Re-order vit pretrained entries for more sensible default weights (no .tag specified)

benchmark.py

@@ -22,7 +22,7 @@ from timm.data import resolve_data_config
 from timm.layers import set_fast_norm
 from timm.models import create_model, is_model, list_models
 from timm.optim import create_optimizer_v2
-from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry
+from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry, ParseKwargs
 
 has_apex = False
 try:
@@ -108,12 +108,15 @@ parser.add_argument('--grad-checkpointing', action='store_true', default=False,
                     help='Enable gradient checkpointing through model blocks/stages')
 parser.add_argument('--amp', action='store_true', default=False,
                     help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.')
+parser.add_argument('--amp-dtype', default='float16', type=str,
+                    help='lower precision AMP dtype (default: float16). Overrides --precision arg if args.amp True.')
 parser.add_argument('--precision', default='float32', type=str,
                     help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)')
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 parser.add_argument('--fast-norm', default=False, action='store_true',
                     help='enable experimental fast-norm')
+parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs)
 
 # codegen (model compilation) options
 scripting_group = parser.add_mutually_exclusive_group()
@@ -124,7 +127,6 @@ scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd optimization.")
 
-
 # train optimizer parameters
 parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                     help='Optimizer (default: "sgd"')
@@ -168,19 +170,21 @@ def count_params(model: nn.Module):
 
 
 def resolve_precision(precision: str):
-    assert precision in ('amp', 'float16', 'bfloat16', 'float32')
+    assert precision in ('amp', 'amp_bfloat16', 'float16', 'bfloat16', 'float32')
-    use_amp = False
+    amp_dtype = None  # amp disabled
     model_dtype = torch.float32
     data_dtype = torch.float32
     if precision == 'amp':
-        use_amp = True
+        amp_dtype = torch.float16
+    elif precision == 'amp_bfloat16':
+        amp_dtype = torch.bfloat16
     elif precision == 'float16':
         model_dtype = torch.float16
         data_dtype = torch.float16
     elif precision == 'bfloat16':
         model_dtype = torch.bfloat16
         data_dtype = torch.bfloat16
-    return use_amp, model_dtype, data_dtype
+    return amp_dtype, model_dtype, data_dtype
 
 
 def profile_deepspeed(model, input_size=(3, 224, 224), batch_size=1, detailed=False):
@@ -228,9 +232,12 @@ class BenchmarkRunner:
         self.model_name = model_name
         self.detail = detail
         self.device = device
-        self.use_amp, self.model_dtype, self.data_dtype = resolve_precision(precision)
+        self.amp_dtype, self.model_dtype, self.data_dtype = resolve_precision(precision)
         self.channels_last = kwargs.pop('channels_last', False)
-        self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=torch.float16) if self.use_amp else suppress
+        if self.amp_dtype is not None:
+            self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=self.amp_dtype)
+        else:
+            self.amp_autocast = suppress
 
         if fuser:
             set_jit_fuser(fuser)
@@ -243,6 +250,7 @@ class BenchmarkRunner:
             drop_rate=kwargs.pop('drop', 0.),
             drop_path_rate=kwargs.pop('drop_path', None),
             drop_block_rate=kwargs.pop('drop_block', None),
+            **kwargs.pop('model_kwargs', {}),
         )
         self.model.to(
             device=self.device,
@@ -560,7 +568,7 @@ def _try_run(
 def benchmark(args):
     if args.amp:
         _logger.warning("Overriding precision to 'amp' since --amp flag set.")
-        args.precision = 'amp'
+        args.precision = 'amp' if args.amp_dtype == 'float16' else '_'.join(['amp', args.amp_dtype])
     _logger.info(f'Benchmarking in {args.precision} precision. '
                  f'{"NHWC" if args.channels_last else "NCHW"} layout. '
                  f'torchscript {"enabled" if args.torchscript else "disabled"}')

inference.py

@@ -20,7 +20,7 @@ import torch
 from timm.data import create_dataset, create_loader, resolve_data_config
 from timm.layers import apply_test_time_pool
 from timm.models import create_model
-from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser
+from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser, ParseKwargs
 
 try:
     from apex import amp
@@ -72,6 +72,8 @@ parser.add_argument('-b', '--batch-size', default=256, type=int,
                     metavar='N', help='mini-batch size (default: 256)')
 parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--in-chans', type=int, default=None, metavar='N',
+                    help='Image input channels (default: None => 3)')
 parser.add_argument('--input-size', default=None, nargs=3, type=int,
                     metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
 parser.add_argument('--use-train-size', action='store_true', default=False,
@@ -110,6 +112,7 @@ parser.add_argument('--amp-dtype', default='float16', type=str,
                     help='lower precision AMP dtype (default: float16)')
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
+parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs)
 
 scripting_group = parser.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', default=False, action='store_true',
@@ -170,12 +173,19 @@ def main():
         set_jit_fuser(args.fuser)
 
     # create model
+    in_chans = 3
+    if args.in_chans is not None:
+        in_chans = args.in_chans
+    elif args.input_size is not None:
+        in_chans = args.input_size[0]
+
     model = create_model(
         args.model,
         num_classes=args.num_classes,
-        in_chans=3,
+        in_chans=in_chans,
         pretrained=args.pretrained,
         checkpoint_path=args.checkpoint,
+        **args.model_kwargs,
     )
     if args.num_classes is None:
         assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'

timm/models/_hub.py

@@ -209,6 +209,7 @@ def push_to_hf_hub(
     private: bool = False,
     create_pr: bool = False,
     model_config: Optional[dict] = None,
+    model_card: Optional[dict] = None,
 ):
     # Create repo if it doesn't exist yet
     repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
@@ -232,9 +233,23 @@ def push_to_hf_hub(
 
         # Add readme if it does not exist
         if not has_readme:
+            model_card = model_card or {}
             model_name = repo_id.split('/')[-1]
             readme_path = Path(tmpdir) / "README.md"
-            readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {model_name}'
+            readme_text = "---\n"
+            readme_text += "tags:\n- image-classification\n- timm\n"
+            readme_text += "library_tag: timm\n"
+            readme_text += f"license: {model_card.get('license', 'apache-2.0')}\n"
+            readme_text += "---\n"
+            readme_text += f"# Model card for {model_name}\n"
+            if 'description' in model_card:
+                readme_text += f"\n{model_card['description']}\n"
+            if 'details' in model_card:
+                readme_text += f"\n## Model Details\n"
+                for k, v in model_card['details'].items():
+                    readme_text += f"- **{k}:** {v}\n"
+            if 'citation' in model_card:
+                readme_text += f"\n## Citation\n```\n{model_card['citation']}```\n"
             readme_path.write_text(readme_text)
 
         # Upload model and return

timm/models/byobnet.py

@@ -218,7 +218,10 @@ def _rep_vgg_bcfg(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0):
 
 
 def interleave_blocks(
-        types: Tuple[str, str], d, every: Union[int, List[int]] = 1, first: bool = False, **kwargs
+        types: Tuple[str, str], d,
+        every: Union[int, List[int]] = 1,
+        first: bool = False,
+        **kwargs,
 ) -> Tuple[ByoBlockCfg]:
     """ interleave 2 block types in stack
     """
@@ -1587,15 +1590,32 @@ class ByobNet(nn.Module):
             in_chans=3,
             global_pool='avg',
             output_stride=32,
-            zero_init_last=True,
             img_size=None,
             drop_rate=0.,
             drop_path_rate=0.,
+            zero_init_last=True,
+            **kwargs,
     ):
+        """
+
+        Args:
+            cfg (ByoModelCfg): Model architecture configuration
+            num_classes (int): Number of classifier classes (default: 1000)
+            in_chans (int): Number of input channels (default: 3)
+            global_pool (str): Global pooling type (default: 'avg')
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            img_size (Union[int, Tuple[int]): Image size for fixed image size models (i.e. self-attn)
+            drop_rate (float): Dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
+            zero_init_last (bool): Zero-init last weight of residual path
+            kwargs (dict): Extra kwargs overlayed onto cfg
+        """
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         self.grad_checkpointing = False
+
+        cfg = replace(cfg, **kwargs)  # overlay kwargs onto cfg
         layers = get_layer_fns(cfg)
         if cfg.fixed_input_size:
             assert img_size is not None, 'img_size argument is required for fixed input size model'

timm/models/convnext.py

@@ -167,7 +167,7 @@ class ConvNeXtStage(nn.Module):
                 conv_bias=conv_bias,
                 use_grn=use_grn,
                 act_layer=act_layer,
-                norm_layer=norm_layer if conv_mlp else norm_layer_cl
+                norm_layer=norm_layer if conv_mlp else norm_layer_cl,
             ))
             in_chs = out_chs
         self.blocks = nn.Sequential(*stage_blocks)
@@ -184,16 +184,6 @@ class ConvNeXtStage(nn.Module):
 class ConvNeXt(nn.Module):
     r""" ConvNeXt
         A PyTorch impl of : `A ConvNet for the 2020s`  - https://arxiv.org/pdf/2201.03545.pdf
-
-    Args:
-        in_chans (int): Number of input image channels. Default: 3
-        num_classes (int): Number of classes for classification head. Default: 1000
-        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
-        dims (tuple(int)): Feature dimension at each stage. Default: [96, 192, 384, 768]
-        drop_rate (float): Head dropout rate
-        drop_path_rate (float): Stochastic depth rate. Default: 0.
-        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
-        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
     """
 
