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https://github.com/huggingface/pytorch-image-models.git
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Finish CaiT cleanup
This commit is contained in:
Ross Wightman
parent
1daa15ecc3
commit
3db12b4b6a
@ -1,19 +1,78 @@
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""" Class-Attention in Image Transformers (CaiT)
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Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
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Original code and weights from https://github.com/facebookresearch/deit, copyright below
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"""
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from functools import partial
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg, overlay_external_default_cfg
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from .layers import trunc_normal_, DropPath
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from .vision_transformer import Mlp, PatchEmbed, _cfg
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from .vision_transformer import Mlp, PatchEmbed
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from .registry import register_model
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__all__ = ['Cait', 'Class_Attention', 'LayerScale_Block_CA', 'LayerScale_Block', 'Attention_talking_head']
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__all__ = ['Cait', 'ClassAttn', 'LayerScaleBlockClassAttn', 'LayerScaleBlock', 'TalkingHeadAttn']
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class Class_Attention(nn.Module):
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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, 384, 384), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = dict(
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cait_xxs24_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/XXS24_224.pth',
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input_size=(3, 224, 224),
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),
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cait_xxs24_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/XXS24_384.pth',
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),
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cait_xxs36_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/XXS36_224.pth',
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input_size=(3, 224, 224),
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),
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cait_xxs36_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/XXS36_384.pth',
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),
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cait_xs24_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/XS24_384.pth',
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),
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cait_s24_224=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/S24_224.pth',
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input_size=(3, 224, 224),
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),
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cait_s24_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/S24_384.pth',
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),
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cait_s36_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/S36_384.pth',
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),
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cait_m36_384=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/M36_384.pth',
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),
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cait_m48_448=_cfg(
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url='https://dl.fbaipublicfiles.com/deit/M48_448.pth',
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input_size=(3, 448, 448),
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),
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)
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class ClassAttn(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications to do CA
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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@ -48,12 +107,12 @@ class Class_Attention(nn.Module):
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return x_cls
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class LayerScale_Block_CA(nn.Module):
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class LayerScaleBlockClassAttn(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications to add CA and LayerScale
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def __init__(
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self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Class_Attention,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=ClassAttn,
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mlp_block=Mlp, init_values=1e-4):
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super().__init__()
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self.norm1 = norm_layer(dim)
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@ -68,15 +127,12 @@ class LayerScale_Block_CA(nn.Module):
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def forward(self, x, x_cls):
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u = torch.cat((x_cls, x), dim=1)
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x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
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x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
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return x_cls
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class Attention_talking_head(nn.Module):
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class TalkingHeadAttn(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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@ -118,12 +174,12 @@ class Attention_talking_head(nn.Module):
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return x
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class LayerScale_Block(nn.Module):
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class LayerScaleBlock(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications to add layerScale
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def __init__(
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self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Attention_talking_head,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
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mlp_block=Mlp, init_values=1e-4):
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super().__init__()
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self.norm1 = norm_layer(dim)
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@ -147,17 +203,22 @@ class Cait(nn.Module):
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# with slight modifications to adapt to our cait models
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None,
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block_layers=LayerScale_Block,
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block_layers_token=LayerScale_Block_CA,
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patch_layer=PatchEmbed, act_layer=nn.GELU,
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attn_block=Attention_talking_head, mlp_block=Mlp,
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num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0.,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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global_pool=None,
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block_layers=LayerScaleBlock,
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block_layers_token=LayerScaleBlockClassAttn,
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patch_layer=PatchEmbed,
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act_layer=nn.GELU,
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attn_block=TalkingHeadAttn,
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mlp_block=Mlp,
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init_scale=1e-4,
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attn_block_token_only=Class_Attention,
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attn_block_token_only=ClassAttn,
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mlp_block_token_only=Mlp,
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depth_token_only=2,
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mlp_ratio_clstk=4.0):
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mlp_ratio_clstk=4.0
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):
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super().__init__()
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self.num_classes = num_classes
