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"""
decouple the trainer with the renderer
"""
import os
import cv2 as cv
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import trimesh
from icecream import ic
from utils.misc_utils import visualize_depth_numpy
from loss.depth_metric import compute_depth_errors
from loss.depth_loss import DepthLoss, DepthSmoothLoss
from models.sparse_neus_renderer import SparseNeuSRenderer
class GenericTrainer(nn.Module):
def __init__(self,
rendering_network_outside,
pyramid_feature_network_lod0,
pyramid_feature_network_lod1,
sdf_network_lod0,
sdf_network_lod1,
variance_network_lod0,
variance_network_lod1,
rendering_network_lod0,
rendering_network_lod1,
n_samples_lod0,
n_importance_lod0,
n_samples_lod1,
n_importance_lod1,
n_outside,
perturb,
alpha_type='div',
conf=None,
timestamp="",
mode='train',
base_exp_dir=None,
):
super(GenericTrainer, self).__init__()
self.conf = conf
self.timestamp = timestamp
self.base_exp_dir = base_exp_dir
self.anneal_start = self.conf.get_float('train.anneal_start', default=0.0)
self.anneal_end = self.conf.get_float('train.anneal_end', default=0.0)
self.anneal_start_lod1 = self.conf.get_float('train.anneal_start_lod1', default=0.0)
self.anneal_end_lod1 = self.conf.get_float('train.anneal_end_lod1', default=0.0)
# network setups
self.rendering_network_outside = rendering_network_outside
self.pyramid_feature_network_geometry_lod0 = pyramid_feature_network_lod0 # 2D pyramid feature network for geometry
self.pyramid_feature_network_geometry_lod1 = pyramid_feature_network_lod1 # use differnet networks for the two lods
# when num_lods==2, may consume too much memeory
self.sdf_network_lod0 = sdf_network_lod0
self.sdf_network_lod1 = sdf_network_lod1
# - warpped by ModuleList to support DataParallel
self.variance_network_lod0 = variance_network_lod0
self.variance_network_lod1 = variance_network_lod1
self.rendering_network_lod0 = rendering_network_lod0
self.rendering_network_lod1 = rendering_network_lod1
self.n_samples_lod0 = n_samples_lod0
self.n_importance_lod0 = n_importance_lod0
self.n_samples_lod1 = n_samples_lod1
self.n_importance_lod1 = n_importance_lod1
self.n_outside = n_outside
self.num_lods = conf.get_int('model.num_lods') # the number of octree lods
self.perturb = perturb
self.alpha_type = alpha_type
# - the two renderers
self.sdf_renderer_lod0 = SparseNeuSRenderer(
self.rendering_network_outside,
self.sdf_network_lod0,
self.variance_network_lod0,
self.rendering_network_lod0,
self.n_samples_lod0,
self.n_importance_lod0,
self.n_outside,
self.perturb,
alpha_type='div',
conf=self.conf)
self.sdf_renderer_lod1 = SparseNeuSRenderer(
self.rendering_network_outside,
self.sdf_network_lod1,
self.variance_network_lod1,
self.rendering_network_lod1,
self.n_samples_lod1,
self.n_importance_lod1,
self.n_outside,
self.perturb,
alpha_type='div',
conf=self.conf)
self.if_fix_lod0_networks = self.conf.get_bool('train.if_fix_lod0_networks')
# sdf network weights
self.sdf_igr_weight = self.conf.get_float('train.sdf_igr_weight')
self.sdf_sparse_weight = self.conf.get_float('train.sdf_sparse_weight', default=0)
self.sdf_decay_param = self.conf.get_float('train.sdf_decay_param', default=100)
self.fg_bg_weight = self.conf.get_float('train.fg_bg_weight', default=0.00)
self.bg_ratio = self.conf.get_float('train.bg_ratio', default=0.0)
self.depth_criterion = DepthLoss()
# - DataParallel mode, cannot modify attributes in forward()
# self.iter_step = 0
self.val_mesh_freq = self.conf.get_int('train.val_mesh_freq')
# - True for finetuning; False for general training
self.if_fitted_rendering = self.conf.get_bool('train.if_fitted_rendering', default=False)
self.prune_depth_filter = self.conf.get_bool('model.prune_depth_filter', default=False)
def get_trainable_params(self):
# set trainable params
self.params_to_train = []
if not self.if_fix_lod0_networks:
# load pretrained featurenet
self.params_to_train += list(self.pyramid_feature_network_geometry_lod0.parameters())
self.params_to_train += list(self.sdf_network_lod0.parameters())
self.params_to_train += list(self.variance_network_lod0.parameters())
if self.rendering_network_lod0 is not None:
self.params_to_train += list(self.rendering_network_lod0.parameters())
if self.sdf_network_lod1 is not None:
# load pretrained featurenet
self.params_to_train += list(self.pyramid_feature_network_geometry_lod1.parameters())
self.params_to_train += list(self.sdf_network_lod1.parameters())
self.params_to_train += list(self.variance_network_lod1.parameters())
if self.rendering_network_lod1 is not None:
self.params_to_train += list(self.rendering_network_lod1.parameters())
return self.params_to_train
def train_step(self, sample,
perturb_overwrite=-1,
background_rgb=None,
alpha_inter_ratio_lod0=0.0,
alpha_inter_ratio_lod1=0.0,
iter_step=0,
):
# * only support batch_size==1
# ! attention: the list of string cannot be splited in DataParallel
batch_idx = sample['batch_idx'][0]
