File size: 11,581 Bytes
0a82b18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# MASt3R heads
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
import mast3r.utils.path_to_dust3r # noqa
from dust3r.heads.postprocess import reg_dense_depth, reg_dense_conf # noqa
from dust3r.heads.dpt_head import PixelwiseTaskWithDPT # noqa
import dust3r.utils.path_to_croco # noqa
from models.blocks import Mlp # noqa
from models.dpt_block import Interpolate # noqa
def reg_desc(desc, mode):
if 'norm' in mode:
desc = desc / desc.norm(dim=-1, keepdim=True)
else:
raise ValueError(f"Unknown desc mode {mode}")
return desc
def postprocess(out, depth_mode, conf_mode, desc_dim=None, desc_mode='norm', two_confs=False, desc_conf_mode=None):
if desc_conf_mode is None:
desc_conf_mode = conf_mode
fmap = out.permute(0, 2, 3, 1) # B,H,W,D
res = dict(pts3d=reg_dense_depth(fmap[..., 0:3], mode=depth_mode))
if conf_mode is not None:
res['conf'] = reg_dense_conf(fmap[..., 3], mode=conf_mode)
if desc_dim is not None:
start = 3 + int(conf_mode is not None)
res['desc'] = reg_desc(fmap[..., start:start + desc_dim], mode=desc_mode)
if two_confs:
res['desc_conf'] = reg_dense_conf(fmap[..., start + desc_dim], mode=desc_conf_mode)
else:
res['desc_conf'] = res['conf'].clone()
return res
class Cat_MLP_LocalFeatures_DPT_Pts3d(PixelwiseTaskWithDPT):
""" Mixture between MLP and DPT head that outputs 3d points and local features (with MLP).
The input for both heads is a concatenation of Encoder and Decoder outputs
"""
def __init__(self, net, has_conf=False, local_feat_dim=16, hidden_dim_factor=4., hooks_idx=None, dim_tokens=None,
num_channels=1, postprocess=None, feature_dim=256, last_dim=32, depth_mode=None, conf_mode=None, head_type="regression", **kwargs):
super().__init__(num_channels=num_channels, feature_dim=feature_dim, last_dim=last_dim, hooks_idx=hooks_idx,
dim_tokens=dim_tokens, depth_mode=depth_mode, postprocess=postprocess, conf_mode=conf_mode, head_type=head_type)
self.local_feat_dim = local_feat_dim
patch_size = net.patch_embed.patch_size
if isinstance(patch_size, tuple):
assert len(patch_size) == 2 and isinstance(patch_size[0], int) and isinstance(
patch_size[1], int), "What is your patchsize format? Expected a single int or a tuple of two ints."
assert patch_size[0] == patch_size[1], "Error, non square patches not managed"
patch_size = patch_size[0]
self.patch_size = patch_size
self.desc_mode = net.desc_mode
self.has_conf = has_conf
self.two_confs = net.two_confs # independent confs for 3D regr and descs
self.desc_conf_mode = net.desc_conf_mode
idim = net.enc_embed_dim + net.dec_embed_dim
self.head_local_features = Mlp(in_features=idim,
hidden_features=int(hidden_dim_factor * idim),
out_features=(self.local_feat_dim + self.two_confs) * self.patch_size**2)
def forward(self, decout, img_shape):
# pass through the heads
pts3d = self.dpt(decout, image_size=(img_shape[0], img_shape[1]))
# recover encoder and decoder outputs
enc_output, dec_output = decout[0], decout[-1]
cat_output = torch.cat([enc_output, dec_output], dim=-1) # concatenate
H, W = img_shape
B, S, D = cat_output.shape
# extract local_features
local_features = self.head_local_features(cat_output) # B,S,D
local_features = local_features.transpose(-1, -2).view(B, -1, H // self.patch_size, W // self.patch_size)
local_features = F.pixel_shuffle(local_features, self.patch_size) # B,d,H,W
# post process 3D pts, descriptors and confidences
out = torch.cat([pts3d, local_features], dim=1)
if self.postprocess:
out = self.postprocess(out,
depth_mode=self.depth_mode,
conf_mode=self.conf_mode,
desc_dim=self.local_feat_dim,
desc_mode=self.desc_mode,
two_confs=self.two_confs,
desc_conf_mode=self.desc_conf_mode)
return out
class MLP_MiniConv_Head(nn.Module):
"""
A special Convolutional head inspired by DPT architecture
A MLP predicts pixelwise feats in lower resolution. Prediction is upsampled to target res and goes through a mini convolutional head
Input : [B, S, D] # S = (H//p) * (W//p)
MLP:
D -> (mlp_hidden_dim) -> out_mlp_dim * (p/2)*2
reshape to [out_mlp_dim, H/2, W/2] (MLP predicts in half-res)
MiniConv head from DPT:
Upsample x2 -> [out_mlp_dim,H,W]
Conv 3x3 -> [conv_inner_dim,H,W]
ReLU
Conv 1x1 -> [odim,H,W]
"""
def __init__(self, idim, mlp_hidden_dim, mlp_odim, conv_inner_dim, odim, patch_size, subpatch=2, **kw):
super().__init__()
self.patch_size = patch_size
self.subpatch = subpatch
self.sub_patch_size = patch_size // subpatch
self.mlp = Mlp(idim, mlp_hidden_dim, mlp_odim * self.sub_patch_size**2, **kw) # D -> mlp_odim*sub_patch_size**2
# DPT conv head
self.head = nn.Sequential(Interpolate(scale_factor=self.subpatch, mode="bilinear", align_corners=True) if self.subpatch != 1 else nn.Identity(),
nn.Conv2d(mlp_odim, conv_inner_dim, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(conv_inner_dim, odim, kernel_size=1, stride=1, padding=0)
)
def forward(self, decout, img_shape):
H, W = img_shape
tokens = decout[-1]
B, S, D = tokens.shape
# extract features
feat = self.mlp(tokens) # [B, S, mlp_odim*sub_patch_size**2]
feat = feat.transpose(-1, -2).reshape(B, -1, H // self.patch_size, W // self.patch_size)
feat = F.pixel_shuffle(feat, self.sub_patch_size) # B,mlp_odim,H/sub,W/sub
return self.head(feat) # B, odim, H, W
class Cat_MLP_LocalFeatures_MiniConv_Pts3d(nn.Module):
""" Mixture between MLP and MLP-Convolutional head that outputs 3d points (with miniconv) and local features (with MLP).
