Add MobileVitV2 support. Fix #1332. Move GroupNorm1 to common layers (used in poolformer + mobilevitv2). Keep ol custom ConvNeXt LayerNorm2d impl as LayerNormExp2d for reference.

timm/models/layers/__init__.py

@@ -25,7 +25,7 @@ from .linear import Linear
 from .mixed_conv2d import MixedConv2d
 from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
 from .non_local_attn import NonLocalAttn, BatNonLocalAttn
-from .norm import GroupNorm, LayerNorm2d
+from .norm import GroupNorm, GroupNorm1, LayerNorm2d
 from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm
 from .padding import get_padding, get_same_padding, pad_same
 from .patch_embed import PatchEmbed

timm/models/layers/create_attn.py

@@ -22,7 +22,7 @@ def get_attn(attn_type):
     if isinstance(attn_type, torch.nn.Module):
         return attn_type
     module_cls = None
-    if attn_type is not None:
+    if attn_type:
         if isinstance(attn_type, str):
             attn_type = attn_type.lower()
             # Lightweight attention modules (channel and/or coarse spatial).

timm/models/layers/norm.py

@@ -14,11 +14,59 @@ class GroupNorm(nn.GroupNorm):
         return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
 
 
+class GroupNorm1(nn.GroupNorm):
+    """ Group Normalization with 1 group.
+    Input: tensor in shape [B, C, *]
+    """
+
+    def __init__(self, num_channels, **kwargs):
+        super().__init__(1, num_channels, **kwargs)
+
+
 class LayerNorm2d(nn.LayerNorm):
-    """ LayerNorm for channels of '2D' spatial BCHW tensors """
-    def __init__(self, num_channels, eps=1e-6):
-        super().__init__(num_channels, eps=eps)
+    """ LayerNorm for channels of '2D' spatial NCHW tensors """
+    def __init__(self, num_channels, eps=1e-6, affine=True):
+        super().__init__(num_channels, eps=eps, elementwise_affine=affine)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return F.layer_norm(
             x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+
+
+def _is_contiguous(tensor: torch.Tensor) -> bool:
+    # jit is oh so lovely :/
+    # if torch.jit.is_tracing():
+    #     return True
+    if torch.jit.is_scripting():
+        return tensor.is_contiguous()
+    else:
+        return tensor.is_contiguous(memory_format=torch.contiguous_format)
+
+
+@torch.jit.script
+def _layer_norm_cf(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
+    s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
+    x = (x - u) * torch.rsqrt(s + eps)
+    x = x * weight[:, None, None] + bias[:, None, None]
+    return x
+
+
+class LayerNormExp2d(nn.LayerNorm):
+    """ LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
+
+    Experimental implementation w/ manual norm for tensors non-contiguous tensors.
+
+    This improves throughput in some scenarios (tested on Ampere GPU), esp w/ channels_last
+    layout. However, benefits are not always clear and can perform worse on other GPUs.
+    """
+
+    def __init__(self, num_channels, eps=1e-6):
+        super().__init__(num_channels, eps=eps)
+
+    def forward(self, x) -> torch.Tensor:
+        if _is_contiguous(x):
+            x = F.layer_norm(
+                x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+        else:
+            x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
+        return x

timm/models/mobilevit.py

@@ -1,7 +1,8 @@
 """ MobileViT
 
 Paper:
-`MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
+V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
+V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
 
 MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets (original copyright below)
 License: https://github.com/apple/ml-cvnets/blob/main/LICENSE (Apple open source)
@@ -13,7 +14,7 @@ Rest of code, ByobNet, and Transformer block hacked together by / Copyright 2022
 # Copyright (C) 2020 Apple Inc. All Rights Reserved.
 #
 import math
-from typing import Union, Callable, Dict, Tuple, Optional
+from typing import Union, Callable, Dict, Tuple, Optional, Sequence
 
 import torch
 from torch import nn
@@ -21,7 +22,7 @@ import torch.nn.functional as F
 
