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
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(" ")