From 719912c3a128c363167206f0c6496736783be4e2 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Fri, 23 Aug 2024 10:14:06 -0700
Subject: [PATCH] Initial mambaout work

---
 timm/models/__init__.py |   1 +
 timm/models/mambaout.py | 480 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 481 insertions(+)
 create mode 100644 timm/models/mambaout.py

diff --git a/timm/models/__init__.py b/timm/models/__init__.py
index 5e723724cc..627a23e317 100644
--- a/timm/models/__init__.py
+++ b/timm/models/__init__.py
@@ -35,6 +35,7 @@
 from .inception_v4 import *
 from .levit import *
 from .maxxvit import *
+from .mambaout import *
 from .metaformer import *
 from .mlp_mixer import *
 from .mobilenetv3 import *
diff --git a/timm/models/mambaout.py b/timm/models/mambaout.py
new file mode 100644
index 0000000000..3acd1d6f4c
--- /dev/null
+++ b/timm/models/mambaout.py
@@ -0,0 +1,480 @@
+"""
+MambaOut models for image classification.
+Some implementations are modified from:
+timm (https://github.com/rwightman/pytorch-image-models),
+MetaFormer (https://github.com/sail-sg/metaformer),
+InceptionNeXt (https://github.com/sail-sg/inceptionnext)
+"""
+from functools import partial
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import trunc_normal_, DropPath, LayerNorm
+from .vision_transformer import LayerScale
+from ._manipulate import checkpoint_seq
+from timm.models.registry import register_model
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+
+class Stem(nn.Module):
+    r""" Code modified from InternImage:
+        https://github.com/OpenGVLab/InternImage
+    """
+
+    def __init__(
+            self,
+            in_chs=3,
+            out_chs=96,
+            mid_norm: bool = True,
+            act_layer=nn.GELU,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            in_chs,
+            out_chs // 2,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm1 = norm_layer(out_chs // 2) if mid_norm else None
+        self.act = act_layer()
+        self.conv2 = nn.Conv2d(
+            out_chs // 2,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm2 = norm_layer(out_chs)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        if self.norm1 is not None:
+            x = x.permute(0, 2, 3, 1)
+            x = self.norm1(x)
+            x = x.permute(0, 3, 1, 2)
+        x = self.act(x)
+        x = self.conv2(x)
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm2(x)
+        return x
+
+
+class DownsampleNormFirst(nn.Module):
+
+    def __init__(
+            self,
+            in_chs=96,
+            out_chs=198,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.norm = norm_layer(in_chs)
+        self.conv = nn.Conv2d(
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = x.permute(0, 3, 1, 2)
+        x = self.conv(x)
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+class Downsample(nn.Module):
+
+    def __init__(
+            self,
+            in_chs=96,
+            out_chs=198,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm = norm_layer(out_chs)
+
+    def forward(self, x):
+        x = x.permute(0, 3, 1, 2)
+        x = self.conv(x)
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm(x)
+        return x
+
+
+class MlpHead(nn.Module):
+    """ MLP classification head
+    """
+
+    def __init__(
+            self,
+            dim,
+            num_classes=1000,
+            act_layer=nn.GELU,
+            mlp_ratio=4,
+            norm_layer=LayerNorm,
+            drop_rate=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(drop_rate)
+
+    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 GatedConvBlock(nn.Module):
+    r""" Our implementation of Gated CNN Block: https://arxiv.org/pdf/1612.08083
+    Args:
+        conv_ratio: control the number of channels to conduct depthwise convolution.
+            Conduct convolution on partial channels can improve paraitcal efficiency.
