File size: 5,168 Bytes
99ec8a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | import os
import torch
import argparse
import numpy as np
import pandas as pd
from pathlib import Path
from datetime import datetime
# mine
from ius.ius import IUS
from ius.ius_eval_parser import IUSEvalParser
from data.loading import IUSEvalDataset
from data.dataloader import to_dataloader
from utils.eval_utils import EPUCNNEval
from utils.train_utils import create_output_folders, save_to_json
from utils.omega_parser import IUSConfig
BASE_PATH = Path(__file__).resolve().parent
def parse_options():
parser = argparse.ArgumentParser()
parser.add_argument("--experiment_folder_name", type=str, required=True,
help="Folder name containing epu configuration & saved ckpt")
parser.add_argument("--cb_vector_tag", type=str, default=None, required=True,
help="cb vector data. If not specified estimates all cb_vectors")
parser.add_argument("--synthetic_images", type=str, default='png',
help="It can be either a single synthetic image file or a folder path containing multiple "
"synthetic images")
parser.add_argument("--synthetic_img_extension", type=str, default='png',
help="Extension of synthetic images")
args = parser.parse_args()
return args
def main():
args = parse_options()
cb_vec_tag = args.cb_vector_tag
synthetic_img_path = args.synthetic_images
synthetic_img_extension = args.synthetic_img_extension
experiment_folder_name = args.experiment_folder_name
# Load saved config
print('Loading configuration...')
saved_epu_folder = (BASE_PATH / "../results/checkpoints").resolve()
saved_epu_folder = os.path.join(saved_epu_folder, experiment_folder_name)
cfg_path = os.path.join(saved_epu_folder, "epu_config.yaml")
cfg = IUSConfig.from_yaml(cfg_path)
# Load EPU for obtaining Contribution Feature Profiles of synthetic images
print('Loading trained model...')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trained_ckpt = os.path.join(saved_epu_folder, f"ckpt_{experiment_folder_name}.pt")
epu = EPUCNNEval(epu_cfg=cfg.model)
trained_epu = epu.load_ckpt(device=device, ckpt_path=trained_ckpt)
# Load Baseline Contribution Feature Profile (cb_vector)
print('IUS class...')
saved_cb_vec_folder = (BASE_PATH / "../results/cb_vectors").resolve()
saved_cb_vec_folder = os.path.join(saved_cb_vec_folder, experiment_folder_name)
cb_vec_path = os.path.join(saved_cb_vec_folder, f"cb_vector_{cb_vec_tag}.npy")
ius = IUS(cb_path=cb_vec_path, cb_tag=cb_vec_tag, device=device)
# Load Synthetic Images & Estimate their Contribution Feature Profiles (c^_vectors)
synthetic_img_path = str((BASE_PATH / ".." / synthetic_img_path).resolve())
parser = IUSEvalParser(path=synthetic_img_path, image_ext=synthetic_img_extension,)
synthetic_dset = IUSEvalDataset(dataconfig=cfg.data_params).get_dataset(parser=parser)
cfg.data_params.data_loading.shuffle = False # update cfg.data_params.data_loading
cfg.data_params.data_loading.batch_size = 1
synthetic_dataloader = to_dataloader(dataset=synthetic_dset, loading_cfg=cfg.data_params.data_loading)
synthetic_c_vecs = trained_epu.calculate_feature_contribution_profiles(
data_loader=synthetic_dataloader,
device=device,
)
ius_score_list = ius.ius_measure(c_vectors=synthetic_c_vecs)
synthetic_filenames = parser.image_filenames
# Setup Saving Dir
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_folder = str((BASE_PATH / "../results/ius_eval").resolve())
output_folder = os.path.join(output_folder, experiment_folder_name)
create_output_folders(output_folder) # skipped if exists
ius_score_pd = pd.DataFrame(
{
"filename": synthetic_filenames,
"ius_measure_score": ius_score_list
}
)
file_saved_ius_scores = os.path.join(output_folder, f"ius_scores_{timestamp}.csv")
ius_score_pd.to_csv(file_saved_ius_scores, index=False)
save_info_dict = {
"epu_ckpt": trained_ckpt,
"config": cfg_path,
"experiment_id": cfg.experiment_saved_folder_name,
"cb_vector_path": cb_vec_path,
"synthetic_images_path": synthetic_img_path,
"synthetic_img_extension": synthetic_img_extension,
"synthetic_samples_num": len(synthetic_filenames),
"timestamp": timestamp,
"ius_scores": file_saved_ius_scores,
}
json_file_path = os.path.join(output_folder, f"ius_scores_info_{timestamp}.json")
save_to_json(save_info_dict, json_file_path)
if __name__ == "__main__":
# python - m scripts.eval_ius - -experiment_folder_name ius_dataset_name_base_one_0000_timestamp - -cb_vector_tag normal - -synthetic_images datasets_synthetic/dataset_name/normal - -synthetic_img_extension png
# python - m scripts.eval_ius - -experiment_folder_name ius_dataset_name_base_one_0000_timestamp - -cb_vector_tag normal - -synthetic_images datasets_synthetic/dataset_name/normal/image_0001.png - -synthetic_img_extension png
main()
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