更换文档检测模型

paddle_detection/ppdet/modeling/tests/test_mstest.py

@@ -0,0 +1,62 @@
+#   Copyright (c) 2021 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import unittest
+from ppdet.core.workspace import load_config
+from ppdet.engine import Trainer
+
+
+class TestMultiScaleInference(unittest.TestCase):
+    def setUp(self):
+        self.set_config()
+
+    def set_config(self):
+        self.mstest_cfg_file = 'configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml'
+
+    # test evaluation with multi scale test
+    def test_eval_mstest(self):
+        cfg = load_config(self.mstest_cfg_file)
+        trainer = Trainer(cfg, mode='eval')
+
+        cfg.weights = 'https://paddledet.bj.bcebos.com/models/faster_rcnn_r34_fpn_1x_coco.pdparams'
+        trainer.load_weights(cfg.weights)
+
+        trainer.evaluate()
+
+    # test inference with multi scale test
+    def test_infer_mstest(self):
+        cfg = load_config(self.mstest_cfg_file)
+        trainer = Trainer(cfg, mode='test')
+
+        cfg.weights = 'https://paddledet.bj.bcebos.com/models/faster_rcnn_r34_fpn_1x_coco.pdparams'
+        trainer.load_weights(cfg.weights)
+        tests_img_root = os.path.join(os.path.dirname(__file__), 'imgs')
+
+        # input images to predict
+        imgs = [
+            'coco2017_val2017_000000000139.jpg',
+            'coco2017_val2017_000000000724.jpg'
+        ]
+        imgs = [os.path.join(tests_img_root, img) for img in imgs]
+        trainer.predict(
+            imgs, draw_threshold=0.5, output_dir='output', save_results=False)
+
+
+if __name__ == '__main__':
+    unittest.main()