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

Modify code structure

python/fastdeploy/vision/evaluation/classify.py

@@ -0,0 +1,82 @@
+# 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 os
+import re
+import time
+import collections
+
+
+def topk_accuracy(topk_list, label_list):
+    match_array = np.logical_or.reduce(topk_list == label_list, axis=1)
+    topk_acc_score = match_array.sum() / match_array.shape[0]
+    return topk_acc_score
+
+
+def eval_classify(model, image_file_path, label_file_path, topk=5):
+    from tqdm import trange
+    import cv2
+    import math
+
+    result_list = []
+    label_list = []
+    image_label_dict = {}
+    assert os.path.isdir(
+        image_file_path), "The image_file_path:{} is not a directory.".format(
+            image_file_path)
+    assert os.path.isfile(
+        label_file_path), "The label_file_path:{} is not a file.".format(
+            label_file_path)
+    assert isinstance(topk, int), "The tok:{} is not int type".format(topk)
+
+    with open(label_file_path, 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            items = line.strip().split()
+            image_name = items[0]
+            label = items[1]
+            image_label_dict[image_name] = int(label)
+    images_num = len(image_label_dict)
+    twenty_percent_images_num = math.ceil(images_num * 0.2)
+    start_time = 0
+    end_time = 0
+    average_inference_time = 0
+    scores = collections.OrderedDict()
+    for (image, label), i in zip(image_label_dict.items(),
+                                 trange(
+                                     images_num, desc='Inference Progress')):
+        if i == twenty_percent_images_num:
+            start_time = time.time()
+
+        label_list.append([label])
+        image_path = os.path.join(image_file_path, image)
+        im = cv2.imread(image_path)
+        result = model.predict(im, topk)
+        result_list.append(result.label_ids)
+        if i == images_num - 1:
+            end_time = time.time()
+    average_inference_time = round(
+        (end_time - start_time) / (images_num - twenty_percent_images_num), 4)
+    topk_acc_score = topk_accuracy(np.array(result_list), np.array(label_list))
+    if topk == 1:
+        scores.update({'topk1': topk_acc_score})
+        scores.update({
+            'topk1_average_inference_time(s)': average_inference_time
+        })
+    elif topk == 5:
+        scores.update({'topk5': topk_acc_score})
+        scores.update({
+            'topk5_average_inference_time(s)': average_inference_time
+        })
+    return scores