     def __init__(
@@ -218,6 +208,28 @@ class ConvNeXt(nn.Module):
             drop_rate=0.,
             drop_path_rate=0.,
     ):
+        """
+        Args:
+            in_chans (int): Number of input image channels (default: 3)
+            num_classes (int): Number of classes for classification head (default: 1000)
+            global_pool (str): Global pooling type (default: 'avg')
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            depths (tuple(int)): Number of blocks at each stage. (default: [3, 3, 9, 3])
+            dims (tuple(int)): Feature dimension at each stage. (default: [96, 192, 384, 768])
+            kernel_sizes (Union[int, List[int]]: Depthwise convolution kernel-sizes for each stage (default: 7)
+            ls_init_value (float): Init value for Layer Scale (default: 1e-6)
+            stem_type (str): Type of stem (default: 'patch')
+            patch_size (int): Stem patch size for patch stem (default: 4)
+            head_init_scale (float): Init scaling value for classifier weights and biases (default: 1)
+            head_norm_first (bool): Apply normalization before global pool + head (default: False)
+            conv_mlp (bool): Use 1x1 conv in MLP, improves speed for small networks w/ chan last (default: False)
+            conv_bias (bool): Use bias layers w/ all convolutions (default: True)
+            use_grn (bool): Use Global Response Norm (ConvNeXt-V2) in MLP (default: False)
+            act_layer (Union[str, nn.Module]): Activation Layer
+            norm_layer (Union[str, nn.Module]): Normalization Layer
+            drop_rate (float): Head dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth rate (default: 0.)
+        """
         super().__init__()
         assert output_stride in (8, 16, 32)
         kernel_sizes = to_ntuple(4)(kernel_sizes)
@@ -279,7 +291,7 @@ class ConvNeXt(nn.Module):
                 use_grn=use_grn,
                 act_layer=act_layer,
                 norm_layer=norm_layer,
-                norm_layer_cl=norm_layer_cl
+                norm_layer_cl=norm_layer_cl,
             ))
             prev_chs = out_chs
             # NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2

timm/models/cspnet.py

@@ -12,7 +12,7 @@ Reference impl via darknet cfg files at https://github.com/WongKinYiu/CrossStage
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
-from dataclasses import dataclass, asdict
+from dataclasses import dataclass, asdict, replace
 from functools import partial
 from typing import Any, Dict, Optional, Tuple, Union
 
@@ -518,7 +518,7 @@ class CrossStage(nn.Module):
             cross_linear=False,
             block_dpr=None,
             block_fn=BottleneckBlock,
-            **block_kwargs
+            **block_kwargs,
     ):
         super(CrossStage, self).__init__()
         first_dilation = first_dilation or dilation
@@ -558,7 +558,7 @@ class CrossStage(nn.Module):
                 bottle_ratio=bottle_ratio,
                 groups=groups,
                 drop_path=block_dpr[i] if block_dpr is not None else 0.,
-                **block_kwargs
+                **block_kwargs,
             ))
             prev_chs = block_out_chs
 
@@ -597,7 +597,7 @@ class CrossStage3(nn.Module):
             cross_linear=False,
             block_dpr=None,
             block_fn=BottleneckBlock,
-            **block_kwargs
+            **block_kwargs,
     ):
         super(CrossStage3, self).__init__()
         first_dilation = first_dilation or dilation
@@ -635,7 +635,7 @@ class CrossStage3(nn.Module):
                 bottle_ratio=bottle_ratio,
                 groups=groups,
                 drop_path=block_dpr[i] if block_dpr is not None else 0.,
-                **block_kwargs
+                **block_kwargs,
             ))
             prev_chs = block_out_chs
 
@@ -668,7 +668,7 @@ class DarkStage(nn.Module):
             avg_down=False,
             block_fn=BottleneckBlock,
             block_dpr=None,
-            **block_kwargs
+            **block_kwargs,
     ):
         super(DarkStage, self).__init__()
         first_dilation = first_dilation or dilation
@@ -715,7 +715,7 @@ def create_csp_stem(
         padding='',
         act_layer=nn.ReLU,
         norm_layer=nn.BatchNorm2d,
-        aa_layer=None
+        aa_layer=None,
 ):
     stem = nn.Sequential()
     feature_info = []
@@ -738,7 +738,7 @@ def create_csp_stem(
             stride=conv_stride,
             padding=padding if i == 0 else '',
             act_layer=act_layer,
-            norm_layer=norm_layer
+            norm_layer=norm_layer,
         ))
         stem_stride *= conv_stride
         prev_chs = chs
@@ -800,7 +800,7 @@ def create_csp_stages(
         cfg: CspModelCfg,
         drop_path_rate: float,
         output_stride: int,
-        stem_feat: Dict[str, Any]
+        stem_feat: Dict[str, Any],
 ):
     cfg_dict = asdict(cfg.stages)
     num_stages = len(cfg.stages.depth)
@@ -868,12 +868,27 @@ class CspNet(nn.Module):
             global_pool='avg',
             drop_rate=0.,
             drop_path_rate=0.,
-            zero_init_last=True
+            zero_init_last=True,
+            **kwargs,
     ):
+        """
+        Args:
+            cfg (CspModelCfg): Model architecture configuration
+            in_chans (int): Number of input channels (default: 3)
+            num_classes (int): Number of classifier classes (default: 1000)
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            global_pool (str): Global pooling type (default: 'avg')
+            drop_rate (float): Dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
+            zero_init_last (bool): Zero-init last weight of residual path
+            kwargs (dict): Extra kwargs overlayed onto cfg
+        """
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         assert output_stride in (8, 16, 32)
+
+        cfg = replace(cfg, **kwargs)  # overlay kwargs onto cfg
         layer_args = dict(
             act_layer=cfg.act_layer,
             norm_layer=cfg.norm_layer,

timm/models/nfnet.py

@@ -17,7 +17,7 @@ Status:
 Hacked together by / copyright Ross Wightman, 2021.
 """
 from collections import OrderedDict
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 from functools import partial
 from typing import Tuple, Optional
 
@@ -159,11 +159,25 @@ class NfCfg:
 
 
 def _nfres_cfg(
-        depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kwargs=None):
+        depths,
+        channels=(256, 512, 1024, 2048),
+        group_size=None,
+        act_layer='relu',
+        attn_layer=None,
+        attn_kwargs=None,
+):
     attn_kwargs = attn_kwargs or {}
     cfg = NfCfg(
-        depths=depths, channels=channels, stem_type='7x7_pool', stem_chs=64, bottle_ratio=0.25,
+        depths=depths,
-        group_size=group_size, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs)
+        channels=channels,
+        stem_type='7x7_pool',
+        stem_chs=64,
+        bottle_ratio=0.25,
+        group_size=group_size,
+        act_layer=act_layer,
+        attn_layer=attn_layer,
+        attn_kwargs=attn_kwargs,
+    )
     return cfg
 
 
@@ -171,28 +185,70 @@ def _nfreg_cfg(depths, channels=(48, 104, 208, 440)):
     num_features = 1280 * channels[-1] // 440
     attn_kwargs = dict(rd_ratio=0.5)
     cfg = NfCfg(
-        depths=depths, channels=channels, stem_type='3x3', group_size=8, width_factor=0.75, bottle_ratio=2.25,
+        depths=depths,
-        num_features=num_features, reg=True, attn_layer='se', attn_kwargs=attn_kwargs)
+        channels=channels,
+        stem_type='3x3',
+        group_size=8,
+        width_factor=0.75,
+        bottle_ratio=2.25,
+        num_features=num_features,
+        reg=True,
+        attn_layer='se',
+        attn_kwargs=attn_kwargs,
+    )
     return cfg
 
 
 def _nfnet_cfg(
-        depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2.,
+        depths,
-        act_layer='gelu', attn_layer='se', attn_kwargs=None):
+        channels=(256, 512, 1536, 1536),
+        group_size=128,
+        bottle_ratio=0.5,
+        feat_mult=2.,
+        act_layer='gelu',
+        attn_layer='se',
+        attn_kwargs=None,
+):
     num_features = int(channels[-1] * feat_mult)
     attn_kwargs = attn_kwargs if attn_kwargs is not None else dict(rd_ratio=0.5)
     cfg = NfCfg(
-        depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=group_size,
+        depths=depths,
-        bottle_ratio=bottle_ratio, extra_conv=True, num_features=num_features, act_layer=act_layer,
+        channels=channels,
-        attn_layer=attn_layer, attn_kwargs=attn_kwargs)
+        stem_type='deep_quad',
+        stem_chs=128,
+        group_size=group_size,
+        bottle_ratio=bottle_ratio,
+        extra_conv=True,
+        num_features=num_features,
+        act_layer=act_layer,
+        attn_layer=attn_layer,
+        attn_kwargs=attn_kwargs,
+    )
     return cfg
 
 
-def _dm_nfnet_cfg(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True):
+def _dm_nfnet_cfg(
+        depths,
+        channels=(256, 512, 1536, 1536),
+        act_layer='gelu',
+        skipinit=True,
+):
     cfg = NfCfg(
-        depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=128,
+        depths=depths,
-        bottle_ratio=0.5, extra_conv=True, gamma_in_act=True, same_padding=True, skipinit=skipinit,
+        channels=channels,
-        num_features=int(channels[-1] * 2.0), act_layer=act_layer, attn_layer='se', attn_kwargs=dict(rd_ratio=0.5))
+        stem_type='deep_quad',
+        stem_chs=128,
+        group_size=128,
+        bottle_ratio=0.5,
+        extra_conv=True,
+        gamma_in_act=True,
+        same_padding=True,
+        skipinit=skipinit,
+        num_features=int(channels[-1] * 2.0),
+        act_layer=act_layer,
+        attn_layer='se',
+        attn_kwargs=dict(rd_ratio=0.5),
+    )
     return cfg
 
 
@@ -278,7 +334,14 @@ def act_with_gamma(act_type, gamma: float = 1.):
 
 class DownsampleAvg(nn.Module):
     def __init__(
-            self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, conv_layer=ScaledStdConv2d):
+            self,
+            in_chs,
+            out_chs,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            conv_layer=ScaledStdConv2d,
+    ):
         """ AvgPool Downsampling as in 'D' ResNet variants. Support for dilation."""
         super(DownsampleAvg, self).__init__()
         avg_stride = stride if dilation == 1 else 1
@@ -299,9 +362,26 @@ class NormFreeBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs=None, stride=1, dilation=1, first_dilation=None,
+            self,
-            alpha=1.0, beta=1.0, bottle_ratio=0.25, group_size=None, ch_div=1, reg=True, extra_conv=False,
+            in_chs,
-            skipinit=False, attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0.):
+            out_chs=None,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            alpha=1.0,
+            beta=1.0,
+            bottle_ratio=0.25,
+            group_size=None,
+            ch_div=1,
+            reg=True,
+            extra_conv=False,
+            skipinit=False,
+            attn_layer=None,
+            attn_gain=2.0,
+            act_layer=None,
+            conv_layer=None,
+            drop_path_rate=0.,
+    ):
         super().__init__()
         first_dilation = first_dilation or dilation
         out_chs = out_chs or in_chs
@@ -316,7 +396,13 @@ class NormFreeBlock(nn.Module):
 
         if in_chs != out_chs or stride != 1 or dilation != first_dilation:
             self.downsample = DownsampleAvg(
-                in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, conv_layer=conv_layer)
+                in_chs,
+                out_chs,
+                stride=stride,
+                dilation=dilation,
+                first_dilation=first_dilation,
+                conv_layer=conv_layer,
+            )
         else:
             self.downsample = None
 
@@ -452,14 +538,33 @@ class NormFreeNet(nn.Module):
             for what it is/does. Approx 8-10% throughput loss.
     """
     def __init__(
-            self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
+            self,
-            drop_rate=0., drop_path_rate=0.
+            cfg: NfCfg,
+            num_classes=1000,
+            in_chans=3,
+            global_pool='avg',
+            output_stride=32,
+            drop_rate=0.,
+            drop_path_rate=0.,
+            **kwargs,
     ):
+        """
+        Args:
+            cfg (NfCfg): Model architecture configuration
+            num_classes (int): Number of classifier classes (default: 1000)
+            in_chans (int): Number of input channels (default: 3)
+            global_pool (str): Global pooling type (default: 'avg')
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            drop_rate (float): Dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
+            kwargs (dict): Extra kwargs overlayed onto cfg
+        """
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         self.grad_checkpointing = False
 
+        cfg = replace(cfg, **kwargs)
         assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})."
         conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d
         if cfg.gamma_in_act:
@@ -472,7 +577,12 @@ class NormFreeNet(nn.Module):
 
         stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div)
         self.stem, stem_stride, stem_feat = create_stem(
-            in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer, act_layer=act_layer)
+            in_chans,
+            stem_chs,
+            cfg.stem_type,
+            conv_layer=conv_layer,
+            act_layer=act_layer,
+        )
 
         self.feature_info = [stem_feat]
         drop_path_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)]

timm/models/regnet.py

@@ -14,7 +14,7 @@ Weights from original impl have been modified
 Hacked together by / Copyright 2020 Ross Wightman
 """
 import math
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 from functools import partial
 from typing import Optional, Union, Callable
 
@@ -237,7 +237,15 @@ def downsample_avg(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_la
 
 
 def create_shortcut(
-        downsample_type, in_chs, out_chs, kernel_size, stride, dilation=(1, 1), norm_layer=None, preact=False):
+        downsample_type,
+        in_chs,
+        out_chs,
+        kernel_size,
+        stride,
+        dilation=(1, 1),
+        norm_layer=None,
+        preact=False,
+):
     assert downsample_type in ('avg', 'conv1x1', '', None)
     if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
         dargs = dict(stride=stride, dilation=dilation[0], norm_layer=norm_layer, preact=preact)
@@ -259,9 +267,21 @@ class Bottleneck(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25,
+            self,
-            downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
+            in_chs,
-            drop_block=None, drop_path_rate=0.):
+            out_chs,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1,
+            group_size=1,
+            se_ratio=0.25,
+            downsample='conv1x1',
+            linear_out=False,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(Bottleneck, self).__init__()
         act_layer = get_act_layer(act_layer)
         bottleneck_chs = int(round(out_chs * bottle_ratio))
@@ -307,9 +327,21 @@ class PreBottleneck(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25,
+            self,
-            downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
+            in_chs,
-            drop_block=None, drop_path_rate=0.):
+            out_chs,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1,
+            group_size=1,
+            se_ratio=0.25,
+            downsample='conv1x1',
+            linear_out=False,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(PreBottleneck, self).__init__()
         norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
         bottleneck_chs = int(round(out_chs * bottle_ratio))
@@ -353,8 +385,16 @@ class RegStage(nn.Module):
     """Stage (sequence of blocks w/ the same output shape)."""
 
     def __init__(
-            self, depth, in_chs, out_chs, stride, dilation,
+            self,
-            drop_path_rates=None, block_fn=Bottleneck, **block_kwargs):
+            depth,
+            in_chs,
+            out_chs,
+            stride,
+            dilation,
+            drop_path_rates=None,
+            block_fn=Bottleneck,
+            **block_kwargs,
+    ):
         super(RegStage, self).__init__()
         self.grad_checkpointing = False
 
@@ -367,8 +407,13 @@ class RegStage(nn.Module):
             name = "b{}".format(i + 1)
             self.add_module(
                 name, block_fn(
-                    block_in_chs, out_chs, stride=block_stride, dilation=block_dilation,
+                    block_in_chs,
-                    drop_path_rate=dpr, **block_kwargs)
+                    out_chs,
+                    stride=block_stride,
+                    dilation=block_dilation,
+                    drop_path_rate=dpr,
+                    **block_kwargs,
+                )
             )
             first_dilation = dilation
 
@@ -389,12 +434,35 @@ class RegNet(nn.Module):
     """
 
     def __init__(
-            self, cfg: RegNetCfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg',
+            self,
-            drop_rate=0., drop_path_rate=0., zero_init_last=True):
+            cfg: RegNetCfg,
+            in_chans=3,
+            num_classes=1000,
+            output_stride=32,
+            global_pool='avg',
+            drop_rate=0.,
+            drop_path_rate=0.,
+            zero_init_last=True,
+            **kwargs,
+    ):
+        """
+
+        Args:
+            cfg (RegNetCfg): Model architecture configuration
+            in_chans (int): Number of input channels (default: 3)
+            num_classes (int): Number of classifier classes (default: 1000)
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            global_pool (str): Global pooling type (default: 'avg')
+            drop_rate (float): Dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
+            zero_init_last (bool): Zero-init last weight of residual path
+            kwargs (dict): Extra kwargs overlayed onto cfg
+        """
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         assert output_stride in (8, 16, 32)
+        cfg = replace(cfg, **kwargs)  # update cfg with extra passed kwargs
 
         # Construct the stem
         stem_width = cfg.stem_width
@@ -461,8 +529,12 @@ class RegNet(nn.Module):
             dict(zip(arg_names, params)) for params in
             zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)]
         common_args = dict(
-            downsample=cfg.downsample, se_ratio=cfg.se_ratio, linear_out=cfg.linear_out,
+            downsample=cfg.downsample,
-            act_layer=cfg.act_layer, norm_layer=cfg.norm_layer)
+            se_ratio=cfg.se_ratio,
+            linear_out=cfg.linear_out,
+            act_layer=cfg.act_layer,
+            norm_layer=cfg.norm_layer,
+        )
         return per_stage_args, common_args
 
     @torch.jit.ignore
@@ -518,7 +590,6 @@ def _init_weights(module, name='', zero_init_last=False):
 
 
 def _filter_fn(state_dict):
-    """ convert patch embedding weight from manual patchify + linear proj to conv"""
     if 'classy_state_dict' in state_dict:
         import re
         state_dict = state_dict['classy_state_dict']['base_model']['model']

timm/models/resnet.py

@@ -16,7 +16,7 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, \
-    create_classifier
+    get_act_layer, get_norm_layer, create_classifier
 from ._builder import build_model_with_cfg
 from ._manipulate import checkpoint_seq
 from ._registry import register_model, model_entrypoint
@@ -500,7 +500,14 @@ class Bottleneck(nn.Module):
 
 
 def downsample_conv(
-        in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        dilation=1,
+        first_dilation=None,
+        norm_layer=None,
+):
     norm_layer = norm_layer or nn.BatchNorm2d
     kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
     first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
@@ -514,7 +521,14 @@ def downsample_conv(
 
 
 def downsample_avg(
-        in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        dilation=1,
+        first_dilation=None,
+        norm_layer=None,
+):
     norm_layer = norm_layer or nn.BatchNorm2d
     avg_stride = stride if dilation == 1 else 1
     if stride == 1 and dilation == 1:
@@ -627,31 +641,6 @@ class ResNet(nn.Module):
 
     SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
         reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
-
-    Parameters
-    ----------
-    block : Block, class for the residual block. Options are BasicBlockGl, BottleneckGl.
-    layers : list of int, number of layers in each block
-    num_classes : int, default 1000, number of classification classes.
-    in_chans : int, default 3, number of input (color) channels.
-    output_stride : int, default 32, output stride of the network, 32, 16, or 8.
-    global_pool : str, Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
-    cardinality : int, default 1, number of convolution groups for 3x3 conv in Bottleneck.
-    base_width : int, default 64, factor determining bottleneck channels. `planes * base_width / 64 * cardinality`
-    stem_width : int, default 64, number of channels in stem convolutions
-    stem_type : str, default ''
-        The type of stem:
-          * '', default - a single 7x7 conv with a width of stem_width
-          * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
-          * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
-    block_reduce_first : int, default 1
-        Reduction factor for first convolution output width of residual blocks, 1 for all archs except senets, where 2
-    down_kernel_size : int, default 1, kernel size of residual block downsample path, 1x1 for most, 3x3 for senets
-    avg_down : bool, default False, use average pooling for projection skip connection between stages/downsample.
-    act_layer : nn.Module, activation layer
-    norm_layer : nn.Module, normalization layer
-    aa_layer : nn.Module, anti-aliasing layer
-    drop_rate : float, default 0. Dropout probability before classifier, for training
     """
 
     def __init__(
@@ -679,6 +668,36 @@ class ResNet(nn.Module):
             zero_init_last=True,
             block_args=None,
     ):
+        """
+        Args:
+            block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck.
+            layers (List[int]) : number of layers in each block
+            num_classes (int): number of classification classes (default 1000)
+            in_chans (int): number of input (color) channels. (default 3)
+            output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
+            global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
+            cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1)
+            base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64)
+            stem_width (int): number of channels in stem convolutions (default 64)
+            stem_type (str): The type of stem (default ''):
+                * '', default - a single 7x7 conv with a width of stem_width
+                * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
+                * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
+            replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution
+            block_reduce_first (int): Reduction factor for first convolution output width of residual blocks,
+                1 for all archs except senets, where 2 (default 1)
+            down_kernel_size (int): kernel size of residual block downsample path,
+                1x1 for most, 3x3 for senets (default: 1)
+            avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False)
+            act_layer (str, nn.Module): activation layer
+            norm_layer (str, nn.Module): normalization layer
+            aa_layer (nn.Module): anti-aliasing layer
+            drop_rate (float): Dropout probability before classifier, for training (default 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default 0.)
+            drop_block_rate (float): Drop block rate (default 0.)
+            zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight)
+            block_args (dict): Extra kwargs to pass through to block module
+        """
         super(ResNet, self).__init__()
         block_args = block_args or dict()
         assert output_stride in (8, 16, 32)
@@ -686,6 +705,9 @@ class ResNet(nn.Module):
         self.drop_rate = drop_rate
         self.grad_checkpointing = False
 
