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FOUND / main_found_evaluate.py

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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.")