481 lines
14 KiB
Python
481 lines
14 KiB
Python
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"""
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MambaOut models for image classification.
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Some implementations are modified from:
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timm (https://github.com/rwightman/pytorch-image-models),
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MetaFormer (https://github.com/sail-sg/metaformer),
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InceptionNeXt (https://github.com/sail-sg/inceptionnext)
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"""
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from functools import partial
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.models.layers import trunc_normal_, DropPath, LayerNorm
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from .vision_transformer import LayerScale
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from ._manipulate import checkpoint_seq
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from timm.models.registry import register_model
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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class Stem(nn.Module):
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r""" Code modified from InternImage:
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https://github.com/OpenGVLab/InternImage
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"""
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def __init__(
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self,
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in_chs=3,
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out_chs=96,
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mid_norm: bool = True,
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act_layer=nn.GELU,
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norm_layer=LayerNorm,
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):
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super().__init__()
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self.conv1 = nn.Conv2d(
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in_chs,
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out_chs // 2,
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kernel_size=3,
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stride=2,
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padding=1
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)
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self.norm1 = norm_layer(out_chs // 2) if mid_norm else None
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self.act = act_layer()
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self.conv2 = nn.Conv2d(
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out_chs // 2,
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out_chs,
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kernel_size=3,
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stride=2,
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padding=1
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)
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self.norm2 = norm_layer(out_chs)
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def forward(self, x):
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x = self.conv1(x)
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if self.norm1 is not None:
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x = x.permute(0, 2, 3, 1)
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x = self.norm1(x)
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x = x.permute(0, 3, 1, 2)
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x = self.act(x)
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x = self.conv2(x)
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x = x.permute(0, 2, 3, 1)
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x = self.norm2(x)
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return x
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class DownsampleNormFirst(nn.Module):
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def __init__(
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self,
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in_chs=96,
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out_chs=198,
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norm_layer=LayerNorm,
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):
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super().__init__()
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self.norm = norm_layer(in_chs)
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self.conv = nn.Conv2d(
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in_chs,
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out_chs,
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kernel_size=3,
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stride=2,
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padding=1
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)
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def forward(self, x):
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x = self.norm(x)
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x = x.permute(0, 3, 1, 2)
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x = self.conv(x)
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x = x.permute(0, 2, 3, 1)
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return x
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class Downsample(nn.Module):
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def __init__(
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self,
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in_chs=96,
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out_chs=198,
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norm_layer=LayerNorm,
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):
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super().__init__()
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self.conv = nn.Conv2d(
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in_chs,
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out_chs,
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kernel_size=3,
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stride=2,
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padding=1
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)
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self.norm = norm_layer(out_chs)
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def forward(self, x):
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x = x.permute(0, 3, 1, 2)
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x = self.conv(x)
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x = x.permute(0, 2, 3, 1)
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x = self.norm(x)
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return x
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class MlpHead(nn.Module):
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""" MLP classification head
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"""
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def __init__(
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self,
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dim,
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num_classes=1000,
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act_layer=nn.GELU,
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mlp_ratio=4,
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norm_layer=LayerNorm,
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drop_rate=0.,
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bias=True,
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):
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super().__init__()
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hidden_features = int(mlp_ratio * dim)
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self.fc1 = nn.Linear(dim, hidden_features, bias=bias)
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self.act = act_layer()
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self.norm = norm_layer(hidden_features)
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self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
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self.head_dropout = nn.Dropout(drop_rate)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.norm(x)
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x = self.head_dropout(x)
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x = self.fc2(x)
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return x
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class GatedConvBlock(nn.Module):
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r""" Our implementation of Gated CNN Block: https://arxiv.org/pdf/1612.08083
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Args:
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conv_ratio: control the number of channels to conduct depthwise convolution.
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Conduct convolution on partial channels can improve paraitcal efficiency.
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The idea of partial channels is from ShuffleNet V2 (https://arxiv.org/abs/1807.11164) and
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also used by InceptionNeXt (https://arxiv.org/abs/2303.16900) and FasterNet (https://arxiv.org/abs/2303.03667)
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"""
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def __init__(
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self,
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dim,
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expansion_ratio=8 / 3,
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kernel_size=7,
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conv_ratio=1.0,
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ls_init_value=None,
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norm_layer=LayerNorm,
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act_layer=nn.GELU,
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drop_path=0.,
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**kwargs
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):
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super().__init__()
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self.norm = norm_layer(dim)
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hidden = int(expansion_ratio * dim)
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self.fc1 = nn.Linear(dim, hidden * 2)
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self.act = act_layer()
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conv_channels = int(conv_ratio * dim)
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self.split_indices = (hidden, hidden - conv_channels, conv_channels)
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self.conv = nn.Conv2d(
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conv_channels,
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conv_channels,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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groups=conv_channels
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)
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self.fc2 = nn.Linear(hidden, dim)
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self.ls = LayerScale(dim) if ls_init_value is not None else nn.Identity()
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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shortcut = x # [B, H, W, C]
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x = self.norm(x)
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x = self.fc1(x)
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g, i, c = torch.split(x, self.split_indices, dim=-1)
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c = c.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W]
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c = self.conv(c)
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c = c.permute(0, 2, 3, 1) # [B, C, H, W] -> [B, H, W, C]
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x = self.fc2(self.act(g) * torch.cat((i, c), dim=-1))
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x = self.ls(x)
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x = self.drop_path(x)
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return x + shortcut
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class MambaOutStage(nn.Module):
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def __init__(
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self,
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dim,
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dim_out: Optional[int] = None,
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depth: int = 4,
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expansion_ratio=8 / 3,
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kernel_size=7,
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conv_ratio=1.0,
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downsample: bool = False,
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ls_init_value: Optional[float] = None,
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norm_layer=LayerNorm,
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act_layer=nn.GELU,
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drop_path=0.,
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):
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super().__init__()
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dim_out = dim_out or dim
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self.grad_checkpointing = False
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if downsample:
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self.downsample = Downsample(dim, dim_out, norm_layer=norm_layer)
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else:
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assert dim == dim_out
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self.downsample = nn.Identity()
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self.blocks = nn.Sequential(*[
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GatedConvBlock(
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dim=dim_out,
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expansion_ratio=expansion_ratio,
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kernel_size=kernel_size,
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conv_ratio=conv_ratio,
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ls_init_value=ls_init_value,
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norm_layer=norm_layer,
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act_layer=act_layer,
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drop_path=drop_path[j] if isinstance(drop_path, (list, tuple)) else drop_path,
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)
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for j in range(depth)
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])
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def forward(self, x):
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x = self.downsample(x)
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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint_seq(self.blocks, x)
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else:
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x = self.blocks(x)
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return x
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class MambaOut(nn.Module):
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r""" MetaFormer
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A PyTorch impl of : `MetaFormer Baselines for Vision` -
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https://arxiv.org/abs/2210.13452
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Args:
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in_chans (int): Number of input image channels. Default: 3.
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num_classes (int): Number of classes for classification head. Default: 1000.
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depths (list or tuple): Number of blocks at each stage. Default: [3, 3, 9, 3].
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 576].
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downsample_layers: (list or tuple): Downsampling layers before each stage.
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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output_norm: norm before classifier head. Default: partial(nn.LayerNorm, eps=1e-6).
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head_fn: classification head. Default: nn.Linear.
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head_dropout (float): dropout for MLP classifier. Default: 0.