+        act_layer = get_act_layer(act_layer)
+        norm_layer = get_norm_layer(norm_layer)
+
         # Stem
         deep_stem = 'deep' in stem_type
         inplanes = stem_width * 2 if deep_stem else 64

timm/models/resnetv2.py

@@ -37,7 +37,7 @@ import torch.nn as nn
 
 from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dB0, EvoNorm2dS0, FilterResponseNormTlu2d, \
-    ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d
+    ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer
 from ._builder import build_model_with_cfg
 from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
 from ._registry import register_model
@@ -276,8 +276,16 @@ class Bottleneck(nn.Module):
 
 class DownsampleConv(nn.Module):
     def __init__(
-            self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, preact=True,
+            self,
-            conv_layer=None, norm_layer=None):
+            in_chs,
+            out_chs,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            preact=True,
+            conv_layer=None,
+            norm_layer=None,
+    ):
         super(DownsampleConv, self).__init__()
         self.conv = conv_layer(in_chs, out_chs, 1, stride=stride)
         self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)
@@ -288,8 +296,16 @@ class DownsampleConv(nn.Module):
 
 class DownsampleAvg(nn.Module):
     def __init__(
-            self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None,
+            self,
-            preact=True, conv_layer=None, norm_layer=None):
+            in_chs,
+            out_chs,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            preact=True,
+            conv_layer=None,
+            norm_layer=None,
+    ):
         """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
         super(DownsampleAvg, self).__init__()
         avg_stride = stride if dilation == 1 else 1
@@ -334,9 +350,18 @@ class ResNetStage(nn.Module):
             drop_path_rate = block_dpr[block_idx] if block_dpr else 0.
             stride = stride if block_idx == 0 else 1
             self.blocks.add_module(str(block_idx), block_fn(
-                prev_chs, out_chs, stride=stride, dilation=dilation, bottle_ratio=bottle_ratio, groups=groups,
+                prev_chs,
-                first_dilation=first_dilation, proj_layer=proj_layer, drop_path_rate=drop_path_rate,
+                out_chs,
-                **layer_kwargs, **block_kwargs))
+                stride=stride,
+                dilation=dilation,
+                bottle_ratio=bottle_ratio,
+                groups=groups,
+                first_dilation=first_dilation,
+                proj_layer=proj_layer,
+                drop_path_rate=drop_path_rate,
+                **layer_kwargs,
+                **block_kwargs,
+            ))
             prev_chs = out_chs
             first_dilation = dilation
             proj_layer = None
@@ -413,21 +438,49 @@ class ResNetV2(nn.Module):
             avg_down=False,
             preact=True,
             act_layer=nn.ReLU,
-            conv_layer=StdConv2d,
             norm_layer=partial(GroupNormAct, num_groups=32),
+            conv_layer=StdConv2d,
             drop_rate=0.,
             drop_path_rate=0.,
             zero_init_last=False,
     ):
+        """
+        Args:
+            layers (List[int]) : number of layers in each block
+            channels (List[int]) : number of channels in each block:
+            num_classes (int): number of classification classes (default 1000)
+            in_chans (int): number of input (color) channels. (default 3)
+            global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
+            output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
+            width_factor (int): channel (width) multiplication factor
+            stem_chs (int): stem width (default: 64)
+            stem_type (str): stem type (default: '' == 7x7)
+            avg_down (bool): average pooling in residual downsampling (default: False)
+            preact (bool): pre-activiation (default: True)
+            act_layer (Union[str, nn.Module]): activation layer
+            norm_layer (Union[str, nn.Module]): normalization layer
+            conv_layer (nn.Module): convolution module
+            drop_rate: classifier dropout rate (default: 0.)
+            drop_path_rate: stochastic depth rate (default: 0.)
+            zero_init_last: zero-init last weight in residual path (default: False)
+        """
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         wf = width_factor
+        norm_layer = get_norm_act_layer(norm_layer, act_layer=act_layer)
+        act_layer = get_act_layer(act_layer)
 
         self.feature_info = []
         stem_chs = make_div(stem_chs * wf)
         self.stem = create_resnetv2_stem(
-            in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
+            in_chans,
+            stem_chs,
+            stem_type,
+            preact,
+            conv_layer=conv_layer,
+            norm_layer=norm_layer,
+        )
         stem_feat = ('stem.conv3' if is_stem_deep(stem_type) else 'stem.conv') if preact else 'stem.norm'
         self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat))
 

timm/models/vision_transformer.py

@@ -697,6 +697,13 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = generate_default_cfgs({
 
+    # re-finetuned augreg 21k FT on in1k weights
+    'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg(
+        hf_hub_id='timm/'),
+    'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg(),
+    'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg(
+        hf_hub_id='timm/'),
+
     # How to train your ViT (augreg) weights, pretrained on 21k FT on in1k
     'vit_tiny_patch16_224.augreg_in21k_ft_in1k': _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--imagenet2012-steps_20k-lr_0.03-res_224.npz',
@@ -751,13 +758,6 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
 
-    # re-finetuned augreg 21k FT on in1k weights
-    'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg(
-        hf_hub_id='timm/'),
-    'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg(),
-    'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg(
-        hf_hub_id='timm/'),
-
     # patch models (weights from official Google JAX impl) pretrained on in21k FT on in1k
     'vit_base_patch16_224.orig_in21k_ft_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
@@ -802,7 +802,6 @@ default_cfgs = generate_default_cfgs({
     'vit_giant_patch14_224.untrained': _cfg(url=''),
     'vit_gigantic_patch14_224.untrained': _cfg(url=''),
 
-
     # patch models, imagenet21k (weights from official Google JAX impl)
     'vit_large_patch32_224.orig_in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
@@ -869,7 +868,6 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
 
-
     # ViT ImageNet-21K-P pretraining by MILL
     'vit_base_patch16_224_miil.in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
@@ -880,7 +878,7 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'),
 
-    # custom timm variants
+    # Custom timm variants
     'vit_base_patch16_rpn_224.in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth',
         hf_hub_id='timm/'),
@@ -896,52 +894,6 @@ default_cfgs = generate_default_cfgs({
     'vit_base_patch16_gap_224': _cfg(),
 
     # CLIP pretrained image tower and related fine-tuned weights
-    'vit_base_patch32_clip_224.laion2b': _cfg(
-        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_base_patch16_clip_224.laion2b': _cfg(
-        #hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K',
-        hf_hub_filename='open_clip_pytorch_model.bin',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
-    'vit_large_patch14_clip_224.laion2b': _cfg(
-        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, crop_pct=1.0, num_classes=768),
-    'vit_huge_patch14_clip_224.laion2b': _cfg(
-        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, crop_pct=1.0, num_classes=1024),
-    'vit_giant_patch14_clip_224.laion2b': _cfg(
-        hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
-        hf_hub_filename='open_clip_pytorch_model.bin',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
-
-    'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
-    'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
-    'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
-        crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
-    'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
-    'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
-        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
-    'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
-    'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg(
-        hf_hub_id='',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
-        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
-
     'vit_base_patch32_clip_224.laion2b_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
@@ -973,28 +925,52 @@ default_cfgs = generate_default_cfgs({
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
         crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
 
-    'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg(
+    'vit_base_patch32_clip_224.openai_ft_in12k_in1k': _cfg(
-        #hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k',
+        # hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
-    'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg(
+    'vit_base_patch32_clip_384.openai_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
-    'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg(
+        crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
+    'vit_base_patch16_clip_224.openai_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
-    'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg(
+    'vit_base_patch16_clip_384.openai_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
+    'vit_large_patch14_clip_224.openai_ft_in12k_in1k': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
+    'vit_large_patch14_clip_336.openai_ft_in12k_in1k': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
 
-    'vit_base_patch32_clip_224.openai': _cfg(
+    'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
-    'vit_base_patch16_clip_224.openai': _cfg(
+    'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
-    'vit_large_patch14_clip_224.openai': _cfg(
+    'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
+    'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
+    'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
+        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
+    'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
+    'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
 
     'vit_base_patch32_clip_224.openai_ft_in1k': _cfg(
         hf_hub_id='timm/',
@@ -1010,30 +986,21 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
 
-    'vit_base_patch32_clip_224.openai_ft_in12k_in1k': _cfg(
+    'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg(
-        #hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k',
+        #hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
-    'vit_base_patch32_clip_384.openai_ft_in12k_in1k': _cfg(
+    'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
-        crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
-    'vit_base_patch16_clip_224.openai_ft_in12k_in1k': _cfg(
-        hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
-    'vit_base_patch16_clip_384.openai_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
-        crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
+    'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg(
-    'vit_large_patch14_clip_224.openai_ft_in12k_in1k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821),
-    'vit_large_patch14_clip_336.openai_ft_in12k_in1k': _cfg(
+    'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg(
         hf_hub_id='timm/',
-        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
-        crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
 
     'vit_base_patch32_clip_224.openai_ft_in12k': _cfg(
-        #hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k',
+        # hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
     'vit_base_patch16_clip_224.openai_ft_in12k': _cfg(
         hf_hub_id='timm/',
@@ -1042,6 +1009,37 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
 
