Question

我正在使用tensorflow对经过训练的模型进行一些推断。

现在，我从张量流中得到结果，

def load_graph():
    ''' This method loads the graph '''
    print 'load graphs'
    pb_graphs = ['/Users/pm/Downloads/frozen_graph.pb']
    graphs = []
    for each in pb_graphs:
        with tf.gfile.GFile(each, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
        with tf.Graph().as_default() as graph:
            tf.import_graph_def(
                graph_def,
                input_map=None,
                return_elements=None,
                name=""
            )
        graphs.append(graph)
    oos_graph = graphs[0]
    print 'graph loaded'
    return oos_graph


def run_inference(graph):
    sess = tf.Session(graph=graph, config=tf.ConfigProto(log_device_placement=False))
    # (1892, 11776),(1892, 12288),(1892, 12800),(1892, 13312),(1892, 9728)]:
    for each in [(1892, 11264)]:
        y,x = each
        print y,x
        y1 = y + 1024
        x1 = x + 1024

        pano_images = pano_img[y:y+1024,x:x+1024]

        x_batch = pano_images.astype('float32')
        pred_boxes = graph.get_tensor_by_name("decoder/cat:0")
        pred_confs = graph.get_tensor_by_name("decoder/shape:0")
        x = graph.get_tensor_by_name("x_in:0")
        feed_dict_testing = {x: x_batch}
        t1 = datetime.now()
        np_pred_confs, np_pred_boxes = sess.run([pred_confs, pred_boxes], feed_dict=feed_dict_testing)      
        t2 = datetime.now()
        delta = t2 - t1
        print 'time taken in seconds == ', delta.total_seconds()
        print 'np_pred_boxes ==', np_pred_boxes.shape
        print 'np_pred_confs ==', np_pred_confs.shape
        print 'np_pred_boxes ==', type(np_pred_boxes.shape)
        print 'np_pred_confs ==', type(np_pred_confs.shape)     
        with sess.as_default():
            img = Image.fromarray(pred_boxes.eval())
            img.save('/Users/pm/Downloads/tf_image.png')

我想拍摄pred_box并生成图像，看看张量流预测了什么。

使用以下代码将张量流预测转换为图像即;将其转换为numpy数组并生成图像。

但它不起作用。读取我可以使用eval（）函数将张量转换为numpy数组。

tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'x_in' with dtype float and shape [1024,1024,3]
     [[Node: x_in = Placeholder[dtype=DT_FLOAT, shape=[1024,1024,3], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

使用张量流预测并将其转换为图像

0 个答案: