Added MVANet ComfyUI plugin

.gitignore

@@ -1,12 +1,21 @@
-**/__pycache__
+/ComfyUI_MVANet/download.sh
+/ComfyUI_MVANet/MVANet_inference.class.py
+/ComfyUI_MVANet/MVANet_inference.execute.py
+/ComfyUI_MVANet/MVANet_inference.function.py
+/ComfyUI_MVANet/MVANet_inference.import.py
+/ComfyUI_MVANet/MVANet_inference.run.sh
+/ComfyUI_MVANet/MVANet_inference.unify.sh
+/ComfyUI_MVANet/.#README.org
 data/
-log/
-pretrain_model/
-git_add.txt
-rm.txt
-main.org
-demo/demo_lip.png
-demo/lip-visualization.jpg
-demo/demo_pascal.png
 demo/demo_atr.png
 demo/demo.jpg
+demo/demo_lip.png
+demo/demo_pascal.png
+demo/lip-visualization.jpg
+/git_add.txt
+log/
+/main.org
+pretrain_model/
+**/__pycache__
+/rm.txt
+/waste.txt

ComfyUI_MVANet/MVANet_inference.py

@@ -0,0 +1,1548 @@
+#!/usr/bin/python3
+import os
+import sys
+
+HOME_DIR = os.environ.get('HOME', '/root')
+MVANET_SOURCE_DIR = HOME_DIR + '/GITHUB/qianyu-dlut/MVANet'
+finetuned_MVANet_model_path = MVANET_SOURCE_DIR + '/model/Model_80.pth'
+pretrained_SwinB_model_path = MVANET_SOURCE_DIR + '/model/swin_base_patch4_window12_384_22kto1k.pth'
+
+import math
+import numpy as np
+import cv2
+import wget
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch.autograd import Variable
+from torch import nn
+from torchvision import transforms
+
+from einops import rearrange
+
+from timm.models import load_checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+torch_device = 'cuda'
+torch_dtype = torch.float16
+
+
+def check_mkdir(dir_name):
+    if not os.path.isdir(dir_name):
+        os.makedirs(dir_name)
+
+
+def SwinT(pretrained=True):
+    model = SwinTransformer(embed_dim=96,
+                            depths=[2, 2, 6, 2],
+                            num_heads=[3, 6, 12, 24],
+                            window_size=7)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_tiny_patch4_window7_224.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def SwinS(pretrained=True):
+    model = SwinTransformer(embed_dim=96,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[3, 6, 12, 24],
+                            window_size=7)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_small_patch4_window7_224.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def SwinB(pretrained=True):
+    model = SwinTransformer(embed_dim=128,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[4, 8, 16, 32],
+                            window_size=12)
+    if pretrained is True:
+        import os
+        model.load_state_dict(torch.load(pretrained_SwinB_model_path,
+                                         map_location='cpu')['model'],
+                              strict=False)
+    return model
+
+
+def SwinL(pretrained=True):
+    model = SwinTransformer(embed_dim=192,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[6, 12, 24, 48],
+                            window_size=12)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_large_patch4_window12_384_22kto1k.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+def make_cbr(in_dim, out_dim):
+    return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                         nn.BatchNorm2d(out_dim), nn.PReLU())
+
+
+def make_cbg(in_dim, out_dim):
+    return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                         nn.BatchNorm2d(out_dim), nn.GELU())
+
+
+def rescale_to(x, scale_factor: float = 2, interpolation='nearest'):
+    return F.interpolate(x, scale_factor=scale_factor, mode=interpolation)
+
+
+def resize_as(x, y, interpolation='bilinear'):
+    return F.interpolate(x, size=y.shape[-2:], mode=interpolation)
+
+
+def image2patches(x):
+    """b c (hg h) (wg w) -> (hg wg b) c h w"""
+    x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2)
+    return x
+
+
+def patches2image(x):
+    """(hg wg b) c h w -> b c (hg h) (wg w)"""
+    x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2)
+    return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+               C)
+    windows = x.permute(0, 1, 3, 2, 4,
+                        5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size,
+                     window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+def mkdir_safe(out_path):
+    if type(out_path) == str:
+        if len(out_path) > 0:
+            if not os.path.exists(out_path):
+                os.mkdir(out_path)
+
+
+def get_model_path():
+    import folder_paths
+    from folder_paths import models_dir
+
+    path_file_model = models_dir
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'MVANet')
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'Model_80.pth')
+
+    return path_file_model
+
+
+def download_model(path):
+    if not os.path.exists(path):
+        wget.download(
+            'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth',
+            out=path)
+
+
+def load_model(model_checkpoint_path):
+    download_model(path=model_checkpoint_path)
+    torch.cuda.set_device(0)
+
+    net = inf_MVANet().to(dtype=torch_dtype, device=torch_device)
+
+    pretrained_dict = torch.load(finetuned_MVANet_model_path,
+                                 map_location=torch_device)
+
+    model_dict = net.state_dict()
+    pretrained_dict = {
+        k: v
+        for k, v in pretrained_dict.items() if k in model_dict
+    }
+    model_dict.update(pretrained_dict)
+    net.load_state_dict(model_dict)
+    net = net.to(dtype=torch_dtype, device=torch_device)
+    net.eval()
+    return net
+
+
+def do_infer_tensor2tensor(img, net):
+
+    img_transform = transforms.Compose(
+        [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+    h_, w_ = img.shape[1], img.shape[2]
+
+    with torch.no_grad():
+
+        img = rearrange(img, 'B H W C -> B C H W')
+
+        img_resize = torch.nn.functional.interpolate(input=img,
+                                                     size=(1024, 1024),
+                                                     mode='bicubic',
+                                                     antialias=True)
+
+        img_var = img_transform(img_resize)
+        img_var = Variable(img_var)
+        img_var = img_var.to(dtype=torch_dtype, device=torch_device)
+
+        mask = []
+
+        mask.append(net(img_var))
+
+        prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
+        prediction = prediction.sigmoid()
+
+        prediction = torch.nn.functional.interpolate(input=prediction,
+                                                     size=(h_, w_),
+                                                     mode='bicubic',
+                                                     antialias=True)
+
+        prediction = prediction.squeeze(0)
+        prediction = prediction.clamp(0, 1)
+        prediction = prediction.detach()
+        prediction = prediction.to(dtype=torch.float32, device='cpu')
+
+        return prediction
+
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class WindowAttention(nn.Module):
+    """ Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self,
+                 dim,
+                 window_size,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :,
+                                         None] - coords_flatten[:,
+                                                                None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :,
+                        0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index",
+                             relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """ Forward function.
+
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        x = x.to(dtype=torch_dtype, device=torch_device)
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[
+            2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+        attn = attn.to(dtype=torch_dtype, device=torch_device)
+        v = v.to(dtype=torch_dtype, device=torch_device)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """ Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=7,
+                 shift_size=0,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(dim,
+                                    window_size=to_2tuple(self.window_size),
+                                    num_heads=num_heads,
+                                    qkv_bias=qkv_bias,
+                                    qk_scale=qk_scale,
+                                    attn_drop=attn_drop,
+                                    proj_drop=drop)
+
+        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(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x,
+                                   shifts=(-self.shift_size, -self.shift_size),
+                                   dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                   C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                   Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x,
+                           shifts=(self.shift_size, self.shift_size),
+                           dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 use_checkpoint=False):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim,
+                                 num_heads=num_heads,
+                                 window_size=window_size,
+                                 shift_size=0 if
+                                 (i % 2 == 0) else window_size // 2,
+                                 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,
+                                 norm_layer=norm_layer) for i in range(depth)
+        ])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1,
+                                         self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                          float(-100.0)).masked_fill(
+                                              attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x,
+                      (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.2,
+                 norm_layer=nn.LayerNorm,
+                 ape=False,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1]
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0],
+                            patches_resolution[1]))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                norm_layer=norm_layer,
+                downsample=PatchMerging if
+                (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+
+        def _init_weights(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)
+
+        if isinstance(pretrained, str):
+            self.apply(_init_weights)
+            load_checkpoint(self, pretrained, strict=False, logger=None)
+        elif pretrained is None:
+            self.apply(_init_weights)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                               size=(Wh, Ww),
+                                               mode='bicubic')
+            x = (x + absolute_pos_embed)  # B Wh*Ww C
+
+        outs = [x.contiguous()]
+        x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W,
+                                 self.num_features[i]).permute(0, 3, 1,
+                                                               2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+class PositionEmbeddingSine:
+
+    def __init__(self,
+                 num_pos_feats=64,
+                 temperature=10000,
+                 normalize=False,
+                 scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+        self.dim_t = torch.arange(0,
+                                  self.num_pos_feats,
+                                  dtype=torch_dtype,
+                                  device=torch_device)
+
+    def __call__(self, b, h, w):
+        mask = torch.zeros([b, h, w], dtype=torch.bool, device=torch_device)
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(dim=1, dtype=torch_dtype)
+        x_embed = not_mask.cumsum(dim=2, dtype=torch_dtype)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) *
+                       self.scale).to(device=torch_device, dtype=torch_dtype)
+            x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) *
+                       self.scale).to(device=torch_device, dtype=torch_dtype)
+
+        dim_t = self.temperature**(2 * (self.dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+            dim=4).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+            dim=4).flatten(3)
+        return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+
+class MCLM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+        super(MCLM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear1 = nn.Linear(d_model, d_model * 2)
+        self.linear2 = nn.Linear(d_model * 2, d_model)
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.activation = get_activation_fn('relu')
+        self.pool_ratios = pool_ratios
+        self.p_poses = []
+        self.g_pos = None
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, l, g):
+        """
+        l: 4,c,h,w
+        g: 1,c,h,w
+        """
+        b, c, h, w = l.size()
+        # 4,c,h,w -> 1,c,2h,2w
+        concated_locs = rearrange(l,
+                                  '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                  hg=2,
+                                  wg=2)
+
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            # b,c,h,w
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+            pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+            if self.g_pos is None:
+                pos_emb = self.positional_encoding(pool.shape[0],
+                                                   pool.shape[2],
+                                                   pool.shape[3])
+                pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+                self.p_poses.append(pos_emb)
+        pools = torch.cat(pools, 0)
+        if self.g_pos is None:
+            self.p_poses = torch.cat(self.p_poses, dim=0)
+            pos_emb = self.positional_encoding(g.shape[0], g.shape[2],
+                                               g.shape[3])
+            self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+        # attention between glb (q) & multisensory concated-locs (k,v)
+        g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+        g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+            g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+        g_hw_b_c = self.norm1(g_hw_b_c)
+        g_hw_b_c = g_hw_b_c + self.dropout2(
+            self.linear2(
+                self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+        g_hw_b_c = self.norm2(g_hw_b_c)
+
+        # attention between origin locs (q) & freashed glb (k,v)
+        l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+        _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+        _g_hw_b_c = rearrange(_g_hw_b_c,
+                              "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                              ng=2,
+                              nw=2)
+        outputs_re = []
+        for i, (_l, _g) in enumerate(
+                zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+            outputs_re.append(self.attention[i + 1](_l, _g,