+    'vit_base_patch32_clip_224.laion2b': _cfg(
+        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_base_patch16_clip_224.laion2b': _cfg(
+        # hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
+    'vit_large_patch14_clip_224.laion2b': _cfg(
+        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, crop_pct=1.0, num_classes=768),
+    'vit_huge_patch14_clip_224.laion2b': _cfg(
+        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, crop_pct=1.0, num_classes=1024),
+    'vit_giant_patch14_clip_224.laion2b': _cfg(
+        hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
+
+    'vit_base_patch32_clip_224.openai': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
+    'vit_base_patch16_clip_224.openai': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
+    'vit_large_patch14_clip_224.openai': _cfg(
+        hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
+
     # experimental (may be removed)
     'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
     'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
@@ -1152,8 +1150,8 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs):
 def vit_tiny_patch16_224(pretrained=False, **kwargs):
     """ ViT-Tiny (Vit-Ti/16)
     """
-    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3)
-    model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1161,8 +1159,8 @@ def vit_tiny_patch16_224(pretrained=False, **kwargs):
 def vit_tiny_patch16_384(pretrained=False, **kwargs):
     """ ViT-Tiny (Vit-Ti/16) @ 384x384.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3)
-    model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1170,8 +1168,8 @@ def vit_tiny_patch16_384(pretrained=False, **kwargs):
 def vit_small_patch32_224(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/32)
     """
-    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6)
-    model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1179,8 +1177,8 @@ def vit_small_patch32_224(pretrained=False, **kwargs):
 def vit_small_patch32_384(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/32) at 384x384.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6)
-    model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1188,8 +1186,8 @@ def vit_small_patch32_384(pretrained=False, **kwargs):
 def vit_small_patch16_224(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
-    model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1197,8 +1195,8 @@ def vit_small_patch16_224(pretrained=False, **kwargs):
 def vit_small_patch16_384(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
-    model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1206,8 +1204,8 @@ def vit_small_patch16_384(pretrained=False, **kwargs):
 def vit_small_patch8_224(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/8)
     """
-    model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6)
-    model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1216,8 +1214,8 @@ def vit_base_patch32_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12)
-    model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1226,8 +1224,8 @@ def vit_base_patch32_384(pretrained=False, **kwargs):
     """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12)
-    model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1236,8 +1234,8 @@ def vit_base_patch16_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12)
-    model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1246,8 +1244,8 @@ def vit_base_patch16_384(pretrained=False, **kwargs):
     """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12)
-    model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1256,8 +1254,8 @@ def vit_base_patch8_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12)
-    model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1265,8 +1263,8 @@ def vit_base_patch8_224(pretrained=False, **kwargs):
 def vit_large_patch32_224(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16)
-    model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1275,8 +1273,8 @@ def vit_large_patch32_384(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16)
-    model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1285,8 +1283,8 @@ def vit_large_patch16_224(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16)
-    model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1295,8 +1293,8 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16)
-    model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1304,8 +1302,8 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
 def vit_large_patch14_224(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/14)
     """
-    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16)
-    model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1313,8 +1311,8 @@ def vit_large_patch14_224(pretrained=False, **kwargs):
 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_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16)
-    model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1322,8 +1320,8 @@ def vit_huge_patch14_224(pretrained=False, **kwargs):
 def vit_giant_patch14_224(pretrained=False, **kwargs):
     """ ViT-Giant (little-g) 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_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16)
-    model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1331,8 +1329,9 @@ def vit_giant_patch14_224(pretrained=False, **kwargs):
 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_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16)
-    model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_gigantic_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1341,8 +1340,9 @@ def vit_base_patch16_224_miil(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False)
-    model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch16_224_miil', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1352,8 +1352,9 @@ def vit_medium_patch16_gap_240(pretrained=False, **kwargs):
     """
     model_kwargs = dict(
         patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
-        global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
-    model = _create_vision_transformer('vit_medium_patch16_gap_240', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_medium_patch16_gap_240', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1363,8 +1364,9 @@ def vit_medium_patch16_gap_256(pretrained=False, **kwargs):
     """
     model_kwargs = dict(
         patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
-        global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
-    model = _create_vision_transformer('vit_medium_patch16_gap_256', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_medium_patch16_gap_256', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1374,8 +1376,9 @@ def vit_medium_patch16_gap_384(pretrained=False, **kwargs):
     """
     model_kwargs = dict(
         patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
-        global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
-    model = _create_vision_transformer('vit_medium_patch16_gap_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_medium_patch16_gap_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1384,9 +1387,9 @@ def vit_base_patch16_gap_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 256x256
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False,
+        patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
-        global_pool=kwargs.get('global_pool', 'avg'), fc_norm=False, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_base_patch16_gap_224', pretrained=pretrained, **model_kwargs)
+        'vit_base_patch16_gap_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1395,8 +1398,9 @@ def vit_base_patch32_clip_224(pretrained=False, **kwargs):
     """ ViT-B/32 CLIP image tower @ 224x224
     """
     model_kwargs = dict(
-        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer('vit_base_patch32_clip_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch32_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1405,8 +1409,9 @@ def vit_base_patch32_clip_384(pretrained=False, **kwargs):
     """ ViT-B/32 CLIP image tower @ 384x384
     """
     model_kwargs = dict(
-        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer('vit_base_patch32_clip_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch32_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1415,8 +1420,9 @@ def vit_base_patch32_clip_448(pretrained=False, **kwargs):
     """ ViT-B/32 CLIP image tower @ 448x448
     """
     model_kwargs = dict(
-        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer('vit_base_patch32_clip_448', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch32_clip_448', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1424,9 +1430,9 @@ def vit_base_patch32_clip_448(pretrained=False, **kwargs):
 def vit_base_patch16_clip_224(pretrained=False, **kwargs):
     """ ViT-B/16 CLIP image tower
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_base_patch16_clip_224', pretrained=pretrained, **model_kwargs)
+        'vit_base_patch16_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1434,9 +1440,9 @@ def vit_base_patch16_clip_224(pretrained=False, **kwargs):
 def vit_base_patch16_clip_384(pretrained=False, **kwargs):
     """ ViT-B/16 CLIP image tower @ 384x384
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_base_patch16_clip_384', pretrained=pretrained, **model_kwargs)
+        'vit_base_patch16_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1444,9 +1450,9 @@ def vit_base_patch16_clip_384(pretrained=False, **kwargs):
 def vit_large_patch14_clip_224(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/14) CLIP image tower
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_large_patch14_clip_224', pretrained=pretrained, **model_kwargs)
+        'vit_large_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1454,9 +1460,9 @@ def vit_large_patch14_clip_224(pretrained=False, **kwargs):
 def vit_large_patch14_clip_336(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/14) CLIP image tower @ 336x336
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_large_patch14_clip_336', pretrained=pretrained, **model_kwargs)
+        'vit_large_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1464,9 +1470,9 @@ def vit_large_patch14_clip_336(pretrained=False, **kwargs):
 def vit_huge_patch14_clip_224(pretrained=False, **kwargs):
     """ ViT-Huge model (ViT-H/14) CLIP image tower.
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_huge_patch14_clip_224', pretrained=pretrained, **model_kwargs)
+        'vit_huge_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1474,9 +1480,9 @@ def vit_huge_patch14_clip_224(pretrained=False, **kwargs):
 def vit_huge_patch14_clip_336(pretrained=False, **kwargs):
     """ ViT-Huge model (ViT-H/14) CLIP image tower @ 336x336
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
-        patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_huge_patch14_clip_336', pretrained=pretrained, **model_kwargs)
+        'vit_huge_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1486,9 +1492,9 @@ def vit_giant_patch14_clip_224(pretrained=False, **kwargs):
     Pretrained weights from CLIP image tower.
     """
     model_kwargs = dict(
-        patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16,
+        patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
-        pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_giant_patch14_clip_224', pretrained=pretrained, **model_kwargs)
+        'vit_giant_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1498,8 +1504,9 @@ def vit_giant_patch14_clip_224(pretrained=False, **kwargs):
 def vit_base_patch32_plus_256(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/32+)
     """
-    model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
-    model = _create_vision_transformer('vit_base_patch32_plus_256', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch32_plus_256', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1507,8 +1514,9 @@ def vit_base_patch32_plus_256(pretrained=False, **kwargs):
 def vit_base_patch16_plus_240(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16+)
     """
-    model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
-    model = _create_vision_transformer('vit_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch16_plus_240', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1517,9 +1525,10 @@ def vit_base_patch16_rpn_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/ residual post-norm
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, class_token=False,
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
-        block_fn=ResPostBlock, global_pool=kwargs.pop('global_pool', 'avg'), **kwargs)
+        class_token=False, block_fn=ResPostBlock, global_pool='avg')
-    model = _create_vision_transformer('vit_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_base_patch16_rpn_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1529,8 +1538,9 @@ def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
     Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5)
-    model = _create_vision_transformer('vit_small_patch16_36x1_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_small_patch16_36x1_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1541,8 +1551,9 @@ def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
     Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
+        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock)
-    model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer(
+        'vit_small_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1551,27 +1562,26 @@ def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
     """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
     """
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock)
-        patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('vit_base_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
+        'vit_base_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
 @register_model
 def eva_large_patch14_196(pretrained=False, **kwargs):
     """ EVA-large model https://arxiv.org/abs/2211.07636 /via MAE MIM pretrain"""
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg', **kwargs)
+    model = _create_vision_transformer(
-    model = _create_vision_transformer('eva_large_patch14_196', pretrained=pretrained, **model_kwargs)
+        'eva_large_patch14_196', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
 @register_model
 def eva_large_patch14_336(pretrained=False, **kwargs):
     """ EVA-large model https://arxiv.org/abs/2211.07636 via MAE MIM pretrain"""
-    model_kwargs = dict(
+    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg', **kwargs)
+    model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
-    model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **model_kwargs)
     return model
 
