Merge branch 'nested_transformer' of https://github.com/alexander-soare/pytorch-image-models into alexander-soare-nested_transformer

convert/convert_nest_flax.py

@@ -0,0 +1,109 @@
+"""
+Convert weights from https://github.com/google-research/nested-transformer
+NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
+"""
+
+import sys
+
+import numpy as np
+import torch
+
+from clu import checkpoint
+
+
+arch_depths = {
+    'nest_base': [2, 2, 20],
+    'nest_small': [2, 2, 20],
+    'nest_tiny': [2, 2, 8],
+}
+
+
+def convert_nest(checkpoint_path, arch):
+    """
+    Expects path to checkpoint which is a dir containing 4 files like in each of these folders
+        - https://console.cloud.google.com/storage/browser/gresearch/nest-checkpoints
+    `arch` is needed to 
+    Returns a state dict that can be used with `torch.nn.Module.load_state_dict`
+    Hint: Follow timm.models.nest.Nest.__init__ and 
+    https://github.com/google-research/nested-transformer/blob/main/models/nest_net.py
+    """
+    assert arch in ['nest_base', 'nest_small', 'nest_tiny'], "Your `arch` is not supported"
+
+    flax_dict = checkpoint.load_state_dict(checkpoint_path)['optimizer']['target']
+    state_dict = {}
+
+    # Patch embedding
+    state_dict['patch_embed.proj.weight'] = torch.tensor(
+        flax_dict['PatchEmbedding_0']['Conv_0']['kernel']).permute(3, 2, 0, 1)
+    state_dict['patch_embed.proj.bias'] = torch.tensor(flax_dict['PatchEmbedding_0']['Conv_0']['bias'])
+    
+    # Positional embeddings
+    posemb_keys = [k for k in flax_dict.keys() if k.startswith('PositionEmbedding')]
+    for i, k in enumerate(posemb_keys):
+        state_dict[f'levels.{i}.pos_embed'] = torch.tensor(flax_dict[k]['pos_embedding'])
+    
+    # Transformer encoders
+    depths = arch_depths[arch]
+    for level in range(len(depths)):
+        for layer in range(depths[level]):
+            global_layer_ix = sum(depths[:level]) + layer
+            # Norms
+            for i in range(2):
+                state_dict[f'levels.{level}.transformer_encoder.{layer}.norm{i+1}.weight'] = torch.tensor(
+                    flax_dict[f'EncoderNDBlock_{global_layer_ix}'][f'LayerNorm_{i}']['scale'])
+                state_dict[f'levels.{level}.transformer_encoder.{layer}.norm{i+1}.bias'] = torch.tensor(
+                    flax_dict[f'EncoderNDBlock_{global_layer_ix}'][f'LayerNorm_{i}']['bias'])
+            # Attention qkv
+            w_q = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_0']['kernel']
+            w_kv = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_1']['kernel']
+            # Pay attention to dims here (maybe get pen and paper)
+            w_kv = np.concatenate(np.split(w_kv, 2, -1), 1)
+            w_qkv = np.concatenate([w_q, w_kv], 1)
+            state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.qkv.weight'] = torch.tensor(w_qkv).flatten(1).permute(1,0)
+            b_q = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_0']['bias']
+            b_kv = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_1']['bias']
+            # Pay attention to dims here (maybe get pen and paper)
+            b_kv = np.concatenate(np.split(b_kv, 2, -1), 0)
+            b_qkv = np.concatenate([b_q, b_kv], 0)
+            state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.qkv.bias'] = torch.tensor(b_qkv).reshape(-1)
+            # Attention proj
+            w_proj = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['proj_kernel']
+            w_proj = torch.tensor(w_proj).permute(2, 1, 0).flatten(1)
+            state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.proj.weight'] = w_proj
+            state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.proj.bias'] = torch.tensor(
+                flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['bias'])
+            # MLP
+            for i in range(2):
+                state_dict[f'levels.{level}.transformer_encoder.{layer}.mlp.fc{i+1}.weight'] = torch.tensor(
+                    flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MlpBlock_0'][f'Dense_{i}']['kernel']).permute(1, 0)
+                state_dict[f'levels.{level}.transformer_encoder.{layer}.mlp.fc{i+1}.bias'] = torch.tensor(
+                    flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MlpBlock_0'][f'Dense_{i}']['bias'])
+
+    # Block aggregations
+    for level in range(len(depths)-1):
+        # Convs
+        state_dict[f'block_aggs.{level}.conv.weight'] = torch.tensor(
+            flax_dict[f'ConvPool_{level}']['Conv_0']['kernel']).permute(3, 2, 0, 1)
+        state_dict[f'block_aggs.{level}.conv.bias'] = torch.tensor(
+            flax_dict[f'ConvPool_{level}']['Conv_0']['bias'])
+        # Norms
+        state_dict[f'block_aggs.{level}.norm.weight'] = torch.tensor(
