Fix grad checkpointing in focalnet

timm/data/dataset.py

@@ -88,6 +88,7 @@ class IterableImageDataset(data.IterableDataset):
             root,
             reader=None,
             split='train',
+            class_map=None,
             is_training=False,
             batch_size=None,
             seed=42,
@@ -102,6 +103,7 @@ class IterableImageDataset(data.IterableDataset):
                 reader,
                 root=root,
                 split=split,
+                class_map=class_map,
                 is_training=is_training,
                 batch_size=batch_size,
                 seed=seed,

timm/data/dataset_factory.py

@@ -157,6 +157,7 @@ def create_dataset(
             root,
             reader=name,
             split=split,
+            class_map=class_map,
             is_training=is_training,
             download=download,
             batch_size=batch_size,
@@ -169,6 +170,7 @@ def create_dataset(
             root,
             reader=name,
             split=split,
+            class_map=class_map,
             is_training=is_training,
             batch_size=batch_size,
             repeats=repeats,

timm/data/readers/reader_tfds.py

@@ -34,6 +34,7 @@ except ImportError as e:
     print("Please install tensorflow_datasets package `pip install tensorflow-datasets`.")
     exit(1)
 
+from .class_map import load_class_map
 from .reader import Reader
 from .shared_count import SharedCount
 
@@ -94,6 +95,7 @@ class ReaderTfds(Reader):
             root,
             name,
             split='train',
+            class_map=None,
             is_training=False,
             batch_size=None,
             download=False,
@@ -151,7 +153,12 @@ class ReaderTfds(Reader):
         # NOTE: the tfds command line app can be used download & prepare datasets if you don't enable download flag
         if download:
             self.builder.download_and_prepare()
-        self.class_to_idx = get_class_labels(self.builder.info) if self.target_name == 'label' else {}
+        self.remap_class = False
+        if class_map:
+            self.class_to_idx = load_class_map(class_map)
+            self.remap_class = True
+        else:
+            self.class_to_idx = get_class_labels(self.builder.info) if self.target_name == 'label' else {}
         self.split_info = self.builder.info.splits[split]
         self.num_samples = self.split_info.num_examples
 
@@ -299,6 +306,8 @@ class ReaderTfds(Reader):
             target_data = sample[self.target_name]
             if self.target_img_mode:
                 target_data = Image.fromarray(target_data, mode=self.target_img_mode)
+            elif self.remap_class:
+                target_data = self.class_to_idx[target_data]
             yield input_data, target_data
             sample_count += 1
             if self.is_training and sample_count >= target_sample_count:

timm/data/readers/reader_wds.py

@@ -29,6 +29,7 @@ except ImportError:
     wds = None
     expand_urls = None
 
+from .class_map import load_class_map
 from .reader import Reader
 from .shared_count import SharedCount
 
@@ -42,13 +43,13 @@ def _load_info(root, basename='info'):
     info_yaml = os.path.join(root, basename + '.yaml')
     err_str = ''
     try:
-        with wds.gopen.gopen(info_json) as f:
+        with wds.gopen(info_json) as f:
             info_dict = json.load(f)
         return info_dict
     except Exception as e:
         err_str = str(e)
     try:
-        with wds.gopen.gopen(info_yaml) as f:
+        with wds.gopen(info_yaml) as f:
             info_dict = yaml.safe_load(f)
         return info_dict
     except Exception:
@@ -110,8 +111,8 @@ def _parse_split_info(split: str, info: Dict):
                 filenames=split_filenames,
             )
     else:
-        if split not in info['splits']:
+        if 'splits' not in info or split not in info['splits']:
-            raise RuntimeError(f"split {split} not found in info ({info['splits'].keys()})")
+            raise RuntimeError(f"split {split} not found in info ({info.get('splits', {}).keys()})")
         split = split
         split_info = info['splits'][split]
         split_info = _info_convert(split_info)
@@ -290,6 +291,7 @@ class ReaderWds(Reader):
             batch_size=None,
             repeats=0,
             seed=42,
+            class_map=None,
             input_name='jpg',
             input_image='RGB',
             target_name='cls',
@@ -320,6 +322,12 @@ class ReaderWds(Reader):
         self.num_samples = self.split_info.num_samples
         if not self.num_samples:
             raise RuntimeError(f'Invalid split definition, no samples found.')
+        self.remap_class = False
+        if class_map:
+            self.class_to_idx = load_class_map(class_map)
+            self.remap_class = True
+        else:
+            self.class_to_idx = {}
 
