| * 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 |
| #+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 |
|
|
