diff --git a/timm/models/__init__.py b/timm/models/__init__.py index a9fbbc26..8cf18e1b 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -28,6 +28,7 @@ from .inception_v3 import * from .inception_v4 import * from .levit import * from .maxxvit import * +from .metaformers import * from .mlp_mixer import * from .mobilenetv3 import * from .mobilevit import * diff --git a/timm/models/metaformers.py b/timm/models/metaformers.py new file mode 100644 index 00000000..8cc373eb --- /dev/null +++ b/timm/models/metaformers.py @@ -0,0 +1,1224 @@ +""" +Poolformer from MetaFormer is Actually What You Need for Vision https://arxiv.org/abs/2111.11418 + +MetaFormer baselines including IdentityFormer, RandFormer, PoolFormerV2, +ConvFormer, and CAFormer as per https://arxiv.org/abs/2210.13452 + +Adapted from https://github.com/sail-sg/metaformer, original copyright below +""" + +# Copyright 2022 Garena Online Private Limited +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections import OrderedDict +from functools import partial + +import torch +import torch.nn as nn +from torch import Tensor + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from timm.layers import trunc_normal_, DropPath, SelectAdaptivePool2d, GroupNorm1 +from timm.layers.helpers import to_2tuple +from ._builder import build_model_with_cfg +from ._features import FeatureInfo +from ._features_fx import register_notrace_function +from ._manipulate import checkpoint_seq +from ._pretrained import generate_default_cfgs +from ._registry import register_model + + + +__all__ = ['MetaFormer'] + + +class Stem(nn.Module): + """ + Stem implemented by a layer of convolution. + Conv2d params constant across all models. + """ + def __init__(self, + in_channels, + out_channels, + norm_layer=None, + ): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=4, + padding=2 + ) + self.norm = norm_layer(out_channels) if norm_layer else nn.Identity() + + def forward(self, x): + x = self.conv(x) + x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + # [B, C, H, W] + return x + +class Downsampling(nn.Module): + """ + Downsampling implemented by a layer of convolution. + """ + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + norm_layer=None, + ): + super().__init__() + self.norm = norm_layer(in_channels) if norm_layer else nn.Identity() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + + def forward(self, x): + x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + x = self.conv(x) + return x + + +class Scale(nn.Module): + """ + Scale vector by element multiplications. + """ + def __init__(self, dim, init_value=1.0, trainable=True): + super().__init__() + self.scale = nn.Parameter(init_value * torch.ones(dim), requires_grad=trainable) + + def forward(self, x): + return x * self.scale + + +class SquaredReLU(nn.Module): + """ + Squared ReLU: https://arxiv.org/abs/2109.08668 + """ + def __init__(self, inplace=False): + super().__init__() + self.relu = nn.ReLU(inplace=inplace) + def forward(self, x): + return torch.square(self.relu(x)) + + +class StarReLU(nn.Module): + """ + StarReLU: s * relu(x) ** 2 + b + """ + def __init__(self, scale_value=1.0, bias_value=0.0, + scale_learnable=True, bias_learnable=True, + mode=None, inplace=False): + super().__init__() + self.inplace = inplace + self.relu = nn.ReLU(inplace=inplace) + self.scale = nn.Parameter(scale_value * torch.ones(1), + requires_grad=scale_learnable) + self.bias = nn.Parameter(bias_value * torch.ones(1), + requires_grad=bias_learnable) + def forward(self, x): + return self.scale * self.relu(x)**2 + self.bias + +class Conv2dChannelsLast(nn.Conv2d): + def forward(self, x): + x = x.permute(0, 3, 1, 2) + return self._conv_forward(x, self.weight, self.bias).permute(0, 2, 3, 1) + + +class Attention(nn.Module): + """ + Vanilla self-attention from Transformer: https://arxiv.org/abs/1706.03762. + Modified from timm. + """ + def __init__(self, dim, head_dim=32, num_heads=None, qkv_bias=False, + attn_drop=0., proj_drop=0., proj_bias=False, **kwargs): + super().__init__() + + self.head_dim = head_dim + self.scale = head_dim ** -0.5 + + self.num_heads = num_heads if num_heads else dim // head_dim + if self.num_heads == 0: + self.num_heads = 1 + + self.attention_dim = self.num_heads * self.head_dim + + self.qkv = nn.Linear(dim, self.attention_dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(self.attention_dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + + def forward(self, x): + B, H, W, C = x.shape + N = H * W + 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) # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, H, W, self.attention_dim) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class RandomMixing(nn.Module): + def __init__(self, num_tokens=196, **kwargs): + super().__init__() + # FIXME no grad breaks tests + self.random_matrix = nn.parameter.Parameter( + data=torch.softmax(torch.rand(num_tokens, num_tokens), dim=-1), + requires_grad=False) + def forward(self, x): + B, H, W, C = x.shape + x = x.reshape(B, H*W, C) + # FIXME change to work with arbitrary input sizes + x = torch.einsum('mn, bnc -> bmc', self.random_matrix, x) + x = x.reshape(B, H, W, C) + return x + + +class LayerNormGeneral(nn.Module): + r""" General LayerNorm for different situations. + + Args: + affine_shape (int, list or tuple): The shape of affine weight and bias. + Usually the affine_shape=C, but in some implementation, like torch.nn.LayerNorm, + the affine_shape is the same as normalized_dim by default. + To adapt to different situations, we offer this argument here. + normalized_dim (tuple or list): Which dims to compute mean and variance. + scale (bool): Flag indicates whether to use scale or not. + bias (bool): Flag indicates whether to use scale or not. + + We give several examples to show how to specify the arguments. + + LayerNorm (https://arxiv.org/abs/1607.06450): + For input shape of (B, *, C) like (B, N, C) or (B, H, W, C), + affine_shape=C, normalized_dim=(-1, ), scale=True, bias=True; + For input shape of (B, C, H, W), + affine_shape=(C, 1, 1), normalized_dim=(1, ), scale=True, bias=True. + + Modified LayerNorm (https://arxiv.org/abs/2111.11418) + that is idental to partial(torch.nn.GroupNorm, num_groups=1): + For input shape of (B, N, C), + affine_shape=C, normalized_dim=(1, 2), scale=True, bias=True; + For input shape of (B, H, W, C), + affine_shape=C, normalized_dim=(1, 2, 3), scale=True, bias=True; + For input shape of (B, C, H, W), + affine_shape=(C, 1, 1), normalized_dim=(1, 2, 3), scale=True, bias=True. + + For the several metaformer baslines, + IdentityFormer, RandFormer and PoolFormerV2 utilize Modified LayerNorm without bias (bias=False); + ConvFormer and CAFormer utilizes LayerNorm without bias (bias=False). + """ + def __init__(self, affine_shape=None, normalized_dim=(-1, ), scale=True, + bias=True, eps=1e-5): + super().__init__() + self.normalized_dim = normalized_dim + self.use_scale = scale + self.use_bias = bias + self.weight = nn.Parameter(torch.ones(affine_shape)) if scale else 1 + self.bias = nn.Parameter(torch.zeros(affine_shape)) if bias else 0 + self.eps = eps + + def forward(self, x): + c = x - x.mean(self.normalized_dim, keepdim=True) + s = c.pow(2).mean(self.normalized_dim, keepdim=True) + x = c / torch.sqrt(s + self.eps) + x = x * self.weight + x = x + self.bias + return x + +class SepConv(nn.Module): + r""" + Inverted separable convolution from MobileNetV2: https://arxiv.org/abs/1801.04381. + """ + def __init__(self, dim, expansion_ratio=2, + act1_layer=StarReLU, act2_layer=nn.Identity, + bias=False, kernel_size=7, padding=3, + **kwargs, ): + super().__init__() + med_channels = int(expansion_ratio * dim) + self.pwconv1 = nn.Linear(dim, med_channels, bias=bias) + self.act1 = act1_layer() + self.dwconv = nn.Conv2d( + med_channels, med_channels, kernel_size=kernel_size, + padding=padding, groups=med_channels, bias=bias) # depthwise conv + self.act2 = act2_layer() + self.pwconv2 = nn.Linear(med_channels, dim, bias=bias) + + def forward(self, x): + x = self.pwconv1(x) + x = self.act1(x) + x = x.permute(0, 3, 1, 2) + x = self.dwconv(x) + x = x.permute(0, 2, 3, 1) + x = self.act2(x) + x = self.pwconv2(x) + return x + + +class Pooling(nn.Module): + """ + Implementation of pooling for PoolFormer: https://arxiv.org/abs/2111.11418 + Modfiled for [B, H, W, C] input + """ + def __init__(self, pool_size=3, **kwargs): + super().