Merge pull request #32 from jaypume/incremental_learning

modify the dataset format to relative path

examples/federated_learning/surface_defect_detection/training_worker/train.py

@@ -16,13 +16,25 @@ import numpy as np
 from tensorflow import keras
 
 import sedna
-from sedna.ml_model import save_model
 from network import GlobalModelInspectionCNN
+from sedna.ml_model import save_model
+
+
+def image_process(line):
+    import keras.preprocessing.image as img_preprocessing
+    file_path, label = line.split(',')
+    img = img_preprocessing.load_img(file_path).resize((128, 128))
+    data = img_preprocessing.img_to_array(img) / 255.0
+    label = [0, 1] if int(label) == 0 else [1, 0]
+    data = np.array(data)
+    label = np.array(label)
+    return [data, label]
 
 
 def main():
     # load dataset.
-    train_data = sedna.load_train_dataset(data_format="txt", with_image=True)
+    train_data = sedna.load_train_dataset(data_format="txt",
+                                          preprocess_fun=image_process)
 
     x = np.array([tup[0] for tup in train_data])
     y = np.array([tup[1] for tup in train_data])

examples/incremental_learning/helmet_detection_incremental_train/training/train.py

@@ -30,8 +30,7 @@ def main():
     class_names = sedna.context.get_parameters("class_names")
 
     # load dataset.
-    train_data = sedna.load_train_dataset(data_format='txt',
-                                            with_image=False)
+    train_data = sedna.load_train_dataset(data_format='txt')
 
     # read parameters from deployment config.
     obj_threshold = sedna.context.get_parameters("obj_threshold")
@@ -88,13 +87,13 @@ def main():
     model = Interface()
 
     sedna.incremental_learning.train(model=model,
-                                       train_data=train_data,
-                                       epochs=epochs,
-                                       batch_size=batch_size,
-                                       class_names=class_names,
-                                       input_shape=input_shape,
-                                       obj_threshold=obj_threshold,
-                                       nms_threshold=nms_threshold)
+                                     train_data=train_data,
+                                     epochs=epochs,
+                                     batch_size=batch_size,
+                                     class_names=class_names,
+                                     input_shape=input_shape,
+                                     obj_threshold=obj_threshold,
+                                     nms_threshold=nms_threshold)
 
 
 if __name__ == '__main__':

lib/sedna/dataset/dataset.py

@@ -18,71 +18,53 @@ choice 1: should be compatible with tensorflow.data.Dataset
 choice 2: a high level Dataset object not compatible with tensorflow,
 but it's unified in our framework.
 """
-
-import fileinput
 import logging
 import os
 
-import numpy as np
-
 from sedna.common.config import BaseConfig
 
 LOG = logging.getLogger(__name__)
 
 
-def _load_dataset(dataset_url, format, **kwargs):
+def _load_dataset(dataset_url, format, preprocess_fun=None, **kwargs):
     if dataset_url is None:
         LOG.warning(f'dataset_url is None, please check the url.')
         return None
     if format == 'txt':
         LOG.info(
             f"dataset format is txt, now loading txt from [{dataset_url}]")
-        if kwargs.get('with_image'):
-            return _load_txt_dataset_with_image(dataset_url)
+        samples = _load_txt_dataset(dataset_url)
+        if preprocess_fun:
+            new_samples = [preprocess_fun(s) for s in samples]
         else:
-            return _load_txt_dataset(dataset_url)
+            new_samples = samples
+        return new_samples
 
 
-def load_train_dataset(data_format, **kwargs):
+def load_train_dataset(data_format, preprocess_fun=None, **kwargs):
     """
     :param data_format: txt
     :param kwargs:
     :return: Dataset
     """
-    return _load_dataset(BaseConfig.train_dataset_url, data_format, **kwargs)
+    return _load_dataset(BaseConfig.train_dataset_url, data_format,
+                         preprocess_fun, **kwargs)
 
 
-def load_test_dataset(data_format, **kwargs):
+def load_test_dataset(data_format, preprocess_fun=None, **kwargs):
     """
     :param data_format: txt
     :param kwargs:
     :return: Dataset
     """
-    return _load_dataset(BaseConfig.test_dataset_url, data_format, **kwargs)
+    return _load_dataset(BaseConfig.test_dataset_url, data_format,
+                         preprocess_fun, **kwargs)
 
 
 def _load_txt_dataset(dataset_url):
     LOG.info(f'dataset_url is {dataset_url}, now reading dataset_url')
-    root_path = BaseConfig.data_path_prefix
+    root_path = os.path.dirname(dataset_url)
     with open(dataset_url) as f:
         lines = f.readlines()
     new_lines = [root_path + os.path.sep + l for l in lines]
     return new_lines
-
-
-def _load_txt_dataset_with_image(dataset_url):
-    import keras.preprocessing.image as img_preprocessing
-    root_path = os.path.dirname(dataset_url)
-    img_data = []
-    img_label = []
-    for line in fileinput.input(dataset_url):
-        file_path, label = line.split(',')
-        file_path = (file_path.replace("\\", os.path.sep)
-                     .replace("/", os.path.sep))
-        file_path = os.path.join(root_path, file_path)
-        img = img_preprocessing.load_img(file_path).resize((128, 128))
-        img_data.append(img_preprocessing.img_to_array(img) / 255.0)
-        img_label += [(0, 1)] if int(label) == 0 else [(1, 0)]
-    data_set = [(np.array(line[0]), np.array(line[1]))
-                for line in zip(img_data, img_label)]
-    return data_set