# FastViT for PyTorch
#
# Original implementation and weights from https://github.com/apple/ml-fastvit
#
# For licensing see accompanying LICENSE file at https://github.com/apple/ml-fastvit/tree/main
# Original work is copyright (C) 2023 Apple Inc. All Rights Reserved.
#
import os
from functools import partial
from typing import Tuple, Optional, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, trunc_normal_, create_conv2d, ConvNormAct, SqueezeExcite, use_fused_attn, \
ClassifierHead
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
def num_groups(group_size, channels):
if not group_size: # 0 or None
return 1 # normal conv with 1 group
else:
# NOTE group_size == 1 -> depthwise conv
assert channels % group_size == 0
return channels // group_size
class MobileOneBlock(nn.Module):
"""MobileOne building block.
This block has a multi-branched architecture at train-time
and plain-CNN style architecture at inference time
For more details, please refer to our paper:
`An Improved One millisecond Mobile Backbone` -
https://arxiv.org/pdf/2206.04040.pdf
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
group_size: int = 0,
inference_mode: bool = False,
use_se: bool = False,
use_act: bool = True,
use_scale_branch: bool = True,
num_conv_branches: int = 1,
act_layer: nn.Module = nn.GELU,
) -> None:
"""Construct a MobileOneBlock module.
Args:
in_chs: Number of channels in the input.
out_chs: Number of channels produced by the block.
kernel_size: Size of the convolution kernel.
stride: Stride size.
dilation: Kernel dilation factor.
group_size: Convolution group size.
inference_mode: If True, instantiates model in inference mode.
use_se: Whether to use SE-ReLU activations.
use_act: Whether to use activation. Default: ``True``
use_scale_branch: Whether to use scale branch. Default: ``True``
num_conv_branches: Number of linear conv branches.
"""
super(MobileOneBlock, self).__init__()
self.inference_mode = inference_mode
self.groups = num_groups(group_size, in_chs)
self.stride = stride
self.dilation = dilation
self.kernel_size = kernel_size
self.in_chs = in_chs
self.out_chs = out_chs
self.num_conv_branches = num_conv_branches
# Check if SE-ReLU is requested
self.se = SqueezeExcite(out_chs, rd_divisor=1) if use_se else nn.Identity()
if inference_mode:
self.reparam_conv = create_conv2d(
in_chs,
out_chs,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=self.groups,
bias=True,
)
else:
# Re-parameterizable skip connection
self.reparam_conv = None
self.identity = (
nn.BatchNorm2d(num_features=in_chs)
if out_chs == in_chs and stride == 1
else None
)
# Re-parameterizable conv branches
if num_conv_branches > 0:
self.conv_kxk = nn.ModuleList([
ConvNormAct(
self.in_chs,
self.out_chs,
kernel_size=kernel_size,
stride=self.stride,
groups=self.groups,
apply_act=False,
) for _ in range(self.num_conv_branches)
])
else:
self.conv_kxk = None
# Re-parameterizable scale branch
self.conv_scale = None
if kernel_size > 1 and use_scale_branch:
self.conv_scale = ConvNormAct(
self.in_chs,
self.out_chs,
kernel_size=1,
stride=self.stride,
groups=self.groups,
apply_act=False
)
self.act = act_layer() if use_act else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply forward pass."""
# Inference mode forward pass.
if self.reparam_conv is not None:
return self.act(self.se(self.reparam_conv(x)))
# Multi-branched train-time forward pass.
# Identity branch output
identity_out = 0
if self.identity is not None:
identity_out = self.identity(x)
# Scale branch output
scale_out = 0
if self.conv_scale is not None:
scale_out = self.conv_scale(x)
# Other kxk conv branches
out = scale_out + identity_out
if self.conv_kxk is not None:
for rc in self.conv_kxk:
out += rc(x)
return self.act(self.se(out))
def reparameterize(self):
"""Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
architecture used at training time to obtain a plain CNN-like structure
for inference.