__init__() + self.pool = nn.AvgPool2d( + pool_size, stride=1, padding=pool_size//2, count_include_pad=False) + + def forward(self, x): + y = x.permute(0, 3, 1, 2) + y = self.pool(y) + y = y.permute(0, 2, 3, 1) + return y - x + + +class Mlp(nn.Module): + """ MLP as used in MetaFormer models, eg Transformer, MLP-Mixer, PoolFormer, MetaFormer baslines and related networks. + Modified from standard timm implementation + """ + def __init__( + self, + dim, + mlp_ratio=4, + out_features=None, + act_layer=StarReLU, + mlp_fn=nn.Linear, + drop=0., + bias=False + ): + super().__init__() + in_features = dim + out_features = out_features or in_features + hidden_features = int(mlp_ratio * in_features) + drop_probs = to_2tuple(drop) + + self.fc1 = mlp_fn(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.fc2 = mlp_fn(hidden_features, out_features, bias=bias) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class MlpHead(nn.Module): + """ MLP classification head + """ + def __init__(self, dim, num_classes=1000, mlp_ratio=4, act_layer=SquaredReLU, + norm_layer=nn.LayerNorm, head_dropout=0., bias=True): + super().__init__() + hidden_features = int(mlp_ratio * dim) + self.fc1 = nn.Linear(dim, hidden_features, bias=bias) + self.act = act_layer() + self.norm = norm_layer(hidden_features) + self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias) + self.head_dropout = nn.Dropout(head_dropout) + + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.norm(x) + x = self.head_dropout(x) + x = self.fc2(x) + return x + + +class MetaFormerBlock(nn.Module): + """ + Implementation of one MetaFormer block. + """ + def __init__( + self, + dim, + token_mixer=nn.Identity, + mlp=Mlp, + mlp_fn=nn.Linear, + mlp_act=StarReLU, + mlp_bias=False, + norm_layer=nn.LayerNorm, + drop=0., drop_path=0., + layer_scale_init_value=None, + res_scale_init_value=None + ): + + super().__init__() + + self.norm1 = norm_layer(dim) + self.token_mixer = token_mixer(dim=dim, drop=drop) + self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.layer_scale1 = Scale(dim=dim, init_value=layer_scale_init_value) \ + if layer_scale_init_value else nn.Identity() + self.res_scale1 = Scale(dim=dim, init_value=res_scale_init_value) \ + if res_scale_init_value else nn.Identity() + + self.norm2 = norm_layer(dim) + self.mlp = mlp( + dim=dim, + drop=drop, + mlp_fn=mlp_fn, + act_layer=mlp_act, + bias=mlp_bias + ) + self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.layer_scale2 = Scale(dim=dim, init_value=layer_scale_init_value) \ + if layer_scale_init_value else nn.Identity() + self.res_scale2 = Scale(dim=dim, init_value=res_scale_init_value) \ + if res_scale_init_value else nn.Identity() + + def forward(self, x): + x = self.res_scale1(x) + \ + self.layer_scale1( + self.drop_path1( + self.token_mixer(self.norm1(x)) + ) + ) + x = self.res_scale2(x) + \ + self.layer_scale2( + self.drop_path2( + self.mlp(self.norm2(x)) + ) + ) + return x + +class MetaFormerStage(nn.Module): + # implementation of a single metaformer stage + def __init__( + self, + in_chs, + out_chs, + depth=2, + downsample_norm=partial(LayerNormGeneral, bias=False, eps=1e-6), + token_mixer=nn.Identity, + mlp=Mlp, + mlp_fn=nn.Linear, + mlp_act=StarReLU, + mlp_bias=False, + norm_layer=partial(LayerNormGeneral, eps=1e-6, bias=False), + dp_rates=[0.]*2, + layer_scale_init_value=None, + res_scale_init_value=None, + ): + super().__init__() + + self.grad_checkpointing = False + + # don't downsample if in_chs and out_chs are the same + self.downsample = nn.Identity() if in_chs == out_chs else Downsampling( + in_chs, + out_chs, + kernel_size=3, + stride=2, + padding=1, + norm_layer=downsample_norm + ) + + self.blocks = nn.Sequential(*[MetaFormerBlock( + dim=out_chs, + token_mixer=token_mixer, + mlp=mlp, + mlp_fn=mlp_fn, + mlp_act=mlp_act, + mlp_bias=mlp_bias, + norm_layer=norm_layer, + drop_path=dp_rates[i], + layer_scale_init_value=layer_scale_init_value, + res_scale_init_value=res_scale_init_value + ) for i in range(depth)]) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + # Permute to channels-first for feature extraction + def forward(self, x: Tensor): + + # [B, C, H, W] -> [B, H, W, C] + x = self.downsample(x).permute(0, 2, 3, 1) + + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint_seq(self.blocks, x) + else: + x = self.blocks(x) + + # [B, H, W, C] -> [B, C, H, W] + x = x.permute(0, 3, 1, 2) + return x + +class MetaFormer(nn.Module): + r""" MetaFormer + A PyTorch impl of : `MetaFormer Baselines for Vision` - + https://arxiv.org/abs/2210.13452 + + Args: + in_chans (int): Number of input image channels. Default: 