meta = sample['meta'][batch_idx] # the scan lighting ref_view info
sizeW = sample['img_wh'][0][0]
sizeH = sample['img_wh'][0][1]
partial_vol_origin = sample['partial_vol_origin'] # [B, 3]
near, far = sample['near_fars'][0, 0, :1], sample['near_fars'][0, 0, 1:]
# the full-size ray variables
sample_rays = sample['rays']
rays_o = sample_rays['rays_o'][0]
rays_d = sample_rays['rays_v'][0]
imgs = sample['images'][0]
intrinsics = sample['intrinsics'][0]
intrinsics_l_4x = intrinsics.clone()
intrinsics_l_4x[:, :2] *= 0.25
w2cs = sample['w2cs'][0]
c2ws = sample['c2ws'][0]
proj_matrices = sample['affine_mats']
scale_mat = sample['scale_mat']
trans_mat = sample['trans_mat']
# *********************** Lod==0 ***********************
if not self.if_fix_lod0_networks:
geometry_feature_maps = self.obtain_pyramid_feature_maps(imgs)
conditional_features_lod0 = self.sdf_network_lod0.get_conditional_volume(
feature_maps=geometry_feature_maps[None, 1:, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices[:,1:],
# proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
lod=0,
)
else:
with torch.no_grad():
geometry_feature_maps = self.obtain_pyramid_feature_maps(imgs, lod=0)
# geometry_feature_maps = self.obtain_pyramid_feature_maps(imgs, lod=0)
conditional_features_lod0 = self.sdf_network_lod0.get_conditional_volume(
feature_maps=geometry_feature_maps[None, 1:, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices[:,1:],
# proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
lod=0,
)
# print("Checker2:, construct cost volume")
con_volume_lod0 = conditional_features_lod0['dense_volume_scale0']
con_valid_mask_volume_lod0 = conditional_features_lod0['valid_mask_volume_scale0']
coords_lod0 = conditional_features_lod0['coords_scale0'] # [1,3,wX,wY,wZ]
# * extract depth maps for all the images
depth_maps_lod0, depth_masks_lod0 = None, None
if self.num_lods > 1:
sdf_volume_lod0 = self.sdf_network_lod0.get_sdf_volume(
con_volume_lod0, con_valid_mask_volume_lod0,
coords_lod0, partial_vol_origin) # [1, 1, dX, dY, dZ]
if self.prune_depth_filter:
depth_maps_lod0_l4x, depth_masks_lod0_l4x = self.sdf_renderer_lod0.extract_depth_maps(
self.sdf_network_lod0, sdf_volume_lod0, intrinsics_l_4x, c2ws,
sizeH // 4, sizeW // 4, near * 1.5, far)
depth_maps_lod0 = F.interpolate(depth_maps_lod0_l4x, size=(sizeH, sizeW), mode='bilinear',
align_corners=True)
depth_masks_lod0 = F.interpolate(depth_masks_lod0_l4x.float(), size=(sizeH, sizeW), mode='nearest')
# *************** losses
loss_lod0, losses_lod0, depth_statis_lod0 = None, None, None
if not self.if_fix_lod0_networks:
render_out = self.sdf_renderer_lod0.render(
rays_o, rays_d, near, far,
self.sdf_network_lod0,
self.rendering_network_lod0,
background_rgb=background_rgb,
alpha_inter_ratio=alpha_inter_ratio_lod0,
# * related to conditional feature
lod=0,
conditional_volume=con_volume_lod0,
conditional_valid_mask_volume=con_valid_mask_volume_lod0,
# * 2d feature maps
feature_maps=geometry_feature_maps,
color_maps=imgs,
w2cs=w2cs,
intrinsics=intrinsics,
img_wh=[sizeW, sizeH],
if_general_rendering=True,
if_render_with_grad=True,
)
loss_lod0, losses_lod0, depth_statis_lod0 = self.cal_losses_sdf(render_out, sample_rays,
iter_step, lod=0)
# *********************** Lod==1 ***********************
loss_lod1, losses_lod1, depth_statis_lod1 = None, None, None
if self.num_lods > 1:
geometry_feature_maps_lod1 = self.obtain_pyramid_feature_maps(imgs, lod=1)
# geometry_feature_maps_lod1 = self.obtain_pyramid_feature_maps(imgs, lod=1)
if self.prune_depth_filter:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf_depthfilter(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0],
depth_maps_lod0, proj_matrices[0],
partial_vol_origin, self.sdf_network_lod0.voxel_size,
near, far, self.sdf_network_lod0.voxel_size, 12)
else:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0])
pre_coords[:, 1:] = pre_coords[:, 1:] * 2
# ? It seems that training gru_fusion, this part should be trainable too
conditional_features_lod1 = self.sdf_network_lod1.get_conditional_volume(
feature_maps=geometry_feature_maps_lod1[None, 1:, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices[:,1:],
# proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
pre_coords=pre_coords,
pre_feats=pre_feats,
)
con_volume_lod1 = conditional_features_lod1['dense_volume_scale1']
con_valid_mask_volume_lod1 = conditional_features_lod1['valid_mask_volume_scale1']
# if not self.if_gru_fusion_lod1:
render_out_lod1 = self.sdf_renderer_lod1.render(
rays_o, rays_d, near, far,
self.sdf_network_lod1,
self.rendering_network_lod1,
background_rgb=background_rgb,
alpha_inter_ratio=alpha_inter_ratio_lod1,
# * related to conditional feature
lod=1,
conditional_volume=con_volume_lod1,
conditional_valid_mask_volume=con_valid_mask_volume_lod1,
# * 2d feature maps
feature_maps=geometry_feature_maps_lod1,
color_maps=imgs,
w2cs=w2cs,
intrinsics=intrinsics,
img_wh=[sizeW, sizeH],
bg_ratio=self.bg_ratio,
)
