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#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2024 Apple Inc. All Rights Reserved.
#
import copy
from functools import partial
from typing import List, Tuple, Optional, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_
from timm.models import register_model
from mobileclip.modules.common.mobileone import MobileOneBlock
from mobileclip.modules.image.replknet import ReparamLargeKernelConv
def _cfg(url="", **kwargs):
return {
"url": url,
"num_classes": 1000,
"input_size": (3, 256, 256),
"pool_size": None,
"crop_pct": 0.95,
"interpolation": "bicubic",
"mean": IMAGENET_DEFAULT_MEAN,
"std": IMAGENET_DEFAULT_STD,
"classifier": "head",
**kwargs,
}
default_cfgs = {
"fastvit_t": _cfg(crop_pct=0.9),
"fastvit_s": _cfg(crop_pct=0.9),
"fastvit_m": _cfg(crop_pct=0.95),
}
def convolutional_stem(
in_channels: int, out_channels: int, inference_mode: bool = False
) -> nn.Sequential:
"""Build convolutional stem with MobileOne blocks.
Args:
in_channels: Number of input channels.
out_channels: Number of output channels.
inference_mode: Flag to instantiate model in inference mode. Default: ``False``
Returns:
nn.Sequential object with stem elements.
"""
return nn.Sequential(
MobileOneBlock(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=2,
padding=1,
groups=1,
inference_mode=inference_mode,
use_se=False,
num_conv_branches=1,
),
MobileOneBlock(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=2,
padding=1,
groups=out_channels,
inference_mode=inference_mode,
use_se=False,
num_conv_branches=1,
),
MobileOneBlock(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
groups=1,
inference_mode=inference_mode,
use_se=False,
num_conv_branches=1,
),
)
class MHSA(nn.Module):
"""Multi-headed Self Attention module.
Source modified from:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
def __init__(
self,
dim: int,
head_dim: int = 32,
qkv_bias: bool = False,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
) -> None:
"""Build MHSA module that can handle 3D or 4D input tensors.
Args:
dim: Number of embedding dimensions.
head_dim: Number of hidden dimensions per head. Default: ``32``
qkv_bias: Use bias or not. Default: ``False``
attn_drop: Dropout rate for attention tensor.
proj_drop: Dropout rate for projection tensor.
"""
super().__init__()
assert dim % head_dim == 0, "dim should be divisible by head_dim"
self.head_dim = head_dim
self.num_heads = dim // head_dim
self.scale = head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
shape = x.shape
B, C, H, W = shape
N = H * W
if len(shape) == 4:
x = torch.flatten(x, start_dim=2).transpose(-2, -1) # (B, N, C)
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)
# trick here to make q@k.t more stable
attn = (q * self.scale) @ k.transpose(-2, -1)
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)
if len(shape) == 4:
x = x.transpose(-2, -1).reshape(B, C, H, W)
return x
class PatchEmbed(nn.Module):
"""Convolutional patch embedding layer."""
def __init__(
self,
patch_size: int,
stride: int,
in_channels: int,
embed_dim: int,
inference_mode: bool = False,
use_se: bool = False,
) -> None:
"""Build patch embedding layer.
Args:
patch_size: Patch size for embedding computation.
stride: Stride for convolutional embedding layer.
in_channels: Number of channels of input tensor.
embed_dim: Number of embedding dimensions.
inference_mode: Flag to instantiate model in inference mode. Default: ``False``
use_se: If ``True`` SE block will be used.
"""
super().__init__()
block = list()
block.append(
ReparamLargeKernelConv(
in_channels=in_channels,
out_channels=embed_dim,
kernel_size=patch_size,
stride=stride,
groups=in_channels,
small_kernel=3,
inference_mode=inference_mode,
use_se=use_se,
)
)
block.append(
MobileOneBlock(
in_channels=embed_dim,
out_channels=embed_dim,
kernel_size=1,
stride=1,
padding=0,
groups=1,
inference_mode=inference_mode,
use_se=False,
num_conv_branches=1,
)
)
self.proj = nn.Sequential(*block)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x)
return x
class RepMixer(nn.Module):
"""Reparameterizable token mixer.
