Delete mamba_vision.py

mamba_vision.py

@@ -1,865 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-
-import torch
-import torch.nn as nn
-from timm.models.registry import register_model
-import math
-from timm.models.layers import trunc_normal_, DropPath, LayerNorm2d
-from timm.models._builder import resolve_pretrained_cfg
-try:
-    from timm.models._builder import _update_default_kwargs as update_args
-except:
-    from timm.models._builder import _update_default_model_kwargs as update_args
-from timm.models.vision_transformer import Mlp, PatchEmbed
-from timm.models.layers import DropPath, trunc_normal_
-from timm.models.registry import register_model
-import torch.nn.functional as F
-from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
-from einops import rearrange, repeat
-from pathlib import Path
-from huggingface_hub import PyTorchModelHubMixin
-
-
-def _cfg(url='', **kwargs):
-    return {'url': url,
-            'num_classes': 1000,
-            'input_size': (3, 224, 224),
-            'pool_size': None,
-            'crop_pct': 0.875,
-            'interpolation': 'bicubic',
-            'fixed_input_size': True,
-            'mean': (0.485, 0.456, 0.406),
-            'std': (0.229, 0.224, 0.225),
-            **kwargs
-            }
-
-
-default_cfgs = {
-    'mamba_vision_T': _cfg(url='https://huggingface.co/nvidia/MambaVision-T-1K/resolve/main/mambavision_tiny_1k.pth.tar',
-                           crop_pct=1.0,
-                           input_size=(3, 224, 224),
-                           crop_mode='center'),
-    'mamba_vision_T2': _cfg(url='https://huggingface.co/nvidia/MambaVision-T2-1K/resolve/main/mambavision_tiny2_1k.pth.tar',
-                            crop_pct=0.98,
-                            input_size=(3, 224, 224),
-                            crop_mode='center'),
-    'mamba_vision_S': _cfg(url='https://huggingface.co/nvidia/MambaVision-S-1K/resolve/main/mambavision_small_1k.pth.tar',
-                           crop_pct=0.93,
-                           input_size=(3, 224, 224),
-                           crop_mode='center'),
-    'mamba_vision_B': _cfg(url='https://huggingface.co/nvidia/MambaVision-B-1K/resolve/main/mambavision_base_1k.pth.tar',
-                           crop_pct=1.0,
-                           input_size=(3, 224, 224),
-                           crop_mode='center'),
-    'mamba_vision_L': _cfg(url='https://huggingface.co/nvidia/MambaVision-L-1K/resolve/main/mambavision_large_1k.pth.tar',
-                           crop_pct=1.0,
-                           input_size=(3, 224, 224),
-                           crop_mode='center'),
-    'mamba_vision_L2': _cfg(url='https://huggingface.co/nvidia/MambaVision-L2-1K/resolve/main/mambavision_large2_1k.pth.tar',
-                            crop_pct=1.0,
-                            input_size=(3, 224, 224),
-                            crop_mode='center')                                
-}
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, C, H, W)
-        window_size: window size
-        h_w: Height of window
-        w_w: Width of window
-    Returns:
-        local window features (num_windows*B, window_size*window_size, C)
-    """
-    B, C, H, W = x.shape
-    x = x.view(B, C, H // window_size, window_size, W // window_size, window_size)
-    windows = x.permute(0, 2, 4, 3, 5, 1).reshape(-1, window_size*window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: local window features (num_windows*B, window_size, window_size, C)
-        window_size: Window size
-        H: Height of image
-        W: Width of image
-    Returns:
-        x: (B, C, H, W)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.reshape(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 5, 1, 3, 2, 4).reshape(B,windows.shape[2], H, W)
-    return x
-
-
-def _load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-    
-    def load(module, prefix=''):
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    
-    if len(err_msg) > 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def _load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = torch.load(filename, map_location=map_location)
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-
-    _load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-class Downsample(nn.Module):
-    """
-    Down-sampling block"
-    """
-
-    def __init__(self,
-                 dim,
-                 keep_dim=False,
-                 ):
-        """
-        Args:
-            dim: feature size dimension.
-            norm_layer: normalization layer.
-            keep_dim: bool argument for maintaining the resolution.
