ComfyUI_AEMatter/README.org

* COMMENT SAMPLE

** AEMatter.import.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.import.py
#+end_src

** AEMatter.function.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
#+end_src

** AEMatter.execute.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
#+end_src

** AEMatter.unify.sh
#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.unify.sh
#+end_src

** AEMatter.run.sh
#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.run.sh
#+end_src

* Code for AEMatter inference

** AEMatter.import.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.import.py
  import cv2
  import math
  import numpy as np
  import os
  import random
  import wget

  import torch
  import torch.nn as nn
  from torch.nn import init
  import torch.nn.functional as F
  import torch.utils.checkpoint as checkpoint

  from collections import OrderedDict
  from einops import rearrange, repeat
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_

  import folder_paths
  from folder_paths import models_dir
#+end_src

** Functions to prepare directory structure and download models
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.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, 'AEMatter')
      mkdir_safe(out_path=path_file_model)

      path_file_model = os.path.join(path_file_model, 'AEM_RWA.ckpt')

      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/AEMatter/AEM_RWA.ckpt?download=true',
              out=path)


  def from_torch_image(image):
      image = image.cpu().numpy() * 255.0
      image = np.clip(image, 0, 255).astype(np.uint8)
      return image


  def to_torch_image(image):
      image = image.astype(dtype=np.float32)
      image /= 255.0
      image = torch.from_numpy(image)
      return image
#+end_src

** AEMatter.function.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
  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
#+end_src

** AEMatter.function.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
  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

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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
          """
          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)

          x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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
#+end_src


** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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.
          """
          # print(x.shape,H,W)
          # 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)  # nW, ww window_size*window_size
          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
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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
#+end_src


** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  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 forward(self, x):
          """Forward function."""
          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).flatten(2).transpose(1, 2)  # B Wh*Ww C
          else:
              x = x.flatten(2).transpose(1, 2)
          x = self.pos_drop(x)

          outs = []
          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()
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.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
#+end_src


** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  class ResBlock(nn.Module):

      def __init__(self, inc, midc):
          super(ResBlock, self).__init__()
          self.conv1 = nn.Conv2d(inc,
                                 midc,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0,
                                 bias=True)
          self.gn1 = nn.GroupNorm(16, midc)
          self.conv2 = nn.Conv2d(midc,
                                 midc,
                                 kernel_size=3,
                                 stride=1,
                                 padding=1,
                                 bias=True)
          self.gn2 = nn.GroupNorm(16, midc)
          self.conv3 = nn.Conv2d(midc,
                                 inc,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0,
                                 bias=True)
          self.relu = nn.LeakyReLU(0.1)

      def forward(self, x):
          x_ = x
          x = self.conv1(x)
          x = self.gn1(x)
          x = self.relu(x)
          x = self.conv2(x)
          x = self.gn2(x)
          x = self.relu(x)
          x = self.conv3(x)
          x = x + x_
          x = self.relu(x)
          return x
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  class AEALblock(nn.Module):

      def __init__(self,
                   d_model,
                   nhead,
                   dim_feedforward=512,
                   dropout=0.0,
                   layer_norm_eps=1e-5,
                   batch_first=True,
                   norm_first=False,
                   width=5):
          super(AEALblock, self).__init__()
          self.self_attn2 = nn.MultiheadAttention(d_model // 2,
                                                  nhead // 2,
                                                  dropout=dropout,
                                                  batch_first=batch_first)
          self.self_attn1 = nn.MultiheadAttention(d_model // 2,
                                                  nhead // 2,
                                                  dropout=dropout,
                                                  batch_first=batch_first)
          self.linear1 = nn.Linear(d_model, dim_feedforward)
          self.dropout = nn.Dropout(dropout)
          self.linear2 = nn.Linear(dim_feedforward, d_model)
          self.norm_first = norm_first
          self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
          self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
          self.dropout1 = nn.Dropout(dropout)
          self.dropout2 = nn.Dropout(dropout)
          self.activation = nn.ReLU()
          self.width = width
          self.trans = nn.Sequential(
              nn.Conv2d(d_model + 512, d_model // 2, 1, 1, 0),
              ResBlock(d_model // 2, d_model // 4),
              nn.Conv2d(d_model // 2, d_model, 1, 1, 0))
          self.gamma = nn.Parameter(torch.zeros(1))

