* Requirements #+begin_src conf :tangle ./requirements.txt einops pillow prodigyopt tensorboard timm torch torchvision #+end_src * Download trained model #+begin_src sh :shebang #!/bin/sh :results output :tangle ./download.sh "efficient_download.sh" \ 'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth' \ 'Model_80.pth' \ '6ca28df33ba8476ac13be329a1b1b8b390da5d8042638fb124df3c067c2fe45bccde4366643b830066cbe0164ddbb978a1987a398b4a987f99d908903b44774f' \ "${HOME}/GITHUB/aravind-h-v/dreambooth_experiments/cloth_segmentation/MVANet_Train/pretrained_model/Model_80.pth" \ ; #+end_src * Swin code ** swin.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py import os os.environ["CUDA_VISIBLE_DEVICES"] ='0' #+end_src ** swin.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py import numpy as np #+end_src ** swin.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint #+end_src ** swin.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py from timm.models import load_checkpoint from timm.models.layers import DropPath from timm.models.layers import to_2tuple from timm.models.layers import trunc_normal_ # from mmdet.utils import get_root_logger #+end_src ** swin.function.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.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 def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x def 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: # model.load_state_dict( # torch.load('./swin_base_patch4_window12_384_22kto1k.pth', # 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 #+end_src ** swin.class.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.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 """ 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 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) # logger = get_root_logger() 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() #+end_src * Main code ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py import os os.environ["CUDA_VISIBLE_DEVICES"] = '0' HOME_DIR = os.environ.get('HOME', '/root') #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py import sys sys.path.append(os.path.dirname(os.path.abspath(__file__))) #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py from datetime import datetime import argparse import numpy as np import random import math #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py import cv2 from PIL import Image from PIL import ImageEnhance #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py from einops import rearrange #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.data as data from torch.autograd import Variable from torch.backends import cudnn from torch.cuda import amp from torch.utils.tensorboard import SummaryWriter from torchvision import transforms #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py from prodigyopt import Prodigy #+end_src ** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py # from model.MVANet import MVANet from swin import SwinB #+end_src ** train.function.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.function.py 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 structure_loss(pred, mask): weit = 1 + 5 * torch.abs( F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask) wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none') wbce = (weit * wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3)) pred = torch.sigmoid(pred) inter = ((pred * mask) * weit).sum(dim=(2, 3)) union = ((pred + mask) * weit).sum(dim=(2, 3)) wiou = 1 - (inter + 1) / (union - inter + 1) return (wbce + wiou).mean() def clip_gradient(optimizer, grad_clip): for group in optimizer.param_groups: for param in group['params']: if param.grad is not None: param.grad.data.clamp_(-grad_clip, grad_clip) def adjust_lr(optimizer, init_lr, epoch, decay_rate=0.1, decay_epoch=5): decay = decay_rate**(epoch // decay_epoch) for param_group in optimizer.param_groups: param_group['lr'] *= decay def truncated_normal_(tensor, mean=0, std=1): size = tensor.shape tmp = tensor.new_empty(size + (4, )).normal_() valid = (tmp < 2) & (tmp > -2) ind = valid.max(-1, keepdim=True)[1] tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1)) tensor.data.mul_(std).add_(mean) def