timm/models/mobilevit.py

""" MobileViT

Paper:
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)

Rest of code, ByobNet, and Transformer block hacked together by / Copyright 2022, Ross Wightman
"""
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2020 Apple Inc. All Rights Reserved.
#
import math
from typing import Callable, Tuple, Optional

import torch
import torch.nn.functional as F
from torch import nn

from timm.layers import to_2tuple, make_divisible, GroupNorm1, ConvMlp, DropPath, is_exportable
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
from .byobnet import register_block, ByoBlockCfg, ByoModelCfg, ByobNet, LayerFn, num_groups
from .vision_transformer import Block as TransformerBlock

__all__ = []


def _inverted_residual_block(d, c, s, br=4.0):
    # inverted residual is a bottleneck block with bottle_ratio > 1 applied to in_chs, linear output, gs=1 (depthwise)
    return ByoBlockCfg(
        type='bottle', d=d, c=c, s=s, gs=1, br=br,
        block_kwargs=dict(bottle_in=True, linear_out=True))


def _mobilevit_block(d, c, s, transformer_dim, transformer_depth, patch_size=4, br=4.0):
    # inverted residual + mobilevit blocks as per MobileViT network
    return (
        _inverted_residual_block(d=d, c=c, s=s, br=br),
        ByoBlockCfg(
            type='mobilevit', d=1, c=c, s=1,
            block_kwargs=dict(
                transformer_dim=transformer_dim,
                transformer_depth=transformer_depth,
                patch_size=patch_size)
        )
    )


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=(
            _inverted_residual_block(d=1, c=16, s=1, br=2.0),
            _inverted_residual_block(d=3, c=24, s=2, br=2.0),
            _mobilevit_block(d=1, c=48, s=2, transformer_dim=64, transformer_depth=2, patch_size=2, br=2.0),
            _mobilevit_block(d=1, c=64, s=2, transformer_dim=80, transformer_depth=4, patch_size=2, br=2.0),
            _mobilevit_block(d=1, c=80, s=2, transformer_dim=96, transformer_depth=3, patch_size=2, br=2.0),
        ),
        stem_chs=16,
        stem_type='3x3',
        stem_pool='',
        downsample='',
        act_layer='silu',
        num_features=320,
    ),

    mobilevit_xs=ByoModelCfg(
        blocks=(
            _inverted_residual_block(d=1, c=32, s=1),
            _inverted_residual_block(d=3, c=48, s=2),
            _mobilevit_block(d=1, c=64, s=2, transformer_dim=96, transformer_depth=2, patch_size=2),
            _mobilevit_block(d=1, c=80, s=2, transformer_dim=120, transformer_depth=4, patch_size=2),
            _mobilevit_block(d=1, c=96, s=2, transformer_dim=144, transformer_depth=3, patch_size=2),
        ),
        stem_chs=16,
        stem_type='3x3',
        stem_pool='',
        downsample='',
        act_layer='silu',
        num_features=384,
    ),

    mobilevit_s=ByoModelCfg(
        blocks=(
            _inverted_residual_block(d=1, c=32, s=1),
            _inverted_residual_block(d=3, c=64, s=2),
            _mobilevit_block(d=1, c=96, s=2, transformer_dim=144, transformer_depth=2, patch_size=2),
            _mobilevit_block(d=1, c=128, s=2, transformer_dim=192, transformer_depth=4, patch_size=2),
            _mobilevit_block(d=1, c=160, s=2, transformer_dim=240, transformer_depth=3, patch_size=2),
        ),
        stem_chs=16,
        stem_type='3x3',
        stem_pool='',
        downsample='',
        act_layer='silu',
        num_features=640,
    ),

    semobilevit_s=ByoModelCfg(
        blocks=(
            _inverted_residual_block(d=1, c=32, s=1),
            _inverted_residual_block(d=3, c=64, s=2),
            _mobilevit_block(d=1, c=96, s=2, transformer_dim=144, transformer_depth=2, patch_size=2),
            _mobilevit_block(d=1, c=128, s=2, transformer_dim=192, transformer_depth=4, patch_size=2),
            _mobilevit_block(d=1, c=160, s=2, transformer_dim=240, transformer_depth=3, patch_size=2),
        ),
        stem_chs=16,
        stem_type='3x3',
        stem_pool='',
        downsample='',
        attn_layer='se',
        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):
    """ MobileViT block
        Paper: https://arxiv.org/abs/2110.02178?context=cs.LG
    """
    def __init__(
            self,
            in_chs: int,
            out_chs: Optional[int] = None,
            kernel_size: int = 3,
            stride: int = 1,
            bottle_ratio: float = 1.0,
            group_size: Optional[int] = None,
            dilation: Tuple[int, int] = (1, 1),
            mlp_ratio: float = 2.0,
            transformer_dim: Optional[int] = None,
            transformer_depth: int = 2,
            patch_size: int = 8,
            num_heads: int = 4,
            attn_drop: float = 0.,
            drop: int = 0.,
            no_fusion: bool = False,
            drop_path_rate: float = 0.,
            layers: LayerFn = None,
            transformer_norm_layer: Callable = nn.LayerNorm,
            **kwargs,  # eat unused args
    ):
        super(MobileVitBlock, 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=stride, groups=groups, dilation=dilation[0])
        self.conv_1x1 = nn.Conv2d(in_chs, transformer_dim, kernel_size=1, bias=False)

        self.transformer = nn.Sequential(*[
            TransformerBlock(
                transformer_dim,
                mlp_ratio=mlp_ratio,
                num_heads=num_heads,
                qkv_bias=True,
                attn_drop=attn_drop,
                proj_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)

        if no_fusion:
            self.conv_fusion = None
        else:
            self.conv_fusion = layers.conv_norm_act(in_chs + out_chs, out_chs, kernel_size=kernel_size, stride=1)

