From 98047ef5e35c18a0dccf16da6a29788e45d7225c Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Tue, 6 Dec 2022 23:14:59 -0800 Subject: [PATCH] Add EVA FT results, hopefully fix BEiT test failures --- README.md | 11 ++- benchmark.py | 6 +- tests/test_models.py | 4 +- timm/models/beit.py | 192 ++++++++++++++++---------------------- timm/models/pretrained.py | 3 +- 5 files changed, 96 insertions(+), 120 deletions(-) diff --git a/README.md b/README.md index 735cb5a4..abcd01a4 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,16 @@ 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 +* 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 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 162ba81b..c44256a3 100644 --- a/timm/models/beit.py +++ b/timm/models/beit.py @@ -1,4 +1,4 @@ -""" 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 @@ -68,82 +68,6 @@ 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 = 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, - ), - '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: num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 # cls to token & token 2 cls & cls to cls @@ -416,6 +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 @@ -425,7 +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, @@ -453,15 +453,6 @@ def beit_base_patch16_384(pretrained=False, **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, - 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( @@ -489,15 +480,6 @@ def beit_large_patch16_512(pretrained=False, **kwargs): return model -@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, - 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 - - @register_model def beitv2_base_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( @@ -507,15 +489,6 @@ def beitv2_base_patch16_224(pretrained=False, **kwargs): return model -@register_model -def beitv2_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=1e-5, **kwargs) - model = _create_beit('beitv2_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs) - return model - - @register_model def beitv2_large_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( @@ -525,15 +498,6 @@ def beitv2_large_patch16_224(pretrained=False, **kwargs): return model -@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, - 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 - - @register_model def eva_giant_patch14_224(pretrained=False, **kwargs): """ EVA-g model https://arxiv.org/abs/2211.07636 """ 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