init_weights(m): if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d: nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu') #nn.init.normal_(m.weight, std=0.001) #nn.init.normal_(m.bias, std=0.001) truncated_normal_(m.bias, mean=0, std=0.001) def init_weights_orthogonal_normal(m): if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d: nn.init.orthogonal_(m.weight) truncated_normal_(m.bias, mean=0, std=0.001) #nn.init.normal_(m.bias, std=0.001) def l2_regularisation(m): l2_reg = None for W in m.parameters(): if l2_reg is None: l2_reg = W.norm(2) else: l2_reg = l2_reg + W.norm(2) return l2_reg def check_mkdir(dir_name): if not os.path.isdir(dir_name): os.makedirs(dir_name) # several data augumentation strategies def cv_random_flip(img, label): flip_flag = random.randint(0, 1) flip_flag2 = random.randint(0, 1) # left right flip if flip_flag == 1: img = img.transpose(Image.FLIP_LEFT_RIGHT) label = label.transpose(Image.FLIP_LEFT_RIGHT) # top bottom flip if flip_flag2 == 1: img = img.transpose(Image.FLIP_TOP_BOTTOM) label = label.transpose(Image.FLIP_TOP_BOTTOM) return img, label def random_crop_full(image, X, Y, TX, TY): image_width = image.size[0] image_height = image.size[1] final_width = image_width * TX final_height = image_height * TY start_x = (1.0 - TX) * X * image_width start_y = (1.0 - TY) * Y * image_height random_region = (start_x, start_y, start_x + final_width, start_y + final_height) return image.crop(random_region) def random_crop(image, X, Y, T): image_width = image.size[0] image_height = image.size[1] final_width = image_width * T final_height = image_height * T start_x = (1.0 - T) * X * image_width start_y = (1.0 - T) * Y * image_height random_region = (start_x, start_y, start_x + final_width, start_y + final_height) return image.crop(random_region) def garment_color_jitter(image, mask): image = np.array(image) mask = np.array(mask) mask = (mask > 127).astype(dtype=np.uint8) image = cv2.cvtColor(src=image, code=cv2.COLOR_RGB2HSV_FULL) image[:, :, 0] += mask * np.random.randint(0, 255) image = cv2.cvtColor(src=image, code=cv2.COLOR_HSV2RGB_FULL) image = Image.fromarray(image) return image def garment_color_jitter_rotate(image, mask, rotate_index=0, shift_amount=0): image = np.array(image) mask = np.array(mask) if rotate_index == 1: image = cv2.rotate(src=image, rotateCode=cv2.ROTATE_90_CLOCKWISE) mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_90_CLOCKWISE) elif rotate_index == 2: image = cv2.rotate(src=image, rotateCode=cv2.ROTATE_180) mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_180) elif rotate_index == 3: image = cv2.rotate(src=image, rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE) mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE) image = cv2.cvtColor(src=image, code=cv2.COLOR_RGB2HSV_FULL).astype(dtype=np.int32) # image[:, :, 0] += mask_tmp * shift_amount image[:, :, 0] += shift_amount image[:, :, 0] %= 255 image = cv2.cvtColor(src=image.astype(np.uint8), code=cv2.COLOR_HSV2RGB_FULL) image = Image.fromarray(image) mask = Image.fromarray(mask) return image, mask def randomCrop_Both(image, label): image, label = garment_color_jitter_rotate( image=image, mask=label, rotate_index=np.random.randint(0, 4), shift_amount=np.random.randint(-4, +4), ) TX = (np.random.rand() * 0.6) + 0.4 TY = (np.random.rand() * 0.6) + 0.4 X = np.random.rand() Y = np.random.rand() return random_crop_full(image, X, Y, TX, TY), random_crop_full(label, X, Y, TX, TY) def randomCrop_Old(image, label): # image, label = garment_color_jitter_rotate( # image=image, # mask=label, # rotate_index=np.random.randint(0, 4), # shift_amount=np.random.randint(0, 256)) # image, label = garment_color_jitter_rotate( # image=image, # mask=label, # rotate_index=np.random.randint(0, 4), # shift_amount=0, # ) T = (np.random.rand() * 0.6) + 0.4 X = np.random.rand() Y = np.random.rand() return random_crop(image, X, Y, T), random_crop(label, X, Y, T) def randomCrop(image, label): return randomCrop_Both(image, label) def randomCrop_original(image, label): image_width = image.size[0] image_height = image.size[1] border = min(image_width, image_height) // 2 