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Finish CaiT cleanup

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pull/609/head
Ross Wightman 4 years ago
parent 1daa15ecc3
commit 3db12b4b6a
1 changed files with 142 additions and 189 deletions
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timm/models/cait.py
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@ -1,19 +1,78 @@
""" Class-Attention in Image Transformers (CaiT)
Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
Original code and weights from https://github.com/facebookresearch/deit, copyright below
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
from functools import partial
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import trunc_normal_, DropPath
from .vision_transformer import Mlp, PatchEmbed, _cfg
from .vision_transformer import Mlp, PatchEmbed
from .registry import register_model
__all__ = ['Cait', 'Class_Attention', 'LayerScale_Block_CA', 'LayerScale_Block', 'Attention_talking_head']
class Class_Attention(nn.Module):
__all__ = ['Cait', 'ClassAttn', 'LayerScaleBlockClassAttn', 'LayerScaleBlock', 'TalkingHeadAttn']
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 384, 384), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = dict(
cait_xxs24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS24_224.pth',
input_size=(3, 224, 224),
),
cait_xxs24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS24_384.pth',
),
cait_xxs36_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS36_224.pth',
input_size=(3, 224, 224),
),
cait_xxs36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS36_384.pth',
),
cait_xs24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XS24_384.pth',
),
cait_s24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S24_224.pth',
input_size=(3, 224, 224),
),
cait_s24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S24_384.pth',
),
cait_s36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S36_384.pth',
),
cait_m36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/M36_384.pth',
),
cait_m48_448=_cfg(
url='https://dl.fbaipublicfiles.com/deit/M48_448.pth',
input_size=(3, 448, 448),
),
)
class ClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to do CA
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
@ -48,12 +107,12 @@ class Class_Attention(nn.Module):
return x_cls
class LayerScale_Block_CA(nn.Module):
class LayerScaleBlockClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add CA and LayerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Class_Attention,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=ClassAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
@ -68,15 +127,12 @@ class LayerScale_Block_CA(nn.Module):
def forward(self, x, x_cls):
u = torch.cat((x_cls, x), dim=1)
x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
return x_cls
class Attention_talking_head(nn.Module):
class TalkingHeadAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
@ -118,12 +174,12 @@ class Attention_talking_head(nn.Module):
return x
class LayerScale_Block(nn.Module):
class LayerScaleBlock(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add layerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Attention_talking_head,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
@ -147,17 +203,22 @@ class Cait(nn.Module):
# with slight modifications to adapt to our cait models
def __init__(
self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None,
block_layers=LayerScale_Block,
block_layers_token=LayerScale_Block_CA,
patch_layer=PatchEmbed, act_layer=nn.GELU,
attn_block=Attention_talking_head, mlp_block=Mlp,
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
global_pool=None,
block_layers=LayerScaleBlock,
block_layers_token=LayerScaleBlockClassAttn,
patch_layer=PatchEmbed,
act_layer=nn.GELU,
attn_block=TalkingHeadAttn,
mlp_block=Mlp,
init_scale=1e-4,
attn_block_token_only=Class_Attention,
attn_block_token_only=ClassAttn,
mlp_block_token_only=Mlp,
depth_token_only=2,
mlp_ratio_clstk=4.0):
mlp_ratio_clstk=4.0
):
super().__init__()
self.num_classes = num_classes
@ -237,211 +298,103 @@ class Cait(nn.Module):
return x
@register_model
def cait_xxs24_224(pretrained=False, **kwargs):
model = Cait(
img_size=224, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/XXS24_224.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 checkpoint_filter_fn(state_dict, model=None):
if 'model' in state_dict:
state_dict = state_dict['model']
checkpoint_no_module = {}
for k, v in state_dict.items():
checkpoint_no_module[k.replace('module.', '')] = v
return checkpoint_no_module
def _create_cait(variant, pretrained=False, default_cfg=None, **kwargs):
if default_cfg is None:
default_cfg = deepcopy(default_cfgs[variant])
overlay_external_default_cfg(default_cfg, kwargs)
default_num_classes = default_cfg['num_classes']
default_img_size = default_cfg['input_size'][-2:]
num_classes = kwargs.pop('num_classes', default_num_classes)
img_size = kwargs.pop('img_size', default_img_size)
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(
Cait, variant, pretrained,
default_cfg=default_cfg,
img_size=img_size,
num_classes=num_classes,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@register_model
def cait_xxs24(pretrained=False, **kwargs):
model = Cait(
img_size=384, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/XXS24_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_xxs24_224(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs24_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs36_224(pretrained=False, **kwargs):
model = Cait(
img_size=224, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/XXS36_224.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)
model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs36_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs36(pretrained=False, **kwargs):
model = Cait(
img_size=384, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/XXS36_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_xxs36_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs36_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xs24(pretrained=False, **kwargs):
model = Cait(
img_size=384, patch_size=16, embed_dim=288, depth=24, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/XS24_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_xs24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=288, depth=24, num_heads=6, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xs24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s24_224(pretrained=False, **kwargs):
model = Cait(
img_size=224, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/S24_224.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)
model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
model = _create_cait('cait_s24_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s24(pretrained=False, **kwargs):
model = Cait(
img_size=384, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, 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/S24_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_s24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
model = _create_cait('cait_s24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s36(pretrained=False, **kwargs):
model = Cait(
img_size=384, patch_size=16, embed_dim=384, depth=36, num_heads=8, 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/S36_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_s36_384(pretrained=False, **kwargs):
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

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