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Easy Deep Learning

Project description

Deep Geo

  • Easy Deep Learning
  • Copyright (c) 2019 InfoLab (Donggun LEE)
  • PIP : https://pypi.org/project/deepgeo/
  • Demo
  • How to install
    pip install deepgeo
    
    • other version
      # 0.0.1
      pip install deepgeo==0.0.1
      
      # 0.0.13
      pip install deepgeo==0.0.13
      
    • requirement
      • Python 3.6
      pip install tensorflow-gpu==1.9.0 exifread>=2.1.2 piexif>=1.1.2 pillow>=6.0.0 matplotlib>=3.1.0 scikit-image>=0.15.0 IPython>=7.5.0 keras>=2.2.4 cython>=0.29.7
      
  • How to use
    • Import DeepGeo
      import deepgeo
      
    • detect
      engine = deepgeo.Engine()
      
      # add model
      engine.add_model('mscoco','maskrcnn','D:/test/config.json')
      
      # create image
      image = deepgeo.Image('uri')
      engine.detect('mscoco',image)
      print(image)
      
      ... etc ...
      
    • extends function
      import sys,os,json
      
      def init_loading_bar(max):
        max_ = str(max)
        sys.stdout.write("%s/0" % max_)
        sys.stdout.flush()
        sys.stdout.write("\b")
      
      def update_loading_bar(num):
        sys.stdout.write("%s" % str(num))
        sys.stdout.flush()
        sys.stdout.write("\b" * len(str(num)))
      
      def final_loading_bar():
        sys.stdout.write("\n")
      
      def fjson_to_imgs(engine, dataset_name, path, image_path):
        file_list = os.listdir(path)
        file_list.sort()
        init_loading_bar(len(file_list))
        count = 0
        for item in file_list:
            count+=1
            update_loading_bar(count)
            if item.find('.json') is not -1:
                data=None
                with open(path+item) as data_file:    
                    data = json.load(data_file)
                engine.add_data('mscoco',dataset_name,deepgeo.Image(data['uri'],image_path,data['annotations']))
                del data
        final_loading_bar()
        del count
        del file_list
      
    • train
      engine = deepgeo.Engine()
      engine.add_model('mscoco', 'maskrcnn', 'D:/test/config.json')
      engine.add_dataset('mscoco','train','maskrcnn')
      engine.add_dataset('mscoco','val','maskrcnn')
      fjson_to_imgs(engine, 'train','D:/test/val2017/json/','D:/test/val2017/images/')
      fjson_to_imgs(engine, 'val','D:/test/val2017/json/','D:/test/val2017/images/')
      
      ## Training...
      print(" > STEP3 : Fine tune all layers")
      engine.set_config('mscoco',{"EPOCHS":160, "LAYERS":'all',"LEARNING_RATE":engine.get_config('mscoco',"LEARNING_RATE")/10})
      engine.train('mscoco',"train","val",None)
      
      ... etc ...
      

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