+                    flax_dict[f'ConvPool_{level}']['LayerNorm_0']['scale'])
+        state_dict[f'block_aggs.{level}.norm.bias'] = torch.tensor(
+                    flax_dict[f'ConvPool_{level}']['LayerNorm_0']['bias'])
+
+    # Final norm
+    state_dict[f'norm.weight'] = torch.tensor(flax_dict['LayerNorm_0']['scale'])
+    state_dict[f'norm.bias'] = torch.tensor(flax_dict['LayerNorm_0']['bias'])
+
+    # Classifier
+    state_dict['head.weight'] = torch.tensor(flax_dict['Dense_0']['kernel']).permute(1, 0)
+    state_dict['head.bias'] = torch.tensor(flax_dict['Dense_0']['bias'])
+
+    return state_dict
+
+
+if __name__ == '__main__':
+    variant = sys.argv[1] # base, small, or tiny
+    state_dict = convert_nest(f'../nested-transformer/checkpoints/nest-{variant[0]}_imagenet', f'nest_{variant}')
+    torch.save(state_dict, f'/home/alexander/.cache/torch/hub/checkpoints/jx_nest_{variant}.pth')

tests/test_models.py

@@ -17,7 +17,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
 # transformer models don't support many of the spatial / feature based model functionalities
 NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
-    'convit_*', 'levit*', 'visformer*', 'deit*']
+    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/__init__.py

@@ -20,6 +20,7 @@ from .levit import *
 from .mlp_mixer import *
 from .mobilenetv3 import *
 from .nasnet import *
+from .nest import *
 from .nfnet import *
 from .pit import *
 from .pnasnet import *

timm/models/nest.py

@@ -0,0 +1,483 @@
+""" Nested Transformer (NesT) in PyTorch
+
+A PyTorch implement of Aggregating Nested Transformers as described in:
+
+'Aggregating Nested Transformers'
+    - https://arxiv.org/abs/2105.12723
+
+The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
+have been converted with convert/convert_nest_flax.py
+
+Acknowledgments:
+* The paper authors for sharing their research, code, and model weights
+* Ross Wightman's existing code off which I based this
+
+Copyright 2021 Alexander Soare
+"""
+
+import collections.abc
+from functools import partial
+import math
+import logging
+
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_
+from .layers.helpers import to_ntuple
+from .layers.create_conv2d import create_conv2d
+from .layers.pool2d_same import create_pool2d
+from .vision_transformer import Block
+from .registry import register_model
+from .helpers import build_model_with_cfg, named_apply
+from .vision_transformer import resize_pos_embed
+
+_logger = logging.getLogger(__name__)
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': [14, 14],
+        'crop_pct': .875, 'interpolation': 'bicubic', 'fixed_input_size': True,
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # (weights from official Google JAX impl)
+    'nest_base': _cfg(),
+    'nest_small': _cfg(),
+    'nest_tiny': _cfg(),
+    'jx_nest_base': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_base.pth'), # TODO
+    'jx_nest_small': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_small.pth'), # TODO
+    'jx_nest_tiny': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_tiny.pth'), # TODO
+}
+
+
+class Attention(nn.Module):
+    """
+    This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
+     an extra "image block" dim
+    """
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, 3*dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        """
+        x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
+        """ 
+        B, T, N, C = x.shape
+        # result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
+        qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale # (B, H, T, N, N)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        # (B, H, T, N, C'), permute -> (B, T, N, C', H)
+        x = (attn @ v).permute(0, 2, 3, 4, 1).reshape(B, T, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x # (B, T, N, C)
+
+
+class TransformerLayer(Block):
+    """
+    This is much like `.vision_transformer.Block` but:
+        - Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
+        - Uses modified Attention layer that handles the "block" dimension
+    """
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__(dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
+                         act_layer=nn.GELU, norm_layer=nn.LayerNorm)
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        y = self.norm1(x)
+        x = x + self.drop_path(self.attn(y))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class BlockAggregation(nn.Module):
+    def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
+        super().__init__()
+        self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
+        self.norm = norm_layer(out_channels)
+        self.pool = create_pool2d('max', kernel_size=3, stride=2, padding=pad_type)
+
+    def forward(self, x):
+        """