 from .byobnet import register_block, ByoBlockCfg, ByoModelCfg, ByobNet, LayerFn, num_groups
 from .fx_features import register_notrace_module
-from .layers import to_2tuple, make_divisible
+from .layers import to_2tuple, make_divisible, LayerNorm2d, GroupNorm1, ConvMlp, DropPath
 from .vision_transformer import Block as TransformerBlock
 from .helpers import build_model_with_cfg
 from .registry import register_model
@@ -48,6 +49,48 @@ default_cfgs = {
     'mobilevit_s': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevit_s-38a5a959.pth'),
     'semobilevit_s': _cfg(),
+
+    'mobilevitv2_050': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_050-49951ee2.pth',
+        crop_pct=0.888),
+    'mobilevitv2_075': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_075-b5556ef6.pth',
+        crop_pct=0.888),
+    'mobilevitv2_100': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_100-e464ef3b.pth',
+        crop_pct=0.888),
+    'mobilevitv2_125': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_125-0ae35027.pth',
+        crop_pct=0.888),
+    'mobilevitv2_150': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150-737c5019.pth',
+        crop_pct=0.888),
+    'mobilevitv2_175': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175-16462ee2.pth',
+        crop_pct=0.888),
+    'mobilevitv2_200': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200-b3422f67.pth',
+        crop_pct=0.888),
+
+    'mobilevitv2_150_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150_in22ft1k-0b555d7b.pth',
+        crop_pct=0.888),
+    'mobilevitv2_175_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175_in22ft1k-4117fa1f.pth',
+        crop_pct=0.888),
+    'mobilevitv2_200_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200_in22ft1k-1d7c8927.pth',
+        crop_pct=0.888),
+
+    'mobilevitv2_150_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150_384_in22ft1k-9e142854.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
+    'mobilevitv2_175_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175_384_in22ft1k-059cbe56.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
+    'mobilevitv2_200_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200_384_in22ft1k-32c87503.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
 }
 
 
@@ -72,6 +115,40 @@ def _mobilevit_block(d, c, s, transformer_dim, transformer_depth, patch_size=4,
     )
 
 
+def _mobilevitv2_block(d, c, s, transformer_depth, patch_size=2, br=2.0, transformer_br=0.5):
+    # inverted residual + mobilevit blocks as per MobileViT network
+    return (
+        _inverted_residual_block(d=d, c=c, s=s, br=br),
+        ByoBlockCfg(
+            type='mobilevit2', d=1, c=c, s=1, br=transformer_br, gs=1,
+            block_kwargs=dict(
+                transformer_depth=transformer_depth,
+                patch_size=patch_size)
+        )
+    )
+
+
+def _mobilevitv2_cfg(multiplier=1.0):
+    chs = (64, 128, 256, 384, 512)
+    if multiplier != 1.0:
+        chs = tuple([int(c * multiplier) for c in chs])
+    cfg = ByoModelCfg(
+        blocks=(
+            _inverted_residual_block(d=1, c=chs[0], s=1, br=2.0),
+            _inverted_residual_block(d=2, c=chs[1], s=2, br=2.0),
+            _mobilevitv2_block(d=1, c=chs[2], s=2, transformer_depth=2),
+            _mobilevitv2_block(d=1, c=chs[3], s=2, transformer_depth=4),
+            _mobilevitv2_block(d=1, c=chs[4], s=2, transformer_depth=3),
+        ),
+        stem_chs=int(32 * multiplier),
+        stem_type='3x3',
+        stem_pool='',
+        downsample='',
+        act_layer='silu',
+    )
+    return cfg
+
+
 model_cfgs = dict(
     mobilevit_xxs=ByoModelCfg(
         blocks=(
@@ -137,11 +214,19 @@ model_cfgs = dict(
         attn_kwargs=dict(rd_ratio=1/8),
         num_features=640,
     ),
+
+    mobilevitv2_050=_mobilevitv2_cfg(.50),
+    mobilevitv2_075=_mobilevitv2_cfg(.75),
+    mobilevitv2_125=_mobilevitv2_cfg(1.25),
+    mobilevitv2_100=_mobilevitv2_cfg(1.0),
+    mobilevitv2_150=_mobilevitv2_cfg(1.5),
+    mobilevitv2_175=_mobilevitv2_cfg(1.75),
+    mobilevitv2_200=_mobilevitv2_cfg(2.0),
 )
 