 
@@ -1579,8 +1589,8 @@ def eva_large_patch14_336(pretrained=False, **kwargs):
 def flexivit_small(pretrained=False, **kwargs):
     """ FlexiViT-Small
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True)
-    model = _create_vision_transformer('flexivit_small', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('flexivit_small', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1588,8 +1598,8 @@ def flexivit_small(pretrained=False, **kwargs):
 def flexivit_base(pretrained=False, **kwargs):
     """ FlexiViT-Base
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True)
-    model = _create_vision_transformer('flexivit_base', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('flexivit_base', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
 
 
@@ -1597,6 +1607,6 @@ def flexivit_base(pretrained=False, **kwargs):
 def flexivit_large(pretrained=False, **kwargs):
     """ FlexiViT-Large
     """
-    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True)
-    model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model

timm/models/vovnet.py

@@ -181,8 +181,18 @@ class SequentialAppendList(nn.Sequential):
 class OsaBlock(nn.Module):
 
     def __init__(
-            self, in_chs, mid_chs, out_chs, layer_per_block, residual=False,
+            self,
-            depthwise=False, attn='', norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path=None):
+            in_chs,
+            mid_chs,
+            out_chs,
+            layer_per_block,
+            residual=False,
+            depthwise=False,
+            attn='',
+            norm_layer=BatchNormAct2d,
+            act_layer=nn.ReLU,
+            drop_path=None,
+    ):
         super(OsaBlock, self).__init__()
 
         self.residual = residual
@@ -232,9 +242,20 @@ class OsaBlock(nn.Module):
 class OsaStage(nn.Module):
 
     def __init__(
-            self, in_chs, mid_chs, out_chs, block_per_stage, layer_per_block, downsample=True,
+            self,
-            residual=True, depthwise=False, attn='ese', norm_layer=BatchNormAct2d, act_layer=nn.ReLU,
+            in_chs,
-            drop_path_rates=None):
+            mid_chs,
+            out_chs,
+            block_per_stage,
+            layer_per_block,
+            downsample=True,
+            residual=True,
+            depthwise=False,
+            attn='ese',
+            norm_layer=BatchNormAct2d,
+            act_layer=nn.ReLU,
+            drop_path_rates=None,
+    ):
         super(OsaStage, self).__init__()
         self.grad_checkpointing = False
 
@@ -270,16 +291,38 @@ class OsaStage(nn.Module):
 class VovNet(nn.Module):
 
     def __init__(
-            self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0., stem_stride=4,
+            self,
-            output_stride=32, norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path_rate=0.):
+            cfg,
-        """ VovNet (v2)
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            output_stride=32,
+            norm_layer=BatchNormAct2d,
+            act_layer=nn.ReLU,
+            drop_rate=0.,
+            drop_path_rate=0.,
+            **kwargs,
+    ):
+        """
+        Args:
+            cfg (dict): Model architecture configuration
+            in_chans (int): Number of input channels (default: 3)
+            num_classes (int): Number of classifier classes (default: 1000)
+            global_pool (str): Global pooling type (default: 'avg')
+            output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
+            norm_layer (Union[str, nn.Module]): normalization layer
+            act_layer (Union[str, nn.Module]): activation layer
+            drop_rate (float): Dropout rate (default: 0.)
+            drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
+            kwargs (dict): Extra kwargs overlayed onto cfg
         """
         super(VovNet, self).__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
-        assert stem_stride in (4, 2)
         assert output_stride == 32  # FIXME support dilation
 
+        cfg = dict(cfg, **kwargs)
+        stem_stride = cfg.get("stem_stride", 4)
         stem_chs = cfg["stem_chs"]
         stage_conv_chs = cfg["stage_conv_chs"]
         stage_out_chs = cfg["stage_out_chs"]
@@ -307,9 +350,15 @@ class VovNet(nn.Module):
         for i in range(4):  # num_stages
             downsample = stem_stride == 2 or i > 0  # first stage has no stride/downsample if stem_stride is 4
             stages += [OsaStage(
-                in_ch_list[i], stage_conv_chs[i], stage_out_chs[i], block_per_stage[i], layer_per_block,
+                in_ch_list[i],
-                downsample=downsample, drop_path_rates=stage_dpr[i], **stage_args)
+                stage_conv_chs[i],
-            ]
+                stage_out_chs[i],
+                block_per_stage[i],
+                layer_per_block,
+                downsample=downsample,
+                drop_path_rates=stage_dpr[i],
+                **stage_args,
+            )]
             self.num_features = stage_out_chs[i]
             current_stride *= 2 if downsample else 1
             self.feature_info += [dict(num_chs=self.num_features, reduction=current_stride, module=f'stages.{i}')]
@@ -324,7 +373,6 @@ class VovNet(nn.Module):
             elif isinstance(m, nn.Linear):
                 nn.init.zeros_(m.bias)
 
-
     @torch.jit.ignore
     def group_matcher(self, coarse=False):
         return dict(

timm/utils/__init__.py

@@ -8,7 +8,7 @@ from .distributed import distribute_bn, reduce_tensor, init_distributed_device,\
 from .jit import set_jit_legacy, set_jit_fuser
 from .log import setup_default_logging, FormatterNoInfo
 from .metrics import AverageMeter, accuracy
-from .misc import natural_key, add_bool_arg
+from .misc import natural_key, add_bool_arg, ParseKwargs
 from .model import unwrap_model, get_state_dict, freeze, unfreeze
 from .model_ema import ModelEma, ModelEmaV2
 from .random import random_seed

timm/utils/misc.py

@@ -2,6 +2,8 @@
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
+import argparse
+import ast
 import re
 
 
@@ -16,3 +18,15 @@ def add_bool_arg(parser, name, default=False, help=''):
     group.add_argument('--' + name, dest=dest_name, action='store_true', help=help)
     group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help)
     parser.set_defaults(**{dest_name: default})
+
+
+class ParseKwargs(argparse.Action):
+    def __call__(self, parser, namespace, values, option_string=None):
+        kw = {}
+        for value in values:
+            key, value = value.split('=')
+            try:
+                kw[key] = ast.literal_eval(value)
+            except ValueError:
+                kw[key] = str(value)  # fallback to string (avoid need to escape on command line)
+        setattr(namespace, self.dest, kw)

train.py

@@ -89,56 +89,58 @@ parser.add_argument('--data-dir', metavar='DIR',
 parser.add_argument('--dataset', metavar='NAME', default='',
                     help='dataset type + name ("<type>/<name>") (default: ImageFolder or ImageTar if empty)')
 group.add_argument('--train-split', metavar='NAME', default='train',
-                    help='dataset train split (default: train)')
+                   help='dataset train split (default: train)')
 group.add_argument('--val-split', metavar='NAME', default='validation',
-                    help='dataset validation split (default: validation)')
+                   help='dataset validation split (default: validation)')
 group.add_argument('--dataset-download', action='store_true', default=False,
-                    help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
+                   help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
 group.add_argument('--class-map', default='', type=str, metavar='FILENAME',
-                    help='path to class to idx mapping file (default: "")')
+                   help='path to class to idx mapping file (default: "")')
 
 # Model parameters
 group = parser.add_argument_group('Model parameters')
 group.add_argument('--model', default='resnet50', type=str, metavar='MODEL',
-                    help='Name of model to train (default: "resnet50")')
+                   help='Name of model to train (default: "resnet50")')
 group.add_argument('--pretrained', action='store_true', default=False,
-                    help='Start with pretrained version of specified network (if avail)')
+                   help='Start with pretrained version of specified network (if avail)')
 group.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
-                    help='Initialize model from this checkpoint (default: none)')
+                   help='Initialize model from this checkpoint (default: none)')
 group.add_argument('--resume', default='', type=str, metavar='PATH',
-                    help='Resume full model and optimizer state from checkpoint (default: none)')
+                   help='Resume full model and optimizer state from checkpoint (default: none)')
 group.add_argument('--no-resume-opt', action='store_true', default=False,
-                    help='prevent resume of optimizer state when resuming model')
+                   help='prevent resume of optimizer state when resuming model')
 group.add_argument('--num-classes', type=int, default=None, metavar='N',
-                    help='number of label classes (Model default if None)')
+                   help='number of label classes (Model default if None)')
 group.add_argument('--gp', default=None, type=str, metavar='POOL',
-                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
+                   help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
 group.add_argument('--img-size', type=int, default=None, metavar='N',
-                    help='Image size (default: None => model default)')
+                   help='Image size (default: None => model default)')
 group.add_argument('--in-chans', type=int, default=None, metavar='N',
-                    help='Image input channels (default: None => 3)')
+                   help='Image input channels (default: None => 3)')
 group.add_argument('--input-size', default=None, nargs=3, type=int,
-                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+                   metavar='N N N',
+                   help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
 group.add_argument('--crop-pct', default=None, type=float,
-                    metavar='N', help='Input image center crop percent (for validation only)')
+                   metavar='N', help='Input image center crop percent (for validation only)')
 group.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
-                    help='Override mean pixel value of dataset')
+                   help='Override mean pixel value of dataset')
 group.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
-                    help='Override std deviation of dataset')
+                   help='Override std deviation of dataset')
 group.add_argument('--interpolation', default='', type=str, metavar='NAME',
-                    help='Image resize interpolation type (overrides model)')
+                   help='Image resize interpolation type (overrides model)')
 group.add_argument('-b', '--batch-size', type=int, default=128, metavar='N',
-                    help='Input batch size for training (default: 128)')
+                   help='Input batch size for training (default: 128)')
 group.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N',
-                    help='Validation batch size override (default: None)')
+                   help='Validation batch size override (default: None)')
 group.add_argument('--channels-last', action='store_true', default=False,
-                    help='Use channels_last memory layout')
+                   help='Use channels_last memory layout')
 group.add_argument('--fuser', default='', type=str,
-                    help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
+                   help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 group.add_argument('--grad-checkpointing', action='store_true', default=False,
-                    help='Enable gradient checkpointing through model blocks/stages')
+                   help='Enable gradient checkpointing through model blocks/stages')
 group.add_argument('--fast-norm', default=False, action='store_true',
-                    help='enable experimental fast-norm')
+                   help='enable experimental fast-norm')
+group.add_argument('--model-kwargs', nargs='*', default={}, action=utils.ParseKwargs)
 
 scripting_group = group.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
@@ -151,199 +153,200 @@ scripting_group.add_argument('--aot-autograd', default=False, action='store_true
 # Optimizer parameters
 group = parser.add_argument_group('Optimizer parameters')
 group.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
-                    help='Optimizer (default: "sgd")')
+                   help='Optimizer (default: "sgd")')
 group.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
-                    help='Optimizer Epsilon (default: None, use opt default)')
+                   help='Optimizer Epsilon (default: None, use opt default)')
 group.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
-                    help='Optimizer Betas (default: None, use opt default)')
+                   help='Optimizer Betas (default: None, use opt default)')
 group.add_argument('--momentum', type=float, default=0.9, metavar='M',
-                    help='Optimizer momentum (default: 0.9)')
+                   help='Optimizer momentum (default: 0.9)')
 group.add_argument('--weight-decay', type=float, default=2e-5,
-                    help='weight decay (default: 2e-5)')
+                   help='weight decay (default: 2e-5)')
 group.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
-                    help='Clip gradient norm (default: None, no clipping)')
+                   help='Clip gradient norm (default: None, no clipping)')
 group.add_argument('--clip-mode', type=str, default='norm',
-                    help='Gradient clipping mode. One of ("norm", "value", "agc")')
+                   help='Gradient clipping mode. One of ("norm", "value", "agc")')
 group.add_argument('--layer-decay', type=float, default=None,
-                    help='layer-wise learning rate decay (default: None)')
+                   help='layer-wise learning rate decay (default: None)')
+group.add_argument('--opt-kwargs', nargs='*', default={}, action=utils.ParseKwargs)
 