loss_lod1, losses_lod1, depth_statis_lod1 = self.cal_losses_sdf(render_out_lod1, sample_rays,
iter_step, lod=1)
# print("Checker3:, compute losses")
# # - extract mesh
if iter_step % self.val_mesh_freq == 0:
torch.cuda.empty_cache()
self.validate_mesh(self.sdf_network_lod0,
self.sdf_renderer_lod0.extract_geometry,
conditional_volume=con_volume_lod0, lod=0,
threshold=0,
# occupancy_mask=con_valid_mask_volume_lod0[0, 0],
mode='train_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat,
trans_mat=trans_mat)
torch.cuda.empty_cache()
if self.num_lods > 1:
self.validate_mesh(self.sdf_network_lod1,
self.sdf_renderer_lod1.extract_geometry,
conditional_volume=con_volume_lod1, lod=1,
# occupancy_mask=con_valid_mask_volume_lod1[0, 0].detach(),
mode='train_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat,
trans_mat=trans_mat)
losses = {
# - lod 0
'loss_lod0': loss_lod0,
'losses_lod0': losses_lod0,
'depth_statis_lod0': depth_statis_lod0,
# - lod 1
'loss_lod1': loss_lod1,
'losses_lod1': losses_lod1,
'depth_statis_lod1': depth_statis_lod1,
}
return losses
def val_step(self, sample,
perturb_overwrite=-1,
background_rgb=None,
alpha_inter_ratio_lod0=0.0,
alpha_inter_ratio_lod1=0.0,
iter_step=0,
chunk_size=512,
save_vis=False,
):
# * only support batch_size==1
# ! attention: the list of string cannot be splited in DataParallel
batch_idx = sample['batch_idx'][0]
meta = sample['meta'][batch_idx] # the scan lighting ref_view info
sizeW = sample['img_wh'][0][0]
sizeH = sample['img_wh'][0][1]
H, W = sizeH, sizeW
partial_vol_origin = sample['partial_vol_origin'] # [B, 3]
near, far = sample['query_near_far'][0, :1], sample['query_near_far'][0, 1:]
# the ray variables
sample_rays = sample['rays']
rays_o = sample_rays['rays_o'][0]
rays_d = sample_rays['rays_v'][0]
rays_ndc_uv = sample_rays['rays_ndc_uv'][0]
imgs = sample['images'][0]
intrinsics = sample['intrinsics'][0]
intrinsics_l_4x = intrinsics.clone()
intrinsics_l_4x[:, :2] *= 0.25
w2cs = sample['w2cs'][0]
c2ws = sample['c2ws'][0]
proj_matrices = sample['affine_mats']
# render_img_idx = sample['render_img_idx'][0]
# true_img = sample['images'][0][render_img_idx]
# - the image to render
scale_mat = sample['scale_mat'] # [1,4,4] used to convert mesh into true scale
trans_mat = sample['trans_mat']
query_c2w = sample['query_c2w'] # [1,4,4]
query_w2c = sample['query_w2c'] # [1,4,4]
true_img = sample['query_image'][0]
true_img = np.uint8(true_img.permute(1, 2, 0).cpu().numpy() * 255)
depth_min, depth_max = near.cpu().numpy(), far.cpu().numpy()
scale_factor = sample['scale_factor'][0].cpu().numpy()
true_depth = sample['query_depth'] if 'query_depth' in sample.keys() else None
if true_depth is not None:
true_depth = true_depth[0].cpu().numpy()
true_depth_colored = visualize_depth_numpy(true_depth, [depth_min, depth_max])[0]
else:
true_depth_colored = None
rays_o = rays_o.reshape(-1, 3).split(chunk_size)
rays_d = rays_d.reshape(-1, 3).split(chunk_size)
# - obtain conditional features
with torch.no_grad():
# - obtain conditional features
geometry_feature_maps = self.obtain_pyramid_feature_maps(imgs, lod=0)
# - lod 0
conditional_features_lod0 = self.sdf_network_lod0.get_conditional_volume(
feature_maps=geometry_feature_maps[None, :, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
lod=0,
)
con_volume_lod0 = conditional_features_lod0['dense_volume_scale0']
con_valid_mask_volume_lod0 = conditional_features_lod0['valid_mask_volume_scale0']
coords_lod0 = conditional_features_lod0['coords_scale0'] # [1,3,wX,wY,wZ]
if self.num_lods > 1:
sdf_volume_lod0 = self.sdf_network_lod0.get_sdf_volume(
con_volume_lod0, con_valid_mask_volume_lod0,
coords_lod0, partial_vol_origin) # [1, 1, dX, dY, dZ]
depth_maps_lod0, depth_masks_lod0 = None, None
if self.prune_depth_filter:
depth_maps_lod0_l4x, depth_masks_lod0_l4x = self.sdf_renderer_lod0.extract_depth_maps(
self.sdf_network_lod0, sdf_volume_lod0,
intrinsics_l_4x, c2ws,
sizeH // 4, sizeW // 4, near * 1.5, far) # - near*1.5 is a experienced number
depth_maps_lod0 = F.interpolate(depth_maps_lod0_l4x, size=(sizeH, sizeW), mode='bilinear',
align_corners=True)
depth_masks_lod0 = F.interpolate(depth_masks_lod0_l4x.float(), size=(sizeH, sizeW), mode='nearest')
#### visualize the depth_maps_lod0 for checking
colored_depth_maps_lod0 = []
for i in range(depth_maps_lod0.shape[0]):
colored_depth_maps_lod0.append(
visualize_depth_numpy(depth_maps_lod0[i, 0].cpu().numpy(), [depth_min, depth_max])[0])
colored_depth_maps_lod0 = np.concatenate(colored_depth_maps_lod0, axis=0).astype(np.uint8)
os.makedirs(os.path.join(self.base_exp_dir, 'depth_maps_lod0'), exist_ok=True)
cv.imwrite(os.path.join(self.base_exp_dir, 'depth_maps_lod0',
'{:0>8d}_{}.png'.format(iter_step, meta)),
colored_depth_maps_lod0[:, :, ::-1])
if self.num_lods > 1:
geometry_feature_maps_lod1 = self.obtain_pyramid_feature_maps(imgs, lod=1)
if self.prune_depth_filter:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf_depthfilter(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0],
