MVANet_Inference/README.org

* 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

** MVANet_inference run
#+begin_src sh :shebang #!/bin/sh :results output :tangle ./MVANet_inference.run.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
pip3 install mmdet==2.23.0
pip3 install mmcv==1.4.8
pip3 install ttach

* 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
  from PIL import Image
  import time
  # import ttach as tta
  import cv2

  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_
#+end_src

** 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

** Device configs
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.execute.py
  torch_device = 'cuda'
  torch_dtype = torch.float16
#+end_src
to(dtype=torch_dtype, device=torch_device)

** 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 load_model(model_checkpoint_path):
      torch.cuda.set_device(0)

      net = inf_MVANet().to(dtype=torch_dtype, device=torch_device)

      pretrained_dict = torch.load(model_checkpoint_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 load_transforms_stripped():
      img_transform = transforms.Compose([
          # transforms.ToTensor(),
          transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ])

      return img_transform


  def load_transforms():
      img_transform = transforms.Compose([
          # transforms.ToTensor(),
          transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ])

      depth_transform = transforms.ToTensor()
      target_transform = transforms.ToTensor()
      to_pil = transforms.ToPILImage()

      transforms_var = tta.Compose([
          tta.HorizontalFlip(),
          tta.Scale(scales=[0.75, 1, 1.25],
                    interpolation='bilinear',
                    align_corners=False),
      ])

      return (img_transform, depth_transform, target_transform, to_pil,
              transforms_var)
#+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)

          return prediction


  def do_infer_modular_cv(input_image_path, output_mask_path, net,
                          all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image_torch(input_image_path)

      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 = []

          for transformer in transforms_var:
              rgb_trans = img_var.to(dtype=torch_dtype, device=torch_device)
              mask.append(net(rgb_trans))

          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)

          save_mask_torch(output_image_path=output_mask_path, mask=prediction)


  def do_infer_modular_cv_2(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image(input_image_path)
      w_, h_ = img.shape[0], img.shape[1]
      img_resize = cv2.resize(img, (1024, 1024), cv2.INTER_CUBIC)

      with torch.no_grad():

          # rgb_png_path = input_image_path
          # img = Image.open(rgb_png_path).convert('RGB')
          # w_, h_ = img.size

          # img_resize = img.resize([256 * 4, 256 * 4], Image.BILINEAR)

          # img_var = Variable(img_transform(img_resize).unsqueeze(0)).to(
          #     dtype=torch_dtype, device=torch_device)

          img_resize = torch.from_numpy(img_resize)
          img_resize = img_resize.to(dtype=torch.float32)
          img_resize /= 255.0
          img_resize = rearrange(img_resize, 'H W C -> C H W')
          img_var = img_transform(img_resize)
          img_var = img_var.unsqueeze(0)
          img_var = Variable(img_var)
          img_var = img_var.to(dtype=torch_dtype, device=torch_device)

          mask = []

          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)


  def do_infer_modular_cv_3(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image(input_image_path)
      w_, h_ = img.shape[0], img.shape[1]

      with torch.no_grad():

          # rgb_png_path = input_image_path
          # img = Image.open(rgb_png_path).convert('RGB')
          # w_, h_ = img.size

          # img_resize = img.resize([256 * 4, 256 * 4], Image.BILINEAR)

          # img_var = Variable(img_transform(img_resize).unsqueeze(0)).to(
          #     dtype=torch_dtype, device=torch_device)

          img_resize = torch.from_numpy(img)
          img_resize = img_resize.to(dtype=torch.float32)
          img_resize = rearrange(img_resize, 'H W C -> C H W')
          img_resize = img_resize.unsqueeze(0)

          img_resize = torch.nn.functional.interpolate(input=img_resize,
                                                       size=(1024, 1024),
                                                       mode='bicubic',
                                                       antialias=True)

          img_resize = img_resize.squeeze(0)
          img_resize = rearrange(img_resize, 'C H W -> H W C')

          img_resize = img_resize.to(dtype=torch.float32)
          img_resize /= 255.0
          img_resize = rearrange(img_resize, 'H W C -> C H W')
          img_var = img_transform(img_resize)
          img_var = img_var.unsqueeze(0)
          img_var = Variable(img_var)
          img_var = img_var.to(dtype=torch_dtype, device=torch_device)

          mask = []

