diff --git a/README.md b/README.md index 798f94f3..994775f1 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,18 @@ 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 to `beit.py`. + * original source: https://github.com/baaivision/EVA + * paper: https://arxiv.org/abs/2211.07636 + +| model | top1 | param_count | gmac | macts | hub | +|:-----------------------------------------|-------:|--------------:|-------:|--------:|:----------------------------------------| +| eva_giant_patch14_560.m30m_ft_in22k_in1k | 89.8 | 1014.4 | 1906.8 | 2577.2 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_336.m30m_ft_in22k_in1k | 89.6 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_336.clip_ft_in1k | 89.4 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_224.clip_ft_in1k | 89.1 | 1012.6 | 267.2 | 192.6 | [link](https://huggingface.co/BAAI/EVA) | + # Dec 5, 2022 * Pre-release (`0.8.0dev0`) of multi-weight support (`model_arch.pretrained_tag`). Install with `pip install --pre timm` @@ -376,6 +388,7 @@ A full version of the list below with source links can be found in the [document * MobileNet-V2 - https://arxiv.org/abs/1801.04381 * Single-Path NAS - https://arxiv.org/abs/1904.02877 * TinyNet - https://arxiv.org/abs/2010.14819 +* EVA - https://arxiv.org/abs/2211.07636 * GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 * GhostNet - https://arxiv.org/abs/1911.11907 * gMLP - https://arxiv.org/abs/2105.08050 diff --git a/benchmark.py b/benchmark.py index 04557a7d..9adeb465 100755 --- a/benchmark.py +++ b/benchmark.py @@ -80,9 +80,11 @@ parser.add_argument('--results-file', default='', type=str, parser.add_argument('--results-format', default='csv', type=str, help='Format for results file one of (csv, json) (default: csv).') parser.add_argument('--num-warm-iter', default=10, type=int, - metavar='N', help='Number of warmup iterations (default: 10)') + help='Number of warmup iterations (default: 10)') parser.add_argument('--num-bench-iter', default=40, type=int, - metavar='N', help='Number of benchmark iterations (default: 40)') + help='Number of benchmark iterations (default: 40)') +parser.add_argument('--device', default='cuda', type=str, + help="device to run benchmark on") # common inference / train args parser.add_argument('--model', '-m', metavar='NAME', default='resnet50', diff --git a/tests/test_models.py b/tests/test_models.py index dd1330eb..87d75cbd 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -27,7 +27,7 @@ NON_STD_FILTERS = [ 'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*', 'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*', - 'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', + 'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', 'eva_*' ] NUM_NON_STD = len(NON_STD_FILTERS) @@ -39,7 +39,7 @@ if 'GITHUB_ACTIONS' in os.environ: '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*', '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*', 'swin*giant*'] - NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*'] + NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*', 'eva_giant*'] else: EXCLUDE_FILTERS = [] NON_STD_EXCLUDE_FILTERS = ['vit_gi*'] diff --git a/timm/models/beit.py b/timm/models/beit.py index 1f6bf82b..c44256a3 100644 --- a/timm/models/beit.py +++ b/timm/models/beit.py @@ -1,8 +1,6 @@ -""" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) +""" 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 @@ -46,72 +60,14 @@ 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 -def _cfg(url='', **kwargs): - return { - 'url': url, - 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, - 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, - 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), - 'first_conv': 'patch_embed.proj', 'classifier': 'head', - **kwargs - } - - -default_cfgs = { - 'beit_base_patch16_224': _cfg( - url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'), - 'beit_base_patch16_384': _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( - 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( - url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'), - 'beit_large_patch16_384': _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( - 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( - 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( - 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( - 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( - 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( - 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 - ), -} - - def gen_relative_position_index(window_size: Tuple[int, int]) -> torch.Tensor: num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 # cls to token & token 2 cls & cls to cls @@ -384,6 +340,82 @@ class Beit(nn.Module): return x +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +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.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_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.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.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.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_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.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.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.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.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, crop_pct=1.0, + ), + '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), crop_pct=1.0, crop_mode='squash'), + '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), crop_pct=1.0, crop_mode='squash'), + '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), crop_pct=1.0, crop_mode='squash'), +}) + + def _beit_checkpoint_filter_fn(state_dict, model): if 'module' in state_dict: # beit v2 didn't strip module @@ -393,7 +425,7 @@ def _beit_checkpoint_filter_fn(state_dict, model): def _create_beit(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): - raise RuntimeError('features_only not implemented for Beit models.') + raise RuntimeError('features_only not implemented for BEiT models.') model = build_model_with_cfg( Beit, variant, pretrained, @@ -415,25 +447,16 @@ 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 -@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, - 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 - - @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 +465,7 @@ 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 @@ -451,52 +474,52 @@ 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 @register_model -def beit_large_patch16_224_in22k(pretrained=False, **kwargs): +def beitv2_base_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, - 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) + patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs) + model = _create_beit('beitv2_base_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model -def beitv2_base_patch16_224(pretrained=False, **kwargs): +def beitv2_large_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, - use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs) - model = _create_beit('beitv2_base_patch16_224', pretrained=pretrained, **model_kwargs) + 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 @register_model -def beitv2_base_patch16_224_in22k(pretrained=False, **kwargs): +def eva_giant_patch14_224(pretrained=False, **kwargs): + """ EVA-g model https://arxiv.org/abs/2211.07636 """ model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, - use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs) - model = _create_beit('beitv2_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs) + 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 beitv2_large_patch16_224(pretrained=False, **kwargs): +def eva_giant_patch14_336(pretrained=False, **kwargs): + """ EVA-g model https://arxiv.org/abs/2211.07636 """ model_kwargs = dict( - patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, - 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) + 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 beitv2_large_patch16_224_in22k(pretrained=False, **kwargs): +def eva_giant_patch14_560(pretrained=False, **kwargs): + """ EVA-g model https://arxiv.org/abs/2211.07636 """ model_kwargs = dict( - patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, - 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) + 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 diff --git a/timm/models/pretrained.py b/timm/models/pretrained.py index 60f38fd4..2ca7ac5a 100644 --- a/timm/models/pretrained.py +++ b/timm/models/pretrained.py @@ -59,10 +59,11 @@ class PretrainedCfg: def filter_pretrained_cfg(cfg, remove_source=False, remove_null=True): filtered_cfg = {} + keep_none = {'pool_size', 'first_conv', 'classifier'} # always keep these keys, even if none for k, v in cfg.items(): if remove_source and k in {'url', 'file', 'hf_hub_id', 'hf_hub_id', 'hf_hub_filename', 'source'}: continue - if remove_null and v is None: + if remove_null and v is None and k not in keep_none: continue filtered_cfg[k] = v return filtered_cfg