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https://github.com/huggingface/pytorch-image-models.git
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MLP-Mixer multi-weight support, hf hub push
This commit is contained in:
Ross Wightman
parent
56b90317cd
commit
b12060996c
@ -48,101 +48,9 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
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from ._builder import build_model_with_cfg
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from ._manipulate import named_apply, checkpoint_seq
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from ._registry import register_model
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from ._registry import generate_default_cfgs, register_model, register_model_deprecations
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__all__ = ['MixerBlock'] # model_registry will add each entrypoint fn to this
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': 0.875, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
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'first_conv': 'stem.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = dict(
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mixer_s32_224=_cfg(),
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mixer_s16_224=_cfg(),
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mixer_b32_224=_cfg(),
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mixer_b16_224=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth',
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),
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mixer_b16_224_in21k=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224_in21k-617b3de2.pth',
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num_classes=21843
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),
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mixer_l32_224=_cfg(),
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mixer_l16_224=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224-92f9adc4.pth',
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),
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mixer_l16_224_in21k=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
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num_classes=21843
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),
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# Mixer ImageNet-21K-P pretraining
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mixer_b16_224_miil_in21k=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil_in21k-2a558a71.pth',
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mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
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),
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mixer_b16_224_miil=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil-9229a591.pth',
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mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
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),
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gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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gmixer_24_224=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmixer_24_224_raa-7daf7ae6.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_12_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_24_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth',
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#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resmlp_24_224_raa-a8256759.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_36_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_big_24_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_12_distilled_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_24_distilled_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_36_distilled_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_big_24_distilled_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_big_24_224_in22ft1k=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_12_224_dino=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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resmlp_24_224_dino=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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gmlp_ti16_224=_cfg(),
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gmlp_s16_224=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
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),
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gmlp_b16_224=_cfg(),
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)
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__all__ = ['MixerBlock', 'MlpMixer'] # model_registry will add each entrypoint fn to this
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class MixerBlock(nn.Module):
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@ -150,8 +58,16 @@ class MixerBlock(nn.Module):
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Based on: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
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"""
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def __init__(
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self, dim, seq_len, mlp_ratio=(0.5, 4.0), mlp_layer=Mlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
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self,
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dim,
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seq_len,
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mlp_ratio=(0.5, 4.0),
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mlp_layer=Mlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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act_layer=nn.GELU,
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drop=0.,
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drop_path=0.,
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):
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super().__init__()
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tokens_dim, channels_dim = [int(x * dim) for x in to_2tuple(mlp_ratio)]
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self.norm1 = norm_layer(dim)
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@ -182,8 +98,17 @@ class ResBlock(nn.Module):
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Based on: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
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"""
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def __init__(
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self, dim, seq_len, mlp_ratio=4, mlp_layer=Mlp, norm_layer=Affine,
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act_layer=nn.GELU, init_values=1e-4, drop=0., drop_path=0.):
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self,
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dim,
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seq_len,
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mlp_ratio=4,
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mlp_layer=Mlp,
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norm_layer=Affine,
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act_layer=nn.GELU,
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init_values=1e-4,
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drop=0.,
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drop_path=0.,
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):
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super().__init__()
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channel_dim = int(dim * mlp_ratio)
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self.norm1 = norm_layer(dim)
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@ -229,8 +154,16 @@ class SpatialGatingBlock(nn.Module):
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Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
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"""
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def __init__(
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self, dim, seq_len, mlp_ratio=4, mlp_layer=GatedMlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
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self,
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dim,
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seq_len,
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mlp_ratio=4,
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mlp_layer=GatedMlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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act_layer=nn.GELU,
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drop=0.,
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drop_path=0.,
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):
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super().__init__()
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channel_dim = int(dim * mlp_ratio)
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self.norm = norm_layer(dim)
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@ -271,13 +204,24 @@ class MlpMixer(nn.Module):
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self.grad_checkpointing = False
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self.stem = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans,
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embed_dim=embed_dim, norm_layer=norm_layer if stem_norm else None)
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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norm_layer=norm_layer if stem_norm else None,
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)
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# FIXME drop_path (stochastic depth scaling rule or all the same?)
