def do_evaluation(ref_dat, BV_measure, d_candi):
dmap = m_misc.depth_val_regression(
BV_measure, d_candi, BV_log=True).squeeze().cpu().numpy() # (256, 768)
gt = ref_dat['dmap_rawsize']
raw_w, raw_h = gt.shape[1], gt.shape[2]
dmap = image.fromarray(dmap)
pred_depth = dmap.resize((raw_h, raw_w), image.NEAREST)
pred_depth = torch.FloatTensor(np.array(pred_depth)).unsqueeze(0)
return pred_depth, gt
def train(nGPU,
model_KV,
optimizer_KV,
t_win_r,
d_candi,
Ref_Dats,
Src_Dats,
Src_CamPoses,
BVs_predict,
Cam_Intrinsics,
refine_dup=False,
weight_var=.001,
loss_type='NLL',
mGPU=False,
Cam_Intrinsics_spatial_up=None,
return_confmap_up=False):
r'''
Perform one single iteration for the training
Support multiple GPU traning.
To do this we treat each trajector as one batch
Inputs:
model_KV -
optimizer_KV -
Ref_Dats - list of ref_dat
Src_Dats - list of list of src_dat: [ list_src_dats_traj_0, ...]
list_src_dats_traj0[iframe] : NCHW
Src_CamPoses - N x V x 4 x 4, where N: batch size (# of batched traj), V: # of src. views
BVs_predict - N x D x H_feat x W_feat
Cam_Intrinsics - list of camera intrinsics for the batched trajectories
refine_dup - if upsample the depth dimension in the refinement net
loss_type = {'L1', 'NLL'}
L1 - we will calculate the mean value from low res. DPV and filter it with DGF to get the L1 loss in high res.;
In additional to that, we will also calculate the variance loss
NLL - we will calulate the NLL loss from the low res. DPV
Outputs:
'''
# Make this work with bs 4 first
# Then make it work with just image input 3
# Then change it to the new dataloader left right
# then change it to the multi process thing
# prepare for the inputs #
ref_frame = torch.cat(tuple([ref_dat['img'] for ref_dat in Ref_Dats]),
dim=0)
src_frames_list = [torch.cat(tuple([src_dat_frame['img'] \
for src_dat_frame in src_dats_traj]), dim=0).unsqueeze(0) \
for src_dats_traj in Src_Dats]
src_frames = torch.cat(tuple(src_frames_list), dim=0)
optimizer_KV.zero_grad()
# If upsample d in the refinement net#
if refine_dup:
dup4_candi = np.linspace(0, d_candi.max(), 4 * len(d_candi))
# kv-net Forward pass #
# model_KV supports multiple-gpus #
BatchIdx_range = torch.FloatTensor(np.arange(nGPU))
IntMs = torch.cat([
cam_intrin['intrinsic_M_cuda'].unsqueeze(0)
for cam_intrin in Cam_Intrinsics
],
dim=0)
unit_ray_Ms_2D = torch.cat([
cam_intrin['unit_ray_array_2D'].unsqueeze(0)
for cam_intrin in Cam_Intrinsics
],
dim=0)
bsize = src_frames.shape[0]
dmap_cur_refined, dmap_refined, d_dpv, kv_dpv = model_KV(
ref_frame=ref_frame.cuda(0),
src_frames=src_frames.cuda(0),
src_cam_poses=Src_CamPoses.cuda(0),
BatchIdx=BatchIdx_range.cuda(0),
cam_intrinsics=None,
BV_predict=BVs_predict,
mGPU=mGPU,
IntMs=IntMs.cuda(0),
unit_ray_Ms_2D=unit_ray_Ms_2D.cuda(0))
# Get losses #
loss = 0.
for ibatch in range(d_dpv.shape[0]):
if loss_type is 'NLL':
# nll loss (d-net) #
depth_ref = Ref_Dats[ibatch]['dmap'].cuda(kv_dpv.get_device())
if refine_dup:
depth_ref_imgsize = Ref_Dats[ibatch][
'dmap_up4_imgsize_digit'].cuda(kv_dpv.get_device())
else:
depth_ref_imgsize = Ref_Dats[ibatch][
'dmap_imgsize_digit'].cuda(kv_dpv.get_device())
loss = loss + F.nll_loss(
d_dpv[ibatch, :, :, :].unsqueeze(0), depth_ref, ignore_index=0)
loss = loss + F.nll_loss(
dmap_cur_refined[ibatch, :, :, :].unsqueeze(0),
depth_ref_imgsize,
ignore_index=0)
if BVs_predict is not None:
if m_misc.valid_dpv(BVs_predict[ibatch, ...]): # refined
loss = loss + F.nll_loss(
kv_dpv[ibatch, :, :, :].unsqueeze(0),
depth_ref,
ignore_index=0)
loss = loss + F.nll_loss(
dmap_refined[ibatch, :, :, :].unsqueeze(0),
depth_ref_imgsize,
ignore_index=0)
dmap_kv_lowres = m_misc.depth_val_regression(
kv_dpv[0, ...].unsqueeze(0), d_candi, BV_log=True)
elif loss_type is 'L1':
if mGPU:
raise Exception('not implemented for multiple GPUs')
# L1 loss #
depth_ref = Ref_Dats[ibatch]['dmap_imgsize'].cuda().unsqueeze(0)
l1_loss_mask = depth_ref > 0.