For more details, please refer to our paper:
`FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
"""
def __init__(
self,
dim,
kernel_size=3,
use_layer_scale=True,
layer_scale_init_value=1e-5,
inference_mode: bool = False,
):
"""Build RepMixer Module.
Args:
dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
kernel_size: Kernel size for spatial mixing. Default: 3
use_layer_scale: If True, learnable layer scale is used. Default: ``True``
layer_scale_init_value: Initial value for layer scale. Default: 1e-5
inference_mode: If True, instantiates model in inference mode. Default: ``False``
"""
super().__init__()
self.dim = dim
self.kernel_size = kernel_size
self.inference_mode = inference_mode
if inference_mode:
self.reparam_conv = nn.Conv2d(
in_channels=self.dim,
out_channels=self.dim,
kernel_size=self.kernel_size,
stride=1,
padding=self.kernel_size // 2,
groups=self.dim,
bias=True,
)
else:
self.norm = MobileOneBlock(
dim,
dim,
kernel_size,
padding=kernel_size // 2,
groups=dim,
use_act=False,
use_scale_branch=False,
num_conv_branches=0,
)
self.mixer = MobileOneBlock(
dim,
dim,
kernel_size,
padding=kernel_size // 2,
groups=dim,
use_act=False,
)
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale = nn.Parameter(
layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if hasattr(self, "reparam_conv"):
x = self.reparam_conv(x)
return x
else:
if self.use_layer_scale:
x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
else:
x = x + self.mixer(x) - self.norm(x)
return x
def reparameterize(self) -> None:
"""Reparameterize mixer and norm into a single
convolutional layer for efficient inference.
"""
if self.inference_mode:
return
self.mixer.reparameterize()
self.norm.reparameterize()
if self.use_layer_scale:
w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
)
b = torch.squeeze(self.layer_scale) * (
self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
)
else:
w = (
self.mixer.id_tensor
+ self.mixer.reparam_conv.weight
- self.norm.reparam_conv.weight
)
b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
self.reparam_conv = nn.Conv2d(
in_channels=self.dim,
out_channels=self.dim,
kernel_size=self.kernel_size,
stride=1,
padding=self.kernel_size // 2,
groups=self.dim,
bias=True,
)
self.reparam_conv.weight.data = w
self.reparam_conv.bias.data = b
for para in self.parameters():
para.detach_()
self.__delattr__("mixer")
self.__delattr__("norm")
if self.use_layer_scale:
self.__delattr__("layer_scale")
class ConvFFN(nn.Module):
"""Convolutional FFN Module."""
def __init__(
self,
in_channels: int,
hidden_channels: Optional[int] = None,
out_channels: Optional[int] = None,
act_layer: nn.Module = nn.GELU,
drop: float = 0.0,
) -> None:
"""Build convolutional FFN module.
Args:
in_channels: Number of input channels.
hidden_channels: Number of channels after expansion. Default: None
out_channels: Number of output channels. Default: None
act_layer: Activation layer. Default: ``GELU``
drop: Dropout rate. Default: ``0.0``.
"""
super().__init__()
out_channels = out_channels or in_channels
hidden_channels = hidden_channels or in_channels
self.conv = nn.Sequential()
self.conv.add_module(
"conv",
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=7,
padding=3,
groups=in_channels,
bias=False,
),
)
self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m: nn.Module) -> None:
if isinstance(m, nn.Conv2d):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class RepCPE(nn.Module):
"""Implementation of conditional positional encoding.
For more details refer to paper:
`Conditional Positional Encodings for Vision Transformers <https://arxiv.org/pdf/2102.10882.pdf>`_
In our implementation, we can reparameterize this module to eliminate a skip connection.
"""
def __init__(
self,
in_channels: int,
embed_dim: int = 768,
spatial_shape: Union[int, Tuple[int, int]] = (7, 7),
inference_mode=False,
) -> None:
"""Build reparameterizable conditional positional encoding
Args:
in_channels: Number of input channels.
embed_dim: Number of embedding dimensions. Default: 768
spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
inference_mode: Flag to instantiate block in inference mode. Default: ``False``
"""
super(RepCPE, self).__init__()
if isinstance(spatial_shape, int):
spatial_shape = tuple([spatial_shape] * 2)
assert isinstance(spatial_shape, Tuple), (
f'"spatial_shape" must by a sequence or int, '
f"get {type(spatial_shape)} instead."
