main_found_evaluate.py

# 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

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description = 'Evaluation of FOUND',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--dataset-eval", type=str, choices=["ECSSD", "DUT-OMRON", "DUTS-TEST"], help="Name of evaluation dataset."
    )
    parser.add_argument(
        "--dataset-set-eval", type=str, default=None, help="Set of the dataset."
    )
    parser.add_argument(
        "--eval-type", type=str, choices=["saliency", "uod"], help="Evaluation type."
    )
    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(
        "--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="/datasets_local",
        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,
        )
    else:
        raise ValueError("Other evaluation method to come.")