def main(argv=None):
if FLAGS.mode == 'train':
if FLAGS.network == None:
raise ValueError('Please supply a network(graph) file.')
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.continue_train != True and tf.gfile.Exists(FLAGS.ckpt):
tf.gfile.DeleteRecursively(FLAGS.ckpt)
if tf.gfile.Exists(FLAGS.ckpt) == False:
tf.gfile.MakeDirs(FLAGS.ckpt)
# WRITE META-DATA
FLAGS.num_of_classes = _num_of_folders(FLAGS.data_dir)
with open(os.path.join(FLAGS.ckpt, "flags.txt"), "w") as text_file:
flags = json.dumps(FLAGS.__dict__["__flags"])
text_file.write(flags)
train(FLAGS.data_dir)
elif FLAGS.mode == 'eval':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.ckpt == FLAGS.event_dir:
FLAGS.event_dir = os.path.join(FLAGS.ckpt, 'event')
#raise ValueError('event_dir cannot be same with ckpt dir!!!')
# READ META-DATA
with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
txt = text_file.read()
flags = json.loads(txt)
FLAGS.num_of_classes = flags['num_of_classes']
FLAGS.image_size = flags['image_size']
FLAGS.image_crop_size = flags['image_crop_size']
FLAGS.grayscale = flags['grayscale']
FLAGS.use_fp16 = flags['use_fp16']
FLAGS.network = flags['network']
FLAGS.learning_rate = flags['learning_rate']
evaluate(FLAGS.data_dir)
elif FLAGS.mode == 'export':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
# READ META-DATA
with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
txt = text_file.read()
flags = json.loads(txt)
FLAGS.num_of_classes = flags['num_of_classes']
FLAGS.image_size = flags['image_size']
FLAGS.image_crop_size = flags['image_crop_size']
FLAGS.grayscale = flags['grayscale']
FLAGS.use_fp16 = flags['use_fp16']
FLAGS.network = flags['network']
FLAGS.learning_rate = flags['learning_rate']
export()
elif FLAGS.mode == 'single':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.image == None:
raise ValueError('Please supply a image file.')
# READ META-DATA
with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
txt = text_file.read()
flags = json.loads(txt)
FLAGS.num_of_classes = flags['num_of_classes']
FLAGS.image_size = flags['image_size']
FLAGS.image_crop_size = flags['image_crop_size']
FLAGS.grayscale = flags['grayscale']
FLAGS.use_fp16 = flags['use_fp16']
FLAGS.network = flags['network']
FLAGS.learning_rate = flags['learning_rate']
evaluate_single()
elif FLAGS.mode == 'single_with_pb':
if FLAGS.pb == None:
raise ValueError('Please supply a pb file.')
if FLAGS.image == None:
raise ValueError('Please supply a image file.')
single_with_pb()
elif FLAGS.mode == 'eval_with_pb':
if FLAGS.pb == None:
raise ValueError('Please supply a pb file.')
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
evaluate_with_pb(FLAGS.pb, FLAGS.data_dir)
elif FLAGS.mode == 'resize':
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.out_dir == None:
raise ValueError('Please supply a out_dir.')
if tf.gfile.Exists(FLAGS.out_dir):
tf.gfile.DeleteRecursively(FLAGS.out_dir)
tf.gfile.MakeDirs(FLAGS.out_dir)
convert_count = CNN.convert_img_to_square_with_pad(
FLAGS.data_dir, FLAGS.out_dir)
print('Complete Succefuly.')
print('Output Dir: {}'.format(FLAGS.out_dir))
print('Total Count: {}'.format(convert_count))
elif FLAGS.mode == 'map_export':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.out_node == None:
raise ValueError('Please supply out_node.')
if FLAGS.data_dir == None:
raise ValueError('Please supply data_dir.')
if tf.gfile.Exists(FLAGS.data_dir) == False:
raise ValueError('data_dir not exists.')
