61 lines
2.2 KiB
Python
Executable File
61 lines
2.2 KiB
Python
Executable File
import logging
|
|
import os
|
|
from datetime import datetime
|
|
|
|
import numpy as np
|
|
|
|
from .eval_detection_voc import eval_detection_voc
|
|
|
|
|
|
def voc_evaluation(dataset, predictions, output_dir, iteration=None):
|
|
class_names = dataset.class_names
|
|
|
|
pred_boxes_list = []
|
|
pred_labels_list = []
|
|
pred_scores_list = []
|
|
gt_boxes_list = []
|
|
gt_labels_list = []
|
|
gt_difficults = []
|
|
|
|
for i in range(len(dataset)):
|
|
image_id, annotation = dataset.get_annotation(i)
|
|
gt_boxes, gt_labels, is_difficult = annotation
|
|
gt_boxes_list.append(gt_boxes)
|
|
gt_labels_list.append(gt_labels)
|
|
gt_difficults.append(is_difficult.astype(np.bool))
|
|
|
|
img_info = dataset.get_img_info(i)
|
|
prediction = predictions[i]
|
|
prediction = prediction.resize((img_info['width'], img_info['height'])).numpy()
|
|
boxes, labels, scores = prediction['boxes'], prediction['labels'], prediction['scores']
|
|
|
|
pred_boxes_list.append(boxes)
|
|
pred_labels_list.append(labels)
|
|
pred_scores_list.append(scores)
|
|
result = eval_detection_voc(pred_bboxes=pred_boxes_list,
|
|
pred_labels=pred_labels_list,
|
|
pred_scores=pred_scores_list,
|
|
gt_bboxes=gt_boxes_list,
|
|
gt_labels=gt_labels_list,
|
|
gt_difficults=gt_difficults,
|
|
iou_thresh=0.5,
|
|
use_07_metric=True)
|
|
logger = logging.getLogger("SSD.inference")
|
|
result_str = "mAP: {:.4f}\n".format(result["map"])
|
|
metrics = {'mAP': result["map"]}
|
|
for i, ap in enumerate(result["ap"]):
|
|
if i == 0: # skip background
|
|
continue
|
|
metrics[class_names[i]] = ap
|
|
result_str += "{:<16}: {:.4f}\n".format(class_names[i], ap)
|
|
logger.info(result_str)
|
|
|
|
if iteration is not None:
|
|
result_path = os.path.join(output_dir, 'result_{:07d}.txt'.format(iteration))
|
|
else:
|
|
result_path = os.path.join(output_dir, 'result_{}.txt'.format(datetime.now().strftime('%Y-%m-%d_%H-%M-%S')))
|
|
with open(result_path, "w") as f:
|
|
f.write(result_str)
|
|
|
|
return dict(metrics=metrics)
|