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from copy import deepcopy
import cv2
import numpy as np
import torch
import torch.nn as nn
import roma
from copy import deepcopy
import tqdm
import os
import matplotlib.pyplot as plt
from cloud_opt.utils import *
from cloud_opt.utils import _check_edges, _compute_img_conf
import cloud_opt.init_all as init_fun
class BaseOptimizer(nn.Module):
"""Optimize a global scene, given a graph-organized observations.
Graph node: images
Graph edges: observations = (pred1, pred2), pred2 is in pred1's coordinate
"""
def __init__(self, *args, **kwargs):
pass
def _init_from_views(
self,
view1s,
view2s,
pred1s,
pred2s, # whatever predictions, they should be organized into pairwise for graph optimization
dist="l1",
conf="log",
min_conf_thr=3,
thr_for_init_conf=False,
base_scale=0.5,
allow_pw_adaptors=False,
pw_break=20,
rand_pose=torch.randn,
empty_cache=False,
verbose=True,
):
super().__init__()
self.edges = [
(int(view1["idx"]), int(view2["idx"]))
for view1, view2 in zip(view1s, view2s)
]
self.dist = ALL_DISTS[dist]
self.n_imgs = _check_edges(self.edges)
self.edge2pts_i = NoGradParamDict(
{ij: pred1s[n]["pts3d_is_self_view"] for n, ij in enumerate(self.str_edges)}
) # ij: the name of the edge
self.edge2pts_j = NoGradParamDict(
{
ij: pred2s[n]["pts3d_in_other_view"]
for n, ij in enumerate(self.str_edges)
}
)
self.edge2conf_i = NoGradParamDict(
{ij: pred1s[n]["conf_self"] for n, ij in enumerate(self.str_edges)}
)
self.edge2conf_j = NoGradParamDict(
{ij: pred2s[n]["conf"] for n, ij in enumerate(self.str_edges)}
)
self.imshapes = get_imshapes(self.edges, pred1s, pred2s)
self.min_conf_thr = min_conf_thr
self.thr_for_init_conf = thr_for_init_conf
self.conf_trf = get_conf_trf(conf)
self.im_conf = _compute_img_conf(
self.imshapes, self.device, self.edges, self.edge2conf_i, self.edge2conf_j
)
for i in range(len(self.im_conf)):
self.im_conf[i].requires_grad = False
self.init_conf_maps = [c.clone() for c in self.im_conf]
self.base_scale = base_scale
self.norm_pw_scale = True
self.pw_break = pw_break
self.POSE_DIM = 7
self.pw_poses = nn.Parameter(
rand_pose((self.n_edges, 1 + self.POSE_DIM))
) # pairwise poses
self.pw_adaptors = nn.Parameter(
torch.zeros((self.n_edges, 2))
) # slight xy/z adaptation
self.pw_adaptors.requires_grad_(allow_pw_adaptors)
self.has_im_poses = False
self.rand_pose = rand_pose
def get_known_poses(self):
if self.has_im_poses:
known_poses_msk = torch.tensor(
[not (p.requires_grad) for p in self.im_poses]
)
known_poses = self.get_im_poses()
return known_poses_msk.sum(), known_poses_msk, known_poses
else:
return 0, None, None
def get_pw_norm_scale_factor(self):
if self.norm_pw_scale:
# normalize scales so that things cannot go south
# we want that exp(scale) ~= self.base_scale
return (np.log(self.base_scale) - self.pw_poses[:, -1].mean()).exp()
else:
return 1 # don't norm scale for known poses
def _set_pose(self, poses, idx, R, T=None, scale=None, force=False):
# all poses == cam-to-world
pose = poses[idx]
if not (pose.requires_grad or force):
return pose
if R.shape == (4, 4):
assert T is None
T = R[:3, 3]
R = R[:3, :3]
if R is not None:
pose.data[0:4] = roma.rotmat_to_unitquat(R)
if T is not None:
pose.data[4:7] = signed_log1p(
T / (scale or 1)
) # translation is function of scale
if scale is not None:
assert poses.shape[-1] in (8, 13)
pose.data[-1] = np.log(float(scale))
return pose
def forward(self, ret_details=False):
pw_poses = self.get_pw_poses() # cam-to-world
pw_adapt = self.get_adaptors()
proj_pts3d = self.get_pts3d()
# pre-compute pixel weights
weight_i = {i_j: self.conf_trf(c) for i_j, c in self.conf_i.items()}