3. + num_classes (int): Number of classes for classification head. Default: 1000. + depths (list or tuple): Number of blocks at each stage. Default: [2, 2, 6, 2]. + dims (int): Feature dimension at each stage. Default: [64, 128, 320, 512]. + token_mixers (list, tuple or token_fcn): Token mixer for each stage. Default: nn.Identity. + mlps (list, tuple or mlp_fcn): Mlp for each stage. Default: Mlp. + norm_layers (list, tuple or norm_fcn): Norm layers for each stage. Default: partial(LayerNormGeneral, eps=1e-6, bias=False). + drop_path_rate (float): Stochastic depth rate. Default: 0. + head_dropout (float): dropout for MLP classifier. Default: 0. + layer_scale_init_values (list, tuple, float or None): Init value for Layer Scale. Default: None. + None means not use the layer scale. Form: https://arxiv.org/abs/2103.17239. + res_scale_init_values (list, tuple, float or None): Init value for Layer Scale. Default: [None, None, 1.0, 1.0]. + None means not use the layer scale. From: https://arxiv.org/abs/2110.09456. + output_norm: norm before classifier head. Default: partial(nn.LayerNorm, eps=1e-6). + head_fn: classification head. Default: nn.Linear. + """ + def __init__( + self, + in_chans=3, + num_classes=1000, + depths=[2, 2, 6, 2], + dims=[64, 128, 320, 512], + downsample_norm=partial(LayerNormGeneral, bias=False, eps=1e-6), + token_mixers=nn.Identity, + mlps=Mlp, + mlp_fn=nn.Linear, + mlp_act=StarReLU, + mlp_bias=False, + norm_layers=partial(LayerNormGeneral, eps=1e-6, bias=False), + drop_path_rate=0., + drop_rate=0.0, + layer_scale_init_values=None, + res_scale_init_values=[None, None, 1.0, 1.0], + output_norm=partial(nn.LayerNorm, eps=1e-6), + head_norm_first=False, + head_fn=nn.Linear, + global_pool = 'avg', + **kwargs, + ): + super().__init__() + self.num_classes = num_classes + self.head_fn = head_fn + self.num_features = dims[-1] + self.drop_rate = drop_rate + self.num_stages = len(depths) + + # convert everything to lists if they aren't indexable + if not isinstance(depths, (list, tuple)): + depths = [depths] # it means the model has only one stage + if not isinstance(dims, (list, tuple)): + dims = [dims] + if not isinstance(token_mixers, (list, tuple)): + token_mixers = [token_mixers] * self.num_stages + if not isinstance(mlps, (list, tuple)): + mlps = [mlps] * self.num_stages + if not isinstance(norm_layers, (list, tuple)): + norm_layers = [norm_layers] * self.num_stages + if not isinstance(layer_scale_init_values, (list, tuple)): + layer_scale_init_values = [layer_scale_init_values] * self.num_stages + if not isinstance(res_scale_init_values, (list, tuple)): + res_scale_init_values = [res_scale_init_values] * self.num_stages + + self.grad_checkpointing = False + self.feature_info = [] + + dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)] + + self.stem = Stem( + in_chans, + dims[0], + norm_layer=downsample_norm + ) + + stages = nn.ModuleList() # each stage consists of multiple metaformer blocks + cur = 0 + last_dim = dims[0] + for i in range(self.num_stages): + stage = MetaFormerStage( + last_dim, + dims[i], + depth=depths[i], + downsample_norm=downsample_norm, + token_mixer=token_mixers[i], + mlp=mlps[i], + mlp_fn=mlp_fn, + mlp_act=mlp_act, + mlp_bias=mlp_bias, + norm_layer=norm_layers[i], + dp_rates=dp_rates[i], + layer_scale_init_value=layer_scale_init_values[i], + res_scale_init_value=res_scale_init_values[i], + ) + + stages.append(stage) + cur += depths[i] + last_dim = dims[i] + self.feature_info += [dict(num_chs=dims[i], reduction=2, module=f'stages.{i}')] + + self.stages = nn.Sequential(*stages) + + # if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets + # otherwise pool -> norm -> fc, similar to ConvNeXt + # drop removed - if using single fc layer, models have no dropout + # if using MlpHead, dropout is handled by MlpHead + if num_classes > 0: + if self.drop_rate > 0.0: + head = self.head_fn(dims[-1], num_classes, head_dropout=self.drop_rate) + else: + head = self.head_fn(dims[-1], num_classes) + else: + head = nn.Identity() + + self.norm_pre = output_norm(self.num_features) if head_norm_first else nn.Identity() + self.head = nn.Sequential(OrderedDict([ + ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)), + ('norm', nn.Identity() if head_norm_first else output_norm(self.num_features)), + ('flatten', nn.Flatten(1) if