update ace2p (#2003)

* update ace2p

* add clean func

* update ace2p

modules/image/semantic_segmentation/ace2p/README.md

@@ -99,20 +99,14 @@
           * data (numpy.ndarray): 图像分割得到的结果，shape 为`H * W`，元素的取值为0-19，表示每个像素的分类结果，映射顺序与下面的调色板相同。
 
     ```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
@@ -176,3 +170,11 @@
 * 1.1.0
 
   适配paddlehub2.0版本
+
+* 1.2.0
+
+  移除 Fluid API
+
+  ```shell
+  $ hub install ace2p == 1.2.0
+  ```
\ No newline at end of file

modules/image/semantic_segmentation/ace2p/README_en.md

@@ -105,19 +105,13 @@
 
 
   - ```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: mMdel 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
@@ -182,3 +176,11 @@
 * 1.1.0
 
   Adapt to paddlehub2.0
+
+* 1.2.0
+
+  Remove Fluid API
+
+  ```shell
+  $ hub install ace2p == 1.2.0
+  ```

modules/image/semantic_segmentation/ace2p/data_feed.py

@@ -6,7 +6,7 @@ from collections import OrderedDict
 import cv2
 import numpy as np
 
-from ace2p.processor import get_direction, get_3rd_point, get_affine_transform
+from .processor import get_affine_transform
 
 __all__ = ['reader']
 
@@ -45,7 +45,7 @@ def preprocess(org_im, scale, rotation):
 
     img_mean = np.array([0.406, 0.456, 0.485]).reshape((1, 1, 3))
     img_std = np.array([0.225, 0.224, 0.229]).reshape((1, 1, 3))
-    image = image.astype(np.float)
+    image = image.astype(np.float32)
     image = (image / 255.0 - img_mean) / img_std
     image = image.transpose(2, 0, 1).astype(np.float32)
 

modules/image/semantic_segmentation/ace2p/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 ace2p.processor import get_palette, postprocess, base64_to_cv2, cv2_to_base64
-from ace2p.data_feed import reader
+from .processor import get_palette, postprocess, base64_to_cv2, cv2_to_base64
+from .data_feed import reader
 
 
 @moduleinfo(
@@ -22,10 +23,11 @@ from ace2p.data_feed import reader
     author="baidu-idl",
     author_email="",
     summary="ACE2P is an image segmentation model for human parsing solution.",
-    version="1.1.0")
-class ACE2P(hub.Module):
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "ace2p_human_parsing")
+    version="1.2.0")
+class ACE2P:
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(
+            self.directory, "ace2p_human_parsing", "model")
         # label list
         label_list_file = os.path.join(self.directory, 'label_list.txt')
         with open(label_list_file, "r") as file:
@@ -39,10 +41,12 @@ class ACE2P(hub.Module):
         """
         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"]
@@ -51,10 +55,10 @@ class ACE2P(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 segmentation(self,
                      images=None,
@@ -114,12 +118,19 @@ class ACE2P(hub.Module):
                     pass
             # feed batch image
             batch_image = np.array([data['image'] for data in batch_data])
-            batch_image = PaddleTensor(batch_image.astype('float32'))
-            data_out = self.gpu_predictor.run([batch_image]) if use_gpu else self.cpu_predictor.run([batch_image])
+
+            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(batch_image.astype('float32'))
+            predictor.run()
+            output_names = predictor.get_output_names()
+            output_handle = predictor.get_output_handle(output_names[0])
+    
             # postprocess one by one
             for i in range(len(batch_data)):
                 out = postprocess(
-                    data_out=data_out[0].as_ndarray()[i],
+                    data_out=output_handle.copy_to_cpu()[i],
                     org_im=batch_data[i]['org_im'],
                     org_im_path=batch_data[i]['org_im_path'],
                     image_info=batch_data[i]['image_info'],
@@ -129,25 +140,6 @@ class ACE2P(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/semantic_segmentation/ace2p/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)
+        fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
+        img = cv2.imread('tests/test.jpg')
+        video = cv2.VideoWriter('tests/test.avi', fourcc,
+                                20.0, tuple(img.shape[:2]))
+        for i in range(40):
+            video.write(img)
+        video.release()
+        cls.module = hub.Module(name="ace2p")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('ace2p_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()