weight_j = {i_j: self.conf_trf(c) for i_j, c in self.conf_j.items()}
loss = 0
if ret_details:
details = -torch.ones((self.n_imgs, self.n_imgs))
for e, (i, j) in enumerate(self.edges):
i_j = edge_str(i, j)
# distance in image i and j
aligned_pred_i = geotrf(pw_poses[e], pw_adapt[e] * self.pred_i[i_j])
aligned_pred_j = geotrf(pw_poses[e], pw_adapt[e] * self.pred_j[i_j])
li = self.dist(proj_pts3d[i], aligned_pred_i, weight=weight_i[i_j]).mean()
lj = self.dist(proj_pts3d[j], aligned_pred_j, weight=weight_j[i_j]).mean()
loss = loss + li + lj
if ret_details:
details[i, j] = li + lj
loss /= self.n_edges # average over all pairs
if ret_details:
return loss, details
return loss
@torch.cuda.amp.autocast(enabled=False)
def compute_global_alignment(self, init=None, niter_PnP=10, **kw):
if init is None:
pass
elif init == "msp" or init == "mst":
init_fun.init_minimum_spanning_tree(self, niter_PnP=niter_PnP)
elif init == "known_poses":
raise NotImplementedError
self.preset_pose(known_poses=self.camera_poses, requires_grad=True)
init_fun.init_from_known_poses(
self, min_conf_thr=self.min_conf_thr, niter_PnP=niter_PnP
)
else:
raise ValueError(f"bad value for {init=}")
return global_alignment_loop(self, **kw)
@property
def str_edges(self):
return [edge_str(i, j) for i, j in self.edges]
@property
def n_edges(self):
return len(self.edges)
def global_alignment_loop(
net,
lr=0.01,
niter=300,
schedule="cosine",
lr_min=1e-3,
temporal_smoothing_weight=0,
depth_map_save_dir=None,
):
params = [p for p in net.parameters() if p.requires_grad]
if not params:
return net
verbose = net.verbose
if verbose:
print("Global alignement - optimizing for:")
print([name for name, value in net.named_parameters() if value.requires_grad])
lr_base = lr
optimizer = torch.optim.Adam(params, lr=lr, betas=(0.9, 0.9))
loss = float("inf")
if verbose:
with tqdm.tqdm(total=niter) as bar:
while bar.n < bar.total:
if bar.n % 500 == 0 and depth_map_save_dir is not None:
if not os.path.exists(depth_map_save_dir):
os.makedirs(depth_map_save_dir)
# visualize the depthmaps
depth_maps = net.get_depthmaps()
for i, depth_map in enumerate(depth_maps):
depth_map_save_path = os.path.join(
depth_map_save_dir, f"depthmaps_{i}_iter_{bar.n}.png"
)
plt.imsave(
depth_map_save_path,
depth_map.detach().cpu().numpy(),
cmap="jet",
)
print(
f"Saved depthmaps at iteration {bar.n} to {depth_map_save_dir}"
)
loss, lr = global_alignment_iter(
net,
bar.n,
niter,
lr_base,
lr_min,
optimizer,
schedule,
temporal_smoothing_weight=temporal_smoothing_weight,
)
bar.set_postfix_str(f"{lr=:g} loss={loss:g}")
bar.update()
else:
for n in range(niter):
loss, _ = global_alignment_iter(
net,
n,
niter,
lr_base,
lr_min,
optimizer,
schedule,
temporal_smoothing_weight=temporal_smoothing_weight,
)
return loss
def global_alignment_iter(
net,
cur_iter,
niter,
lr_base,
lr_min,
optimizer,
schedule,
temporal_smoothing_weight=0,
):
t = cur_iter / niter
if schedule == "cosine":
lr = cosine_schedule(t, lr_base, lr_min)
elif schedule == "linear":
lr = linear_schedule(t, lr_base, lr_min)
elif schedule.startswith("cycle"):
try:
num_cycles = int(schedule[5:])
except ValueError:
num_cycles = 2
lr = cycled_linear_schedule(t, lr_base, lr_min, num_cycles=num_cycles)
else:
raise ValueError(f"bad lr {schedule=}")
adjust_learning_rate_by_lr(optimizer, lr)
optimizer.zero_grad()
if net.empty_cache:
torch.cuda.empty_cache()
loss = net(epoch=cur_iter)
if net.empty_cache:
torch.cuda.empty_cache()
loss.backward()
if net.empty_cache:
torch.cuda.empty_cache()
optimizer.step()
return float(loss), lr
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