Upload painformer.py

architecture/painformer.py

@@ -0,0 +1,664 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from timm.models.registry import register_model
+from timm.models.vision_transformer import _cfg
+import math
+import numpy as np
+from pytorch_wavelets import DWTForward, DWTInverse # (or import DWT, IDWT)
+
+
+class SpectralGatingNetwork(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        # this weights are valid for h=14 and w=8
+        if dim == 64: #96 for large model, 64 for small and base model
+            self.h = 56 #H
+            self.w = 29 #(W/2)+1            
+            self.complex_weight = nn.Parameter(torch.randn(self.h, self.w, dim, 2, dtype=torch.float32) * 0.02)
+        if dim ==128:
+            self.h = 28 #H
+            self.w = 15 #(W/2)+1, this is due to rfft2
+            self.complex_weight = nn.Parameter(torch.randn(self.h, self.w, dim, 2, dtype=torch.float32) * 0.02)
+        if dim == 96: #96 for large model, 64 for small and base model
+            self.h = 56 #H
+            self.w = 29 #(W/2)+1            
+            self.complex_weight = nn.Parameter(torch.randn(self.h, self.w, dim, 2, dtype=torch.float32) * 0.02)
+        if dim ==192:
+            self.h = 28 #H
+            self.w = 15 #(W/2)+1, this is due to rfft2
+            self.complex_weight = nn.Parameter(torch.randn(self.h, self.w, dim, 2, dtype=torch.float32) * 0.02)
+
+    def forward(self, x, H, W):
+        # print('wno',x.shape) #CIFAR100 image :[128, 196, 384]
+        B, N, C = x.shape 
+        # print('wno B, N, C',B, N, C) #CIFAR100 image : 128 196 384
+        x = x.view(B, H, W, C)
+        # B, H, W, C=x.shape
+        x = x.to(torch.float32) 
+        # print(x.dtype)
+        # Add above for this error, RuntimeError: Input type (torch.cuda.HalfTensor) and weight type (torch.cuda.FloatTensor) should be the same
+        x = torch.fft.rfft2(x, dim=(1, 2), norm='ortho')
+        # print('wno',x.shape)
+        weight = torch.view_as_complex(self.complex_weight)
+        # print('weight',weight.shape)
+        x = x * weight
+        x = torch.fft.irfft2(x, s=(H, W), dim=(1, 2), norm='ortho')
+        # print('wno',x.shape)
+        x = x.reshape(B, N, C)# permute is not same as reshape or view
+        return x
+        #return x, weight
+
+
+def rand_bbox(size, lam, scale=1):
+    W = size[1] // scale
+    H = size[2] // scale
+    cut_rat = np.sqrt(1. - lam)
+    cut_w = np.int(W * cut_rat)
+    cut_h = np.int(H * cut_rat)
+
+    # uniform
+    cx = np.random.randint(W)
+    cy = np.random.randint(H)
+
+    bbx1 = np.clip(cx - cut_w // 2, 0, W)
+    bby1 = np.clip(cy - cut_h // 2, 0, H)
+    bbx2 = np.clip(cx + cut_w // 2, 0, W)
+    bby2 = np.clip(cy + cut_h // 2, 0, H)
+
+    return bbx1, bby1, bbx2, bby2
+
+class ClassAttention(nn.Module):
+    def __init__(self, dim, num_heads):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.head_dim = head_dim
+        self.scale = head_dim**-0.5
+        self.kv = nn.Linear(dim, dim * 2)
+        self.q = nn.Linear(dim, dim)
+        self.proj = nn.Linear(dim, dim)
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        B, N, C = x.shape
+        kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+        q = self.q(x[:, :1, :]).reshape(B, self.num_heads, 1, self.head_dim)
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        cls_embed = (attn @ v).transpose(1, 2).reshape(B, 1, self.head_dim * self.num_heads)
+        cls_embed = self.proj(cls_embed)
+        return cls_embed
+
+class FFN(nn.Module):
+    def __init__(self, in_features, hidden_features):
+        super().__init__()
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = nn.GELU()
+        self.fc2 = nn.Linear(hidden_features, in_features)
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+class ClassBlock(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        self.attn = ClassAttention(dim, num_heads)
+        self.mlp = FFN(dim, int(dim * mlp_ratio))
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        cls_embed = x[:, :1]
+        cls_embed = cls_embed + self.attn(self.norm1(x))
+        cls_embed = cls_embed + self.mlp(self.norm2(cls_embed))
+        return torch.cat([cls_embed, x[:, 1:]], dim=1)
+
+class PVT2FFN(nn.Module):
+    def __init__(self, in_features, hidden_features):
+        super().__init__()
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.dwconv = DWConv(hidden_features)
+        self.act = nn.GELU()
+        self.fc2 = nn.Linear(hidden_features, in_features)
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x, H, W):
+        x = self.fc1(x)
+        x = self.dwconv(x, H, W)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads):
+        super().__init__()
+        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
+
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim)
+        self.kv = nn.Linear(dim, dim * 2)
+        self.proj = nn.Linear(dim, dim)
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        #return x
+        return x, attn
+
+class Block(nn.Module):
+    def __init__(self, 
+        dim, 
+        num_heads, 
+        mlp_ratio,
+        drop_path=0., 
+        norm_layer=nn.LayerNorm, 
+        sr_ratio=1, 
+        block_type = 'wave'
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+
+        if block_type == 'std_att':
+            self.attn = Attention(dim, num_heads)
+        else:
+            self.attn = SpectralGatingNetwork(dim)
+        self.mlp = PVT2FFN(in_features=dim, hidden_features=int(dim * mlp_ratio))
+        self.drop_path = DropPath(drop_path) if drop_path > 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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    # def forward(self, x, H, W): ## !!!!!!!!!!!!!!!!