+        x is expected to have shape (B, C, H, W)
+        """
+        assert x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims'
+        assert x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims'
+        x = self.conv(x)
+        # Layer norm done over channel dim only
+        x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        x = self.pool(x)
+        return x # (B, C, H//2, W//2)
+
+
+def blockify(x, block_size: int):
+    """image to blocks
+    Args:
+        x (Tensor): with shape (B, H, W, C)
+        block_size (int): edge length of a single square block in units of H, W
+    """
+    B, H, W, C  = x.shape
+    assert H % block_size == 0, '`block_size` must divide input height evenly'
+    assert W % block_size == 0, '`block_size` must divide input width evenly'
+    grid_height = H // block_size
+    grid_width = W // block_size
+    x = x.reshape(B, grid_height, block_size, grid_width, block_size, C)
+    x = x.permute(0, 1, 3, 2, 4, 5)
+    x = x.reshape(B, grid_height * grid_width, -1, C)
+    return x # (B, T, N, C)
+
+
+def deblockify(x, block_size: int):
+    """blocks to image
+    Args:
+        x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block
+        block_size (int): edge length of a single square block in units of desired H, W
+    """
+    B, T, _, C= x.shape
+    grid_size = int(math.sqrt(T))
+    x = x.reshape(B, grid_size, grid_size, block_size, block_size, C)
+    x = x.permute(0, 1, 3, 2, 4, 5)
+    height = width = grid_size * block_size
+    x = x.reshape(B, height, width, C)
+    return x # (B, H, W, C)
+    
+
+class NestLevel(nn.Module):
+    """ Single hierarchical level of a Nested Transformer
+    """
+    def __init__(self, num_blocks, block_size, seq_length, num_heads, depth, embed_dim, mlp_ratio=4., qkv_bias=True,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rates=[], norm_layer=None, act_layer=None):
+        super().__init__()
+        self.block_size = block_size
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_blocks, seq_length, embed_dim))
+        # Transformer encoder
+        if len(drop_path_rates):
+            assert len(drop_path_rates) == depth, 'Must provide as many drop path rates as there are transformer layers'
+        self.transformer_encoder = nn.Sequential(*[
+            TransformerLayer(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_rates[i],
+                norm_layer=norm_layer, act_layer=act_layer)
+            for i in range(depth)])
+
+    def forward(self, x):
+        """
+        expects x as (B, C, H, W)
+        """
+        # Switch to channels last for transformer
+        x = x.permute(0, 2, 3, 1) # (B, H', W', C)
+        x = blockify(x, self.block_size) # (B, T, N, C')
+        x = x + self.pos_embed
+        x = self.transformer_encoder(x) # (B, T, N, C')
+        x = deblockify(x, self.block_size) # (B, H', W', C')
+        # Channel-first for block aggregation, and generally to replicate convnet feature map at each stage
+        x = x.permute(0, 3, 1, 2) # (B, C, H', W')
+        return x
+
+
+class Nest(nn.Module):
+    """ Nested Transformer (NesT)
+
+    A PyTorch impl of : `Aggregating Nested Transformers`
+        - https://arxiv.org/abs/2105.12723
+    """
+
+    def __init__(self, img_size=224, in_chans=3, patch_size=4, num_levels=3, embed_dims=(128, 256, 512),
+                 num_heads=(4, 8, 16), depths=(2, 2, 20), num_classes=1000, mlp_ratio=4., qkv_bias=True, pad_type='',
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.5, norm_layer=None, act_layer=None, weight_init='',
+                 global_pool='avg'):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            in_chans (int): number of input channels
+            patch_size (int): patch size
+            num_levels (int): number of block hierarchies (T_d in the paper)
+            embed_dims (int, tuple): embedding dimensions of each level
+            num_heads (int, tuple): number of attention heads for each level
+            depths (int, tuple): number of transformer layers for each level
+            num_classes (int): number of classes for classification head
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim for MLP of transformer layers
+            qkv_bias (bool): enable bias for qkv if True
+            drop_rate (float): dropout rate for MLP of transformer layers, MSA final projection layer, and classifier
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate
+            norm_layer: (nn.Module): normalization layer for transformer layers
+            act_layer: (nn.Module): activation layer in MLP of transformer layers
+            weight_init: (str): weight init scheme
+            global_pool: (str): type of pooling operation to apply to final feature map
+
+        Notes:
+            - Default values follow NesT-B from the original Jax code.
