update pyramidbox_face_detection (#1975)

* update pyramidbox_face_detection

* update

* add clean func

* update save inference model

modules/image/face_detection/pyramidbox_face_detection/README.md

@@ -100,19 +100,13 @@
 
 
   - ```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
@@ -165,6 +159,11 @@
 * 1.1.0
 
   修复numpy数据读取问题
+
+* 1.2.0
+
+  修复无法导出推理模型的问题
+
   - ```shell
-    $ hub install pyramidbox_face_detection==1.1.0
+    $ hub install pyramidbox_face_detection==1.2.0
     ```

modules/image/face_detection/pyramidbox_face_detection/README_en.md

@@ -99,19 +99,13 @@
 
 
   - ```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
@@ -164,6 +158,11 @@
 * 1.1.0
 
   Fix the problem of reading numpy
+
+* 1.2.0
+
+  Fix a bug of save_inference_model
+
   - ```shell
-    $ hub install pyramidbox_face_detection==1.1.0
+    $ hub install pyramidbox_face_detection==1.2.0
     ```

modules/image/face_detection/pyramidbox_face_detection/module.py

@@ -7,13 +7,14 @@ import argparse
 import os
 
 import numpy as np
-import paddle.fluid as fluid
-import paddlehub as hub
-from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
+import paddle
+import paddle.jit
+import paddle.static
+from paddle.inference import Config, create_predictor
 from paddlehub.module.module import moduleinfo, runnable, serving
 
-from pyramidbox_face_detection.data_feed import reader
-from pyramidbox_face_detection.processor import postprocess, base64_to_cv2
+from .data_feed import reader
+from .processor import postprocess, base64_to_cv2
 
 
 @moduleinfo(
@@ -22,20 +23,22 @@ from pyramidbox_face_detection.processor import postprocess, base64_to_cv2
     author="baidu-vis",
     author_email="",
     summary="Baidu's PyramidBox model for face detection.",
-    version="1.1.0")
-class PyramidBoxFaceDetection(hub.Module):
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "pyramidbox_face_detection_widerface")
+    version="1.2.0")
+class PyramidBoxFaceDetection:
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(self.directory, "pyramidbox_face_detection_widerface", "model")
         self._set_config()
 
     def _set_config(self):
         """
         predictor config setting
         """
-        cpu_config = AnalysisConfig(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_paddle_predictor(cpu_config)
+        self.cpu_predictor = create_predictor(cpu_config)
 
         try:
             _places = os.environ["CUDA_VISIBLE_DEVICES"]
@@ -44,10 +47,10 @@ class PyramidBoxFaceDetection(hub.Module):
         except:
             use_gpu = False
         if use_gpu:
-            gpu_config = AnalysisConfig(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_paddle_predictor(gpu_config)
+            self.gpu_predictor = create_predictor(gpu_config)
 
     def face_detection(self,
                        images=None,
@@ -95,11 +98,17 @@ class PyramidBoxFaceDetection(hub.Module):
         # process one by one
         for element in reader(images, paths):
             image = np.expand_dims(element['image'], axis=0).astype('float32')
-            image_tensor = PaddleTensor(image.copy())
-            data_out = self.gpu_predictor.run([image_tensor]) if use_gpu else self.cpu_predictor.run([image_tensor])
+            predictor = self.gpu_predictor if use_gpu else self.cpu_predictor
+            input_names = predictor.get_input_names()
+            input_handle = predictor.get_input_handle(input_names[0])
+            input_handle.copy_from_cpu(image)
+            predictor.run()
+            output_names = predictor.get_output_names()
+            output_handle = predictor.get_output_handle(output_names[0])
+            output = np.expand_dims(output_handle.copy_to_cpu(), axis=1)
             # print(len(data_out))  # 1
             out = postprocess(
-                data_out=data_out[0].as_ndarray(),
+                data_out=output_handle.copy_to_cpu(),
                 org_im=element['org_im'],
                 org_im_path=element['org_im_path'],
                 org_im_width=element['org_im_width'],
@@ -110,25 +119,6 @@ class PyramidBoxFaceDetection(hub.Module):
             res.append(out)
         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 = fluid.CPUPlace()
-        exe = fluid.Executor(place)
-
-        program, feeded_var_names, target_vars = fluid.io.load_inference_model(
-            dirname=self.default_pretrained_model_path, executor=exe)
-
-        fluid.io.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/face_detection/pyramidbox_face_detection/processor.py

@@ -5,12 +5,11 @@ from __future__ import print_function
 
 import os
 import time
-from collections import OrderedDict
 
 import base64
 import cv2
 import numpy as np
-from PIL import Image, ImageDraw
+from PIL import ImageDraw
 
 __all__ = ['base64_to_cv2', 'postprocess']
 

modules/image/face_detection/pyramidbox_face_detection/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="pyramidbox_face_detection")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('detection_result')
+
+    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(
+            cv2.error,
+            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()