Mindspore demo:dog croissants classification

Signed-off-by: Lj1ang <2872509481@qq.com>

examples/incremental-learning-dog-croissants-classification.Dockerfile

@@ -1,6 +1,5 @@
-FROM mindspore/mindspore-cpu:1.8.1
-
-COPY ../lib/requirements.txt /home
+FROM mindspore/mindspore-cpu:1.7.1
+COPY lib/requirements.txt /home
 # install requirements of sedna lib
 RUN pip install -r /home/requirements.txt
 RUN pip install Pillow
@@ -10,9 +9,9 @@ RUN pip install mindvision
 ENV PYTHONPATH "/home/lib"
 
 WORKDIR /home/work
-COPY ../lib /home/lib
+COPY lib /home/lib
 
-COPY incremental_learning/dog_croissants_classification/training  /home/work/
+COPY examples/incremental_learning/dog_croissants_classification/training  /home/work/
 
 
 ENTRYPOINT ["python"]

examples/incremental_learning/dog_croissants_classification/README.md

@@ -1,5 +1,4 @@
-
-
+# Dog-Croissants-classification Demo
 ## Prepare Model
 auto-download
 
@@ -11,7 +10,7 @@ mkdir -p /data/dog_croissants/
 mkdir /output
 ```
 
-TODO:download dataset
+TODO:download dataset. I have no idea where I should put dataset 
 ```shell
 
