update animegan_v2_paprika_54 (#2092)

* update animegan_v2_paprika_54

* update animegan_v2_paprika_54

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md

@@ -135,14 +135,10 @@
 
   初始发布
 
-* 1.0.1
+* 1.1.0
 
-  适配paddlehub2.0
-
-* 1.0.2
-
-  删除batch_size选项
+  移除 Fluid API
 
   - ```shell
-    $ hub install animegan_v2_paprika_54==1.0.2
-    ```
\ No newline at end of file
+    $ hub install animegan_v2_paprika_54==1.1.0
+    ```

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/README_en.md

@@ -11,7 +11,7 @@
 |Data indicators|-|
 
 
-## I. Basic Information 
+## I. Basic Information
 
 - ### Application Effect Display
 
@@ -142,10 +142,10 @@
 
   First release.
 
-* 1.0.1
+* 1.1.0
 
-  Support paddlehub2.0.
+  Remove Fluid API
 
-* 1.0.2
-
-  Delete batch_size.
+  - ```shell
+    $ hub install animegan_v2_paprika_54==1.1.0
+    ```

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/model.py

 import os
-import numpy as np
 
-from paddle.inference import create_predictor, Config
+import numpy as np
+from paddle.inference import Config
+from paddle.inference import create_predictor
 
-__all__ = ['Model']
+__all__ = ['InferenceModel']
 
 
-class Model():
+class InferenceModel:
     # 初始化函数
-    def __init__(self, modelpath, use_gpu=False, use_mkldnn=True, combined=True):
-        # 加载模型预测器
-        self.predictor = self.load_model(modelpath, use_gpu, use_mkldnn, combined)
+    def __init__(self, modelpath, use_gpu=False, gpu_id=0, use_mkldnn=False, cpu_threads=1):
+        '''
+        init the inference model
+        modelpath: inference model path
+        use_gpu: use gpu or not
+        use_mkldnn: use mkldnn or not
+        '''
+        # 加载模型配置
+        self.config = self.load_config(modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads)
 
-        # 获取模型的输入输出
-        self.input_names = self.predictor.get_input_names()
-        self.output_names = self.predictor.get_output_names()
-        self.input_handle = self.predictor.get_input_handle(self.input_names[0])
-        self.output_handle = self.predictor.get_output_handle(self.output_names[0])
+    # 打印函数
+    def __repr__(self):
+        '''
+        get the numbers and name of inputs and outputs
+        '''
+        return 'input_num: %d\ninput_names: %s\noutput_num: %d\noutput_names: %s' % (
+            self.input_num, str(self.input_names), self.output_num, str(self.output_names))
 
-    # 模型加载函数
-    def load_model(self, modelpath, use_gpu, use_mkldnn, combined):
+    # 类调用函数
+    def __call__(self, *input_datas, batch_size=1):
+        '''
+        call function
+        '''
+        return self.forward(*input_datas, batch_size=batch_size)
+
+    # 模型参数加载函数
+    def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads):
+        '''
+        load the model config
+        modelpath: inference model path
+        use_gpu: use gpu or not
+        use_mkldnn: use mkldnn or not
+        '''
         # 对运行位置进行配置
         if use_gpu:
             try:
                 int(os.environ.get('CUDA_VISIBLE_DEVICES'))
             except Exception:
                 print(
-                    'Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU.'
+                    '''Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU. Now switch to CPU to continue...'''
                 )
                 use_gpu = False
 