+                                                    _g)[0])  # (h w) 1 c
+        outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+        l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+        l_hw_b_c = self.norm1(l_hw_b_c)
+        l_hw_b_c = l_hw_b_c + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+        l_hw_b_c = self.norm2(l_hw_b_c)
+
+        l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+        return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+class inf_MCLM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+        super(inf_MCLM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear1 = nn.Linear(d_model, d_model * 2)
+        self.linear2 = nn.Linear(d_model * 2, d_model)
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.activation = get_activation_fn('relu')
+        self.pool_ratios = pool_ratios
+        self.p_poses = []
+        self.g_pos = None
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, l, g):
+        """
+        l: 4,c,h,w
+        g: 1,c,h,w
+        """
+        b, c, h, w = l.size()
+        # 4,c,h,w -> 1,c,2h,2w
+        concated_locs = rearrange(l,
+                                  '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                  hg=2,
+                                  wg=2)
+        self.p_poses = []
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            # b,c,h,w
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+            pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+            # if self.g_pos is None:
+            pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2],
+                                               pool.shape[3])
+            pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+            self.p_poses.append(pos_emb)
+        pools = torch.cat(pools, 0)
+        # if self.g_pos is None:
+        self.p_poses = torch.cat(self.p_poses, dim=0)
+        pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3])
+        self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+        # attention between glb (q) & multisensory concated-locs (k,v)
+        g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+        g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+            g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+        g_hw_b_c = self.norm1(g_hw_b_c)
+        g_hw_b_c = g_hw_b_c + self.dropout2(
+            self.linear2(
+                self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+        g_hw_b_c = self.norm2(g_hw_b_c)
+
+        # attention between origin locs (q) & freashed glb (k,v)
+        l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+        _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+        _g_hw_b_c = rearrange(_g_hw_b_c,
+                              "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                              ng=2,
+                              nw=2)
+        outputs_re = []
+        for i, (_l, _g) in enumerate(
+                zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+            outputs_re.append(self.attention[i + 1](_l, _g,
+                                                    _g)[0])  # (h w) 1 c
+        outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+        l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+        l_hw_b_c = self.norm1(l_hw_b_c)
+        l_hw_b_c = l_hw_b_c + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+        l_hw_b_c = self.norm2(l_hw_b_c)
+
+        l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+        return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+class MCRM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+        super(MCRM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.sigmoid = nn.Sigmoid()
+        self.activation = get_activation_fn('relu')
+        self.sal_conv = nn.Conv2d(d_model, 1, 1)
+        self.pool_ratios = pool_ratios
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+        # b(4),c,h,w
+        patched_glb = rearrange(glb,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+
+        # generate token attention map
+        token_attention_map = self.sigmoid(self.sal_conv(glb))
+        token_attention_map = F.interpolate(token_attention_map,
+                                            size=patches2image(loc).shape[-2:],
+                                            mode='nearest')
+        loc = loc * rearrange(token_attention_map,
+                              'b c (hg h) (wg w) -> (hg wg b) c h w',
+                              hg=2,
+                              wg=2)
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+            pools.append(rearrange(pool,
+                                   'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+        # nl(4),c,nphw -> nl(4),nphw,1,c
+        pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+        loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+        outputs = []
+        for i, q in enumerate(
+                loc_.unbind(dim=0)):  # traverse all local patches
+            # np*hw,1,c
+            v = pools[i]
+            k = v
+            outputs.append(self.attention[i](q, k, v)[0])
+        outputs = torch.cat(outputs, 1)
+        src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+        src = self.norm1(src)
+        src = src + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(src)).clone())))
+        src = self.norm2(src)
+
+        src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+        glb = glb + F.interpolate(patches2image(src),
+                                  size=glb.shape[-2:],
+                                  mode='nearest')  # freshed glb
+        return torch.cat((src, glb), 0), token_attention_map
+
+
+class inf_MCRM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+        super(inf_MCRM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.sigmoid = nn.Sigmoid()
+        self.activation = get_activation_fn('relu')
+        self.sal_conv = nn.Conv2d(d_model, 1, 1)
+        self.pool_ratios = pool_ratios
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+        # b(4),c,h,w
+        patched_glb = rearrange(glb,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+
+        # generate token attention map
+        token_attention_map = self.sigmoid(self.sal_conv(glb))
+        token_attention_map = F.interpolate(token_attention_map,
+                                            size=patches2image(loc).shape[-2:],
+                                            mode='nearest')
+        loc = loc * rearrange(token_attention_map,
+                              'b c (hg h) (wg w) -> (hg wg b) c h w',
+                              hg=2,
+                              wg=2)
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+            pools.append(rearrange(pool,
+                                   'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+        # nl(4),c,nphw -> nl(4),nphw,1,c
+        pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+        loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+        outputs = []
+        for i, q in enumerate(
+                loc_.unbind(dim=0)):  # traverse all local patches
+            # np*hw,1,c
+            v = pools[i]
+            k = v
+            outputs.append(self.attention[i](q, k, v)[0])
+        outputs = torch.cat(outputs, 1)
+        src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+        src = self.norm1(src)
+        src = src + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(src)).clone())))
+        src = self.norm2(src)
+
+        src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+        glb = glb + F.interpolate(patches2image(src),
+                                  size=glb.shape[-2:],
+                                  mode='nearest')  # freshed glb
+        return torch.cat((src, glb), 0)
+
+
+# model for single-scale training
+class MVANet(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.backbone = SwinB(pretrained=True)
+        emb_dim = 128
+        self.sideout5 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout4 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout3 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout2 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout1 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        self.output5 = make_cbr(1024, emb_dim)
+        self.output4 = make_cbr(512, emb_dim)
+        self.output3 = make_cbr(256, emb_dim)
+        self.output2 = make_cbr(128, emb_dim)
+        self.output1 = make_cbr(128, emb_dim)
+
+        self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8])
+        self.conv1 = make_cbr(emb_dim, emb_dim)
+        self.conv2 = make_cbr(emb_dim, emb_dim)
+        self.conv3 = make_cbr(emb_dim, emb_dim)
+        self.conv4 = make_cbr(emb_dim, emb_dim)
+        self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8])
+
+        self.insmask_head = nn.Sequential(
+            nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+            nn.BatchNorm2d(384), nn.PReLU(),
+            nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+            nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+        self.shallow = nn.Sequential(
+            nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+        self.upsample1 = make_cbg(emb_dim, emb_dim)
+        self.upsample2 = make_cbg(emb_dim, emb_dim)
+        self.output = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        for m in self.modules():
+            if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                m.inplace = True
+
+    def forward(self, x):
+        x = x.to(dtype=torch_dtype, device=torch_device)
+        shallow = self.shallow(x)
+        glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+        loc = image2patches(x)
+        input = torch.cat((loc, glb), dim=0)
+        feature = self.backbone(input)
+        e5 = self.output5(feature[4])  # (5,128,16,16)
+        e4 = self.output4(feature[3])  # (5,128,32,32)
+        e3 = self.output3(feature[2])  # (5,128,64,64)
+        e2 = self.output2(feature[1])  # (5,128,128,128)
+        e1 = self.output1(feature[0])  # (5,128,128,128)
+        loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+        e5 = self.multifieldcrossatt(loc_e5, glb_e5)  # (4,128,16,16)
+
+        e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4))
+        e4 = self.conv4(e4)
+        e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3))
+        e3 = self.conv3(e3)
+        e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2))
+        e2 = self.conv2(e2)
+        e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1))
+        e1 = self.conv1(e1)
+        loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+        output1_cat = patches2image(loc_e1)  # (1,128,256,256)
+        # add glb feat in
+        output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+        # merge
+        final_output = self.insmask_head(output1_cat)  # (1,128,256,256)
+        # shallow feature merge
+        final_output = final_output + resize_as(shallow, final_output)
+        final_output = self.upsample1(rescale_to(final_output))
+        final_output = rescale_to(final_output +
+                                  resize_as(shallow, final_output))
+        final_output = self.upsample2(final_output)
+        final_output = self.output(final_output)
+        ####
+        sideout5 = self.sideout5(e5).to(dtype=torch_dtype, device=torch_device)
+        sideout4 = self.sideout4(e4)
+        sideout3 = self.sideout3(e3)
+        sideout2 = self.sideout2(e2)
+        sideout1 = self.sideout1(e1)
+        #######glb_sideouts ######
+        glb5 = self.sideout5(glb_e5)
+        glb4 = sideout4[-1, :, :, :].unsqueeze(0)
+        glb3 = sideout3[-1, :, :, :].unsqueeze(0)
+        glb2 = sideout2[-1, :, :, :].unsqueeze(0)
+        glb1 = sideout1[-1, :, :, :].unsqueeze(0)
+        ####### concat 4 to 1 #######
+        sideout1 = patches2image(sideout1[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout2 = patches2image(sideout2[:-1]).to(
+            dtype=torch_dtype,
+            device=torch_device)  ####(5,c,h,w) -> (1 c 2h,2w)
+        sideout3 = patches2image(sideout3[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout4 = patches2image(sideout4[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout5 = patches2image(sideout5[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        if self.training:
+            return sideout5, sideout4, sideout3, sideout2, sideout1, final_output, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1
+        else:
+            return final_output
+
+
+# model for multi-scale testing
+class inf_MVANet(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # self.backbone = SwinB(pretrained=True)
+        self.backbone = SwinB(pretrained=False)
+
+        emb_dim = 128
+        self.output5 = make_cbr(1024, emb_dim)
+        self.output4 = make_cbr(512, emb_dim)
+        self.output3 = make_cbr(256, emb_dim)
+        self.output2 = make_cbr(128, emb_dim)
+        self.output1 = make_cbr(128, emb_dim)
+
+        self.multifieldcrossatt = inf_MCLM(emb_dim, 1, [1, 4, 8])
+        self.conv1 = make_cbr(emb_dim, emb_dim)
+        self.conv2 = make_cbr(emb_dim, emb_dim)
+        self.conv3 = make_cbr(emb_dim, emb_dim)
+        self.conv4 = make_cbr(emb_dim, emb_dim)
+        self.dec_blk1 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk2 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk3 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk4 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+
+        self.insmask_head = nn.Sequential(
+            nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+            nn.BatchNorm2d(384), nn.PReLU(),
+            nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+            nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+        self.shallow = nn.Sequential(
+            nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+        self.upsample1 = make_cbg(emb_dim, emb_dim)
+        self.upsample2 = make_cbg(emb_dim, emb_dim)
+        self.output = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        for m in self.modules():
+            if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                m.inplace = True
+
+    def forward(self, x):
+        shallow = self.shallow(x)
+        glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+        loc = image2patches(x)
+        input = torch.cat((loc, glb), dim=0)
+        feature = self.backbone(input)
+        e5 = self.output5(feature[4])
+        e4 = self.output4(feature[3])
+        e3 = self.output3(feature[2])
+        e2 = self.output2(feature[1])
+        e1 = self.output1(feature[0])
+        loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+        e5_cat = self.multifieldcrossatt(loc_e5, glb_e5)
+
+        e4 = self.conv4(self.dec_blk4(e4 + resize_as(e5_cat, e4)))
+        e3 = self.conv3(self.dec_blk3(e3 + resize_as(e4, e3)))
+        e2 = self.conv2(self.dec_blk2(e2 + resize_as(e3, e2)))
