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Default conv_mlp to False across the board for ConvNeXt, causing issues on more setups than it's improving right now...

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Ross Wightman 2022-01-16 14:20:08 -08:00
parent b669f4a588
commit e0c4eec4b6
1 changed files with 10 additions and 10 deletions
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20
timm/models/convnext.py
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@ -116,7 +116,7 @@ class ConvNeXtBlock(nn.Module):
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=True, mlp_ratio=4, norm_layer=None):
def __init__(self, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=False, mlp_ratio=4, norm_layer=None):
super().__init__()
if not norm_layer:
norm_layer = partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
@ -148,7 +148,7 @@ class ConvNeXtBlock(nn.Module):
class ConvNeXtStage(nn.Module):
def __init__(
self, in_chs, out_chs, stride=2, depth=2, dp_rates=None, ls_init_value=1.0, conv_mlp=True,
self, in_chs, out_chs, stride=2, depth=2, dp_rates=None, ls_init_value=1.0, conv_mlp=False,
norm_layer=None, cl_norm_layer=None, cross_stage=False):
super().__init__()
@ -190,7 +190,7 @@ class ConvNeXt(nn.Module):
def __init__(
self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32, patch_size=4,
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), ls_init_value=1e-6, conv_mlp=True,
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), ls_init_value=1e-6, conv_mlp=False,
head_init_scale=1., head_norm_first=False, norm_layer=None, drop_rate=0., drop_path_rate=0.,
):
super().__init__()
@ -356,7 +356,7 @@ def convnext_base(pretrained=False, **kwargs):
@register_model
def convnext_large(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large', pretrained=pretrained, **model_args)
return model
@ -370,14 +370,14 @@ def convnext_base_in22ft1k(pretrained=False, **kwargs):
@register_model
def convnext_large_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22ft1k', pretrained=pretrained, **model_args)
return model
@ -391,14 +391,14 @@ def convnext_base_384_in22ft1k(pretrained=False, **kwargs):
@register_model
def convnext_large_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@ -412,14 +412,14 @@ def convnext_base_in22k(pretrained=False, **kwargs):
@register_model
def convnext_large_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], conv_mlp=False, **kwargs)
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22k', pretrained=pretrained, **model_args)
return model