image-match/python/py/Lib/site-packages/timm/models/gcvit.py

""" Global Context ViT

From scratch implementation of GCViT in the style of timm swin_transformer_v2_cr.py

Global Context Vision Transformers -https://arxiv.org/abs/2206.09959

@article{hatamizadeh2022global,
  title={Global Context Vision Transformers},
  author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and Molchanov, Pavlo},
  journal={arXiv preprint arXiv:2206.09959},
  year={2022}
}

Free of any code related to NVIDIA GCVit impl at https://github.com/NVlabs/GCVit.
The license for this code release is Apache 2.0 with no commercial restrictions.

However, weight files adapted from NVIDIA GCVit impl ARE under a non-commercial share-alike license
(https://creativecommons.org/licenses/by-nc-sa/4.0/) until I have a chance to train new ones...

Hacked together by / Copyright 2022, Ross Wightman
"""
import math
from functools import partial
from typing import Callable, List, Optional, Tuple, Type, Union

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import (
    DropPath,
    calculate_drop_path_rates,
    to_2tuple,
    to_ntuple,
    Mlp,
    ClassifierHead,
    LayerNorm2d,
    LayerScale,
    get_attn,
    get_act_layer,
    get_norm_layer,
    RelPosBias,
    _assert,
)
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._features_fx import register_notrace_function
from ._manipulate import named_apply, checkpoint
from ._registry import register_model, generate_default_cfgs

__all__ = ['GlobalContextVit']


class MbConvBlock(nn.Module):
    """ A depthwise separable / fused mbconv style residual block with SE, `no norm.
    """
    def __init__(
            self,
            in_chs: int,
            out_chs: Optional[int] = None,
            expand_ratio: float = 1.0,
            attn_layer: str = 'se',
            bias: bool = False,
            act_layer: Type[nn.Module] = nn.GELU,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        attn_kwargs = dict(act_layer=act_layer, **dd)
        if isinstance(attn_layer, str) and attn_layer == 'se' or attn_layer == 'eca':
            attn_kwargs['rd_ratio'] = 0.25
            attn_kwargs['bias'] = False
        attn_layer = get_attn(attn_layer)
        out_chs = out_chs or in_chs
        mid_chs = int(expand_ratio * in_chs)

        self.conv_dw = nn.Conv2d(in_chs, mid_chs, 3, 1, 1, groups=in_chs, bias=bias, **dd)
        self.act = act_layer()
        self.se = attn_layer(mid_chs, **attn_kwargs)
        self.conv_pw = nn.Conv2d(mid_chs, out_chs, 1, 1, 0, bias=bias, **dd)

    def forward(self, x):
        shortcut = x
        x = self.conv_dw(x)
        x = self.act(x)
        x = self.se(x)
        x = self.conv_pw(x)
        x = x + shortcut
        return x


class Downsample2d(nn.Module):
    def __init__(
            self,
            dim: int,
            dim_out: Optional[int] = None,
            reduction: str = 'conv',
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = LayerNorm2d,  # NOTE in NCHW
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        dim_out = dim_out or dim

        self.norm1 = norm_layer(dim, **dd) if norm_layer is not None else nn.Identity()
        self.conv_block = MbConvBlock(dim, act_layer=act_layer, **dd)
        assert reduction in ('conv', 'max', 'avg')
        if reduction == 'conv':
            self.reduction = nn.Conv2d(dim, dim_out, 3, 2, 1, bias=False, **dd)
        elif reduction == 'max':
            assert dim == dim_out
            self.reduction = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        else:
            assert dim == dim_out
            self.reduction = nn.AvgPool2d(kernel_size=2)
        self.norm2 = norm_layer(dim_out, **dd) if norm_layer is not None else nn.Identity()

    def forward(self, x):
        x = self.norm1(x)
        x = self.conv_block(x)
        x = self.reduction(x)
        x = self.norm2(x)
        return x


class FeatureBlock(nn.Module):
    def __init__(
            self,
            dim: int,
            levels: int = 0,
            reduction: str = 'max',
            act_layer: Type[nn.Module] = nn.GELU,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        reductions = levels
        levels = max(1, levels)
        if reduction == 'avg':
            pool_fn = partial(nn.AvgPool2d, kernel_size=2)
        else:
            pool_fn = partial(nn.MaxPool2d, kernel_size=3, stride=2, padding=1)
        self.blocks = nn.Sequential()
        for i in range(levels):
            self.blocks.add_module(f'conv{i+1}', MbConvBlock(dim, act_layer=act_layer, **dd))
            if reductions:
                self.blocks.add_module(f'pool{i+1}', pool_fn())
                reductions -= 1

    def forward(self, x):
        return self.blocks(x)