      def forward(
          self,
          src,
          feats,
      ):
          src = self.gamma * self.trans(torch.cat([src, feats], 1)) + src
          b, c, h, w = src.shape
          x1 = src[:, 0:c // 2]
          x1_ = rearrange(x1, 'b c (h1 h2) w -> b c h1 h2 w', h2=self.width)
          x1_ = rearrange(x1_, 'b c h1 h2 w -> (b h1) (h2 w) c')
          x2 = src[:, c // 2:]
          x2_ = rearrange(x2, 'b c h (w1 w2) -> b c h w1 w2', w2=self.width)
          x2_ = rearrange(x2_, 'b c h w1 w2 -> (b w1) (h w2) c')
          x = rearrange(src, 'b c h w-> b (h w) c')
          x = self.norm1(x + self._sa_block(x1_, x2_, h, w))
          x = self.norm2(x + self._ff_block(x))
          x = rearrange(x, 'b (h w) c->b c h w', h=h, w=w)
          return x

      def _sa_block(self, x1, x2, h, w):
          x1 = self.self_attn1(x1,
                               x1,
                               x1,
                               attn_mask=None,
                               key_padding_mask=None,
                               need_weights=False)[0]

          x2 = self.self_attn2(x2,
                               x2,
                               x2,
                               attn_mask=None,
                               key_padding_mask=None,
                               need_weights=False)[0]

          x1 = rearrange(x1,
                         '(b h1) (h2 w) c-> b (h1 h2 w) c',
                         h2=self.width,
                         h1=h // self.width)
          x2 = rearrange(x2,
                         ' (b w1) (h w2) c-> b (h w1 w2) c',
                         w2=self.width,
                         w1=w // self.width)
          x = torch.cat([x1, x2], dim=2)
          return self.dropout1(x)

      def _ff_block(self, x):
          x = self.linear2(self.dropout(self.activation(self.linear1(x))))
          return self.dropout2(x)
#+end_src

** AEMatter.class.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  class AEMatter(nn.Module):

      def __init__(self):
          super(AEMatter, self).__init__()
          trans = SwinTransformer(pretrain_img_size=224,
                                  embed_dim=96,
                                  depths=[2, 2, 6, 2],
                                  num_heads=[3, 6, 12, 24],
                                  window_size=7,
                                  ape=False,
                                  drop_path_rate=0.2,
                                  patch_norm=True,
                                  use_checkpoint=False)

          # trans.load_state_dict(torch.load(
          #     '/home/asd/Desktop/swin_tiny_patch4_window7_224.pth',
          #     map_location="cpu")["model"],
          #                       strict=False)

          trans.patch_embed.proj = nn.Conv2d(64, 96, 3, 2, 1)

          self.start_conv0 = nn.Sequential(nn.Conv2d(6, 48, 3, 1, 1),
                                           nn.PReLU(48))

          self.start_conv = nn.Sequential(nn.Conv2d(48, 64, 3, 2,
                                                    1), nn.PReLU(64),
                                          nn.Conv2d(64, 64, 3, 1, 1),
                                          nn.PReLU(64))