+    #     x = x + self.drop_path(self.attn(self.norm1(x), H, W)) 
+    #     x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
+    #     return x
+    
+    
+    def forward(self, x, H, W):
+        attn_output, attn_weights = self.attn(self.norm1(x), H, W) if isinstance(self.attn, Attention) else (self.attn(self.norm1(x), H, W), None)
+        x = x + self.drop_path(attn_output)
+        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
+
+        # Optionally return attention weights for visualization or analysis
+        return (x, attn_weights) if attn_weights is not None else x
+
+
+class DownSamples(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.proj = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1)
+        self.norm = nn.LayerNorm(out_channels)
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        x = self.proj(x)
+        _, _, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        return x, H, W
+
+class Stem(nn.Module):
+    def __init__(self, in_channels, stem_hidden_dim, out_channels):
+        super().__init__()
+        hidden_dim = stem_hidden_dim
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, hidden_dim, kernel_size=7, stride=2,
+                      padding=3, bias=False),  # 112x112
+            nn.BatchNorm2d(hidden_dim),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1,
+                      padding=1, bias=False),  # 112x112
+            nn.BatchNorm2d(hidden_dim),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1,
+                      padding=1, bias=False),  # 112x112
+            nn.BatchNorm2d(hidden_dim),
+            nn.ReLU(inplace=True),
+        )
+        self.proj = nn.Conv2d(hidden_dim,
+                              out_channels,
+                              kernel_size=3,
+                              stride=2,
+                              padding=1)
+        self.norm = nn.LayerNorm(out_channels)
+
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.proj(x)
+        _, _, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        return x, H, W
+
+class SpectFormer(nn.Module):
+    def __init__(self, 
+        in_chans=3, 
+        num_classes=1000, 
+        stem_hidden_dim = 32,
+        embed_dims=[64, 128, 320, 448],
+        num_heads=[2, 4, 10, 14], 
+        mlp_ratios=[8, 8, 4, 4], 
+        drop_path_rate=0., 
+        norm_layer=nn.LayerNorm,
+        depths=[3, 4, 6, 3], 
+        sr_ratios=[4, 2, 1, 1], 
+        num_stages=4,
+        token_label=False,
+        **kwargs
+    ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.depths = depths
+        self.num_stages = num_stages
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+        cur = 0
+
+        for i in range(num_stages):
+            if i == 0:
+                patch_embed = Stem(in_chans, stem_hidden_dim, embed_dims[i])
+            else:
+                patch_embed = DownSamples(embed_dims[i - 1], embed_dims[i])
+
+            block = nn.ModuleList([Block(
+                dim = embed_dims[i], 
+                num_heads = num_heads[i], 
+                mlp_ratio = mlp_ratios[i], 
+                drop_path=dpr[cur + j], 
+                norm_layer=norm_layer,
+                sr_ratio = sr_ratios[i],
+                block_type='wave' if i < 2 else 'std_att')
+            for j in range(depths[i])])
+
+            norm = norm_layer(embed_dims[i])
+            cur += depths[i]
+
+            setattr(self, f"patch_embed{i + 1}", patch_embed)
+            setattr(self, f"block{i + 1}", block)
+            setattr(self, f"norm{i + 1}", norm)
+
+        post_layers = ['ca']
+        self.post_network = nn.ModuleList([
+            ClassBlock(
+                dim = embed_dims[-1], 
+                num_heads = num_heads[-1], 
+                mlp_ratio = mlp_ratios[-1],
+                norm_layer=norm_layer)
+            for _ in range(len(post_layers))
+        ])
+
+        # classification head
+        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
+        ##################################### token_label #####################################
+        self.return_dense = token_label
+        self.mix_token = token_label
+        self.beta = 1.0
+        self.pooling_scale = 8
+        if self.return_dense:
+            self.aux_head = nn.Linear(
+                embed_dims[-1],
+                num_classes) if num_classes > 0 else nn.Identity()
+        ##################################### token_label #####################################
+
+        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, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def forward_cls(self, x):
+        B, N, C = x.shape
+        cls_tokens = x.mean(dim=1, keepdim=True)
+        x = torch.cat((cls_tokens, x), dim=1)
+        for block in self.post_network:
+            x = block(x)
+        return x
+
+    ########## Normal block without Attention Maps ##########
+    # def forward_features(self, x):
+    #     B = x.shape[0]
+    #     for i in range(self.num_stages):
+    #         patch_embed = getattr(self, f"patch_embed{i + 1}")
+    #         block = getattr(self, f"block{i + 1}")
+    #         x, H, W = patch_embed(x)
+    #         for blk in block:
+    #             x = blk(x, H, W)
+    #             tokens = x
+            
+    #         if i != self.num_stages - 1:
+    #             norm = getattr(self, f"norm{i + 1}")
+    #             x = norm(x)
+    #             x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+
+    #     x = self.forward_cls(x)[:, 0]
+    #     norm = getattr(self, f"norm{self.num_stages}")
+    #     x = norm(x)
+    #     return x, tokens
+    
+    ########### You can create Attention Maps with this block ##########
+    def forward_features(self, x):
+        B = x.shape[0]
+        attention_maps = []  # Collect attention maps if available
+        tokens = None  # Initialize tokens to ensure scope coverage
+    
+        for i in range(self.num_stages):
+            patch_embed = getattr(self, f"patch_embed{i + 1}")
+            block = getattr(self, f"block{i + 1}")
+            x, H, W = patch_embed(x)
+            
+            for blk in block:
+                outputs = blk(x, H, W)
+                if isinstance(outputs, tuple):
+                    x, attn_weights = outputs
+                    attention_maps.append(attn_weights)  # Store attention maps
+                else:
+                    x = outputs
+            
+            tokens = x  # Update tokens with the latest block output
+    
+            if i != self.num_stages - 1:
+                norm = getattr(self, f"norm{i + 1}")
+                x = norm(x)
+                x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+    
+        x = self.forward_cls(x)[:, 0]  # Further processing for classification token
+        norm = getattr(self, f"norm{self.num_stages}")
+        x = norm(x)
+        return x, tokens, attention_maps
+
+
+    ########## Normal block without Attention Maps ##########
+    # def forward(self, x):
+    #     if not self.return_dense:
+    #         x, tokens = self.forward_features(x)
+    #         x = self.head(x)
+    #         return x, tokens
+    #     else:
+    #         x, H, W = self.forward_embeddings(x)
+    #         # mix token, see token labeling for details.
+    #         if self.mix_token and self.training:
+    #             lam = np.random.beta(self.beta, self.beta)
+    #             patch_h, patch_w = x.shape[1] // self.pooling_scale, x.shape[
+    #                 2] // self.pooling_scale
+    #             bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam, scale=self.pooling_scale)
+    #             temp_x = x.clone()
+    #             sbbx1,sbby1,sbbx2,sbby2=self.pooling_scale*bbx1,self.pooling_scale*bby1,\
+    #                                     self.pooling_scale*bbx2,self.pooling_scale*bby2
+    #             temp_x[:, sbbx1:sbbx2, sbby1:sbby2, :] = x.flip(0)[:, sbbx1:sbbx2, sbby1:sbby2, :]
+    #             x = temp_x
+    #         else:
+    #             bbx1, bby1, bbx2, bby2 = 0, 0, 0, 0
+            
+    #         x = self.forward_tokens(x, H, W)
+    #         x_cls = self.head(x[:, 0])
+    #         x_aux = self.aux_head(
+    #             x[:, 1:]
+    #         )  # generate classes in all feature tokens, see token labeling
+
+    #         if not self.training:
+    #             return x_cls + 0.5 * x_aux.max(1)[0]
+
+    #         if self.mix_token and self.training:  # reverse "mix token", see token labeling for details.
