Add hf hub entries for laion2b clip models, add huggingface_hub depen…

…dency, update some setup/reqs, torch >= 1.7

requirements.txt

@@ -1,3 +1,4 @@
-torch>=1.4.0
-torchvision>=0.5.0
+torch>=1.7
+torchvision
 pyyaml
+huggingface_hub

setup.py

@@ -25,13 +25,15 @@
         #   3 - Alpha
         #   4 - Beta
         #   5 - Production/Stable
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 4 - Beta',
         'Intended Audience :: Education',
         'Intended Audience :: Science/Research',
         'License :: OSI Approved :: Apache Software License',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
         'Topic :: Scientific/Engineering',
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
         'Topic :: Software Development',
@@ -40,9 +42,10 @@
     ],
 
     # Note that this is a string of words separated by whitespace, not a list.
-    keywords='pytorch pretrained models efficientnet mobilenetv3 mnasnet',
+    keywords='pytorch pretrained models efficientnet mobilenetv3 mnasnet resnet vision transformer vit',
     packages=find_packages(exclude=['convert', 'tests', 'results']),
     include_package_data=True,
-    install_requires=['torch >= 1.4', 'torchvision'],
+    install_requires=['torch >= 1.7', 'torchvision', 'pyyaml', 'huggingface_hub'],
     python_requires='>=3.6',
 )
+

tests/test_models.py

@@ -38,7 +38,7 @@
     EXCLUDE_FILTERS = [
         '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
         '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
-        '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_g*', 'swin*huge*',
+        '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*',
         'swin*giant*']
     NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*']
 else:

timm/data/constants.py

@@ -5,3 +5,5 @@
 IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
 IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
 IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
+OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711)

timm/models/vision_transformer.py

@@ -30,7 +30,8 @@
 import torch.nn.functional as F
 import torch.utils.checkpoint
 
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD,\
+    OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
 from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply, adapt_input_conv, checkpoint_seq
 from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
 from .registry import register_model
@@ -106,7 +107,7 @@ def _cfg(url='', **kwargs):
     'vit_large_patch14_224': _cfg(url=''),
     'vit_huge_patch14_224': _cfg(url=''),
     'vit_giant_patch14_224': _cfg(url=''),
-    'vit_gee_patch14_224': _cfg(url=''),
+    'vit_gigantic_patch14_224': _cfg(url=''),
 
 
     # patch models, imagenet21k (weights from official Google JAX impl)
@@ -179,17 +180,21 @@ def _cfg(url='', **kwargs):
     'vit_base_patch16_18x2_224': _cfg(url=''),
 
     'vit_base_patch32_224_clip_laion2b': _cfg(
-        hf_hub_id='',
-        num_classes=512),
+        hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
     'vit_large_patch14_224_clip_laion2b': _cfg(
-        hf_hub_id='',
-        num_classes=768),
+        hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=768),
     'vit_huge_patch14_224_clip_laion2b': _cfg(
-        hf_hub_id='',
-        num_classes=1024),
+        hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),
     'vit_giant_patch14_224_clip_laion2b': _cfg(
-        hf_hub_id='',
-        num_classes=1024),
+        hf_hub_id='CLIP-ViT-g-14-laion2B-s12B-b42K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),
 
 }
 
@@ -960,12 +965,11 @@ def vit_giant_patch14_224(pretrained=False, **kwargs):
 
 
 @register_model
-def vit_gee_patch14_224(pretrained=False, **kwargs):
-    """ ViT-GEE (big-G) model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
-    As per https://twitter.com/wightmanr/status/1570549064667889666
+def vit_gigantic_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Gigantic (big-G) model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
     """
     model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
-    model = _create_vision_transformer('vit_gee_patch14_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
     return model