-        """
-
-        super().__init__()
-        if keep_dim:
-            dim_out = dim
-        else:
-            dim_out = 2 * dim
-        self.reduction = nn.Sequential(
-            nn.Conv2d(dim, dim_out, 3, 2, 1, bias=False),
-        )
-
-    def forward(self, x):
-        x = self.reduction(x)
-        return x
-
-
-class PatchEmbed(nn.Module):
-    """
-    Patch embedding block"
-    """
-
-    def __init__(self, in_chans=3, in_dim=64, dim=96):
-        """
-        Args:
-            in_chans: number of input channels.
-            dim: feature size dimension.
-        """
-        # in_dim = 1
-        super().__init__()
-        self.proj = nn.Identity()
-        self.conv_down = nn.Sequential(
-            nn.Conv2d(in_chans, in_dim, 3, 2, 1, bias=False),
-            nn.BatchNorm2d(in_dim, eps=1e-4),
-            nn.ReLU(),
-            nn.Conv2d(in_dim, dim, 3, 2, 1, bias=False),
-            nn.BatchNorm2d(dim, eps=1e-4),
-            nn.ReLU()
-            )
-
-    def forward(self, x):
-        x = self.proj(x)
-        x = self.conv_down(x)
-        return x
-
-
-class ConvBlock(nn.Module):
-
-    def __init__(self, dim,
-                 drop_path=0.,
-                 layer_scale=None,
-                 kernel_size=3):
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
-        self.norm1 = nn.BatchNorm2d(dim, eps=1e-5)
-        self.act1 = nn.GELU(approximate= 'tanh')
-        self.conv2 = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
-        self.norm2 = nn.BatchNorm2d(dim, eps=1e-5)
-        self.layer_scale = layer_scale
-        if layer_scale is not None and type(layer_scale) in [int, float]:
-            self.gamma = nn.Parameter(layer_scale * torch.ones(dim))
-            self.layer_scale = True
-        else:
-            self.layer_scale = False
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
-    def forward(self, x):
-        input = x
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.act1(x)
-        x = self.conv2(x)
-        x = self.norm2(x)
-        if self.layer_scale:
-            x = x * self.gamma.view(1, -1, 1, 1)
-        x = input + self.drop_path(x)
-        return x
-
-
-class MambaVisionMixer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        d_state=16,
-        d_conv=4,
-        expand=2,
-        dt_rank="auto",
-        dt_min=0.001,
-        dt_max=0.1,
-        dt_init="random",
-        dt_scale=1.0,
-        dt_init_floor=1e-4,
-        conv_bias=True,
-        bias=False,
-        use_fast_path=True, 
-        layer_idx=None,
-        device=None,
-        dtype=None,
-    ):
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        self.d_model = d_model
-        self.d_state = d_state
-        self.d_conv = d_conv
-        self.expand = expand
-        self.d_inner = int(self.expand * self.d_model)
-        self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == "auto" else dt_rank
-        self.use_fast_path = use_fast_path
-        self.layer_idx = layer_idx
-        self.in_proj = nn.Linear(self.d_model, self.d_inner, bias=bias, **factory_kwargs)    
-        self.x_proj = nn.Linear(
-            self.d_inner//2, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs
-        )
-        self.dt_proj = nn.Linear(self.dt_rank, self.d_inner//2, bias=True, **factory_kwargs)
-        dt_init_std = self.dt_rank**-0.5 * dt_scale
-        if dt_init == "constant":
-            nn.init.constant_(self.dt_proj.weight, dt_init_std)
-        elif dt_init == "random":
-            nn.init.uniform_(self.dt_proj.weight, -dt_init_std, dt_init_std)
-        else:
-            raise NotImplementedError
-        dt = torch.exp(
-            torch.rand(self.d_inner//2, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
-            + math.log(dt_min)
-        ).clamp(min=dt_init_floor)
-        inv_dt = dt + torch.log(-torch.expm1(-dt))
-        with torch.no_grad():
-            self.dt_proj.bias.copy_(inv_dt)
-        self.dt_proj.bias._no_reinit = True
-        A = repeat(
-            torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),
-            "n -> d n",
-            d=self.d_inner//2,
-        ).contiguous()
-        A_log = torch.log(A)
-        self.A_log = nn.Parameter(A_log)
-        self.A_log._no_weight_decay = True
-        self.D = nn.Parameter(torch.ones(self.d_inner//2, device=device))
-        self.D._no_weight_decay = True
-        self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)
-        self.conv1d_x = nn.Conv1d(
-            in_channels=self.d_inner//2,
-            out_channels=self.d_inner//2,
-            bias=conv_bias//2,
-            kernel_size=d_conv,
-            groups=self.d_inner//2,
