update deeplabv3p_xception65_humanseg (#2008)

* update deeplabv3p_xception65_humanseg

* update save inference model

modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/README.md

@@ -70,11 +70,11 @@
 
     ```python
     def segmentation(images=None,
-                    paths=None,
-                    batch_size=1,
-                    use_gpu=False,
-                    visualization=False,
-                    output_dir='humanseg_output')
+                     paths=None,
+                     batch_size=1,
+                     use_gpu=False,
+                     visualization=False,
+                     output_dir='humanseg_output')
     ```
 
     - 预测API，用于人像分割。
@@ -95,20 +95,14 @@
       * data (numpy.ndarray): 人像分割结果，仅包含Alpha通道，取值为0-255 (0为全透明，255为不透明)，也即取值越大的像素点越可能为人体，取值越小的像素点越可能为背景。
 
     ```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
-      * model\_filename: 模型文件名称，默认为\_\_model\_\_
-      * params\_filename: 参数文件名称，默认为\_\_params\_\_(仅当`combined`为True时生效)
-      * combined: 是否将参数保存到统一的一个文件中
+      * dirname: 模型保存路径
 
 
 ## 四、服务部署
@@ -175,10 +169,10 @@
 
    修复预测后处理图像数据超过[0,255]范围
 
-* 1.1.2
+* 1.2.0
 
    移除 fluid api
 
   - ```shell
-    $ hub install deeplabv3p_xception65_humanseg==1.1.2
+    $ hub install deeplabv3p_xception65_humanseg==1.2.0
     ```

modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/README_en.md

@@ -70,11 +70,11 @@
 
     - ```python
       def segmentation(images=None,
-                      paths=None,
-                      batch_size=1,
-                      use_gpu=False,
-                      visualization=False,
-                      output_dir='humanseg_output')
+                       paths=None,
+                       batch_size=1,
+                       use_gpu=False,
+                       visualization=False,
+                       output_dir='humanseg_output')
       ```
 
       - Prediction API, generating segmentation result.
@@ -94,19 +94,13 @@
               * data (numpy.ndarray): The result of portrait segmentation.
 
     - ```python
-      def save_inference_model(dirname,
-                              model_filename=None,
-                              params_filename=None,
-                              combined=True)
+      def save_inference_model(dirname)
       ```
 
       - Save the model to the specified path.
 
       - **Parameters**
-        * dirname: Save path.
-        * model\_filename: Model file name，defalt is \_\_model\_\_
-        * params\_filename: Parameter file name，defalt is \_\_params\_\_(Only takes effect when `combined` is True)
-        * combined: Whether to save the parameters to a unified file.
+        * dirname: Model save path.
 
 
 ## IV. Server Deployment
@@ -171,10 +165,10 @@
 
    Fix the bug of image value out of range
 
-* 1.1.2
+* 1.2.0
 
    Remove fluid api
 
   - ```shell
-    $ hub install deeplabv3p_xception65_humanseg==1.1.2
+    $ hub install deeplabv3p_xception65_humanseg==1.2.0
     ```

modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/data_feed.py

@@ -5,7 +5,6 @@ from collections import OrderedDict
 
 import cv2
 import numpy as np
-from PIL import Image
 
 __all__ = ['reader']
 

modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/module.py

@@ -8,14 +8,13 @@ import os
 
 import numpy as np
 import paddle
-from deeplabv3p_xception65_humanseg.data_feed import reader
-from deeplabv3p_xception65_humanseg.processor import base64_to_cv2
-from deeplabv3p_xception65_humanseg.processor import cv2_to_base64
-from deeplabv3p_xception65_humanseg.processor import postprocess
+from .data_feed import reader
+from .processor import base64_to_cv2
+from .processor import cv2_to_base64
+from .processor import postprocess
 from paddle.inference import Config
 from paddle.inference import create_predictor
 
-import paddlehub as hub
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -26,18 +25,20 @@ from paddlehub.module.module import serving
             author="baidu-vis",
             author_email="",
             summary="DeepLabv3+ is a semantic segmentation model.",
-            version="1.1.2")
-class DeeplabV3pXception65HumanSeg(hub.Module):
+            version="1.2.0")
+class DeeplabV3pXception65HumanSeg:
 
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "deeplabv3p_xception65_humanseg_model")
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(self.directory, "deeplabv3p_xception65_humanseg_model", "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)
@@ -49,7 +50,7 @@ class DeeplabV3pXception65HumanSeg(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)
@@ -134,24 +135,6 @@ class DeeplabV3pXception65HumanSeg(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 = 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/semantic_segmentation/deeplabv3p_xception65_humanseg/processor.py

@@ -5,7 +5,6 @@ from __future__ import print_function
 
 import os
 import time
-from collections import OrderedDict
 
 import base64
 import cv2

modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import numpy as np
+import paddlehub as hub
+
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://unsplash.com/photos/pg_WCHWSdT8/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjYyNDM2ODI4&force=true&w=640'
+        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="deeplabv3p_xception65_humanseg")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('humanseg_output')
+
+    def test_segmentation1(self):
+        results = self.module.segmentation(
+            paths=['tests/test.jpg'],
+            use_gpu=False,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_segmentation2(self):
+        results = self.module.segmentation(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_segmentation3(self):
+        results = self.module.segmentation(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=True
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_segmentation4(self):
+        results = self.module.segmentation(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=True,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_segmentation5(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.segmentation,
+            paths=['no.jpg']
+        )
+
+    def test_segmentation6(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.segmentation,
+            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()