l1_loss_mask = l1_loss_mask.type_as(depth_ref)
loss_BV_cur_L1 = \
F.l1_loss( dmap_cur_refined* l1_loss_mask, depth_ref.cuda().squeeze(1) * l1_loss_mask)
if m_misc.valid_dpv(BVs_predict[ibatch, ...]):
loss_KV_L1 = F.l1_loss(
dmap_refined * l1_loss_mask,
depth_ref.cuda().squeeze(1) * l1_loss_mask)
# variance #
dmap_d_lowres = m_misc.depth_val_regression(d_dpv,
d_candi,
BV_log=True)
loss_BV_cur_var = torch.mean(
m_misc.depth_var(d_dpv, dmap_d_lowres, d_candi))
if m_misc.valid_dpv(BVs_predict[ibatch, ...]):
dmap_kv_lowres = m_misc.depth_val_regression(kv_dpv,
d_candi,
BV_log=True)
loss_KV_var = torch.mean(
m_misc.depth_var(kv_dpv, dmap_kv_lowres, d_candi))
loss = loss_BV_cur_L1 + loss_KV_L1 + weight_var * (
loss_KV_var + loss_BV_cur_var)
else:
loss = loss_BV_cur_L1 + weight_var * loss_BV_cur_var
dmap_kv_lowres = dmap_d_lowres
# Backward pass #
if mGPU:
loss = loss / torch.tensor(float(bsize)).cuda(loss.get_device())
loss.backward()
optimizer_KV.step()
# BV_predict estimation (3D re-sampling) #
d_dpv = d_dpv.detach()
kv_dpv = kv_dpv.detach()
r_dpv = dmap_cur_refined.detach(
) if dmap_cur_refined is not -1 else dmap_refined.detach()
BVs_predict_out = []
for ibatch in range(d_dpv.shape[0]):
rel_Rt = Src_CamPoses[ibatch, t_win_r, :, :].inverse()
BV_predict = warp_homo.resample_vol_cuda(src_vol = kv_dpv[ibatch, ...].unsqueeze(0),
rel_extM = rel_Rt.cuda(kv_dpv.get_device()),
cam_intrinsic = Cam_Intrinsics[ibatch],
d_candi = d_candi,
padding_value = math.log(1. / float(len(d_candi))) \
).clamp(max=0, min=-1000.).unsqueeze(0)
BVs_predict_out.append(BV_predict)
BVs_predict_out = torch.cat(BVs_predict_out, dim=0)
# logging (for single GPU) #
depth_ref_lowres = Ref_Dats[0]['dmap_raw'].cpu().squeeze().numpy()
depth_kv_lres_log = dmap_kv_lowres[0, ...].detach().cpu().squeeze().numpy()
dmap_log_lres = np.hstack([depth_kv_lres_log, depth_ref_lowres])
if dmap_refined.dim() < 4: # refined depth map
depth_kv_hres_log = dmap_refined.detach().cpu().squeeze().numpy()
depth_ref_highres = depth_ref.detach().cpu().squeeze().numpy()
else: # refined dpv
if refine_dup:
depth_kv_hres_log = m_misc.depth_val_regression(
dmap_refined[0, ...].unsqueeze(0), dup4_candi,
BV_log=True).detach().cpu().squeeze().numpy()
else:
depth_kv_hres_log = m_misc.depth_val_regression(
dmap_refined[0, ...].unsqueeze(0), d_candi,
BV_log=True).detach().cpu().squeeze().numpy()
depth_ref_imgsize_raw = Ref_Dats[0]['dmap_imgsize'].squeeze().cpu(
).numpy()
dmap_log_hres = np.hstack([depth_kv_hres_log, depth_ref_imgsize_raw])
if return_confmap_up:
confmap_up = torch.exp(dmap_refined[0, ...].detach())
confmap_up, _ = torch.max(confmap_up, dim=0)
return r_dpv, BVs_predict_out, loss, dmap_log_lres, dmap_log_hres, confmap_up.cpu(
)
else:
return r_dpv, BVs_predict_out, loss, dmap_log_lres, dmap_log_hres
def export_res_refineNet(ref_dat, BV_measure, d_candi, res_fldr, batch_idx, diff_vrange_ratio=4,
cam_pose = None, cam_intrinM = None, output_pngs = False, save_mat=True, output_dmap_ref=True):
'''
export results
'''
# img_in #
img_up = ref_dat['img']
img_in_raw = img_up.squeeze().cpu().permute(1,2,0).numpy()
img_in = (img_in_raw - img_in_raw.min()) / (img_in_raw.max()-img_in_raw.min()) * 255.
# confMap #
confMap_log, _ = torch.max(BV_measure, dim=1)
confMap_log = torch.exp(confMap_log.squeeze().cpu())
confMap_log = confMap_log.cpu().numpy()
# depth map #
nDepth = len(d_candi)
dmap_height, dmap_width = BV_measure.shape[2], BV_measure.shape[3]
dmap = m_misc.depth_val_regression(BV_measure, d_candi, BV_log = True).squeeze().cpu().numpy()
# save up-sampeled results #
resfldr = res_fldr
m_misc.m_makedir(resfldr)
img_up_path ='%s/input.png'%(resfldr,)
conf_up_path = '%s/conf.png'%(resfldr,)
dmap_raw_path = '%s/dmap_raw.png'%(resfldr,)
final_res_up = '%s/res_%05d.png'%(resfldr, batch_idx)
if output_dmap_ref: # output GT depth
ref_up = '%s/dmap_ref.png'%(resfldr,)
res_up_diff = '%s/dmaps_diff.png'%(resfldr,)
dmap_ref = ref_dat['dmap_imgsize']
dmap_ref = dmap_ref.squeeze().cpu().numpy()
mask_dmap = (dmap_ref > 0 ).astype(np.float)
dmap_diff_raw = np.abs(dmap_ref - dmap ) * mask_dmap
dmaps_diff = dmap_diff_raw
plt.imsave(res_up_diff, dmaps_diff, vmin=0, vmax=d_candi.max()/ diff_vrange_ratio )
plt.imsave(ref_up, dmap_ref, vmax= d_candi.max(), vmin=0, cmap='gray')
plt.imsave(conf_up_path, confMap_log, vmin=0, vmax=1, cmap='jet')
plt.imsave(dmap_raw_path, dmap, vmin=0., vmax =d_candi.max(), cmap='gray' )
plt.imsave(img_up_path, img_in.astype(np.uint8))
# output the depth as .mat files #
fname_mat = '%s/depth_%05d.mat'%(resfldr, batch_idx)
img_path = ref_dat['img_path']
if save_mat:
if not output_dmap_ref:
mdict = { 'dmap': dmap, 'img': img_in_raw, 'confMap': confMap_log, 'img_path': img_path}
elif cam_pose is None:
mdict = {'dmap_ref': dmap_ref, 'dmap': dmap, 'img': img_in_raw, 'confMap': confMap_log,
'img_path': img_path}
else:
mdict = {'dmap_ref': dmap_ref, 'dmap': dmap,
'img': img_in_raw, 'cam_pose': cam_pose,
'confMap':confMap_log, 'cam_intrinM': cam_intrinM,
'img_path': img_path }
sio.savemat(fname_mat, mdict)
print('export to %s'%(final_res_up))
if output_dmap_ref:
cat_imgs((img_up_path, conf_up_path, dmap_raw_path, res_up_diff, ref_up), final_res_up)
else:
cat_imgs((img_up_path, conf_up_path, dmap_raw_path), final_res_up)
if output_pngs:
import cv2
png_fldr = '%s/output_pngs'%(res_fldr, )
m_misc.m_makedir( png_fldr )
depth_png = (dmap * 1000 ).astype(np.uint16)
img_in_png = _un_normalize( img_in_raw ); img_in_png = (img_in_png * 255).astype(np.uint8)
confMap_png = (confMap_log*255).astype(np.uint8)
cv2.imwrite( '%s/d_%05d.png'%(png_fldr, batch_idx), depth_png)
cv2.imwrite( '%s/rgb_%05d.png'%(png_fldr, batch_idx), img_in_png)
cv2.imwrite( '%s/conf_%05d.png'%(png_fldr, batch_idx), confMap_png)
if output_dmap_ref:
depth_ref_png = (dmap_ref * 1000).astype(np.uint16)
cv2.imwrite( '%s/dref_%05d.png'%(png_fldr, batch_idx), depth_ref_png)
def main():
import argparse
print('Parsing the arguments...')