-        # 加载模型参数
-        if combined:
-            model = os.path.join(modelpath, "__model__")
-            params = os.path.join(modelpath, "__params__")
+        if os.path.isdir(modelpath):
+            if os.path.exists(os.path.join(modelpath, "__params__")):
+                # __model__ + __params__
+                model = os.path.join(modelpath, "__model__")
+                params = os.path.join(modelpath, "__params__")
+                config = Config(model, params)
+            elif os.path.exists(os.path.join(modelpath, "params")):
+                # model + params
+                model = os.path.join(modelpath, "model")
+                params = os.path.join(modelpath, "params")
+                config = Config(model, params)
+            elif os.path.exists(os.path.join(modelpath, "__model__")):
+                # __model__ + others
+                config = Config(modelpath)
+            else:
+                raise Exception("Error! Can\'t find the model in: %s. Please check your model path." %
+                                os.path.abspath(modelpath))
+        elif os.path.exists(modelpath + ".pdmodel"):
+            # *.pdmodel + *.pdiparams
+            model = modelpath + ".pdmodel"
+            params = modelpath + ".pdiparams"
             config = Config(model, params)
+        elif isinstance(modelpath, Config):
+            config = modelpath
         else:
-            config = Config(modelpath)
+            raise Exception("Error! Can\'t find the model in: %s. Please check your model path." %
+                            os.path.abspath(modelpath))
 
         # 设置参数
         if use_gpu:
-            config.enable_use_gpu(100, 0)
+            config.enable_use_gpu(100, gpu_id)
         else:
             config.disable_gpu()
+            config.set_cpu_math_library_num_threads(cpu_threads)
             if use_mkldnn:
                 config.enable_mkldnn()
+
         config.disable_glog_info()
-        config.switch_ir_optim(True)
-        config.enable_memory_optim()
-        config.switch_use_feed_fetch_ops(False)
-        config.switch_specify_input_names(True)
 
-        # 通过参数加载模型预测器
-        predictor = create_predictor(config)
+        # 返回配置
+        return config
 
-        # 返回预测器
-        return predictor
+    # 预测器创建函数
+    def eval(self):
+        '''
+        create the model predictor by model config
+        '''
+        # 创建预测器
+        self.predictor = create_predictor(self.config)
 
-    # 模型预测函数
-    def predict(self, input_datas):
-        outputs = []
+        # 获取模型的输入输出名称
+        self.input_names = self.predictor.get_input_names()
+        self.output_names = self.predictor.get_output_names()
+
+        # 获取模型的输入输出节点数量
+        self.input_num = len(self.input_names)
+        self.output_num = len(self.output_names)
+
+        # 获取输入
+        self.input_handles = []
+        for input_name in self.input_names:
+            self.input_handles.append(self.predictor.get_input_handle(input_name))
+
+        # 获取输出
+        self.output_handles = []
+        for output_name in self.output_names:
+            self.output_handles.append(self.predictor.get_output_handle(output_name))
+
+    # 前向计算函数
+    def forward(self, *input_datas, batch_size=1):
+        """
+        model inference
+        batch_size: batch size
+        *input_datas: x1, x2, ..., xn
+        """
+        # 切分输入数据
+        datas_num = input_datas[0].shape[0]
+        split_num = datas_num // batch_size + \
+                    1 if datas_num % batch_size != 0 else datas_num // batch_size
+        input_datas = [np.array_split(input_data, split_num) for input_data in input_datas]
 
         # 遍历输入数据进行预测
-        for input_data in input_datas:
-            inputs = input_data.copy()
-            self.input_handle.copy_from_cpu(inputs)
+        outputs = {}
+        for step in range(split_num):
+            for i in range(self.input_num):
+                input_data = input_datas[i][step].copy()
+                self.input_handles[i].copy_from_cpu(input_data)
+
             self.predictor.run()
-            output = self.output_handle.copy_to_cpu()
-            outputs.append(output)
+
+            for i in range(self.output_num):
+                output = self.output_handles[i].copy_to_cpu()
+                if i in outputs:
+                    outputs[i].append(output)
+                else:
+                    outputs[i] = [output]
 
         # 预测结果合并
-        outputs = np.concatenate(outputs, 0)
+        for key in outputs.keys():
+            outputs[key] = np.concatenate(outputs[key], 0)
+
+        outputs = [v for v in outputs.values()]
 