-            **factory_kwargs,
-        )
-        self.conv1d_z = nn.Conv1d(
-            in_channels=self.d_inner//2,
-            out_channels=self.d_inner//2,
-            bias=conv_bias//2,
-            kernel_size=d_conv,
-            groups=self.d_inner//2,
-            **factory_kwargs,
-        )
-
-    def forward(self, hidden_states):
-        """
-        hidden_states: (B, L, D)
-        Returns: same shape as hidden_states
-        """
-        _, seqlen, _ = hidden_states.shape
-        xz = self.in_proj(hidden_states)
-        xz = rearrange(xz, "b l d -> b d l")
-        x, z = xz.chunk(2, dim=1)
-        A = -torch.exp(self.A_log.float())
-        x = F.silu(F.conv1d(input=x, weight=self.conv1d_x.weight, bias=self.conv1d_x.bias, padding='same', groups=self.d_inner//2))
-        z = F.silu(F.conv1d(input=z, weight=self.conv1d_z.weight, bias=self.conv1d_z.bias, padding='same', groups=self.d_inner//2))
-        x_dbl = self.x_proj(rearrange(x, "b d l -> (b l) d"))
-        dt, B, C = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=-1)
-        dt = rearrange(self.dt_proj(dt), "(b l) d -> b d l", l=seqlen)
-        B = rearrange(B, "(b l) dstate -> b dstate l", l=seqlen).contiguous()
-        C = rearrange(C, "(b l) dstate -> b dstate l", l=seqlen).contiguous()
-        y = selective_scan_fn(x, 
-                              dt, 
-                              A, 
-                              B, 
-                              C, 
-                              self.D.float(), 
-                              z=None, 
-                              delta_bias=self.dt_proj.bias.float(), 
-                              delta_softplus=True, 
-                              return_last_state=None)
-        
-        y = torch.cat([y, z], dim=1)
-        y = rearrange(y, "b d l -> b l d")
-        out = self.out_proj(y)
-        return out
-    
-
-class Attention(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=False,
-            qk_norm=False,
-            attn_drop=0.,
-            proj_drop=0.,
-            norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        assert dim % num_heads == 0
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-        self.fused_attn = True
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        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)
-        q, k = self.q_norm(q), self.k_norm(k)
-
-        if self.fused_attn:
-            x = F.scaled_dot_product_attention(
-             q, k, v,
-                dropout_p=self.attn_drop.p,
-            )
-        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)
-        return x
-
-
-class Block(nn.Module):
-    def __init__(self, 
-                 dim, 
-                 num_heads, 
-                 counter, 
-                 transformer_blocks, 
-                 mlp_ratio=4., 
-                 qkv_bias=False, 
-                 qk_scale=False, 
-                 drop=0., 
-                 attn_drop=0.,
-                 drop_path=0., 
-                 act_layer=nn.GELU, 
-                 norm_layer=nn.LayerNorm, 
-                 Mlp_block=Mlp,
-                 layer_scale=None,
-                 ):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        if counter in transformer_blocks:
-            self.mixer = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_norm=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-            norm_layer=norm_layer,
-        )
-        else:
-            self.mixer = MambaVisionMixer(d_model=dim, 
-                                          d_state=8,  
-                                          d_conv=3,    
-                                          expand=1
-                                          )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-        use_layer_scale = layer_scale is not None and type(layer_scale) in [int, float]
-        self.gamma_1 = nn.Parameter(layer_scale * torch.ones(dim))  if use_layer_scale else 1
-        self.gamma_2 = nn.Parameter(layer_scale * torch.ones(dim))  if use_layer_scale else 1
-
-    def forward(self, x):
-        x = x + self.drop_path(self.gamma_1 * self.mixer(self.norm1(x)))
-        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
-        return x
-
-
-class MambaVisionLayer(nn.Module):
-    """
-    MambaVision layer"
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size,
-                 conv=False,
-                 downsample=True,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 layer_scale=None,
-                 layer_scale_conv=None,
-                 transformer_blocks = [],
-    ):
-        """
-        Args:
-            dim: feature size dimension.
