File size: 10,782 Bytes
16a0f31 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 |
# -----------------------------------------------------------------------------
# Do Not Alter This File!
# -----------------------------------------------------------------------------
# The following code is part of the logic used for loading and evaluating your
# output scores. Please DO NOT modify this section, as upon your submission,
# the whole evaluation logic will be overwritten by the original code.
# -----------------------------------------------------------------------------
import warnings
import os
from pathlib import Path
import csv
import json
import torch
import datasets.rayan_dataset as rayan_dataset
from evaluation.utils.metrics import compute_metrics
warnings.filterwarnings("ignore")
class BaseEval:
def __init__(self, cfg):
self.cfg = cfg
self.device = torch.device(
"cuda:{}".format(cfg["device"]) if torch.cuda.is_available() else "cpu"
)
self.path = cfg["datasets"]["data_path"]
self.dataset = cfg["datasets"]["dataset_name"]
self.save_csv = cfg["testing"]["save_csv"]
self.save_json = cfg["testing"]["save_json"]
self.categories = cfg["datasets"]["class_name"]
if isinstance(self.categories, str):
if self.categories.lower() == "all":
if self.dataset == "rayan_dataset":
self.categories = self.get_available_class_names(self.path)
else:
self.categories = [self.categories]
self.output_dir = cfg["testing"]["output_dir"]
os.makedirs(self.output_dir, exist_ok=True)
self.scores_dir = cfg["testing"]["output_scores_dir"]
self.class_name_mapping_dir = cfg["testing"]["class_name_mapping_dir"]
self.leaderboard_metric_weights = {
"image_auroc": 1.2,
"image_ap": 1.1,
"image_f1": 1.1,
"pixel_auroc": 1.0,
"pixel_aupro": 1.4,
"pixel_ap": 1.3,
"pixel_f1": 1.3,
}
def get_available_class_names(self, root_data_path):
all_items = os.listdir(root_data_path)
folder_names = [
item
for item in all_items
if os.path.isdir(os.path.join(root_data_path, item))
]
return folder_names
def load_datasets(self, category):
dataset_classes = {
"rayan_dataset": rayan_dataset.RayanDataset,
}
dataset_splits = {
"rayan_dataset": rayan_dataset.DatasetSplit.TEST,
}
test_dataset = dataset_classes[self.dataset](
source=self.path,
split=dataset_splits[self.dataset],
classname=category,
)
return test_dataset
def get_category_metrics(self, category):
print(f"Loading scores of '{category}'")
gt_sp, pr_sp, gt_px, pr_px, _ = self.load_category_scores(category)
print(f"Computing metrics for '{category}'")
image_metric, pixel_metric = compute_metrics(gt_sp, pr_sp, gt_px, pr_px)
return image_metric, pixel_metric
def load_category_scores(self, category):
raise NotImplementedError()
def get_scores_path_for_image(self, image_path):
"""example image_path: './data/photovoltaic_module/test/good/037.png'"""
path = Path(image_path)
category, split, anomaly_type = path.parts[-4:-1]
image_name = path.stem
return os.path.join(
self.scores_dir, category, split, anomaly_type, f"{image_name}_scores.json"
)
def calc_leaderboard_score(self, **metrics):
weighted_sum = 0
total_weight = 0
for key, weight in self.leaderboard_metric_weights.items():
metric = metrics.get(key)
weighted_sum += metric * weight
total_weight += weight
if total_weight == 0:
return 0
return weighted_sum / total_weight
def main(self):
image_auroc_list = []
image_f1_list = []
image_ap_list = []
pixel_auroc_list = []
pixel_f1_list = []
pixel_ap_list = []
pixel_aupro_list = []
leaderboard_score_list = []
for category in self.categories:
image_metric, pixel_metric = self.get_category_metrics(
category=category,
)
image_auroc, image_f1, image_ap = image_metric
pixel_auroc, pixel_f1, pixel_ap, pixel_aupro = pixel_metric
leaderboard_score = self.calc_leaderboard_score(
image_auroc=image_auroc,
image_f1=image_f1,