)
assert len(spatial_shape) == 2, (
f'Length of "spatial_shape" should be 2, '
f"got {len(spatial_shape)} instead."
)
self.spatial_shape = spatial_shape
self.embed_dim = embed_dim
self.in_channels = in_channels
self.groups = embed_dim
if inference_mode:
self.reparam_conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.embed_dim,
kernel_size=self.spatial_shape,
stride=1,
padding=int(self.spatial_shape[0] // 2),
groups=self.embed_dim,
bias=True,
)
else:
self.pe = nn.Conv2d(
in_channels,
embed_dim,
spatial_shape,
1,
int(spatial_shape[0] // 2),
bias=True,
groups=embed_dim,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if hasattr(self, "reparam_conv"):
x = self.reparam_conv(x)
return x
else:
x = self.pe(x) + x
return x
def reparameterize(self) -> None:
# Build equivalent Id tensor
input_dim = self.in_channels // self.groups
kernel_value = torch.zeros(
(
self.in_channels,
input_dim,
self.spatial_shape[0],
self.spatial_shape[1],
),
dtype=self.pe.weight.dtype,
device=self.pe.weight.device,
)
for i in range(self.in_channels):
kernel_value[
i,
i % input_dim,
self.spatial_shape[0] // 2,
self.spatial_shape[1] // 2,
] = 1
id_tensor = kernel_value
# Reparameterize Id tensor and conv
w_final = id_tensor + self.pe.weight
b_final = self.pe.bias
# Introduce reparam conv
self.reparam_conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.embed_dim,
kernel_size=self.spatial_shape,
stride=1,
padding=int(self.spatial_shape[0] // 2),
groups=self.embed_dim,
bias=True,
)
self.reparam_conv.weight.data = w_final
self.reparam_conv.bias.data = b_final
for para in self.parameters():
para.detach_()
self.__delattr__("pe")
class RepMixerBlock(nn.Module):
"""Implementation of Metaformer block with RepMixer as token mixer.
For more details on Metaformer structure, please refer to:
`MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
"""
def __init__(
self,
dim: int,
kernel_size: int = 3,
mlp_ratio: float = 4.0,
act_layer: nn.Module = nn.GELU,
drop: float = 0.0,
drop_path: float = 0.0,
use_layer_scale: bool = True,
layer_scale_init_value: float = 1e-5,
inference_mode: bool = False,
):
"""Build RepMixer Block.
Args:
dim: Number of embedding dimensions.
kernel_size: Kernel size for repmixer. Default: 3
mlp_ratio: MLP expansion ratio. Default: 4.0
act_layer: Activation layer. Default: ``nn.GELU``
drop: Dropout rate. Default: 0.0
drop_path: Drop path rate. Default: 0.0
use_layer_scale: Flag to turn on layer scale. Default: ``True``
layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
inference_mode: Flag to instantiate block in inference mode. Default: ``False``
"""
super().__init__()
self.token_mixer = RepMixer(
dim,
kernel_size=kernel_size,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
inference_mode=inference_mode,
)
assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
mlp_ratio
)
mlp_hidden_dim = int(dim * mlp_ratio)
self.convffn = ConvFFN(
in_channels=dim,
hidden_channels=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
# Drop Path
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
# Layer Scale
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale = nn.Parameter(
layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
)
def forward(self, x):
if self.use_layer_scale:
x = self.token_mixer(x)
x = x + self.drop_path(self.layer_scale * self.convffn(x))
else:
x = self.token_mixer(x)
x = x + self.drop_path(self.convffn(x))
return x
class AttentionBlock(nn.Module):
"""Implementation of metaformer block with MHSA as token mixer.
For more details on Metaformer structure, please refer to:
`MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
"""
def __init__(
self,
dim: int,
mlp_ratio: float = 4.0,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.BatchNorm2d,
drop: float = 0.0,
drop_path: float = 0.0,
use_layer_scale: bool = True,
layer_scale_init_value: float = 1e-5,
):
"""Build Attention Block.