parser = argparse.ArgumentParser()
# exp name #
parser.add_argument(
'--exp_name',
required=True,
type=str,
help='The name of the experiment. Used to naming the folders')
# about testing #
parser.add_argument('--img_name_pattern',
type=str,
default='*.png',
help='image name pattern')
parser.add_argument('--model_path',
type=str,
default='.',
help='The pre-trained model path for KV-net')
parser.add_argument('--split_file',
type=str,
default='.',
help='The split txt file')
parser.add_argument('--t_win',
type=int,
default=2,
help='The radius of the temporal window; default=2')
parser.add_argument('--d_min',
type=float,
default=0,
help='The minimal depth value; default=0')
parser.add_argument('--d_max',
type=float,
default=5,
help='The maximal depth value; default=15')
parser.add_argument('--ndepth',
type=int,
default=64,
help='The # of candidate depth values; default= 128')
parser.add_argument('--sigma_soft_max',
type=float,
default=10.,
help='sigma_soft_max, default = 500.')
parser.add_argument(
'--feature_dim',
type=int,
default=64,
help='The feature dimension for the feature extractor; default=64')
# about pose #
parser.add_argument('--intrin_path',
type=str,
required=True,
help='camera intrinic path, saved as .mat')
parser.add_argument(
'--dso_res_path',
type=str,
default='dso_res/result_dso.txt',
help=
'if use DSO pose, specify the path to the DSO results. Should be a .txt file'
)
parser.add_argument('--opt_next_frame', action='store_true', help='')
parser.add_argument('--use_gt_R', action='store_true', help='')
parser.add_argument('--use_gt_t', action='store_true', help='')
parser.add_argument('--use_dso_R', action='store_true', help='')
parser.add_argument('--use_dso_t', action='store_true', help='')
parser.add_argument('--min_frame_idx', type=int, help=' ', default=0)
parser.add_argument('--max_frame_idx', type=int, help=' ', default=10000)
parser.add_argument('--refresh_frames', type=int, help=' ', default=1000)
parser.add_argument('--LBA_max_iter', type=int, help=' ')
parser.add_argument('--opt_r', type=int, default=1, help=' ')
parser.add_argument('--opt_t', type=int, default=1, help=' ')
parser.add_argument('--LBA_step', type=float, help=' ')
parser.add_argument('--frame_interv', type=int, default=5, help=' ')
# about dataset #
parser.add_argument('--dataset',
type=str,
default='7scenes',
help='Dataset name: {scanNet, 7scenes}')
parser.add_argument('--dataset_path',
type=str,
default='.',
help='Path to the dataset')
# about output #
parser.add_argument('--output_pngs',
action='store_true',
help='if output pngs')
# para config. #
args = parser.parse_args()
exp_name = args.exp_name
dataset_name = args.dataset
t_win_r = args.t_win
nDepth = args.ndepth
d_candi = np.linspace(args.d_min, args.d_max, nDepth)
sigma_soft_max = args.sigma_soft_max #10.#500.
dnet_feature_dim = args.feature_dim
frame_interv = args.frame_interv
d_candi_dmap_ref = d_candi
nDepth_dmap_ref = nDepth
# Initialize data-loader, model and optimizer #
# ===== Dataset selection ======== #
dataset_path = args.dataset_path
if dataset_name == 'scanNet':
# deal with 1-frame scanNet data
import mdataloader.scanNet as dl_scanNet
dataset_init = dl_scanNet.ScanNet_dataset
split_txt = './mdataloader/scanNet_split/scannet_val.txt' if args.split_file == '.' else args.split_file
if not dataset_path == '.':
# if specify the path, we will assume we are using 1-frame-interval scanNet video #
fun_get_paths = lambda traj_indx: dl_scanNet.get_paths_1frame(
traj_indx,
database_path_base=dataset_path,
split_txt=split_txt)
dat_indx_step = 5 #pick this value to make sure the camera baseline is big enough
else:
fun_get_paths = lambda traj_indx: dl_scanNet.get_paths(
traj_indx, frame_interv=5, split_txt=split_txt)
dat_indx_step = 1
img_size = [384, 256]
# trajectory index for training #
n_scenes, _, _, _, _ = fun_get_paths(0)
traj_Indx = np.arange(0, n_scenes)
elif dataset_name == '7scenes':
# 7 scenes video #
import mdataloader.dl_7scenes as dl_7scenes
img_size = [384, 256]
dataset_init = dl_7scenes.SevenScenesDataset
dat_indx_step = 5 # pick this value to make sure the camera baseline is big enough
# trajectory index for training #
split_file = None if args.split_file == '.' else args.split_file
fun_get_paths = lambda traj_indx: dl_7scenes.get_paths_1frame(
traj_indx,
database_path_base=dataset_path,
split_txt=split_file,
)
elif dataset_name == 'single_folder':
# images in a single folder specified by the user #
import mdataloader.mdata as mdata
img_size = [384, 256]