if FLAGS.map_dir == None:
raise ValueError('Please supply map_dir.')
if tf.gfile.Exists(FLAGS.map_dir) == False:
raise ValueError('map_dir not exists.')
map_export(FLAGS.pb, FLAGS.out_node, FLAGS.data_dir, FLAGS.map_dir)
elif FLAGS.mode == 'map_inference':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.out_node == None:
raise ValueError('Please supply out_node.')
if FLAGS.image == None:
raise ValueError('Please supply image.')
if tf.gfile.Exists(FLAGS.image) == False:
raise ValueError('image not exists.')
if FLAGS.map_dir == None:
raise ValueError('Please supply map_dir.')
if tf.gfile.Exists(FLAGS.map_dir) == False:
raise ValueError('map_dir not exists.')
if FLAGS.label == None:
print(bcolors.WARNING + 'WARNING: Label is not supplied!!' +
bcolors.ENDC)
#raise ValueError('Please supply label.')
map_inference(FLAGS.pb, FLAGS.out_node, FLAGS.image, FLAGS.map_dir,
FLAGS.label)
elif FLAGS.mode == 'map_distance':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.out_node == None:
raise ValueError('Please supply out_node.')
if FLAGS.image == None:
raise ValueError('Please supply image.')
if tf.gfile.Exists(FLAGS.image) == False:
raise ValueError('image not exists.')
if FLAGS.map_dir == None:
raise ValueError('Please supply map_dir.')
if tf.gfile.Exists(FLAGS.map_dir) == False:
raise ValueError('map_dir not exists.')
if FLAGS.label == None:
raise ValueError('Please supply label.')
if FLAGS.target_label == None:
raise ValueError('Please supply target_label.')
map_distance(FLAGS.pb, FLAGS.out_node, FLAGS.image, FLAGS.map_dir,
FLAGS.label, FLAGS.target_label)
elif FLAGS.mode == 'auto':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.data_dir == None:
raise ValueError('Please supply data_dir.')
if tf.gfile.Exists(FLAGS.data_dir) == False:
raise ValueError('data_dir not exists.')
if FLAGS.out_dir == None:
raise ValueError('Please supply out_dir.')
if tf.gfile.Exists(FLAGS.out_dir):
tf.gfile.DeleteRecursively(FLAGS.out_dir)
tf.gfile.MakeDirs(FLAGS.out_dir)
auto_classifier(FLAGS.pb, FLAGS.data_dir, FLAGS.out_dir)
def main(argv=None):
if FLAGS.mode == 'train':
if FLAGS.network == None:
raise ValueError('Please supply a network(graph) file.')
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.continue_train != True and tf.gfile.Exists(FLAGS.ckpt):
tf.gfile.DeleteRecursively(FLAGS.ckpt)
if tf.gfile.Exists(FLAGS.ckpt) == False:
tf.gfile.MakeDirs(FLAGS.ckpt)
# WRITE META-DATA
# FLAGS.num_of_classes = _num_of_folders(FLAGS.data_dir)
# with open(os.path.join(FLAGS.ckpt, "flags.txt"), "w") as text_file:
# flags = json.dumps(FLAGS.__dict__["flags"])
# text_file.write(flags)
train()
elif FLAGS.mode == 'eval':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.ckpt == FLAGS.event_dir:
raise ValueError('event_dir cannot be same with ckpt dir!!!')