"""
if self.reparam_conv is not None:
return
kernel, bias = self._get_kernel_bias()
self.reparam_conv = create_conv2d(
in_channels=self.in_chs,
out_channels=self.out_chs,
kernel_size=self.kernel_size,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
bias=True,
)
self.reparam_conv.weight.data = kernel
self.reparam_conv.bias.data = bias
# Delete un-used branches
for name, para in self.named_parameters():
if 'reparam_conv' in name:
continue
para.detach_()
self.__delattr__("conv_kxk")
self.__delattr__("conv_scale")
if hasattr(self, "identity"):
self.__delattr__("identity")
self.inference_mode = True
def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
"""Method to obtain re-parameterized kernel and bias.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
Returns:
Tuple of (kernel, bias) after fusing branches.
"""
# get weights and bias of scale branch
kernel_scale = 0
bias_scale = 0
if self.conv_scale is not None:
kernel_scale, bias_scale = self._fuse_bn_tensor(self.conv_scale)
# Pad scale branch kernel to match conv branch kernel size.
pad = self.kernel_size // 2
kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
# get weights and bias of skip branch
kernel_identity = 0
bias_identity = 0
if self.identity is not None:
kernel_identity, bias_identity = self._fuse_bn_tensor(self.identity)
# get weights and bias of conv branches
kernel_conv = 0
bias_conv = 0
if self.conv_kxk is not None:
for ix in range(self.num_conv_branches):
_kernel, _bias = self._fuse_bn_tensor(self.conv_kxk[ix])
kernel_conv += _kernel
bias_conv += _bias
kernel_final = kernel_conv + kernel_scale + kernel_identity
bias_final = bias_conv + bias_scale + bias_identity
return kernel_final, bias_final
def _fuse_bn_tensor(
self, branch: Union[nn.Sequential, nn.BatchNorm2d]
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Method to fuse batchnorm layer with preceeding conv layer.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
Args:
branch: Sequence of ops to be fused.
Returns:
Tuple of (kernel, bias) after fusing batchnorm.
"""
if isinstance(branch, ConvNormAct):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, "id_tensor"):
input_dim = self.in_chs // self.groups
kernel_value = torch.zeros(
(self.in_chs, input_dim, self.kernel_size, self.kernel_size),
dtype=branch.weight.dtype,
device=branch.weight.device,
)
for i in range(self.in_chs):
kernel_value[
i, i % input_dim, self.kernel_size // 2, self.kernel_size // 2
] = 1
self.id_tensor = kernel_value
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
class ReparamLargeKernelConv(nn.Module):
"""Building Block of RepLKNet
This class defines overparameterized large kernel conv block
introduced in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int,
stride: int,
group_size: int,
small_kernel: Optional[int] = None,
inference_mode: bool = False,
act_layer: Optional[nn.Module] = None,
) -> None:
"""Construct a ReparamLargeKernelConv module.
Args:
in_chs: Number of input channels.
out_chs: Number of output channels.
kernel_size: Kernel size of the large kernel conv branch.
stride: Stride size. Default: 1
group_size: Group size. Default: 1
small_kernel: Kernel size of small kernel conv branch.
inference_mode: If True, instantiates model in inference mode. Default: ``False``
act_layer: Activation module. Default: ``nn.GELU``
"""
super(ReparamLargeKernelConv, self).__init__()
self.stride = stride
self.groups = num_groups(group_size, in_chs)
self.in_chs = in_chs
self.out_chs = out_chs
self.kernel_size = kernel_size
self.small_kernel = small_kernel
if inference_mode:
self.reparam_conv = create_conv2d(
in_chs,
out_chs,
kernel_size=kernel_size,
stride=stride,
dilation=1,
groups=self.groups,
bias=True,
)
else:
self.reparam_conv = None
self.large_conv = ConvNormAct(
in_chs,
out_chs,
kernel_size=kernel_size,
stride=self.stride,
groups=self.groups,
apply_act=False,
)
if small_kernel is not None:
assert (
small_kernel <= kernel_size
), "The kernel size for re-param cannot be larger than the large kernel!"
self.small_conv = ConvNormAct(
in_chs,
out_chs,
kernel_size=small_kernel,
stride=self.stride,
groups=self.groups,
apply_act=False,
)
# FIXME output of this act was not used in original impl, likely due to bug
self.act = act_layer() if act_layer is not None else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.reparam_conv is not None:
out = self.reparam_conv(x)
else:
out = self.large_conv(x)
if self.small_conv is not None:
out = out + self.small_conv(x)
out = self.act(out)
return out
def get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
"""Method to obtain re-parameterized kernel and bias.
Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
Returns:
Tuple of (kernel, bias) after fusing branches.
"""
eq_k, eq_b = self._fuse_bn(self.large_conv.conv, self.large_conv.bn)
if hasattr(self, "small_conv"):
small_k, small_b = self._fuse_bn(self.small_conv.conv, self.small_conv.bn)
eq_b += small_b
eq_k += nn.functional.pad(
small_k, [(self.kernel_size - self.small_kernel) // 2] * 4
)
return eq_k, eq_b
def reparameterize(self) -> None:
"""
Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
architecture used at training time to obtain a plain CNN-like structure
for inference.
"""
eq_k, eq_b = self.get_kernel_bias()
self.reparam_conv = create_conv2d(
self.in_chs,
self.out_chs,
kernel_size=self.kernel_size,
stride=self.stride,
groups=self.groups,
bias=True,
)
self.reparam_conv.weight.data = eq_k
self.reparam_conv.bias.data = eq_b
self.__delattr__("large_conv")
if hasattr(self, "small_conv"):
self.__delattr__("small_conv")
@staticmethod
def _fuse_bn(
conv: torch.Tensor, bn: nn.BatchNorm2d
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Method to fuse batchnorm layer with conv layer.
Args:
conv: Convolutional kernel weights.
bn: Batchnorm 2d layer.
Returns:
Tuple of (kernel, bias) after fusing batchnorm.
"""
kernel = conv.weight
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
def convolutional_stem(
in_chs: int,
out_chs: int,
act_layer: nn.Module = nn.GELU,
inference_mode: bool = False
) -> nn.Sequential:
"""Build convolutional stem with MobileOne blocks.
Args:
in_chs: Number of input channels.
out_chs: 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_chs=in_chs,
out_chs=out_chs,
kernel_size=3,
stride=2,
act_layer=act_layer,
inference_mode=inference_mode,
),
MobileOneBlock(
in_chs=out_chs,
out_chs=out_chs,
kernel_size=3,
stride=2,
group_size=1,
act_layer=act_layer,
inference_mode=inference_mode,
),
MobileOneBlock(
in_chs=out_chs,
out_chs=out_chs,
kernel_size=1,
stride=1,
act_layer=act_layer,
inference_mode=inference_mode,
),
)
class Attention(nn.Module):
"""Multi-headed Self Attention module.
Source modified from:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
fused_attn: torch.jit.Final[bool]
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.fused_attn = use_fused_attn()
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:
B, C, H, W = x.shape
N = H * W
x = x.flatten(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)
if self.fused_attn:
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
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_chs: int,
embed_dim: int,
act_layer: nn.Module = nn.GELU,
lkc_use_act: bool = False,
inference_mode: bool = False,
) -> None:
"""Build patch embedding layer.
Args:
patch_size: Patch size for embedding computation.
stride: Stride for convolutional embedding layer.
in_chs: Number of channels of input tensor.
embed_dim: Number of embedding dimensions.
