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# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
"""
Inference wrapper for MASt3R + Sparse GA
"""
import os
import tempfile
import warnings
import torch
from dust3r.image_pairs import make_pairs
from mast3r.cloud_opt.sparse_ga import sparse_global_alignment
from mast3r.model import load_model
from mapanything.models.external.vggt.utils.rotation import mat_to_quat
from mapanything.utils.geometry import (
convert_ray_dirs_depth_along_ray_pose_trans_quats_to_pointmap,
convert_z_depth_to_depth_along_ray,
depthmap_to_camera_frame,
get_rays_in_camera_frame,
)
class MASt3RSGAWrapper(torch.nn.Module):
def __init__(
self,
name,
ckpt_path,
cache_dir,
scene_graph="complete",
sparse_ga_lr1=0.07,
sparse_ga_niter1=300,
sparse_ga_lr2=0.01,
sparse_ga_niter2=300,
sparse_ga_optim_level="refine+depth",
sparse_ga_shared_intrinsics=False,
sparse_ga_matching_conf_thr=5.0,
**kwargs,
):
super().__init__()
self.name = name
self.ckpt_path = ckpt_path
self.cache_dir = cache_dir
self.scene_graph = scene_graph
self.sparse_ga_lr1 = sparse_ga_lr1
self.sparse_ga_niter1 = sparse_ga_niter1
self.sparse_ga_lr2 = sparse_ga_lr2
self.sparse_ga_niter2 = sparse_ga_niter2
self.sparse_ga_optim_level = sparse_ga_optim_level
self.sparse_ga_shared_intrinsics = sparse_ga_shared_intrinsics
self.sparse_ga_matching_conf_thr = sparse_ga_matching_conf_thr
# Init the model and load the checkpoint
self.model = load_model(self.ckpt_path, device="cpu")
def forward(self, views):
"""
Forward pass wrapper for MASt3R using the sparse global aligner.
Assumption:
- The batch size of input views is 1.
Args:
views (List[dict]): List of dictionaries containing the input views' images and instance information.
Each dictionary should contain the following keys, where B is the batch size and is 1:
"img" (tensor): Image tensor of shape (B, C, H, W).
"data_norm_type" (list): ["dust3r"]
"label" (list): ["scene_name"]
"instance" (list): ["image_name"]
Returns:
List[dict]: A list containing the final outputs for the input views.
"""
# Check the batch size of input views
batch_size_per_view, _, height, width = views[0]["img"].shape
device = views[0]["img"].device
num_views = len(views)
assert batch_size_per_view == 1, (
f"Batch size of input views should be 1, but got {batch_size_per_view}."
)
# Check the data norm type
data_norm_type = views[0]["data_norm_type"][0]
assert data_norm_type == "dust3r", (
"MASt3R expects a normalized image with the DUSt3R normalization scheme applied"
)
# Convert the input views to the expected input format
images = []
image_paths = []
for view in views:
images.append(
dict(
img=view["img"].cpu(),
idx=len(images),
instance=str(len(images)),
true_shape=torch.tensor(view["img"].shape[-2:])[None]
.repeat(batch_size_per_view, 1)
.numpy(),
)
)
view_name = os.path.join(view["label"][0], view["instance"][0])
image_paths.append(view_name)
# Make image pairs and run inference
# Sparse GA (forward mast3r -> matching -> 3D optim -> 2D refinement -> triangulation)
pairs = make_pairs(
images, scene_graph=self.scene_graph, prefilter=None, symmetrize=True
)
with torch.enable_grad():
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)
tempfile.mkdtemp(dir=self.cache_dir)
scene = sparse_global_alignment(
image_paths,
pairs,
self.cache_dir,
self.model,
lr1=self.sparse_ga_lr1,
niter1=self.sparse_ga_niter1,
lr2=self.sparse_ga_lr2,
niter2=self.sparse_ga_niter2,
device=device,
opt_depth="depth" in self.sparse_ga_optim_level,
shared_intrinsics=self.sparse_ga_shared_intrinsics,
matching_conf_thr=self.sparse_ga_matching_conf_thr,
verbose=False,
)
# Make sure scene is not None
if scene is None:
raise RuntimeError("Global optimization failed.")
# Get the predictions
intrinsics = scene.intrinsics
c2w_poses = scene.get_im_poses()
_, depths, _ = scene.get_dense_pts3d()
# Convert the output to the MapAnything format
with torch.autocast("cuda", enabled=False):
res = []
for view_idx in range(num_views):
# Get the current view predictions
curr_view_intrinsic = intrinsics[view_idx].unsqueeze(0)
curr_view_pose = c2w_poses[view_idx].unsqueeze(0)
curr_view_depth_z = (
depths[view_idx].reshape((height, width)).unsqueeze(0)
)
# Convert the pose to quaternions and translation
curr_view_cam_translations = curr_view_pose[..., :3, 3]
curr_view_cam_quats = mat_to_quat(curr_view_pose[..., :3, :3])
# Get the camera frame pointmaps
curr_view_pts3d_cam, _ = depthmap_to_camera_frame(
curr_view_depth_z, curr_view_intrinsic
)
# Convert the z depth to depth along ray
curr_view_depth_along_ray = convert_z_depth_to_depth_along_ray(
curr_view_depth_z, curr_view_intrinsic
)
curr_view_depth_along_ray = curr_view_depth_along_ray.unsqueeze(-1)
# Get the ray directions on the unit sphere in the camera frame
_, curr_view_ray_dirs = get_rays_in_camera_frame(
curr_view_intrinsic, height, width, normalize_to_unit_sphere=True
)
# Get the pointmaps
curr_view_pts3d = (
convert_ray_dirs_depth_along_ray_pose_trans_quats_to_pointmap(
curr_view_ray_dirs,
curr_view_depth_along_ray,
curr_view_cam_translations,
curr_view_cam_quats,
)
)
# Append the outputs to the result list
res.append(
{
"pts3d": curr_view_pts3d,
"pts3d_cam": curr_view_pts3d_cam,
"ray_directions": curr_view_ray_dirs,
"depth_along_ray": curr_view_depth_along_ray,
"cam_trans": curr_view_cam_translations,
"cam_quats": curr_view_cam_quats,
}
)
return res
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