          self.trans = trans
          self.conv1 = nn.Sequential(
              nn.Conv2d(in_channels=640 + 768,
                        out_channels=256,
                        kernel_size=1,
                        stride=1,
                        padding=0,
                        bias=True))
          self.conv2 = nn.Sequential(
              nn.Conv2d(in_channels=256 + 384,
                        out_channels=256,
                        kernel_size=1,
                        stride=1,
                        padding=0,
                        bias=True), )
          self.conv3 = nn.Sequential(
              nn.Conv2d(in_channels=256 + 192,
                        out_channels=192,
                        kernel_size=1,
                        stride=1,
                        padding=0,
                        bias=True), )
          self.conv4 = nn.Sequential(
              nn.Conv2d(in_channels=192 + 96,
                        out_channels=128,
                        kernel_size=1,
                        stride=1,
                        padding=0,
                        bias=True), )
          self.ctran0 = BasicLayer(256, 3, 8, 7, drop_path=0.09)
          self.ctran1 = BasicLayer(256, 3, 8, 7, drop_path=0.07)
          self.ctran2 = BasicLayer(192, 3, 6, 7, drop_path=0.05)
          self.ctran3 = BasicLayer(128, 3, 4, 7, drop_path=0.03)
          self.conv5 = nn.Sequential(
              nn.Conv2d(in_channels=192,
                        out_channels=64,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True), nn.PReLU(64),
              nn.Conv2d(in_channels=64,
                        out_channels=64,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True), nn.PReLU(64),
              nn.Conv2d(in_channels=64,
                        out_channels=48,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True), nn.PReLU(48))
          self.convo = nn.Sequential(
              nn.Conv2d(in_channels=48 + 48 + 6,
                        out_channels=32,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True), nn.PReLU(32),
              nn.Conv2d(in_channels=32,
                        out_channels=32,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True), nn.PReLU(32),
              nn.Conv2d(in_channels=32,
                        out_channels=1,
                        kernel_size=3,
                        stride=1,
                        padding=1,
                        bias=True))
          self.up = nn.Upsample(scale_factor=2,
                                mode='bilinear',
                                align_corners=False)
          self.upn = nn.Upsample(scale_factor=2, mode='nearest')
          self.apptrans = nn.Sequential(
              nn.Conv2d(256 + 384, 256, 1, 1, bias=True), ResBlock(256, 128),
              ResBlock(256, 128), nn.Conv2d(256, 512, 2, 2, bias=True),
              ResBlock(512, 128))
          self.emb = nn.Sequential(nn.Conv2d(768, 640, 1, 1, 0),
                                   ResBlock(640, 160))
          self.embdp = nn.Sequential(nn.Conv2d(640, 640, 1, 1, 0))
          self.h2l = nn.Conv2d(768, 256, 1, 1, 0)
          self.width = 5
          self.trans1 = AEALblock(d_model=640,
                                  nhead=20,
                                  dim_feedforward=2048,
                                  dropout=0.2,
                                  width=self.width)
          self.trans2 = AEALblock(d_model=640,
                                  nhead=20,
                                  dim_feedforward=2048,
                                  dropout=0.2,
                                  width=self.width)
          self.trans3 = AEALblock(d_model=640,
                                  nhead=20,
                                  dim_feedforward=2048,
                                  dropout=0.2,
                                  width=self.width)

      def aeal(self, x, sem):
          xe = self.emb(x)
          x_ = xe
          x_ = self.embdp(x_)
          b, c, h1, w1 = x_.shape
          bnew_ph = int(np.ceil(h1 / self.width) * self.width) - h1
          bnew_pw = int(np.ceil(w1 / self.width) * self.width) - w1
          newph1 = bnew_ph // 2
          newph2 = bnew_ph - newph1
          newpw1 = bnew_pw // 2
          newpw2 = bnew_pw - newpw1
          x_ = F.pad(x_, (newpw1, newpw2, newph1, newph2))
          sem = F.pad(sem, (newpw1, newpw2, newph1, newph2))
          x_ = self.trans1(x_, sem)
          x_ = self.trans2(x_, sem)
          x_ = self.trans3(x_, sem)
          x_ = x_[:, :, newph1:h1 + newph1, newpw1:w1 + newpw1]
          return x_

      def forward(self, x, y):
          inputs = torch.cat((x, y), 1)
          x = self.start_conv0(inputs)
          x_ = self.start_conv(x)
          x1, x2, x3, x4 = self.trans(x_)
          x4h = self.h2l(x4)
          x3s = self.apptrans(torch.cat([x3, self.upn(x4h)], 1))
          x4_ = self.aeal(x4, x3s)
          x4 = torch.cat((x4, x4_), 1)
          X4 = self.conv1(x4)
          wh, ww = X4.shape[2], X4.shape[3]
          X4 = rearrange(X4, 'b c h w -> b (h w) c')
          X4, _, _, _, _, _ = self.ctran0(X4, wh, ww)
          X4 = rearrange(X4, 'b (h w) c -> b c h w', h=wh, w=ww)
          X3 = self.up(X4)
          X3 = torch.cat((x3, X3), 1)
          X3 = self.conv2(X3)
          wh, ww = X3.shape[2], X3.shape[3]
          X3 = rearrange(X3, 'b c h w -> b (h w) c')
          X3, _, _, _, _, _ = self.ctran1(X3, wh, ww)
          X3 = rearrange(X3, 'b (h w) c -> b c h w', h=wh, w=ww)
          X2 = self.up(X3)
          X2 = torch.cat((x2, X2), 1)
          X2 = self.conv3(X2)
          wh, ww = X2.shape[2], X2.shape[3]
          X2 = rearrange(X2, 'b c h w -> b (h w) c')
          X2, _, _, _, _, _ = self.ctran2(X2, wh, ww)
          X2 = rearrange(X2, 'b (h w) c -> b c h w', h=wh, w=ww)
          X1 = self.up(X2)
          X1 = torch.cat((x1, X1), 1)
          X1 = self.conv4(X1)
          wh, ww = X1.shape[2], X1.shape[3]
          X1 = rearrange(X1, 'b c h w -> b (h w) c')
          X1, _, _, _, _, _ = self.ctran3(X1, wh, ww)
          X1 = rearrange(X1, 'b (h w) c -> b c h w', h=wh, w=ww)
          X0 = self.up(X1)
          X0 = torch.cat((x_, X0), 1)
          X0 = self.conv5(X0)
          X = self.up(X0)
          X = torch.cat((inputs, x, X), 1)
          alpha = self.convo(X)
          alpha = torch.clamp(alpha, min=0, max=1)
          return alpha
#+end_src

