| |
| |
| |
| |
| |
| import torch |
| import torch.distributed as dist |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from functools import partial |
| import warnings |
| from einops import rearrange |
|
|
| from timm.models.layers import DropPath, to_2tuple, trunc_normal_ |
| from timm.models.registry import register_model |
| from timm.models.vision_transformer import _cfg |
| import math |
| from mmcv.cnn import ConvModule |
| from modules.commons.loralib.layers import MergedLoRALinear, LoRALinear, LoRAConv2d, LoRAConv3d |
|
|
| from utils.commons.hparams import hparams |
|
|
|
|
| def resize(input, |
| size=None, |
| scale_factor=None, |
| mode='nearest', |
| align_corners=None, |
| warning=True): |
| if warning: |
| if size is not None and align_corners: |
| input_h, input_w = tuple(int(x) for x in input.shape[2:]) |
| output_h, output_w = tuple(int(x) for x in size) |
| if output_h > input_h or output_w > output_h: |
| if ((output_h > 1 and output_w > 1 and input_h > 1 |
| and input_w > 1) and (output_h - 1) % (input_h - 1) |
| and (output_w - 1) % (input_w - 1)): |
| warnings.warn( |
| f'When align_corners={align_corners}, ' |
| 'the output would more aligned if ' |
| f'input size {(input_h, input_w)} is `x+1` and ' |
| f'out size {(output_h, output_w)} is `nx+1`') |
| if isinstance(size, torch.Size): |
| size = tuple(int(x) for x in size) |
| return F.interpolate(input, size, scale_factor, mode, align_corners) |
|
|
|
|
| class HeadMLP(nn.Module): |
| """ |
| Linear Embedding |
| """ |
| def __init__(self, input_dim=2048, embed_dim=768): |
| super().__init__() |
| self.proj = nn.Linear(input_dim, embed_dim) |
|
|
| def forward(self, x): |
| x = x.flatten(2).transpose(1, 2) |
| x = self.proj(x) |
| return x |
|
|
|
|
| class Mlp(nn.Module): |
| 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.dwconv = DWConv(hidden_features) |
| self.act = act_layer() |
| self.fc2 = nn.Linear(hidden_features, out_features) |
| self.drop = nn.Dropout(drop) |
|
|
| self.apply(self._init_weights) |
|
|
| def _init_weights(self, 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) |
| elif isinstance(m, nn.Conv2d): |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| fan_out //= m.groups |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x, H, W): |
| x = self.fc1(x) |
| x = self.dwconv(x, H, W) |
| x = self.act(x) |
| x = self.drop(x) |
| x = self.fc2(x) |
| x = self.drop(x) |
| return x |
|
|
|
|
| class Attention(nn.Module): |
| def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1): |
| super().__init__() |
| assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." |
|
|
| self.dim = dim |
| self.num_heads = num_heads |
| head_dim = dim // num_heads |
| self.scale = qk_scale or head_dim ** -0.5 |
|
|
| self.q = nn.Linear(dim, dim, bias=qkv_bias) |
| self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) |
| self.attn_drop = nn.Dropout(attn_drop) |
| self.proj = nn.Linear(dim, dim) |
| self.proj_drop = nn.Dropout(proj_drop) |
|
|
| self.sr_ratio = sr_ratio |
| if sr_ratio > 1: |
| self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) |
| self.norm = nn.LayerNorm(dim) |
|
|
| self.apply(self._init_weights) |
|
|
| def _init_weights(self, 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) |
| elif isinstance(m, nn.Conv2d): |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| fan_out //= m.groups |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x, H, W): |
| B, N, C = x.shape |
| q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) |
|
|
| if self.sr_ratio > 1: |
| x_ = x.permute(0, 2, 1).reshape(B, C, H, W) |
| x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) |
| x_ = self.norm(x_) |
| kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) |
| else: |
| kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) |
| k, v = kv[0], kv[1] |
|
|
| attn = (q @ k.transpose(-2, -1)) * self.scale |
| attn = attn.softmax(dim=-1) |
| 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 Block(nn.Module): |
|
|
| def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., |
| drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1): |
| super().__init__() |
| self.norm1 = norm_layer(dim) |
| self.attn = Attention( |
| dim, |
| num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, |
| attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) |
| |
| 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.apply(self._init_weights) |
|
|
| def _init_weights(self, 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) |
| elif isinstance(m, nn.Conv2d): |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| fan_out //= m.groups |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x, H, W): |
| x = x + self.drop_path(self.attn(self.norm1(x), H, W)) |
| x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) |
|
|
| return x |
|
|
|
|
| class OverlapPatchEmbed(nn.Module): |
| """ Image to Patch Embedding |
| """ |
|
|
| def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768): |
| super().__init__() |
| img_size = to_2tuple(img_size) |
| patch_size = to_2tuple(patch_size) |
|
|
| self.img_size = img_size |
| self.patch_size = patch_size |
| self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1] |
| self.num_patches = self.H * self.W |
| self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, |
| padding=(patch_size[0] // 2, patch_size[1] // 2)) |
| self.norm = nn.LayerNorm(embed_dim) |
|
|
| self.apply(self._init_weights) |
|
|
| def _init_weights(self, 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) |
| elif isinstance(m, nn.Conv2d): |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| fan_out //= m.groups |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x): |
| x = self.proj(x) |
| _, _, H, W = x.shape |
| x = x.flatten(2).transpose(1, 2) |
| x = self.norm(x) |
|
|
| return x, H, W |
|
|
|
|
| class MixVisionTransformer(nn.Module): |
| def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], |
| num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., |
| attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, |
| depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]): |
| super().__init__() |
| self.num_classes = num_classes |
| self.depths = depths |
|
|
| |
| self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans, |
| embed_dim=embed_dims[0]) |
| self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], |
| embed_dim=embed_dims[1]) |
| self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], |
| embed_dim=embed_dims[2]) |
| self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2], |
| embed_dim=embed_dims[3]) |
|
|
| |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] |
| cur = 0 |
| self.block1 = nn.ModuleList([Block( |
| dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, |
| sr_ratio=sr_ratios[0]) |
| for i in range(depths[0])]) |
| self.norm1 = norm_layer(embed_dims[0]) |
|
|
| cur += depths[0] |
| self.block2 = nn.ModuleList([Block( |
| dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, |
| sr_ratio=sr_ratios[1]) |
| for i in range(depths[1])]) |
| self.norm2 = norm_layer(embed_dims[1]) |
|
|
| cur += depths[1] |
| self.block3 = nn.ModuleList([Block( |
| dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, |
| sr_ratio=sr_ratios[2]) |
| for i in range(depths[2])]) |
| self.norm3 = norm_layer(embed_dims[2]) |
|
|
| cur += depths[2] |
| self.block4 = nn.ModuleList([Block( |
| dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, |
| sr_ratio=sr_ratios[3]) |
| for i in range(depths[3])]) |
| self.norm4 = norm_layer(embed_dims[3]) |
|
|
| |
| |
|
|
| self.apply(self._init_weights) |
|
|
| def _init_weights(self, 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) |
| elif isinstance(m, nn.Conv2d): |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| fan_out //= m.groups |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def reset_drop_path(self, drop_path_rate): |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] |
| cur = 0 |
| for i in range(self.depths[0]): |
| self.block1[i].drop_path.drop_prob = dpr[cur + i] |
|
|