inference_mode: Flag to instantiate model in inference mode. Default: ``False``
"""
super().__init__()
self.proj = nn.Sequential(
ReparamLargeKernelConv(
in_chs=in_chs,
out_chs=embed_dim,
kernel_size=patch_size,
stride=stride,
group_size=1,
small_kernel=3,
inference_mode=inference_mode,
act_layer=act_layer if lkc_use_act else None, # NOTE original weights didn't use this act
),
MobileOneBlock(
in_chs=embed_dim,
out_chs=embed_dim,
kernel_size=1,
stride=1,
act_layer=act_layer,
inference_mode=inference_mode,
)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x)
return x
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim, 1, 1))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
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,
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
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(
self.dim,
self.dim,
kernel_size=self.kernel_size,
stride=1,
padding=self.kernel_size // 2,
groups=self.dim,
bias=True,
)
else:
self.reparam_conv = None
self.norm = MobileOneBlock(
dim,
dim,
kernel_size,
group_size=1,
use_act=False,
use_scale_branch=False,
num_conv_branches=0,
)
self.mixer = MobileOneBlock(
dim,
dim,
kernel_size,
group_size=1,
use_act=False,
)
if layer_scale_init_value is not None:
self.layer_scale = LayerScale2d(dim, layer_scale_init_value)
else:
self.layer_scale = nn.Identity
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.reparam_conv is not None:
x = self.reparam_conv(x)
else:
x = x + self.layer_scale(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 isinstance(self.layer_scale, LayerScale2d):
w = self.mixer.id_tensor + self.layer_scale.gamma.unsqueeze(-1) * (
self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
)
b = torch.squeeze(self.layer_scale.gamma) * (
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 = create_conv2d(
self.dim,
self.dim,
kernel_size=self.kernel_size,
stride=1,
groups=self.dim,
bias=True,
)
self.reparam_conv.weight.data = w
self.reparam_conv.bias.data = b
for name, para in self.named_parameters():
if 'reparam_conv' in name:
continue
para.detach_()
self.__delattr__("mixer")
self.__delattr__("norm")
self.__delattr__("layer_scale")
class ConvMlp(nn.Module):
"""Convolutional FFN Module."""
def __init__(
self,
in_chs: int,
hidden_channels: Optional[int] = None,
out_chs: Optional[int] = None,
act_layer: nn.Module = nn.GELU,
drop: float = 0.0,
) -> None:
"""Build convolutional FFN module.
Args:
in_chs: Number of input channels.
hidden_channels: Number of channels after expansion. Default: None
out_chs: Number of output channels. Default: None
act_layer: Activation layer. Default: ``GELU``
drop: Dropout rate. Default: ``0.0``.
"""
super().__init__()
out_chs = out_chs or in_chs
hidden_channels = hidden_channels or in_chs
self.conv = ConvNormAct(
in_chs,
out_chs,
kernel_size=7,
groups=in_chs,
apply_act=False,
)
self.fc1 = nn.Conv2d(in_chs, hidden_channels, kernel_size=1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_channels, out_chs, 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 RepConditionalPosEnc(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,
dim: int,
dim_out: Optional[int] = None,
spatial_shape: Union[int, Tuple[int, int]] = (7, 7),
inference_mode=False,
) -> None:
"""Build reparameterizable conditional positional encoding
Args:
dim: Number of input channels.
dim_out: 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(RepConditionalPosEnc, 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.dim = dim
self.dim_out = dim_out or dim
self.groups = dim
if inference_mode:
self.reparam_conv = nn.Conv2d(
self.dim,
self.dim_out,
kernel_size=self.spatial_shape,
stride=1,
padding=spatial_shape[0] // 2,
groups=self.groups,
bias=True,
)
else:
self.reparam_conv = None
self.pos_enc = nn.Conv2d(
self.dim,
self.dim_out,
spatial_shape,
1,
int(spatial_shape[0] // 2),
groups=self.groups,
bias=True,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.reparam_conv is not None:
x = self.reparam_conv(x)
else:
x = self.pos_enc(x) + x
return x
def reparameterize(self) -> None:
# Build equivalent Id tensor
input_dim = self.dim // self.groups
kernel_value = torch.zeros(
(
self.dim,
input_dim,
self.spatial_shape[0],
self.spatial_shape[1],
),
dtype=self.pos_enc.weight.dtype,
device=self.pos_enc.weight.device,
)
for i in range(self.dim):
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.pos_enc.weight
b_final = self.pos_enc.bias
# Introduce reparam conv
self.reparam_conv = nn.Conv2d(
self.dim,
self.dim_out,
kernel_size=self.spatial_shape,
stride=1,
padding=int(self.spatial_shape[0] // 2),
groups=self.groups,
bias=True,
)
self.reparam_conv.weight.data = w_final
