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# Copyright 2022 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai
#
# 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.
import argparse
from model import FoundModel
from misc import load_config
from datasets.datasets import build_dataset
from evaluation.saliency import evaluate_saliency
from evaluation.uod import evaluation_unsupervised_object_discovery
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description = 'Evaluation of FOUND',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--eval-type",
type=str,
choices=["saliency", "uod"],
help="Evaluation type."
)
parser.add_argument(
"--dataset-eval",
type=str,
choices=["ECSSD", "DUT-OMRON", "DUTS-TEST", "VOC07", "VOC12", "COCO20k"],
help="Name of evaluation dataset."
)
parser.add_argument(
"--dataset-set-eval",
type=str,
default=None,
help="Set of the dataset."
)
parser.add_argument(
"--apply-bilateral",
action="store_true",
help="use bilateral solver."
)
parser.add_argument(
"--evaluation-mode",
type=str,
default="multi",
choices=["single", "multi"],
help="Type of evaluation."
)
parser.add_argument(
"--model-weights",
type=str,
default="data/weights/decoder_weights.pt",
)
parser.add_argument(
"--dataset-dir",
type=str,
default="/datasets_local",
)
parser.add_argument(
"--config",
type=str,
default="configs/found_DUTS-TR.yaml",
)
args = parser.parse_args()
print(args.__dict__)
# Configuration
config = load_config(args.config)
# ------------------------------------
# Load the model
model = FoundModel(vit_model=config.model["pre_training"],
vit_arch=config.model["arch"],
vit_patch_size=config.model["patch_size"],
enc_type_feats=config.found["feats"],
bkg_type_feats=config.found["feats"],
bkg_th=config.found["bkg_th"])
# Load weights
model.decoder_load_weights(args.model_weights)
model.eval()
print(f"Model {args.model_weights} loaded correctly.")
# ------------------------------------
# Build the validation set
val_dataset = build_dataset(
root_dir=args.dataset_dir,
dataset_name=args.dataset_eval,
dataset_set=args.dataset_set_eval,
for_eval=True,
evaluation_type=args.eval_type,
)
print(f"\nBuilding dataset {val_dataset.name} (#{len(val_dataset)} images)")
# ------------------------------------
# Training
print(f"\nStarted evaluation on {val_dataset.name}")
if args.eval_type == "saliency":
evaluate_saliency(
val_dataset,
model=model,
evaluation_mode=args.evaluation_mode,
apply_bilateral=args.apply_bilateral,
)
elif args.eval_type == "uod":
if args.apply_bilateral:
raise ValueError("Not implemented.")
evaluation_unsupervised_object_discovery(
val_dataset,
model=model,
evaluation_mode=args.evaluation_mode,
)
else:
raise ValueError("Other evaluation method to come.") |