class Stem(nn.Module):
    def __init__(
            self,
            in_chs: int = 3,
            out_chs: int = 96,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = LayerNorm2d,  # NOTE stem in NCHW
            device=None,
            dtype=None,
    ):
        super().__init__()
        dd = {'device': device, 'dtype': dtype}
        self.conv1 = nn.Conv2d(in_chs, out_chs, kernel_size=3, stride=2, padding=1, **dd)
        self.down = Downsample2d(out_chs, act_layer=act_layer, norm_layer=norm_layer, **dd)

    def forward(self, x):
        x = self.conv1(x)
        x = self.down(x)
        return x


class WindowAttentionGlobal(nn.Module):

    def __init__(
            self,
            dim: int,
            num_heads: int,
            window_size: Tuple[int, int],
            use_global: bool = True,
            qkv_bias: bool = True,
            attn_drop: float = 0.,
            proj_drop: float = 0.,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        window_size = to_2tuple(window_size)
        self.window_size = window_size
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.use_global = use_global

        self.rel_pos = RelPosBias(window_size=window_size, num_heads=num_heads, **dd)
        if self.use_global:
            self.qkv = nn.Linear(dim, dim * 2, bias=qkv_bias, **dd)
        else:
            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias, **dd)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim, **dd)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, q_global: Optional[torch.Tensor] = None):
        B, N, C = x.shape
        if self.use_global and q_global is not None:
            _assert(x.shape[-1] == q_global.shape[-1], 'x and q_global seq lengths should be equal')

            kv = self.qkv(x)
            kv = kv.reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
            k, v = kv.unbind(0)

            q = q_global.repeat(B // q_global.shape[0], 1, 1, 1)
            q = q.reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        else:
            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
            q, k, v = qkv.unbind(0)
        q = q * self.scale

        attn = q @ k.transpose(-2, -1).contiguous()  # NOTE contiguous() fixes an odd jit bug in PyTorch 2.0
        attn = self.rel_pos(attn)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


def window_partition(x, window_size: Tuple[int, int]):
    B, H, W, C = x.shape
    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
    return windows


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
    H, W = img_size
    C = windows.shape[-1]
    x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
    return x


class GlobalContextVitBlock(nn.Module):
    def __init__(
            self,
            dim: int,
            feat_size: Tuple[int, int],
            num_heads: int,
            window_size: int = 7,
            mlp_ratio: float = 4.,
            use_global: bool = True,
            qkv_bias: bool = True,
            layer_scale: Optional[float] = None,
            proj_drop: float = 0.,
            attn_drop: float = 0.,
            drop_path: float = 0.,
            attn_layer: Callable = WindowAttentionGlobal,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        feat_size = to_2tuple(feat_size)
        window_size = to_2tuple(window_size)
        self.window_size = window_size
        self.num_windows = int((feat_size[0] // window_size[0]) * (feat_size[1] // window_size[1]))

        self.norm1 = norm_layer(dim, **dd)
        self.attn = attn_layer(
            dim,
            num_heads=num_heads,
            window_size=window_size,
            use_global=use_global,
            qkv_bias=qkv_bias,
            attn_drop=attn_drop,
            proj_drop=proj_drop,
            **dd,
        )
        self.ls1 = LayerScale(dim, layer_scale, **dd) if layer_scale is not None else nn.Identity()
        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.norm2 = norm_layer(dim, **dd)
        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=proj_drop, **dd)
        self.ls2 = LayerScale(dim, layer_scale, **dd) if layer_scale is not None else nn.Identity()
        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def _window_attn(self, x, q_global: Optional[torch.Tensor] = None):
        B, H, W, C = x.shape
        x_win = window_partition(x, self.window_size)
        x_win = x_win.view(-1, self.window_size[0] * self.window_size[1], C)
        attn_win = self.attn(x_win, q_global)
        x = window_reverse(attn_win, self.window_size, (H, W))
        return x

    def forward(self, x, q_global: Optional[torch.Tensor] = None):
        x = x + self.drop_path1(self.ls1(self._window_attn(self.norm1(x), q_global)))
        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
        return x


class GlobalContextVitStage(nn.Module):
    def __init__(
            self,
            dim: int,
            depth: int,
            num_heads: int,
            feat_size: Tuple[int, int],
            window_size: Tuple[int, int],
            downsample: bool = True,
            global_norm: bool = False,
            stage_norm: bool = False,
            mlp_ratio: float = 4.,
            qkv_bias: bool = True,
            layer_scale: Optional[float] = None,
            proj_drop: float = 0.,
            attn_drop: float = 0.,
            drop_path: Union[List[float], float] = 0.0,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            norm_layer_cl: Type[nn.Module] = LayerNorm2d,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        if downsample:
            self.downsample = Downsample2d(
                dim=dim,
                dim_out=dim * 2,
                norm_layer=norm_layer,
                **dd,
            )
            dim = dim * 2
            feat_size = (feat_size[0] // 2, feat_size[1] // 2)
        else:
            self.downsample = nn.Identity()
        self.feat_size = feat_size
        window_size = to_2tuple(window_size)