-            depth: number of layers in each stage.
-            window_size: window size in each stage.
-            conv: bool argument for conv stage flag.
-            downsample: bool argument for down-sampling.
-            mlp_ratio: MLP ratio.
-            num_heads: number of heads in each stage.
-            qkv_bias: bool argument for query, key, value learnable bias.
-            qk_scale: bool argument to scaling query, key.
-            drop: dropout rate.
-            attn_drop: attention dropout rate.
-            drop_path: drop path rate.
-            norm_layer: normalization layer.
-            layer_scale: layer scaling coefficient.
-            layer_scale_conv: conv layer scaling coefficient.
-            transformer_blocks: list of transformer blocks.
-        """
-
-        super().__init__()
-        self.conv = conv
-        self.transformer_block = False
-        if conv:
-            self.blocks = nn.ModuleList([ConvBlock(dim=dim,
-                                                   drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                                   layer_scale=layer_scale_conv)
-                                                   for i in range(depth)])
-            self.transformer_block = False
-        else:
-            self.transformer_block = True
-            self.blocks = nn.ModuleList([Block(dim=dim,
-                                               counter=i, 
-                                               transformer_blocks=transformer_blocks,
-                                               num_heads=num_heads,
-                                               mlp_ratio=mlp_ratio,
-                                               qkv_bias=qkv_bias,
-                                               qk_scale=qk_scale,
-                                               drop=drop,
-                                               attn_drop=attn_drop,
-                                               drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                               layer_scale=layer_scale)
-                                               for i in range(depth)])
-            self.transformer_block = True
-
-        self.downsample = None if not downsample else Downsample(dim=dim)
-        self.do_gt = False
-        self.window_size = window_size
-
-    def forward(self, x):
-        _, _, H, W = x.shape
-
-        if self.transformer_block:
-            pad_r = (self.window_size - W % self.window_size) % self.window_size
-            pad_b = (self.window_size - H % self.window_size) % self.window_size
-            if pad_r > 0 or pad_b > 0:
-                x = torch.nn.functional.pad(x, (0,pad_r,0,pad_b))
-                _, _, Hp, Wp = x.shape
-            else:
-                Hp, Wp = H, W
-            x = window_partition(x, self.window_size)
-
-        for _, blk in enumerate(self.blocks):
-            x = blk(x)
-        if self.transformer_block:
-            x = window_reverse(x, self.window_size, Hp, Wp)
-            if pad_r > 0 or pad_b > 0:
-                x = x[:, :, :H, :W].contiguous()
-        if self.downsample is None:
-            return x
-        return self.downsample(x)
-
-
-class MambaVision(nn.Module, PyTorchModelHubMixin):
-    """
-    MambaVision,
-    """
-
-    def __init__(self,
-                 dim,
-                 in_dim,
-                 depths,
-                 window_size,
-                 mlp_ratio,
-                 num_heads,
-                 drop_path_rate=0.2,
-                 in_chans=3,
-                 num_classes=1000,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 layer_scale=None,
-                 layer_scale_conv=None,
-                 **kwargs):
-        """
-        Args:
-            dim: feature size dimension.
-            depths: number of layers in each stage.
-            window_size: window size in each stage.
-            mlp_ratio: MLP ratio.
-            num_heads: number of heads in each stage.
-            drop_path_rate: drop path rate.
-            in_chans: number of input channels.
-            num_classes: number of classes.
-            qkv_bias: bool argument for query, key, value learnable bias.
-            qk_scale: bool argument to scaling query, key.
-            drop_rate: dropout rate.
-            attn_drop_rate: attention dropout rate.
-            norm_layer: normalization layer.
-            layer_scale: layer scaling coefficient.
-            layer_scale_conv: conv layer scaling coefficient.