depth_maps_lod0, proj_matrices[0],
partial_vol_origin, self.sdf_network_lod0.voxel_size,
near, far, self.sdf_network_lod0.voxel_size, 12)
else:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0])
pre_coords[:, 1:] = pre_coords[:, 1:] * 2
with torch.no_grad():
conditional_features_lod1 = self.sdf_network_lod1.get_conditional_volume(
feature_maps=geometry_feature_maps_lod1[None, :, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
pre_coords=pre_coords,
pre_feats=pre_feats,
)
con_volume_lod1 = conditional_features_lod1['dense_volume_scale1']
con_valid_mask_volume_lod1 = conditional_features_lod1['valid_mask_volume_scale1']
out_rgb_fine = []
out_normal_fine = []
out_depth_fine = []
out_rgb_fine_lod1 = []
out_normal_fine_lod1 = []
out_depth_fine_lod1 = []
# out_depth_fine_explicit = []
if save_vis:
for rays_o_batch, rays_d_batch in zip(rays_o, rays_d):
# ****** lod 0 ****
render_out = self.sdf_renderer_lod0.render(
rays_o_batch, rays_d_batch, near, far,
self.sdf_network_lod0,
self.rendering_network_lod0,
background_rgb=background_rgb,
alpha_inter_ratio=alpha_inter_ratio_lod0,
# * related to conditional feature
lod=0,
conditional_volume=con_volume_lod0,
conditional_valid_mask_volume=con_valid_mask_volume_lod0,
# * 2d feature maps
feature_maps=geometry_feature_maps,
color_maps=imgs,
w2cs=w2cs,
intrinsics=intrinsics,
img_wh=[sizeW, sizeH],
query_c2w=query_c2w,
if_render_with_grad=False,
)
feasible = lambda key: ((key in render_out) and (render_out[key] is not None))
if feasible('depth'):
out_depth_fine.append(render_out['depth'].detach().cpu().numpy())
# if render_out['color_coarse'] is not None:
if feasible('color_fine'):
out_rgb_fine.append(render_out['color_fine'].detach().cpu().numpy())
if feasible('gradients') and feasible('weights'):
if render_out['inside_sphere'] is not None:
out_normal_fine.append((render_out['gradients'] * render_out['weights'][:,
:self.n_samples_lod0 + self.n_importance_lod0,
None] * render_out['inside_sphere'][
..., None]).sum(dim=1).detach().cpu().numpy())
else:
out_normal_fine.append((render_out['gradients'] * render_out['weights'][:,
:self.n_samples_lod0 + self.n_importance_lod0,
None]).sum(dim=1).detach().cpu().numpy())
del render_out
# ****************** lod 1 **************************
if self.num_lods > 1:
for rays_o_batch, rays_d_batch in zip(rays_o, rays_d):
render_out_lod1 = self.sdf_renderer_lod1.render(
rays_o_batch, rays_d_batch, near, far,
self.sdf_network_lod1,
self.rendering_network_lod1,
background_rgb=background_rgb,
alpha_inter_ratio=alpha_inter_ratio_lod1,
# * related to conditional feature
lod=1,
conditional_volume=con_volume_lod1,
conditional_valid_mask_volume=con_valid_mask_volume_lod1,
# * 2d feature maps
feature_maps=geometry_feature_maps_lod1,
color_maps=imgs,
w2cs=w2cs,
intrinsics=intrinsics,
img_wh=[sizeW, sizeH],
query_c2w=query_c2w,
if_render_with_grad=False,
)
feasible = lambda key: ((key in render_out_lod1) and (render_out_lod1[key] is not None))
if feasible('depth'):
out_depth_fine_lod1.append(render_out_lod1['depth'].detach().cpu().numpy())
# if render_out['color_coarse'] is not None:
if feasible('color_fine'):
out_rgb_fine_lod1.append(render_out_lod1['color_fine'].detach().cpu().numpy())
if feasible('gradients') and feasible('weights'):
if render_out_lod1['inside_sphere'] is not None:
out_normal_fine_lod1.append((render_out_lod1['gradients'] * render_out_lod1['weights'][:,
:self.n_samples_lod1 + self.n_importance_lod1,
None] *
render_out_lod1['inside_sphere'][
..., None]).sum(dim=1).detach().cpu().numpy())
else:
out_normal_fine_lod1.append((render_out_lod1['gradients'] * render_out_lod1['weights'][:,
:self.n_samples_lod1 + self.n_importance_lod1,
None]).sum(
dim=1).detach().cpu().numpy())
del render_out_lod1
# - save visualization of lod 0
self.save_visualization(true_img, true_depth_colored, out_depth_fine, out_normal_fine,
query_w2c[0], out_rgb_fine, H, W,
depth_min, depth_max, iter_step, meta, "val_lod0", true_depth=true_depth, scale_factor=scale_factor)
if self.num_lods > 1:
self.save_visualization(true_img, true_depth_colored, out_depth_fine_lod1, out_normal_fine_lod1,
query_w2c[0], out_rgb_fine_lod1, H, W,
depth_min, depth_max, iter_step, meta, "val_lod1", true_depth=true_depth, scale_factor=scale_factor)
# - extract mesh
if (iter_step % self.val_mesh_freq == 0):
torch.cuda.empty_cache()
self.validate_mesh(self.sdf_network_lod0,
self.sdf_renderer_lod0.extract_geometry,
conditional_volume=con_volume_lod0, lod=0,
threshold=0,
# occupancy_mask=con_valid_mask_volume_lod0[0, 0],
mode='val_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat, trans_mat=trans_mat)
torch.cuda.empty_cache()
if self.num_lods > 1:
self.validate_mesh(self.sdf_network_lod1,
self.sdf_renderer_lod1.extract_geometry,
conditional_volume=con_volume_lod1, lod=1,
# occupancy_mask=con_valid_mask_volume_lod1[0, 0].detach(),
mode='val_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat, trans_mat=trans_mat)