self.reparam_conv.bias.data = b_final
for name, para in self.named_parameters():
if 'reparam_conv' in name:
continue
para.detach_()
self.__delattr__("pos_enc")
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,
proj_drop: float = 0.0,
drop_path: float = 0.0,
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``
proj_drop: Dropout rate. Default: 0.0
drop_path: Drop path rate. Default: 0.0
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,
layer_scale_init_value=layer_scale_init_value,
inference_mode=inference_mode,
)
self.mlp = ConvMlp(
in_chs=dim,
hidden_channels=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
if layer_scale_init_value is not None:
self.layer_scale = LayerScale2d(dim, layer_scale_init_value)
else:
self.layer_scale = nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x):
x = self.token_mixer(x)
x = x + self.drop_path(self.layer_scale(self.mlp(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,
proj_drop: float = 0.0,
drop_path: float = 0.0,
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``
proj_drop: Dropout rate. Default: 0.0
drop_path: Drop path rate. Default: 0.0
layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
"""
super().__init__()
self.norm = norm_layer(dim)
self.token_mixer = Attention(dim=dim)
if layer_scale_init_value is not None:
self.layer_scale_1 = LayerScale2d(dim, layer_scale_init_value)
else:
self.layer_scale_1 = nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.mlp = ConvMlp(
in_chs=dim,
hidden_channels=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
if layer_scale_init_value is not None:
self.layer_scale_2 = LayerScale2d(dim, layer_scale_init_value)
else:
self.layer_scale_2 = nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.layer_scale_1(self.token_mixer(self.norm(x))))
x = x + self.drop_path2(self.layer_scale_2(self.mlp(x)))
return x
class FastVitStage(nn.Module):
def __init__(
self,
dim: int,
dim_out: int,
depth: int,
token_mixer_type: str,
downsample: bool = True,
down_patch_size: int = 7,
down_stride: int = 2,
pos_emb_layer: Optional[nn.Module] = None,
kernel_size: int = 3,
mlp_ratio: float = 4.0,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.BatchNorm2d,
proj_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
layer_scale_init_value: Optional[float] = 1e-5,
lkc_use_act=False,
inference_mode=False,
):
"""FastViT stage.
Args:
dim: Number of embedding dimensions.
depth: Number of blocks in 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.
proj_drop_rate: Dropout rate.
drop_path_rate: Drop path rate.
layer_scale_init_value: Layer scale value at initialization.
inference_mode: Flag to instantiate block in inference mode.
"""
super().__init__()
self.grad_checkpointing = False
if downsample:
self.downsample = PatchEmbed(
patch_size=down_patch_size,
stride=down_stride,
in_chs=dim,
embed_dim=dim_out,
act_layer=act_layer,
lkc_use_act=lkc_use_act,
inference_mode=inference_mode,
)
else:
assert dim == dim_out
self.downsample = nn.Identity()
if pos_emb_layer is not None:
self.pos_emb = pos_emb_layer(dim_out, inference_mode=inference_mode)
else:
self.pos_emb = nn.Identity()
blocks = []
for block_idx in range(depth):
if token_mixer_type == "repmixer":
blocks.append(RepMixerBlock(
dim_out,
kernel_size=kernel_size,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
proj_drop=proj_drop_rate,
drop_path=drop_path_rate[block_idx],
layer_scale_init_value=layer_scale_init_value,
inference_mode=inference_mode,
))
elif token_mixer_type == "attention":
blocks.append(AttentionBlock(
dim_out,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer,
proj_drop=proj_drop_rate,
drop_path=drop_path_rate[block_idx],
layer_scale_init_value=layer_scale_init_value,
))
else:
raise ValueError(
"Token mixer type: {} not supported".format(token_mixer_type)
)
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.pos_emb(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class FastVit(nn.Module):
fork_feat: torch.jit.Final[bool]
"""
This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_
"""
def __init__(
self,
in_chans: int = 3,
layers: Tuple[int, ...] = (2, 2, 6, 2),
token_mixers: Tuple[str, ...] = ("repmixer", "repmixer", "repmixer", "repmixer"),
embed_dims: Tuple[int, ...] = (64, 128, 256, 512),
mlp_ratios: Tuple[float, ...] = (4,) * 4,
downsamples: Tuple[bool, ...] = (False, True, True, True),
repmixer_kernel_size: int = 3,
num_classes: int = 1000,
pos_embs: Tuple[Optional[nn.Module], ...] = (None,) * 4,
down_patch_size: int = 7,
down_stride: int = 2,
drop_rate: float = 0.0,
proj_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
layer_scale_init_value: float = 1e-5,
fork_feat: bool = False,
