Python __初始化()缺少1个必需的位置参数:';sess';在张量流中
我试图在这个脚本中使用类,它对目录“test_images”中的多个图像执行图像分类。我以前很少使用类,所以我对如何在这种情况下正确应用它们有点困惑。错误为:Python __初始化()缺少1个必需的位置参数:';sess';在张量流中,python,class,tensorflow,image-recognition,Python,Class,Tensorflow,Image Recognition,我试图在这个脚本中使用类,它对目录“test_images”中的多个图像执行图像分类。我以前很少使用类,所以我对如何在这种情况下正确应用它们有点困惑。错误为:TypeError:\uuuu init\uuuu()缺少1个必需的位置参数:“sess”。任何帮助都将不胜感激 代码如下: def image_recognition_algorithm(): def load_graph(model_file): graph = tf.Graph() graph_def = tf.Gr
TypeError:\uuuu init\uuuu()缺少1个必需的位置参数:“sess”
。任何帮助都将不胜感激
代码如下:
def image_recognition_algorithm():
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
input_mean=0, input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.read_file(file_name, input_name)
image_reader = tf.image.decode_jpeg(file_reader, channels = 3, name='jpeg_reader')
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0);
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
return result
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
class initiate_session():
def __init__(self, sess):
self.sess = sess
graph = load_graph(model_file)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name);
output_operation = graph.get_operation_by_name(output_name);
config = tf.ConfigProto(device_count={"CPU": 4},
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=4)
self.sess = tf.Session(graph=graph, config = config)
start = time.time()
results = self.sess.run(output_operation.outputs[0],
{input_operation.outputs[0]: t})
end=time.time()
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
for i in top_k:
print(file_name, labels[i], results[i])
return [file_name] + list(results)
image_list = [f for f in listdir('test_images') if isfile(join('test_images', f))]
res_list = []
for image in image_list:
if image.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')):
res_list.append(main(join('test_images', image)))
def main(self, file_name):
model_file = "tf_files/retrained_graph.pb"
label_file = "tf_files/retrained_labels.txt"
input_height = 299
input_width = 299
input_mean = 128
input_std = 128
input_layer = "Mul"
output_layer = "final_result"
t = read_tensor_from_image_file(file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
if __name__ == '__main__':
initiate_session().main()
您的
initiate\u会话。
方法有两个参数,self
,自动作为对自身的引用传入;以及sess
,需要传入。当您在此处实例化initiate_session
时:
if __name__ == '__main__':
initiate_session().main()
您需要传入一个sess
参数
但是,在您的情况下,我认为您要做的是将sess
参数删除到\uuu init\uuu()
方法中,因为您将在构造函数的后面分配给self.sess
,如下所示:
self.sess = tf.Session(graph=graph, config = config)
删除的sess
参数和行
self.sess = sess
应该可以解决您的问题。启动会话
在\uuuuu init\uuuu
中接受一个参数,但您没有通过任何验证!我会试试这个!