| cur += self.depths[0] |
| for i in range(self.depths[1]): |
| self.block2[i].drop_path.drop_prob = dpr[cur + i] |
|
|
| cur += self.depths[1] |
| for i in range(self.depths[2]): |
| self.block3[i].drop_path.drop_prob = dpr[cur + i] |
|
|
| cur += self.depths[2] |
| for i in range(self.depths[3]): |
| self.block4[i].drop_path.drop_prob = dpr[cur + i] |
|
|
| def freeze_patch_emb(self): |
| self.patch_embed1.requires_grad = False |
|
|
| @torch.jit.ignore |
| def no_weight_decay(self): |
| return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} |
|
|
| def get_classifier(self): |
| return self.head |
|
|
| def reset_classifier(self, num_classes, global_pool=''): |
| self.num_classes = num_classes |
| self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() |
|
|
| def forward_features(self, x): |
| B = x.shape[0] |
| outs = [] |
|
|
| |
| x, H, W = self.patch_embed1(x) |
| for i, blk in enumerate(self.block1): |
| x = blk(x, H, W) |
| x = self.norm1(x) |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() |
| outs.append(x) |
|
|
| |
| x, H, W = self.patch_embed2(x) |
| for i, blk in enumerate(self.block2): |
| x = blk(x, H, W) |
| x = self.norm2(x) |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() |
| outs.append(x) |
|
|
| |
| x, H, W = self.patch_embed3(x) |
| for i, blk in enumerate(self.block3): |
| x = blk(x, H, W) |
| x = self.norm3(x) |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() |
| outs.append(x) |
|
|
| |
| x, H, W = self.patch_embed4(x) |
| for i, blk in enumerate(self.block4): |
| x = blk(x, H, W) |
| x = self.norm4(x) |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() |
| outs.append(x) |
|
|
| return outs |
|
|
| def forward(self, x): |
| x = self.forward_features(x) |
| |
|
|
| return x |
|
|
|
|
| class DWConv(nn.Module): |
| def __init__(self, dim=768): |
| super(DWConv, self).__init__() |
| self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) |
|
|
| def forward(self, x, H, W): |
| B, N, C = x.shape |
| x = x.transpose(1, 2).view(B, C, H, W) |
| x = self.dwconv(x) |
| x = x.flatten(2).transpose(1, 2) |
| return x |
|
|
|
|
| class mit_b0(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b0, self).__init__( |
| patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b0.pth'), strict=False) |
|
|
|
|
| class mit_b1(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b1, self).__init__( |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b1.pth'), strict=False) |
|
|
|
|
|
|
| class mit_b2(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b2, self).__init__( |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b2.pth'), strict=False) |
|
|
|
|
|
|
| class mit_b3(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b3, self).__init__( |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b3.pth'), strict=False) |
|
|
|
|
| class mit_b4(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b4, self).__init__( |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b4.pth'), strict=False) |
|
|
|
|
| class mit_b5(MixVisionTransformer): |
| def __init__(self, **kwargs): |
| super(mit_b5, self).__init__( |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], |
| drop_rate=0.0, drop_path_rate=0.1) |
| self.load_state_dict(torch.load('checkpoints/pretrained_ckpts/mit_b5.pth'), strict=False) |
| |
|
|
| class SegFormerHead(nn.Module): |
| """ |
| SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers |
| """ |
| def __init__(self, segformer_scale='b3'): |
| super().__init__() |
| self.segformer_scale = segformer_scale |
| |
| self.in_channels = [64, 128, 320, 512] if self.segformer_scale != 'b0' else [32, 64, 160, 256] |
| self.feature_strides = [4, 8, 16, 32] |
| self.in_index = [0, 1, 2, 3] |
| self.input_transform='multiple_select' |
| self.dropout = nn.Dropout2d(0.1) |
| c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels |
| embedding_dim = self.embedding_dim = 256 |
| self.linear_c4 = HeadMLP(input_dim=c4_in_channels, embed_dim=embedding_dim) |
| self.linear_c3 = HeadMLP(input_dim=c3_in_channels, embed_dim=embedding_dim) |