+        e1 = self.conv1(self.dec_blk1(e1 + resize_as(e2, e1)))
+        loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+        # after decoder, concat loc features to a whole one, and merge
+        output1_cat = patches2image(loc_e1)
+        # add glb feat in
+        output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+        # merge
+        final_output = self.insmask_head(output1_cat)
+        # shallow feature merge
+        final_output = final_output + resize_as(shallow, final_output)
+        final_output = self.upsample1(rescale_to(final_output))
+        final_output = rescale_to(final_output +
+                                  resize_as(shallow, final_output))
+        final_output = self.upsample2(final_output)
+        final_output = self.output(final_output)
+        return final_output
+
+
+class load_MVANet_Model:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ("MVANet_Model", )
+    FUNCTION = "test"
+    CATEGORY = "MVANet"
+
+    def test(self):
+        return (load_model(get_model_path()), )
+
+
+class run_MVANet_inference:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "MVANet_Model": ("MVANet_Model", ),
+            },
+        }
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "test"
+    CATEGORY = "MVANet"
+
+    def test(
+        self,
+        image,
+        MVANet_Model,
+    ):
+        ret = do_infer_tensor2tensor(img=image, net=MVANet_Model)
+
+        return (ret, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "load_MVANet_Model": load_MVANet_Model,
+    "run_MVANet_inference": run_MVANet_inference
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "load_MVANet_Model": "load MVANet Model",
+    "load_MVANet_Model": "load MVANet Model"
+}

ComfyUI_MVANet/README.org

@@ -0,0 +1,1694 @@
+* COMMENT Sample
+
+** Shell script to download
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./download.sh
+#+end_src
+
+** MVANet_inference import
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.import.py
+#+end_src
+
+** MVANet_inference function
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
+#+end_src
+
+** MVANet_inference class
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.class.py
+#+end_src
+
+** MVANet_inference execute
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.execute.py
+#+end_src
+
+** MVANet_inference unify
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./MVANet_inference.unify.sh
+#+end_src
+
+* Download the code:
+
+** Function to download
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./download.sh
+  get_repo(){
+      DIR_REPO="${HOME}/GITHUB/$('echo' "${1}" | 'sed' 's/^git@github.com://g ; s@^https://github.com/@@g ; s@.git$@@g' )"
+      DIR_BASE="$('dirname' '--' "${DIR_REPO}")"
+      mkdir -pv -- "${DIR_BASE}"
+      cd "${DIR_BASE}"
+      git clone "${1}"
+      cd "${DIR_REPO}"
+      git pull
+      git submodule update --recursive --init
+  }
+#+end_src
+
+** Download
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./download.sh
+  get_repo 'https://github.com/qianyu-dlut/MVANet.git'
+#+end_src
+
+* Dependencies
+#+begin_src conf :tangle ./requirements.txt
+  timm
+  einops
+  wget
+#+end_src
+
+* Python inference
+
+** Important configs
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.import.py
+  import os
+  import sys
+
+  HOME_DIR = os.environ.get('HOME', '/root')
+  MVANET_SOURCE_DIR = HOME_DIR + '/GITHUB/qianyu-dlut/MVANet'
+  finetuned_MVANet_model_path = MVANET_SOURCE_DIR + '/model/Model_80.pth'
+  pretrained_SwinB_model_path = MVANET_SOURCE_DIR + '/model/swin_base_patch4_window12_384_22kto1k.pth'
+#+end_src
+
+** MVANet_inference import
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.import.py
+  import math
+  import numpy as np
+  import cv2
+  import wget
+
+  import torch
+  import torch.nn as nn
+  import torch.nn.functional as F
+  import torch.utils.checkpoint as checkpoint
+  from torch.autograd import Variable
+  from torch import nn
+  from torchvision import transforms
+
+  from einops import rearrange
+
+  from timm.models import load_checkpoint
+  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+  torch_device = 'cuda'
+  torch_dtype = torch.float16
+#+end_src
+
+** COMMENT Load image using CV
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
+  def load_image(input_image_path):
+      img = cv2.imread(input_image_path, cv2.IMREAD_COLOR)
+      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+      return img
+
+
+  def load_image_torch(input_image_path):
+      img = cv2.imread(input_image_path, cv2.IMREAD_COLOR)
+      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+      img = torch.from_numpy(img)
+      img = img.to(dtype=torch.float32)
+      img /= 255.0
+      img = img.unsqueeze(0)
+      return img
+
+
+  def save_mask(output_image_path, mask):
+      cv2.imwrite(output_image_path, mask)
+
+
+  def save_mask_torch(output_image_path, mask):
+      mask = mask.detach().cpu()
+      mask *= 255.0
+      mask = mask.clamp(0, 255)
+      print(mask.shape)
+      mask = mask.squeeze(0)
+      mask = mask.to(dtype=torch.uint8)
+      print(mask.shape)
+      mask = mask.numpy()
+      print(mask.shape)
+      cv2.imwrite(output_image_path, mask)
+#+end_src
+
+** MVANet_inference function
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
+  def check_mkdir(dir_name):
+      if not os.path.isdir(dir_name):
+          os.makedirs(dir_name)
+
+
+  def SwinT(pretrained=True):
+      model = SwinTransformer(embed_dim=96,
+                              depths=[2, 2, 6, 2],
+                              num_heads=[3, 6, 12, 24],
+                              window_size=7)
+      if pretrained is True:
+          model.load_state_dict(torch.load(
+              'data/backbone_ckpt/swin_tiny_patch4_window7_224.pth',
+              map_location='cpu')['model'],
+                                strict=False)
+
+      return model
+
+
+  def SwinS(pretrained=True):
+      model = SwinTransformer(embed_dim=96,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[3, 6, 12, 24],
+                              window_size=7)
+      if pretrained is True:
+          model.load_state_dict(torch.load(
+              'data/backbone_ckpt/swin_small_patch4_window7_224.pth',
+              map_location='cpu')['model'],
+                                strict=False)
+
+      return model
+
+
+  def SwinB(pretrained=True):
+      model = SwinTransformer(embed_dim=128,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[4, 8, 16, 32],
+                              window_size=12)
+      if pretrained is True:
+          import os
+          model.load_state_dict(torch.load(pretrained_SwinB_model_path,
+                                           map_location='cpu')['model'],
+                                strict=False)
+      return model
+
+
+  def SwinL(pretrained=True):
+      model = SwinTransformer(embed_dim=192,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[6, 12, 24, 48],
+                              window_size=12)
+      if pretrained is True:
+          model.load_state_dict(torch.load(
+              'data/backbone_ckpt/swin_large_patch4_window12_384_22kto1k.pth',
+              map_location='cpu')['model'],
+                                strict=False)
+
+      return model
+
+
+  def get_activation_fn(activation):
+      """Return an activation function given a string"""
+      if activation == "relu":
+          return F.relu
+      if activation == "gelu":
+          return F.gelu
+      if activation == "glu":
+          return F.glu
+      raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+  def make_cbr(in_dim, out_dim):
+      return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                           nn.BatchNorm2d(out_dim), nn.PReLU())
+
+
+  def make_cbg(in_dim, out_dim):
+      return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                           nn.BatchNorm2d(out_dim), nn.GELU())
+
+
+  def rescale_to(x, scale_factor: float = 2, interpolation='nearest'):
+      return F.interpolate(x, scale_factor=scale_factor, mode=interpolation)
+
+
+  def resize_as(x, y, interpolation='bilinear'):
+      return F.interpolate(x, size=y.shape[-2:], mode=interpolation)
+
+
+  def image2patches(x):
+      """b c (hg h) (wg w) -> (hg wg b) c h w"""
+      x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2)
+      return x
+
+
+  def patches2image(x):
+      """(hg wg b) c h w -> b c (hg h) (wg w)"""
+      x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2)
+      return x
+
+
+  def window_partition(x, window_size):
+      """
+      Args:
+          x: (B, H, W, C)
+          window_size (int): window size
+
+      Returns:
+          windows: (num_windows*B, window_size, window_size, C)
+      """
+      B, H, W, C = x.shape
+      x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+                 C)
+      windows = x.permute(0, 1, 3, 2, 4,
+                          5).contiguous().view(-1, window_size, window_size, C)
+      return windows
+
+
+  def window_reverse(windows, window_size, H, W):
+      """
+      Args:
+          windows: (num_windows*B, window_size, window_size, C)
+          window_size (int): Window size
+          H (int): Height of image
+          W (int): Width of image
+
+      Returns:
+          x: (B, H, W, C)
+      """
+      B = int(windows.shape[0] / (H * W / window_size / window_size))
+      x = windows.view(B, H // window_size, W // window_size, window_size,
+                       window_size, -1)
+      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+      return x
+#+end_src
+
+** MVANet_inference class
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.class.py
+  class Mlp(nn.Module):
+      """ Multilayer perceptron."""
+
+      def __init__(self,
+                   in_features,
+                   hidden_features=None,
+                   out_features=None,
+                   act_layer=nn.GELU,
+                   drop=0.):
+          super().__init__()
+          out_features = out_features or in_features
+          hidden_features = hidden_features or in_features
+          self.fc1 = nn.Linear(in_features, hidden_features)
+          self.act = act_layer()
+          self.fc2 = nn.Linear(hidden_features, out_features)
+          self.drop = nn.Dropout(drop)
+
+      def forward(self, x):
+          x = self.fc1(x)
+          x = self.act(x)
+          x = self.drop(x)
+          x = self.fc2(x)
+          x = self.drop(x)
+          return x
+
+
+  class WindowAttention(nn.Module):
+      """ Window based multi-head self attention (W-MSA) module with relative position bias.
+      It supports both of shifted and non-shifted window.
+
+      Args:
+          dim (int): Number of input channels.
+          window_size (tuple[int]): The height and width of the window.
+          num_heads (int): Number of attention heads.
+          qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+          attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+          proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+      """
+
+      def __init__(self,
+                   dim,
+                   window_size,
+                   num_heads,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   attn_drop=0.,
+                   proj_drop=0.):
+
+          super().__init__()
+          self.dim = dim
+          self.window_size = window_size  # Wh, Ww
+          self.num_heads = num_heads
+          head_dim = dim // num_heads
+          self.scale = qk_scale or head_dim**-0.5
+
+          # define a parameter table of relative position bias
+          self.relative_position_bias_table = nn.Parameter(
+              torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                          num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+          # get pair-wise relative position index for each token inside the window
+          coords_h = torch.arange(self.window_size[0])
+          coords_w = torch.arange(self.window_size[1])
+          coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+          coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+          relative_coords = coords_flatten[:, :,
+                                           None] - coords_flatten[:,
+                                                                  None, :]  # 2, Wh*Ww, Wh*Ww
+          relative_coords = relative_coords.permute(
+              1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+          relative_coords[:, :,
+                          0] += self.window_size[0] - 1  # shift to start from 0
+          relative_coords[:, :, 1] += self.window_size[1] - 1
+          relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+          relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+          self.register_buffer("relative_position_index",
+                               relative_position_index)
+
+          self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+          self.attn_drop = nn.Dropout(attn_drop)
+          self.proj = nn.Linear(dim, dim)
+          self.proj_drop = nn.Dropout(proj_drop)
+
+          trunc_normal_(self.relative_position_bias_table, std=.02)
+          self.softmax = nn.Softmax(dim=-1)
+
+      def forward(self, x, mask=None):
+          """ Forward function.
+
+          Args:
+              x: input features with shape of (num_windows*B, N, C)
+              mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+          """
+          x = x.to(dtype=torch_dtype, device=torch_device)
+          B_, N, C = x.shape
+          qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                    C // self.num_heads).permute(2, 0, 3, 1, 4)
+          q, k, v = qkv[0], qkv[1], qkv[
+              2]  # make torchscript happy (cannot use tensor as tuple)
+
+          q = q * self.scale
+          attn = (q @ k.transpose(-2, -1))
+
+          relative_position_bias = self.relative_position_bias_table[
+              self.relative_position_index.view(-1)].view(
+                  self.window_size[0] * self.window_size[1],
+                  self.window_size[0] * self.window_size[1],
+                  -1)  # Wh*Ww,Wh*Ww,nH
+          relative_position_bias = relative_position_bias.permute(
+              2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+          attn = attn + relative_position_bias.unsqueeze(0)
+
+          if mask is not None:
+              nW = mask.shape[0]
+              attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                               N) + mask.unsqueeze(1).unsqueeze(0)
+              attn = attn.view(-1, self.num_heads, N, N)
+              attn = self.softmax(attn)
+          else:
+              attn = self.softmax(attn)
+
+          attn = self.attn_drop(attn)
+          attn = attn.to(dtype=torch_dtype, device=torch_device)
+          v = v.to(dtype=torch_dtype, device=torch_device)
+
+          x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+          x = self.proj(x)
+          x = self.proj_drop(x)
+          return x
+
+
+  class SwinTransformerBlock(nn.Module):
+      """ Swin Transformer Block.
