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ssd/data/datasets/evaluation/voc/__init__.py

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+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)