| self.linear_c2 = HeadMLP(input_dim=c2_in_channels, embed_dim=embedding_dim) |
| self.linear_c1 = HeadMLP(input_dim=c1_in_channels, embed_dim=embedding_dim) |
|
|
| if dist.is_initialized(): |
| self.linear_fuse = ConvModule( |
| in_channels=embedding_dim*4, |
| out_channels=embedding_dim, |
| kernel_size=1, |
| norm_cfg=dict(type='SyncBN', requires_grad=True) |
| ) |
| else: |
| self.linear_fuse = ConvModule( |
| in_channels=embedding_dim*4, |
| out_channels=embedding_dim, |
| kernel_size=1, |
| norm_cfg=dict(type='BN', requires_grad=True) |
| ) |
|
|
| def _transform_inputs(self, inputs): |
| """Transform inputs for decoder. |
| |
| Args: |
| inputs (list[Tensor]): List of multi-level img features. |
| |
| Returns: |
| Tensor: The transformed inputs |
| """ |
|
|
| if self.input_transform == 'multiple_select': |
| inputs = [inputs[i] for i in self.in_index] |
| else: |
| inputs = inputs[self.in_index] |
|
|
| return inputs |
| |
| def forward(self, inputs): |
| x = self._transform_inputs(inputs) |
| c1, c2, c3, c4 = x |
|
|
| |
| n, _, h, w = c4.shape |
|
|
| _c4 = self.linear_c4(c4).permute(0,2,1).reshape(n, -1, c4.shape[2], c4.shape[3]) |
| _c4 = resize(_c4, size=c1.size()[2:],mode='bilinear',align_corners=False) |
|
|
| _c3 = self.linear_c3(c3).permute(0,2,1).reshape(n, -1, c3.shape[2], c3.shape[3]) |
| _c3 = resize(_c3, size=c1.size()[2:],mode='bilinear',align_corners=False) |
|
|
| _c2 = self.linear_c2(c2).permute(0,2,1).reshape(n, -1, c2.shape[2], c2.shape[3]) |
| _c2 = resize(_c2, size=c1.size()[2:],mode='bilinear',align_corners=False) |
|
|
| _c1 = self.linear_c1(c1).permute(0,2,1).reshape(n, -1, c1.shape[2], c1.shape[3]) |
|
|
| _c = self.linear_fuse(torch.cat([_c4, _c3, _c2, _c1], dim=1)) |
|
|
| x = self.dropout(_c) |
|
|
| return x |
|
|
|
|
| |
| from modules.eg3ds.models.networks_stylegan2 import Conv2dLayer |
| class conv(nn.Module): |
| def __init__(self, num_in_layers, num_out_layers, kernel_size, up=1, down=1, lora_args=None): |
| super(conv, self).__init__() |
| self.conv = Conv2dLayer(num_in_layers, num_out_layers, kernel_size, activation='elu', up=up, down=down, lora_args=lora_args) |
| self.bn = nn.InstanceNorm2d( |
| num_out_layers, track_running_stats=False, affine=True |
| ) |
|
|
| def forward(self, x): |
| return self.bn(self.conv(x)) |
|
|
|
|
| class SegFormerImg2PlaneBackbone(nn.Module): |
| def __init__(self, mode='b3'): |
| super().__init__() |
| mode2cls = { |
| 'b0': mit_b0, |
| 'b1': mit_b1, |
| 'b2': mit_b2, |
| 'b3': mit_b3, |
| 'b4': mit_b4, |
| 'b5': mit_b5, |
| } |
| self.mode = mode |
| self.mix_vit = mode2cls[mode]() |
| self.fuse_head = SegFormerHead(mode) |
|
|
| self.to_plane_cnn = nn.Sequential(*[ |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.UpsamplingBilinear2d(scale_factor=2.), |
| nn.Conv2d(in_channels=256, out_channels=96, kernel_size=3, stride=1, padding=1), |
| ]) |
|
|
| def forward(self, x): |
| """ |
| x: [B, 3, H=512, W=512] |
| return: |
| plane: [B, 96, H=256, W=256] |
| """ |
|
|
| feats = self.mix_vit(x) |
| fused_feat = self.fuse_head(feats) |
|
|
| planes = self.to_plane_cnn(fused_feat) |
|
|
| planes = planes.view(len(planes), 3, -1, planes.shape[-2], planes.shape[-1]) |
| planes_xy = planes[:,0] |
| planes_xy = torch.flip(planes_xy, [2]) |
| planes_xz = planes[:,1] |
| planes_xz = torch.flip(planes_xz, [2]) |
| planes_zy = planes[:,2] |
| planes_zy = torch.flip(planes_zy, [2, 3]) |
| planes = torch.stack([planes_xy, planes_xz, planes_zy], dim=1) |
| |
| return planes |
|
|
|
|
| class TemporalAttNet(nn.Module): |
| """ |
| Used to smooth the secc_plane with a window input |
| """ |
| def __init__(self, in_dim=96, seq_len=5): |
| super().__init__() |
| self.seq_len = seq_len |
| self.conv2d_layers = nn.Sequential(*[ |
| |
| nn.Conv3d(in_dim, 64, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.Conv3d(64, 64, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1,2,2), count_include_pad=False), |
| |
| nn.Conv3d(64, 32, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.Conv3d(32, 32, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1,2,2), count_include_pad=False), |
