形状必须为 0 级，但对于具有输入形状的“file_reader”（操作：“ReadFile”）为 1 级：[0]

Question

我试图运行下面的代码，但它向我抛出了这个错误：“形状必须为 0 级，但对于 'file_reader'（操作：'ReadFile'）的输入形状为 1 级：[0]”。我试图从我的目录中读取 jpeg 格式的图像列表，然后将它们一个接一个地分类。任何帮助将不胜感激！

代码：

class image_recognition_algorithm():

def __init__(self, file_name, model_file, label_file):
    self.model_file = model_file
    self.label_file = label_file
    self.file_name = file_name

def load_graph(self):
    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(self, file_name, input_height=299, input_width=299,
                input_mean=128, input_std=128):
    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(self, 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

def main(self, file_name):
    self.model_file = "tf_files/retrained_graph.pb"
    self.label_file = "tf_files/retrained_labels.txt"
    self.input_height = 299
    self.input_width = 299
    self.input_mean = 128
    self.input_std = 128
    input_layer = "Mul"
    output_layer = "final_result"

    graph = self.load_graph()
    t = self.read_tensor_from_image_file(file_name,
                                         input_height = self.input_height,
                                         input_width = self.input_width,
                                         input_mean = self.input_mean,
                                         input_std = self.input_std)


    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])

if __name__ == '__main__':
            model_file = "tf_files/retrained_graph.pb"
            label_file = "tf_files/retrained_labels.txt"
            list_of_imgs = []
            img_dir = "./test_images/"
            for img in os.listdir("."):
                       img = os.path.join(img_dir, img)
                       a = cv2.imread(img)
                       if img.lower().endswith(".jpg"):
                           list_of_imgs.append(a.flatten())
            file_name = np.array(list_of_imgs)
            image_recognition_algorithm_obj = 
            image_recognition_algorithm(model_file, label_file, file_name)
            image_recognition_algorithm_obj.main(file_name)

Answer 1

您收到此错误是因为您将一个 numpy 数组提供给 tf.read_file 函数。

您需要为每个图像名称作为字符串调用tf.read_file。
正如它在doc for tf.read_file 中所说，输入filename: A Tensor of type string.

示例用法：

file_reader = tf.read_file("test.jpeg", "file_reader")