easyvolcap: fix renaming utils

zju3dv · Apr 5, 2024 · e31d9fa · e31d9fa
1 parent 62c2fca
commit e31d9fa
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Showing 8 changed files with 370 additions and 367 deletions.
diff --git a/easyvolcap/models/networks/embedders/geometry_image_based_embedder.py b/easyvolcap/models/networks/embedders/geometry_image_based_embedder.py
@@ -5,8 +5,8 @@
 from torch.nn import functional as F
 from easyvolcap.engine import EMBEDDERS, REGRESSORS
 from easyvolcap.utils.base_utils import dotdict
-from easyvolcap.utils.enerf_utils import sample_feature_volume, sample_geometry_feature_image, FeatureAgg, FeatureNet
-from easyvolcap.utils.ibr_utils import get_src_inps, get_src_feats, prepare_caches, compute_src_inps, compute_src_feats
+from easyvolcap.utils.enerf_utils import FeatureAgg, FeatureNet
+from easyvolcap.utils.ibr_utils import get_src_inps, get_src_feats, prepare_caches, compute_src_inps, compute_src_feats, sample_feature_volume, sample_geometry_feature_image
 from easyvolcap.utils.image_utils import interpolate_image, pad_image
 from easyvolcap.utils.data_utils import to_x
 

diff --git a/easyvolcap/models/networks/embedders/image_based_embedder.py b/easyvolcap/models/networks/embedders/image_based_embedder.py
@@ -6,7 +6,8 @@
 from easyvolcap.utils.base_utils import dotdict
 from easyvolcap.utils.data_utils import to_x
 from easyvolcap.utils.chunk_utils import chunkify
-from easyvolcap.utils.enerf_utils import sample_feature_volume, sample_feature_image, FeatureAgg
+from easyvolcap.utils.ibr_utils import sample_feature_volume, sample_feature_image
+from easyvolcap.utils.enerf_utils import FeatureAgg
 
 
 @EMBEDDERS.register_module()

diff --git a/easyvolcap/models/networks/regressors/image_based_spherical_harmonics.py b/easyvolcap/models/networks/regressors/image_based_spherical_harmonics.py
@@ -4,7 +4,6 @@
 from easyvolcap.engine import REGRESSORS
 from easyvolcap.utils.sh_utils import eval_sh
 from easyvolcap.utils.base_utils import dotdict
-from easyvolcap.utils.enerf_utils import sample_feature_volume, sample_feature_image, FeatureAgg, FeatureNet
 from easyvolcap.models.networks.regressors.mlp_regressor import MlpRegressor
 
 

diff --git a/easyvolcap/models/samplers/cost_volume_sampler.py b/easyvolcap/models/samplers/cost_volume_sampler.py
@@ -18,11 +18,11 @@
 from easyvolcap.utils.timer_utils import timer  # global timer
 from easyvolcap.utils.base_utils import dotdict
 from easyvolcap.utils.data_utils import to_x
-from easyvolcap.utils.ibr_utils import get_src_inps, get_src_feats, prepare_caches, compute_src_inps, compute_src_feats
-from easyvolcap.utils.nerf_utils import linear_sampling, ray2xyz, volume_rendering
-from easyvolcap.utils.prop_utils import s_vals_to_z_vals, z_vals_to_s_vals
 from easyvolcap.utils.image_utils import interpolate_image, pad_image
-from easyvolcap.utils.enerf_utils import MinCostRegNet, CostRegNet, FeatureNet, get_proj_mats, build_cost_vol, depth_regression, render_debug_cost_volume
+from easyvolcap.utils.prop_utils import s_vals_to_z_vals, z_vals_to_s_vals
+from easyvolcap.utils.nerf_utils import linear_sampling, ray2xyz, volume_rendering
+from easyvolcap.utils.ibr_utils import get_src_inps, get_src_feats, prepare_caches, compute_src_inps, compute_src_feats, get_proj_mats, build_cost_vol, depth_regression, render_debug_cost_volume
+from easyvolcap.utils.enerf_utils import MinCostRegNet, CostRegNet, FeatureNet
 
 # ? How do we share 2D convolutions between multiple levels?
 # We use a single level network for this, allow more customization since cas_enerf isn't multiple NeRFs stacked together -> instead they are highly intertwined

diff --git a/easyvolcap/utils/depth_utils.py b/easyvolcap/utils/depth_utils.py
@@ -8,7 +8,7 @@
 from easyvolcap.utils.console_utils import *
 from easyvolcap.utils.color_utils import colormap
 from easyvolcap.utils.ray_utils import create_meshgrid
-from easyvolcap.utils.enerf_utils import depth_regression
+from easyvolcap.utils.ibr_utils import depth_regression
 from easyvolcap.utils.math_utils import affine_inverse, torch_inverse_3x3
 
 

diff --git a/easyvolcap/utils/enerf_utils.py b/easyvolcap/utils/enerf_utils.py
diff --git a/easyvolcap/utils/ibr_utils.py b/easyvolcap/utils/ibr_utils.py
diff --git a/scripts/points/ibr_colorization.py b/scripts/points/ibr_colorization.py
@@ -11,16 +11,16 @@
 For every point, then find the closest ranking view, and use that view's color
 This way we can deal with non-uniform camera distribution
 """
+
 import torch
 import torch.nn.functional as F
 from easyvolcap.utils.console_utils import *
 from easyvolcap.utils.viewer_utils import Camera
-from easyvolcap.utils.net_utils import load_pretrained
 from easyvolcap.utils.parallel_utils import parallel_execution
 from easyvolcap.utils.easy_utils import read_camera, write_camera
 from easyvolcap.utils.chunk_utils import multi_gather, multi_scatter
-from easyvolcap.utils.data_utils import load_pts, export_pts, to_cuda, to_cpu, to_tensor, load_image
-from easyvolcap.utils.enerf_utils import sample_geometry_feature_image
+from easyvolcap.utils.data_utils import load_pts, export_pts, to_cuda, load_image
+from easyvolcap.utils.ibr_utils import sample_geometry_feature_image
 
 
 @catch_throw