+
+      Args:
+          dim (int): Number of input channels.
+          num_heads (int): Number of attention heads.
+          window_size (int): Window size.
+          shift_size (int): Shift size for SW-MSA.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float, optional): Stochastic depth rate. Default: 0.0
+          act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self,
+                   dim,
+                   num_heads,
+                   window_size=7,
+                   shift_size=0,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   act_layer=nn.GELU,
+                   norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.num_heads = num_heads
+          self.window_size = window_size
+          self.shift_size = shift_size
+          self.mlp_ratio = mlp_ratio
+          assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+          self.norm1 = norm_layer(dim)
+          self.attn = WindowAttention(dim,
+                                      window_size=to_2tuple(self.window_size),
+                                      num_heads=num_heads,
+                                      qkv_bias=qkv_bias,
+                                      qk_scale=qk_scale,
+                                      attn_drop=attn_drop,
+                                      proj_drop=drop)
+
+          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(in_features=dim,
+                         hidden_features=mlp_hidden_dim,
+                         act_layer=act_layer,
+                         drop=drop)
+
+          self.H = None
+          self.W = None
+
+      def forward(self, x, mask_matrix):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+              mask_matrix: Attention mask for cyclic shift.
+          """
+          B, L, C = x.shape
+          H, W = self.H, self.W
+          assert L == H * W, "input feature has wrong size"
+
+          shortcut = x
+          x = self.norm1(x)
+          x = x.view(B, H, W, C)
+
+          # pad feature maps to multiples of window size
+          pad_l = pad_t = 0
+          pad_r = (self.window_size - W % self.window_size) % self.window_size
+          pad_b = (self.window_size - H % self.window_size) % self.window_size
+          x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+          _, Hp, Wp, _ = x.shape
+
+          # cyclic shift
+          if self.shift_size > 0:
+              shifted_x = torch.roll(x,
+                                     shifts=(-self.shift_size, -self.shift_size),
+                                     dims=(1, 2))
+              attn_mask = mask_matrix
+          else:
+              shifted_x = x
+              attn_mask = None
+
+          # partition windows
+          x_windows = window_partition(
+              shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+          x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                     C)  # nW*B, window_size*window_size, C
+
+          # W-MSA/SW-MSA
+          attn_windows = self.attn(
+              x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+          # merge windows
+          attn_windows = attn_windows.view(-1, self.window_size,
+                                           self.window_size, C)
+          shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                     Wp)  # B H' W' C
+
+          # reverse cyclic shift
+          if self.shift_size > 0:
+              x = torch.roll(shifted_x,
+                             shifts=(self.shift_size, self.shift_size),
+                             dims=(1, 2))
+          else:
+              x = shifted_x
+
+          if pad_r > 0 or pad_b > 0:
+              x = x[:, :H, :W, :].contiguous()
+
+          x = x.view(B, H * W, C)
+
+          # FFN
+          x = shortcut + self.drop_path(x)
+          x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+          return x
+
+
+  class PatchMerging(nn.Module):
+      """ Patch Merging Layer
+
+      Args:
+          dim (int): Number of input channels.
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self, dim, norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+          self.norm = norm_layer(4 * dim)
+
+      def forward(self, x, H, W):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+          B, L, C = x.shape
+          assert L == H * W, "input feature has wrong size"
+
+          x = x.view(B, H, W, C)
+
+          # padding
+          pad_input = (H % 2 == 1) or (W % 2 == 1)
+          if pad_input:
+              x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+          x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+          x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+          x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+          x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+          x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+          x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+          x = self.norm(x)
+          x = self.reduction(x)
+
+          return x
+
+
+  class BasicLayer(nn.Module):
+      """ A basic Swin Transformer layer for one stage.
+
+      Args:
+          dim (int): Number of feature channels
+          depth (int): Depths of this stage.
+          num_heads (int): Number of attention head.
+          window_size (int): Local window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+          norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+          downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   dim,
+                   depth,
+                   num_heads,
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   norm_layer=nn.LayerNorm,
+                   downsample=None,
+                   use_checkpoint=False):
+          super().__init__()
+          self.window_size = window_size
+          self.shift_size = window_size // 2
+          self.depth = depth
+          self.use_checkpoint = use_checkpoint
+
+          # build blocks
+          self.blocks = nn.ModuleList([
+              SwinTransformerBlock(dim=dim,
+                                   num_heads=num_heads,
+                                   window_size=window_size,
+                                   shift_size=0 if
+                                   (i % 2 == 0) else window_size // 2,
+                                   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,
+                                   norm_layer=norm_layer) for i in range(depth)
+          ])
+
+          # patch merging layer
+          if downsample is not None:
+              self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+          else:
+              self.downsample = None
+
+      def forward(self, x, H, W):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+
+          # calculate attention mask for SW-MSA
+          Hp = int(np.ceil(H / self.window_size)) * self.window_size
+          Wp = int(np.ceil(W / self.window_size)) * self.window_size
+          img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+          h_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          w_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          cnt = 0
+          for h in h_slices:
+              for w in w_slices:
+                  img_mask[:, h, w, :] = cnt
+                  cnt += 1
+
+          mask_windows = window_partition(
+              img_mask, self.window_size)  # nW, window_size, window_size, 1
+          mask_windows = mask_windows.view(-1,
+                                           self.window_size * self.window_size)
+          attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+          attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                            float(-100.0)).masked_fill(
+                                                attn_mask == 0, float(0.0))
+
+          for blk in self.blocks:
+              blk.H, blk.W = H, W
+              if self.use_checkpoint:
+                  x = checkpoint.checkpoint(blk, x, attn_mask)
+              else:
+                  x = blk(x, attn_mask)
+          if self.downsample is not None:
+              x_down = self.downsample(x, H, W)
+              Wh, Ww = (H + 1) // 2, (W + 1) // 2
+              return x, H, W, x_down, Wh, Ww
+          else:
+              return x, H, W, x, H, W
+
+
+  class PatchEmbed(nn.Module):
+      """ Image to Patch Embedding
+
+      Args:
+          patch_size (int): Patch token size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          norm_layer (nn.Module, optional): Normalization layer. Default: None
+      """
+
+      def __init__(self,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   norm_layer=None):
+          super().__init__()
+          patch_size = to_2tuple(patch_size)
+          self.patch_size = patch_size
+
+          self.in_chans = in_chans
+          self.embed_dim = embed_dim
+
+          self.proj = nn.Conv2d(in_chans,
+                                embed_dim,
+                                kernel_size=patch_size,
+                                stride=patch_size)
+          if norm_layer is not None:
+              self.norm = norm_layer(embed_dim)
+          else:
+              self.norm = None
+
+      def forward(self, x):
+          """Forward function."""
+          # padding
+          _, _, H, W = x.size()
+          if W % self.patch_size[1] != 0:
+              x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+          if H % self.patch_size[0] != 0:
+              x = F.pad(x,
+                        (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+          x = self.proj(x)  # B C Wh Ww
+          if self.norm is not None:
+              Wh, Ww = x.size(2), x.size(3)
+              x = x.flatten(2).transpose(1, 2)
+              x = self.norm(x)
+              x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+          return x
+
+
+  class SwinTransformer(nn.Module):
+      """ Swin Transformer backbone.
+          A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+            https://arxiv.org/pdf/2103.14030
+
+      Args:
+          pretrain_img_size (int): Input image size for training the pretrained model,
+              used in absolute postion embedding. Default 224.
+          patch_size (int | tuple(int)): Patch size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          depths (tuple[int]): Depths of each Swin Transformer stage.
+          num_heads (tuple[int]): Number of attention head of each stage.
+          window_size (int): Window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+          drop_rate (float): Dropout rate.
+          attn_drop_rate (float): Attention dropout rate. Default: 0.
+          drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+          norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+          ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+          patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+          out_indices (Sequence[int]): Output from which stages.
+          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+              -1 means not freezing any parameters.
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   pretrain_img_size=224,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   depths=[2, 2, 6, 2],
+                   num_heads=[3, 6, 12, 24],
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop_rate=0.,
+                   attn_drop_rate=0.,
+                   drop_path_rate=0.2,
+                   norm_layer=nn.LayerNorm,
+                   ape=False,
+                   patch_norm=True,
+                   out_indices=(0, 1, 2, 3),
+                   frozen_stages=-1,
+                   use_checkpoint=False):
+          super().__init__()
+
+          self.pretrain_img_size = pretrain_img_size
+          self.num_layers = len(depths)
+          self.embed_dim = embed_dim
+          self.ape = ape
+          self.patch_norm = patch_norm
+          self.out_indices = out_indices
+          self.frozen_stages = frozen_stages
+
+          # split image into non-overlapping patches
+          self.patch_embed = PatchEmbed(
+              patch_size=patch_size,
+              in_chans=in_chans,
+              embed_dim=embed_dim,
+              norm_layer=norm_layer if self.patch_norm else None)
+
+          # absolute position embedding
+          if self.ape:
+              pretrain_img_size = to_2tuple(pretrain_img_size)
+              patch_size = to_2tuple(patch_size)
+              patches_resolution = [
+                  pretrain_img_size[0] // patch_size[0],
+                  pretrain_img_size[1] // patch_size[1]
+              ]
+
+              self.absolute_pos_embed = nn.Parameter(
+                  torch.zeros(1, embed_dim, patches_resolution[0],
+                              patches_resolution[1]))
+              trunc_normal_(self.absolute_pos_embed, std=.02)
+
+          self.pos_drop = nn.Dropout(p=drop_rate)
+
+          # stochastic depth
+          dpr = [
+              x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+          ]  # stochastic depth decay rule
+
+          # build layers
+          self.layers = nn.ModuleList()
+          for i_layer in range(self.num_layers):
+              layer = BasicLayer(
+                  dim=int(embed_dim * 2**i_layer),
+                  depth=depths[i_layer],
+                  num_heads=num_heads[i_layer],
+                  window_size=window_size,
+                  mlp_ratio=mlp_ratio,
+                  qkv_bias=qkv_bias,
+                  qk_scale=qk_scale,
+                  drop=drop_rate,
+                  attn_drop=attn_drop_rate,
+                  drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                  norm_layer=norm_layer,
+                  downsample=PatchMerging if
+                  (i_layer < self.num_layers - 1) else None,
+                  use_checkpoint=use_checkpoint)
+              self.layers.append(layer)
+
+          num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+          self.num_features = num_features
+
+          # add a norm layer for each output
+          for i_layer in out_indices:
+              layer = norm_layer(num_features[i_layer])
+              layer_name = f'norm{i_layer}'
+              self.add_module(layer_name, layer)
+
+          self._freeze_stages()
+
+      def _freeze_stages(self):
+          if self.frozen_stages >= 0:
+              self.patch_embed.eval()
+              for param in self.patch_embed.parameters():
+                  param.requires_grad = False
+
+          if self.frozen_stages >= 1 and self.ape:
+              self.absolute_pos_embed.requires_grad = False
+
+          if self.frozen_stages >= 2:
+              self.pos_drop.eval()
+              for i in range(0, self.frozen_stages - 1):
+                  m = self.layers[i]
+                  m.eval()
+                  for param in m.parameters():
+                      param.requires_grad = False
+
+      def init_weights(self, pretrained=None):
+          """Initialize the weights in backbone.
