update falsr_c (#1989)

* update falsr_c

* update version

* add clean func

* update falsr_c

modules/image/Image_editing/super_resolution/falsr_c/README.md

@@ -68,8 +68,7 @@
 - ### 3、API
 
   - ```python
-    def reconstruct(self,
-                   images=None,
+    def reconstruct(images=None,
                     paths=None,
                     use_gpu=False,
                     visualization=False,
@@ -93,21 +92,14 @@
         * data (numpy.ndarray): 超分辨后图像。
 
   - ```python
-    def save_inference_model(self,
-                            dirname='falsr_c_save_model',
-                            model_filename=None,
-                            params_filename=None,
-                            combined=False)
+    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
 
 
 
@@ -166,3 +158,11 @@
 
   初始发布
 
+
+* 1.1.0
+
+  移除 fluid API
+
+  ```shell
+  $ hub install falsr_c == 1.1.0
+  ```

modules/image/Image_editing/super_resolution/falsr_c/README_en.md

@@ -71,8 +71,7 @@
 - ### 3、API
 
   - ```python
-    def reconstruct(self,
-                   images=None,
+    def reconstruct(images=None,
                     paths=None,
                     use_gpu=False,
                     visualization=False,
@@ -95,21 +94,14 @@
         * data (numpy.ndarray): Result of super resolution.
 
   - ```python
-    def save_inference_model(self,
-                            dirname='falsr_c_save_model',
-                            model_filename=None,
-                            params_filename=None,
-                            combined=False)
+    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.
 
 
 
@@ -170,4 +162,11 @@
   First release
 
 
+- 1.1.0
 
+  Remove Fluid API
+
+
+  ```shell
+  $ hub install falsr_c == 1.1.0
+  ```

modules/image/Image_editing/super_resolution/falsr_c/data_feed.py

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

modules/image/Image_editing/super_resolution/falsr_c/module.py

@@ -18,13 +18,14 @@ import os
 import argparse
 
 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 falsr_c.data_feed import reader
-from falsr_c.processor import postprocess, base64_to_cv2, cv2_to_base64, check_dir
+from .data_feed import reader
+from .processor import postprocess, base64_to_cv2, cv2_to_base64, check_dir
 
 
 @moduleinfo(
@@ -33,21 +34,22 @@ from falsr_c.processor import postprocess, base64_to_cv2, cv2_to_base64, check_d
     author="paddlepaddle",
     author_email="",
     summary="falsr_c is a super resolution model.",
-    version="1.0.0")
-class Falsr_C(hub.Module):
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "falsr_c_model")
+    version="1.1.0")
+class Falsr_C:
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(self.directory, "falsr_c_model", "model")
         self._set_config()
 
     def _set_config(self):
         """
         predictor config setting
         """
-        self.model_file_path = self.default_pretrained_model_path
-        cpu_config = AnalysisConfig(self.model_file_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"]
@@ -56,10 +58,10 @@ class Falsr_C(hub.Module):
         except:
             use_gpu = False
         if use_gpu:
-            gpu_config = AnalysisConfig(self.model_file_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 reconstruct(self, images=None, paths=None, use_gpu=False, visualization=False, output_dir="falsr_c_output"):
         """
@@ -96,11 +98,18 @@ class Falsr_C(hub.Module):
         for i in range(total_num):
             image_y = np.array([all_data[i]['img_y']])
             image_scale_pbpr = np.array([all_data[i]['img_scale_pbpr']])
-            image_y = PaddleTensor(image_y.copy())
-            image_scale_pbpr = PaddleTensor(image_scale_pbpr.copy())
-            output = self.gpu_predictor.run([image_y, image_scale_pbpr]) if use_gpu else self.cpu_predictor.run(
-                [image_y, image_scale_pbpr])
-            output = np.expand_dims(output[0].as_ndarray(), axis=1)
+
+            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_y.copy())
+            input_handle = predictor.get_input_handle(input_names[1])
+            input_handle.copy_from_cpu(image_scale_pbpr.copy())
+
+            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)
             out = postprocess(
                 data_out=output,
                 org_im=all_data[i]['org_im'],
@@ -111,29 +120,6 @@ class Falsr_C(hub.Module):
             res.append(out)
         return res
 
-    def save_inference_model(self,
-                             dirname='falsr_c_save_model',
-                             model_filename=None,
-                             params_filename=None,
-                             combined=False):
-        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/Image_editing/super_resolution/falsr_c/processor.py

@@ -52,7 +52,6 @@ def postprocess(data_out, org_im, org_im_shape, org_im_path, output_dir, visuali
             result['data'] = sr
         else:
             result['data'] = sr
-    print("result['data'] shape", result['data'].shape)
 
     return result
 

modules/image/Image_editing/super_resolution/falsr_c/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/1sLIu1XKQrY/download?ixid=MnwxMjA3fDB8MXxhbGx8MTJ8fHx8fHwyfHwxNjYyMzQxNDUx&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="falsr_c")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('falsr_c_output')
+
+    def test_reconstruct1(self):
+        results = self.module.reconstruct(
+            paths=['tests/test.jpg'],
+            use_gpu=False,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_reconstruct2(self):
+        results = self.module.reconstruct(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_reconstruct3(self):
+        results = self.module.reconstruct(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=True
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_reconstruct4(self):
+        results = self.module.reconstruct(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=True,
+            visualization=False
+        )
+        self.assertIsInstance(results[0]['data'], np.ndarray)
+
+    def test_reconstruct5(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.reconstruct,
+            paths=['no.jpg']
+        )
+
+    def test_reconstruct6(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.reconstruct,
+            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()