Args:
dim: Number of embedding dimensions.
mlp_ratio: MLP expansion ratio. Default: 4.0
act_layer: Activation layer. Default: ``nn.GELU``
norm_layer: Normalization layer. Default: ``nn.BatchNorm2d``
drop: Dropout rate. Default: 0.0
drop_path: Drop path rate. Default: 0.0
use_layer_scale: Flag to turn on layer scale. Default: ``True``
layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
"""
super().__init__()
self.norm = norm_layer(dim)
self.token_mixer = MHSA(dim=dim)
assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
mlp_ratio
)
mlp_hidden_dim = int(dim * mlp_ratio)
self.convffn = ConvFFN(
in_channels=dim,
hidden_channels=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
# Drop path
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
# Layer Scale
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
)
def forward(self, x):
if self.use_layer_scale:
x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(self.norm(x)))
x = x + self.drop_path(self.layer_scale_2 * self.convffn(x))
else:
x = x + self.drop_path(self.token_mixer(self.norm(x)))
x = x + self.drop_path(self.convffn(x))
return x
def basic_blocks(
dim: int,
block_index: int,
num_blocks: List[int],
token_mixer_type: str,
kernel_size: int = 3,
mlp_ratio: float = 4.0,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.BatchNorm2d,
drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
use_layer_scale: bool = True,
layer_scale_init_value: float = 1e-5,
inference_mode=False,
) -> nn.Sequential:
"""Build FastViT blocks within a stage.
Args:
dim: Number of embedding dimensions.
block_index: block index.
num_blocks: List containing number of blocks per stage.
token_mixer_type: Token mixer type.
kernel_size: Kernel size for repmixer.
mlp_ratio: MLP expansion ratio.
act_layer: Activation layer.
norm_layer: Normalization layer.
drop_rate: Dropout rate.
drop_path_rate: Drop path rate.
use_layer_scale: Flag to turn on layer scale regularization.
layer_scale_init_value: Layer scale value at initialization.
inference_mode: Flag to instantiate block in inference mode.
Returns:
nn.Sequential object of all the blocks within the stage.
"""
blocks = []
for block_idx in range(num_blocks[block_index]):
block_dpr = (
drop_path_rate
* (block_idx + sum(num_blocks[:block_index]))
/ (sum(num_blocks) - 1)
)
if token_mixer_type == "repmixer":
blocks.append(
RepMixerBlock(
dim,
kernel_size=kernel_size,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
drop=drop_rate,
drop_path=block_dpr,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
inference_mode=inference_mode,
)
)
elif token_mixer_type == "attention":
blocks.append(
AttentionBlock(
dim,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer,
drop=drop_rate,
drop_path=block_dpr,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
)
)
else:
raise ValueError(
"Token mixer type: {} not supported".format(token_mixer_type)
)
blocks = nn.Sequential(*blocks)
return blocks
class FastViT(nn.Module):
"""
This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_
"""
def __init__(
self,
layers,
token_mixers: Tuple[str, ...],
embed_dims=None,
mlp_ratios=None,
downsamples=None,
se_downsamples=None,
repmixer_kernel_size=3,
norm_layer: nn.Module = nn.BatchNorm2d,
act_layer: nn.Module = nn.GELU,
num_classes=1000,
pos_embs=None,
down_patch_size=7,
down_stride=2,
drop_rate=0.0,
drop_path_rate=0.0,
use_layer_scale=True,
layer_scale_init_value=1e-5,
init_cfg=None,
pretrained=None,
cls_ratio=2.0,
inference_mode=False,
**kwargs,
) -> None:
super().__init__()
self.num_classes = num_classes
if pos_embs is None:
pos_embs = [None] * len(layers)
if se_downsamples is None:
se_downsamples = [False] * len(layers)
# Convolutional stem
self.patch_embed = convolutional_stem(3, embed_dims[0], inference_mode)
# Build the main stages of the network architecture
network = []
for i in range(len(layers)):
# Add position embeddings if requested
if pos_embs[i] is not None:
network.append(
pos_embs[i](
embed_dims[i], embed_dims[i], inference_mode=inference_mode
)
)
stage = basic_blocks(
embed_dims[i],
i,
layers,
token_mixer_type=token_mixers[i],
kernel_size=repmixer_kernel_size,
mlp_ratio=mlp_ratios[i],
act_layer=act_layer,
norm_layer=norm_layer,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
inference_mode=inference_mode,
)
network.append(stage)
if i >= len(layers) - 1:
break
# Patch merging/downsampling between stages.
if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
network.append(
PatchEmbed(
patch_size=down_patch_size,
stride=down_stride,
in_channels=embed_dims[i],
embed_dim=embed_dims[i + 1],
inference_mode=inference_mode,
use_se=se_downsamples[i + 1],
)
)
self.network = nn.ModuleList(network)
# Classifier head
self.conv_exp = MobileOneBlock(
in_channels=embed_dims[-1],
out_channels=int(embed_dims[-1] * cls_ratio),
kernel_size=3,
stride=1,
padding=1,
groups=embed_dims[-1],
inference_mode=inference_mode,
use_se=True,
num_conv_branches=1,
)
self.head = (
nn.Linear(int(embed_dims[-1] * cls_ratio), num_classes)
if num_classes > 0
else nn.Identity()
)
self.apply(self.cls_init_weights)
self.init_cfg = copy.deepcopy(init_cfg)
def cls_init_weights(self, m: nn.Module) -> None:
"""Init. for classification"""
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward_embeddings(self, x: torch.Tensor) -> torch.Tensor:
x = self.patch_embed(x)
return x
def forward_tokens(self, x: torch.Tensor) -> torch.Tensor:
for idx, block in enumerate(self.network):
x = block(x)
# output only the features of last layer for image classification
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
# input embedding
x = self.forward_embeddings(x)
# through backbone
x = self.forward_tokens(x)
# for image classification
x = self.conv_exp(x)
cls_out = self.head(x)
return cls_out
@register_model
def mci0(pretrained=False, **kwargs):
"""Instantiate MCi0 model variant."""
layers = [2, 6, 10, 2]
embed_dims = [64, 128, 256, 512]
mlp_ratios = [3, 3, 3, 3]
downsamples = [True, True, True, True]
se_downsamples = [False, False, True, True]
pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
model = FastViT(
layers,
token_mixers=token_mixers,
embed_dims=embed_dims,
pos_embs=pos_embs,
mlp_ratios=mlp_ratios,
downsamples=downsamples,
se_downsamples=se_downsamples,
**kwargs,
)
model.default_cfg = default_cfgs["fastvit_s"]
if pretrained:
raise ValueError("Functionality not implemented.")
return model
@register_model
def mci1(pretrained=False, **kwargs):
"""Instantiate MCi1 model variant."""
layers = [4, 12, 20, 4]
embed_dims = [64, 128, 256, 512]
mlp_ratios = [3, 3, 3, 3]
downsamples = [True, True, True, True]
se_downsamples = [False, False, True, True]
pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
model = FastViT(
layers,
token_mixers=token_mixers,
embed_dims=embed_dims,
pos_embs=pos_embs,
mlp_ratios=mlp_ratios,
downsamples=downsamples,
se_downsamples=se_downsamples,
**kwargs,
)
model.default_cfg = default_cfgs["fastvit_s"]
if pretrained:
raise ValueError("Functionality not implemented.")
return model
@register_model
def mci2(pretrained=False, **kwargs):
"""Instantiate MCi2 model variant."""
layers = [4, 12, 24, 4]
embed_dims = [80, 160, 320, 640]
mlp_ratios = [3, 3, 3, 3]
downsamples = [True, True, True, True]
se_downsamples = [False, False, True, True]
pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
model = FastViT(
layers,
token_mixers=token_mixers,
embed_dims=embed_dims,
pos_embs=pos_embs,
mlp_ratios=mlp_ratios,
downsamples=downsamples,
se_downsamples=se_downsamples,
**kwargs,
)
model.default_cfg = default_cfgs["fastvit_m"]
if pretrained:
raise ValueError("Functionality not implemented.")
return model
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