 # Learning rate schedule parameters
 group = parser.add_argument_group('Learning rate schedule parameters')
 group.add_argument('--sched', type=str, default='cosine', metavar='SCHEDULER',
-                    help='LR scheduler (default: "step"')
+                   help='LR scheduler (default: "step"')
 group.add_argument('--sched-on-updates', action='store_true', default=False,
-                    help='Apply LR scheduler step on update instead of epoch end.')
+                   help='Apply LR scheduler step on update instead of epoch end.')
 group.add_argument('--lr', type=float, default=None, metavar='LR',
-                    help='learning rate, overrides lr-base if set (default: None)')
+                   help='learning rate, overrides lr-base if set (default: None)')
 group.add_argument('--lr-base', type=float, default=0.1, metavar='LR',
-                    help='base learning rate: lr = lr_base * global_batch_size / base_size')
+                   help='base learning rate: lr = lr_base * global_batch_size / base_size')
 group.add_argument('--lr-base-size', type=int, default=256, metavar='DIV',
-                    help='base learning rate batch size (divisor, default: 256).')
+                   help='base learning rate batch size (divisor, default: 256).')
 group.add_argument('--lr-base-scale', type=str, default='', metavar='SCALE',
-                    help='base learning rate vs batch_size scaling ("linear", "sqrt", based on opt if empty)')
+                   help='base learning rate vs batch_size scaling ("linear", "sqrt", based on opt if empty)')
 group.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
-                    help='learning rate noise on/off epoch percentages')
+                   help='learning rate noise on/off epoch percentages')
 group.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
-                    help='learning rate noise limit percent (default: 0.67)')
+                   help='learning rate noise limit percent (default: 0.67)')
 group.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
-                    help='learning rate noise std-dev (default: 1.0)')
+                   help='learning rate noise std-dev (default: 1.0)')
 group.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
-                    help='learning rate cycle len multiplier (default: 1.0)')
+                   help='learning rate cycle len multiplier (default: 1.0)')
 group.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
-                    help='amount to decay each learning rate cycle (default: 0.5)')
+                   help='amount to decay each learning rate cycle (default: 0.5)')
 group.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
-                    help='learning rate cycle limit, cycles enabled if > 1')
+                   help='learning rate cycle limit, cycles enabled if > 1')
 group.add_argument('--lr-k-decay', type=float, default=1.0,
-                    help='learning rate k-decay for cosine/poly (default: 1.0)')
+                   help='learning rate k-decay for cosine/poly (default: 1.0)')
 group.add_argument('--warmup-lr', type=float, default=1e-5, metavar='LR',
-                    help='warmup learning rate (default: 1e-5)')
+                   help='warmup learning rate (default: 1e-5)')
 group.add_argument('--min-lr', type=float, default=0, metavar='LR',
-                    help='lower lr bound for cyclic schedulers that hit 0 (default: 0)')
+                   help='lower lr bound for cyclic schedulers that hit 0 (default: 0)')
 group.add_argument('--epochs', type=int, default=300, metavar='N',
-                    help='number of epochs to train (default: 300)')
+                   help='number of epochs to train (default: 300)')
 group.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
-                    help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
+                   help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
 group.add_argument('--start-epoch', default=None, type=int, metavar='N',
-                    help='manual epoch number (useful on restarts)')
+                   help='manual epoch number (useful on restarts)')
 group.add_argument('--decay-milestones', default=[90, 180, 270], type=int, nargs='+', metavar="MILESTONES",
-                    help='list of decay epoch indices for multistep lr. must be increasing')
+                   help='list of decay epoch indices for multistep lr. must be increasing')
 group.add_argument('--decay-epochs', type=float, default=90, metavar='N',
-                    help='epoch interval to decay LR')
+                   help='epoch interval to decay LR')
 group.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
-                    help='epochs to warmup LR, if scheduler supports')
+                   help='epochs to warmup LR, if scheduler supports')
 group.add_argument('--warmup-prefix', action='store_true', default=False,
-                    help='Exclude warmup period from decay schedule.'),
+                   help='Exclude warmup period from decay schedule.'),
 group.add_argument('--cooldown-epochs', type=int, default=0, metavar='N',
-                    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
+                   help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
 group.add_argument('--patience-epochs', type=int, default=10, metavar='N',
-                    help='patience epochs for Plateau LR scheduler (default: 10)')
+                   help='patience epochs for Plateau LR scheduler (default: 10)')
 group.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
-                    help='LR decay rate (default: 0.1)')
+                   help='LR decay rate (default: 0.1)')
 
 # Augmentation & regularization parameters
 group = parser.add_argument_group('Augmentation and regularization parameters')
 group.add_argument('--no-aug', action='store_true', default=False,
-                    help='Disable all training augmentation, override other train aug args')
+                   help='Disable all training augmentation, override other train aug args')
 group.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
-                    help='Random resize scale (default: 0.08 1.0)')
+                   help='Random resize scale (default: 0.08 1.0)')
-group.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
+group.add_argument('--ratio', type=float, nargs='+', default=[3. / 4., 4. / 3.], metavar='RATIO',
-                    help='Random resize aspect ratio (default: 0.75 1.33)')
+                   help='Random resize aspect ratio (default: 0.75 1.33)')
 group.add_argument('--hflip', type=float, default=0.5,
-                    help='Horizontal flip training aug probability')
+                   help='Horizontal flip training aug probability')
 group.add_argument('--vflip', type=float, default=0.,
-                    help='Vertical flip training aug probability')
+                   help='Vertical flip training aug probability')
 group.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
-                    help='Color jitter factor (default: 0.4)')
+                   help='Color jitter factor (default: 0.4)')
 group.add_argument('--aa', type=str, default=None, metavar='NAME',
-                    help='Use AutoAugment policy. "v0" or "original". (default: None)'),
+                   help='Use AutoAugment policy. "v0" or "original". (default: None)'),
 group.add_argument('--aug-repeats', type=float, default=0,
-                    help='Number of augmentation repetitions (distributed training only) (default: 0)')
+                   help='Number of augmentation repetitions (distributed training only) (default: 0)')
 group.add_argument('--aug-splits', type=int, default=0,
-                    help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
+                   help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
 group.add_argument('--jsd-loss', action='store_true', default=False,
-                    help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
+                   help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
 group.add_argument('--bce-loss', action='store_true', default=False,
-                    help='Enable BCE loss w/ Mixup/CutMix use.')
+                   help='Enable BCE loss w/ Mixup/CutMix use.')
 group.add_argument('--bce-target-thresh', type=float, default=None,
-                    help='Threshold for binarizing softened BCE targets (default: None, disabled)')
+                   help='Threshold for binarizing softened BCE targets (default: None, disabled)')
 group.add_argument('--reprob', type=float, default=0., metavar='PCT',
-                    help='Random erase prob (default: 0.)')
+                   help='Random erase prob (default: 0.)')
 group.add_argument('--remode', type=str, default='pixel',
-                    help='Random erase mode (default: "pixel")')
+                   help='Random erase mode (default: "pixel")')
 group.add_argument('--recount', type=int, default=1,
-                    help='Random erase count (default: 1)')
+                   help='Random erase count (default: 1)')
 group.add_argument('--resplit', action='store_true', default=False,
-                    help='Do not random erase first (clean) augmentation split')
+                   help='Do not random erase first (clean) augmentation split')
 group.add_argument('--mixup', type=float, default=0.0,
-                    help='mixup alpha, mixup enabled if > 0. (default: 0.)')
+                   help='mixup alpha, mixup enabled if > 0. (default: 0.)')
 group.add_argument('--cutmix', type=float, default=0.0,
-                    help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
+                   help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
 group.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
-                    help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
+                   help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
 group.add_argument('--mixup-prob', type=float, default=1.0,
-                    help='Probability of performing mixup or cutmix when either/both is enabled')
+                   help='Probability of performing mixup or cutmix when either/both is enabled')
 group.add_argument('--mixup-switch-prob', type=float, default=0.5,
-                    help='Probability of switching to cutmix when both mixup and cutmix enabled')
+                   help='Probability of switching to cutmix when both mixup and cutmix enabled')
 group.add_argument('--mixup-mode', type=str, default='batch',
-                    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
+                   help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
 group.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
-                    help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
+                   help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
 group.add_argument('--smoothing', type=float, default=0.1,
-                    help='Label smoothing (default: 0.1)')
+                   help='Label smoothing (default: 0.1)')
 group.add_argument('--train-interpolation', type=str, default='random',
-                    help='Training interpolation (random, bilinear, bicubic default: "random")')
+                   help='Training interpolation (random, bilinear, bicubic default: "random")')
 group.add_argument('--drop', type=float, default=0.0, metavar='PCT',
-                    help='Dropout rate (default: 0.)')
+                   help='Dropout rate (default: 0.)')
 group.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
-                    help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
+                   help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
 group.add_argument('--drop-path', type=float, default=None, metavar='PCT',
-                    help='Drop path rate (default: None)')
+                   help='Drop path rate (default: None)')
 group.add_argument('--drop-block', type=float, default=None, metavar='PCT',
-                    help='Drop block rate (default: None)')
+                   help='Drop block rate (default: None)')
 
 # Batch norm parameters (only works with gen_efficientnet based models currently)
 group = parser.add_argument_group('Batch norm parameters', 'Only works with gen_efficientnet based models currently.')
 group.add_argument('--bn-momentum', type=float, default=None,
-                    help='BatchNorm momentum override (if not None)')
+                   help='BatchNorm momentum override (if not None)')
 group.add_argument('--bn-eps', type=float, default=None,
-                    help='BatchNorm epsilon override (if not None)')
+                   help='BatchNorm epsilon override (if not None)')
 group.add_argument('--sync-bn', action='store_true',
-                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
+                   help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
 group.add_argument('--dist-bn', type=str, default='reduce',
-                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
+                   help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
 group.add_argument('--split-bn', action='store_true',
-                    help='Enable separate BN layers per augmentation split.')
+                   help='Enable separate BN layers per augmentation split.')
 
