Update REAMDE.md. Sneak in g/G (giant / gigantic?) ViT defs from scaling paper

README.md

@@ -23,11 +23,14 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
-### Jan 6, 2022
+### Jan 14, 2022
-* Version 0.5.2 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
+* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
-* Tried training a few small / mobile optimized models, a few are good so far, more on the way...
+* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
+* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
   * `mnasnet_small` - 65.6 top-1
-  * `lcnet_100` - 72.1 top-1
+  * `mobilenetv2_050` - 65.9
+  * `lcnet_100/075/050` - 72.1 / 68.8 / 63.1
+  * `semnasnet_075` - 73
   * `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0
 * TinyNet models added by [rsomani95](https://github.com/rsomani95)
 * LCNet added via MobileNetV3 architecture

tests/test_models.py

@@ -28,12 +28,12 @@ NON_STD_FILTERS = [
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures
-if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
+if 'GITHUB_ACTIONS' in os.environ:
     # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
     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*']
+        '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_gi*']
 else:
     EXCLUDE_FILTERS = []
 
@@ -255,7 +255,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
 EXCLUDE_JIT_FILTERS = [
     '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
     'dla*', 'hrnet*', 'ghostnet*',  # hopefully fix at some point
-    'vit_large_*', 'vit_huge_*',
+    'vit_large_*', 'vit_huge_*', 'vit_gi*',
 ]
 
 
@@ -334,7 +334,7 @@ def _create_fx_model(model, train=False):
     return fx_model
 
 
-EXCLUDE_FX_FILTERS = []
+EXCLUDE_FX_FILTERS = ['vit_gi*']
 # not enough memory to run fx on more models than other tests
 if 'GITHUB_ACTIONS' in os.environ:
     EXCLUDE_FX_FILTERS += [

timm/models/vision_transformer.py

@@ -105,6 +105,10 @@ default_cfgs = {
             'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
         input_size=(3, 384, 384), crop_pct=1.0),
 
+    'vit_huge_patch14_224': _cfg(url=''),
+    'vit_giant_patch14_224': _cfg(url=''),
+    'vit_gigantic_patch14_224': _cfg(url=''),
+
     # patch models, imagenet21k (weights from official Google JAX impl)
     'vit_tiny_patch16_224_in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
@@ -715,6 +719,33 @@ def vit_base_patch32_sam_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_huge_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_giant_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_gigantic_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Gigantic 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_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Tiny (Vit-Ti/16).