+            - `embed_dims`, `num_heads`, `depths` should be ints or tuples with length `num_levels`.
+            - For those following the paper, Table A1 may have errors!
+                - https://github.com/google-research/nested-transformer/issues/2
+        """
+        super().__init__()
+
+        for param_name in ['embed_dims', 'num_heads', 'depths']:
+            param_value = locals()[param_name]
+            if isinstance(param_value, collections.abc.Sequence):
+                assert len(param_value) == num_levels, f'Require `len({param_name}) == num_levels`'
+
+        embed_dims = to_ntuple(num_levels)(embed_dims)
+        num_heads = to_ntuple(num_levels)(num_heads)
+        depths = to_ntuple(num_levels)(depths)
+        self.num_classes = num_classes
+        self.num_features = embed_dims[-1]
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+        self.drop_rate = drop_rate
+        self.num_levels = num_levels
+        if isinstance(img_size, collections.abc.Sequence):
+            assert img_size[0] == img_size[1], 'Model only handles square inputs'
+            img_size = img_size[0]
+        assert img_size % patch_size == 0, '`patch_size` must divide `img_size` evenly'
+        self.patch_size = patch_size
+
+        # Number of blocks at each level
+        self.num_blocks = 4**(np.arange(num_levels)[::-1])
+        assert (img_size // patch_size) % np.sqrt(self.num_blocks[0]) == 0, \
+                    'First level blocks don\'t fit evenly. Check `img_size`, `patch_size`, and `num_levels`'
+
+        # Block edge size in units of patches
+        # Hint: (img_size // patch_size) gives number of patches along edge of image. sqrt(self.num_blocks[0]) is the
+        #  number of blocks along edge of image
+        self.block_size = int((img_size // patch_size) // np.sqrt(self.num_blocks[0]))
+        
+        # Patch embedding
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dims[0])
+        self.num_patches = self.patch_embed.num_patches
+        self.seq_length = self.num_patches // self.num_blocks[0]
+
+        # Build up each hierarchical level
+        self.levels = nn.ModuleList([])
+        self.block_aggs = nn.ModuleList([])
+        drop_path_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        for lix in range(self.num_levels):
+            dpr = drop_path_rates[sum(depths[:lix]):sum(depths[:lix+1])]
+            self.levels.append(NestLevel(
+                self.num_blocks[lix], self.block_size, self.seq_length, num_heads[lix], depths[lix],
+                embed_dims[lix], mlp_ratio, qkv_bias, drop_rate, attn_drop_rate, dpr, norm_layer,
+                act_layer))            
+            if lix < self.num_levels - 1:
+                self.block_aggs.append(BlockAggregation(
+                    embed_dims[lix], embed_dims[lix+1], norm_layer, pad_type=pad_type))
+            else:
+                # Required for zipped iteration over levels and ls_block_agg together
+                self.block_aggs.append(nn.Identity())
+
+        # Final normalization layer
+        self.norm = norm_layer(embed_dims[-1])
+
+        # Classifier
+        self.global_pool, self.head = create_classifier(
+            self.num_features, self.num_classes, pool_type=global_pool)
+
+        self.init_weights(weight_init)
+
+    def init_weights(self, mode=''):
+        assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
+        head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
+        for level in self.levels:
+            trunc_normal_(level.pos_embed, std=.02, a=-2, b=2)
+        if mode.startswith('jax'):
+            named_apply(partial(_init_nest_weights, head_bias=head_bias, jax_impl=True), self)
+        else:
+            named_apply(_init_nest_weights, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed'}
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.num_classes = num_classes
+        self.global_pool, self.head = create_classifier(
+            self.num_features, self.num_classes, pool_type=global_pool)
+
+    def forward_features(self, x):
+        """ x shape (B, C, H, W)
+        """
+        B, _, H, W = x.shape
+        x = self.patch_embed(x)
+        x = x.reshape(B, H//self.patch_size, W//self.patch_size, -1) # (B, H', W', C')
+        x = x.permute(0, 3, 1, 2)
+        # NOTE: TorchScript won't let us subscript module lists with integer variables, so we iterate instead
+        for level, block_agg in zip(self.levels, self.block_aggs):
+            x = level(x)
+            x = block_agg(x)
+        # Layer norm done over channel dim only
+        x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        return x
+
+    def forward(self, x):
+        """ x shape (B, C, H, W)
+        """
+        x = self.forward_features(x)