 # Model Exponential Moving Average
 group = parser.add_argument_group('Model exponential moving average parameters')
 group.add_argument('--model-ema', action='store_true', default=False,
-                    help='Enable tracking moving average of model weights')
+                   help='Enable tracking moving average of model weights')
 group.add_argument('--model-ema-force-cpu', action='store_true', default=False,
-                    help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
+                   help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
 group.add_argument('--model-ema-decay', type=float, default=0.9998,
-                    help='decay factor for model weights moving average (default: 0.9998)')
+                   help='decay factor for model weights moving average (default: 0.9998)')
 
 # Misc
 group = parser.add_argument_group('Miscellaneous parameters')
 group.add_argument('--seed', type=int, default=42, metavar='S',
-                    help='random seed (default: 42)')
+                   help='random seed (default: 42)')
 group.add_argument('--worker-seeding', type=str, default='all',
-                    help='worker seed mode (default: all)')
+                   help='worker seed mode (default: all)')
 group.add_argument('--log-interval', type=int, default=50, metavar='N',
-                    help='how many batches to wait before logging training status')
+                   help='how many batches to wait before logging training status')
 group.add_argument('--recovery-interval', type=int, default=0, metavar='N',
-                    help='how many batches to wait before writing recovery checkpoint')
+                   help='how many batches to wait before writing recovery checkpoint')
 group.add_argument('--checkpoint-hist', type=int, default=10, metavar='N',
-                    help='number of checkpoints to keep (default: 10)')
+                   help='number of checkpoints to keep (default: 10)')
 group.add_argument('-j', '--workers', type=int, default=4, metavar='N',
-                    help='how many training processes to use (default: 4)')
+                   help='how many training processes to use (default: 4)')
 group.add_argument('--save-images', action='store_true', default=False,
-                    help='save images of input bathes every log interval for debugging')
+                   help='save images of input bathes every log interval for debugging')
 group.add_argument('--amp', action='store_true', default=False,
-                    help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
+                   help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
 group.add_argument('--amp-dtype', default='float16', type=str,
-                    help='lower precision AMP dtype (default: float16)')
+                   help='lower precision AMP dtype (default: float16)')
 group.add_argument('--amp-impl', default='native', type=str,
-                    help='AMP impl to use, "native" or "apex" (default: native)')
+                   help='AMP impl to use, "native" or "apex" (default: native)')
 group.add_argument('--no-ddp-bb', action='store_true', default=False,
-                    help='Force broadcast buffers for native DDP to off.')
+                   help='Force broadcast buffers for native DDP to off.')
 group.add_argument('--pin-mem', action='store_true', default=False,
-                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
+                   help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
 group.add_argument('--no-prefetcher', action='store_true', default=False,
-                    help='disable fast prefetcher')
+                   help='disable fast prefetcher')
 group.add_argument('--output', default='', type=str, metavar='PATH',
-                    help='path to output folder (default: none, current dir)')
+                   help='path to output folder (default: none, current dir)')
 group.add_argument('--experiment', default='', type=str, metavar='NAME',
-                    help='name of train experiment, name of sub-folder for output')
+                   help='name of train experiment, name of sub-folder for output')
 group.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
-                    help='Best metric (default: "top1"')
+                   help='Best metric (default: "top1"')
 group.add_argument('--tta', type=int, default=0, metavar='N',
-                    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
+                   help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
 group.add_argument("--local_rank", default=0, type=int)
 group.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
-                    help='use the multi-epochs-loader to save time at the beginning of every epoch')
+                   help='use the multi-epochs-loader to save time at the beginning of every epoch')
 group.add_argument('--log-wandb', action='store_true', default=False,
-                    help='log training and validation metrics to wandb')
+                   help='log training and validation metrics to wandb')
 
 
 def _parse_args():
@@ -371,8 +374,6 @@ def main():
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.benchmark = True
 
-    if args.data and not args.data_dir:
-        args.data_dir = args.data
     args.prefetcher = not args.no_prefetcher
     device = utils.init_distributed_device(args)
     if args.distributed:
@@ -383,14 +384,6 @@ def main():
         _logger.info(f'Training with a single process on 1 device ({args.device}).')
     assert args.rank >= 0
 
-    if utils.is_primary(args) and args.log_wandb:
-        if has_wandb:
-            wandb.init(project=args.experiment, config=args)
-        else:
-            _logger.warning(
-                "You've requested to log metrics to wandb but package not found. "
-                "Metrics not being logged to wandb, try `pip install wandb`")
-
     # resolve AMP arguments based on PyTorch / Apex availability
     use_amp = None
     amp_dtype = torch.float16
@@ -432,6 +425,7 @@ def main():
         bn_eps=args.bn_eps,
         scriptable=args.torchscript,
         checkpoint_path=args.initial_checkpoint,
+        **args.model_kwargs,
     )
     if args.num_classes is None:
         assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
@@ -504,7 +498,11 @@ def main():
                 f'Learning rate ({args.lr}) calculated from base learning rate ({args.lr_base}) '
                 f'and global batch size ({global_batch_size}) with {args.lr_base_scale} scaling.')
 
-    optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))
+    optimizer = create_optimizer_v2(
+        model,
+        **optimizer_kwargs(cfg=args),
+        **args.opt_kwargs,
+    )
 
     # setup automatic mixed-precision (AMP) loss scaling and op casting
     amp_autocast = suppress  # do nothing
@@ -559,6 +557,8 @@ def main():
         # NOTE: EMA model does not need to be wrapped by DDP
 
     # create the train and eval datasets
+    if args.data and not args.data_dir:
+        args.data_dir = args.data
     dataset_train = create_dataset(
         args.dataset,
         root=args.data_dir,
@@ -712,6 +712,14 @@ def main():
         with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
             f.write(args_text)
 
+    if utils.is_primary(args) and args.log_wandb:
+        if has_wandb:
+            wandb.init(project=args.experiment, config=args)
+        else:
+            _logger.warning(
+                "You've requested to log metrics to wandb but package not found. "
+                "Metrics not being logged to wandb, try `pip install wandb`")
+
     # setup learning rate schedule and starting epoch
     updates_per_epoch = len(loader_train)
     lr_scheduler, num_epochs = create_scheduler_v2(

validate.py

@@ -26,7 +26,7 @@ from timm.data import create_dataset, create_loader, resolve_data_config, RealLa
 from timm.layers import apply_test_time_pool, set_fast_norm
 from timm.models import create_model, load_checkpoint, is_model, list_models
 from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_fuser, \
-    decay_batch_step, check_batch_size_retry
+    decay_batch_step, check_batch_size_retry, ParseKwargs
 
 try:
     from apex import amp
@@ -71,6 +71,8 @@ parser.add_argument('-b', '--batch-size', default=256, type=int,
                     metavar='N', help='mini-batch size (default: 256)')
 parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--in-chans', type=int, default=None, metavar='N',
+                    help='Image input channels (default: None => 3)')
 parser.add_argument('--input-size', default=None, nargs=3, type=int,
                     metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
 parser.add_argument('--use-train-size', action='store_true', default=False,
@@ -123,6 +125,8 @@ parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 parser.add_argument('--fast-norm', default=False, action='store_true',
                     help='enable experimental fast-norm')
+parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs)
+
 
 scripting_group = parser.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', default=False, action='store_true',
@@ -181,13 +185,20 @@ def validate(args):
         set_fast_norm()
 
     # create model
+    in_chans = 3
+    if args.in_chans is not None:
+        in_chans = args.in_chans
+    elif args.input_size is not None:
+        in_chans = args.input_size[0]
+
     model = create_model(
         args.model,
         pretrained=args.pretrained,
         num_classes=args.num_classes,
-        in_chans=3,
+        in_chans=in_chans,
         global_pool=args.gp,
         scriptable=args.torchscript,
+        **args.model_kwargs,
     )
     if args.num_classes is None:
         assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
@@ -232,8 +243,9 @@ def validate(args):
 
     criterion = nn.CrossEntropyLoss().to(device)
 
+    root_dir = args.data or args.data_dir
     dataset = create_dataset(
-        root=args.data,
+        root=root_dir,
         name=args.dataset,
         split=args.split,
         download=args.dataset_download,
@@ -389,7 +401,7 @@ def main():
         if args.model == 'all':
             # validate all models in a list of names with pretrained checkpoints
             args.pretrained = True
-            model_names = list_models(pretrained=True, exclude_filters=['*_in21k', '*_in22k', '*_dino'])
+            model_names = list_models('convnext*', pretrained=True, exclude_filters=['*_in21k', '*_in22k', '*in12k', '*_dino', '*fcmae'])
             model_cfgs = [(n, '') for n in model_names]
         elif not is_model(args.model):
             # model name doesn't exist, try as wildcard filter