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self.blocks = nn.Sequential(*[
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block_layer(
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embed_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer,
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act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
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embed_dim,
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self.stem.num_patches,
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mlp_ratio,
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mlp_layer=mlp_layer,
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norm_layer=norm_layer,
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act_layer=act_layer,
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drop=drop_rate,
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drop_path=drop_path_rate,
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)
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for _ in range(num_blocks)])
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self.norm = norm_layer(embed_dim)
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self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
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@ -320,11 +264,14 @@ class MlpMixer(nn.Module):
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x = self.norm(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool == 'avg':
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x = x.mean(dim=1)
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x = self.head(x)
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return x if pre_logits else self.head(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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@ -384,12 +331,107 @@ def _create_mixer(variant, pretrained=False, **kwargs):
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raise RuntimeError('features_only not implemented for MLP-Mixer models.')
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model = build_model_with_cfg(
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MlpMixer, variant, pretrained,
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MlpMixer,
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variant,
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pretrained,
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pretrained_filter_fn=checkpoint_filter_fn,
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**kwargs)
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**kwargs,
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)
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return model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': 0.875, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
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'first_conv': 'stem.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = generate_default_cfgs({
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'mixer_s32_224.untrained': _cfg(),
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'mixer_s16_224.untrained': _cfg(),
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'mixer_b32_224.untrained': _cfg(),
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'mixer_b16_224.goog_in21k_ft_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth',
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),
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'mixer_b16_224.goog_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224_in21k-617b3de2.pth',
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num_classes=21843
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),
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'mixer_l32_224.untrained': _cfg(),
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'mixer_l16_224.goog_in21k_ft_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224-92f9adc4.pth',
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),
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'mixer_l16_224.goog_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
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num_classes=21843
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),
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# Mixer ImageNet-21K-P pretraining
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'mixer_b16_224.miil_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil_in21k-2a558a71.pth',
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mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
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),
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'mixer_b16_224.miil_in21k_ft_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil-9229a591.pth',
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mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
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),
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'gmixer_12_224.untrained': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'gmixer_24_224.ra3_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmixer_24_224_raa-7daf7ae6.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_12_224.fb_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_24_224.fb_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth',
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#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resmlp_24_224_raa-a8256759.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_36_224.fb_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_big_24_224.fb_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_12_224.fb_distilled_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_24_224.fb_distilled_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_36_224.fb_distilled_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_big_24_224.fb_distilled_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_big_24_224.fb_in22k_ft_in1k': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_12_224.fb_dino': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'resmlp_24_224.fb_dino': _cfg(
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url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'gmlp_ti16_224.untrained': _cfg(),
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'gmlp_s16_224.ra3_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
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),
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'gmlp_b16_224.untrained': _cfg(),
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})
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@register_model
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def mixer_s32_224(pretrained=False, **kwargs):
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""" Mixer-S/32 224x224
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@ -430,16 +472,6 @@ def mixer_b16_224(pretrained=False, **kwargs):
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return model
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@register_model
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def mixer_b16_224_in21k(pretrained=False, **kwargs):
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""" Mixer-B/16 224x224. ImageNet-21k pretrained weights.
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Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
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"""
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model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
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model = _create_mixer('mixer_b16_224_in21k', pretrained=pretrained, **model_args)
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return model
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@register_model
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def mixer_l32_224(pretrained=False, **kwargs):
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""" Mixer-L/32 224x224.
|
||||
@ -460,40 +492,10 @@ def mixer_l16_224(pretrained=False, **kwargs):
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def mixer_l16_224_in21k(pretrained=False, **kwargs):
|
||||
""" Mixer-L/16 224x224. ImageNet-21k pretrained weights.