torch.cuda.empty_cache()
def export_mesh_step(self, sample,
perturb_overwrite=-1,
background_rgb=None,
alpha_inter_ratio_lod0=0.0,
alpha_inter_ratio_lod1=0.0,
iter_step=0,
chunk_size=512,
save_vis=False,
):
# * only support batch_size==1
# ! attention: the list of string cannot be splited in DataParallel
batch_idx = sample['batch_idx'][0]
meta = sample['meta'][batch_idx] # the scan lighting ref_view info
sizeW = sample['img_wh'][0][0]
sizeH = sample['img_wh'][0][1]
H, W = sizeH, sizeW
partial_vol_origin = sample['partial_vol_origin'] # [B, 3]
near, far = sample['query_near_far'][0, :1], sample['query_near_far'][0, 1:]
# the ray variables
sample_rays = sample['rays']
rays_o = sample_rays['rays_o'][0]
rays_d = sample_rays['rays_v'][0]
rays_ndc_uv = sample_rays['rays_ndc_uv'][0]
imgs = sample['images'][0]
intrinsics = sample['intrinsics'][0]
intrinsics_l_4x = intrinsics.clone()
intrinsics_l_4x[:, :2] *= 0.25
w2cs = sample['w2cs'][0]
c2ws = sample['c2ws'][0]
# target_candidate_w2cs = sample['target_candidate_w2cs'][0]
proj_matrices = sample['affine_mats']
# - the image to render
scale_mat = sample['scale_mat'] # [1,4,4] used to convert mesh into true scale
trans_mat = sample['trans_mat']
query_c2w = sample['query_c2w'] # [1,4,4]
query_w2c = sample['query_w2c'] # [1,4,4]
true_img = sample['query_image'][0]
true_img = np.uint8(true_img.permute(1, 2, 0).cpu().numpy() * 255)
depth_min, depth_max = near.cpu().numpy(), far.cpu().numpy()
scale_factor = sample['scale_factor'][0].cpu().numpy()
true_depth = sample['query_depth'] if 'query_depth' in sample.keys() else None
if true_depth is not None:
true_depth = true_depth[0].cpu().numpy()
true_depth_colored = visualize_depth_numpy(true_depth, [depth_min, depth_max])[0]
else:
true_depth_colored = None
rays_o = rays_o.reshape(-1, 3).split(chunk_size)
rays_d = rays_d.reshape(-1, 3).split(chunk_size)
# import time
# jha_begin1 = time.time()
# - obtain conditional features
with torch.no_grad():
# - obtain conditional features
geometry_feature_maps = self.obtain_pyramid_feature_maps(imgs, lod=0)
# - lod 0
conditional_features_lod0 = self.sdf_network_lod0.get_conditional_volume(
feature_maps=geometry_feature_maps[None, :, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
lod=0,
)
# jha_end1 = time.time()
# print("get_conditional_volume: ", jha_end1 - jha_begin1)
# jha_begin2 = time.time()
con_volume_lod0 = conditional_features_lod0['dense_volume_scale0']
con_valid_mask_volume_lod0 = conditional_features_lod0['valid_mask_volume_scale0']
coords_lod0 = conditional_features_lod0['coords_scale0'] # [1,3,wX,wY,wZ]
if self.num_lods > 1:
sdf_volume_lod0 = self.sdf_network_lod0.get_sdf_volume(
con_volume_lod0, con_valid_mask_volume_lod0,
coords_lod0, partial_vol_origin) # [1, 1, dX, dY, dZ]
depth_maps_lod0, depth_masks_lod0 = None, None
if self.num_lods > 1:
geometry_feature_maps_lod1 = self.obtain_pyramid_feature_maps(imgs, lod=1)
if self.prune_depth_filter:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf_depthfilter(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0],
depth_maps_lod0, proj_matrices[0],
partial_vol_origin, self.sdf_network_lod0.voxel_size,
near, far, self.sdf_network_lod0.voxel_size, 12)
else:
pre_coords, pre_feats = self.sdf_renderer_lod0.get_valid_sparse_coords_by_sdf(
sdf_volume_lod0[0], coords_lod0[0], con_valid_mask_volume_lod0[0], con_volume_lod0[0])
pre_coords[:, 1:] = pre_coords[:, 1:] * 2
with torch.no_grad():
conditional_features_lod1 = self.sdf_network_lod1.get_conditional_volume(
feature_maps=geometry_feature_maps_lod1[None, :, :, :, :],
partial_vol_origin=partial_vol_origin,
proj_mats=proj_matrices,
sizeH=sizeH,
sizeW=sizeW,
pre_coords=pre_coords,
pre_feats=pre_feats,
)
con_volume_lod1 = conditional_features_lod1['dense_volume_scale1']
con_valid_mask_volume_lod1 = conditional_features_lod1['valid_mask_volume_scale1']
out_rgb_fine = []
out_normal_fine = []
out_depth_fine = []
out_rgb_fine_lod1 = []
out_normal_fine_lod1 = []
out_depth_fine_lod1 = []
# jha_end2 = time.time()
# print("interval before starting mesh export: ", jha_end2 - jha_begin2)
# - extract mesh
if (iter_step % self.val_mesh_freq == 0):
torch.cuda.empty_cache()
# jha_begin3 = time.time()
self.validate_colored_mesh(
density_or_sdf_network=self.sdf_network_lod0,
func_extract_geometry=self.sdf_renderer_lod0.extract_geometry,
conditional_volume=con_volume_lod0,
conditional_valid_mask_volume = con_valid_mask_volume_lod0,
feature_maps=geometry_feature_maps,
color_maps=imgs,
w2cs=w2cs,
target_candidate_w2cs=None,
intrinsics=intrinsics,
rendering_network=self.rendering_network_lod0,
rendering_projector=self.sdf_renderer_lod0.rendering_projector,
lod=0,
threshold=0,
query_c2w=query_c2w,
mode='val_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat, trans_mat=trans_mat
)
torch.cuda.empty_cache()
# jha_end3 = time.time()
# print("validate_colored_mesh_test_time: ", jha_end3 - jha_begin3)
if self.num_lods > 1:
self.validate_colored_mesh(
density_or_sdf_network=self.sdf_network_lod1,
func_extract_geometry=self.sdf_renderer_lod1.extract_geometry,
conditional_volume=con_volume_lod1,
conditional_valid_mask_volume = con_valid_mask_volume_lod1,
feature_maps=geometry_feature_maps,
color_maps=imgs,
w2cs=w2cs,
target_candidate_w2cs=None,
intrinsics=intrinsics,
rendering_network=self.rendering_network_lod1,
rendering_projector=self.sdf_renderer_lod1.rendering_projector,
lod=1,
threshold=0,
query_c2w=query_c2w,
mode='val_bg', meta=meta,
iter_step=iter_step, scale_mat=scale_mat, trans_mat=trans_mat
)
torch.cuda.empty_cache()
def save_visualization(self, true_img, true_colored_depth, out_depth, out_normal, w2cs, out_color, H, W,
depth_min, depth_max, iter_step, meta, comment, out_color_mlp=[], true_depth=None, scale_factor=1.0):
if len(out_color) > 0:
img_fine = (np.concatenate(out_color, axis=0).reshape([H, W, 3]) * 256).clip(0, 255)
if len(out_color_mlp) > 0:
img_mlp = (np.concatenate(out_color_mlp, axis=0).reshape([H, W, 3]) * 256).clip(0, 255)
if len(out_normal) > 0:
normal_img = np.concatenate(out_normal, axis=0)
rot = w2cs[:3, :3].detach().cpu().numpy()
# - convert normal from world space to camera space
normal_img = (np.matmul(rot[None, :, :],
normal_img[:, :, None]).reshape([H, W, 3]) * 128 + 128).clip(0, 255)
if len(out_depth) > 0:
pred_depth = np.concatenate(out_depth, axis=0).reshape([H, W])
pred_depth_colored = visualize_depth_numpy(pred_depth, [depth_min, depth_max])[0]
if len(out_depth) > 0:
os.makedirs(os.path.join(self.base_exp_dir, 'depths_' + comment), exist_ok=True)
if true_colored_depth is not None:
if true_depth is not None:
depth_error_map = np.abs(true_depth - pred_depth) * 2.0 / scale_factor
# [256, 256, 1] -> [256, 256, 3]
depth_error_map = np.tile(depth_error_map[:, :, None], [1, 1, 3])
print("meta: ", meta)
print("scale_factor: ", scale_factor)
print("depth_error_mean: ", depth_error_map.mean())
depth_visualized = np.concatenate(
[(depth_error_map * 255).astype(np.uint8), true_colored_depth, pred_depth_colored, true_img], axis=1)[:, :, ::-1]
# print("depth_visualized.shape: ", depth_visualized.shape)
# write depth error result text on img, the input is a numpy array of [256, 1024, 3]
# cv.putText(depth_visualized.copy(), "depth_error_mean: {:.4f}".format(depth_error_map.mean()), (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
else:
depth_visualized = np.concatenate(
[true_colored_depth, pred_depth_colored, true_img])[:, :, ::-1]
cv.imwrite(
os.path.join(self.base_exp_dir, 'depths_' + comment,
'{:0>8d}_{}.png'.format(iter_step, meta)), depth_visualized
)
else:
cv.imwrite(
os.path.join(self.base_exp_dir, 'depths_' + comment,
'{:0>8d}_{}.png'.format(iter_step, meta)),
np.concatenate(
[pred_depth_colored, true_img])[:, :, ::-1])
if len(out_color) > 0:
os.makedirs(os.path.join(self.base_exp_dir, 'synthesized_color_' + comment), exist_ok=True)
cv.imwrite(os.path.join(self.base_exp_dir, 'synthesized_color_' + comment,
'{:0>8d}_{}.png'.format(iter_step, meta)),
np.concatenate(
[img_fine, true_img])[:, :, ::-1]) # bgr2rgb
# compute psnr (image pixel lie in [0, 255])
mse_loss = np.mean((img_fine - true_img) ** 2)
psnr = 10 * np.log10(255 ** 2 / mse_loss)
print("PSNR: ", psnr)
if len(out_color_mlp) > 0:
os.makedirs(os.path.join(self.base_exp_dir, 'synthesized_color_mlp_' + comment), exist_ok=True)
cv.imwrite(os.path.join(self.base_exp_dir, 'synthesized_color_mlp_' + comment,
'{:0>8d}_{}.png'.format(iter_step, meta)),
np.concatenate(
[img_mlp, true_img])[:, :, ::-1]) # bgr2rgb
if len(out_normal) > 0:
os.makedirs(os.path.join(self.base_exp_dir, 'normals_' + comment), exist_ok=True)
cv.imwrite(os.path.join(self.base_exp_dir, 'normals_' + comment,
'{:0>8d}_{}.png'.format(iter_step, meta)),
normal_img[:, :, ::-1])
def forward(self, sample,
perturb_overwrite=-1,
background_rgb=None,
alpha_inter_ratio_lod0=0.0,
alpha_inter_ratio_lod1=0.0,
iter_step=0,
mode='train',
save_vis=False,
):
if mode == 'train':
return self.train_step(sample,
perturb_overwrite=perturb_overwrite,
background_rgb=background_rgb,
alpha_inter_ratio_lod0=alpha_inter_ratio_lod0,
alpha_inter_ratio_lod1=alpha_inter_ratio_lod1,
iter_step=iter_step
)
elif mode == 'val':
import time
begin = time.time()
result = self.val_step(sample,
perturb_overwrite=perturb_overwrite,
background_rgb=background_rgb,
alpha_inter_ratio_lod0=alpha_inter_ratio_lod0,
alpha_inter_ratio_lod1=alpha_inter_ratio_lod1,
iter_step=iter_step,
save_vis=save_vis,
)
end = time.time()
print("val_step time: ", end - begin)
return result
elif mode == 'export_mesh':
import time
begin = time.time()
result = self.export_mesh_step(sample,
perturb_overwrite=perturb_overwrite,
background_rgb=background_rgb,
alpha_inter_ratio_lod0=alpha_inter_ratio_lod0,
alpha_inter_ratio_lod1=alpha_inter_ratio_lod1,
iter_step=iter_step,
save_vis=save_vis,
)
end = time.time()
print("export mesh time: ", end - begin)