        feat_levels = int(math.log2(min(feat_size) / min(window_size)))
        self.global_block = FeatureBlock(dim, feat_levels, **dd)
        self.global_norm = norm_layer_cl(dim, **dd) if global_norm else nn.Identity()

        self.blocks = nn.ModuleList([
            GlobalContextVitBlock(
                dim=dim,
                num_heads=num_heads,
                feat_size=feat_size,
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                use_global=(i % 2 != 0),
                layer_scale=layer_scale,
                proj_drop=proj_drop,
                attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                act_layer=act_layer,
                norm_layer=norm_layer_cl,
                **dd,
            )
            for i in range(depth)
        ])
        self.norm = norm_layer_cl(dim, **dd) if stage_norm else nn.Identity()
        self.dim = dim
        self.feat_size = feat_size
        self.grad_checkpointing = False

    def forward(self, x):
        # input NCHW, downsample & global block are 2d conv + pooling
        x = self.downsample(x)
        global_query = self.global_block(x)

        # reshape NCHW --> NHWC for transformer blocks
        x = x.permute(0, 2, 3, 1)
        global_query = self.global_norm(global_query.permute(0, 2, 3, 1))
        for blk in self.blocks:
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint(blk, x, global_query)
            else:
                x = blk(x, global_query)
        x = self.norm(x)
        x = x.permute(0, 3, 1, 2).contiguous()  # back to NCHW
        return x


class GlobalContextVit(nn.Module):
    def __init__(
            self,
            in_chans: int = 3,
            num_classes: int = 1000,
            global_pool: str = 'avg',
            img_size: Union[int, Tuple[int, int]] = 224,
            window_ratio: Tuple[int, ...] = (32, 32, 16, 32),
            window_size: Optional[Union[int, Tuple[int, ...]]] = None,
            embed_dim: int = 64,
            depths: Tuple[int, ...] = (3, 4, 19, 5),
            num_heads: Tuple[int, ...] = (2, 4, 8, 16),
            mlp_ratio: float = 3.0,
            qkv_bias: bool = True,
            layer_scale: Optional[float] = None,
            drop_rate: float = 0.,
            proj_drop_rate: float = 0.,
            attn_drop_rate: float = 0.,
            drop_path_rate: float = 0.,
            weight_init: str = '',
            act_layer: str = 'gelu',
            norm_layer: str = 'layernorm2d',
            norm_layer_cl: str = 'layernorm',
            norm_eps: float = 1e-5,
            device=None,
            dtype=None,
    ):
        super().__init__()
        dd = {'device': device, 'dtype': dtype}
        act_layer = get_act_layer(act_layer)
        norm_layer = partial(get_norm_layer(norm_layer), eps=norm_eps)
        norm_layer_cl = partial(get_norm_layer(norm_layer_cl), eps=norm_eps)
        self.feature_info = []
        img_size = to_2tuple(img_size)
        feat_size = tuple(d // 4 for d in img_size)  # stem reduction by 4
        self.global_pool = global_pool
        self.num_classes = num_classes
        self.in_chans = in_chans
        self.drop_rate = drop_rate
        num_stages = len(depths)
        self.num_features = self.head_hidden_size = int(embed_dim * 2 ** (num_stages - 1))
        if window_size is not None:
            window_size = to_ntuple(num_stages)(window_size)
        else:
            assert window_ratio is not None
            window_size = tuple([(img_size[0] // r, img_size[1] // r) for r in to_ntuple(num_stages)(window_ratio)])

        self.stem = Stem(
            in_chs=in_chans,
            out_chs=embed_dim,
            act_layer=act_layer,
            norm_layer=norm_layer,
            **dd,
        )

        dpr = calculate_drop_path_rates(drop_path_rate, depths, stagewise=True)
        stages = []
        for i in range(num_stages):
            last_stage = i == num_stages - 1
            stage_scale = 2 ** max(i - 1, 0)
            stages.append(GlobalContextVitStage(
                dim=embed_dim * stage_scale,
                depth=depths[i],
                num_heads=num_heads[i],
                feat_size=(feat_size[0] // stage_scale, feat_size[1] // stage_scale),
                window_size=window_size[i],
                downsample=i != 0,
                stage_norm=last_stage,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                layer_scale=layer_scale,
                proj_drop=proj_drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[i],
                act_layer=act_layer,
                norm_layer=norm_layer,
                norm_layer_cl=norm_layer_cl,
                **dd,
            ))
            self.feature_info += [dict(num_chs=stages[-1].dim, reduction=2**(i+2), module=f'stages.{i}')]
        self.stages = nn.Sequential(*stages)