 
 @register_notrace_module
-class MobileViTBlock(nn.Module):
+class MobileVitBlock(nn.Module):
     """ MobileViT block
         Paper: https://arxiv.org/abs/2110.02178?context=cs.LG
     """
@@ -165,9 +250,9 @@ class MobileViTBlock(nn.Module):
             drop_path_rate: float = 0.,
             layers: LayerFn = None,
             transformer_norm_layer: Callable = nn.LayerNorm,
-            downsample: str = ''
+            **kwargs,  # eat unused args
     ):
-        super(MobileViTBlock, self).__init__()
+        super(MobileVitBlock, self).__init__()
 
         layers = layers or LayerFn()
         groups = num_groups(group_size, in_chs)
@@ -241,7 +326,270 @@ class MobileViTBlock(nn.Module):
         return x
 
 
-register_block('mobilevit', MobileViTBlock)
+class LinearSelfAttention(nn.Module):
+    """
+    This layer applies a self-attention with linear complexity, as described in `https://arxiv.org/abs/2206.02680`
+    This layer can be used for self- as well as cross-attention.
+    Args:
+        embed_dim (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`
+        attn_drop (float): Dropout value for context scores. Default: 0.0
+        bias (bool): Use bias in learnable layers. Default: True
+    Shape:
+        - Input: :math:`(N, C, P, N)` where :math:`N` is the batch size, :math:`C` is the input channels,
+        :math:`P` is the number of pixels in the patch, and :math:`N` is the number of patches
+        - Output: same as the input
+    .. note::
+        For MobileViTv2, we unfold the feature map [B, C, H, W] into [B, C, P, N] where P is the number of pixels
+        in a patch and N is the number of patches. Because channel is the first dimension in this unfolded tensor,
+        we use point-wise convolution (instead of a linear layer). This avoids a transpose operation (which may be
+        expensive on resource-constrained devices) that may be required to convert the unfolded tensor from
+        channel-first to channel-last format in case of a linear layer.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+
+        self.qkv_proj = nn.Conv2d(
+            in_channels=embed_dim,
+            out_channels=1 + (2 * embed_dim),
+            bias=bias,
+            kernel_size=1,
+        )
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = nn.Conv2d(
+            in_channels=embed_dim,
+            out_channels=embed_dim,
+            bias=bias,
+            kernel_size=1,
+        )
+        self.out_drop = nn.Dropout(proj_drop)
+
+    def _forward_self_attn(self, x: torch.Tensor) -> torch.Tensor:
+        # [B, C, P, N] --> [B, h + 2d, P, N]
+        qkv = self.qkv_proj(x)
+
+        # Project x into query, key and value
+        # Query --> [B, 1, P, N]
+        # value, key --> [B, d, P, N]
+        query, key, value = qkv.split([1, self.embed_dim, self.embed_dim], dim=1)
+
+        # apply softmax along N dimension
+        context_scores = F.softmax(query, dim=-1)
+        context_scores = self.attn_drop(context_scores)
+
+        # Compute context vector
+        # [B, d, P, N] x [B, 1, P, N] -> [B, d, P, N] --> [B, d, P, 1]
+        context_vector = (key * context_scores).sum(dim=-1, keepdim=True)
+
+        # combine context vector with values
+        # [B, d, P, N] * [B, d, P, 1] --> [B, d, P, N]
+        out = F.relu(value) * context_vector.expand_as(value)
+        out = self.out_proj(out)
+        out = self.out_drop(out)
+        return out
+
+    @torch.jit.ignore()
+    def _forward_cross_attn(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # x --> [B, C, P, N]
+        # x_prev = [B, C, P, M]
+        batch_size, in_dim, kv_patch_area, kv_num_patches = x.shape
+        q_patch_area, q_num_patches = x.shape[-2:]
+
+        assert (
+            kv_patch_area == q_patch_area
+        ), "The number of pixels in a patch for query and key_value should be the same"
+
+        # compute query, key, and value
+        # [B, C, P, M] --> [B, 1 + d, P, M]
+        qk = F.conv2d(
+            x_prev,
+            weight=self.qkv_proj.weight[:self.embed_dim + 1],
+            bias=self.qkv_proj.bias[:self.embed_dim + 1],
+        )
+
+        # [B, 1 + d, P, M] --> [B, 1, P, M], [B, d, P, M]
+        query, key = qk.split([1, self.embed_dim], dim=1)
+        # [B, C, P, N] --> [B, d, P, N]
+        value = F.conv2d(
+            x,
+            weight=self.qkv_proj.weight[self.embed_dim + 1],
+            bias=self.qkv_proj.bias[self.embed_dim + 1] if self.qkv_proj.bias is not None else None,
+        )
+
+        # apply softmax along M dimension
+        context_scores = F.softmax(query, dim=-1)
+        context_scores = self.attn_drop(context_scores)
+
+        # compute context vector
+        # [B, d, P, M] * [B, 1, P, M] -> [B, d, P, M] --> [B, d, P, 1]
+        context_vector = (key * context_scores).sum(dim=-1, keepdim=True)
+
+        # combine context vector with values
+        # [B, d, P, N] * [B, d, P, 1] --> [B, d, P, N]
+        out = F.relu(value) * context_vector.expand_as(value)
+        out = self.out_proj(out)
+        out = self.out_drop(out)
+        return out
+
+    def forward(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if x_prev is None:
+            return self._forward_self_attn(x)
+        else:
+            return self._forward_cross_attn(x, x_prev=x_prev)
+
+