return result
def obtain_pyramid_feature_maps(self, imgs, lod=0):
"""
get feature maps of all conditional images
:param imgs:
:return:
"""
if lod == 0:
extractor = self.pyramid_feature_network_geometry_lod0
elif lod >= 1:
extractor = self.pyramid_feature_network_geometry_lod1
pyramid_feature_maps = extractor(imgs)
# * the pyramid features are very important, if only use the coarst features, hard to optimize
fused_feature_maps = torch.cat([
F.interpolate(pyramid_feature_maps[0], scale_factor=4, mode='bilinear', align_corners=True),
F.interpolate(pyramid_feature_maps[1], scale_factor=2, mode='bilinear', align_corners=True),
pyramid_feature_maps[2]
], dim=1)
return fused_feature_maps
def cal_losses_sdf(self, render_out, sample_rays, iter_step=-1, lod=0):
# loss weight schedule; the regularization terms should be added in later training stage
def get_weight(iter_step, weight):
if lod == 1:
anneal_start = self.anneal_end if lod == 0 else self.anneal_end_lod1
anneal_end = self.anneal_end if lod == 0 else self.anneal_end_lod1
anneal_end = anneal_end * 2
else:
anneal_start = self.anneal_start if lod == 0 else self.anneal_start_lod1
anneal_end = self.anneal_end if lod == 0 else self.anneal_end_lod1
anneal_end = anneal_end * 2
if iter_step < 0:
return weight
if anneal_end == 0.0:
return weight
elif iter_step < anneal_start:
return 0.0
else:
return np.min(
[1.0,
(iter_step - anneal_start) / (anneal_end - anneal_start)]) * weight
rays_o = sample_rays['rays_o'][0]
rays_d = sample_rays['rays_v'][0]
true_rgb = sample_rays['rays_color'][0]
if 'rays_depth' in sample_rays.keys():
true_depth = sample_rays['rays_depth'][0]
else:
true_depth = None
mask = sample_rays['rays_mask'][0]
color_fine = render_out['color_fine']
color_fine_mask = render_out['color_fine_mask']
depth_pred = render_out['depth']
variance = render_out['variance']
cdf_fine = render_out['cdf_fine']
weight_sum = render_out['weights_sum']
gradient_error_fine = render_out['gradient_error_fine']
sdf = render_out['sdf']
# * color generated by mlp
color_mlp = render_out['color_mlp']
color_mlp_mask = render_out['color_mlp_mask']
if color_fine is not None:
# Color loss
color_mask = color_fine_mask if color_fine_mask is not None else mask
color_mask = color_mask[..., 0]
color_error = (color_fine[color_mask] - true_rgb[color_mask])
# print("Nan number", torch.isnan(color_error).sum())
# print("Color error shape", color_error.shape)
color_fine_loss = F.l1_loss(color_error, torch.zeros_like(color_error).to(color_error.device),
reduction='mean')
# print(color_fine_loss)
psnr = 20.0 * torch.log10(
1.0 / (((color_fine[color_mask] - true_rgb[color_mask]) ** 2).mean() / (3.0)).sqrt())
else:
color_fine_loss = 0.
psnr = 0.
if color_mlp is not None:
# Color loss
color_mlp_mask = color_mlp_mask[..., 0]
color_error_mlp = (color_mlp[color_mlp_mask] - true_rgb[color_mlp_mask])
color_mlp_loss = F.l1_loss(color_error_mlp,
torch.zeros_like(color_error_mlp).to(color_error_mlp.device),
reduction='mean')
psnr_mlp = 20.0 * torch.log10(
1.0 / (((color_mlp[color_mlp_mask] - true_rgb[color_mlp_mask]) ** 2).mean() / (3.0)).sqrt())
else:
color_mlp_loss = 0.
psnr_mlp = 0.
# depth loss is only used for inference, not included in total loss
if true_depth is not None:
# depth_loss = self.depth_criterion(depth_pred, true_depth, mask)
depth_loss = self.depth_criterion(depth_pred, true_depth)
# # depth evaluation
# depth_statis = compute_depth_errors(depth_pred.detach().cpu().numpy(), true_depth.cpu().numpy())
# depth_statis = numpy2tensor(depth_statis, device=rays_o.device)
depth_statis = None
else:
depth_loss = 0.
depth_statis = None
sparse_loss_1 = torch.exp(
-1 * torch.abs(render_out['sdf_random']) * self.sdf_decay_param).mean() # - should equal
sparse_loss_2 = torch.exp(-1 * torch.abs(sdf) * self.sdf_decay_param).mean()
sparse_loss = (sparse_loss_1 + sparse_loss_2) / 2
sdf_mean = torch.abs(sdf).mean()
sparseness_1 = (torch.abs(sdf) < 0.01).to(torch.float32).mean()
sparseness_2 = (torch.abs(sdf) < 0.02).to(torch.float32).mean()
# Eikonal loss
gradient_error_loss = gradient_error_fine
# ! the first 50k, don't use bg constraint
fg_bg_weight = 0.0 if iter_step < 50000 else get_weight(iter_step, self.fg_bg_weight)
# Mask loss, optional
# The images of DTU dataset contain large black regions (0 rgb values),
# can use this data prior to make fg more clean
background_loss = 0.0
fg_bg_loss = 0.0
if self.fg_bg_weight > 0 and torch.mean((mask < 0.5).to(torch.float32)) > 0.02:
weights_sum_fg = render_out['weights_sum_fg']
fg_bg_error = (weights_sum_fg - mask)[mask < 0.5]
fg_bg_loss = F.l1_loss(fg_bg_error,
torch.zeros_like(fg_bg_error).to(fg_bg_error.device),
reduction='mean')
loss = 1.0 * depth_loss + color_fine_loss + color_mlp_loss + \
sparse_loss * get_weight(iter_step, self.sdf_sparse_weight) + \
fg_bg_loss * fg_bg_weight + \
gradient_error_loss * self.sdf_igr_weight # ! gradient_error_loss need a mask
losses = {
"loss": loss,
"depth_loss": depth_loss,
