Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add eva models to beit.py

Browse Source
pull/1582/head
Ross Wightman 2 years ago
parent da6644b6ba
commit 781f174fe6
2 changed files with 88 additions and 24 deletions
Show Stats Download Patch File Download Diff File

3
README.md
Unescape View File

@ -21,6 +21,9 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before
## What's New
# Dec 6, 2022
* Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain from https://github.com/baaivision/EVA
# Dec 5, 2022
* Pre-release (`0.8.0dev0`) of multi-weight support (`model_arch.pretrained_tag`). Install with `pip install --pre timm`

109
timm/models/beit.py
Unescape View File

@ -1,8 +1,6 @@
""" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
Model from official source: https://github.com/microsoft/unilm/tree/master/beit
and
https://github.com/microsoft/unilm/tree/master/beit2
@inproceedings{beit,
title={{BEiT}: {BERT} Pre-Training of Image Transformers},
@ -12,6 +10,8 @@ year={2022},
url={https://openreview.net/forum?id=p-BhZSz59o4}
}
BEiT-v2 from https://github.com/microsoft/unilm/tree/master/beit2
@article{beitv2,
title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers},
author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei},
@ -21,6 +21,17 @@ archivePrefix={arXiv},
primaryClass={cs.CV}
}
EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2211.07636},
year={2022}
}
At this point only the 1k fine-tuned classification weights and model configs have been added,
see original source above for pre-training models and procedure.
@ -37,6 +48,9 @@ Modifications by / Copyright 2021 Ross Wightman, original copyrights below
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
# EVA models Copyright (c) 2022 BAAI-Vision
import math
from functools import partial
from typing import Optional, Tuple
@ -46,9 +60,10 @@ import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from .helpers import build_model_with_cfg
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
from .pretrained import generate_default_cfgs
from .registry import register_model
from .vision_transformer import checkpoint_filter_fn
@ -64,52 +79,72 @@ def _cfg(url='', **kwargs):
}
default_cfgs = {
'beit_base_patch16_224': _cfg(
default_cfgs = generate_default_cfgs({
'beit_base_patch16_224.in22k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'),
'beit_base_patch16_384': _cfg(
'beit_base_patch16_384.in22k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_base_patch16_224_in22k': _cfg(
'beit_base_patch16_224.in22k_ft_in22k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth',
num_classes=21841,
),
'beit_large_patch16_224': _cfg(
'beit_large_patch16_224.in22k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'),
'beit_large_patch16_384': _cfg(
'beit_large_patch16_384.in22k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_large_patch16_512': _cfg(
'beit_large_patch16_512.in22k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
input_size=(3, 512, 512), crop_pct=1.0,
),
'beit_large_patch16_224_in22k': _cfg(
'beit_large_patch16_224.in22k_ft_in22k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth',
num_classes=21841,
),
'beitv2_base_patch16_224': _cfg(
'beitv2_base_patch16_224.in1k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_base_patch16_224_in22k': _cfg(
'beitv2_base_patch16_224.in1k_ft_in22k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth',
num_classes=21841,
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_large_patch16_224': _cfg(
'beitv2_large_patch16_224.in1k_ft_in22k_in1k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21kto1k.pth',
crop_pct=0.95,
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_large_patch16_224_in22k': _cfg(
'beitv2_large_patch16_224.in1k_ft_in22k': _cfg(
url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth',
num_classes=21841,
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
}
'eva_giant_patch14_224.clip_ft_in1k': _cfg(
hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
),
'eva_giant_patch14_336.clip_ft_in1k': _cfg(
hf_hub_id='BAAI/EVA',
hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336)),
'eva_giant_patch14_336.m30m_ft_in22k_in1k': _cfg(
hf_hub_id='BAAI/EVA',
hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
input_size=(3, 336, 336)),
'eva_giant_patch14_560.m30m_ft_in22k_in1k': _cfg(
hf_hub_id='BAAI/EVA',
hf_hub_filename='eva_21k_1k_560px_psz14_ema_89p7.pt',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
input_size=(3, 560, 560)),
})
def gen_relative_position_index(window_size: Tuple[int, int]) -> torch.Tensor:
@ -415,7 +450,7 @@ def beit_base_patch16_224(pretrained=False, **kwargs):
@register_model
def beit_base_patch16_384(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@ -424,7 +459,7 @@ def beit_base_patch16_384(pretrained=False, **kwargs):
@register_model
def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
patch_size=16, embed_dim=768, depth=12, num_heads=12,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
model = _create_beit('beit_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
return model
@ -433,7 +468,7 @@ def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
@register_model
def beit_large_patch16_224(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@ -442,7 +477,7 @@ def beit_large_patch16_224(pretrained=False, **kwargs):
@register_model
def beit_large_patch16_384(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@ -451,7 +486,7 @@ def beit_large_patch16_384(pretrained=False, **kwargs):
@register_model
def beit_large_patch16_512(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **model_kwargs)
return model
@ -460,7 +495,7 @@ def beit_large_patch16_512(pretrained=False, **kwargs):
@register_model
def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
return model
@ -487,7 +522,7 @@ def beitv2_base_patch16_224_in22k(pretrained=False, **kwargs):
@register_model
def beitv2_large_patch16_224(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beitv2_large_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@ -496,7 +531,33 @@ def beitv2_large_patch16_224(pretrained=False, **kwargs):
@register_model
def beitv2_large_patch16_224_in22k(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beitv2_large_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
return model
def eva_giant_patch14_224(pretrained=False, **kwargs):
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_kwargs = dict(
patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408, **kwargs)
model = _create_beit('eva_giant_patch14_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def eva_giant_patch14_336(pretrained=False, **kwargs):
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_kwargs = dict(
patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408, **kwargs)
model = _create_beit('eva_giant_patch14_336', pretrained=pretrained, **model_kwargs)
return model
@register_model
def eva_giant_patch14_560(pretrained=False, **kwargs):
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_kwargs = dict(
patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408, **kwargs)
model = _create_beit('eva_giant_patch14_560', pretrained=pretrained, **model_kwargs)
return model

Write Preview
Loading…
Cancel
Save