crop_win_width = np.random.randint(image_width - border, image_width) crop_win_height = np.random.randint(image_height - border, image_height) random_region = ((image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1, (image_height + crop_win_height) >> 1) return image.crop(random_region), label.crop(random_region) def randomRotation(image, label): mode = Image.BICUBIC if random.random() > 0.8: random_angle = np.random.randint(-15, 15) image = image.rotate(random_angle, mode) label = label.rotate(random_angle, mode) return image, label def colorEnhance(image): bright_intensity = random.randint(5, 15) / 10.0 image = ImageEnhance.Brightness(image).enhance(bright_intensity) contrast_intensity = random.randint(5, 15) / 10.0 image = ImageEnhance.Contrast(image).enhance(contrast_intensity) color_intensity = random.randint(0, 20) / 10.0 image = ImageEnhance.Color(image).enhance(color_intensity) sharp_intensity = random.randint(0, 30) / 10.0 image = ImageEnhance.Sharpness(image).enhance(sharp_intensity) return image def randomGaussian(image, mean=0.1, sigma=0.35): def gaussianNoisy(im, mean=mean, sigma=sigma): for _i in range(len(im)): im[_i] += random.gauss(mean, sigma) return im img = np.asarray(image) width, height = img.shape img = gaussianNoisy(img[:].flatten(), mean, sigma) img = img.reshape([width, height]) return Image.fromarray(np.uint8(img)) def randomPeper(img): img = np.array(img) noiseNum = int(0.0015 * img.shape[0] * img.shape[1]) for i in range(noiseNum): randX = random.randint(0, img.shape[0] - 1) randY = random.randint(0, img.shape[1] - 1) if random.randint(0, 1) == 0: img[randX, randY] = 0 else: img[randX, randY] = 255 return Image.fromarray(img) # dataloader for training def get_loader(image_root, gt_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=False): print('DEBUG 6') dataset = DISDataset(image_root, gt_root, trainsize) print('DEBUG 7') data_loader = data.DataLoader(dataset=dataset, batch_size=batchsize, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) print('DEBUG 8') return data_loader #+end_src ** train.class.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py class AvgMeter(object): def __init__(self, num=40): self.num = num self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 self.losses = [] def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count self.losses.append(val) def show(self): a = len(self.losses) b = np.maximum(a - self.num, 0) c = self.losses[b:] #print(c) #d = torch.mean(torch.stack(c)) #print(d) return torch.mean(torch.stack(c)) class Running_Avg(object): def __init__(self, weight=0.999): self.weight = weight self.reset() def reset(self): self.n = 0 self.val = 0 def update(self, val, n=1): self.val = (self.weight * self.val) + ((1 - self.weight) * val) self.n = (self.weight * self.n) + ((1 - self.weight) * n) def show(self): if self.n == 0: return 0 else: return self.val / self.n #+end_src ** Main training dataset *** COMMENT Original #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py # dataset for training # The current loader is not using the normalized depth maps for training and test. If you use the normalized depth maps # (e.g., 0 represents background and 1 represents foreground.), the performance will be further improved. class DISDataset(data.Dataset): def __init__(self, image_root, gt_root, trainsize): self.trainsize = trainsize self.images = [ image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png') or f.endswith('tif') ] self.gts = [ gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg') or f.endswith('.png') or f.endswith('tif') ] self.images = sorted(self.images) self.gts = sorted(self.gts) self.filter_files() self.size = len(self.images) self.img_transform = transforms.Compose([ transforms.Resize((self.trainsize, self.trainsize)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.gt_transform = transforms.Compose([ transforms.Resize((self.trainsize, self.trainsize)), transforms.ToTensor() ]) def __getitem__(self, index): image = self.rgb_loader(self.images[index]) gt = self.binary_loader(self.gts[index]) image, gt = cv_random_flip(image, gt) image, gt = randomCrop(image, gt) image, gt = randomRotation(image, gt) image = colorEnhance(image) image = self.img_transform(image) gt = self.gt_transform(gt) return image, gt def filter_files(self): assert len(self.images) == len(self.gts) and len(self.gts) == len( self.images) images = [] gts = [] for img_path, gt_path in zip(self.images, self.gts): img = Image.open(img_path) gt = Image.open(gt_path) if img.size == gt.size: images.append(img_path) gts.append(gt_path) self.images = images self.gts = gts def rgb_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('RGB') def binary_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('L') def resize(self, img, gt): assert img.size == gt.size w, h = img.size if h < self.trainsize or w < self.trainsize: h = max(h, self.trainsize) w = max(w, self.trainsize) return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST) else: return img, gt def __len__(self): return self.size #+end_src *** Changed #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py # dataset for training # The current loader is not using the normalized depth maps for training and test. If you use the normalized depth maps # (e.g., 0 represents background and 1 represents foreground.), the performance will be further improved. class DISDataset(data.Dataset): def __init__(self, image_root, gt_root, trainsize): self.trainsize = trainsize end_pattern = '_segm.png' files = list(f for f in os.listdir(gt_root) if f.endswith(end_pattern)) files.sort() self.gts = list(gt_root + f for f in files) self.images = list(image_root + f[0:-len(end_pattern)] + '.jpg' for f in files) self.size = len(self.images) self.img_transform = transforms.Compose([ transforms.Resize((self.trainsize, self.trainsize)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.gt_transform = transforms.Compose([ transforms.Resize((self.trainsize, self.trainsize)), transforms.ToTensor() ]) def __getitem__(self, index): image = self.rgb_loader(self.images[index]) gt = self.binary_loader(self.gts[index]) image, gt = cv_random_flip(image, gt) image, gt = randomCrop(image, gt) image, gt = randomRotation(image, gt) image = colorEnhance(image) image = self.img_transform(image) gt = self.gt_transform(gt) return image, gt def filter_files(self): assert len(self.images) == len(self.gts) and len(self.gts) == len( self.images) images = [] gts = [] for img_path, gt_path in zip(self.images, self.gts): img = Image.open(img_path) gt = Image.open(gt_path) if img.size == gt.size: images.append(img_path) gts.append(gt_path) self.images = images self.gts = gts def rgb_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('RGB') def binary_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('L') def resize(self, img, gt): assert img.size == gt.size w, h = img.size if h < self.trainsize or w < self.trainsize: h = max(h, self.trainsize) w = max(w, self.trainsize) return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST) else: return img, gt def __len__(self): return self.size #+end_src ** train.class.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py # test dataset and loader class test_dataset: def __init__(self, image_root, depth_root, testsize): self.testsize = testsize self.images = [ image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') ] self.depths = [ depth_root + f for f in os.listdir(depth_root) if f.endswith('.bmp') or f.endswith('.png') ] self.images = sorted(self.images) self.depths = sorted(self.depths) self.transform = transforms.Compose([ transforms.Resize((self.testsize, self.testsize)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) # self.gt_transform = transforms.Compose([ # transforms.Resize((self.trainsize, self.trainsize)), # transforms.ToTensor()]) self.depths_transform = transforms.Compose([ transforms.Resize((self.testsize, self.testsize)), transforms.ToTensor() ]) self.size = len(self.images) self.index = 0 def load_data(self): image = self.rgb_loader(self.images[self.index]) HH = image.size[0] WW = image.size[1] image = self.transform(image).unsqueeze(0) depth = self.rgb_loader(self.depths[self.index]) depth = self.depths_transform(depth).unsqueeze(0) name = self.images[self.index].split('/')[-1] # image_for_post=self.rgb_loader(self.images[self.index]) # image_for_post=image_for_post.resize(gt.size) if