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"""
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def __init__(
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self,
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in_chans=3,
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num_classes=1000,
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depths=(3, 3, 9, 3),
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dims=(96, 192, 384, 576),
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norm_layer=LayerNorm,
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act_layer=nn.GELU,
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conv_ratio=1.0,
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kernel_size=7,
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ls_init_value=None,
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drop_path_rate=0.,
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drop_rate=0.,
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output_norm=LayerNorm,
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head_fn=MlpHead,
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**kwargs,
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):
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super().__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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if not isinstance(depths, (list, tuple)):
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depths = [depths] # it means the model has only one stage
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if not isinstance(dims, (list, tuple)):
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dims = [dims]
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num_stage = len(depths)
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self.num_stage = num_stage
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self.stem = Stem(in_chans, dims[0], act_layer=act_layer, norm_layer=norm_layer)
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prev_dim = dims[0]
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dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
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self.stages = nn.ModuleList()
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cur = 0
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for i in range(num_stage):
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dim = dims[i]
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stage = MambaOutStage(
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dim=prev_dim,
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dim_out=dim,
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depth=depths[i],
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kernel_size=kernel_size,
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conv_ratio=conv_ratio,
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downsample=i > 0,
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ls_init_value=ls_init_value,
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norm_layer=norm_layer,
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act_layer=act_layer,
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drop_path=dp_rates[i],
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)
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self.stages.append(stage)
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prev_dim = dim
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cur += depths[i]
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self.norm = output_norm(prev_dim)
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self.head = head_fn(prev_dim, num_classes, drop_rate=drop_rate)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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@torch.jit.ignore
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def no_weight_decay(self):
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return {'norm'}
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def forward_features(self, x):
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x = self.stem(x)
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for s in self.stages:
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x = s(x)
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return x
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def forward_head(self, x):
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x = x.mean((1, 2))
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x = self.norm(x)
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x = self.head(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': 1.0, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'mambaout_femto': _cfg(
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url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_femto.pth'),
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'mambaout_kobe': _cfg(
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url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_kobe.pth'),
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'mambaout_tiny': _cfg(
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url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_tiny.pth'),
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'mambaout_small': _cfg(
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url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_small.pth'),
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'mambaout_base': _cfg(
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url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_base.pth'),
|
||
|
|
'mambaout_small_rw': _cfg(),
|
||
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|
'mambaout_base_rw': _cfg(),
|
||
|
|
}
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||
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|
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||
|
|
|
||
|
|
# a series of MambaOut models
|
||
|
|
@register_model
|
||
|
|
def mambaout_femto(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 3, 9, 3],
|
||
|
|
dims=[48, 96, 192, 288],
|
||
|
|
**kwargs)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_femto']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
# Kobe Memorial Version with 24 Gated CNN blocks
|
||
|
|
@register_model
|
||
|
|
def mambaout_kobe(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 3, 15, 3],
|
||
|
|
dims=[48, 96, 192, 288],
|
||
|
|
**kwargs)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_kobe']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def mambaout_tiny(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 3, 9, 3],
|
||
|
|
dims=[96, 192, 384, 576],
|
||
|
|
**kwargs)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_tiny']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def mambaout_small(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 4, 27, 3],
|
||
|
|
dims=[96, 192, 384, 576],
|
||
|
|
**kwargs)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_small']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def mambaout_base(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 4, 27, 3],
|
||
|
|
dims=[128, 256, 512, 768],
|
||
|
|
**kwargs)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_base']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def mambaout_small_rw(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=[3, 4, 27, 3],
|
||
|
|
dims=[96, 192, 384, 576],
|
||
|
|
ls_init_value=1e-6,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_small']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def mambaout_base_rw(pretrained=False, **kwargs):
|
||
|
|
model = MambaOut(
|
||
|
|
depths=(3, 4, 27, 3),
|
||
|
|
dims=(128, 256, 512, 768),
|
||
|
|
ls_init_value=1e-6,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
model.default_cfg = default_cfgs['mambaout_base']
|
||
|
|
if pretrained:
|
||
|
|
state_dict = torch.hub.load_state_dict_from_url(
|
||
|
|
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|