add evaluator unit test

tests/data/dummy_coco.json

@@ -31,6 +31,7 @@
             "score": 1.0,
             "category_id": 1,
             "id": 1,
+            "iscrowd": 0,
             "image_id": 0
         },
         {
@@ -44,6 +45,7 @@
             "score": 1.0,
             "category_id": 2,
             "id": 2,
+            "iscrowd": 0,
             "image_id": 0
         },
         {
@@ -57,6 +59,7 @@
             "score": 1.0,
             "category_id": 1,
             "id": 3,
+            "iscrowd": 0,
             "image_id": 1
         }
     ],

tests/test_evaluator/test_coco_detection.py

@@ -0,0 +1,30 @@
+import tempfile
+
+from nanodet.data.dataset import build_dataset
+from nanodet.evaluator import build_evaluator
+
+
+def test_coco_detection():
+    dummy_results = {
+        0: {0: [[0, 0, 20, 20, 1]], 1: [[0, 0, 20, 20, 1]]},
+        1: {0: [[0, 0, 20, 20, 1]]},
+    }
+
+    cfg = dict(
+        name="CocoDataset",
+        img_path="./tests/data",
+        ann_path="./tests/data/dummy_coco.json",
+        input_size=[320, 320],  # [w,h]
+        keep_ratio=True,
+        pipeline=dict(normalize=[[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]),
+    )
+    dataset = build_dataset(cfg, "train")
+
+    eval_cfg = dict(name="CocoDetectionEvaluator", save_key="mAP")
+
+    evaluator = build_evaluator(eval_cfg, dataset)
+    tmp_dir = tempfile.TemporaryDirectory()
+    eval_results = evaluator.evaluate(
+        results=dummy_results, save_dir=tmp_dir.name, rank=-1
+    )
+    assert eval_results["mAP"] == 1