         # Distributed world state
         self.dist_rank = 0
@@ -431,7 +439,10 @@ class ReaderWds(Reader):
         i = 0
         # _logger.info(f'start {i}, {self.worker_id}')  # FIXME temporary debug
         for sample in ds:
-            yield sample[self.image_key], sample[self.target_key]
+            target = sample[self.target_key]
+            if self.remap_class:
+                target = self.class_to_idx[target]
+            yield sample[self.image_key], target
             i += 1
         # _logger.info(f'end {i}, {self.worker_id}')  # FIXME temporary debug
 

timm/models/__init__.py

@@ -17,6 +17,7 @@ from .edgenext import *
 from .efficientformer import *
 from .efficientformer_v2 import *
 from .efficientnet import *
+from .focalnet import *
 from .gcvit import *
 from .ghostnet import *
 from .gluon_resnet import *

timm/models/focalnet.py

@@ -0,0 +1,637 @@
+""" FocalNet
+
+As described in `Focal Modulation Networks` - https://arxiv.org/abs/2203.11926
+
+Significant modifications and refactoring from the original impl at https://github.com/microsoft/FocalNet
+
+This impl is/has:
+* fully convolutional, NCHW tensor layout throughout, seemed to have minimal performance impact but more flexible
+* re-ordered downsample / layer so that striding always at beginning of layer (stage)
+* no input size constraints or input resolution/H/W tracking through the model
+* torchscript fixed and a number of quirks cleaned up
+* feature extraction support via `features_only=True`
+"""
+# --------------------------------------------------------
+# FocalNets -- Focal Modulation Networks
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Jianwei Yang (jianwyan@microsoft.com)
+# --------------------------------------------------------
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import Mlp, DropPath, LayerNorm2d, trunc_normal_, ClassifierHead, NormMlpClassifierHead
+from ._builder import build_model_with_cfg
+from ._manipulate import named_apply
+from ._registry import register_model
+
+__all__ = ['FocalNet']
+
+
+class FocalModulation(nn.Module):
+    def __init__(
+            self,
+            dim,
+            focal_window,
+            focal_level,
+            focal_factor=2,
+            bias=True,
+            use_post_norm=False,
+            normalize_modulator=False,
+            proj_drop=0.,
+            norm_layer=LayerNorm2d,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.focal_window = focal_window
+        self.focal_level = focal_level
+        self.focal_factor = focal_factor
+        self.use_post_norm = use_post_norm
+        self.normalize_modulator = normalize_modulator
+        self.input_split = [dim, dim, self.focal_level + 1]
+
+        self.f = nn.Conv2d(dim, 2 * dim + (self.focal_level + 1), kernel_size=1, bias=bias)
+        self.h = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
+
+        self.act = nn.GELU()
+        self.proj = nn.Conv2d(dim, dim, kernel_size=1)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.focal_layers = nn.ModuleList()
+
+        self.kernel_sizes = []
+        for k in range(self.focal_level):
+            kernel_size = self.focal_factor * k + self.focal_window
+            self.focal_layers.append(nn.Sequential(
+                nn.Conv2d(dim, dim, kernel_size=kernel_size, groups=dim, padding=kernel_size // 2, bias=False),
+                nn.GELU(),
+            ))
+            self.kernel_sizes.append(kernel_size)
+        self.norm = norm_layer(dim) if self.use_post_norm else nn.Identity()
+
+    def forward(self, x):
+        """
+        Args:
+            x: input features with shape of (B, H, W, C)
+        """
+        C = x.shape[1]
+
+        # pre linear projection
+        x = self.f(x)
+        q, ctx, gates = torch.split(x, self.input_split, 1)
+
+        # context aggreation
+        ctx_all = 0
+        for l, focal_layer in enumerate(self.focal_layers):
+            ctx = focal_layer(ctx)
+            ctx_all = ctx_all + ctx * gates[:, l:l + 1]
+        ctx_global = self.act(ctx.mean((2, 3), keepdim=True))
+        ctx_all = ctx_all + ctx_global * gates[:, self.focal_level:]
+
+        # normalize context
+        if self.normalize_modulator:
+            ctx_all = ctx_all / (self.focal_level + 1)
+
+        # focal modulation
+        x_out = q * self.h(ctx_all)
+        x_out = self.norm(x_out)
+
+        # post linear projection
+        x_out = self.proj(x_out)
+        x_out = self.proj_drop(x_out)
+        return x_out
+
+
+class LayerScale2d(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        gamma = self.gamma.view(1, -1, 1, 1)
+        return x.mul_(gamma) if self.inplace else x * gamma
+
+
+class FocalNetBlock(nn.Module):
+    r""" Focal Modulation Network Block.
+
+    Args:
+        dim (int): Number of input channels.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        proj_drop (float, optional): Dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        focal_level (int): Number of focal levels.
