resnetrs152(pretrained=False, **kwargs): def resnetrs200(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs200', pretrained, **model_args) @@ -1125,7 +1125,7 @@ def resnetrs200(pretrained=False, **kwargs): def resnetrs270(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs270', pretrained, **model_args) @@ -1134,7 +1134,7 @@ def resnetrs270(pretrained=False, **kwargs): def resnetrs350(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs350', pretrained, **model_args) @@ -1142,7 +1142,7 @@ def resnetrs350(pretrained=False, **kwargs): def resnetrs420(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se'), **kwargs) + avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) return _create_resnet('resnetrs420', pretrained, **model_args)