 
@@ -24,7 +23,10 @@ download checkpoint
 mkdir -p /models/base_model
 mkdir -p /models/deploy_model
 cd /models/base_model
-#wget https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt
+curl https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt -o base_model.ckpt
+cd ../deploy_model
+curl https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt -o deploy_model.ckpt
+
 ```
 ## build docker file
 ```shell
@@ -197,8 +199,3 @@ kubectl delete model initial-model
 kubectl delete model deploy-model
 kubectl delete IncrementalLearningJob dog-croissants-classification-demo
 ```
-```shell
-ctr -n k8s.io image pull registry.aliyuncs.com/google_containers/pause:3.5
-ctr -n k8s.io image tag registry.aliyuncs.com/google_containers/pause:3.5 k8s.gcr.io/pause:3.5
-
-```

examples/incremental_learning/dog_croissants_classification/dog_croissants_classification.yaml

@@ -96,4 +96,4 @@ spec:
             hostPath:
               path: /incremental_learning/he/
               type: DirectoryOrCreate
-  outputDir: "/output"
+  outputDir: "/output"

examples/incremental_learning/dog_croissants_classification/training/dataset.py

@@ -47,12 +47,3 @@ class ImgDataset(BaseDataSource):
                               num_parallel_workers=num_parallel_workers)
         dataset = dataset.batch(batch_size, drop_remainder=True)
         return dataset
-
-    '''
-    def download_dataset(self):
-        dataset_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/beginner/DogCroissants.zip"
-        path = "./datasets"
-        dl = DownLoad()
-        dl.download_and_extract_archive(dataset_url, path)
-    '''
-

examples/incremental_learning/dog_croissants_classification/training/eval.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import os.path
 
 from sedna.common.config import Context
@@ -45,4 +46,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

examples/incremental_learning/dog_croissants_classification/training/inference.py

@@ -11,10 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import glob
-import os
-
 
+import glob
 from PIL import Image
 from sedna.common.config import Context
 from sedna.core.incremental_learning import IncrementalLearning
@@ -23,15 +21,16 @@ import shutil
 import mindspore as ms
 from mobilenet_v2 import mobilenet_v2_fine_tune
 
-
 he_saved_url = Context.get_parameters("HE_SAVED_URL", './tmp')
 
+
 def output_deal(is_hard_example, infer_image_path):
     img_name=infer_image_path.split(r"/")[-1]
     img_category = infer_image_path.split(r"/")[-2]
     if is_hard_example:
         shutil.copy(infer_image_path,f"{he_saved_url}/{img_category}_{img_name}")
 
+
 def main():
 
     hard_example_mining = IncrementalLearning.get_hem_algorithm_from_config(
@@ -64,6 +63,6 @@ def main():
             print(f"{each_img}--->{results}-->{hard_example}")
             output_deal(is_hard_example, each_img)
 
+
 if __name__ == "__main__":
     main()
-

examples/incremental_learning/dog_croissants_classification/training/interface.py

@@ -13,10 +13,7 @@
 # limitations under the License.
 
 from __future__ import division
-
-
 import os
-
 import PIL
 import numpy as np
 from PIL import Image
@@ -26,47 +23,36 @@ from mindvision.engine.loss import CrossEntropySmooth
 from mindvision.engine.callback import ValAccMonitor
 from mobilenet_v2 import mobilenet_v2_fine_tune
 
-
 os.environ['BACKEND_TYPE'] = 'MINDSPORE'
 
+
 def preprocess(img:PIL.Image.Image):
-    #image=Image.open(img_path).convert("RGB").resize((224 ,224))
     image=img.convert("RGB").resize((224,224))
     mean = np.array([0.485 * 255, 0.456 * 255, 0.406 * 255])
     std = np.array([0.229 * 255, 0.224 * 255, 0.225 * 255])
     image = np.array(image)
     image = (image - mean) / std
     image = image.astype(np.float32)
-
     image = np.transpose(image, (2, 0, 1))
     image = np.expand_dims(image, axis=0)
     return image
 
-class Estimator:
-
 
+class Estimator:
     def __init__(self,**kwargs):
         self.trained_ckpt_url=None
 
-
-    # TODO:save url
-    # example : https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.0/train.html#id3
-    def train(self, train_data,base_model_url, trained_ckpt_url, valid_data=None,epochs=10, **kwargs):
+    def train(self, train_data,base_model_url, trained_ckpt_url, valid_data=None, epochs=10, **kwargs):
         network=mobilenet_v2_fine_tune(base_model_url).get_train_network()
-        network_opt=nn.Momentum(params=network.trainable_params(),learning_rate=0.01,momentum=0.9)
+        network_opt=nn.Momentum(params=network.trainable_params(), learning_rate=0.01, momentum=0.9)
         network_loss=CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=0.1, classes_num=2)
-        metrics = {"Accuracy" : nn.Accuracy()}
+        metrics = {"Accuracy": nn.Accuracy()}
         model=ms.Model(network, loss_fn=network_loss, optimizer=network_opt, metrics=metrics)
         num_epochs = epochs
-        #best_ckpt_name=deploy_model_url.split(r"/")[-1]
-        #ckpt_dir=deploy_model_url.replace(best_ckpt_name, "")
         model.train(num_epochs, train_data, callbacks=[ValAccMonitor(model, valid_data, num_epochs, save_best_ckpt=True, ckpt_directory=trained_ckpt_url), ms.TimeMonitor()])
-        self.trained_ckpt_url=trained_ckpt_url+"/best.ckpt"
-        # sedna will save model checkpoint in the path which is the value of MODEL_URL or MODEL_PATH
-        #ms.save_checkpoint(network, deploy_model_url)
+        self.trained_ckpt_url = trained_ckpt_url+"/best.ckpt"
 
-
-    def evaluate(self,data,model_path="",class_name="",input_shape=(224,224),**kwargs):
+    def evaluate(self,data,model_path="", class_name="", input_shape=(224, 224), **kwargs):
         # load