-        """
-        super().__init__()
-        num_features = int(dim * 2 ** (len(depths) - 1))
-        self.num_classes = num_classes
-        self.patch_embed = PatchEmbed(in_chans=in_chans, in_dim=in_dim, dim=dim)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
-        self.levels = nn.ModuleList()
-        for i in range(len(depths)):
-            conv = True if (i == 0 or i == 1) else False
-            level = MambaVisionLayer(dim=int(dim * 2 ** i),
-                                     depth=depths[i],
-                                     num_heads=num_heads[i],
-                                     window_size=window_size[i],
-                                     mlp_ratio=mlp_ratio,
-                                     qkv_bias=qkv_bias,
-                                     qk_scale=qk_scale,
-                                     conv=conv,
-                                     drop=drop_rate,
-                                     attn_drop=attn_drop_rate,
-                                     drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],
-                                     downsample=(i < 3),
-                                     layer_scale=layer_scale,
-                                     layer_scale_conv=layer_scale_conv,
-                                     transformer_blocks=list(range(depths[i]//2+1, depths[i])) if depths[i]%2!=0 else list(range(depths[i]//2, depths[i])),
-                                     )
-            self.levels.append(level)
-        self.norm = nn.BatchNorm2d(num_features)
-        self.avgpool = nn.AdaptiveAvgPool2d(1)
-        self.head = nn.Linear(num_features, num_classes) if num_classes > 0 else nn.Identity()
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, LayerNorm2d):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.BatchNorm2d):
-            nn.init.ones_(m.weight)
-            nn.init.zeros_(m.bias)
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'rpb'}
-
-    def forward_features(self, x):
-        x = self.patch_embed(x)
-        for level in self.levels:
-            x = level(x)
-        x = self.norm(x)
-        x = self.avgpool(x)
-        x = torch.flatten(x, 1)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-    def _load_state_dict(self, 
-                         pretrained, 
-                         strict: bool = False):
-        _load_checkpoint(self, 
-                         pretrained, 
-                         strict=strict)
-
-
-@register_model
-def mamba_vision_T(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_T.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_T').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[1, 3, 8, 4],
-                        num_heads=[2, 4, 8, 16],
-                        window_size=[8, 8, 14, 7],
-                        dim=80,
-                        in_dim=32,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.2,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model
-
-
-@register_model
-def mamba_vision_T2(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_T2.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_T2').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[1, 3, 11, 4],
-                        num_heads=[2, 4, 8, 16],
-                        window_size=[8, 8, 14, 7],
-                        dim=80,
-                        in_dim=32,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.2,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model
-
-
-@register_model
-def mamba_vision_S(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_S.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_S').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[3, 3, 7, 5],
-                        num_heads=[2, 4, 8, 16],
-                        window_size=[8, 8, 14, 7],
-                        dim=96,
-                        in_dim=64,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.2,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model
-
-
-@register_model
-def mamba_vision_B(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_B.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_B').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[3, 3, 10, 5],
-                        num_heads=[2, 4, 8, 16],
-                        window_size=[8, 8, 14, 7],
-                        dim=128,
-                        in_dim=64,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.3,
-                        layer_scale=1e-5,
-                        layer_scale_conv=None,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model
-
-
-@register_model
-def mamba_vision_L(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_L.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_L').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[3, 3, 10, 5],
-                        num_heads=[4, 8, 16, 32],
-                        window_size=[8, 8, 14, 7],
-                        dim=196,
-                        in_dim=64,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.3,
-                        layer_scale=1e-5,
-                        layer_scale_conv=None,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model
-
-
-@register_model
-def mamba_vision_L2(pretrained=False, **kwargs):
-    model_path = kwargs.pop("model_path", "/tmp/mamba_vision_L2.pth.tar")
-    pretrained_cfg = resolve_pretrained_cfg('mamba_vision_L2').to_dict()
-    update_args(pretrained_cfg, kwargs, kwargs_filter=None)
-    model = MambaVision(depths=[3, 3, 12, 5],
-                        num_heads=[4, 8, 16, 32],
-                        window_size=[8, 8, 14, 7],
-                        dim=196,
-                        in_dim=64,
-                        mlp_ratio=4,
-                        resolution=224,
-                        drop_path_rate=0.3,
-                        layer_scale=1e-5,
-                        layer_scale_conv=None,
-                        **kwargs)
-    model.pretrained_cfg = pretrained_cfg
-    model.default_cfg = model.pretrained_cfg
-    if pretrained:
-        if not Path(model_path).is_file():
-            url = model.default_cfg['url']
-            torch.hub.download_url_to_file(url=url, dst=model_path)
-        model._load_state_dict(model_path)
-    return model