"color_fine_loss": color_fine_loss,
"color_mlp_loss": color_mlp_loss,
"gradient_error_loss": gradient_error_loss,
"background_loss": background_loss,
"sparse_loss": sparse_loss,
"sparseness_1": sparseness_1,
"sparseness_2": sparseness_2,
"sdf_mean": sdf_mean,
"psnr": psnr,
"psnr_mlp": psnr_mlp,
"weights_sum": render_out['weights_sum'],
"weights_sum_fg": render_out['weights_sum_fg'],
"alpha_sum": render_out['alpha_sum'],
"variance": render_out['variance'],
"sparse_weight": get_weight(iter_step, self.sdf_sparse_weight),
"fg_bg_weight": fg_bg_weight,
"fg_bg_loss": fg_bg_loss, # added by jha, bug of sparseNeuS
}
losses = torch.tensor(losses, device=rays_o.device)
return loss, losses, depth_statis
@torch.no_grad()
def validate_mesh(self, density_or_sdf_network, func_extract_geometry, world_space=True, resolution=360,
threshold=0.0, mode='val',
# * 3d feature volume
conditional_volume=None, lod=None, occupancy_mask=None,
bound_min=[-1, -1, -1], bound_max=[1, 1, 1], meta='', iter_step=0, scale_mat=None,
trans_mat=None
):
bound_min = torch.tensor(bound_min, dtype=torch.float32)
bound_max = torch.tensor(bound_max, dtype=torch.float32)
vertices, triangles, fields = func_extract_geometry(
density_or_sdf_network,
bound_min, bound_max, resolution=resolution,
threshold=threshold, device=conditional_volume.device,
# * 3d feature volume
conditional_volume=conditional_volume, lod=lod,
occupancy_mask=occupancy_mask
)
if scale_mat is not None:
scale_mat_np = scale_mat.cpu().numpy()
vertices = vertices * scale_mat_np[0][0, 0] + scale_mat_np[0][:3, 3][None]
if trans_mat is not None: # w2c_ref_inv
trans_mat_np = trans_mat.cpu().numpy()
vertices_homo = np.concatenate([vertices, np.ones_like(vertices[:, :1])], axis=1)
vertices = np.matmul(trans_mat_np, vertices_homo[:, :, None])[:, :3, 0]
mesh = trimesh.Trimesh(vertices, triangles)
os.makedirs(os.path.join(self.base_exp_dir, 'meshes_' + mode), exist_ok=True)
mesh.export(os.path.join(self.base_exp_dir, 'meshes_' + mode,
'mesh_{:0>8d}_{}_lod{:0>1d}.ply'.format(iter_step, meta, lod)))
def validate_colored_mesh(self, density_or_sdf_network, func_extract_geometry, world_space=True, resolution=360,
threshold=0.0, mode='val',
# * 3d feature volume
conditional_volume=None,
conditional_valid_mask_volume=None,
feature_maps=None,
color_maps = None,
w2cs=None,
target_candidate_w2cs=None,
intrinsics=None,
rendering_network=None,
rendering_projector=None,
query_c2w=None,
lod=None, occupancy_mask=None,
bound_min=[-1, -1, -1], bound_max=[1, 1, 1], meta='', iter_step=0, scale_mat=None,
trans_mat=None
):
bound_min = torch.tensor(bound_min, dtype=torch.float32)
bound_max = torch.tensor(bound_max, dtype=torch.float32)
# import time
# jha_begin4 = time.time()
vertices, triangles, fields = func_extract_geometry(
density_or_sdf_network,
bound_min, bound_max, resolution=resolution,
threshold=threshold, device=conditional_volume.device,
# * 3d feature volume
conditional_volume=conditional_volume, lod=lod,
occupancy_mask=occupancy_mask
)
# jha_end4 = time.time()
# print("[TEST]: func_extract_geometry time", jha_end4 - jha_begin4)
# import time
# jha_begin5 = time.time()
with torch.no_grad():
ren_geo_feats, ren_rgb_feats, ren_ray_diff, ren_mask, _, _ = rendering_projector.compute_view_independent(
torch.tensor(vertices).to(conditional_volume),
lod=lod, # JHA EDITED
# * 3d geometry feature volumes
geometryVolume=conditional_volume[0],
geometryVolumeMask=conditional_valid_mask_volume[0],
sdf_network=density_or_sdf_network,
# * 2d rendering feature maps
rendering_feature_maps=feature_maps, # [n_view, 56, 256, 256]
color_maps=color_maps,
w2cs=w2cs,
target_candidate_w2cs=target_candidate_w2cs,
intrinsics=intrinsics,
img_wh=[256,256],
query_img_idx=0, # the index of the N_views dim for rendering
query_c2w=query_c2w,
)
vertices_color, rendering_valid_mask = rendering_network(
ren_geo_feats, ren_rgb_feats, ren_ray_diff, ren_mask)
# jha_end5 = time.time()
# print("[TEST]: rendering_network time", jha_end5 - jha_begin5)
if scale_mat is not None:
scale_mat_np = scale_mat.cpu().numpy()
vertices = vertices * scale_mat_np[0][0, 0] + scale_mat_np[0][:3, 3][None]
if trans_mat is not None: # w2c_ref_inv
trans_mat_np = trans_mat.cpu().numpy()
vertices_homo = np.concatenate([vertices, np.ones_like(vertices[:, :1])], axis=1)
vertices = np.matmul(trans_mat_np, vertices_homo[:, :, None])[:, :3, 0]
vertices_color = np.array(vertices_color.squeeze(0).cpu() * 255, dtype=np.uint8)
mesh = trimesh.Trimesh(vertices, triangles, vertex_colors=vertices_color)
os.makedirs(os.path.join(self.base_exp_dir, 'meshes_' + mode, 'lod{:0>1d}'.format(lod)), exist_ok=True)
# mesh.export(os.path.join(self.base_exp_dir, 'meshes_' + mode, 'lod{:0>1d}'.format(lod),
# 'mesh_{:0>8d}_{}_lod{:0>1d}.ply'.format(iter_step, meta, lod)))
# MODIFIED
mesh.export(os.path.join(self.base_exp_dir, 'meshes_' + mode, 'lod{:0>1d}'.format(lod),
'mesh_{:0>8d}_gradio_lod{:0>1d}.ply'.format(iter_step, lod))) |