global_pool else nn.Identity()), + ('fc', head)])) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, (nn.Conv2d, nn.Linear)): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + for stage in self.stages: + stage.set_grad_checkpointing(enable=enable) + + @torch.jit.ignore + def get_classifier(self): + return self.head.fc + + def reset_classifier(self, num_classes=0, global_pool=None): + if global_pool is not None: + self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity() + if num_classes > 0: + if self.drop_rate > 0.0: + head = self.head_fn(dims[-1], num_classes, head_dropout=self.drop_rate) + else: + head = self.head_fn(dims[-1], num_classes) + else: + head = nn.Identity() + self.head.fc = head + + def forward_head(self, x: Tensor, pre_logits: bool = False): + # NOTE nn.Sequential in head broken down since can't call head[:-1](x) in torchscript :( + x = self.head.global_pool(x) + x = self.head.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + x = self.head.flatten(x) + return x if pre_logits else self.head.fc(x) + + def forward_features(self, x: Tensor): + x = self.stem(x) + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint_seq(self.stages, x) + else: + x = self.stages(x) + x = self.norm_pre(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + + def forward(self, x: Tensor): + x = self.forward_features(x) + x = self.forward_head(x) + return x + +# FIXME convert to group matcher +# this works but it's long and breaks backwards compatability with weights from the poolformer-only impl +def checkpoint_filter_fn(state_dict, model): + import re + out_dict = {} + for k, v in state_dict.items(): + + k = re.sub(r'layer_scale_([0-9]+)', r'layer_scale\1.scale', k) + k = k.replace('network.1', 'downsample_layers.1') + k = k.replace('network.3', 'downsample_layers.2') + k = k.replace('network.5', 'downsample_layers.3') + k = k.replace('network.2', 'network.1') + k = k.replace('network.4', 'network.2') + k = k.replace('network.6', 'network.3') + k = k.replace('network', 'stages') + + k = re.sub(r'downsample_layers.([0-9]+)', r'stages.\1.downsample', k) + k = k.replace('downsample.proj', 'downsample.conv') + k = k.replace('patch_embed.proj', 'patch_embed.conv') + k = re.sub(r'([0-9]+).([0-9]+)', r'\1.blocks.\2', k) + k = k.replace('stages.0.downsample', 'patch_embed') + k = k.replace('patch_embed', 'stem') + k = k.replace('post_norm', 'norm') + k = k.replace('pre_norm', 'norm') + k = re.sub(r'^head', 'head.fc', k) + k = re.sub(r'^norm', 'head.norm', k) + out_dict[k] = v + return out_dict + + +def _create_metaformer(variant, pretrained=False, **kwargs): + default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (2, 2, 6, 2)))) + out_indices = kwargs.pop('out_indices', default_out_indices) + + model = build_model_with_cfg( + MetaFormer, + variant, + pretrained, + pretrained_filter_fn=checkpoint_filter_fn, + feature_cfg=dict(flatten_sequential=True, out_indices = out_indices), + **kwargs) + + return model + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 1.0, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'classifier': 'head.fc', 'first_conv': 'stem.conv', + **kwargs + } + +default_cfgs = generate_default_cfgs({ + 'poolformerv1_s12.sail_in1k': _cfg( + url='https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s12.pth.tar', + crop_pct=0.9), + 'poolformerv1_s24.sail_in1k': _cfg( + url='https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s24.pth.tar', + crop_pct=0.9), + 'poolformerv1_s36.sail_in1k': _cfg( + url='https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s36.pth.tar', + crop_pct=0.9), + 'poolformerv1_m36.sail_in1k': _cfg( + url='https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m36.pth.tar', + crop_pct=0.95), + 'poolformerv1_m48.sail_in1k': _cfg( + url='https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m48.pth.tar', + crop_pct=0.95), + + 'identityformer_s12.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s12.pth'), + 'identityformer_s24.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s24.pth'), + 'identityformer_s36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s36.pth'), + 'identityformer_m36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_m36.pth'), + 'identityformer_m48.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_m48.pth'), + + + 'randformer_s12.