** Function to load model
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
  def get_AEMatter_model(path_model_checkpoint):

      download_model(path=path_model_checkpoint)

      matmodel = AEMatter()
      matmodel.load_state_dict(
          torch.load(path_model_checkpoint, map_location='cpu')['model'])

      matmodel = matmodel.cuda()
      matmodel.eval()

      return matmodel
#+end_src

** Function to do inference
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
  def do_infer(rawimg, trimap, matmodel):
      trimap_nonp = trimap.copy()
      h, w, c = rawimg.shape
      nonph, nonpw, _ = rawimg.shape
      newh = (((h - 1) // 32) + 1) * 32
      neww = (((w - 1) // 32) + 1) * 32
      padh = newh - h
      padh1 = int(padh / 2)
      padh2 = padh - padh1
      padw = neww - w
      padw1 = int(padw / 2)
      padw2 = padw - padw1

      rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
                                      cv2.BORDER_REFLECT)

      trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
                                      cv2.BORDER_REFLECT)

      h_pad, w_pad, _ = rawimg_pad.shape
      tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
      tritemp[:, :, 0] = (trimap_pad == 0)
      tritemp[:, :, 1] = (trimap_pad == 128)
      tritemp[:, :, 2] = (trimap_pad == 255)
      tritempimgs = np.transpose(tritemp, (2, 0, 1))
      tritempimgs = tritempimgs[np.newaxis, :, :, :]
      img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
      img = np.array(img, np.float32)
      img = img / 255.
      img = torch.from_numpy(img).cuda()
      tritempimgs = torch.from_numpy(tritempimgs).cuda()
      with torch.no_grad():
          pred = matmodel(img, tritempimgs)
          pred = pred.detach().cpu().numpy()[0]
          pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
          preda = pred[
              0:1,
          ] * 255
          preda = np.transpose(preda, (1, 2, 0))
          preda = preda * (trimap_nonp[:, :, None]
                           == 128) + (trimap_nonp[:, :, None] == 255) * 255
      preda = np.array(preda, np.uint8)
      return preda
#+end_src

** Load ComfyUI AEMatter model
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.class.py
  class load_AEMatter_Model:

      def __init__(self):
          pass

      @classmethod
      def INPUT_TYPES(s):
          return {
              "required": {},
          }

      RETURN_TYPES = ("AEMatter_Model", )
      FUNCTION = "test"
      CATEGORY = "AEMatter"

      def test(self):
          return (get_AEMatter_model(get_model_path()), )


  class run_AEMatter_inference:

      def __init__(self):
          pass

      @classmethod
      def INPUT_TYPES(s):
          return {
              "required": {
                  "image": ("IMAGE", ),
                  "trimap": ("MASK", ),
                  "AEMatter_Model": ("AEMatter_Model", ),
              },
          }

      RETURN_TYPES = ("MASK", )
      FUNCTION = "test"
      CATEGORY = "AEMatter"

      def test(
          self,
          image,
          trimap,
          AEMatter_Model,
      ):

          ret = []
          batch_size = image.shape[0]

          for i in range(batch_size):
              tmp_i = from_torch_image(image[i])
              tmp_m = from_torch_image(trimap[i])
              tmp = do_infer(tmp_i, tmp_m, AEMatter_Model)
              ret.append(tmp)

          ret = to_torch_image(np.array(ret))
          ret = ret.squeeze(-1)
          print(ret.shape)

          return ret
#+end_src

** Main function
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.function.py
  def main():
      ptrimap = '/home/asd/Desktop/demo/retriever_trimap.png'
      pimgs = '/home/asd/Desktop/demo/retriever_rgb.png'
      p_outs = 'alpha.png'

      matmodel = get_AEMatter_model(
          path_model_checkpoint='/home/asd/Desktop/AEM_RWA.ckpt')