name.endswith('.jpg'): name = name.split('.jpg')[0] + '.png' self.index += 1 self.index = self.index % self.size return image, depth, HH, WW, name def rgb_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('RGB') def binary_loader(self, path): with open(path, 'rb') as f: img = Image.open(f) return img.convert('L') def __len__(self): return self.size 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.float32, device='cuda') def __call__(self, b, h, w): mask = torch.zeros([b, h, w], dtype=torch.bool, device='cuda') assert mask is not None not_mask = ~mask y_embed = not_mask.cumsum(dim=1, dtype=torch.float32) x_embed = not_mask.cumsum(dim=2, dtype=torch.float32) if self.normalize: eps = 1e-6 y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale).cuda() x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale).cuda() 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): 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).cuda() 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]).cuda() sideout2 = patches2image( sideout2[:-1]).cuda() ####(5,c,h,w) -> (1 c 2h,2w) sideout3 = patches2image(sideout3[:-1]).cuda() sideout4 = patches2image(sideout4[:-1]).cuda() sideout5 = patches2image(sideout5[:-1]).cuda() 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) 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]) print(e5.shape) 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 ** train.execute.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.execute.py writer = SummaryWriter() cudnn.benchmark = True parser = argparse.ArgumentParser() parser.add_argument('--epoch', type=int, default=80, help='epoch number') parser.add_argument('--lr_gen', type=float, default=1e-5, help='learning rate') parser.add_argument('--batchsize', type=int, default=1, help='training batch size') parser.add_argument('--trainsize', type=int, default=1024, help='training dataset size') parser.add_argument('--decay_rate', type=float, default=0.9, help='decay rate of learning rate') parser.add_argument('--decay_epoch', type=int, default=80, help='every n epochs decay learning rate') opt = parser.parse_args() print('Generator Learning Rate: {}'.format(opt.lr_gen)) # build models if hasattr(torch.cuda, 'empty_cache'): torch.cuda.empty_cache() generator = MVANet() generator.cuda() print('DEBUG 3') pretrained_dict = torch.load( HOME_DIR + '/GITHUB/aravind-h-v/dreambooth_experiments/cloth_segmentation/MVANet_Train/pretrained_model/Model_80.pth', map_location='cuda') model_dict = generator.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) generator.load_state_dict(model_dict) generator_params = generator.parameters() # generator_optimizer = torch.optim.Adam(generator_params, opt.lr_gen) generator_optimizer = Prodigy(generator_params, lr=1., weight_decay=0.01) print('DEBUG 4') image_root = './data/image/' gt_root = './data/mask/' train_loader = get_loader(image_root, gt_root, batchsize=opt.batchsize, trainsize=opt.trainsize) print('DEBUG 5') total_step = len(train_loader) to_pil = transforms.ToPILImage() ## define loss print('DEBUG 2') CE = torch.nn.BCELoss() mse_loss = torch.nn.MSELoss(size_average=True, reduce=True) size_rates = [1] criterion = nn.BCEWithLogitsLoss().cuda() criterion_mae = nn.L1Loss().cuda() criterion_mse = nn.MSELoss().cuda() use_fp16 = True scaler = amp.GradScaler(enabled=use_fp16) print('DEBUG 1') for epoch in range(1, opt.epoch + 1): torch.cuda.empty_cache() generator.train() # loss_record = AvgMeter() loss_record = Running_Avg() print('Generator Learning Rate: {}'.format( generator_optimizer.param_groups[0]['lr'])) for i, pack in enumerate(train_loader, start=1): torch.cuda.empty_cache() for rate in size_rates: torch.cuda.empty_cache() generator_optimizer.zero_grad() images, gts = pack images = Variable(images) gts = Variable(gts) images = images.cuda() gts = gts.cuda() trainsize = int(round(opt.trainsize * rate / 32) * 32) if rate != 1: images = F.upsample(images, size=(trainsize, trainsize), mode='bilinear', align_corners=True) gts = F.upsample(gts, size=(trainsize, trainsize), mode='bilinear', align_corners=True) b, c, h, w = gts.size() target_1 = F.upsample(gts, size=h // 