update yolov3_darknet53_vehicles (#1957)

* update yolov3_darknet53_vehicles

* update gpu config

* update

* add clean func

* update save inference model

modules/image/object_detection/yolov3_darknet53_vehicles/README.md

@@ -100,19 +100,13 @@
         - save\_path (str, optional): 识别结果的保存路径 (仅当visualization=True时存在)
 
   - ```python
-    def save_inference_model(dirname,
-                             model_filename=None,
-                             params_filename=None,
-                             combined=True)
+    def save_inference_model(dirname)
     ```
     - 将模型保存到指定路径。
 
     - **参数**
 
-      - dirname: 存在模型的目录名称； <br/>
-      - model\_filename: 模型文件名称，默认为\_\_model\_\_； <br/>
-      - params\_filename: 参数文件名称，默认为\_\_params\_\_(仅当`combined`为True时生效)；<br/>
-      - combined: 是否将参数保存到统一的一个文件中。
+      - dirname: 模型保存路径 <br/>
 
 
 ## 四、服务部署
@@ -170,6 +164,10 @@
 
   移除 fluid api
 
+* 1.1.0
+
+   修复推理模型无法导出的问题
+
   - ```shell
-    $ hub install yolov3_darknet53_vehicles==1.0.3
+    $ hub install yolov3_darknet53_vehicles==1.1.0
     ```

modules/image/object_detection/yolov3_darknet53_vehicles/README_en.md

@@ -100,19 +100,13 @@
         - save\_path (str, optional): output path for saving results
 
   - ```python
-    def save_inference_model(dirname,
-                             model_filename=None,
-                             params_filename=None,
-                             combined=True)
+    def save_inference_model(dirname)
     ```
     - Save model to specific path
 
     - **Parameters**
 
-      - dirname: output dir for saving model
-      - model\_filename: filename for saving model
-      - params\_filename: filename for saving parameters
-      - combined: whether save parameters into one file
+      - dirname: model save path
 
 
 ## IV.Server Deployment
@@ -170,6 +164,10 @@
 
   Remove fluid api
 
+* 1.1.0
+
+   Fix bug of save_inference_model
+
   - ```shell
-    $ hub install yolov3_darknet53_vehicles==1.0.3
+    $ hub install yolov3_darknet53_vehicles==1.1.0
     ```

modules/image/object_detection/yolov3_darknet53_vehicles/module.py

@@ -8,30 +8,29 @@ from functools import partial
 
 import numpy as np
 import paddle
+import paddle.jit
+import paddle.static
 from paddle.inference import Config
 from paddle.inference import create_predictor
-from yolov3_darknet53_vehicles.data_feed import reader
-from yolov3_darknet53_vehicles.processor import base64_to_cv2
-from yolov3_darknet53_vehicles.processor import load_label_info
-from yolov3_darknet53_vehicles.processor import postprocess
+from .data_feed import reader
+from .processor import base64_to_cv2
+from .processor import load_label_info
+from .processor import postprocess
 
-import paddlehub as hub
-from paddlehub.common.paddle_helper import add_vars_prefix
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
 
 
 @moduleinfo(name="yolov3_darknet53_vehicles",
-            version="1.0.3",
+            version="1.1.0",
             type="CV/object_detection",
             summary="Baidu's YOLOv3 model for vehicles detection, with backbone DarkNet53.",
             author="paddlepaddle",
             author_email="paddle-dev@baidu.com")
-class YOLOv3DarkNet53Vehicles(hub.Module):
-
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "yolov3_darknet53_vehicles_model")
+class YOLOv3DarkNet53Vehicles:
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(self.directory, "yolov3_darknet53_vehicles_model", "model")
         self.label_names = load_label_info(os.path.join(self.directory, "label_file.txt"))
         self._set_config()
 
@@ -49,7 +48,9 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
         """
 