      # matmodel = AEMatter()
      # matmodel.load_state_dict(
      #     torch.load('/home/asd/Desktop/AEM_RWA.ckpt',
      #                map_location='cpu')['model'])

      # matmodel = matmodel.cuda()
      # matmodel.eval()

      rawimg = pimgs
      trimap = ptrimap
      rawimg = cv2.imread(rawimg, cv2.IMREAD_COLOR)
      trimap = cv2.imread(trimap, cv2.IMREAD_GRAYSCALE)
      trimap_nonp = trimap.copy()
      h, w, c = rawimg.shape
      nonph, nonpw, _ = rawimg.shape
      newh = (((h - 1) // 32) + 1) * 32
      neww = (((w - 1) // 32) + 1) * 32
      padh = newh - h
      padh1 = int(padh / 2)
      padh2 = padh - padh1
      padw = neww - w
      padw1 = int(padw / 2)
      padw2 = padw - padw1
      rawimg_pad = cv2.copyMakeBorder(rawimg, padh1, padh2, padw1, padw2,
                                      cv2.BORDER_REFLECT)
      trimap_pad = cv2.copyMakeBorder(trimap, padh1, padh2, padw1, padw2,
                                      cv2.BORDER_REFLECT)
      h_pad, w_pad, _ = rawimg_pad.shape
      tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
      tritemp[:, :, 0] = (trimap_pad == 0)
      tritemp[:, :, 1] = (trimap_pad == 128)
      tritemp[:, :, 2] = (trimap_pad == 255)
      tritempimgs = np.transpose(tritemp, (2, 0, 1))
      tritempimgs = tritempimgs[np.newaxis, :, :, :]
      img = np.transpose(rawimg_pad, (2, 0, 1))[np.newaxis, ::-1, :, :]
      img = np.array(img, np.float32)
      img = img / 255.
      img = torch.from_numpy(img).cuda()
      tritempimgs = torch.from_numpy(tritempimgs).cuda()
      with torch.no_grad():
          pred = matmodel(img, tritempimgs)
          pred = pred.detach().cpu().numpy()[0]
          pred = pred[:, padh1:padh1 + h, padw1:padw1 + w]
          preda = pred[
              0:1,
          ] * 255
          preda = np.transpose(preda, (1, 2, 0))
          preda = preda * (trimap_nonp[:, :, None]
                           == 128) + (trimap_nonp[:, :, None] == 255) * 255
      preda = np.array(preda, np.uint8)
      cv2.imwrite(p_outs, preda)

#+end_src

** Comfyui Dictionary
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
  NODE_CLASS_MAPPINGS = {
      'load_AEMatter_Model': load_AEMatter_Model,
      'run_AEMatter_inference': run_AEMatter_inference,
  }

  NODE_DISPLAY_NAME_MAPPINGS = {
      'load_AEMatter_Model': 'load_AEMatter_Model',
      'run_AEMatter_inference': 'run_AEMatter_inference',
  }
#+end_src

** COMMENT AEMatter.execute.py
#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./AEMatter.execute.py
  if __name__ == '__main__':
      # main()

      rawimg = cv2.imread('/home/asd/Desktop/demo/retriever_rgb.png',
                          cv2.IMREAD_COLOR)

      trimap = cv2.imread('/home/asd/Desktop/demo/retriever_trimap.png',
                          cv2.IMREAD_GRAYSCALE)

      do_infer(rawimg, trimap,
               get_AEMatter_model('/home/asd/Desktop/AEM_RWA.ckpt'))
#+end_src

** AEMatter.unify.sh
#+begin_src sh :shebang #!/bin/sh :results output :tangle ./AEMatter.unify.sh
  . "${HOME}/dbnew.sh"

  cat \
        'AEMatter.import.py' \
        'AEMatter.function.py' \
        'AEMatter.class.py' \
        'AEMatter.execute.py' \
      | expand | yapf3 \
      > 'AEMatter.py' \
  ;

  cp 'AEMatter.py' '__init__.py'
#+end_src

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

#+RESULTS:

* COMMENT WORK SPACE

** ESHELL
#+begin_src elisp
  (save-buffer)
  (org-babel-tangle)
  (shell-command "./AEMatter.unify.sh")
#+end_src

#+RESULTS:
: 0

** SHELL
#+begin_src sh :shebang #!/bin/sh :results output 
  realpath .
  cd /home/asd/GITHUB/aravind-h-v/dreambooth_experiments/AEMatter
#+end_src

#+RESULTS:

** SHELL
#+begin_src sh :shebang #!/bin/sh :results output 
  ls
#+end_src