dataset_init = mdata.mData
dat_indx_step = 5 # pick this value to make sure the camera baseline is big enough
fun_get_paths = lambda traj_indx: mdata.get_paths_1frame(
traj_indx, dataset_path, args.img_name_pattern)
traj_Indx = [0] #dummy
fldr_path, img_paths, dmap_paths, poses, intrin_path = fun_get_paths(
traj_Indx[0])
if dataset_name == 'single_folder':
intrin_path = args.intrin_path
dataset = dataset_init(
True,
img_paths,
dmap_paths,
poses,
intrin_path=intrin_path,
img_size=img_size,
digitize=True,
d_candi=d_candi_dmap_ref,
resize_dmap=.25,
)
dataset_Himgsize = dataset_init(
True,
img_paths,
dmap_paths,
poses,
intrin_path=intrin_path,
img_size=img_size,
digitize=True,
d_candi=d_candi_dmap_ref,
resize_dmap=.5,
)
dataset_imgsize = dataset_init(
True,
img_paths,
dmap_paths,
poses,
intrin_path=intrin_path,
img_size=img_size,
digitize=True,
d_candi=d_candi_dmap_ref,
resize_dmap=1,
)
# ================================ #
print('Initnializing the KV-Net')
model_KVnet = m_kvnet.KVNET(\
feature_dim = dnet_feature_dim,
cam_intrinsics = dataset.cam_intrinsics,
d_candi = d_candi, sigma_soft_max = sigma_soft_max,
KVNet_feature_dim = dnet_feature_dim,
d_upsample_ratio_KV_net = None,
t_win_r = t_win_r, if_refined = True)
model_KVnet = torch.nn.DataParallel(model_KVnet)
model_KVnet.cuda()
model_path_KV = args.model_path
print('loading KV_net at %s' % (model_path_KV))
utils_model.load_pretrained_model(model_KVnet, model_path_KV)
print('Done')
for traj_idx in traj_Indx:
scene_path_info = []
print('Getting the paths for traj_%d' % (traj_idx))
fldr_path, img_seq_paths, dmap_seq_paths, poses, intrin_path = fun_get_paths(
traj_idx)
res_fldr = '../results/%s/traj_%d' % (exp_name, traj_idx)
m_misc.m_makedir(res_fldr)
dataset.set_paths(img_seq_paths, dmap_seq_paths, poses)
if dataset_name == 'scanNet':
# the camera intrinsic may be slightly different for different trajectories in scanNet #
dataset.get_cam_intrinsics(intrin_path)
print('Done')
if args.min_frame_idx > 0:
frame_idxs = np.arange(args.min_frame_idx - t_win_r,
args.min_frame_idx + t_win_r)
dat_array = [dataset[idx] for idx in frame_idxs]
else:
dat_array = [dataset[idx] for idx in range(t_win_r * 2 + 1)]
DMaps_meas = []
dso_res_path = args.dso_res_path
print('init initial pose from DSO estimations ...')
traj_extMs = init_traj_extMs(traj_len=len(dataset),
dso_res_path=dso_res_path,
if_filter=True,
min_idx=args.min_frame_idx,
max_idx=args.max_frame_idx)
traj_extMs_init = copy_list(traj_extMs)
traj_length = min(len(dataset), len(traj_extMs))
first_frame = True
for frame_cnt, ref_indx in enumerate(
range(t_win_r * dat_indx_step + args.min_frame_idx,
traj_length - t_win_r * dat_indx_step - dat_indx_step)):
# ref_indx: the frame index for the reference frame #
# Read ref. and src. data in the local time window #
ref_dat, src_dats = m_misc.split_frame_list(dat_array, t_win_r)
src_frame_idx = [ idx for idx in range(
ref_indx - t_win_r * dat_indx_step, ref_indx, dat_indx_step) ] + \
[ idx for idx in range(
ref_indx + dat_indx_step, ref_indx + t_win_r*dat_indx_step+1, dat_indx_step) ]
valid_seq = dso_io.valid_poses(traj_extMs, src_frame_idx)
# only look at a subset of frames #
if ref_indx < args.min_frame_idx:
valid_seq = False
if ref_indx > args.max_frame_idx or ref_indx >= traj_length - t_win_r * dat_indx_step - dat_indx_step:
break
if frame_cnt == 0 or valid_seq is False:
BVs_predict = None
# refresh #
if ref_indx % args.refresh_frames == 0:
print('REFRESH !')
BVs_predict = None
BVs_predict_in = None
first_frame = True
traj_extMs = copy_list(traj_extMs_init)
if valid_seq: # if the sequence does not contain invalid pose estimation
# Get poses #
src_cam_extMs = [traj_extMs[i] for i in src_frame_idx]
ref_cam_extM = traj_extMs[ref_indx]
src_cam_poses = [
warp_homo.get_rel_extrinsicM(ref_cam_extM, src_cam_extM_)
for src_cam_extM_ in src_cam_extMs
]
src_cam_poses = [
torch.from_numpy(pose.astype(
np.float32)).cuda().unsqueeze(0)
for pose in src_cam_poses
]
# Load the gt pose if available #
if 'extM' in dataset[0]:
src_cam_extMs_ref = [
dataset[i]['extM'] for i in src_frame_idx
]
ref_cam_extM_ref = dataset[ref_indx]['extM']
src_cam_poses_ref = [ warp_homo.get_rel_extrinsicM(ref_cam_extM_ref, src_cam_extM_) \
for src_cam_extM_ in src_cam_extMs_ref ]
src_cam_poses_ref = [ torch.from_numpy(pose.astype(np.float32)).cuda().unsqueeze(0) \
for pose in src_cam_poses_ref ]
# -- Determine the scale, mapping from DSO scale to our working scale -- #
if frame_cnt == 0 or BVs_predict is None: # the first window for the traj.
