updete resnet50_vd_animals (#2070)

modules/image/classification/resnet50_vd_animals/README.md

@@ -168,10 +168,10 @@
 
   初始发布
 
-* 1.0.1
+* 1.1.0
 
-  移除 fluid api
+  移除 Fluid API
 
   - ```shell
-    $ hub install resnet50_vd_animals==1.0.1
+    $ hub install resnet50_vd_animals==1.1.0
     ```

modules/image/classification/resnet50_vd_animals/README_en.md

@@ -171,10 +171,10 @@
 
   First release
 
-* 1.0.1
+* 1.1.0
 
-  Remove fluid api
+  Remove Fluid API
 
   - ```shell
-    $ hub install resnet50_vd_animals==1.0.1
+    $ hub install resnet50_vd_animals==1.1.0
     ```

modules/image/classification/resnet50_vd_animals/data_feed.py

@@ -3,7 +3,6 @@ import os
 import time
 from collections import OrderedDict
 
-import cv2
 import numpy as np
 from PIL import Image
 

modules/image/classification/resnet50_vd_animals/module.py

@@ -7,15 +7,12 @@ import ast
 import os
 
 import numpy as np
-import paddle
 from paddle.inference import Config
 from paddle.inference import create_predictor
-from resnet50_vd_animals.data_feed import reader
-from resnet50_vd_animals.processor import base64_to_cv2
-from resnet50_vd_animals.processor import postprocess
 
-import paddlehub as hub
-from paddlehub.common.paddle_helper import add_vars_prefix
+from .data_feed import reader
+from .processor import base64_to_cv2
+from .processor import postprocess
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -28,10 +25,10 @@ from paddlehub.module.module import serving
     author_email="",
     summary="ResNet50vd is a image classfication model, this module is trained with Baidu's self-built animals dataset.",
     version="1.0.1")
-class ResNet50vdAnimals(hub.Module):
+class ResNet50vdAnimals:
 
-    def _initialize(self):
-        self.default_pretrained_model_path = os.path.join(self.directory, "model")
+    def __init__(self):
+        self.default_pretrained_model_path = os.path.join(self.directory, "model", "model")
         label_file = os.path.join(self.directory, "label_list.txt")
         with open(label_file, 'r', encoding='utf-8') as file:
             self.label_list = file.read().split("\n")[:-1]
@@ -65,7 +62,9 @@ class ResNet50vdAnimals(hub.Module):
         """
 
         # create default cpu predictor
-        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)
@@ -76,7 +75,7 @@ class ResNet50vdAnimals(hub.Module):
         npu_id = self._get_device_id("FLAGS_selected_npus")
         if npu_id != -1:
             # use npu
-            npu_config = Config(self.default_pretrained_model_path)
+            npu_config = Config(model, params)
             npu_config.disable_glog_info()
             npu_config.enable_npu(device_id=npu_id)
             self.npu_predictor = create_predictor(npu_config)
@@ -85,7 +84,7 @@ class ResNet50vdAnimals(hub.Module):
         gpu_id = self._get_device_id("CUDA_VISIBLE_DEVICES")
         if gpu_id != -1:
             # 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=gpu_id)
             self.gpu_predictor = create_predictor(gpu_config)
@@ -94,7 +93,7 @@ class ResNet50vdAnimals(hub.Module):
         xpu_id = self._get_device_id("XPU_VISIBLE_DEVICES")
         if xpu_id != -1:
             # use xpu
-            xpu_config = Config(self.default_pretrained_model_path)
+            xpu_config = Config(model, params)
             xpu_config.disable_glog_info()
             xpu_config.enable_xpu(100)
             self.xpu_predictor = create_predictor(xpu_config)
@@ -165,24 +164,6 @@ class ResNet50vdAnimals(hub.Module):
             res += 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/classification/resnet50_vd_animals/processor.py

@@ -4,9 +4,8 @@ from __future__ import division
 from __future__ import print_function
 
 import base64
-import cv2
-import os
 
+import cv2
 import numpy as np
 
 
@@ -18,7 +17,6 @@ def base64_to_cv2(b64str):
 
 
 def softmax(x):
-    orig_shape = x.shape
     if len(x.shape) > 1:
         tmp = np.max(x, axis=1)
         x -= tmp.reshape((x.shape[0], 1))

modules/image/classification/resnet50_vd_animals/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://unsplash.com/photos/brFsZ7qszSY/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8OHx8ZG9nfGVufDB8fHx8MTY2MzA1ODQ1MQ&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="resnet50_vd_animals")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+
+    def test_classification1(self):
+        results = self.module.classification(paths=['tests/test.jpg'])
+        data = results[0]
+        self.assertTrue('威尔士柯基' in data)
+        self.assertTrue(data['威尔士柯基'] > 0.5)
+
+    def test_classification2(self):
+        results = self.module.classification(images=[cv2.imread('tests/test.jpg')])
+        data = results[0]
+        self.assertTrue('威尔士柯基' in data)
+        self.assertTrue(data['威尔士柯基'] > 0.5)
+
+    def test_classification3(self):
+        results = self.module.classification(images=[cv2.imread('tests/test.jpg')], use_gpu=True)
+        data = results[0]
+        self.assertTrue('威尔士柯基' in data)
+        self.assertTrue(data['威尔士柯基'] > 0.5)
+
+    def test_classification4(self):
+        self.assertRaises(AssertionError, self.module.classification, paths=['no.jpg'])
+
+    def test_classification5(self):
+        self.assertRaises(TypeError, self.module.classification, 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()