        # Classifier head
        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate, **dd)

        if weight_init:
            named_apply(partial(self._init_weights, scheme=weight_init), self)

    def _init_weights(self, module, name, scheme='vit'):
        # note Conv2d left as default init
        if scheme == 'vit':
            if isinstance(module, nn.Linear):
                nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    if 'mlp' in name:
                        nn.init.normal_(module.bias, std=1e-6)
                    else:
                        nn.init.zeros_(module.bias)
        else:
            if isinstance(module, nn.Linear):
                nn.init.normal_(module.weight, std=.02)
                if module.bias is not None:
                    nn.init.zeros_(module.bias)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {
            k for k, _ in self.named_parameters()
            if any(n in k for n in ["relative_position_bias_table", "rel_pos.mlp"])}

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        matcher = dict(
            stem=r'^stem',  # stem and embed
            blocks=r'^stages\.(\d+)'
        )
        return matcher

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for s in self.stages:
            s.grad_checkpointing = enable

    @torch.jit.ignore
    def get_classifier(self) -> nn.Module:
        return self.head.fc

    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None, device=None, dtype=None):
        dd = {'device': device, 'dtype': dtype}
        self.num_classes = num_classes
        if global_pool is None:
            global_pool = self.head.global_pool.pool_type
        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, **dd)

    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """ Forward features that returns intermediates.

        Args:
            x: Input image tensor
            indices: Take last n blocks if int, all if None, select matching indices if sequence
            norm: Apply norm layer to compatible intermediates
            stop_early: Stop iterating over blocks when last desired intermediate hit
            output_fmt: Shape of intermediate feature outputs
            intermediates_only: Only return intermediate features
        Returns:

        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        take_indices, max_index = feature_take_indices(len(self.stages), indices)

        # forward pass
        x = self.stem(x)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.stages
        else:
            stages = self.stages[:max_index + 1]

        for feat_idx, stage in enumerate(stages):
            x = stage(x)
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        take_indices, max_index = feature_take_indices(len(self.stages), indices)
        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices

    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
        x = self.stem(x)
        x = self.stages(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def _create_gcvit(variant, pretrained=False, **kwargs):
    model = build_model_with_cfg(
        GlobalContextVit, variant, pretrained,
        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
        **kwargs
    )
    return model


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.conv1', 'classifier': 'head.fc',
        'fixed_input_size': True,
        'license': 'apache-2.0',
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    'gcvit_xxtiny.in1k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xxtiny_224_nvidia-d1d86009.pth'),
    'gcvit_xtiny.in1k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xtiny_224_nvidia-274b92b7.pth'),
    'gcvit_tiny.in1k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_tiny_224_nvidia-ac783954.pth'),
    'gcvit_small.in1k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_small_224_nvidia-4e98afa2.pth'),
    'gcvit_base.in1k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_base_224_nvidia-f009139b.pth'),
})


@register_model
def gcvit_xxtiny(pretrained=False, **kwargs) -> GlobalContextVit:
    model_kwargs = dict(
        depths=(2, 2, 6, 2),
        num_heads=(2, 4, 8, 16),
        **kwargs)
    return _create_gcvit('gcvit_xxtiny', pretrained=pretrained, **model_kwargs)


@register_model
def gcvit_xtiny(pretrained=False, **kwargs) -> GlobalContextVit:
    model_kwargs = dict(
        depths=(3, 4, 6, 5),
        num_heads=(2, 4, 8, 16),
        **kwargs)
    return _create_gcvit('gcvit_xtiny', pretrained=pretrained, **model_kwargs)


@register_model
def gcvit_tiny(pretrained=False, **kwargs) -> GlobalContextVit:
    model_kwargs = dict(
        depths=(3, 4, 19, 5),
        num_heads=(2, 4, 8, 16),
        **kwargs)
    return _create_gcvit('gcvit_tiny', pretrained=pretrained, **model_kwargs)


@register_model
def gcvit_small(pretrained=False, **kwargs) -> GlobalContextVit:
    model_kwargs = dict(
        depths=(3, 4, 19, 5),
        num_heads=(3, 6, 12, 24),
        embed_dim=96,
        mlp_ratio=2,
        layer_scale=1e-5,
        **kwargs)
    return _create_gcvit('gcvit_small', pretrained=pretrained, **model_kwargs)


@register_model
def gcvit_base(pretrained=False, **kwargs) -> GlobalContextVit:
    model_kwargs = dict(
        depths=(3, 4, 19, 5),
        num_heads=(4, 8, 16, 32),
        embed_dim=128,
        mlp_ratio=2,
        layer_scale=1e-5,
        **kwargs)
    return _create_gcvit('gcvit_base', pretrained=pretrained, **model_kwargs)