_, t_norm_single = get_fb(src_cam_poses,
dataset.cam_intrinsics,
src_cam_pose_next=None)
# We need to heurisitcally determine scale_ without using GT pose #
t_norms = get_t_norms(traj_extMs, dat_indx_step)
scale_ = d_candi.max() / (
dataset.cam_intrinsics['focal_length'] *
np.array(t_norm_single).max() / 2)
scale_ = d_candi.max() / (
dataset.cam_intrinsics['focal_length'] *
np.array(t_norms).max())
scale_ = d_candi.max() / (
dataset.cam_intrinsics['focal_length'] *
np.array(t_norms).mean() / 2)
rescale_traj_t(traj_extMs, scale_)
traj_extMs_dso = copy_list(traj_extMs)
# Get poses #
src_cam_extMs = [traj_extMs[i] for i in src_frame_idx]
ref_cam_extM = traj_extMs[ref_indx]
src_cam_poses = [
warp_homo.get_rel_extrinsicM(ref_cam_extM,
src_cam_extM_)
for src_cam_extM_ in src_cam_extMs
]
src_cam_poses = [
torch.from_numpy(pose.astype(
np.float32)).cuda().unsqueeze(0)
for pose in src_cam_poses
]
# src_cam_poses size: N V 4 4 #
src_cam_poses = torch.cat(src_cam_poses, dim=0).unsqueeze(0)
src_frames = [m_misc.get_entries_list_dict(src_dats, 'img')]
cam_pose_next = traj_extMs[ref_indx + 1]
cam_pose_next = torch.FloatTensor(
warp_homo.get_rel_extrinsicM(traj_extMs[ref_indx],
cam_pose_next)).cuda()
BVs_predict_in = None if frame_cnt == 0 or BVs_predict is None \
else BVs_predict
BVs_measure, BVs_predict = test_KVNet.test(
model_KVnet,
d_candi,
Ref_Dats=[ref_dat],
Src_Dats=[src_dats],
Cam_Intrinsics=[dataset.cam_intrinsics],
t_win_r=t_win_r,
Src_CamPoses=src_cam_poses,
BV_predict=BVs_predict_in,
R_net=True,
cam_pose_next=cam_pose_next,
ref_indx=ref_indx)
# export_res.export_res_refineNet(ref_dat, BVs_measure, d_candi_dmap_ref,
# res_fldr, ref_indx,
# save_mat = True, output_pngs = args.output_pngs, output_dmap_ref=False)
export_res.export_res_img(ref_dat, BVs_measure,
d_candi_dmap_ref, res_fldr,
frame_cnt)
scene_path_info.append(
[frame_cnt, dataset[ref_indx]['img_path']])
# UPDATE dat_array #
if dat_indx_step > 1: # use one-interval video and the frame interval is larger than 5
print('updating array ...')
dat_array = update_dat_array(dat_array,
dataset,
data_interv=1,
frame_interv=5,
ref_indx=ref_indx,
t_win_r=t_win_r)
print('done')
else:
dat_array.pop(0)
new_dat = dataset[ref_indx + t_win_r + 1]
dat_array.append(new_dat)
# OPTMIZE POSES #
idx_ref_ = ref_indx + 1
cam_pose_nextframe = traj_extMs[idx_ref_]
cam_pose_nextframe = torch.FloatTensor(
warp_homo.get_rel_extrinsicM(traj_extMs[ref_indx],
cam_pose_nextframe)).cuda()
# get depth and confidence map #
BV_tmp_ = warp_homo.resample_vol_cuda(\
src_vol = BVs_measure, rel_extM = cam_pose_nextframe.inverse(),
cam_intrinsic = dataset_imgsize.cam_intrinsics,
d_candi = d_candi, d_candi_new = d_candi,
padding_value = math.log(1. / float(len(d_candi)))
).clamp(max=0, min=-1000.)