          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)


  def do_infer_modular_cv_4(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image(input_image_path)
      w_, h_ = img.shape[0], img.shape[1]

      with torch.no_grad():

          img_resize = torch.from_numpy(img)
          img_resize = img_resize.to(dtype=torch.float32)
          img_resize /= 255.0
          img_resize = img_resize.unsqueeze(0)

          img_resize = rearrange(img_resize, 'B H W C -> B C H W')

          img_resize = torch.nn.functional.interpolate(input=img_resize,
                                                       size=(1024, 1024),
                                                       mode='bicubic',
                                                       antialias=True)

          img_resize = img_resize.squeeze(0)
          img_var = img_transform(img_resize)
          img_var = img_var.unsqueeze(0)
          img_var = Variable(img_var)
          img_var = img_var.to(dtype=torch_dtype, device=torch_device)

          mask = []

          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)


  def do_infer_modular_cv_5(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image(input_image_path)
      w_, h_ = img.shape[0], img.shape[1]

      with torch.no_grad():

          img_resize = torch.from_numpy(img)
          img_resize = img_resize.to(dtype=torch.float32)
          img_resize /= 255.0
          img_resize = img_resize.unsqueeze(0)

          img_resize = rearrange(img_resize, 'B H W C -> B C H W')

          img_resize = torch.nn.functional.interpolate(input=img_resize,
                                                       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 = []

          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)


  def do_infer_modular_cv_6(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image(input_image_path)
      w_, h_ = img.shape[0], img.shape[1]

      with torch.no_grad():

          img_resize = torch.from_numpy(img)
          img_resize = img_resize.to(dtype=torch.float32)
          img_resize /= 255.0
          img_resize = img_resize.unsqueeze(0)

          img_resize = rearrange(img_resize, 'B H W C -> B C H W')

          img_resize = torch.nn.functional.interpolate(input=img_resize,
                                                       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 = []

          for transformer in transforms_var:
              rgb_trans = img_var.to(dtype=torch_dtype, device=torch_device)
              mask.append(net(rgb_trans))

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)


  def do_infer_modular_cv_7(input_image_path, output_mask_path, net,
                            all_transforms):

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      img = load_image_torch(input_image_path)

      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 = []

          for transformer in transforms_var:
              rgb_trans = img_var.to(dtype=torch_dtype, device=torch_device)
              mask.append(net(rgb_trans))

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)
#+end_src

** Function for modular inference
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
  def do_infer_modular(input_image_path, output_mask_path, net, all_transforms):
      # net = load_model(finetuned_MVANet_model_path)

      (img_transform, depth_transform, target_transform, to_pil,
       transforms_var) = all_transforms

      with torch.no_grad():
          rgb_png_path = input_image_path
          img = Image.open(rgb_png_path).convert('RGB')

          w_, h_ = img.size
          # img_resize = img.resize([(w_ // 2) * 2, (h_ // 2) * 2], Image.BILINEAR)
          img_resize = img.resize([256 * 4, 256 * 4], Image.BILINEAR)
          # img_resize = img
          img_var = Variable(img_transform(img_resize).unsqueeze(0)).to(
              dtype=torch_dtype, device=torch_device)
          mask = []
          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save(output_mask_path)
#+end_src

** Function for inference
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
  def do_infer():
      torch.cuda.set_device(0)
      args = {'crf_refine': True, 'save_results': True}

      img_transform = transforms.Compose([
          transforms.ToTensor(),
          transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ])

      depth_transform = transforms.ToTensor()
      target_transform = transforms.ToTensor()
      to_pil = transforms.ToPILImage()

      transforms_var = tta.Compose([
          tta.HorizontalFlip(),
          tta.Scale(scales=[0.75, 1, 1.25],
                    interpolation='bilinear',
                    align_corners=False),
      ])

