Add beitv2 224x224 checkpoints from https://github.com/microsoft/unilm/tree/master/beit2

timm/models/beit.py

@@ -1,6 +1,25 @@
 """ 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},
+author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
+booktitle={International Conference on Learning Representations},
+year={2022},
+url={https://openreview.net/forum?id=p-BhZSz59o4}
+}
+
+@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},
+year={2022},
+eprint={2208.06366},
+archivePrefix={arXiv},
+primaryClass={cs.CV}
+}
 
 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.
@@ -27,6 +46,7 @@ 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 .helpers import build_model_with_cfg
 from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
 from .registry import register_model
@@ -69,6 +89,26 @@ default_cfgs = {
         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
+    ),
 }
 
 
@@ -417,3 +457,39 @@ def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
         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(
+        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_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(
+        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)
+    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, 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)
+    return model