dmap_ref = m_misc.depth_val_regression(BVs_measure,
d_candi,
BV_log=True).squeeze()
conf_map_ref, _ = torch.max(BVs_measure.squeeze(), dim=0)
dmap_kf = m_misc.depth_val_regression(BV_tmp_.unsqueeze(0),
d_candi,
BV_log=True).squeeze()
conf_map_kf, _ = torch.max(BV_tmp_.squeeze(), dim=0)
# setup optimization #
cams_intrin = [
dataset.cam_intrinsics, dataset_Himgsize.cam_intrinsics,
dataset_imgsize.cam_intrinsics
]
dw_scales = [4, 2, 1]
LBA_max_iter = args.LBA_max_iter #10 # 20
LBA_step = args.LBA_step #.05 #.01
if LBA_max_iter <= 1: # do not do optimization
LBA_step = 0.
opt_vars = [args.opt_r, args.opt_t]
# initialization for the first time window #
if first_frame:
first_frame = False
# optimize the pose for all frames within the window #
if LBA_max_iter <= 1: # for debugging: using GT pose initialization #
rel_pose_inits_all_frame, srcs_idx_all_frame = m_misc.get_twin_rel_pose(
traj_extMs,
idx_ref_,
t_win_r * dat_indx_step,
1,
use_gt_R=True,
use_gt_t=True,
dataset=dataset,
add_noise_gt=False,
noise_sigmas=None)
else:
rel_pose_inits_all_frame, srcs_idx_all_frame = m_misc.get_twin_rel_pose(
traj_extMs,
ref_indx,
t_win_r * dat_indx_step,
1,
use_gt_R=False,
use_gt_t=False,
dataset=dataset,
)
# opt. #
img_ref = dataset[ref_indx]['img']
imgs_src = [dataset[i]['img'] for i in srcs_idx_all_frame]
conf_map_ref = torch.exp(conf_map_ref).squeeze()**2
rel_pose_opt = opt_pose_numerical.local_BA_direct(
img_ref,
imgs_src,
dmap_ref.unsqueeze(0).unsqueeze(0),
conf_map_ref.unsqueeze(0).unsqueeze(0),
cams_intrin,
dw_scales,
rel_pose_inits_all_frame,
max_iter=LBA_max_iter,
step=LBA_step,
opt_vars=opt_vars)
# update #
for idx, srcidx in enumerate(srcs_idx_all_frame):
traj_extMs[srcidx] = np.matmul(
rel_pose_opt[idx].cpu().numpy(),
traj_extMs[ref_indx])
# for next frame #
if LBA_max_iter <= 1: # for debugging: using GT pose init.
rel_pose_opt, srcs_idx = m_misc.get_twin_rel_pose(
traj_extMs,
idx_ref_,
t_win_r,
dat_indx_step,
use_gt_R=True,
use_gt_t=True,
dataset=dataset,
add_noise_gt=False,
noise_sigmas=None,
)
else:
rel_pose_inits, srcs_idx = m_misc.get_twin_rel_pose(
traj_extMs,
idx_ref_,
t_win_r,
dat_indx_step,
use_gt_R=args.use_gt_R,
use_dso_R=args.use_dso_R,
use_gt_t=args.use_gt_t,
use_dso_t=args.use_dso_t,
dataset=dataset,
traj_extMs_dso=traj_extMs_dso,
opt_next_frame=args.opt_next_frame)
img_ref = dataset[idx_ref_]['img']
_, src_dats_opt = m_misc.split_frame_list(
dat_array, t_win_r)
imgs_src = [dat_['img'] for dat_ in src_dats_opt]
img_ref = dataset[idx_ref_]['img']
imgs_src = [dataset[i] for i in srcs_idx]
imgs_src = [img_['img'] for img_ in imgs_src]
# opt. #
conf_map_kf = torch.exp(conf_map_kf).squeeze()**2
rel_pose_opt = \
opt_pose_numerical.local_BA_direct_parallel(
img_ref, imgs_src,
dmap_kf.unsqueeze(0).unsqueeze(0),
conf_map_kf.unsqueeze(0).unsqueeze(0), cams_intrin,
dw_scales, rel_pose_inits, max_iter = LBA_max_iter,
step = LBA_step, opt_vars = opt_vars)
# update #
print('idx_ref_: %d' % (idx_ref_))
print('srcs_idx : ')
print(srcs_idx)
print('updating pose ...')
for idx, srcidx in enumerate(srcs_idx):
traj_extMs[srcidx] = np.matmul(
rel_pose_opt[idx].cpu().numpy(), traj_extMs[idx_ref_])
print('done')
else: # if the sequence contains invalid pose estimation
BVs_predict = None
print('frame_cnt :%d, include invalid poses' % (frame_cnt))
# UPDATE dat_array #
if dat_indx_step > 1: # use one-interval video and the frame interval is larger than 5
print('updating array ...')