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@ -237,211 +298,103 @@ class Cait(nn.Module):
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return x
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@register_model
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def cait_xxs24_224(pretrained=False, **kwargs):
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model = Cait(
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img_size=224, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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def checkpoint_filter_fn(state_dict, model=None):
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if 'model' in state_dict:
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state_dict = state_dict['model']
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checkpoint_no_module = {}
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for k, v in state_dict.items():
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checkpoint_no_module[k.replace('module.', '')] = v
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return checkpoint_no_module
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/XXS24_224.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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def _create_cait(variant, pretrained=False, default_cfg=None, **kwargs):
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if default_cfg is None:
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default_cfg = deepcopy(default_cfgs[variant])
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overlay_external_default_cfg(default_cfg, kwargs)
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default_num_classes = default_cfg['num_classes']
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default_img_size = default_cfg['input_size'][-2:]
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num_classes = kwargs.pop('num_classes', default_num_classes)
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img_size = kwargs.pop('img_size', default_img_size)
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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model = build_model_with_cfg(
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Cait, variant, pretrained,
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default_cfg=default_cfg,
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img_size=img_size,
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num_classes=num_classes,
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pretrained_filter_fn=checkpoint_filter_fn,
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**kwargs)
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return model
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@register_model
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def cait_xxs24(pretrained=False, **kwargs):
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model = Cait(
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img_size=384, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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def cait_xxs24_224(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_xxs24_224', pretrained=pretrained, **model_args)
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return model
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/XXS24_384.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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@register_model
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def cait_xxs24_384(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_xxs24_384', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_xxs36_224(pretrained=False, **kwargs):
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model = Cait(
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img_size=224, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/XXS36_224.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_xxs36_224', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_xxs36(pretrained=False, **kwargs):
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model = Cait(
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img_size=384, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/XXS36_384.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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def cait_xxs36_384(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_xxs36_384', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_xs24(pretrained=False, **kwargs):
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model = Cait(
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img_size=384, patch_size=16, embed_dim=288, depth=24, num_heads=6, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/XS24_384.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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def cait_xs24_384(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=288, depth=24, num_heads=6, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_xs24_384', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_s24_224(pretrained=False, **kwargs):
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model = Cait(
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img_size=224, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/S24_224.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_s24_224', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_s24(pretrained=False, **kwargs):
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model = Cait(
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img_size=384, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/S24_384.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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def cait_s24_384(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
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model = _create_cait('cait_s24_384', pretrained=pretrained, **model_args)
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return model
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@register_model
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def cait_s36(pretrained=False, **kwargs):
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model = Cait(
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img_size=384, patch_size=16, embed_dim=384, depth=36, num_heads=8, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/S36_384.pth",
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map_location="cpu", check_hash=True
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)
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checkpoint_no_module = {}
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for k in model.state_dict().keys():
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checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
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model.load_state_dict(checkpoint_no_module)
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def cait_s36_384(pretrained=False, **kwargs):
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model_args = dict(patch_size=16, embed_dim=384, depth=36, num_heads=8, init_scale=1e-6, **kwargs)
|
||||
model = _create_cait('cait_s36_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def cait_m36(pretrained=False, **kwargs):
|
||||
model = Cait(
|
||||
img_size=384, patch_size=16, embed_dim=768, depth=36, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
|
||||
|
||||
model.default_cfg = _cfg()
|
||||
if pretrained:
|
||||
checkpoint = torch.hub.load_state_dict_from_url(
|
||||
url="https://dl.fbaipublicfiles.com/deit/M36_384.pth",
|
||||
map_location="cpu", check_hash=True
|
||||
)
|
||||
checkpoint_no_module = {}
|
||||
for k in model.state_dict().keys():
|
||||
checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
|
||||
|
||||
model.load_state_dict(checkpoint_no_module)
|
||||
|
||||
def cait_m36_384(pretrained=False, **kwargs):
|
||||
model_args = dict(patch_size=16, embed_dim=768, depth=36, num_heads=16, init_scale=1e-6, **kwargs)
|
||||
model = _create_cait('cait_m36_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def cait_m48(pretrained=False, **kwargs):
|
||||
model = Cait(
|
||||
img_size=448, patch_size=16, embed_dim=768, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
|
||||
|
||||
model.default_cfg = _cfg()
|
||||
if pretrained:
|
||||
checkpoint = torch.hub.load_state_dict_from_url(
|
||||
url="https://dl.fbaipublicfiles.com/deit/M48_448.pth",
|
||||
map_location="cpu", check_hash=True
|
||||
)
|
||||
checkpoint_no_module = {}
|
||||
for k in model.state_dict().keys():
|
||||
checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
|
||||
|
||||
model.load_state_dict(checkpoint_no_module)
|
||||
|
||||
return model
|
||||
def cait_m48_448(pretrained=False, **kwargs):
|
||||
model_args = dict(patch_size=16, embed_dim=768, depth=48, num_heads=16, init_scale=1e-6, **kwargs)
|
||||
model = _create_cait('cait_m48_448', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user