+
+          Args:
+              pretrained (str, optional): Path to pre-trained weights.
+                  Defaults to None.
+          """
+
+          def _init_weights(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)
+
+          if isinstance(pretrained, str):
+              self.apply(_init_weights)
+              load_checkpoint(self, pretrained, strict=False, logger=None)
+          elif pretrained is None:
+              self.apply(_init_weights)
+          else:
+              raise TypeError('pretrained must be a str or None')
+
+      def forward(self, x):
+          x = self.patch_embed(x)
+
+          Wh, Ww = x.size(2), x.size(3)
+          if self.ape:
+              # interpolate the position embedding to the corresponding size
+              absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                                 size=(Wh, Ww),
+                                                 mode='bicubic')
+              x = (x + absolute_pos_embed)  # B Wh*Ww C
+
+          outs = [x.contiguous()]
+          x = x.flatten(2).transpose(1, 2)
+          x = self.pos_drop(x)
+          for i in range(self.num_layers):
+              layer = self.layers[i]
+              x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+              if i in self.out_indices:
+                  norm_layer = getattr(self, f'norm{i}')
+                  x_out = norm_layer(x_out)
+
+                  out = x_out.view(-1, H, W,
+                                   self.num_features[i]).permute(0, 3, 1,
+                                                                 2).contiguous()
+                  outs.append(out)
+
+          return tuple(outs)
+
+      def train(self, mode=True):
+          """Convert the model into training mode while keep layers freezed."""
+          super(SwinTransformer, self).train(mode)
+          self._freeze_stages()
+
+
+  class PositionEmbeddingSine:
+
+      def __init__(self,
+                   num_pos_feats=64,
+                   temperature=10000,
+                   normalize=False,
+                   scale=None):
+          super().__init__()
+          self.num_pos_feats = num_pos_feats
+          self.temperature = temperature
+          self.normalize = normalize
+          if scale is not None and normalize is False:
+              raise ValueError("normalize should be True if scale is passed")
+          if scale is None:
+              scale = 2 * math.pi
+          self.scale = scale
+          self.dim_t = torch.arange(0,
+                                    self.num_pos_feats,
+                                    dtype=torch_dtype,
+                                    device=torch_device)
+
+      def __call__(self, b, h, w):
+          mask = torch.zeros([b, h, w], dtype=torch.bool, device=torch_device)
+          assert mask is not None
+          not_mask = ~mask
+          y_embed = not_mask.cumsum(dim=1, dtype=torch_dtype)
+          x_embed = not_mask.cumsum(dim=2, dtype=torch_dtype)
+          if self.normalize:
+              eps = 1e-6
+              y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) *
+                         self.scale).to(device=torch_device, dtype=torch_dtype)
+              x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) *
+                         self.scale).to(device=torch_device, dtype=torch_dtype)
+
+          dim_t = self.temperature**(2 * (self.dim_t // 2) / self.num_pos_feats)
+
+          pos_x = x_embed[:, :, :, None] / dim_t
+          pos_y = y_embed[:, :, :, None] / dim_t
+          pos_x = torch.stack(
+              (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+              dim=4).flatten(3)
+          pos_y = torch.stack(
+              (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+              dim=4).flatten(3)
+          return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+
+  class MCLM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+          super(MCLM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear1 = nn.Linear(d_model, d_model * 2)
+          self.linear2 = nn.Linear(d_model * 2, d_model)
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.activation = get_activation_fn('relu')
+          self.pool_ratios = pool_ratios
+          self.p_poses = []
+          self.g_pos = None
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, l, g):
+          """
+          l: 4,c,h,w
+          g: 1,c,h,w
+          """
+          b, c, h, w = l.size()
+          # 4,c,h,w -> 1,c,2h,2w
+          concated_locs = rearrange(l,
+                                    '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                    hg=2,
+                                    wg=2)
+
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              # b,c,h,w
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+              pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+              if self.g_pos is None:
+                  pos_emb = self.positional_encoding(pool.shape[0],
+                                                     pool.shape[2],
+                                                     pool.shape[3])
+                  pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+                  self.p_poses.append(pos_emb)
+          pools = torch.cat(pools, 0)
+          if self.g_pos is None:
+              self.p_poses = torch.cat(self.p_poses, dim=0)
+              pos_emb = self.positional_encoding(g.shape[0], g.shape[2],
+                                                 g.shape[3])
+              self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+          # attention between glb (q) & multisensory concated-locs (k,v)
+          g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+          g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+              g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+          g_hw_b_c = self.norm1(g_hw_b_c)
+          g_hw_b_c = g_hw_b_c + self.dropout2(
+              self.linear2(
+                  self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+          g_hw_b_c = self.norm2(g_hw_b_c)
+
+          # attention between origin locs (q) & freashed glb (k,v)
+          l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+          _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+          _g_hw_b_c = rearrange(_g_hw_b_c,
+                                "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                                ng=2,
+                                nw=2)
+          outputs_re = []
+          for i, (_l, _g) in enumerate(
+                  zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+              outputs_re.append(self.attention[i + 1](_l, _g,
+                                                      _g)[0])  # (h w) 1 c
+          outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+          l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+          l_hw_b_c = self.norm1(l_hw_b_c)
+          l_hw_b_c = l_hw_b_c + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+          l_hw_b_c = self.norm2(l_hw_b_c)
+
+          l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+          return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+  class inf_MCLM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+          super(inf_MCLM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear1 = nn.Linear(d_model, d_model * 2)
+          self.linear2 = nn.Linear(d_model * 2, d_model)
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.activation = get_activation_fn('relu')
+          self.pool_ratios = pool_ratios
+          self.p_poses = []
+          self.g_pos = None
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, l, g):
+          """
+          l: 4,c,h,w
+          g: 1,c,h,w
+          """
+          b, c, h, w = l.size()
+          # 4,c,h,w -> 1,c,2h,2w
+          concated_locs = rearrange(l,
+                                    '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                    hg=2,
+                                    wg=2)
+          self.p_poses = []
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              # b,c,h,w
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+              pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+              # if self.g_pos is None:
+              pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2],
+                                                 pool.shape[3])
+              pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+              self.p_poses.append(pos_emb)
+          pools = torch.cat(pools, 0)
+          # if self.g_pos is None:
+          self.p_poses = torch.cat(self.p_poses, dim=0)
+          pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3])
+          self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+          # attention between glb (q) & multisensory concated-locs (k,v)
+          g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+          g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+              g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+          g_hw_b_c = self.norm1(g_hw_b_c)
+          g_hw_b_c = g_hw_b_c + self.dropout2(
+              self.linear2(
+                  self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+          g_hw_b_c = self.norm2(g_hw_b_c)
+
+          # attention between origin locs (q) & freashed glb (k,v)
+          l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+          _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+          _g_hw_b_c = rearrange(_g_hw_b_c,
+                                "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                                ng=2,
+                                nw=2)
+          outputs_re = []
+          for i, (_l, _g) in enumerate(
+                  zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+              outputs_re.append(self.attention[i + 1](_l, _g,
+                                                      _g)[0])  # (h w) 1 c
+          outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+          l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+          l_hw_b_c = self.norm1(l_hw_b_c)
+          l_hw_b_c = l_hw_b_c + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+          l_hw_b_c = self.norm2(l_hw_b_c)
+
+          l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+          return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+  class MCRM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+          super(MCRM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.sigmoid = nn.Sigmoid()
+          self.activation = get_activation_fn('relu')
+          self.sal_conv = nn.Conv2d(d_model, 1, 1)
+          self.pool_ratios = pool_ratios
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, x):
+          b, c, h, w = x.size()
+          loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+          # b(4),c,h,w
+          patched_glb = rearrange(glb,
+                                  'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                  hg=2,
+                                  wg=2)
+
+          # generate token attention map
+          token_attention_map = self.sigmoid(self.sal_conv(glb))
+          token_attention_map = F.interpolate(token_attention_map,
+                                              size=patches2image(loc).shape[-2:],
+                                              mode='nearest')
+          loc = loc * rearrange(token_attention_map,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+              pools.append(rearrange(pool,
+                                     'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+          # nl(4),c,nphw -> nl(4),nphw,1,c
+          pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+          loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+          outputs = []
+          for i, q in enumerate(
+                  loc_.unbind(dim=0)):  # traverse all local patches
+              # np*hw,1,c
+              v = pools[i]
+              k = v
+              outputs.append(self.attention[i](q, k, v)[0])
+          outputs = torch.cat(outputs, 1)
+          src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+          src = self.norm1(src)
+          src = src + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(src)).clone())))
+          src = self.norm2(src)
+
+          src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+          glb = glb + F.interpolate(patches2image(src),
+                                    size=glb.shape[-2:],
+                                    mode='nearest')  # freshed glb
+          return torch.cat((src, glb), 0), token_attention_map
+
+
+  class inf_MCRM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+          super(inf_MCRM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.sigmoid = nn.Sigmoid()
+          self.activation = get_activation_fn('relu')
+          self.sal_conv = nn.Conv2d(d_model, 1, 1)
+          self.pool_ratios = pool_ratios
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, x):
+          b, c, h, w = x.size()
+          loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+          # b(4),c,h,w
+          patched_glb = rearrange(glb,
+                                  'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                  hg=2,
+                                  wg=2)
+
+          # generate token attention map
+          token_attention_map = self.sigmoid(self.sal_conv(glb))
+          token_attention_map = F.interpolate(token_attention_map,
+                                              size=patches2image(loc).shape[-2:],
+                                              mode='nearest')
+          loc = loc * rearrange(token_attention_map,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+              pools.append(rearrange(pool,
+                                     'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+          # nl(4),c,nphw -> nl(4),nphw,1,c
+          pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+          loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+          outputs = []
+          for i, q in enumerate(
+                  loc_.unbind(dim=0)):  # traverse all local patches
+              # np*hw,1,c
+              v = pools[i]
+              k = v
+              outputs.append(self.attention[i](q, k, v)[0])
