update ultra_light_fast_generic_face_detector_1mb_320 (#1965)

* update ultra_light_fast_generic_face_detector_1mb

* add clean func

* update save inference model

modules/image/face_detection/ultra_light_fast_generic_face_detector_1mb_320/README.md

@@ -102,19 +102,13 @@
 
 
   - ```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
@@ -168,6 +162,10 @@
 
   移除 fluid api
 
+* 1.2.0
+  
+  修复无法导出推理模型的问题
+
   - ```shell
-    $ hub install ultra_light_fast_generic_face_detector_1mb_320==1.1.3
+    $ hub install ultra_light_fast_generic_face_detector_1mb_320==1.2.0
     ```

modules/image/face_detection/ultra_light_fast_generic_face_detector_1mb_320/README_en.md

@@ -101,19 +101,13 @@
 
 
   - ```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
@@ -167,6 +161,10 @@
 
   Remove fluid api
 
+* 1.2.0
+
+  Fix a bug of save_inference_model
+
   - ```shell
-    $ hub install ultra_light_fast_generic_face_detector_1mb_320==1.1.3
+    $ hub install ultra_light_fast_generic_face_detector_1mb_320==1.2.0
     ```

modules/image/face_detection/ultra_light_fast_generic_face_detector_1mb_320/module.py

@@ -10,11 +10,10 @@ import numpy as np
 import paddle
 from paddle.inference import Config
 from paddle.inference import create_predictor
-from ultra_light_fast_generic_face_detector_1mb_320.data_feed import reader
-from ultra_light_fast_generic_face_detector_1mb_320.processor import base64_to_cv2
-from ultra_light_fast_generic_face_detector_1mb_320.processor import postprocess
+from .data_feed import reader
+from .processor import base64_to_cv2
+from .processor import postprocess
 
-import paddlehub as hub
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -27,19 +26,20 @@ from paddlehub.module.module import serving
     author_email="paddle-dev@baidu.com",
     summary=
     "Ultra-Light-Fast-Generic-Face-Detector-1MB is a high-performance object detection model release on https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB.",
-    version="1.1.3")
-class FaceDetector320(hub.Module):
-
-    def _initialize(self):
+    version="1.2.0")
+class FaceDetector320:
+    def __init__(self):
         self.default_pretrained_model_path = os.path.join(self.directory,
-                                                          "ultra_light_fast_generic_face_detector_1mb_320")
+                                                          "ultra_light_fast_generic_face_detector_1mb_320", "model")
         self._set_config()
 
     def _set_config(self):
         """
         predictor config setting
         """
-        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)
@@ -51,29 +51,11 @@ class FaceDetector320(hub.Module):
         except:
             use_gpu = False
         if 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=0)
             self.gpu_predictor = create_predictor(gpu_config)
 
-    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)
-
     def face_detection(self,
                        images=None,
                        paths=None,

modules/image/face_detection/ultra_light_fast_generic_face_detector_1mb_320/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import paddlehub as hub
+
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://ai-studio-static-online.cdn.bcebos.com/7799a8ccc5f6471b9d56fb6eff94f82a08b70ca2c7594d3f99877e366c0a2619'
+        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="ultra_light_fast_generic_face_detector_1mb_320")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('face_detector_320_predict_output')
+
+    def test_face_detection1(self):
+        results = self.module.face_detection(
+            paths=['tests/test.jpg'],
+            use_gpu=False,
+            visualization=False
+        )
+        bbox = results[0]['data'][0]
+
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+        
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(1000 < left < 4000)
+        self.assertTrue(1000 < right < 4000)
+        self.assertTrue(0 < top < 2000)
+        self.assertTrue(0 < bottom < 2000)
+
+    def test_face_detection2(self):
+        results = self.module.face_detection(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=False
+        )
+        bbox = results[0]['data'][0]
+
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+        
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(1000 < left < 4000)
+        self.assertTrue(1000 < right < 4000)
+        self.assertTrue(0 < top < 2000)
+        self.assertTrue(0 < bottom < 2000)
+
+    def test_face_detection3(self):
+        results = self.module.face_detection(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=True
+        )
+        bbox = results[0]['data'][0]
+
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+        
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(1000 < left < 4000)
+        self.assertTrue(1000 < right < 4000)
+        self.assertTrue(0 < top < 2000)
+        self.assertTrue(0 < bottom < 2000)
+
+    def test_face_detection4(self):
+        results = self.module.face_detection(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=True,
+            visualization=False
+        )
+        bbox = results[0]['data'][0]
+
+        confidence = bbox['confidence']
+        left = bbox['left']
+        right = bbox['right']
+        top = bbox['top']
+        bottom = bbox['bottom']
+        
+        self.assertTrue(confidence > 0.5)
+        self.assertTrue(1000 < left < 4000)
+        self.assertTrue(1000 < right < 4000)
+        self.assertTrue(0 < top < 2000)
+        self.assertTrue(0 < bottom < 2000)
+
+    def test_face_detection5(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.face_detection,
+            paths=['no.jpg']
+        )
+
+    def test_face_detection6(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.face_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()