image_ap=image_ap,
pixel_auroc=pixel_auroc,
pixel_aupro=pixel_aupro,
pixel_f1=pixel_f1,
pixel_ap=pixel_ap,
)
image_auroc_list.append(image_auroc)
image_f1_list.append(image_f1)
image_ap_list.append(image_ap)
pixel_auroc_list.append(pixel_auroc)
pixel_f1_list.append(pixel_f1)
pixel_ap_list.append(pixel_ap)
pixel_aupro_list.append(pixel_aupro)
leaderboard_score_list.append(leaderboard_score)
print(category)
print(
"[image level] auroc:{}, f1:{}, ap:{}".format(
image_auroc * 100,
image_f1 * 100,
image_ap * 100,
)
)
print(
"[pixel level] auroc:{}, f1:{}, ap:{}, aupro:{}".format(
pixel_auroc * 100,
pixel_f1 * 100,
pixel_ap * 100,
pixel_aupro * 100,
)
)
print(
"leaderboard score:{}".format(
leaderboard_score * 100,
)
)
image_auroc_mean = sum(image_auroc_list) / len(image_auroc_list)
image_f1_mean = sum(image_f1_list) / len(image_f1_list)
image_ap_mean = sum(image_ap_list) / len(image_ap_list)
pixel_auroc_mean = sum(pixel_auroc_list) / len(pixel_auroc_list)
pixel_f1_mean = sum(pixel_f1_list) / len(pixel_f1_list)
pixel_ap_mean = sum(pixel_ap_list) / len(pixel_ap_list)
pixel_aupro_mean = sum(pixel_aupro_list) / len(pixel_aupro_list)
leaderboard_score_mean = sum(leaderboard_score_list) / len(
leaderboard_score_list
)
print("mean")
print(
"[image level] auroc:{}, f1:{}, ap:{}".format(
image_auroc_mean * 100, image_f1_mean * 100, image_ap_mean * 100
)
)
print(
"[pixel level] auroc:{}, f1:{}, ap:{}, aupro:{}".format(
pixel_auroc_mean * 100,
pixel_f1_mean * 100,
pixel_ap_mean * 100,
pixel_aupro_mean * 100,
)
)
print(
"leaderboard score:{}".format(
leaderboard_score_mean * 100,
)
)
# Save the final results as a csv file
if self.save_csv:
with open(self.class_name_mapping_dir, "r") as f:
class_name_mapping_dict = json.load(f)
csv_data = [
[
"Category",
"pixel_auroc",
"pixel_f1",
"pixel_ap",
"pixel_aupro",
"image_auroc",
"image_f1",
"image_ap",
"leaderboard_score",
]
]
for i, category in enumerate(self.categories):
csv_data.append(
[
class_name_mapping_dict[category],
pixel_auroc_list[i] * 100,
pixel_f1_list[i] * 100,
pixel_ap_list[i] * 100,
pixel_aupro_list[i] * 100,
image_auroc_list[i] * 100,
image_f1_list[i] * 100,
image_ap_list[i] * 100,
leaderboard_score_list[i] * 100,
]
)
csv_data.append(
[
"mean",
pixel_auroc_mean * 100,
pixel_f1_mean * 100,
pixel_ap_mean * 100,
pixel_aupro_mean * 100,
image_auroc_mean * 100,
image_f1_mean * 100,
image_ap_mean * 100,
leaderboard_score_mean * 100,
]
)
csv_file_path = os.path.join(self.output_dir, "results.csv")
with open(csv_file_path, mode="w", newline="") as file:
writer = csv.writer(file)
writer.writerows(csv_data)
# Save the final results as a json file
if self.save_json:
json_data = []
with open(self.class_name_mapping_dir, "r") as f:
class_name_mapping_dict = json.load(f)
for i, category in enumerate(self.categories):
json_data.append(
{
"Category": class_name_mapping_dict[category],
"pixel_auroc": pixel_auroc_list[i] * 100,
"pixel_f1": pixel_f1_list[i] * 100,
"pixel_ap": pixel_ap_list[i] * 100,
"pixel_aupro": pixel_aupro_list[i] * 100,
"image_auroc": image_auroc_list[i] * 100,
"image_f1": image_f1_list[i] * 100,
"image_ap": image_ap_list[i] * 100,
"leaderboard_score": leaderboard_score_list[i] * 100,
}
)
json_data.append(
{
"Category": "mean",
"pixel_auroc": pixel_auroc_mean * 100,
"pixel_f1": pixel_f1_mean * 100,
"pixel_ap": pixel_ap_mean * 100,
"pixel_aupro": pixel_aupro_mean * 100,
"image_auroc": image_auroc_mean * 100,
"image_f1": image_f1_mean * 100,
"image_ap": image_ap_mean * 100,
"leaderboard_score": leaderboard_score_mean * 100,
}
)
json_file_path = os.path.join(self.output_dir, "results.json")
with open(json_file_path, mode="w") as file:
final_json = {
"result": leaderboard_score_mean * 100,
"metadata": json_data,
}
json.dump(final_json, file, indent=4)
|