+          outputs = torch.cat(outputs, 1)
+          src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+          src = self.norm1(src)
+          src = src + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(src)).clone())))
+          src = self.norm2(src)
+
+          src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+          glb = glb + F.interpolate(patches2image(src),
+                                    size=glb.shape[-2:],
+                                    mode='nearest')  # freshed glb
+          return torch.cat((src, glb), 0)
+
+
+  # model for single-scale training
+  class MVANet(nn.Module):
+
+      def __init__(self):
+          super().__init__()
+          self.backbone = SwinB(pretrained=True)
+          emb_dim = 128
+          self.sideout5 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout4 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout3 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout2 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout1 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          self.output5 = make_cbr(1024, emb_dim)
+          self.output4 = make_cbr(512, emb_dim)
+          self.output3 = make_cbr(256, emb_dim)
+          self.output2 = make_cbr(128, emb_dim)
+          self.output1 = make_cbr(128, emb_dim)
+
+          self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8])
+          self.conv1 = make_cbr(emb_dim, emb_dim)
+          self.conv2 = make_cbr(emb_dim, emb_dim)
+          self.conv3 = make_cbr(emb_dim, emb_dim)
+          self.conv4 = make_cbr(emb_dim, emb_dim)
+          self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8])
+
+          self.insmask_head = nn.Sequential(
+              nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+              nn.BatchNorm2d(384), nn.PReLU(),
+              nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+              nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+          self.shallow = nn.Sequential(
+              nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+          self.upsample1 = make_cbg(emb_dim, emb_dim)
+          self.upsample2 = make_cbg(emb_dim, emb_dim)
+          self.output = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          for m in self.modules():
+              if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                  m.inplace = True
+
+      def forward(self, x):
+          x = x.to(dtype=torch_dtype, device=torch_device)
+          shallow = self.shallow(x)
+          glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+          loc = image2patches(x)
+          input = torch.cat((loc, glb), dim=0)
+          feature = self.backbone(input)
+          e5 = self.output5(feature[4])  # (5,128,16,16)
+          e4 = self.output4(feature[3])  # (5,128,32,32)
+          e3 = self.output3(feature[2])  # (5,128,64,64)
+          e2 = self.output2(feature[1])  # (5,128,128,128)
+          e1 = self.output1(feature[0])  # (5,128,128,128)
+          loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+          e5 = self.multifieldcrossatt(loc_e5, glb_e5)  # (4,128,16,16)
+
+          e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4))
+          e4 = self.conv4(e4)
+          e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3))
+          e3 = self.conv3(e3)
+          e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2))
+          e2 = self.conv2(e2)
+          e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1))
+          e1 = self.conv1(e1)
+          loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+          output1_cat = patches2image(loc_e1)  # (1,128,256,256)
+          # add glb feat in
+          output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+          # merge
+          final_output = self.insmask_head(output1_cat)  # (1,128,256,256)
+          # shallow feature merge
+          final_output = final_output + resize_as(shallow, final_output)
+          final_output = self.upsample1(rescale_to(final_output))
+          final_output = rescale_to(final_output +
+                                    resize_as(shallow, final_output))
+          final_output = self.upsample2(final_output)
+          final_output = self.output(final_output)
+          ####
+          sideout5 = self.sideout5(e5).to(dtype=torch_dtype, device=torch_device)
+          sideout4 = self.sideout4(e4)
+          sideout3 = self.sideout3(e3)
+          sideout2 = self.sideout2(e2)
+          sideout1 = self.sideout1(e1)
+          #######glb_sideouts ######
+          glb5 = self.sideout5(glb_e5)
+          glb4 = sideout4[-1, :, :, :].unsqueeze(0)
+          glb3 = sideout3[-1, :, :, :].unsqueeze(0)
+          glb2 = sideout2[-1, :, :, :].unsqueeze(0)
+          glb1 = sideout1[-1, :, :, :].unsqueeze(0)
+          ####### concat 4 to 1 #######
+          sideout1 = patches2image(sideout1[:-1]).to(dtype=torch_dtype,
+                                                     device=torch_device)
+          sideout2 = patches2image(sideout2[:-1]).to(
+              dtype=torch_dtype,
+              device=torch_device)  ####(5,c,h,w) -> (1 c 2h,2w)
+          sideout3 = patches2image(sideout3[:-1]).to(dtype=torch_dtype,
+                                                     device=torch_device)
+          sideout4 = patches2image(sideout4[:-1]).to(dtype=torch_dtype,
+                                                     device=torch_device)
+          sideout5 = patches2image(sideout5[:-1]).to(dtype=torch_dtype,
+                                                     device=torch_device)
+          if self.training:
+              return sideout5, sideout4, sideout3, sideout2, sideout1, final_output, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1
+          else:
+              return final_output
+
+
+  # model for multi-scale testing
+  class inf_MVANet(nn.Module):
+
+      def __init__(self):
+          super().__init__()
+          # self.backbone = SwinB(pretrained=True)
+          self.backbone = SwinB(pretrained=False)
+
+          emb_dim = 128
+          self.output5 = make_cbr(1024, emb_dim)
+          self.output4 = make_cbr(512, emb_dim)
+          self.output3 = make_cbr(256, emb_dim)
+          self.output2 = make_cbr(128, emb_dim)
+          self.output1 = make_cbr(128, emb_dim)
+
+          self.multifieldcrossatt = inf_MCLM(emb_dim, 1, [1, 4, 8])
+          self.conv1 = make_cbr(emb_dim, emb_dim)
+          self.conv2 = make_cbr(emb_dim, emb_dim)
+          self.conv3 = make_cbr(emb_dim, emb_dim)
+          self.conv4 = make_cbr(emb_dim, emb_dim)
+          self.dec_blk1 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk2 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk3 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk4 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+
+          self.insmask_head = nn.Sequential(
+              nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+              nn.BatchNorm2d(384), nn.PReLU(),
+              nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+              nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+          self.shallow = nn.Sequential(
+              nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+          self.upsample1 = make_cbg(emb_dim, emb_dim)
+          self.upsample2 = make_cbg(emb_dim, emb_dim)
+          self.output = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          for m in self.modules():
+              if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                  m.inplace = True
+
+      def forward(self, x):
+          shallow = self.shallow(x)
+          glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+          loc = image2patches(x)
+          input = torch.cat((loc, glb), dim=0)
+          feature = self.backbone(input)
+          e5 = self.output5(feature[4])
+          e4 = self.output4(feature[3])
+          e3 = self.output3(feature[2])
+          e2 = self.output2(feature[1])
+          e1 = self.output1(feature[0])
+          loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+          e5_cat = self.multifieldcrossatt(loc_e5, glb_e5)
+
+          e4 = self.conv4(self.dec_blk4(e4 + resize_as(e5_cat, e4)))
+          e3 = self.conv3(self.dec_blk3(e3 + resize_as(e4, e3)))
+          e2 = self.conv2(self.dec_blk2(e2 + resize_as(e3, e2)))
+          e1 = self.conv1(self.dec_blk1(e1 + resize_as(e2, e1)))
+          loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+          # after decoder, concat loc features to a whole one, and merge
+          output1_cat = patches2image(loc_e1)
+          # add glb feat in
+          output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+          # merge
+          final_output = self.insmask_head(output1_cat)
+          # shallow feature merge
+          final_output = final_output + resize_as(shallow, final_output)
+          final_output = self.upsample1(rescale_to(final_output))
+          final_output = rescale_to(final_output +
+                                    resize_as(shallow, final_output))
+          final_output = self.upsample2(final_output)
+          final_output = self.output(final_output)
+          return final_output
+#+end_src
+
+** Function to load model
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
+  def mkdir_safe(out_path):
+      if type(out_path) == str:
+          if len(out_path) > 0:
+              if not os.path.exists(out_path):
+                  os.mkdir(out_path)
+
+
+  def get_model_path():
+      import folder_paths
+      from folder_paths import models_dir
+
+      path_file_model = models_dir
+      mkdir_safe(out_path=path_file_model)
+
+      path_file_model = os.path.join(path_file_model, 'MVANet')
+      mkdir_safe(out_path=path_file_model)
+
+      path_file_model = os.path.join(path_file_model, 'Model_80.pth')
+
+      return path_file_model
+
+
+  def download_model(path):
+      if not os.path.exists(path):
+          wget.download(
+              'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth',
+              out=path)
+
+
+  def load_model(model_checkpoint_path):
+      download_model(path=model_checkpoint_path)
+      torch.cuda.set_device(0)
+
+      net = inf_MVANet().to(dtype=torch_dtype, device=torch_device)
+
+      pretrained_dict = torch.load(finetuned_MVANet_model_path,
+                                   map_location=torch_device)
+
+      model_dict = net.state_dict()
+      pretrained_dict = {
+          k: v
+          for k, v in pretrained_dict.items() if k in model_dict
+      }
+      model_dict.update(pretrained_dict)
+      net.load_state_dict(model_dict)
+      net = net.to(dtype=torch_dtype, device=torch_device)
+      net.eval()
+      return net
+#+end_src
+
+** Function for modular inference CV
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
+  def do_infer_tensor2tensor(img, net):
+
+      img_transform = transforms.Compose(
+          [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+      h_, w_ = img.shape[1], img.shape[2]
+
+      with torch.no_grad():
+
+          img = rearrange(img, 'B H W C -> B C H W')
+
+          img_resize = torch.nn.functional.interpolate(input=img,
+                                                       size=(1024, 1024),
+                                                       mode='bicubic',
+                                                       antialias=True)
+
+          img_var = img_transform(img_resize)
+          img_var = Variable(img_var)
+          img_var = img_var.to(dtype=torch_dtype, device=torch_device)
+
+          mask = []
+
+          mask.append(net(img_var))
+
+          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
+          prediction = prediction.sigmoid()
+
+          prediction = torch.nn.functional.interpolate(input=prediction,
+                                                       size=(h_, w_),
+                                                       mode='bicubic',
+                                                       antialias=True)
+
+          prediction = prediction.squeeze(0)
+          prediction = prediction.clamp(0, 1)
+          prediction = prediction.detach()
+          prediction = prediction.to(dtype=torch.float32, device='cpu')
+
+          return prediction
+#+end_src
+
+** Comfyui wrapper classes
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.class.py
+  class load_MVANet_Model:
+
+      def __init__(self):
+          pass
+
+      @classmethod
+      def INPUT_TYPES(s):
+          return {
+              "required": {},
+          }
+
+      RETURN_TYPES = ("MVANet_Model", )
+      FUNCTION = "test"
+      CATEGORY = "MVANet"
+
+      def test(self):
+          return (load_model(get_model_path()), )
+
+
+  class run_MVANet_inference:
+
+      def __init__(self):
+          pass
+
+      @classmethod
+      def INPUT_TYPES(s):
+          return {
+              "required": {
+                  "image": ("IMAGE", ),
+                  "MVANet_Model": ("MVANet_Model", ),
+              },
+          }
+
+      RETURN_TYPES = ("MASK", )
+      FUNCTION = "test"
+      CATEGORY = "MVANet"
+
+      def test(
+          self,
+          image,
+          MVANet_Model,
+      ):
+          ret = do_infer_tensor2tensor(img=image, net=MVANet_Model)
+
+          return (ret, )
+#+end_src
+
+** MVANet_inference execute
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.execute.py
+  NODE_CLASS_MAPPINGS = {
+      "load_MVANet_Model": load_MVANet_Model,
+      "run_MVANet_inference": run_MVANet_inference
+  }
+
+  NODE_DISPLAY_NAME_MAPPINGS = {
+      "load_MVANet_Model": "load MVANet Model",
+      "load_MVANet_Model": "load MVANet Model"
+  }
+#+end_src
+
+** MVANet_inference unify
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./MVANet_inference.unify.sh
+  . "${HOME}/dbnew.sh"
+
+  (
+      echo '#!/usr/bin/python3'
+      cat \
+          './MVANet_inference.import.py' \
+          './MVANet_inference.function.py' \
+          './MVANet_inference.class.py' \
+          './MVANet_inference.execute.py' \
+      | expand | yapf3 \
+      | grep -v '#!/usr/bin/python3' \
+      ;
+  ) > './MVANet_inference.py' \
+  ;
+
+  cp './MVANet_inference.py' '__init__.py'
+#+end_src
+
+* WORK SPACE
+
+** elisp
+#+begin_src elisp
+  (save-buffer)
+  (org-babel-tangle)
+  (shell-command "./MVANet_inference.unify.sh")
+#+end_src
+
+#+RESULTS:
+: 0
+
+** sh
+#+begin_src sh :shebang #!/bin/sh :results output 
+  realpath .
