Modify file structure to separate python and cpp code (#223)

Modify code structure

python/fastdeploy/vision/evaluation/detection.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import copy
+import collections
+import math
+
+
+def eval_detection(model,
+                   data_dir,
+                   ann_file,
+                   conf_threshold=None,
+                   nms_iou_threshold=None,
+                   plot=False):
+    from .utils import CocoDetection
+    from .utils import COCOMetric
+    import cv2
+    from tqdm import trange
+    import time
+
+    if conf_threshold is not None or nms_iou_threshold is not None:
+        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"
+        assert isinstance(conf_threshold, (
+            float,
+            int)), "The conf_threshold:{} need to be int or float".format(
+                conf_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
+    image_num = eval_dataset.num_samples
+    eval_dataset.data_fields = {
+        'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'is_crowd'
+    }
+    eval_metric = COCOMetric(
+        coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False)
+    scores = collections.OrderedDict()
+    twenty_percent_image_num = math.ceil(image_num * 0.2)
+    start_time = 0
+    end_time = 0
+    average_inference_time = 0
+    for image_info, i in zip(all_image_info,
+                             trange(
+                                 image_num, desc="Inference Progress")):
+        if i == twenty_percent_image_num:
+            start_time = time.time()
+        im = cv2.imread(image_info["image"])
+        im_id = image_info["im_id"]
+        if conf_threshold is None and nms_iou_threshold is None:
+            result = model.predict(im.copy())
+        else:
+            result = model.predict(im, conf_threshold, nms_iou_threshold)
+        pred = {
+            'bbox':
+            [[c] + [s] + b
+             for b, s, c in zip(result.boxes, result.scores, result.label_ids)
+             ],
+            'bbox_num': len(result.boxes),
+            'im_id': im_id
+        }
+        eval_metric.update(im_id, pred)
+        if i == image_num - 1:
+            end_time = time.time()
+    average_inference_time = round(
+        (end_time - start_time) / (image_num - twenty_percent_image_num), 4)
+    eval_metric.accumulate()
+    eval_details = eval_metric.details
+    scores.update(eval_metric.get())
+    scores.update({'average_inference_time(s)': average_inference_time})
+    eval_metric.reset()
+    return scores