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Updates on examples #174

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Mar 9, 2023
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Updates on examples #174

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5b63c49
move torch data transfer into dataloader
97littleleaf11 Mar 9, 2023
f199ab7
Update README
97littleleaf11 Mar 9, 2023
0687da8
use args.data_root
97littleleaf11 Mar 9, 2023
1cd5e19
Remove redundant check
97littleleaf11 Mar 9, 2023
2b05a8e
Fix isort
97littleleaf11 Mar 9, 2023
7917d52
Fix black
97littleleaf11 Mar 9, 2023
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -132,7 +132,7 @@ optimizer.step()

## Examples:

Before running those example scripts, please check the script about which dataset it is needed, and download the dataset first.
Before running those example scripts, please check the script about which dataset is needed, and download the dataset first. You could use `--data_root` to specify the path.

```bash
# clone the repo with submodules.
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16 changes: 11 additions & 5 deletions examples/datasets/dnerf_synthetic.py
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Expand Up @@ -86,6 +86,7 @@ def __init__(
near: float = None,
far: float = None,
batch_over_images: bool = True,
device: str = "cuda:0",
):
super().__init__()
assert split in self.SPLITS, "%s" % split
Expand All @@ -106,18 +107,23 @@ def __init__(
self.focal,
self.timestamps,
) = _load_renderings(root_fp, subject_id, split)
self.images = torch.from_numpy(self.images).to(torch.uint8)
self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32)
self.timestamps = torch.from_numpy(self.timestamps).to(torch.float32)[
:, None
]
self.images = torch.from_numpy(self.images).to(device).to(torch.uint8)
self.camtoworlds = (
torch.from_numpy(self.camtoworlds).to(device).to(torch.float32)
)
self.timestamps = (
torch.from_numpy(self.timestamps)
.to(device)
.to(torch.float32)[:, None]
)
self.K = torch.tensor(
[
[self.focal, 0, self.WIDTH / 2.0],
[0, self.focal, self.HEIGHT / 2.0],
[0, 0, 1],
],
dtype=torch.float32,
device=device,
) # (3, 3)
assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH)

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9 changes: 6 additions & 3 deletions examples/datasets/nerf_360_v2.py
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Expand Up @@ -169,6 +169,7 @@ def __init__(
far: float = None,
batch_over_images: bool = True,
factor: int = 1,
device: str = "cuda:0",
):
super().__init__()
assert split in self.SPLITS, "%s" % split
Expand All @@ -186,9 +187,11 @@ def __init__(
self.images, self.camtoworlds, self.K = _load_colmap(
root_fp, subject_id, split, factor
)
self.images = torch.from_numpy(self.images).to(torch.uint8)
self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32)
self.K = torch.tensor(self.K).to(torch.float32)
self.images = torch.from_numpy(self.images).to(device).to(torch.uint8)
self.camtoworlds = (
torch.from_numpy(self.camtoworlds).to(device).to(torch.float32)
)
self.K = torch.tensor(self.K).to(device).to(torch.float32)
self.height, self.width = self.images.shape[1:3]

def __len__(self):
Expand Down
8 changes: 6 additions & 2 deletions examples/datasets/nerf_synthetic.py
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Expand Up @@ -79,6 +79,7 @@ def __init__(
near: float = None,
far: float = None,
batch_over_images: bool = True,
device: str = "cuda:0",
):
super().__init__()
assert split in self.SPLITS, "%s" % split
Expand Down Expand Up @@ -109,15 +110,18 @@ def __init__(
self.images, self.camtoworlds, self.focal = _load_renderings(
root_fp, subject_id, split
)
self.images = torch.from_numpy(self.images).to(torch.uint8)
self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32)
self.images = torch.from_numpy(self.images).to(device).to(torch.uint8)
self.camtoworlds = (
torch.from_numpy(self.camtoworlds).to(device).to(torch.float32)
)
self.K = torch.tensor(
[
[self.focal, 0, self.WIDTH / 2.0],
[0, self.focal, self.HEIGHT / 2.0],
[0, 0, 1],
],
dtype=torch.float32,
device=device,
) # (3, 3)
assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH)

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23 changes: 10 additions & 13 deletions examples/train_mlp_dnerf.py
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Expand Up @@ -4,7 +4,7 @@

import argparse
import math
import os
import pathlib
import time

import imageio
Expand All @@ -24,6 +24,12 @@
set_random_seed(42)

parser = argparse.ArgumentParser()
parser.add_argument(
"--data_root",
type=str,
default=str(pathlib.Path.cwd() / "data/dnerf"),
help="the root dir of the dataset",
)
parser.add_argument(
"--train_split",
type=str,
Expand Down Expand Up @@ -91,31 +97,22 @@
gamma=0.33,
)
# setup the dataset
data_root_fp = "/home/ruilongli/data/dnerf/"
target_sample_batch_size = 1 << 16
grid_resolution = 128

train_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split=args.train_split,
num_rays=target_sample_batch_size // render_n_samples,
)
train_dataset.images = train_dataset.images.to(device)
train_dataset.camtoworlds = train_dataset.camtoworlds.to(device)
train_dataset.K = train_dataset.K.to(device)
train_dataset.timestamps = train_dataset.timestamps.to(device)

test_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split="test",
num_rays=None,
)
test_dataset.images = test_dataset.images.to(device)
test_dataset.camtoworlds = test_dataset.camtoworlds.to(device)
test_dataset.K = test_dataset.K.to(device)
test_dataset.timestamps = test_dataset.timestamps.to(device)

occupancy_grid = OccupancyGrid(
roi_aabb=args.aabb,
Expand Down Expand Up @@ -191,7 +188,7 @@
f"n_rendering_samples={n_rendering_samples:d} | num_rays={len(pixels):d} |"
)

if step >= 0 and step % max_steps == 0 and step > 0:
if step > 0 and step % max_steps == 0:
# evaluation
radiance_field.eval()

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23 changes: 10 additions & 13 deletions examples/train_mlp_nerf.py
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Expand Up @@ -4,7 +4,7 @@

import argparse
import math
import os
import pathlib
import time

import imageio
Expand All @@ -23,6 +23,12 @@
set_random_seed(42)

parser = argparse.ArgumentParser()
parser.add_argument(
"--data_root",
type=str,
default=str(pathlib.Path.cwd() / "data/nerf_synthetic"),
help="the root dir of the dataset",
)
parser.add_argument(
"--train_split",
type=str,
Expand Down Expand Up @@ -112,40 +118,31 @@
if args.scene == "garden":
from datasets.nerf_360_v2 import SubjectLoader

data_root_fp = "/home/ruilongli/data/360_v2/"
target_sample_batch_size = 1 << 16
train_dataset_kwargs = {"color_bkgd_aug": "random", "factor": 4}
test_dataset_kwargs = {"factor": 4}
grid_resolution = 128
else:
from datasets.nerf_synthetic import SubjectLoader

data_root_fp = "/home/ruilongli/data/nerf_synthetic/"
target_sample_batch_size = 1 << 16
grid_resolution = 128

train_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split=args.train_split,
num_rays=target_sample_batch_size // render_n_samples,
**train_dataset_kwargs,
)

train_dataset.images = train_dataset.images.to(device)
train_dataset.camtoworlds = train_dataset.camtoworlds.to(device)
train_dataset.K = train_dataset.K.to(device)

test_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split="test",
num_rays=None,
**test_dataset_kwargs,
)
test_dataset.images = test_dataset.images.to(device)
test_dataset.camtoworlds = test_dataset.camtoworlds.to(device)
test_dataset.K = test_dataset.K.to(device)

occupancy_grid = OccupancyGrid(
roi_aabb=args.aabb,
Expand Down Expand Up @@ -217,7 +214,7 @@
f"n_rendering_samples={n_rendering_samples:d} | num_rays={len(pixels):d} |"
)

if step >= 0 and step % max_steps == 0 and step > 0:
if step > 0 and step % max_steps == 0:
# evaluation
radiance_field.eval()

Expand Down
23 changes: 10 additions & 13 deletions examples/train_ngp_nerf.py
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Expand Up @@ -4,7 +4,7 @@

import argparse
import math
import os
import pathlib
import time

import imageio
Expand All @@ -23,6 +23,12 @@
set_random_seed(42)

parser = argparse.ArgumentParser()
parser.add_argument(
"--data_root",
type=str,
default=str(pathlib.Path.cwd() / "data"),
help="the root dir of the dataset",
)
parser.add_argument(
"--train_split",
type=str,
Expand Down Expand Up @@ -87,40 +93,31 @@
if args.unbounded:
from datasets.nerf_360_v2 import SubjectLoader

data_root_fp = "/home/ruilongli/data/360_v2/"
target_sample_batch_size = 1 << 20
train_dataset_kwargs = {"color_bkgd_aug": "random", "factor": 4}
test_dataset_kwargs = {"factor": 4}
grid_resolution = 256
else:
from datasets.nerf_synthetic import SubjectLoader

data_root_fp = "/home/ruilongli/data/nerf_synthetic/"
target_sample_batch_size = 1 << 18
grid_resolution = 128

train_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split=args.train_split,
num_rays=target_sample_batch_size // render_n_samples,
**train_dataset_kwargs,
)

train_dataset.images = train_dataset.images.to(device)
train_dataset.camtoworlds = train_dataset.camtoworlds.to(device)
train_dataset.K = train_dataset.K.to(device)

test_dataset = SubjectLoader(
subject_id=args.scene,
root_fp=data_root_fp,
root_fp=args.data_root,
split="test",
num_rays=None,
**test_dataset_kwargs,
)
test_dataset.images = test_dataset.images.to(device)
test_dataset.camtoworlds = test_dataset.camtoworlds.to(device)
test_dataset.K = test_dataset.K.to(device)

if args.auto_aabb:
camera_locs = torch.cat(
Expand Down Expand Up @@ -260,7 +257,7 @@ def occ_eval_fn(x):
f"n_rendering_samples={n_rendering_samples:d} | num_rays={len(pixels):d} |"
)

if step >= 0 and step % max_steps == 0 and step > 0:
if step > 0 and step % max_steps == 0:
# evaluation
radiance_field.eval()

Expand Down