      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()
      with torch.no_grad():
          rgb_png_path = '/home/asd/DATASETS/SD_BG_SWAP_TEST/comfyui_outputs/4/output_fooocus/bgswap-output.png'
          img = Image.open(rgb_png_path).convert('RGB')
          w_, h_ = img.size
          # img_resize = img.resize([(w_ // 2) * 2, (h_ // 2) * 2], Image.BILINEAR)
          img_resize = img.resize([256 * 4 , 256 * 4 ], Image.BILINEAR)
          # img_resize = img
          img_var = Variable(img_transform(img_resize).unsqueeze(0),
                             volatile=True).cuda()
          mask = []
          for transformer in transforms_var:
              rgb_trans = transformer.augment_image(img_var)
              rgb_trans = rgb_trans.to(dtype=torch_dtype, device=torch_device)
              model_output = net(rgb_trans)
              deaug_mask = transformer.deaugment_mask(model_output)
              mask.append(deaug_mask)

          prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
          prediction = prediction.sigmoid()
          prediction = to_pil(prediction.data.squeeze(0).cpu())
          prediction = prediction.resize((w_, h_), Image.BILINEAR)
          prediction.save('./tmp.png')
#+end_src

** MVANet_inference function
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.function.py
  def main(item):
      net = inf_MVANet().cuda()
      pretrained_dict = torch.load(os.path.join(ckpt_path, item + '.pth'),
                                   map_location='cuda')
      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.eval()
      with torch.no_grad():
          for name, root in to_test.items():
              root1 = os.path.join(root, 'images')
              img_list = [os.path.splitext(f) for f in os.listdir(root1)]
              for idx, img_name in enumerate(img_list):

                  print('predicting for %s: %d / %d' %
                        (name, idx + 1, len(img_list)))
                  rgb_png_path = os.path.join(root, 'images',
                                              img_name[0] + '.png')
                  rgb_jpg_path = os.path.join(root, 'images',
                                              img_name[0] + '.jpg')
                  if os.path.exists(rgb_png_path):
                      img = Image.open(rgb_png_path).convert('RGB')
                  else:
                      img = Image.open(rgb_jpg_path).convert('RGB')
                  w_, h_ = img.size
                  img_resize = img.resize([1024, 1024], Image.BILINEAR)
                  img_var = Variable(img_transform(img_resize).unsqueeze(0),
                                     volatile=True).cuda()
                  mask = []
                  for transformer in transforms_var:
                      rgb_trans = transformer.augment_image(img_var)
                      model_output = net(rgb_trans)
                      deaug_mask = transformer.deaugment_mask(model_output)
                      mask.append(deaug_mask)

                  prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
                  prediction = prediction.sigmoid()
                  prediction = to_pil(prediction.data.squeeze(0))
                  prediction = prediction.resize((w_, h_), Image.BILINEAR)
                  if args['save_results']:
                      check_mkdir(os.path.join(ckpt_path, item, name))
                      prediction.save(
                          os.path.join(ckpt_path, item, name,
                                       img_name[0] + '.png'))
#+end_src

** MVANet_inference execute
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./MVANet_inference.execute.py
  def do_merge(path_image, path_mask, path_out):
      image = cv2.imread(path_image, cv2.IMREAD_COLOR)
      mask = cv2.imread(path_mask, cv2.IMREAD_GRAYSCALE)
      mask = (mask > 127).astype(dtype=np.uint8) * 255
      out = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
      out[:, :, 0:3] = image
      out[:, :, 3] = mask
      cv2.imwrite(path_out, out)


  if __name__ == '__main__':

      # do_infer_modular_cv(
      #     input_image_path=
      #     '/home/asd/DATASETS/SD_BG_SWAP_TEST/comfyui_outputs/4/output_fooocus/bgswap-output.png',
      #     output_mask_path='./tmp.png',
      #     net=load_model(finetuned_MVANet_model_path),
      #     all_transforms=load_transforms(),
      # )