|
||||
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
|
||||
"""
|
||||
model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
|
||||
model = _create_mixer('mixer_l16_224_in21k', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def mixer_b16_224_miil(pretrained=False, **kwargs):
|
||||
""" Mixer-B/16 224x224. ImageNet-21k pretrained weights.
|
||||
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
|
||||
"""
|
||||
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
|
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model = _create_mixer('mixer_b16_224_miil', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def mixer_b16_224_miil_in21k(pretrained=False, **kwargs):
|
||||
""" Mixer-B/16 224x224. ImageNet-1k pretrained weights.
|
||||
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
|
||||
"""
|
||||
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
|
||||
model = _create_mixer('mixer_b16_224_miil_in21k', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def gmixer_12_224(pretrained=False, **kwargs):
|
||||
""" Glu-Mixer-12 224x224
|
||||
Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
|
||||
Experiment by Ross Wightman, adding SwiGLU to MLP-Mixer
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=(1.0, 4.0),
|
||||
@ -505,7 +507,7 @@ def gmixer_12_224(pretrained=False, **kwargs):
|
||||
@register_model
|
||||
def gmixer_24_224(pretrained=False, **kwargs):
|
||||
""" Glu-Mixer-24 224x224
|
||||
Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
|
||||
Experiment by Ross Wightman, adding SwiGLU to MLP-Mixer
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=(1.0, 4.0),
|
||||
@ -561,92 +563,6 @@ def resmlp_big_24_224(pretrained=False, **kwargs):
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_12_distilled_224(pretrained=False, **kwargs):
|
||||
""" ResMLP-12
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_12_distilled_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_24_distilled_224(pretrained=False, **kwargs):
|
||||
""" ResMLP-24
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
|
||||
block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_24_distilled_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_36_distilled_224(pretrained=False, **kwargs):
|
||||
""" ResMLP-36
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
|
||||
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_36_distilled_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_big_24_distilled_224(pretrained=False, **kwargs):
|
||||
""" ResMLP-B-24
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
|
||||
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_big_24_distilled_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_big_24_224_in22ft1k(pretrained=False, **kwargs):
|
||||
""" ResMLP-B-24
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
|
||||
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_big_24_224_in22ft1k', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_12_224_dino(pretrained=False, **kwargs):
|
||||
""" ResMLP-12
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
|
||||
Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_12_224_dino', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def resmlp_24_224_dino(pretrained=False, **kwargs):
|
||||
""" ResMLP-24
|
||||
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
|
||||
|
||||
Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
|
||||
block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
|
||||
model = _create_mixer('resmlp_24_224_dino', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def gmlp_ti16_224(pretrained=False, **kwargs):
|
||||
""" gMLP-Tiny
|
||||
@ -681,3 +597,18 @@ def gmlp_b16_224(pretrained=False, **kwargs):
|
||||
mlp_layer=GatedMlp, **kwargs)
|
||||
model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
register_model_deprecations(__name__, {
|
||||
'mixer_b16_224_in21k': 'mixer_b16_224.goog_in21k_ft_in1k',
|
||||
'mixer_l16_224_in21k': 'mixer_l16_224.goog_in21k_ft_in1k',
|
||||
'mixer_b16_224_miil': 'mixer_b16_224.miil_in21k_ft_in1k',
|
||||
'mixer_b16_224_miil_in21k': 'mixer_b16_224.miil_in21k',
|
||||
'resmlp_12_distilled_224': 'resmlp_12_224.fb_distilled_in1k',
|
||||
'resmlp_24_distilled_224': 'resmlp_24_224.fb_distilled_in1k',
|
||||
'resmlp_36_distilled_224': 'resmlp_36_224.fb_distilled_in1k',
|
||||
'resmlp_big_24_distilled_224': 'resmlp_big_24_224.fb_distilled_in1k',
|
||||
'resmlp_big_24_224_in22ft1k': 'resmlp_big_24_224.fb_in22k_ft_in1k',
|
||||
'resmlp_12_224_dino': 'resmlp_12_224',
|
||||
'resmlp_24_224_dino': 'resmlp_24_224',
|
||||
})
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user