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s12.pth'), + 'randformer_s24.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s24.pth'), + 'randformer_s36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s36.pth'), + 'randformer_m36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_m36.pth'), + 'randformer_m48.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_m48.pth'), + + 'poolformerv2_s12.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s12.pth'), + 'poolformerv2_s24.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s24.pth'), + 'poolformerv2_s36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s36.pth'), + 'poolformerv2_m36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_m36.pth'), + 'poolformerv2_m48.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_m48.pth'), + + + + 'convformer_s18.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18.pth', + classifier='head.fc.fc2'), + 'convformer_s18.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_s18.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_in21ft1k.pth', + classifier='head.fc.fc2'), + 'convformer_s18.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_s18.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'convformer_s36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36.pth', + classifier='head.fc.fc2'), + 'convformer_s36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_s36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'convformer_s36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_s36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'convformer_m36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36.pth', + classifier='head.fc.fc2'), + 'convformer_m36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_m36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'convformer_m36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_m36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'convformer_b36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36.pth', + classifier='head.fc.fc2'), + 'convformer_b36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_b36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'convformer_b36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'convformer_b36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + + 'caformer_s18.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18.pth', + classifier='head.fc.fc2'), + 'caformer_s18.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_s18.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_in21ft1k.pth', + classifier='head.fc.fc2'), + 'caformer_s18.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_s18.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'caformer_s36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36.pth', + classifier='head.fc.fc2'), + 'caformer_s36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_s36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'caformer_s36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_s36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'caformer_m36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36.pth', + classifier='head.fc.fc2'), + 'caformer_m36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_m36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'caformer_m36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_m36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), + + 'caformer_b36.sail_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36.pth', + classifier='head.fc.fc2'), + 'caformer_b36.sail_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_384.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_b36.sail_in22k_ft_in1k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_in21ft1k.pth', + classifier='head.fc.fc2'), + 'caformer_b36.sail_in22k_ft_in1k_384': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_384_in21ft1k.pth', + classifier='head.fc.fc2', input_size=(3, 384, 384), pool_size=(12,12)), + 'caformer_b36.sail_in22k': _cfg( + url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_in21k.pth', + classifier='head.fc.fc2', num_classes=21841), +}) + +# FIXME fully merge poolformerv1, rename to poolformer to succeed poolformer.py + +@register_model +def poolformerv1_s12(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[2, 2, 6, 2], + dims=[64, 128, 