      # net = load_model(
      #     HOME_DIR + '/dreambooth_experiments/MVANet/MVANet_cloth_segment_14.pth')

      # net = load_model(
      #     HOME_DIR +
      #     '/dreambooth_experiments/MVANet/new_type_crop_with_midshot.pth')

      # net = load_model('/home/asd/MODEL_CHECKPOINTS/MVANet/SKIN_SEGMENTATION/1/Model_4.pth')

      net = load_model('/home/asd/MODEL_CHECKPOINTS/MVANet/SKIN_SEGMENTATION/3/Model_14.pth')


      # net = load_model(HOME_DIR +
      #                  '/dreambooth_experiments/MVANet/mvanet_normal_crop_2.pth')

      DATA_DIR_BASE = HOME_DIR + '/DATASETS/cloth_segmentation_test_images.dir/cloth_segmentation_test_images/'

      images = (
          '1370', '1371', '1372', '1373', '1374', '1375', '1376', '1377', '1378',
          '1379', '1380', '1381', '1382', '1383', '1384', '1385', '1386', '1387',
          '1388', '1389', '1390', '1391', '1392', '1393', '1394', '1395', '1396',
          '1397', '1398', '1399', '1400', '1401', '1402', '1403', '1404', '1405',
          '1406', '1407', '1408', '1409', '1410', '1411', '1412', '1413', '1414',
          '1415', '1539', '1541', '1542', '1543', '17320', '4129', '4190',
          '4191', '4192', '4193', '4202', '4203', '4204', '4207', '4208', '4209',
          '4210', '4213', '4214', '4221', '4222', '4223', '4224', '4225', '4226',
          '4227', '4228', '4229', '4230', '4231', '4232', '4233', '4234', '4235',
          '4236', '4237', '4238', '4239', '4240', '4241', '4242', '4251', '4252',
          '4253', '4254', '4255', '4256', '4257', '4258', '4259', '4260', '4261',
          '4262', '4263', '4264', '6581', '6642', '6647', '6656', '6660', '6690',
          '6696', '6724', '6767', '6771', '6788', '6791', '6807', '6821', '6824',
          '6833', '6847', '6850', '6879', '6941', '7001', '7070', '7083', '7092',
          '7093', '7119', '7191', '7220', '7252', '7264', '7276', '7278', '7281',
          '7290', '7301', '7312', '7340', '7398', '7404', '7412', '7429', '7439',
          '7478', '7491', '7631', '7687', '7699', '7719', '7770', '7784', '7793',
          '7811', '7829', '7861', '7864', '7868', '7980', '7987', '7990', '8069',
          '8083', '8100', '8108', '8227', '8323', '8329', '8358', '8383', '8401',
          '8415', '8488', '8515', '8518', '8560', '8565', '8595', '8639', '8676',
          '8690', '8691', '8701', '8703', '8723', '8726', '8756', '8783', '8801',
          '8820', '8826', '8842', '8865', '8874', '8875', '8882', '8911', '8946',
          '8947', '8969', '8979', '8983')

      masks = [DATA_DIR_BASE + i + '/garment_mask.png' for i in images]
      out = [DATA_DIR_BASE + i + '/garment_transparent.png' for i in images]

      images = [DATA_DIR_BASE + i + '/original.jpg' for i in images]

      for i in range(len(images)):
          image = images[i]
          image = load_image_torch(image)
          mask = do_infer_tensor2tensor(image, net)
          save_mask_torch(output_image_path=masks[i], mask=mask)
          do_merge(path_image=images[i], path_mask=masks[i], path_out=out[i])

      # img = load_image_torch(
      #     '/home/asd/DATASETS/SD_BG_SWAP_TEST/comfyui_outputs/4/output_fooocus/bgswap-output.png'
      # )
      # # all_transforms = load_transforms()
      # masks = do_infer_tensor2tensor(img, net)
      # save_mask_torch(output_image_path='./tmp.png', mask=masks)
#+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' \
  ;
#+end_src

** MVANet_inference run
#+begin_src sh :shebang #!/bin/sh :results output :tangle ./MVANet_inference.run.sh
  . "${HOME}/dbnew.sh"
  python3 './MVANet_inference.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