cls_ratio: float = 2.0,
global_pool: str = 'avg',
norm_layer: nn.Module = nn.BatchNorm2d,
act_layer: nn.Module = nn.GELU,
lkc_use_act: bool = False,
inference_mode: bool = False,
) -> None:
super().__init__()
self.num_classes = 0 if fork_feat else num_classes
self.fork_feat = fork_feat
self.global_pool = global_pool
self.feature_info = []
# Convolutional stem
self.stem = convolutional_stem(
in_chans,
embed_dims[0],
act_layer,
inference_mode,
)
# Build the main stages of the network architecture
prev_dim = embed_dims[0]
scale = 1
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(layers)).split(layers)]
stages = []
for i in range(len(layers)):
downsample = downsamples[i] or prev_dim != embed_dims[i]
stage = FastVitStage(
dim=prev_dim,
dim_out=embed_dims[i],
depth=layers[i],
downsample=downsample,
down_patch_size=down_patch_size,
down_stride=down_stride,
pos_emb_layer=pos_embs[i],
token_mixer_type=token_mixers[i],
kernel_size=repmixer_kernel_size,
mlp_ratio=mlp_ratios[i],
act_layer=act_layer,
norm_layer=norm_layer,
proj_drop_rate=proj_drop_rate,
drop_path_rate=dpr[i],
layer_scale_init_value=layer_scale_init_value,
lkc_use_act=lkc_use_act,
inference_mode=inference_mode,
)
stages.append(stage)
prev_dim = embed_dims[i]
if downsample:
scale *= 2
self.feature_info += [dict(num_chs=prev_dim, reduction=4 * scale, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.num_features = prev_dim
# For segmentation and detection, extract intermediate output
if self.fork_feat:
# add a norm layer for each output
self.out_indices = [0, 2, 4, 6]
for i_emb, i_layer in enumerate(self.out_indices):
if i_emb == 0 and os.environ.get("FORK_LAST3", None):
"""For RetinaNet, `start_level=1`. The first norm layer will not used.
cmd: `FORK_LAST3=1 python -m torch.distributed.launch ...`
"""
layer = nn.Identity()
else:
layer = norm_layer(embed_dims[i_emb])
layer_name = f"norm{i_layer}"
self.add_module(layer_name, layer)
else:
# Classifier head
self.num_features = final_features = int(embed_dims[-1] * cls_ratio)
self.final_conv = MobileOneBlock(
in_chs=embed_dims[-1],
out_chs=final_features,
kernel_size=3,
stride=1,
group_size=1,
inference_mode=inference_mode,
use_se=True,
act_layer=act_layer,
num_conv_branches=1,
)
self.head = ClassifierHead(
final_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
self.apply(self._init_weights)
def _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)
@torch.jit.ignore
def no_weight_decay(self):
return set()
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem', # stem and embed
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+).pos_emb', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
@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):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
# input embedding
x = self.stem(x)
outs = []
for idx, block in enumerate(self.stages):
x = block(x)
if self.fork_feat:
if idx in self.out_indices:
norm_layer = getattr(self, f"norm{idx}")
x_out = norm_layer(x)
outs.append(x_out)
if self.fork_feat:
# output the features of four stages for dense prediction
return outs
x = self.final_conv(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False):
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
if self.fork_feat:
return x
x = self.forward_head(x)
return x
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": IMAGENET_DEFAULT_MEAN,
"std": IMAGENET_DEFAULT_STD,
'first_conv': ('stem.0.conv_kxk.0.conv', 'stem.0.conv_scale.conv'),
"classifier": "head.fc",
**kwargs,
}
default_cfgs = generate_default_cfgs({
"fastvit_t8.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_t12.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_s12.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_sa12.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_sa24.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_sa36.apple_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_ma36.apple_in1k": _cfg(
hf_hub_id='timm/',
crop_pct=0.95
),
"fastvit_t8.apple_dist_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_t12.apple_dist_in1k": _cfg(
hf_hub_id='timm/'),
"fastvit_s12.apple_dist_in1k": _cfg(
hf_hub_id='timm/',),
"fastvit_sa12.apple_dist_in1k": _cfg(
hf_hub_id='timm/',),
"fastvit_sa24.apple_dist_in1k": _cfg(
hf_hub_id='timm/',),
"fastvit_sa36.apple_dist_in1k": _cfg(
hf_hub_id='timm/',),
"fastvit_ma36.apple_dist_in1k": _cfg(
hf_hub_id='timm/',
crop_pct=0.95
),
})
def _create_fastvit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
FastVit,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs
)
return model
@register_model
def fastvit_t8(pretrained=False, **kwargs):
"""Instantiate FastViT-T8 model variant."""