+class LinearTransformerBlock(nn.Module):
+    """
+    This class defines the pre-norm transformer encoder with linear self-attention in `MobileViTv2 paper <>`_
+    Args:
+        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, P, N)`
+        mlp_ratio (float): Inner dimension ratio of the FFN relative to embed_dim
+        drop (float): Dropout rate. Default: 0.0
+        attn_drop (float): Dropout rate for attention in multi-head attention. Default: 0.0
+        drop_path (float): Stochastic depth rate Default: 0.0
+        norm_layer (Callable): Normalization layer. Default: layer_norm_2d
+    Shape:
+        - Input: :math:`(B, C_{in}, P, N)` where :math:`B` is batch size, :math:`C_{in}` is input embedding dim,
+            :math:`P` is number of pixels in a patch, and :math:`N` is number of patches,
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        mlp_ratio: float = 2.0,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        act_layer=None,
+        norm_layer=None,
+    ) -> None:
+        super().__init__()
+        act_layer = act_layer or nn.SiLU
+        norm_layer = norm_layer or GroupNorm1
+
+        self.norm1 = norm_layer(embed_dim)
+        self.attn = LinearSelfAttention(embed_dim=embed_dim, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path1 = DropPath(drop_path)
+
+        self.norm2 = norm_layer(embed_dim)
+        self.mlp = ConvMlp(
+            in_features=embed_dim,
+            hidden_features=int(embed_dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop)
+        self.drop_path2 = DropPath(drop_path)
+
+    def forward(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if x_prev is None:
+            # self-attention
+            x = x + self.drop_path1(self.attn(self.norm1(x)))
+        else:
+            # cross-attention
+            res = x
+            x = self.norm1(x)  # norm
+            x = self.attn(x, x_prev)  # attn
+            x = self.drop_path1(x) + res  # residual
+
+        # Feed forward network
+        x = x + self.drop_path2(self.mlp(self.norm2(x)))
+        return x
+
+
+@register_notrace_module
+class MobileVitV2Block(nn.Module):
+    """
+    This class defines the `MobileViTv2 block <>`_
+    """
+
+    def __init__(
+        self,
+        in_chs: int,
+        out_chs: Optional[int] = None,
+        kernel_size: int = 3,
+        bottle_ratio: float = 1.0,
+        group_size: Optional[int] = 1,
+        dilation: Tuple[int, int] = (1, 1),
+        mlp_ratio: float = 2.0,
+        transformer_dim: Optional[int] = None,
+        transformer_depth: int = 2,
+        patch_size: int = 8,
+        attn_drop: float = 0.,
+        drop: int = 0.,
+        drop_path_rate: float = 0.,
+        layers: LayerFn = None,
+        transformer_norm_layer: Callable = GroupNorm1,
+        **kwargs,  # eat unused args
+    ):
+        super(MobileVitV2Block, self).__init__()
+        layers = layers or LayerFn()
+        groups = num_groups(group_size, in_chs)
+        out_chs = out_chs or in_chs
+        transformer_dim = transformer_dim or make_divisible(bottle_ratio * in_chs)
+
+        self.conv_kxk = layers.conv_norm_act(
+            in_chs, in_chs, kernel_size=kernel_size,
+            stride=1, groups=groups, dilation=dilation[0])
+        self.conv_1x1 = nn.Conv2d(in_chs, transformer_dim, kernel_size=1, bias=False)
+
+        self.transformer = nn.Sequential(*[
+            LinearTransformerBlock(
+                transformer_dim,
+                mlp_ratio=mlp_ratio,
+                attn_drop=attn_drop,
+                drop=drop,
+                drop_path=drop_path_rate,
+                act_layer=layers.act,
+                norm_layer=transformer_norm_layer
+            )
+            for _ in range(transformer_depth)
+        ])
+        self.norm = transformer_norm_layer(transformer_dim)
+
+        self.conv_proj = layers.conv_norm_act(transformer_dim, out_chs, kernel_size=1, stride=1, apply_act=False)
+
+        self.patch_size = to_2tuple(patch_size)
+        self.patch_area = self.patch_size[0] * self.patch_size[1]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, C, H, W = x.shape
+        patch_h, patch_w = self.patch_size
+        new_h, new_w = math.ceil(H / patch_h) * patch_h, math.ceil(W / patch_w) * patch_w
+        num_patch_h, num_patch_w = new_h // patch_h, new_w // patch_w  # n_h, n_w
+        num_patches = num_patch_h * num_patch_w  # N
+        if new_h != H or new_w != W:
+            x = F.interpolate(x, size=(new_h, new_w), mode="bilinear", align_corners=True)
+
+        # Local representation
+        x = self.conv_kxk(x)
+        x = self.conv_1x1(x)
+
+        # Unfold (feature map -> patches), [B, C, H, W] -> [B, C, P, N]
+        C = x.shape[1]
+        x = x.reshape(B, C, num_patch_h, patch_h, num_patch_w, patch_w).permute(0, 1, 3, 5, 2, 4)
+        x = x.reshape(B, C, -1, num_patches)
+
+        # Global representations
+        x = self.transformer(x)
+        x = self.norm(x)
+
+        # Fold (patches -> feature map), [B, C, P, N] --> [B, C, H, W]
+        x = x.reshape(B, C, patch_h, patch_w, num_patch_h, num_patch_w).permute(0, 1, 4, 2, 5, 3)
+        x = x.reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
+
+        x = self.conv_proj(x)
+        return x
+
+
+register_block('mobilevit', MobileVitBlock)
+register_block('mobilevit2', MobileVitV2Block)
 