+        focal_window (int): Focal window size at first focal level
+        layerscale_value (float): Initial layerscale value
+        use_post_norm (bool): Whether to use layernorm after modulation
+    """
+
+    def __init__(
+            self,
+            dim,
+            mlp_ratio=4.,
+            focal_level=1,
+            focal_window=3,
+            use_post_norm=False,
+            use_post_norm_in_modulation=False,
+            normalize_modulator=False,
+            layerscale_value=1e-4,
+            proj_drop=0.,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=LayerNorm2d,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+
+        self.focal_window = focal_window
+        self.focal_level = focal_level
+        self.use_post_norm = use_post_norm
+
+        self.norm1 = norm_layer(dim) if not use_post_norm else nn.Identity()
+        self.modulation = FocalModulation(
+            dim,
+            focal_window=focal_window,
+            focal_level=self.focal_level,
+            use_post_norm=use_post_norm_in_modulation,
+            normalize_modulator=normalize_modulator,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.norm1_post = norm_layer(dim) if use_post_norm else nn.Identity()
+        self.ls1 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm2 = norm_layer(dim) if not use_post_norm else nn.Identity()
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+            use_conv=True,
+        )
+        self.norm2_post = norm_layer(dim) if use_post_norm else nn.Identity()
+        self.ls2 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        shortcut = x
+
+        # Focal Modulation
+        x = self.norm1(x)
+        x = self.modulation(x)
+        x = self.norm1_post(x)
+        x = shortcut + self.drop_path1(self.ls1(x))
+
+        # FFN
+        x = x + self.drop_path2(self.ls2(self.norm2_post(self.mlp(self.norm2(x)))))
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """ A basic Focal Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        depth (int): Number of blocks.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (bool): Downsample layer at start of the layer. Default: True
+        focal_level (int): Number of focal levels
+        focal_window (int): Focal window size at first focal level
+        layerscale_value (float): Initial layerscale value
+        use_post_norm (bool): Whether to use layer norm after modulation
+    """
+
+    def __init__(
+            self,
+            dim,
+            out_dim,
+            depth,
+            mlp_ratio=4.,
+            downsample=True,
+            focal_level=1,
+            focal_window=1,
+            use_overlap_down=False,
+            use_post_norm=False,
+            use_post_norm_in_modulation=False,
+            normalize_modulator=False,
+            layerscale_value=1e-4,
+            proj_drop=0.,
+            drop_path=0.,
+            norm_layer=LayerNorm2d,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.depth = depth
+        self.grad_checkpointing = False
+
+        if downsample:
+            self.downsample = Downsample(
+                in_chs=dim,
+                out_chs=out_dim,
+                stride=2,
+                overlap=use_overlap_down,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.downsample = nn.Identity()
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            FocalNetBlock(
+                dim=out_dim,
+                mlp_ratio=mlp_ratio,
+                focal_level=focal_level,
+                focal_window=focal_window,
+                use_post_norm=use_post_norm,
+                use_post_norm_in_modulation=use_post_norm_in_modulation,
+                normalize_modulator=normalize_modulator,
+                layerscale_value=layerscale_value,
+                proj_drop=proj_drop,
+                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                norm_layer=norm_layer,
+            )
+            for i in range(depth)])
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+
+    def forward(self, x):
+        x = self.downsample(x)
+        for blk in self.blocks:
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        return x
+
+
+class Downsample(nn.Module):
+    r"""
+    Args:
+        in_chs (int): Number of input image channels
+        out_chs (int): Number of linear projection output channels
+        stride (int): Downsample stride. Default: 4.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(
+            self,
+            in_chs,
+            out_chs,
+            stride=4,
+            overlap=False,
+            norm_layer=None,
+    ):
+        super().__init__()
+        self.stride = stride
+        padding = 0
+        kernel_size = stride
+        if overlap:
+            assert stride in (2, 4)
+            if stride == 4:
+                kernel_size, padding = 7, 2
+            elif stride == 2:
+                kernel_size, padding = 3, 1
+        self.proj = nn.Conv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.norm = norm_layer(out_chs) if norm_layer is not None else nn.Identity()