320, 512], + downsample_norm=None, + token_mixers=Pooling, + mlp_fn=partial(Conv2dChannelsLast, kernel_size=1), + mlp_act=nn.GELU, + mlp_bias=True, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=True), + layer_scale_init_values=1e-5, + res_scale_init_values=None, + **kwargs) + return _create_metaformer('poolformerv1_s12', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv1_s24(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[4, 4, 12, 4], + dims=[64, 128, 320, 512], + downsample_norm=None, + token_mixers=Pooling, + mlp_fn=partial(Conv2dChannelsLast, kernel_size=1), + mlp_act=nn.GELU, + mlp_bias=True, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=True), + layer_scale_init_values=1e-5, + res_scale_init_values=None, + **kwargs) + return _create_metaformer('poolformerv1_s24', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv1_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[64, 128, 320, 512], + downsample_norm=None, + token_mixers=Pooling, + mlp_fn=partial(Conv2dChannelsLast, kernel_size=1), + mlp_act=nn.GELU, + mlp_bias=True, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=True), + layer_scale_init_values=1e-6, + res_scale_init_values=None, + **kwargs) + return _create_metaformer('poolformerv1_s36', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv1_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[96, 192, 384, 768], + downsample_norm=None, + token_mixers=Pooling, + mlp_fn=partial(Conv2dChannelsLast, kernel_size=1), + mlp_act=nn.GELU, + mlp_bias=True, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=True), + layer_scale_init_values=1e-6, + res_scale_init_values=None, + **kwargs) + return _create_metaformer('poolformerv1_m36', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv1_m48(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[8, 8, 24, 8], + dims=[96, 192, 384, 768], + downsample_norm=None, + token_mixers=Pooling, + mlp_fn=partial(Conv2dChannelsLast, kernel_size=1), + mlp_act=nn.GELU, + mlp_bias=True, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=True), + layer_scale_init_values=1e-6, + res_scale_init_values=None, + **kwargs) + return _create_metaformer('poolformerv1_m48', pretrained=pretrained, **model_kwargs) + +@register_model +def identityformer_s12(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[2, 2, 6, 2], + dims=[64, 128, 320, 512], + token_mixers=nn.Identity, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('identityformer_s12', pretrained=pretrained, **model_kwargs) + +@register_model +def identityformer_s24(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[4, 4, 12, 4], + dims=[64, 128, 320, 512], + token_mixers=nn.Identity, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('identityformer_s24', pretrained=pretrained, **model_kwargs) + +@register_model +def identityformer_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[64, 128, 320, 512], + token_mixers=nn.Identity, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('identityformer_s36', pretrained=pretrained, **model_kwargs) + +@register_model +def identityformer_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[96, 192, 384, 768], + token_mixers=nn.Identity, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('identityformer_m36', pretrained=pretrained, **model_kwargs) + +@register_model +def identityformer_m48(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[8, 8, 24, 8], + dims=[96, 192, 384, 768], + token_mixers=nn.Identity, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('identityformer_m48', pretrained=pretrained, **model_kwargs) + +@register_model +def randformer_s12(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[2, 2, 6, 2], + dims=[64, 128, 320, 512], + token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)], + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('randformer_s12', pretrained=pretrained, **model_kwargs) + +@register_model +def randformer_s24(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[4, 4, 12, 4], + dims=[64, 128, 320, 512], + token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)], + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('randformer_s24', pretrained=pretrained, **model_kwargs) + +@register_model +def randformer_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[64, 128, 320, 