model_args = dict(
layers=(2, 2, 4, 2),
embed_dims=(48, 96, 192, 384),
mlp_ratios=(3, 3, 3, 3),
token_mixers=("repmixer", "repmixer", "repmixer", "repmixer")
)
return _create_fastvit('fastvit_t8', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_t12(pretrained=False, **kwargs):
"""Instantiate FastViT-T12 model variant."""
model_args = dict(
layers=(2, 2, 6, 2),
embed_dims=(64, 128, 256, 512),
mlp_ratios=(3, 3, 3, 3),
token_mixers=("repmixer", "repmixer", "repmixer", "repmixer"),
)
return _create_fastvit('fastvit_t12', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_s12(pretrained=False, **kwargs):
"""Instantiate FastViT-S12 model variant."""
model_args = dict(
layers=(2, 2, 6, 2),
embed_dims=(64, 128, 256, 512),
mlp_ratios=(4, 4, 4, 4),
token_mixers=("repmixer", "repmixer", "repmixer", "repmixer"),
)
return _create_fastvit('fastvit_s12', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_sa12(pretrained=False, **kwargs):
"""Instantiate FastViT-SA12 model variant."""
model_args = dict(
layers=(2, 2, 6, 2),
embed_dims=(64, 128, 256, 512),
mlp_ratios=(4, 4, 4, 4),
pos_embs=(None, None, None, partial(RepConditionalPosEnc, spatial_shape=(7, 7))),
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
)
return _create_fastvit('fastvit_sa12', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_sa24(pretrained=False, **kwargs):
"""Instantiate FastViT-SA24 model variant."""
model_args = dict(
layers=(4, 4, 12, 4),
embed_dims=(64, 128, 256, 512),
mlp_ratios=(4, 4, 4, 4),
pos_embs=(None, None, None, partial(RepConditionalPosEnc, spatial_shape=(7, 7))),
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
)
return _create_fastvit('fastvit_sa24', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_sa36(pretrained=False, **kwargs):
"""Instantiate FastViT-SA36 model variant."""
model_args = dict(
layers=(6, 6, 18, 6),
embed_dims=(64, 128, 256, 512),
mlp_ratios=(4, 4, 4, 4),
pos_embs=(None, None, None, partial(RepConditionalPosEnc, spatial_shape=(7, 7))),
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
)
return _create_fastvit('fastvit_sa36', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def fastvit_ma36(pretrained=False, **kwargs):
"""Instantiate FastViT-MA36 model variant."""
model_args = dict(
layers=(6, 6, 18, 6),
embed_dims=(76, 152, 304, 608),
mlp_ratios=(4, 4, 4, 4),
pos_embs=(None, None, None, partial(RepConditionalPosEnc, spatial_shape=(7, 7))),
token_mixers=("repmixer", "repmixer", "repmixer", "attention")
)
return _create_fastvit('fastvit_ma36', pretrained=pretrained, **dict(model_args, **kwargs))