         # 返回预测结果
-        return outputs
+        return tuple(outputs) if len(outputs) > 1 else outputs[0]

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py

 import os
 
-from paddlehub import Module
-from paddlehub.module.module import moduleinfo, serving
-
-from animegan_v2_paprika_54.model import Model
-from animegan_v2_paprika_54.processor import base64_to_cv2, cv2_to_base64, Processor
+from .model import InferenceModel
+from .processor import base64_to_cv2
+from .processor import cv2_to_base64
+from .processor import Processor
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import serving
 
 
 @moduleinfo(
@@ -13,16 +14,18 @@ from animegan_v2_paprika_54.processor import base64_to_cv2, cv2_to_base64, Proce
     author="jm12138",  # 作者名称
     author_email="jm12138@qq.com",  # 作者邮箱
     summary="animegan_v2_paprika_54",  # 模型介绍
-    version="1.0.2"  # 版本号
+    version="1.1.0"  # 版本号
 )
-class Animegan_V2_Paprika_54(Module):
+class Animegan_V2_Paprika_54:
     # 初始化函数
-    def __init__(self, name=None, use_gpu=False):
+    def __init__(self, use_gpu=False, use_mkldnn=False):
         # 设置模型路径
-        self.model_path = os.path.join(self.directory, "animegan_v2_paprika_54")
+        self.model_path = os.path.join(self.directory, "animegan_v2_paprika_54", "model")
 
         # 加载模型
-        self.model = Model(modelpath=self.model_path, use_gpu=use_gpu, use_mkldnn=False, combined=False)
+        self.model = InferenceModel(modelpath=self.model_path, use_gpu=use_gpu, use_mkldnn=use_mkldnn)
+
+        self.model.eval()
 
     # 关键点检测函数
     def style_transfer(self,
@@ -33,11 +36,18 @@ class Animegan_V2_Paprika_54(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(
-            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(images=images,
+                              paths=paths,
+                              batch_size=1,
+                              output_dir=output_dir,
+                              min_size=min_size,
+                              max_size=max_size)
 
         # 模型预测
-        outputs = self.model.predict(processor.input_datas)
+        outputs = []
+        for input_data in processor.input_datas:
+            output = self.model(input_data)
+            outputs.append(output)
 
         # 结果后处理
         results = processor.postprocess(outputs, visualization)

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/processor.py

+import base64
 import os
-import cv2
 import time
-import base64
+
+import cv2
 import numpy as np
 
 __all__ = ['base64_to_cv2', 'cv2_to_base64', 'Processor']

modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import numpy as np
+import requests
+
+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/mJaD10XeD7w/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8M3x8Y2F0fGVufDB8fHx8MTY2MzczNDc3Mw&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)
+        img = cv2.imread('tests/test.jpg')
+        img = cv2.resize(img, (0, 0), fx=0.25, fy=0.25)
+        cv2.imwrite('tests/test.jpg', img)
+        cls.module = hub.Module(name="animegan_v2_paprika_54")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('output')
+
+    def test_style_transfer1(self):
+        results = self.module.style_transfer(paths=['tests/test.jpg'])
+        self.assertIsInstance(results[0], np.ndarray)
+
+    def test_style_transfer2(self):
+        results = self.module.style_transfer(paths=['tests/test.jpg'], visualization=True)
+        self.assertIsInstance(results[0], np.ndarray)
+
+    def test_style_transfer3(self):
+        results = self.module.style_transfer(images=[cv2.imread('tests/test.jpg')])
+        self.assertIsInstance(results[0], np.ndarray)
+
+    def test_style_transfer4(self):
+        results = self.module.style_transfer(images=[cv2.imread('tests/test.jpg')], visualization=True)
+        self.assertIsInstance(results[0], np.ndarray)
+
+    def test_style_transfer5(self):
+        self.assertRaises(AssertionError, self.module.style_transfer, paths=['no.jpg'])
+
+    def test_style_transfer6(self):
+        self.assertRaises(cv2.error, self.module.style_transfer, images=['tests/test.jpg'])
+
+
+if __name__ == "__main__":
+    unittest.main()