Evaluation support model containing nms (#51)

* Detection evaluation function

* Add license

* Fix python import problem

* Modify requirement.txt

* Add requirements.txt

* Evaluation support model containing nms

* Delete useless code

Co-authored-by: Jason <jiangjiajun@baidu.com>

fastdeploy/vision/evaluation/detection.py

@@ -22,18 +22,21 @@ import collections
 
 
 def eval_detection(model,
-                   conf_threshold,
-                   nms_iou_threshold,
                    data_dir,
                    ann_file,
+                   conf_threshold=None,
+                   nms_iou_threshold=None,
                    plot=False):
-    assert isinstance(conf_threshold, (
+    if conf_threshold is not None or nms_iou_threshold is not None:
-        float, int
+        assert conf_threshold is not None and nms_iou_threshold is not None, "The conf_threshold and nms_iou_threshold should be setted at the same time"
-    )), "The conf_threshold:{} need to be int or float".format(conf_threshold)
+        assert isinstance(conf_threshold, (
-    assert isinstance(nms_iou_threshold, (
+            float,
-        float,
+            int)), "The conf_threshold:{} need to be int or float".format(
-        int)), "The nms_iou_threshold:{} need to be int or float".format(
+                conf_threshold)
-            nms_iou_threshold)
+        assert isinstance(nms_iou_threshold, (
+            float,
+            int)), "The nms_iou_threshold:{} need to be int or float".format(
+                nms_iou_threshold)
     eval_dataset = CocoDetection(
         data_dir=data_dir, ann_file=ann_file, shuffle=False)
     all_image_info = eval_dataset.file_list
@@ -49,7 +52,10 @@ def eval_detection(model,
                                  image_num, desc="Inference Progress")):
         im = cv2.imread(image_info["image"])
         im_id = image_info["im_id"]
-        result = model.predict(im, conf_threshold, nms_iou_threshold)
+        if conf_threshold is None and nms_iou_threshold is None:
+            result = model.predict(im)
+        else:
+            result = model.predict(im, conf_threshold, nms_iou_threshold)
         pred = {
             'bbox':
             [[c] + [s] + b