+            The idea of partial channels is from ShuffleNet V2 (https://arxiv.org/abs/1807.11164) and
+            also used by InceptionNeXt (https://arxiv.org/abs/2303.16900) and FasterNet (https://arxiv.org/abs/2303.03667)
+    """
+
+    def __init__(
+            self,
+            dim,
+            expansion_ratio=8 / 3,
+            kernel_size=7,
+            conv_ratio=1.0,
+            ls_init_value=None,
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            drop_path=0.,
+            **kwargs
+    ):
+        super().__init__()
+        self.norm = norm_layer(dim)
+        hidden = int(expansion_ratio * dim)
+        self.fc1 = nn.Linear(dim, hidden * 2)
+        self.act = act_layer()
+        conv_channels = int(conv_ratio * dim)
+        self.split_indices = (hidden, hidden - conv_channels, conv_channels)
+        self.conv = nn.Conv2d(
+            conv_channels,
+            conv_channels,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+            groups=conv_channels
+        )
+        self.fc2 = nn.Linear(hidden, dim)
+        self.ls = LayerScale(dim) if ls_init_value is not None else nn.Identity()
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        shortcut = x  # [B, H, W, C]
+        x = self.norm(x)
+        x = self.fc1(x)
+        g, i, c = torch.split(x, self.split_indices, dim=-1)
+        c = c.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        c = self.conv(c)
+        c = c.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        x = self.fc2(self.act(g) * torch.cat((i, c), dim=-1))
+        x = self.ls(x)
+        x = self.drop_path(x)
+        return x + shortcut
+
+
+class MambaOutStage(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            dim_out: Optional[int] = None,
+            depth: int = 4,
+            expansion_ratio=8 / 3,
+            kernel_size=7,
+            conv_ratio=1.0,
+            downsample: bool = False,
+            ls_init_value: Optional[float] = None,
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            drop_path=0.,
+    ):
+        super().__init__()
+        dim_out = dim_out or dim
+        self.grad_checkpointing = False
+
+        if downsample:
+            self.downsample = Downsample(dim, dim_out, norm_layer=norm_layer)
+        else:
+            assert dim == dim_out
+            self.downsample = nn.Identity()
+
+        self.blocks = nn.Sequential(*[
+            GatedConvBlock(
+                dim=dim_out,
+                expansion_ratio=expansion_ratio,
+                kernel_size=kernel_size,
+                conv_ratio=conv_ratio,
+                ls_init_value=ls_init_value,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                drop_path=drop_path[j] if isinstance(drop_path, (list, tuple)) else drop_path,
+            )
+            for j in range(depth)
+        ])
+
+    def forward(self, x):
+        x = self.downsample(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 MambaOut(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: [3, 3, 9, 3].
+        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 576].
+        downsample_layers: (list or tuple): Downsampling layers before each stage.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        output_norm: norm before classifier head. Default: partial(nn.LayerNorm, eps=1e-6).
+        head_fn: classification head. Default: nn.Linear.
+        head_dropout (float): dropout for MLP classifier. Default: 0.
+    """
+
+    def __init__(
+            self,
+            in_chans=3,
+            num_classes=1000,
+            depths=(3, 3, 9, 3),
+            dims=(96, 192, 384, 576),
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            conv_ratio=1.0,
+            kernel_size=7,
+            ls_init_value=None,
+            drop_path_rate=0.,
+            drop_rate=0.,
+            output_norm=LayerNorm,
+            head_fn=MlpHead,
+            **kwargs,
+    ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.drop_rate = drop_rate
+        if not isinstance(depths, (list, tuple)):
+            depths = [depths]  # it means the model has only one stage
+        if not isinstance(dims, (list, tuple)):
+            dims = [dims]
+
+        num_stage = len(depths)
+        self.num_stage = num_stage
+
+        self.stem = Stem(in_chans, dims[0], act_layer=act_layer, norm_layer=norm_layer)
+        prev_dim = dims[0]
+        dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        self.stages = nn.ModuleList()
+        cur = 0
+        for i in range(num_stage):
+            dim = dims[i]
+            stage = MambaOutStage(
+                dim=prev_dim,
+                dim_out=dim,
+                depth=depths[i],
+                kernel_size=kernel_size,
+                conv_ratio=conv_ratio,
+                downsample=i > 0,
+                ls_init_value=ls_init_value,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                drop_path=dp_rates[i],
+            )
+            self.stages.append(stage)
+            prev_dim = dim
+            cur += depths[i]
+
+        self.norm = output_norm(prev_dim)
+
+        self.head = head_fn(prev_dim, num_classes, drop_rate=drop_rate)
+
+        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 no_weight_decay(self):
+        return {'norm'}
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        for s in self.stages:
+            x = s(x)
+        return x
+
+    def forward_head(self, x):
+        x = x.mean((1, 2))
+        x = self.norm(x)
+        x = self.head(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': 1.0, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'mambaout_femto': _cfg(
+        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_femto.pth'),
+    'mambaout_kobe': _cfg(
+        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_kobe.pth'),
+    'mambaout_tiny': _cfg(
+        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_tiny.pth'),
+    'mambaout_small': _cfg(
+        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_small.pth'),
+    'mambaout_base': _cfg(
+        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_base.pth'),
+    'mambaout_small_rw': _cfg(),
+    'mambaout_base_rw': _cfg(),
+}
+
+
+# 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