 
 def _create_mobilevit(variant, cfg_variant=None, pretrained=False, **kwargs):
@@ -252,6 +600,14 @@ def _create_mobilevit(variant, cfg_variant=None, pretrained=False, **kwargs):
         **kwargs)
 
 
+def _create_mobilevit2(variant, cfg_variant=None, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        ByobNet, variant, pretrained,
+        model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
+        feature_cfg=dict(flatten_sequential=True),
+        **kwargs)
+
+
 @register_model
 def mobilevit_xxs(pretrained=False, **kwargs):
     return _create_mobilevit('mobilevit_xxs', pretrained=pretrained, **kwargs)
@@ -270,3 +626,74 @@ def mobilevit_s(pretrained=False, **kwargs):
 @register_model
 def semobilevit_s(pretrained=False, **kwargs):
     return _create_mobilevit('semobilevit_s', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_050(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_050', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_075(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_075', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_100(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_100', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_125(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_125', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_200', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_150_in22ft1k', cfg_variant='mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_175_in22ft1k', cfg_variant='mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_200_in22ft1k', cfg_variant='mobilevitv2_200', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_150_384_in22ft1k', cfg_variant='mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_175_384_in22ft1k', cfg_variant='mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_200_384_in22ft1k', cfg_variant='mobilevitv2_200', pretrained=pretrained, **kwargs)

timm/models/poolformer.py

@@ -26,7 +26,7 @@ import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg, checkpoint_seq
-from .layers import DropPath, trunc_normal_, to_2tuple, ConvMlp
+from .layers import DropPath, trunc_normal_, to_2tuple, ConvMlp, GroupNorm1
 from .registry import register_model
 
 
@@ -80,15 +80,6 @@ class PatchEmbed(nn.Module):
         return x
 
 
-class GroupNorm1(nn.GroupNorm):
-    """ Group Normalization with 1 group.
-    Input: tensor in shape [B, C, H, W]
-    """
-
-    def __init__(self, num_channels, **kwargs):
-        super().__init__(1, num_channels, **kwargs)
-
-
 class Pooling(nn.Module):
     def __init__(self, pool_size=3):
         super().__init__()