# Copyright (c) 2020 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 os from unittest import TestCase, main os.environ['CUDA_VISIBLE_DEVICES'] = '1' import cv2 import numpy as np import paddlehub as hub class EfficientNetB2TestCase(TestCase): def setUp(self): self.module = hub.Module(name='efficientnetb2_imagenet') self.test_images = [ "../image_dataset/classification/animals/dog.jpeg", "../image_dataset/keypoint_detection/girl2.jpg" ] self.true_mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3).tolist() self.true_std = np.array([0.229, 0.224, 0.225]).reshape(1, 3).tolist() def test_classifcation(self): results_1 = self.module.classify(paths=self.test_images, use_gpu=True) results_2 = self.module.classify(paths=self.test_images, use_gpu=False) for index, res in enumerate(results_1): self.assertTrue(res.keys(), results_2[index].keys()) diff = list(res.values())[0] - list(results_2[index].values())[0] self.assertTrue((diff < 1e-5)) test_images = [cv2.imread(img) for img in self.test_images] results_3 = self.module.classify(images=test_images, use_gpu=False) for index, res in enumerate(results_1): self.assertTrue(res.keys(), results_3[index].keys()) results_4 = self.module.classify( images=test_images, use_gpu=True, top_k=2) for res in results_4: self.assertEqual(len(res.keys()), 2) def test_common_apis(self): width = self.module.get_expected_image_width() height = self.module.get_expected_image_height() mean = self.module.get_pretrained_images_mean() std = self.module.get_pretrained_images_std() self.assertEqual(width, 224) self.assertEqual(height, 224) self.assertEqual(mean.tolist(), self.true_mean) self.assertEqual(std.tolist(), self.true_std) if __name__ == '__main__': main()