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#
# 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.
# --------------------------------------------------------------------------
# More information about Marigold:
# https://marigoldmonodepth.github.io
# https://marigoldcomputervision.github.io
# Efficient inference pipelines are now part of diffusers:
# https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage
# https://huggingface.co/docs/diffusers/api/pipelines/marigold
# Examples of trained models and live demos:
# https://huggingface.co/prs-eth
# Related projects:
# https://rollingdepth.github.io/
# https://marigolddepthcompletion.github.io/
# Citation (BibTeX):
# https://github.com/prs-eth/Marigold#-citation
# If you find Marigold useful, we kindly ask you to cite our papers.
# --------------------------------------------------------------------------
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
import argparse
import logging
import numpy as np
import os
import torch
from omegaconf import OmegaConf
from tabulate import tabulate
from torch.utils.data import DataLoader
from torchmetrics.image import (
PeakSignalNoiseRatio,
StructuralSimilarityIndexMeasure,
LearnedPerceptualImagePatchSimilarity,
)
from tqdm.auto import tqdm
from marigold.util.image_util import srgb2linear, linear2srgb
from src.dataset import DatasetMode, get_dataset
from src.util.metric import MetricTracker, compute_iid_metric
if "__main__" == __name__:
logging.basicConfig(level=logging.INFO)
# -------------------- Arguments --------------------
parser = argparse.ArgumentParser(
description="Marigold : Intrinsic Image Decomposition : Metrics Evaluation"
)
parser.add_argument(
"--prediction_dir",
type=str,
required=True,
help="Directory with predictions obtained from inference.",
)
parser.add_argument(
"--target_names",
nargs="+",
default=["albedo", "material"],
type=str,
help="A list of predicted targets to evaluate.",
)
parser.add_argument(
"--targets_to_eval_in_linear_space",
nargs="*",
default=[None],
type=str,
help="A list of targets to evaluate in linear space (as opposed to sRGB by default). Defaults to an empty list.",
)
parser.add_argument(
"--dataset_config",
type=str,
required=True,
help="Path to the config file of the evaluation dataset.",
)
parser.add_argument(
"--base_data_dir",
type=str,
required=True,
help="Base path to the datasets.",
)
parser.add_argument(
"--output_dir", type=str, required=True, help="Output directory."
)
parser.add_argument(
"--use_mask", action="store_true", help="Evaluate only in the masked region."
)
parser.add_argument("--no_cuda", action="store_true", help="Run without cuda.")
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
# -------------------- Device --------------------
cuda_avail = torch.cuda.is_available() and not args.no_cuda
device = torch.device("cuda" if cuda_avail else "cpu")
logging.info(f"Device: {device}")
# -------------------- Initialize Metrics --------------------
psnr_metric = PeakSignalNoiseRatio(data_range=1.0).to(device)
ssim_metric = StructuralSimilarityIndexMeasure(data_range=1.0).to(device)
lpips_metric = LearnedPerceptualImagePatchSimilarity(normalize=True).to(device)
target_names = args.target_names
targets_to_eval_in_linear_space = args.targets_to_eval_in_linear_space
for tn in targets_to_eval_in_linear_space:
if tn is not None and tn not in target_names:
raise ValueError(
f"'{tn}' specified in targets_to_eval_in_linear_space does not belong to the predicted targets: "
f"{target_names=}"
)
metrics_dict = {}
for target_name in target_names:
metrics_dict["psnr_" + target_name] = psnr_metric
metrics_dict["ssim_" + target_name] = ssim_metric
metrics_dict["lpips_" + target_name] = lpips_metric
# -------------------- Data --------------------
cfg_data = OmegaConf.load(args.dataset_config)
dataset = get_dataset(
cfg_data, base_data_dir=args.base_data_dir, mode=DatasetMode.EVAL
)
dataloader = DataLoader(dataset, batch_size=1, num_workers=0)
# -------------------- Eval metrics --------------------
metric_tracker = MetricTracker(*metrics_dict.keys())
metric_tracker.reset()
# -------------------- Results Dictionary --------------------
results = {}
# -------------------- Per-sample metrics file --------------------
per_sample_filename = os.path.join(args.output_dir, "per_sample_metrics.csv")
# write title
with open(per_sample_filename, "w+") as f:
f.write("filename,")
f.write(",".join(metrics_dict.keys()))
f.write("\n")
# -------------------- Evaluate --------------------
for data in tqdm(dataloader, desc="Evaluating"):
rgb_name = data["rgb_relative_path"][0]
# load predictions
rgb_basename = os.path.basename(rgb_name)
scene_dir = os.path.join(args.prediction_dir, os.path.dirname(rgb_name))
rgb_basename_without_extension = os.path.splitext(rgb_basename)[0]
sample_metric = []
for target_name in target_names:
target_gt = data[target_name].to(device)
pred_basename_target = (
rgb_basename_without_extension + "_" + target_name + ".npy"
)
pred_path_target = os.path.join(scene_dir, pred_basename_target)
if not os.path.exists(pred_path_target):
logging.warning(f"Can't find prediction: {pred_path_target}")
continue
target_pred = (
torch.from_numpy(np.load((pred_path_target))).unsqueeze(0).to(device)
) # [1,3,H,W]
# IID Appearance model predicts all modalities in sRGB space
if target_name in targets_to_eval_in_linear_space:
target_gt = srgb2linear(target_gt)
target_pred = srgb2linear(target_pred)
# Hypersim GT and IID Lighting model predictions are in linear space
# We evaluate albedo in sRGB space
if (
"hypersim" in cfg_data.name
and len(target_names) == 3
and target_name == "albedo"
):
# linear --> sRGB
target_gt = linear2srgb(target_gt)
target_pred = linear2srgb(target_pred)
for metric_name in ("psnr", "ssim", "lpips"):
_metric_name = metric_name + "_" + target_name
if args.use_mask:
_mask_name = "mask" + "_" + target_name
valid_mask = data[_mask_name].to(device)
else:
valid_mask = None
_metric = compute_iid_metric(
target_pred,
target_gt,
target_name,
metric_name,
metrics_dict[_metric_name],
valid_mask,
)
sample_metric.append(_metric.__str__())
metric_tracker.update(_metric_name, _metric)
# Save per-sample metric
with open(per_sample_filename, "a+") as f:
f.write(rgb_name + ",")
f.write(",".join(sample_metric))
f.write("\n")
# -------------------- Save metrics to file --------------------
eval_text = f"Evaluation metrics:\n\
of predictions: {args.prediction_dir}\n\
on dataset: {dataset.disp_name}\n\
with samples in: {dataset.filename_ls_path}\n"
eval_text += tabulate(
[metric_tracker.result().keys(), metric_tracker.result().values()]
)
metrics_filename = "eval_metrics"
metrics_filename += ".txt"
_save_to = os.path.join(args.output_dir, metrics_filename)
with open(_save_to, "w+") as f:
f.write(eval_text)
logging.info(f"Evaluation metrics saved to {_save_to}")
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