512], + token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)], + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('randformer_s36', pretrained=pretrained, **model_kwargs) + +@register_model +def randformer_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[96, 192, 384, 768], + token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)], + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('randformer_m36', pretrained=pretrained, **model_kwargs) + +@register_model +def randformer_m48(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[8, 8, 24, 8], + dims=[96, 192, 384, 768], + token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)], + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('randformer_m48', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv2_s12(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[2, 2, 6, 2], + dims=[64, 128, 320, 512], + token_mixers=Pooling, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('poolformerv2_s12', pretrained=pretrained, **model_kwargs) + +@register_model +def poolformerv2_s24(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[4, 4, 12, 4], + dims=[64, 128, 320, 512], + token_mixers=Pooling, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('poolformerv2_s24', pretrained=pretrained, **model_kwargs) + + + +@register_model +def poolformerv2_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[64, 128, 320, 512], + token_mixers=Pooling, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('poolformerv2_s36', pretrained=pretrained, **model_kwargs) + + + +@register_model +def poolformerv2_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[6, 6, 18, 6], + dims=[96, 192, 384, 768], + token_mixers=Pooling, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('poolformerv2_m36', pretrained=pretrained, **model_kwargs) + + +@register_model +def poolformerv2_m48(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[8, 8, 24, 8], + dims=[96, 192, 384, 768], + token_mixers=Pooling, + norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False), + **kwargs) + return _create_metaformer('poolformerv2_m48', pretrained=pretrained, **model_kwargs) + + + +@register_model +def convformer_s18(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 3, 9, 3], + dims=[64, 128, 320, 512], + token_mixers=SepConv, + head_fn=MlpHead, + **kwargs) + return _create_metaformer('convformer_s18', pretrained=pretrained, **model_kwargs) + + + + +@register_model +def convformer_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[64, 128, 320, 512], + token_mixers=SepConv, + head_fn=MlpHead, + **kwargs) + return _create_metaformer('convformer_s36', pretrained=pretrained, **model_kwargs) + + +@register_model +def convformer_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[96, 192, 384, 576], + token_mixers=SepConv, + head_fn=MlpHead, + **kwargs) + return _create_metaformer('convformer_m36', pretrained=pretrained, **model_kwargs) + + + +@register_model +def convformer_b36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[128, 256, 512, 768], + token_mixers=SepConv, + head_fn=MlpHead, + **kwargs) + return _create_metaformer('convformer_b36', pretrained=pretrained, **model_kwargs) + + + + +@register_model +def caformer_s18(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 3, 9, 3], + dims=[64, 128, 320, 512], + token_mixers=[SepConv, SepConv, Attention, Attention], + head_fn=MlpHead, + **kwargs) + return _create_metaformer('caformer_s18', pretrained=pretrained, **model_kwargs) + + + +@register_model +def caformer_s36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[64, 128, 320, 512], + token_mixers=[SepConv, SepConv, Attention, Attention], + head_fn=MlpHead, + **kwargs) + return _create_metaformer('caformer_s36', pretrained=pretrained, **model_kwargs) + + +@register_model +def caformer_m36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[96, 192, 384, 576], + token_mixers=[SepConv, SepConv, Attention, Attention], + head_fn=MlpHead, + **kwargs) + return _create_metaformer('caformer_m36', pretrained=pretrained, **model_kwargs) + + +@register_model +def caformer_b36(pretrained=False, **kwargs): + model_kwargs = dict( + depths=[3, 12, 18, 3], + dims=[128, 256, 512, 768], + token_mixers=[SepConv, SepConv, Attention, Attention], + head_fn=MlpHead, + **kwargs) + return _create_metaformer('caformer_b36', pretrained=pretrained, **model_kwargs)