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = self.norm(x)
+        return x
+
+
+class FocalNet(nn.Module):
+    r""" Focal Modulation Networks (FocalNets)
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Focal Transformer layer.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        focal_levels (list): How many focal levels at all stages. Note that this excludes the finest-grain level.
+            Default: [1, 1, 1, 1]
+        focal_windows (list): The focal window size at all stages. Default: [7, 5, 3, 1]
+        use_overlap_down (bool): Whether to use convolutional embedding.
+        use_post_norm (bool): Whether to use layernorm after modulation (it helps stablize training of large models)
+        layerscale_value (float): Value for layer scale. Default: 1e-4
+        drop_rate (float): Dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+
+    """
+
+    def __init__(
+            self,
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            embed_dim=96,
+            depths=(2, 2, 6, 2),
+            mlp_ratio=4.,
+            focal_levels=(2, 2, 2, 2),
+            focal_windows=(3, 3, 3, 3),
+            use_overlap_down=False,
+            use_post_norm=False,
+            use_post_norm_in_modulation=False,
+            normalize_modulator=False,
+            head_hidden_size=None,
+            head_init_scale=1.0,
+            layerscale_value=None,
+            drop_rate=0.,
+            proj_drop_rate=0.,
+            drop_path_rate=0.1,
+            norm_layer=partial(LayerNorm2d, eps=1e-5),
+            **kwargs,
+    ):
+        super().__init__()
+
+        self.num_layers = len(depths)
+        embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)]
+
+        self.num_classes = num_classes
+        self.embed_dim = embed_dim
+        self.num_features = embed_dim[-1]
+        self.feature_info = []
+
+        self.stem = Downsample(
+            in_chs=in_chans,
+            out_chs=embed_dim[0],
+            overlap=use_overlap_down,
+            norm_layer=norm_layer,
+        )
+        in_dim = embed_dim[0]
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+        layers = []
+        for i_layer in range(self.num_layers):
+            out_dim = embed_dim[i_layer]
+            layer = BasicLayer(
+                dim=in_dim,
+                out_dim=out_dim,
+                depth=depths[i_layer],
+                mlp_ratio=mlp_ratio,
+                downsample=i_layer > 0,
+                focal_level=focal_levels[i_layer],
+                focal_window=focal_windows[i_layer],
+                use_overlap_down=use_overlap_down,
+                use_post_norm=use_post_norm,
+                use_post_norm_in_modulation=use_post_norm_in_modulation,
+                normalize_modulator=normalize_modulator,
+                layerscale_value=layerscale_value,
+                proj_drop=proj_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                norm_layer=norm_layer,
+            )
+            in_dim = out_dim
+            layers += [layer]
+            self.feature_info += [dict(num_chs=out_dim, reduction=4 * 2 ** i_layer, module=f'layers.{i_layer}')]
+
+        self.layers = nn.Sequential(*layers)
+
+        if head_hidden_size:
+            self.norm = nn.Identity()
+            self.head = NormMlpClassifierHead(
+                self.num_features,
+                num_classes,
+                hidden_size=head_hidden_size,
+                pool_type=global_pool,
+                drop_rate=drop_rate,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.norm = norm_layer(self.num_features)
+            self.head = ClassifierHead(
+                self.num_features,
+                num_classes,
+                pool_type=global_pool,
+                drop_rate=drop_rate
+            )
+
+        named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {''}
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+        for l in self.layers:
+            l.set_grad_checkpointing(enable=enable)
+
+    @torch.jit.ignore
+    def get_classifier(self):
+        return self.classifier.fc
+
+    def reset_classifier(self, num_classes, global_pool=None):
+        self.classifier.reset(num_classes, global_pool=global_pool)
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        x = self.layers(x)
+        x = self.norm(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        return self.head(x, pre_logits=pre_logits)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def _init_weights(module, name=None, head_init_scale=1.0):
+    if isinstance(module, nn.Conv2d):
+        trunc_normal_(module.weight, std=.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+        if name and 'head.fc' in name:
+            module.weight.data.mul_(head_init_scale)
+            module.bias.data.mul_(head_init_scale)