| |
| nn.Conv3d(32, 16, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.Conv3d(16, 16, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1)), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1,2,2), count_include_pad=False), |
| ]) |
|
|
| self.conv3d_layers = nn.Sequential(*[ |
| |
| nn.Conv3d(16, 8, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1,2,2), count_include_pad=False), |
| |
| nn.Conv3d(8, 8, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1,2,2), count_include_pad=False), |
| |
| nn.Conv3d(8, 4, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(0.02, True), |
| nn.Conv3d(4, 2, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(0.02, True), |
| nn.Conv3d(2, 1, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(0.02, True), |
| nn.AvgPool3d(kernel_size=(1, 7, 7), stride=1, count_include_pad=False), |
| ]) |
|
|
| self.to_attention_weights = nn.Sequential( |
| nn.Linear(in_features=self.seq_len, out_features=self.seq_len, bias=True), |
| nn.Softmax(dim=1) |
| ) |
|
|
| def forward(self, x): |
| """ |
| x: [B, C, T, H, W] |
| y: [B, T] attention weights |
| out: [B, C, H, W] |
| """ |
| b,c,t,h,w = x.shape |
| y = F.interpolate(x, size=(t, 224, 224), mode='trilinear') |
| y = self.conv2d_layers(y) |
| y = self.conv3d_layers(y) |
| y = y.squeeze(1, 3, 4) |
| assert y.ndim == 2 |
| y = y.reshape([b, 1, t, 1, 1]) |
| out = (y * x).sum(dim=2) |
| return out |
|
|
|
|
| class SegFormerSECC2PlaneBackbone(nn.Module): |
| def __init__(self, mode='b0', out_channels=96, pncc_cond_mode='cano_src_tgt', lora_args=None): |
| super().__init__() |
| mode2cls = { |
| 'b0': mit_b0, |
| 'b1': mit_b1, |
| 'b2': mit_b2, |
| 'b3': mit_b3, |
| 'b4': mit_b4, |
| 'b5': mit_b5, |
| } |
| self.mode = mode |
| self.pncc_cond_mode = pncc_cond_mode |
| in_dim = 9 if pncc_cond_mode == 'cano_src_tgt' else 6 |
| self.prenet = Conv2dLayer(in_dim, 3, 1, lora_args=lora_args) |
| self.mix_vit = mode2cls[mode]() |
| self.fuse_head = SegFormerHead(mode) |
| if lora_args is None: |
| self.to_plane_cnn = nn.Sequential(*[ |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.UpsamplingBilinear2d(scale_factor=2.), |
| nn.Conv2d(in_channels=256, out_channels=out_channels, kernel_size=3, stride=1, padding=1), |
| ]) |
| else: |
| lora_r = self.lora_r = lora_args.get("lora_r", 8) |
| self.to_plane_cnn = nn.Sequential(*[ |
| LoRAConv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, r=lora_r), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| LoRAConv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, r=lora_r), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| LoRAConv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, r=lora_r), |
| nn.LeakyReLU(negative_slope=0.01, inplace=True), |
| nn.UpsamplingBilinear2d(scale_factor=2.), |
| LoRAConv2d(in_channels=256, out_channels=out_channels, kernel_size=3, stride=1, padding=1, r=lora_r), |
| ]) |
| |
| |
| |
|
|
| def forward(self, x): |
| """ |
| x: [B, 3, H=512, W=512] or [B, 3, T, H, W] |
| return: |
| plane: [B, 96, H=256, W=256] |
| """ |
| |
| |
| |
| x = self.prenet(x) |
| feats = self.mix_vit(x) |
| fused_feat = self.fuse_head(feats) |
| planes = self.to_plane_cnn(fused_feat) |
|
|
| |
| |
| |
|
|
| planes = planes.view(len(planes), 3, -1, planes.shape[-2], planes.shape[-1]) |
| planes_xy = planes[:,0] |
| planes_xy = torch.flip(planes_xy, [2]) |
| planes_xz = planes[:,1] |
| planes_xz = torch.flip(planes_xz, [2]) |
| planes_zy = planes[:,2] |
| planes_zy = torch.flip(planes_zy, [2, 3]) |
| planes = torch.stack([planes_xy, planes_xz, planes_zy], dim=1) |
| |
| return planes |
|
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| if __name__ == '__main__': |
| import tqdm |
| img2plane = SegFormerTexture2PlaneBackbone() |
| img2plane.cuda() |
| x = torch.randn([4, 3, 512, 512]).cuda() |
| idx = torch.randint(low=0, high=128*128, size=[4, 3, 256*256]).cuda() |
| for _ in tqdm.trange(100): |
| y = img2plane(x, idx) |
| print(" ") |