         # create default cpu predictor
-        cpu_config = Config(self.default_pretrained_model_path)
+        model = self.default_pretrained_model_path+'.pdmodel'
+        params = self.default_pretrained_model_path+'.pdiparams'
+        cpu_config = Config(model, params)
         cpu_config.disable_glog_info()
         cpu_config.disable_gpu()
         self.cpu_predictor = create_predictor(cpu_config)
@@ -60,7 +61,7 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
         npu_id = self._get_device_id("FLAGS_selected_npus")
         if npu_id != -1:
             # use npu
-            npu_config = Config(self.default_pretrained_model_path)
+            npu_config = Config(model, params)
             npu_config.disable_glog_info()
             npu_config.enable_npu(device_id=npu_id)
             self.npu_predictor = create_predictor(npu_config)
@@ -69,7 +70,7 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
         gpu_id = self._get_device_id("CUDA_VISIBLE_DEVICES")
         if gpu_id != -1:
             # use gpu
-            gpu_config = Config(self.default_pretrained_model_path)
+            gpu_config = Config(model, params)
             gpu_config.disable_glog_info()
             gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=gpu_id)
             self.gpu_predictor = create_predictor(gpu_config)
@@ -78,7 +79,7 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
         xpu_id = self._get_device_id("XPU_VISIBLE_DEVICES")
         if xpu_id != -1:
             # use xpu
-            xpu_config = Config(self.default_pretrained_model_path)
+            xpu_config = Config(model, params)
             xpu_config.disable_glog_info()
             xpu_config.enable_xpu(100)
             self.xpu_predictor = create_predictor(xpu_config)
@@ -169,24 +170,6 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
             res.extend(output)
         return res
 
-    def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True):
-        if combined:
-            model_filename = "__model__" if not model_filename else model_filename
-            params_filename = "__params__" if not params_filename else params_filename
-        place = paddle.CPUPlace()
-        exe = paddle.Executor(place)
-
-        program, feeded_var_names, target_vars = paddle.static.load_inference_model(
-            dirname=self.default_pretrained_model_path, executor=exe)
-
-        paddle.static.save_inference_model(dirname=dirname,
-                                           main_program=program,
-                                           executor=exe,
-                                           feeded_var_names=feeded_var_names,
-                                           target_vars=target_vars,
-                                           model_filename=model_filename,
-                                           params_filename=params_filename)
-
     @serving
     def serving_method(self, images, **kwargs):
         """

modules/image/object_detection/yolov3_darknet53_vehicles/processor.py

@@ -88,7 +88,7 @@ def load_label_info(file_path):
 
 def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True):
     """
-    postprocess the lod_tensor produced by fluid.Executor.run
+    postprocess the lod_tensor produced by Executor.run
 
     Args:
         paths (list[str]): The paths of images.

modules/image/object_detection/yolov3_darknet53_vehicles/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import paddlehub as hub
+
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://ai-studio-static-online.cdn.bcebos.com/036990d3d8654d789c2138492155d9dd95dba2a2fc8e410ab059eea42b330f59'
+        if not os.path.exists('tests'):
+            os.makedirs('tests')
+        response = requests.get(img_url)
+        assert response.status_code == 200, 'Network Error.'
+        with open('tests/test.jpg', 'wb') as f:
+            f.write(response.content)
+        cls.module = hub.Module(name="yolov3_darknet53_vehicles")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('yolov3_vehicles_detect_output')
+
+    def test_object_detection1(self):
+        results = self.module.object_detection(
+            paths=['tests/test.jpg']
+        )
+        bbox = results[0]['data'][0]
+        label = bbox['label']
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+
+        self.assertEqual(label, 'car')
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(2000 < left < 4000)
+        self.assertTrue(4000 < right < 6000)
+        self.assertTrue(1000 < top < 3000)
+        self.assertTrue(2000 < bottom < 5000)
+
+    def test_object_detection2(self):
+        results = self.module.object_detection(
+            images=[cv2.imread('tests/test.jpg')]
+        )
+        bbox = results[0]['data'][0]
+        label = bbox['label']
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+
+        self.assertEqual(label, 'car')
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(2000 < left < 4000)
+        self.assertTrue(4000 < right < 6000)
+        self.assertTrue(1000 < top < 3000)
+        self.assertTrue(2000 < bottom < 5000)
+
+    def test_object_detection3(self):
+        results = self.module.object_detection(
+            images=[cv2.imread('tests/test.jpg')],
+            visualization=False
+        )
+        bbox = results[0]['data'][0]
+        label = bbox['label']
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+
+        self.assertEqual(label, 'car')
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(2000 < left < 4000)
+        self.assertTrue(4000 < right < 6000)
+        self.assertTrue(1000 < top < 3000)
+        self.assertTrue(2000 < bottom < 5000)
+
+    def test_object_detection4(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.object_detection,
+            paths=['no.jpg']
+        )
+
+    def test_object_detection5(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.object_detection,
+            images=['test.jpg']
+        )
+
+    def test_save_inference_model(self):
+        self.module.save_inference_model('./inference/model')
+
+        self.assertTrue(os.path.exists('./inference/model.pdmodel'))
+        self.assertTrue(os.path.exists('./inference/model.pdiparams'))
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file