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.proj', 'classifier': 'head.fc',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    "focalnet_tiny_srf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth'),
+    "focalnet_small_srf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth'),
+    "focalnet_base_srf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth'),
+    "focalnet_tiny_lrf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth'),
+    "focalnet_small_lrf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth'),
+    "focalnet_base_lrf": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth'),
+    "focalnet_large_fl3": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
+        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
+    "focalnet_large_fl4": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
+        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
+    "focalnet_xlarge_fl3": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
+        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
+    "focalnet_xlarge_fl4": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
+        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
+    "focalnet_huge_fl3": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_huge_lrf_224.pth',
+        num_classes=0),
+    "focalnet_huge_fl4": _cfg(
+        url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_huge_lrf_224_fl4.pth',
+        num_classes=0),
+}
+
+
+def checkpoint_filter_fn(state_dict, model: FocalNet):
+    if 'stem.proj.weight' in state_dict:
+        return
+    import re
+    out_dict = {}
+    if 'model' in state_dict:
+        state_dict = state_dict['model']
+    dest_dict = model.state_dict()
+    for k, v in state_dict.items():
+        k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
+        k = k.replace('patch_embed', 'stem')
+        k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
+        if 'norm' in k and k not in dest_dict:
+            k = re.sub(r'norm([0-9])', r'norm\1_post', k)
+        k = k.replace('ln.', 'norm.')
+        k = k.replace('head', 'head.fc')
+        if dest_dict[k].shape != v.shape:
+            v = v.reshape(dest_dict[k].shape)
+        out_dict[k] = v
+    return out_dict
+
+
+def _create_focalnet(variant, pretrained=False, **kwargs):
+    default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
+    out_indices = kwargs.pop('out_indices', default_out_indices)
+
+    model = build_model_with_cfg(
+        FocalNet, variant, pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
+        **kwargs)
+    return model
+
+
+@register_model
+def focalnet_tiny_srf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, **kwargs)
+    return _create_focalnet('focalnet_tiny_srf', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_small_srf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, **kwargs)
+    return _create_focalnet('focalnet_small_srf', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_base_srf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, **kwargs)
+    return _create_focalnet('focalnet_base_srf', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_tiny_lrf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
+    return _create_focalnet('focalnet_tiny_lrf', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_small_lrf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
+    return _create_focalnet('focalnet_small_lrf', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_base_lrf(pretrained=False, **kwargs):
+    model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], **kwargs)
+    return _create_focalnet('focalnet_base_lrf', pretrained=pretrained, **model_kwargs)
+
+
+# FocalNet large+ models
+@register_model
+def focalnet_large_fl3(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[3, 3, 3, 3], focal_windows=[5] * 4,
+        use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_large_fl3', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_large_fl4(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[4, 4, 4, 4],
+        use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_large_fl4', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_xlarge_fl3(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[3, 3, 3, 3], focal_windows=[5] * 4,
+        use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_xlarge_fl3', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_xlarge_fl4(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[4, 4, 4, 4],
+        use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_xlarge_fl4', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_huge_fl3(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[3, 3, 3, 3], focal_windows=[3] * 4,
+        use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_huge_fl3', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def focalnet_huge_fl4(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[4, 4, 4, 4],
+        use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
+    return _create_focalnet('focalnet_huge_fl4', pretrained=pretrained, **model_kwargs)
+