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# Last modified: 2024-05-24
# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import argparse
import logging
import os
import numpy as np
import torch
from omegaconf import OmegaConf
from PIL import Image
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from marigold import MarigoldPipeline
from src.util.seeding import seed_all
from src.dataset import (
BaseDepthDataset,
DatasetMode,
get_dataset,
get_pred_name,
)
if "__main__" == __name__:
logging.basicConfig(level=logging.INFO)
# -------------------- Arguments --------------------
parser = argparse.ArgumentParser(
description="Run single-image depth estimation using Marigold."
)
parser.add_argument(
"--checkpoint",
type=str,
default="prs-eth/marigold-v1-0",
help="Checkpoint path or hub name.",
)
# dataset setting
parser.add_argument(
"--dataset_config",
type=str,
required=True,
help="Path to config file of evaluation dataset.",
)
parser.add_argument(
"--base_data_dir",
type=str,
required=True,
help="Path to base data directory.",
)
parser.add_argument(
"--output_dir", type=str, required=True, help="Output directory."
)
# inference setting
parser.add_argument(
"--denoise_steps",
type=int,
default=50, # quantitative evaluation uses 50 steps
help="Diffusion denoising steps, more steps results in higher accuracy but slower inference speed.",
)
parser.add_argument(
"--ensemble_size",
type=int,
default=10,
help="Number of predictions to be ensembled, more inference gives better results but runs slower.",
)
parser.add_argument(
"--half_precision",
"--fp16",
action="store_true",
help="Run with half-precision (16-bit float), might lead to suboptimal result.",
)
# resolution setting
parser.add_argument(
"--processing_res",
type=int,
default=0,
help="Maximum resolution of processing. 0 for using input image resolution. Default: 0.",
)
parser.add_argument(
"--output_processing_res",
action="store_true",
help="When input is resized, out put depth at resized operating resolution. Default: False.",
)
parser.add_argument(
"--resample_method",
type=str,
default="bilinear",
help="Resampling method used to resize images. This can be one of 'bilinear' or 'nearest'.",
)
parser.add_argument("--seed", type=int, default=None, help="Random seed.")
args = parser.parse_args()
checkpoint_path = args.checkpoint
dataset_config = args.dataset_config
base_data_dir = args.base_data_dir
output_dir = args.output_dir
denoise_steps = args.denoise_steps
ensemble_size = args.ensemble_size
if ensemble_size > 15:
logging.warning("Running with large ensemble size will be slow.")
half_precision = args.half_precision
processing_res = args.processing_res
match_input_res = not args.output_processing_res
if 0 == processing_res and match_input_res is False:
logging.warning(
"Processing at native resolution without resizing output might NOT lead to exactly the same resolution, due to the padding and pooling properties of conv layers."
)
resample_method = args.resample_method
seed = args.seed
print(f"arguments: {args}")
# -------------------- Preparation --------------------
# Print out config
logging.info(
f"Inference settings: checkpoint = `{checkpoint_path}`, "
f"with denoise_steps = {denoise_steps}, ensemble_size = {ensemble_size}, "
f"processing resolution = {processing_res}, seed = {seed}; "
f"dataset config = `{dataset_config}`."
)
# Random seed
if seed is None:
import time
seed = int(time.time())
seed_all(seed)
def check_directory(directory):
if os.path.exists(directory):
response = (
input(
f"The directory '{directory}' already exists. Are you sure to continue? (y/n): "
)
.strip()
.lower()
)
if "y" == response:
pass
elif "n" == response:
print("Exiting...")
exit()
else:
print("Invalid input. Please enter 'y' (for Yes) or 'n' (for No).")
check_directory(directory) # Recursive call to ask again
check_directory(output_dir)
os.makedirs(output_dir, exist_ok=True)
logging.info(f"output dir = {output_dir}")
# -------------------- Device --------------------
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
logging.warning("CUDA is not available. Running on CPU will be slow.")
logging.info(f"device = {device}")
# -------------------- Data --------------------
cfg_data = OmegaConf.load(dataset_config)
dataset: BaseDepthDataset = get_dataset(
cfg_data, base_data_dir=base_data_dir, mode=DatasetMode.RGB_ONLY
)
dataloader = DataLoader(dataset, batch_size=1, num_workers=0)
# -------------------- Model --------------------
if half_precision:
dtype = torch.float16
variant = "fp16"
logging.warning(
f"Running with half precision ({dtype}), might lead to suboptimal result."
)
else:
dtype = torch.float32
variant = None
pipe = MarigoldPipeline.from_pretrained(
checkpoint_path, variant=variant, torch_dtype=dtype
)
try:
pipe.enable_xformers_memory_efficient_attention()
except ImportError:
logging.debug("run without xformers")
pipe = pipe.to(device)
logging.info(
f"scale_invariant: {pipe.scale_invariant}, shift_invariant: {pipe.shift_invariant}"
)
# -------------------- Inference and saving --------------------
with torch.no_grad():
for batch in tqdm(
dataloader, desc=f"Inferencing on {dataset.disp_name}", leave=True
):
# Read input image
rgb_int = batch["rgb_int"].squeeze().numpy().astype(np.uint8) # [3, H, W]
rgb_int = np.moveaxis(rgb_int, 0, -1) # [H, W, 3]
input_image = Image.fromarray(rgb_int)
# Predict depth
pipe_out = pipe(
input_image,
denoising_steps=denoise_steps,
ensemble_size=ensemble_size,
processing_res=processing_res,
match_input_res=match_input_res,
batch_size=0,
color_map=None,
show_progress_bar=False,
resample_method=resample_method,
)
depth_pred: np.ndarray = pipe_out.depth_np
# Save predictions
rgb_filename = batch["rgb_relative_path"][0]
rgb_basename = os.path.basename(rgb_filename)
scene_dir = os.path.join(output_dir, os.path.dirname(rgb_filename))
if not os.path.exists(scene_dir):
os.makedirs(scene_dir)
pred_basename = get_pred_name(
rgb_basename, dataset.name_mode, suffix=".npy"
)
save_to = os.path.join(scene_dir, pred_basename)
if os.path.exists(save_to):
logging.warning(f"Existing file: '{save_to}' will be overwritten")
np.save(save_to, depth_pred)
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