         network = mobilenet_v2_fine_tune(model_path).get_eval_network()
         # eval
@@ -79,27 +65,16 @@ class Estimator:
         print(acc)
         return acc
 
-
-    def predict(self, data,model, input_shape=None, **kwargs):
-        # load
-
+    def predict(self, data, model, input_shape=None, **kwargs):
         # preprocess
         preprocessed_data=preprocess(data)
         # predict
         pre=model.predict(ms.Tensor(preprocessed_data))
-        result=np.argmax(pre)
-        class_name={0:"Croissants", 1:"Dog"}
-        #print(class_name[result])
-        #return class_name[result]
         return pre
 
-    def load(self, model_url):
-        pass
-
     def save(self, model_path=None):
         if not model_path:
             return
-        #model_dir, model_name = os.path.split(model_path)
         network = mobilenet_v2_fine_tune(self.trained_ckpt_url).get_eval_network()
         ms.save_checkpoint(network, model_path)
 

examples/incremental_learning/dog_croissants_classification/training/mobilenet_v2.py

@@ -1,27 +1,31 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import mindspore as ms
 from mindvision.classification.models import mobilenet_v2
 from mindvision.dataset import DownLoad
 
 
-
 class mobilenet_v2_fine_tune:
-    # TODO: save model
-    def __init__(self, base_model_url=None):
-        models_download_url = "https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt"
-        dl=DownLoad()
-        if base_model_url==None:
-            dl.download_url(models_download_url)
-        else:
-            dl.download_url(models_download_url,filename=base_model_url )
+    def __init__(self, base_model_url):
         self.network = mobilenet_v2(num_classes=2, resize=224)
-        #print("base_model_url == "+base_model_url)
         self.param_dict = ms.load_checkpoint(base_model_url)
 
-
     def get_train_network(self):
-        self.filter_list = [x.name for x in self.network.head.classifier.get_parameters()]
+        filter_list = [x.name for x in self.network.head.classifier.get_parameters()]
         for key in list(self.param_dict.keys()):
-            for name in self.filter_list:
+            for name in filter_list:
                 if name in key:
                     print("Delete parameter from checkpoint: ", key)
                     del self.param_dict[key]
@@ -31,4 +35,4 @@ class mobilenet_v2_fine_tune:
 
     def get_eval_network(self):
         ms.load_param_into_net(self.network, self.param_dict)
-        return self.network
+        return self.network

examples/incremental_learning/dog_croissants_classification/training/train.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import os
 
 from sedna.common.config import Context, BaseConfig
@@ -44,7 +45,6 @@ def main():
     else:
         print("valid_dataset_url : NULL")
 
-
     train_data = ImgDataset(data_type="train").parse(path=train_dataset_url,
                                                      train=True,
                                                      image_shape=input_shape,
@@ -62,6 +62,7 @@ def main():
                                       valid_data=valid_data,
                                       epochs=1)
 
+
 if __name__ == "__main__":
     main()
     print("train_phase_done")

incremental-learning-dog-croissants-classification.Dockerfile

@@ -1,18 +0,0 @@
-FROM mindspore/mindspore-cpu:1.7.1
-
-COPY lib/requirements.txt /home
-# install requirements of sedna lib
-RUN pip install -r /home/requirements.txt
-RUN pip install Pillow
-RUN pip install numpy
-RUN pip install mindvision
-
-ENV PYTHONPATH "/home/lib"
-
-WORKDIR /home/work
-COPY lib /home/lib
-
-COPY examples/incremental_learning/dog_croissants_classification/training  /home/work/
-
-
-ENTRYPOINT ["python"]

lib/sedna/algorithms/hard_example_mining/hard_example_mining.py

@@ -178,6 +178,7 @@ class IBTFilter(BaseFilter, abc.ABC):
         return (len(confidence_score_list) / len(infer_result)
                 >= (1 - self.threshold_img))
 
+
 @ClassFactory.register(ClassType.HEM, alias="Random")
 class RandomFilter(BaseFilter):
     """judge a image is hard example or not randomly
@@ -197,9 +198,9 @@ class RandomFilter(BaseFilter):
                 `True` means hard sample, `False` means not.
             """
     def __init__(self, random_ratio=0.3, **kwargs):
-        self.random_ratio=random_ratio
+        self.random_ratio = random_ratio
 
     def __call__(self, *args, **kwargs):
-        if random.uniform(0,1) < self.random_ratio:
+        if random.uniform(0, 1) < self.random_ratio:
             return True
         return False

lib/sedna/backend/mindspore/__init__.py

@@ -26,11 +26,14 @@ class MSBackend(BackendBase):
                                         **kwargs)
         self.framework = "mindspore"
         if self.use_npu:
-            context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+            context.set_context(mode=context.GRAPH_MODE,
+                                device_target="Ascend")
         elif self.use_cuda:
-            context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+            context.set_context(mode=context.GRAPH_MODE,
+                                device_target="GPU")
         else:
-            context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+            context.set_context(mode=context.GRAPH_MODE,
+                                device_target="CPU")
 
         if callable(self.estimator):
             self.estimator = self.estimator()
@@ -70,4 +73,4 @@ class MSBackend(BackendBase):
         """todo: no support yet"""
 
     def set_weights(self, weights):
-        """todo: no support yet"""
+        """todo: no support yet"""