        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:
        shortcut = x

        # Local representation
        x = self.conv_kxk(x)
        x = self.conv_1x1(x)

        # Unfold (feature map -> patches)
        patch_h, patch_w = self.patch_size
        B, C, H, W = x.shape
        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
        interpolate = False
        if new_h != H or new_w != W:
            # Note: Padding can be done, but then it needs to be handled in attention function.
            x = F.interpolate(x, size=(new_h, new_w), mode="bilinear", align_corners=False)
            interpolate = True

        # [B, C, H, W] --> [B * C * n_h, n_w, p_h, p_w]
        x = x.reshape(B * C * num_patch_h, patch_h, num_patch_w, patch_w).transpose(1, 2)
        # [B * C * n_h, n_w, p_h, p_w] --> [BP, N, C] where P = p_h * p_w and N = n_h * n_w
        x = x.reshape(B, C, num_patches, self.patch_area).transpose(1, 3).reshape(B * self.patch_area, num_patches, -1)

        # Global representations
        x = self.transformer(x)
        x = self.norm(x)

        # Fold (patch -> feature map)
        # [B, P, N, C] --> [B*C*n_h, n_w, p_h, p_w]
        x = x.contiguous().view(B, self.patch_area, num_patches, -1)
        x = x.transpose(1, 3).reshape(B * C * num_patch_h, num_patch_w, patch_h, patch_w)
        # [B*C*n_h, n_w, p_h, p_w] --> [B*C*n_h, p_h, n_w, p_w] --> [B, C, H, W]
        x = x.transpose(1, 2).reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
        if interpolate:
            x = F.interpolate(x, size=(H, W), mode="bilinear", align_corners=False)

        x = self.conv_proj(x)
        if self.conv_fusion is not None:
            x = self.conv_fusion(torch.cat((shortcut, x), dim=1))
        return x


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]
        self.coreml_exportable = is_exportable()

    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]
        if self.coreml_exportable:
            x = F.unfold(x, kernel_size=(patch_h, patch_w), stride=(patch_h, patch_w))
        else:
            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]
        if self.coreml_exportable:
            # adopted from https://github.com/apple/ml-cvnets/blob/main/cvnets/modules/mobilevit_block.py#L609-L624
            x = x.reshape(B, C * patch_h * patch_w, num_patch_h, num_patch_w)
            x = F.pixel_shuffle(x, upscale_factor=patch_h)
        else:
            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):
    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)


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)


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
        'crop_pct': 0.9, 'interpolation': 'bicubic',
        'mean': (0., 0., 0.), 'std': (1., 1., 1.),
        'first_conv': 'stem.conv', 'classifier': 'head.fc',
        'fixed_input_size': False,
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    'mobilevit_xxs.cvnets_in1k': _cfg(hf_hub_id='timm/'),
    'mobilevit_xs.cvnets_in1k': _cfg(hf_hub_id='timm/'),
    'mobilevit_s.cvnets_in1k': _cfg(hf_hub_id='timm/'),

    'mobilevitv2_050.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_075.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_100.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_125.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_150.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_175.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_200.cvnets_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),

    'mobilevitv2_150.cvnets_in22k_ft_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_175.cvnets_in22k_ft_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),
    'mobilevitv2_200.cvnets_in22k_ft_in1k': _cfg(
        hf_hub_id='timm/',
        crop_pct=0.888),

    'mobilevitv2_150.cvnets_in22k_ft_in1k_384': _cfg(
        hf_hub_id='timm/',
        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
    'mobilevitv2_175.cvnets_in22k_ft_in1k_384': _cfg(
        hf_hub_id='timm/',
        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
    'mobilevitv2_200.cvnets_in22k_ft_in1k_384': _cfg(
        hf_hub_id='timm/',
        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
})


@register_model
def mobilevit_xxs(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevit_xxs', pretrained=pretrained, **kwargs)


@register_model
def mobilevit_xs(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevit_xs', pretrained=pretrained, **kwargs)


@register_model
def mobilevit_s(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevit_s', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_050(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_050', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_075(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_075', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_100(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_100', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_125(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_125', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_150(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_150', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_175(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_175', pretrained=pretrained, **kwargs)


@register_model
def mobilevitv2_200(pretrained=False, **kwargs) -> ByobNet:
    return _create_mobilevit('mobilevitv2_200', pretrained=pretrained, **kwargs)


register_model_deprecations(__name__, {
    'mobilevitv2_150_in22ft1k': 'mobilevitv2_150.cvnets_in22k_ft_in1k',
    'mobilevitv2_175_in22ft1k': 'mobilevitv2_175.cvnets_in22k_ft_in1k',
    'mobilevitv2_200_in22ft1k': 'mobilevitv2_200.cvnets_in22k_ft_in1k',

    'mobilevitv2_150_384_in22ft1k': 'mobilevitv2_150.cvnets_in22k_ft_in1k_384',
    'mobilevitv2_175_384_in22ft1k': 'mobilevitv2_175.cvnets_in22k_ft_in1k_384',
    'mobilevitv2_200_384_in22ft1k': 'mobilevitv2_200.cvnets_in22k_ft_in1k_384',
})