simply contains two MLP_MiniConv_Head: one for 3D points and one for features.
The input for both heads is a concatenation of Encoder and Decoder outputs
"""
def __init__(self, net, has_conf=False, local_feat_dim=16, hidden_dim_factor=4., mlp_odim=24, conv_inner_dim=100, subpatch=2, **kw):
super().__init__()
self.local_feat_dim = local_feat_dim
patch_size = net.patch_embed.patch_size
if isinstance(patch_size, tuple):
assert len(patch_size) == 2 and isinstance(patch_size[0], int) and isinstance(
patch_size[1], int), "What is your patchsize format? Expected a single int or a tuple of two ints."
assert patch_size[0] == patch_size[1], "Error, non square patches not managed"
patch_size = patch_size[0]
self.patch_size = patch_size
self.depth_mode = net.depth_mode
self.conf_mode = net.conf_mode
self.desc_mode = net.desc_mode
self.desc_conf_mode = net.desc_conf_mode
self.has_conf = has_conf
self.two_confs = net.two_confs # independent confs for 3D regr and descs
idim = net.enc_embed_dim + net.dec_embed_dim
self.head_pts3d = MLP_MiniConv_Head(idim=idim,
mlp_hidden_dim=int(hidden_dim_factor * idim),
mlp_odim=mlp_odim + self.has_conf,
conv_inner_dim=conv_inner_dim,
odim=3 + self.has_conf,
subpatch=subpatch,
patch_size=self.patch_size,
**kw)
self.head_local_features = Mlp(in_features=idim,
hidden_features=int(hidden_dim_factor * idim),
out_features=(self.local_feat_dim + self.two_confs) * self.patch_size**2)
def forward(self, decout, img_shape):
enc_output, dec_output = decout[0], decout[-1] # recover encoder and decoder outputs
cat_output = torch.cat([enc_output, dec_output], dim=-1) # concatenate
# pass through the heads
pts3d = self.head_pts3d([cat_output], img_shape)
H, W = img_shape
B, S, D = cat_output.shape
# extract 3D points
local_features = self.head_local_features(cat_output) # B,S,D
local_features = local_features.transpose(-1, -2).view(B, -1, H // self.patch_size, W // self.patch_size)
local_features = F.pixel_shuffle(local_features, self.patch_size) # B,d,H,W
# post process 3D pts, descriptors and confidences
out = postprocess(torch.cat([pts3d, local_features], dim=1),
depth_mode=self.depth_mode,
conf_mode=self.conf_mode,
desc_dim=self.local_feat_dim,
desc_mode=self.desc_mode,
two_confs=self.two_confs, desc_conf_mode=self.desc_conf_mode)
return out
def mast3r_head_factory(head_type, output_mode, net, has_conf=False):
"""" build a prediction head for the decoder
"""
if head_type == 'catmlp+dpt' and output_mode.startswith('pts3d+desc'):
local_feat_dim = int(output_mode[10:])
assert net.dec_depth > 9
l2 = net.dec_depth
feature_dim = 256
last_dim = feature_dim // 2
out_nchan = 3
ed = net.enc_embed_dim
dd = net.dec_embed_dim
return Cat_MLP_LocalFeatures_DPT_Pts3d(net, local_feat_dim=local_feat_dim, has_conf=has_conf,
num_channels=out_nchan + has_conf,
feature_dim=feature_dim,
last_dim=last_dim,
hooks_idx=[0, l2 * 2 // 4, l2 * 3 // 4, l2],
dim_tokens=[ed, dd, dd, dd],
postprocess=postprocess,
depth_mode=net.depth_mode,
conf_mode=net.conf_mode,
head_type='regression')
elif head_type == 'catconv' and output_mode.startswith('pts3d+desc'):
local_feat_dim = int(output_mode[10:])
# more params (anounced by a ':' and comma separated)
kw = {}
if ':' in head_type:
kw = eval("dict(" + head_type[8:] + ")")
return Cat_MLP_LocalFeatures_MiniConv_Pts3d(net, local_feat_dim=local_feat_dim, has_conf=has_conf, **kw)
else:
raise NotImplementedError(
f"unexpected {head_type=} and {output_mode=}")
|