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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# dpt head implementation for DUST3R
# Downstream heads assume inputs of size B x N x C (where N is the number of tokens) ;
# or if it takes as input the output at every layer, the attribute return_all_layers should be set to True
# the forward function also takes as input a dictionnary img_info with key "height" and "width"
# for PixelwiseTask, the output will be of dimension B x num_channels x H x W
# --------------------------------------------------------
from einops import rearrange
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
# import dust3r.utils.path_to_croco
from .dpt_block import DPTOutputAdapter, Interpolate, make_fusion_block
from .head_modules import UnetExtractor, AppearanceTransformer, _init_weights
from .postprocess import postprocess
# class DPTOutputAdapter_fix(DPTOutputAdapter):
# """
# Adapt croco's DPTOutputAdapter implementation for dust3r:
# remove duplicated weigths, and fix forward for dust3r
# """
#
# def init(self, dim_tokens_enc=768):
# super().init(dim_tokens_enc)
# # these are duplicated weights
# del self.act_1_postprocess
# del self.act_2_postprocess
# del self.act_3_postprocess
# del self.act_4_postprocess
#
# self.scratch.refinenet1 = make_fusion_block(256 * 2, False, 1, expand=True)
# self.scratch.refinenet2 = make_fusion_block(256 * 2, False, 1, expand=True)
# self.scratch.refinenet3 = make_fusion_block(256 * 2, False, 1, expand=True)
# # self.scratch.refinenet4 = make_fusion_block(256 * 2, False, 1)
#
# self.depth_encoder = UnetExtractor(in_channel=3)
# self.feat_up = Interpolate(scale_factor=2, mode="bilinear", align_corners=True)
# self.out_conv = nn.Conv2d(256+3+4, 256, kernel_size=3, padding=1)
# self.out_relu = nn.ReLU(inplace=True)
#
# self.input_merger = nn.Sequential(
# # nn.Conv2d(256+3+3+1, 256, kernel_size=3, padding=1),
# nn.Conv2d(256+3+3, 256, kernel_size=3, padding=1),
# nn.ReLU(),
# )
#
# def forward(self, encoder_tokens: List[torch.Tensor], depths, imgs, image_size=None, conf=None):
# assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
# # H, W = input_info['image_size']
# image_size = self.image_size if image_size is None else image_size
# H, W = image_size
# # Number of patches in height and width
# N_H = H // (self.stride_level * self.P_H)
# N_W = W // (self.stride_level * self.P_W)
#
# # Hook decoder onto 4 layers from specified ViT layers
# layers = [encoder_tokens[hook] for hook in self.hooks]
#
# # Extract only task-relevant tokens and ignore global tokens.
# layers = [self.adapt_tokens(l) for l in layers]
#
# # Reshape tokens to spatial representation
# layers = [rearrange(l, 'b (nh nw) c -> b c nh nw', nh=N_H, nw=N_W) for l in layers]
#
# layers = [self.act_postprocess[idx](l) for idx, l in enumerate(layers)]
# # Project layers to chosen feature dim
# layers = [self.scratch.layer_rn[idx](l) for idx, l in enumerate(layers)]
#
# # get depth features
# depth_features = self.depth_encoder(depths)
# depth_feature1, depth_feature2, depth_feature3 = depth_features
#
# # Fuse layers using refinement stages
# path_4 = self.scratch.refinenet4(layers[3])[:, :, :layers[2].shape[2], :layers[2].shape[3]]
# path_3 = self.scratch.refinenet3(torch.cat([path_4, depth_feature3], dim=1), torch.cat([layers[2], depth_feature3], dim=1))
# path_2 = self.scratch.refinenet2(torch.cat([path_3, depth_feature2], dim=1), torch.cat([layers[1], depth_feature2], dim=1))
# path_1 = self.scratch.refinenet1(torch.cat([path_2, depth_feature1], dim=1), torch.cat([layers[0], depth_feature1], dim=1))
# # path_3 = self.scratch.refinenet3(path_4, layers[2], depth_feature3)
# # path_2 = self.scratch.refinenet2(path_3, layers[1], depth_feature2)
# # path_1 = self.scratch.refinenet1(path_2, layers[0], depth_feature1)
#
# path_1 = self.feat_up(path_1)
# path_1 = torch.cat([path_1, imgs, depths], dim=1)
# if conf is not None:
# path_1 = torch.cat([path_1, conf], dim=1)
# path_1 = self.input_merger(path_1)
#
# # Output head
# out = self.head(path_1)
#
# return out
class DPTOutputAdapter_fix(DPTOutputAdapter):
"""
Adapt croco's DPTOutputAdapter implementation for dust3r:
remove duplicated weigths, and fix forward for dust3r
"""
def init(self, dim_tokens_enc=768):
super().init(dim_tokens_enc)
# these are duplicated weights
del self.act_1_postprocess
del self.act_2_postprocess
del self.act_3_postprocess
del self.act_4_postprocess
self.feat_up = Interpolate(scale_factor=2, mode="bilinear", align_corners=True)
self.input_merger = nn.Sequential(
# nn.Conv2d(256+3+3+1, 256, kernel_size=3, padding=1),
# nn.Conv2d(3+6, 256, 7, 1, 3),
nn.Conv2d(3, 256, 7, 1, 3),
nn.ReLU(),
)
def forward(self, encoder_tokens: List[torch.Tensor], depths, imgs, image_size=None, conf=None):
assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
# H, W = input_info['image_size']
image_size = self.image_size if image_size is None else image_size
H, W = image_size
# Number of patches in height and width
N_H = H // (self.stride_level * self.P_H)
N_W = W // (self.stride_level * self.P_W)
# Hook decoder onto 4 layers from specified ViT layers
layers = [encoder_tokens[hook] for hook in self.hooks]
# Extract only task-relevant tokens and ignore global tokens.
layers = [self.adapt_tokens(l) for l in layers]
# Reshape tokens to spatial representation
layers = [rearrange(l, 'b (nh nw) c -> b c nh nw', nh=N_H, nw=N_W) for l in layers]
layers = [self.act_postprocess[idx](l) for idx, l in enumerate(layers)]
# Project layers to chosen feature dim
layers = [self.scratch.layer_rn[idx](l) for idx, l in enumerate(layers)]
# Fuse layers using refinement stages
path_4 = self.scratch.refinenet4(layers[3])[:, :, :layers[2].shape[2], :layers[2].shape[3]]
path_3 = self.scratch.refinenet3(path_4, layers[2])
path_2 = self.scratch.refinenet2(path_3, layers[1])
path_1 = self.scratch.refinenet1(path_2, layers[0])
direct_img_feat = self.input_merger(imgs)
# imgs = imgs.permute(0, 2, 3, 1).flatten(1, 2).contiguous()
# # Pachify
# patch_size = self.patch_size
# hh = H // patch_size[0]
# ww = W // patch_size[1]
# direct_img_feat = rearrange(imgs, "b (hh ph ww pw) d -> b (hh ww) (ph pw d)", hh=hh, ww=ww, ph=patch_size[0], pw=patch_size[1])
# actually, we just do interpolate here
# path_1 = self.feat_up(path_1)
path_1 = F.interpolate(path_1, size=(H, W), mode='bilinear', align_corners=True)
path_1 = path_1 + direct_img_feat
# path_1 = torch.cat([path_1, imgs], dim=1)
# Output head
out = self.head(path_1)
return out, [path_4, path_3, path_2]
class PixelwiseTaskWithDPT(nn.Module):
""" DPT module for dust3r, can return 3D points + confidence for all pixels"""
def __init__(self, *, n_cls_token=0, hooks_idx=None, dim_tokens=None,
output_width_ratio=1, num_channels=1, postprocess=None, depth_mode=None, conf_mode=None, **kwargs):
super(PixelwiseTaskWithDPT, self).__init__()
self.return_all_layers = True # backbone needs to return all layers
self.postprocess = postprocess
self.depth_mode = depth_mode