# READ META-DATA
# with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
# txt = text_file.read()
# flags = json.loads(txt)
FLAGS.num_of_classes = 2
FLAGS.image_size = 100
FLAGS.image_crop_size = 80
FLAGS.grayscale = True
FLAGS.use_fp16 = False
FLAGS.network = 'nn_default'
FLAGS.learning_rate = 0.005
evaluate()
elif FLAGS.mode == 'export':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
# READ META-DATA
# with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
# txt = text_file.read()
# flags = json.loads(txt)
# FLAGS.num_of_classes = flags['num_of_classes']
# FLAGS.image_size = flags['image_size']
# FLAGS.image_crop_size = flags['image_crop_size']
# FLAGS.grayscale = flags['grayscale']
# FLAGS.use_fp16 = flags['use_fp16']
# FLAGS.network = flags['network']
# FLAGS.learning_rate = flags['learning_rate']
export()
elif FLAGS.mode == 'export-serving':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
# READ META-DATA
with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
txt = text_file.read()
flags = json.loads(txt)
FLAGS.num_of_classes = flags['num_of_classes']
FLAGS.image_size = flags['image_size']
FLAGS.image_crop_size = flags['image_crop_size']
FLAGS.grayscale = flags['grayscale']
FLAGS.use_fp16 = flags['use_fp16']
FLAGS.network = flags['network']
FLAGS.learning_rate = flags['learning_rate']
export_serving()
elif FLAGS.mode == 'single':
if FLAGS.ckpt == None:
raise ValueError('Please supply a ckpt dir.')
if FLAGS.image == None:
raise ValueError('Please supply a image file.')
# READ META-DATA
# with open(os.path.join(FLAGS.ckpt, "flags.txt"), "r") as text_file:
# txt = text_file.read()
# flags = json.loads(txt)
# FLAGS.num_of_classes = flags['num_of_classes']
# FLAGS.image_size = flags['image_size']
# FLAGS.image_crop_size = flags['image_crop_size']
# FLAGS.grayscale = flags['grayscale']
# FLAGS.use_fp16 = flags['use_fp16']
# FLAGS.network = flags['network']
# FLAGS.learning_rate = flags['learning_rate']
evaluate_single()
elif FLAGS.mode == 'pb':
if FLAGS.pb == None:
raise ValueError('Please supply a pb file.')
if FLAGS.image == None:
raise ValueError('Please supply a image file.')
evaluate_with_pb()
elif FLAGS.mode == 'resize':
if FLAGS.data_dir == None:
raise ValueError('Please supply a data_dir.')
if FLAGS.out_dir == None:
raise ValueError('Please supply a out_dir.')
if tf.gfile.Exists(FLAGS.out_dir):
tf.gfile.DeleteRecursively(FLAGS.out_dir)
tf.gfile.MakeDirs(FLAGS.out_dir)
convert_count = CNN.convert_img_to_square_with_pad(
FLAGS.data_dir, FLAGS.out_dir)
print('Complete Succefuly.')
print('Output Dir: {}'.format(FLAGS.out_dir))
print('Total Count: {}'.format(convert_count))
elif FLAGS.mode == 'map-export':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.out_node == None:
raise ValueError('Please supply out_node.')
if FLAGS.data_dir == None:
raise ValueError('Please supply data_dir.')
if tf.gfile.Exists(FLAGS.data_dir) == False:
raise ValueError('data_dir not exists.')
if FLAGS.map == None:
raise ValueError('Please supply map.')
if tf.gfile.Exists(FLAGS.map) == True:
tf.gfile.Remove(FLAGS.map)
map_export(FLAGS.pb, FLAGS.out_node, FLAGS.data_dir, FLAGS.map)
elif FLAGS.mode == 'map-inference':
if FLAGS.pb == None:
raise ValueError('Please supply pb.')
if tf.gfile.Exists(FLAGS.pb) == False:
raise ValueError('Pb not exists.')
if FLAGS.out_node == None:
raise ValueError('Please supply out_node.')
if FLAGS.image == None:
raise ValueError('Please supply image.')
if tf.gfile.Exists(FLAGS.image) == False:
raise ValueError('image not exists.')
if FLAGS.map == None:
raise ValueError('Please supply map.')
if tf.gfile.Exists(FLAGS.map) == False:
raise ValueError('Map not exists.')
map_inference(FLAGS.pb, FLAGS.out_node, FLAGS.image, FLAGS.map)