dat_array = update_dat_array(dat_array,
dataset,
data_interv=1,
frame_interv=5,
ref_indx=ref_indx,
t_win_r=t_win_r)
print('done')
else:
dat_array.pop(0)
new_dat = dataset[ref_indx + t_win_r + 1]
dat_array.append(new_dat)
m_misc.save_ScenePathInfo('%s/scene_path_info.txt' % (res_fldr),
scene_path_info)
def forward(self,
ref_frame,
src_frames,
src_cam_poses,
BatchIdx,
cam_intrinsics=None,
BV_predict=None,
mGPU=False,
IntMs=None,
unit_ray_Ms_2D=None):
r'''
Inputs:
ref_frame - NCHW format tensor on GPU, N = 1
src_frames - NVCHW: V - # of source views, N = 1
src_cam_poses - N x V x4 x4 - relative cam poses, N = 1
BatchIdx - e.g. for 4 gpus: [0,1,2,3], used for indexing list input for multi-gpu training
cam_intrinsics - list of cam_intrinsics dict.
BV_predict - NDHW tensor, the predicted BV, from the last reference frame, N=1
Outputs:
dmap_cur_refined, dmap_kv_refined, BV_cur, BV_KV
if refined on dpv, then dmap_cur_refined and dmap_kv_refined are refined dpvs
NOTE:
1. We should put ref_frame and src_frames and src_cam_poses into GPU before running the forward pass
2. The purpose of enforcing N=1 is for multi-gpu running
'''
if isinstance(BV_predict, torch.Tensor):
if m_misc.valid_dpv(BV_predict):
assert BV_predict.shape[0] == 1
# D-Net #
if (self.if_refined is False) or (self.if_refined is True
and self.refineNet_name != 'DPV'):
BV_cur = self.d_net(ref_frame,
src_frames,
src_cam_poses,
BV_predict=None,
debug_ipdb=False)
else:
BV_cur, d_net_features = self.d_net(ref_frame,
src_frames,
src_cam_poses,
BV_predict=None,
debug_ipdb=False)
d_net_features.append(ref_frame)
if self.if_refined:
dmap_cur_lowres = m_misc.depth_val_regression(
BV_cur, self.d_candi, BV_log=True).unsqueeze(0)
if self.refineNet_name == 'DGF':
dmap_cur_refined = self.r_net(dmap_cur_lowres, ref_frame)
elif self.refineNet_name == 'DPV':
dmap_cur_refined = self.r_net(torch.exp(BV_cur),
img_features=d_net_features)
else:
dmap_cur_refined = -1
if not isinstance(BV_predict, torch.Tensor):
#If the first time win., then return only BV_cur
return dmap_cur_refined, dmap_cur_refined, BV_cur, BV_cur
elif not m_misc.valid_dpv(BV_predict):
return dmap_cur_refined, dmap_cur_refined, BV_cur, BV_cur
else:
# KV-Net #
down_sample_rate = ref_frame.shape[3] / BV_cur.shape[3]
ref_frame_dw = F.avg_pool2d(ref_frame,
int(down_sample_rate)).cuda()
src_frames_dw = [
F.avg_pool2d(src_frame_.unsqueeze(0),
int(down_sample_rate)).cuda()
for src_frame_ in src_frames.squeeze(0)
]
Rs_src = [pose[:3, :3] for pose in src_cam_poses.squeeze(0)]
ts_src = [pose[:3, 3] for pose in src_cam_poses.squeeze(0)]
# Warp the src-frames to the ref. view #
if mGPU:
WAPRED_src_frames = warp_homo.warp_img_feats_mgpu(
src_frames_dw, self.d_candi, Rs_src, ts_src, IntMs,
unit_ray_Ms_2D)
else:
cam_intrin = cam_intrinsics[int(BatchIdx)]
WAPRED_src_frames = warp_homo.warp_img_feats_v3(
src_frames_dw,
self.d_candi,
Rs_src,
ts_src,
cam_intrin,
)
ref_frame_dw_rep = torch.transpose(
ref_frame_dw.repeat([len(self.d_candi), 1, 1, 1]), 0, 1)
# Input to the KV-net #
kvnet_in_vol = torch.cat(
(torch.cat(tuple(WAPRED_src_frames),
dim=0), ref_frame_dw_rep, BV_cur - BV_predict),
dim=0).unsqueeze(0)
# Run KV-net #
BV_gain = self.kv_net(kvnet_in_vol)
# Add back to BV_predict #
DPV = torch.squeeze(BV_gain, dim=1) + BV_predict
DPV = F.log_softmax(DPV, dim=1)
if self.if_refined:
dmap_lowres = m_misc.depth_val_regression(
DPV, self.d_candi, BV_log=True).unsqueeze(0)
if self.refineNet_name == 'DGF':
dmap_refined = self.r_net(dmap_lowres, ref_frame)
elif self.refineNet_name == 'DPV':
dmap_refined = self.r_net(torch.exp(DPV),
img_features=d_net_features)
else:
dmap_refined = -1
return dmap_cur_refined, dmap_refined, BV_cur, DPV