4, mode='nearest') target_2 = F.upsample(gts, size=h // 8, mode='nearest').cuda() target_3 = F.upsample(gts, size=h // 16, mode='nearest').cuda() target_4 = F.upsample(gts, size=h // 32, mode='nearest').cuda() target_5 = F.upsample(gts, size=h // 64, mode='nearest').cuda() with amp.autocast(enabled=use_fp16): sideout5, sideout4, sideout3, sideout2, sideout1, final, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1 = generator.forward( images) loss1 = structure_loss(sideout5, target_4) loss2 = structure_loss(sideout4, target_3) loss3 = structure_loss(sideout3, target_2) loss4 = structure_loss(sideout2, target_1) loss5 = structure_loss(sideout1, target_1) loss6 = structure_loss(final, gts) loss7 = structure_loss(glb5, target_5) loss8 = structure_loss(glb4, target_4) loss9 = structure_loss(glb3, target_3) loss10 = structure_loss(glb2, target_2) loss11 = structure_loss(glb1, target_2) loss12 = structure_loss(tokenattmap4, target_3) loss13 = structure_loss(tokenattmap3, target_2) loss14 = structure_loss(tokenattmap2, target_1) loss15 = structure_loss(tokenattmap1, target_1) loss = loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + 0.3 * ( loss7 + loss8 + loss9 + loss10 + loss11) + 0.3 * (loss12 + loss13 + loss14 + loss15) Loss_loc = loss1 + loss2 + loss3 + loss4 + loss5 + loss6 Loss_glb = loss7 + loss8 + loss9 + loss10 + loss11 Loss_map = loss12 + loss13 + loss14 + loss15 writer.add_scalar('loss', loss.item(), epoch * len(train_loader) + i) generator_optimizer.zero_grad() scaler.scale(loss).backward() scaler.step(generator_optimizer) scaler.update() if rate == 1: loss_record.update(loss.data, opt.batchsize) if i % 10 == 0 or i == total_step: print( '{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], gen Loss: {:.4f}' .format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record.show())) if i % 8000 == 0 or i == total_step: save_path = './saved_model/' if not os.path.exists(save_path): os.mkdir(save_path) torch.save( generator.state_dict(), save_path + 'Model' + '_%d' % epoch + '_%d' % i + '.pth') # adjust_lr(generator_optimizer, opt.lr_gen, epoch, opt.decay_rate, # opt.decay_epoch) # save checkpoints every 20 epochs # if epoch % 20 == 0: if True: save_path = './saved_model/' if not os.path.exists(save_path): os.mkdir(save_path) save_path = './saved_model/MVANet/' if not os.path.exists(save_path): os.mkdir(save_path) torch.save(generator.state_dict(), save_path + 'Model' + '_%d' % epoch + '.pth') #+end_src * SAMPLE ** train *** train.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py #+end_src *** train.function.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.function.py #+end_src *** train.class.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py #+end_src *** train.execute.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.execute.py #+end_src ** swin *** swin.import.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py #+end_src *** swin.function.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.function.py #+end_src *** swin.class.py #+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.class.py #+end_src * UNIFY #+begin_src sh :shebang #!/bin/sh :results output :tangle ./train.unify.sh . "${HOME}/dbnew.sh" echo '#!/usr/bin/python3' > './train.py' cat \ './train.import.py' \ './train.function.py' \ './train.class.py' \ './train.execute.py' \ | expand | yapf3 \ | grep -v '^#!/usr/bin/python3$' \ >> './train.py' \ ; echo '#!/usr/bin/python3' > './swin.py' cat \ './swin.import.py' \ './swin.function.py' \ './swin.class.py' \ | expand | yapf3 \ | grep -v '^#!/usr/bin/python3$' \ >> './swin.py' \ ; rm -vf -- \ './swin.class.py' \ './swin.function.py' \ './swin.import.py' \ './train.class.py' \ './train.execute.py' \ './train.function.py' \ './train.import.py' \ './train.unify.sh' \ ; #+end_src * Run #+begin_src sh :shebang #!/bin/sh :results output :tangle ./run.sh . "${HOME}/dbnew.sh" cd "$('dirname' '--' "${0}")" pip3 install -r './requirements.txt' python3 ./train.py --batchsize 4 #+end_src * WORK SPACE ** ELISP #+begin_src elisp (save-buffer) (org-babel-tangle) (shell-command "./train.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/cloth_segmentation/MVANet_Train #+end_src