+    #             x_aux = x_aux.reshape(x_aux.shape[0], patch_h, patch_w, x_aux.shape[-1])
+
+    #             temp_x = x_aux.clone()
+    #             temp_x[:, bbx1:bbx2, bby1:bby2, :] = x_aux.flip(0)[:, bbx1:bbx2, bby1:bby2, :]
+    #             x_aux = temp_x
+
+    #             x_aux = x_aux.reshape(x_aux.shape[0], patch_h * patch_w, x_aux.shape[-1])
+
+    #         return x_cls, x_aux, (bbx1, bby1, bbx2, bby2)
+
+
+
+
+    ########### You can create Attention Maps with this block ##########
+    def forward(self, x):
+        attention_maps = []  # Initialize to collect attention maps from all blocks
+    
+        if not self.return_dense:
+            # Retrieve main output, tokens, and attention maps
+            x, tokens, new_attention_maps = self.forward_features(x)
+            attention_maps.extend(new_attention_maps)  # Collect new attention maps
+            x = self.head(x)
+            return x, tokens, attention_maps
+        else:
+            # For dense token labeling and feature manipulation
+            x, H, W = self.forward_embeddings(x)
+            x, new_attention_maps = self.forward_tokens(x, H, W)  # Adjusted to return attention maps
+            attention_maps.extend(new_attention_maps)  # Collect new attention maps
+    
+            if self.mix_token and self.training:
+                lam = np.random.beta(self.beta, self.beta)
+                patch_h, patch_w = x.shape[1] // self.pooling_scale, x.shape[2] // self.pooling_scale
+                bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam, scale=self.pooling_scale)
+                sbbx1, sbby1, sbbx2, sbby2 = self.pooling_scale * bbx1, self.pooling_scale * bby1, self.pooling_scale * bbx2, self.pooling_scale * bby2
+                temp_x = x.clone()
+                temp_x[:, sbbx1:sbbx2, sbby1:sbby2, :] = x.flip(0)[:, sbbx1:sbbx2, sbby1:sbby2, :]
+                x = temp_x
+            else:
+                bbx1, bby1, bbx2, bby2 = 0, 0, 0, 0  # Default to zero if no mixing
+    
+            x_cls = self.head(x[:, 0])
+            x_aux = self.aux_head(x[:, 1:])  # Class prediction for all feature tokens
+    
+            if not self.training:
+                return x_cls + 0.5 * x_aux.max(1)[0], attention_maps
+    
+            return x_cls, x_aux, (bbx1, bby1, bbx2, bby2), attention_maps
+
+
+
+
+
+
+
+    def forward_tokens(self, x, H, W):
+        B = x.shape[0]
+        x = x.view(B, -1, x.size(-1))
+
+        for i in range(self.num_stages):
+            if i != 0:
+                patch_embed = getattr(self, f"patch_embed{i + 1}")
+                x, H, W = patch_embed(x)
+
+            block = getattr(self, f"block{i + 1}")
+            for blk in block:
+                x = blk(x, H, W)
+
+            if i != self.num_stages - 1:
+                norm = getattr(self, f"norm{i + 1}")
+                x = norm(x)
+                x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+
+        x = self.forward_cls(x)
+        norm = getattr(self, f"norm{self.num_stages}")
+        x = norm(x)    
+        return x
+
+    def forward_embeddings(self, x):
+        patch_embed = getattr(self, f"patch_embed{0 + 1}")
+        x, H, W = patch_embed(x)
+        x = x.view(x.size(0), H, W, -1)
+        return x, H, W
+
+
+class DWConv(nn.Module):
+    def __init__(self, dim=768):
+        super(DWConv, self).__init__()
+        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        x = x.transpose(1, 2).view(B, C, H, W)
+        x = self.dwconv(x)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+
+@register_model
+def painformer(pretrained=False, **kwargs):
+    model = SpectFormer(
+        stem_hidden_dim = 64,
+        embed_dims = [64, 128, 320, 160],
+        num_heads = [2, 4, 10, 16], 
+        mlp_ratios = [8, 8, 4, 4], 
+        norm_layer = partial(nn.LayerNorm, eps=1e-6), 
+        depths = [3, 4, 12, 3], 
+        sr_ratios = [4, 2, 1, 1], 
+        **kwargs)
+    model.default_cfg = _cfg()
+    return model