+  cd /home/asd/GITHUB/aravind-h-v/dreambooth_experiments/MVANet
+#+end_src

ComfyUI_MVANet/__init__.py

@@ -0,0 +1,1548 @@
+#!/usr/bin/python3
+import os
+import sys
+
+HOME_DIR = os.environ.get('HOME', '/root')
+MVANET_SOURCE_DIR = HOME_DIR + '/GITHUB/qianyu-dlut/MVANet'
+finetuned_MVANet_model_path = MVANET_SOURCE_DIR + '/model/Model_80.pth'
+pretrained_SwinB_model_path = MVANET_SOURCE_DIR + '/model/swin_base_patch4_window12_384_22kto1k.pth'
+
+import math
+import numpy as np
+import cv2
+import wget
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch.autograd import Variable
+from torch import nn
+from torchvision import transforms
+
+from einops import rearrange
+
+from timm.models import load_checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+torch_device = 'cuda'
+torch_dtype = torch.float16
+
+
+def check_mkdir(dir_name):
+    if not os.path.isdir(dir_name):
+        os.makedirs(dir_name)
+
+
+def SwinT(pretrained=True):
+    model = SwinTransformer(embed_dim=96,
+                            depths=[2, 2, 6, 2],
+                            num_heads=[3, 6, 12, 24],
+                            window_size=7)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_tiny_patch4_window7_224.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def SwinS(pretrained=True):
+    model = SwinTransformer(embed_dim=96,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[3, 6, 12, 24],
+                            window_size=7)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_small_patch4_window7_224.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def SwinB(pretrained=True):
+    model = SwinTransformer(embed_dim=128,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[4, 8, 16, 32],
+                            window_size=12)
+    if pretrained is True:
+        import os
+        model.load_state_dict(torch.load(pretrained_SwinB_model_path,
+                                         map_location='cpu')['model'],
+                              strict=False)
+    return model
+
+
+def SwinL(pretrained=True):
+    model = SwinTransformer(embed_dim=192,
+                            depths=[2, 2, 18, 2],
+                            num_heads=[6, 12, 24, 48],
+                            window_size=12)
+    if pretrained is True:
+        model.load_state_dict(torch.load(
+            'data/backbone_ckpt/swin_large_patch4_window12_384_22kto1k.pth',
+            map_location='cpu')['model'],
+                              strict=False)
+
+    return model
+
+
+def get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+def make_cbr(in_dim, out_dim):
+    return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                         nn.BatchNorm2d(out_dim), nn.PReLU())
+
+
+def make_cbg(in_dim, out_dim):
+    return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                         nn.BatchNorm2d(out_dim), nn.GELU())
+
+
+def rescale_to(x, scale_factor: float = 2, interpolation='nearest'):
+    return F.interpolate(x, scale_factor=scale_factor, mode=interpolation)
+
+
+def resize_as(x, y, interpolation='bilinear'):
+    return F.interpolate(x, size=y.shape[-2:], mode=interpolation)
+
+
+def image2patches(x):
+    """b c (hg h) (wg w) -> (hg wg b) c h w"""
+    x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2)
+    return x
+
+
+def patches2image(x):
+    """(hg wg b) c h w -> b c (hg h) (wg w)"""
+    x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2)
+    return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+               C)
+    windows = x.permute(0, 1, 3, 2, 4,
+                        5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size,
+                     window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+def mkdir_safe(out_path):
+    if type(out_path) == str:
+        if len(out_path) > 0:
+            if not os.path.exists(out_path):
+                os.mkdir(out_path)
+
+
+def get_model_path():
+    import folder_paths
+    from folder_paths import models_dir
+
+    path_file_model = models_dir
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'MVANet')
+    mkdir_safe(out_path=path_file_model)
+
+    path_file_model = os.path.join(path_file_model, 'Model_80.pth')
+
+    return path_file_model
+
+
+def download_model(path):
+    if not os.path.exists(path):
+        wget.download(
+            'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth',
+            out=path)
+
+
+def load_model(model_checkpoint_path):
+    download_model(path=model_checkpoint_path)
+    torch.cuda.set_device(0)
+
+    net = inf_MVANet().to(dtype=torch_dtype, device=torch_device)
+
+    pretrained_dict = torch.load(finetuned_MVANet_model_path,
+                                 map_location=torch_device)
+
+    model_dict = net.state_dict()
+    pretrained_dict = {
+        k: v
+        for k, v in pretrained_dict.items() if k in model_dict
+    }
+    model_dict.update(pretrained_dict)
+    net.load_state_dict(model_dict)
+    net = net.to(dtype=torch_dtype, device=torch_device)
+    net.eval()
+    return net
+
+
+def do_infer_tensor2tensor(img, net):
+
+    img_transform = transforms.Compose(
+        [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+    h_, w_ = img.shape[1], img.shape[2]
+
+    with torch.no_grad():
+
+        img = rearrange(img, 'B H W C -> B C H W')
+
+        img_resize = torch.nn.functional.interpolate(input=img,
+                                                     size=(1024, 1024),
+                                                     mode='bicubic',
+                                                     antialias=True)
+
+        img_var = img_transform(img_resize)
+        img_var = Variable(img_var)
+        img_var = img_var.to(dtype=torch_dtype, device=torch_device)
+
+        mask = []
+
+        mask.append(net(img_var))
+
+        prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
+        prediction = prediction.sigmoid()
+
+        prediction = torch.nn.functional.interpolate(input=prediction,
+                                                     size=(h_, w_),
+                                                     mode='bicubic',
+                                                     antialias=True)
+
+        prediction = prediction.squeeze(0)
+        prediction = prediction.clamp(0, 1)
+        prediction = prediction.detach()
+        prediction = prediction.to(dtype=torch.float32, device='cpu')
+
+        return prediction
+
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class WindowAttention(nn.Module):
+    """ Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self,
+                 dim,
+                 window_size,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop=0.,
+                 proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :,
+                                         None] - coords_flatten[:,
+                                                                None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :,
+                        0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index",
+                             relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """ Forward function.
+
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        x = x.to(dtype=torch_dtype, device=torch_device)
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[
+            2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+        attn = attn.to(dtype=torch_dtype, device=torch_device)
+        v = v.to(dtype=torch_dtype, device=torch_device)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """ Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=7,
+                 shift_size=0,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(dim,
+                                    window_size=to_2tuple(self.window_size),
+                                    num_heads=num_heads,
+                                    qkv_bias=qkv_bias,
+                                    qk_scale=qk_scale,
+                                    attn_drop=attn_drop,
+                                    proj_drop=drop)
+
+        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(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x,
+                                   shifts=(-self.shift_size, -self.shift_size),
+                                   dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                   C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                   Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x,
+                           shifts=(self.shift_size, self.shift_size),
+                           dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 use_checkpoint=False):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim,
+                                 num_heads=num_heads,
+                                 window_size=window_size,
+                                 shift_size=0 if
+                                 (i % 2 == 0) else window_size // 2,
+                                 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,
+                                 norm_layer=norm_layer) for i in range(depth)
+        ])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size,
+                          -self.shift_size), slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1,
+                                         self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                          float(-100.0)).masked_fill(
+                                              attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x,
+                      (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_chans=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.2,
+                 norm_layer=nn.LayerNorm,
+                 ape=False,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1]
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0],
+                            patches_resolution[1]))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                norm_layer=norm_layer,
+                downsample=PatchMerging if
+                (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+
+        def _init_weights(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)
+
+        if isinstance(pretrained, str):
+            self.apply(_init_weights)
+            load_checkpoint(self, pretrained, strict=False, logger=None)
+        elif pretrained is None:
+            self.apply(_init_weights)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                               size=(Wh, Ww),
+                                               mode='bicubic')
+            x = (x + absolute_pos_embed)  # B Wh*Ww C
+
+        outs = [x.contiguous()]
+        x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W,
+                                 self.num_features[i]).permute(0, 3, 1,
+                                                               2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+class PositionEmbeddingSine:
+
+    def __init__(self,
+                 num_pos_feats=64,
+                 temperature=10000,
+                 normalize=False,
+                 scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+        self.dim_t = torch.arange(0,
+                                  self.num_pos_feats,
+                                  dtype=torch_dtype,
+                                  device=torch_device)
+
+    def __call__(self, b, h, w):
+        mask = torch.zeros([b, h, w], dtype=torch.bool, device=torch_device)
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(dim=1, dtype=torch_dtype)
+        x_embed = not_mask.cumsum(dim=2, dtype=torch_dtype)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) *
+                       self.scale).to(device=torch_device, dtype=torch_dtype)
+            x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) *
+                       self.scale).to(device=torch_device, dtype=torch_dtype)
+
+        dim_t = self.temperature**(2 * (self.dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+            dim=4).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+            dim=4).flatten(3)
+        return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+
+class MCLM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+        super(MCLM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear1 = nn.Linear(d_model, d_model * 2)
+        self.linear2 = nn.Linear(d_model * 2, d_model)
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.activation = get_activation_fn('relu')
+        self.pool_ratios = pool_ratios
+        self.p_poses = []
+        self.g_pos = None
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, l, g):
+        """
+        l: 4,c,h,w
+        g: 1,c,h,w
+        """
+        b, c, h, w = l.size()
+        # 4,c,h,w -> 1,c,2h,2w
+        concated_locs = rearrange(l,
+                                  '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                  hg=2,
+                                  wg=2)
+
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            # b,c,h,w
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+            pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+            if self.g_pos is None:
+                pos_emb = self.positional_encoding(pool.shape[0],
+                                                   pool.shape[2],
+                                                   pool.shape[3])
+                pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+                self.p_poses.append(pos_emb)
+        pools = torch.cat(pools, 0)
+        if self.g_pos is None:
+            self.p_poses = torch.cat(self.p_poses, dim=0)
+            pos_emb = self.positional_encoding(g.shape[0], g.shape[2],
+                                               g.shape[3])
+            self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+        # attention between glb (q) & multisensory concated-locs (k,v)
+        g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+        g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+            g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+        g_hw_b_c = self.norm1(g_hw_b_c)
+        g_hw_b_c = g_hw_b_c + self.dropout2(
+            self.linear2(
+                self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+        g_hw_b_c = self.norm2(g_hw_b_c)
+
+        # attention between origin locs (q) & freashed glb (k,v)
+        l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+        _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+        _g_hw_b_c = rearrange(_g_hw_b_c,
+                              "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                              ng=2,
+                              nw=2)
+        outputs_re = []
+        for i, (_l, _g) in enumerate(
+                zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+            outputs_re.append(self.attention[i + 1](_l, _g,
+                                                    _g)[0])  # (h w) 1 c
+        outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+        l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+        l_hw_b_c = self.norm1(l_hw_b_c)
+        l_hw_b_c = l_hw_b_c + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+        l_hw_b_c = self.norm2(l_hw_b_c)
+
+        l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+        return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+class inf_MCLM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+        super(inf_MCLM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear1 = nn.Linear(d_model, d_model * 2)
+        self.linear2 = nn.Linear(d_model * 2, d_model)
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.activation = get_activation_fn('relu')
+        self.pool_ratios = pool_ratios
+        self.p_poses = []
+        self.g_pos = None
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, l, g):
+        """
+        l: 4,c,h,w
+        g: 1,c,h,w
+        """
+        b, c, h, w = l.size()
+        # 4,c,h,w -> 1,c,2h,2w
+        concated_locs = rearrange(l,
+                                  '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                  hg=2,
+                                  wg=2)
+        self.p_poses = []
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            # b,c,h,w