+        x = self.global_pool(x)
+        if self.drop_rate > 0.:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        return self.head(x)
+
+
+def _init_nest_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
+    """ NesT weight initialization
+    Can replicate Jax implementation. Otherwise follows vision_transformer.py
+    """
+    if isinstance(module, nn.Linear):
+        if name.startswith('head'):
+            if jax_impl:
+                trunc_normal_(module.weight, std=.02, a=-2, b=2)
+            else:
+                nn.init.zeros_(module.weight)
+            nn.init.constant_(module.bias, head_bias)
+        else:
+            trunc_normal_(module.weight, std=.02, a=-2, b=2)
+            if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+    elif jax_impl and isinstance(module, nn.Conv2d):
+        trunc_normal_(module.weight, std=.02, a=-2, b=2)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
+        nn.init.zeros_(module.bias)
+        nn.init.ones_(module.weight)
+
+
+def resize_pos_embed(posemb, posemb_new):
+    """
+    Rescale the grid of position embeddings when loading from state_dict
+    Expected shape of position embeddings is (1, T, N, C), and considers only square images
+    """
+    _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
+    seq_length_old = posemb.shape[2]
+    num_blocks_new, seq_length_new = posemb_new.shape[1:3]
+    size_new = int(math.sqrt(num_blocks_new*seq_length_new))
+    # First change to (1, C, H, W)
+    posemb = deblockify(posemb, int(math.sqrt(seq_length_old))).permute(0, 3, 1, 2)
+    posemb = F.interpolate(posemb, size=[size_new, size_new], mode='bilinear')
+    # Now change to new (1, T, N, C)
+    posemb = blockify(posemb.permute(0, 2, 3, 1), int(math.sqrt(seq_length_new)))
+    return posemb
+
+
+def checkpoint_filter_fn(state_dict, model):
+    """ resize positional embeddings of pretrained weights """
+    pos_embed_keys = [k for k in state_dict.keys() if k.startswith('pos_embed_')]
+    for k in pos_embed_keys:
+        if state_dict[k].shape != getattr(model, k).shape:
+            state_dict[k] = resize_pos_embed(state_dict[k], getattr(model, k))
+    return state_dict
+
+
+def _create_nest(variant, pretrained=False, default_cfg=None, **kwargs):
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Vision Transformer models.')
+
+    default_cfg = default_cfg or default_cfgs[variant]
+    model = build_model_with_cfg(
+        Nest, variant, pretrained,
+        default_cfg=default_cfg,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        **kwargs)
+
+    return model
+
+
+@register_model
+def nest_base(pretrained=False, **kwargs):
+    """ Nest-B @ 224x224
+    """
+    model_kwargs = dict(
+        embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), drop_path_rate=0.5, **kwargs)
+    model = _create_nest('nest_base', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def nest_small(pretrained=False, **kwargs):
+    """ Nest-S @ 224x224
+    """
+    model_kwargs = dict(
+        embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), drop_path_rate=0.3, **kwargs)
+    model = _create_nest('nest_small', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def nest_tiny(pretrained=False, **kwargs):
+    """ Nest-T @ 224x224
+    """
+    model_kwargs = dict(
+        embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), drop_path_rate=0.2, **kwargs)
+    model = _create_nest('nest_tiny', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def jx_nest_base(pretrained=False, **kwargs):
+    """ Nest-B @ 224x224, Pretrained weights converted from official Jax impl.
+    """
+    kwargs['pad_type'] = 'same'
+    kwargs['weight_init'] = 'jax'
+    model_kwargs = dict(
+        embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), drop_path_rate=0.5, **kwargs)
+    model = _create_nest('jx_nest_base', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def jx_nest_small(pretrained=False, **kwargs):
+    """ Nest-S @ 224x224, Pretrained weights converted from official Jax impl.
+    """
+    kwargs['pad_type'] = 'same'
+    kwargs['weight_init'] = 'jax'
+    model_kwargs = dict(
+        embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), drop_path_rate=0.3, **kwargs)
+    model = _create_nest('jx_nest_small', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def jx_nest_tiny(pretrained=False, **kwargs):
+    """ Nest-T @ 224x224, Pretrained weights converted from official Jax impl.
+    """
+    kwargs['pad_type'] = 'same'
+    kwargs['weight_init'] = 'jax'
+    model_kwargs = dict(
+        embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), drop_path_rate=0.2, **kwargs)
+    model = _create_nest('jx_nest_tiny', pretrained=pretrained, **model_kwargs)
+    return model