self.conf_mode = conf_mode
assert n_cls_token == 0, "Not implemented"
dpt_args = dict(output_width_ratio=output_width_ratio,
num_channels=num_channels,
**kwargs)
if hooks_idx is not None:
dpt_args.update(hooks=hooks_idx)
self.dpt = DPTOutputAdapter_fix(**dpt_args)
dpt_init_args = {} if dim_tokens is None else {'dim_tokens_enc': dim_tokens}
self.dpt.init(**dpt_init_args)
def forward(self, x, depths, imgs, img_info, conf=None):
out, interm_feats = self.dpt(x, depths, imgs, image_size=(img_info[0], img_info[1]), conf=conf)
if self.postprocess:
out = self.postprocess(out, self.depth_mode, self.conf_mode)
return out, interm_feats
class AttnBasedAppearanceHead(nn.Module):
"""
Attention head Appearence Reconstruction
"""
def __init__(self, num_channels, patch_size, feature_dim, last_dim, hooks_idx, dim_tokens, postprocess, depth_mode, conf_mode, head_type='gs_params'):
super().__init__()
self.num_channels = num_channels
self.patch_size = patch_size
self.hooks = hooks_idx
assert len(set(dim_tokens)) == 1
self.tokenizer = nn.Linear(3 * self.patch_size[0] ** 2, dim_tokens[0], bias=False)
self.attn_processor = AppearanceTransformer(num_layers=4, attn_dim=dim_tokens[0] * 2, head_dim=feature_dim)
self.token_decoder = nn.Sequential(
nn.LayerNorm(dim_tokens[0] * 2, bias=False),
nn.Linear(
dim_tokens[0] * 2, self.num_channels * (self.patch_size[0] ** 2),
bias=False,
)
)
self.token_decoder.apply(_init_weights)
def img_pts_tokenizer(self, imgs, pts3d):
B, V, _, H, W = imgs.shape
pts3d = pts3d.flatten(2, 3).contiguous()
imgs = imgs.permute(0, 1, 3, 4, 2).flatten(2, 3).contiguous()
mean = pts3d.mean(dim=-2, keepdim=True) # (B, V, 1, 3)
z_median = torch.median(torch.norm(pts3d, dim=-1, keepdim=True), dim=2, keepdim=True)[0] # (B, V, 1, 1)
pts3d_normed = (pts3d - mean) / (z_median + 1e-8) # (B, V, N, 3)
input = imgs #torch.cat([pts3d_normed, imgs], dim=-1) # (B, V, H*W, 9)
# Pachify
patch_size = self.patch_size
hh = H // patch_size[0]
ww = W // patch_size[1]
input = rearrange(input, "b v (hh ph ww pw) d -> (b v) (hh ww) (ph pw d)", hh=hh, ww=ww, ph=patch_size[0], pw=patch_size[1])
# Tokenize the input images
input_tokens = self.tokenizer(input)
return input_tokens
def forward(self, x, depths, imgs, img_info, conf=None):
B, V, H, W = img_info
input_tokens = rearrange(self.img_pts_tokenizer(imgs, depths), "(b v) l d -> b (v l) d", b=B, v=V)
# Hook decoder onto 4 layers from specified ViT layers
layer_tokens = [rearrange(x[hook].detach(), "(b v) l d -> b (v l) d", b=B, v=V) for hook in self.hooks]
tokens = self.attn_processor(torch.cat([input_tokens, layer_tokens[-1]], dim=-1))
gaussian_params = self.token_decoder(tokens)
patch_size = self.patch_size
hh = H // patch_size[0]
ww = W // patch_size[1]
gaussians = rearrange(gaussian_params, "b (v hh ww) (ph pw d) -> b (v hh ph ww pw) d", v=V, hh=hh, ww=ww, ph=patch_size[0], pw=patch_size[1])
return gaussians.view(B, V, H*W, -1)
def create_gs_dpt_head(net, has_conf=False, out_nchan=3, postprocess_func=postprocess):
"""
return PixelwiseTaskWithDPT for given net params
"""
assert net.dec_depth > 9
l2 = net.dec_depth
feature_dim = net.feature_dim
last_dim = feature_dim//2
ed = net.enc_embed_dim
dd = net.dec_embed_dim
try:
patch_size = net.patch_size
except:
patch_size = (16, 16)
return PixelwiseTaskWithDPT(num_channels=out_nchan + has_conf,
patch_size=patch_size,
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_func,
depth_mode=net.depth_mode,
conf_mode=net.conf_mode,
head_type='gs_params') |