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+            pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+            # if self.g_pos is None:
+            pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2],
+                                               pool.shape[3])
+            pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+            self.p_poses.append(pos_emb)
+        pools = torch.cat(pools, 0)
+        # if self.g_pos is None:
+        self.p_poses = torch.cat(self.p_poses, dim=0)
+        pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3])
+        self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+        # attention between glb (q) & multisensory concated-locs (k,v)
+        g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+        g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+            g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+        g_hw_b_c = self.norm1(g_hw_b_c)
+        g_hw_b_c = g_hw_b_c + self.dropout2(
+            self.linear2(
+                self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+        g_hw_b_c = self.norm2(g_hw_b_c)
+
+        # attention between origin locs (q) & freashed glb (k,v)
+        l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+        _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+        _g_hw_b_c = rearrange(_g_hw_b_c,
+                              "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                              ng=2,
+                              nw=2)
+        outputs_re = []
+        for i, (_l, _g) in enumerate(
+                zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+            outputs_re.append(self.attention[i + 1](_l, _g,
+                                                    _g)[0])  # (h w) 1 c
+        outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+        l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+        l_hw_b_c = self.norm1(l_hw_b_c)
+        l_hw_b_c = l_hw_b_c + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+        l_hw_b_c = self.norm2(l_hw_b_c)
+
+        l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+        return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+class MCRM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+        super(MCRM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.sigmoid = nn.Sigmoid()
+        self.activation = get_activation_fn('relu')
+        self.sal_conv = nn.Conv2d(d_model, 1, 1)
+        self.pool_ratios = pool_ratios
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+        # b(4),c,h,w
+        patched_glb = rearrange(glb,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+
+        # generate token attention map
+        token_attention_map = self.sigmoid(self.sal_conv(glb))
+        token_attention_map = F.interpolate(token_attention_map,
+                                            size=patches2image(loc).shape[-2:],
+                                            mode='nearest')
+        loc = loc * rearrange(token_attention_map,
+                              'b c (hg h) (wg w) -> (hg wg b) c h w',
+                              hg=2,
+                              wg=2)
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+            pools.append(rearrange(pool,
+                                   'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+        # nl(4),c,nphw -> nl(4),nphw,1,c
+        pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+        loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+        outputs = []
+        for i, q in enumerate(
+                loc_.unbind(dim=0)):  # traverse all local patches
+            # np*hw,1,c
+            v = pools[i]
+            k = v
+            outputs.append(self.attention[i](q, k, v)[0])
+        outputs = torch.cat(outputs, 1)
+        src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+        src = self.norm1(src)
+        src = src + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(src)).clone())))
+        src = self.norm2(src)
+
+        src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+        glb = glb + F.interpolate(patches2image(src),
+                                  size=glb.shape[-2:],
+                                  mode='nearest')  # freshed glb
+        return torch.cat((src, glb), 0), token_attention_map
+
+
+class inf_MCRM(nn.Module):
+
+    def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+        super(inf_MCRM, self).__init__()
+        self.attention = nn.ModuleList([
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+            nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+        ])
+
+        self.linear3 = nn.Linear(d_model, d_model * 2)
+        self.linear4 = nn.Linear(d_model * 2, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.sigmoid = nn.Sigmoid()
+        self.activation = get_activation_fn('relu')
+        self.sal_conv = nn.Conv2d(d_model, 1, 1)
+        self.pool_ratios = pool_ratios
+        self.positional_encoding = PositionEmbeddingSine(
+            num_pos_feats=d_model // 2, normalize=True)
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+        # b(4),c,h,w
+        patched_glb = rearrange(glb,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+
+        # generate token attention map
+        token_attention_map = self.sigmoid(self.sal_conv(glb))
+        token_attention_map = F.interpolate(token_attention_map,
+                                            size=patches2image(loc).shape[-2:],
+                                            mode='nearest')
+        loc = loc * rearrange(token_attention_map,
+                              'b c (hg h) (wg w) -> (hg wg b) c h w',
+                              hg=2,
+                              wg=2)
+        pools = []
+        for pool_ratio in self.pool_ratios:
+            tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+            pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+            pools.append(rearrange(pool,
+                                   'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+        # nl(4),c,nphw -> nl(4),nphw,1,c
+        pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+        loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+        outputs = []
+        for i, q in enumerate(
+                loc_.unbind(dim=0)):  # traverse all local patches
+            # np*hw,1,c
+            v = pools[i]
+            k = v
+            outputs.append(self.attention[i](q, k, v)[0])
+        outputs = torch.cat(outputs, 1)
+        src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+        src = self.norm1(src)
+        src = src + self.dropout2(
+            self.linear4(
+                self.dropout(self.activation(self.linear3(src)).clone())))
+        src = self.norm2(src)
+
+        src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+        glb = glb + F.interpolate(patches2image(src),
+                                  size=glb.shape[-2:],
+                                  mode='nearest')  # freshed glb
+        return torch.cat((src, glb), 0)
+
+
+# model for single-scale training
+class MVANet(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.backbone = SwinB(pretrained=True)
+        emb_dim = 128
+        self.sideout5 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout4 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout3 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout2 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+        self.sideout1 = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        self.output5 = make_cbr(1024, emb_dim)
+        self.output4 = make_cbr(512, emb_dim)
+        self.output3 = make_cbr(256, emb_dim)
+        self.output2 = make_cbr(128, emb_dim)
+        self.output1 = make_cbr(128, emb_dim)
+
+        self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8])
+        self.conv1 = make_cbr(emb_dim, emb_dim)
+        self.conv2 = make_cbr(emb_dim, emb_dim)
+        self.conv3 = make_cbr(emb_dim, emb_dim)
+        self.conv4 = make_cbr(emb_dim, emb_dim)
+        self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8])
+
+        self.insmask_head = nn.Sequential(
+            nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+            nn.BatchNorm2d(384), nn.PReLU(),
+            nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+            nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+        self.shallow = nn.Sequential(
+            nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+        self.upsample1 = make_cbg(emb_dim, emb_dim)
+        self.upsample2 = make_cbg(emb_dim, emb_dim)
+        self.output = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        for m in self.modules():
+            if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                m.inplace = True
+
+    def forward(self, x):
+        x = x.to(dtype=torch_dtype, device=torch_device)
+        shallow = self.shallow(x)
+        glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+        loc = image2patches(x)
+        input = torch.cat((loc, glb), dim=0)
+        feature = self.backbone(input)
+        e5 = self.output5(feature[4])  # (5,128,16,16)
+        e4 = self.output4(feature[3])  # (5,128,32,32)
+        e3 = self.output3(feature[2])  # (5,128,64,64)
+        e2 = self.output2(feature[1])  # (5,128,128,128)
+        e1 = self.output1(feature[0])  # (5,128,128,128)
+        loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+        e5 = self.multifieldcrossatt(loc_e5, glb_e5)  # (4,128,16,16)
+
+        e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4))
+        e4 = self.conv4(e4)
+        e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3))
+        e3 = self.conv3(e3)
+        e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2))
+        e2 = self.conv2(e2)
+        e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1))
+        e1 = self.conv1(e1)
+        loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+        output1_cat = patches2image(loc_e1)  # (1,128,256,256)
+        # add glb feat in
+        output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+        # merge
+        final_output = self.insmask_head(output1_cat)  # (1,128,256,256)
+        # shallow feature merge
+        final_output = final_output + resize_as(shallow, final_output)
+        final_output = self.upsample1(rescale_to(final_output))
+        final_output = rescale_to(final_output +
+                                  resize_as(shallow, final_output))
+        final_output = self.upsample2(final_output)
+        final_output = self.output(final_output)
+        ####
+        sideout5 = self.sideout5(e5).to(dtype=torch_dtype, device=torch_device)
+        sideout4 = self.sideout4(e4)
+        sideout3 = self.sideout3(e3)
+        sideout2 = self.sideout2(e2)
+        sideout1 = self.sideout1(e1)
+        #######glb_sideouts ######
+        glb5 = self.sideout5(glb_e5)
+        glb4 = sideout4[-1, :, :, :].unsqueeze(0)
+        glb3 = sideout3[-1, :, :, :].unsqueeze(0)
+        glb2 = sideout2[-1, :, :, :].unsqueeze(0)
+        glb1 = sideout1[-1, :, :, :].unsqueeze(0)
+        ####### concat 4 to 1 #######
+        sideout1 = patches2image(sideout1[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout2 = patches2image(sideout2[:-1]).to(
+            dtype=torch_dtype,
+            device=torch_device)  ####(5,c,h,w) -> (1 c 2h,2w)
+        sideout3 = patches2image(sideout3[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout4 = patches2image(sideout4[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        sideout5 = patches2image(sideout5[:-1]).to(dtype=torch_dtype,
+                                                   device=torch_device)
+        if self.training:
+            return sideout5, sideout4, sideout3, sideout2, sideout1, final_output, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1
+        else:
+            return final_output
+
+
+# model for multi-scale testing
+class inf_MVANet(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # self.backbone = SwinB(pretrained=True)
+        self.backbone = SwinB(pretrained=False)
+
+        emb_dim = 128
+        self.output5 = make_cbr(1024, emb_dim)
+        self.output4 = make_cbr(512, emb_dim)
+        self.output3 = make_cbr(256, emb_dim)
+        self.output2 = make_cbr(128, emb_dim)
+        self.output1 = make_cbr(128, emb_dim)
+
+        self.multifieldcrossatt = inf_MCLM(emb_dim, 1, [1, 4, 8])
+        self.conv1 = make_cbr(emb_dim, emb_dim)
+        self.conv2 = make_cbr(emb_dim, emb_dim)
+        self.conv3 = make_cbr(emb_dim, emb_dim)
+        self.conv4 = make_cbr(emb_dim, emb_dim)
+        self.dec_blk1 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk2 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk3 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+        self.dec_blk4 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+
+        self.insmask_head = nn.Sequential(
+            nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+            nn.BatchNorm2d(384), nn.PReLU(),
+            nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+            nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+        self.shallow = nn.Sequential(
+            nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+        self.upsample1 = make_cbg(emb_dim, emb_dim)
+        self.upsample2 = make_cbg(emb_dim, emb_dim)
+        self.output = nn.Sequential(
+            nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+        for m in self.modules():
+            if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                m.inplace = True
+
+    def forward(self, x):
+        shallow = self.shallow(x)
+        glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+        loc = image2patches(x)
+        input = torch.cat((loc, glb), dim=0)
+        feature = self.backbone(input)
+        e5 = self.output5(feature[4])
+        e4 = self.output4(feature[3])
+        e3 = self.output3(feature[2])
+        e2 = self.output2(feature[1])
+        e1 = self.output1(feature[0])
+        loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+        e5_cat = self.multifieldcrossatt(loc_e5, glb_e5)
+
+        e4 = self.conv4(self.dec_blk4(e4 + resize_as(e5_cat, e4)))
+        e3 = self.conv3(self.dec_blk3(e3 + resize_as(e4, e3)))
+        e2 = self.conv2(self.dec_blk2(e2 + resize_as(e3, e2)))
+        e1 = self.conv1(self.dec_blk1(e1 + resize_as(e2, e1)))
+        loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+        # after decoder, concat loc features to a whole one, and merge
+        output1_cat = patches2image(loc_e1)
+        # add glb feat in
+        output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+        # merge
+        final_output = self.insmask_head(output1_cat)
+        # shallow feature merge
+        final_output = final_output + resize_as(shallow, final_output)
+        final_output = self.upsample1(rescale_to(final_output))
+        final_output = rescale_to(final_output +
+                                  resize_as(shallow, final_output))
+        final_output = self.upsample2(final_output)
+        final_output = self.output(final_output)
+        return final_output
+
+
+class load_MVANet_Model:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ("MVANet_Model", )
+    FUNCTION = "test"
+    CATEGORY = "MVANet"
+
+    def test(self):
+        return (load_model(get_model_path()), )
+
+
+class run_MVANet_inference:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "MVANet_Model": ("MVANet_Model", ),
+            },
+        }
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "test"
+    CATEGORY = "MVANet"
+
+    def test(
+        self,
+        image,
+        MVANet_Model,
+    ):
+        ret = do_infer_tensor2tensor(img=image, net=MVANet_Model)
+
+        return (ret, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "load_MVANet_Model": load_MVANet_Model,
+    "run_MVANet_inference": run_MVANet_inference
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "load_MVANet_Model": "load MVANet Model",
+    "load_MVANet_Model": "load MVANet Model"
+}

ComfyUI_MVANet/requirements.txt

@@ -0,0 +1,3 @@
+timm
+einops
+wget