Created by: newdimitri

您好，研发老师，我在macos、Python3.7、paddlepaddle 1.2下运行https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/object_detection 中的代码时，出现错误：

Traceback (most recent call last):
  File "train.py", line 317, in <module>
    val_file_list=val_file_list)
  File "train.py", line 164, in train
    is_train=True)
  File "train.py", line 86, in build_program
    with fluid.program.devices_num_guard(main_prog, startup_prog):
AttributeError: module 'paddle.fluid' has no attribute 'program'

运行指令为：

python3 -u train.py --batch_size=64 --dataset='coco2017' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/' --use_gpu=false

出错代码的前后文为：

def build_program(main_prog, startup_prog, train_params, is_train):
    image_shape = train_params['image_shape']
    class_num = train_params['class_num']
    ap_version = train_params['ap_version']
    outs = []
    with fluid.program.devices_num_guard(main_prog, startup_prog):
        py_reader = fluid.layers.py_reader(
            capacity=64,
            shapes=[[-1] + image_shape, [-1, 4], [-1, 1], [-1, 1]],
            lod_levels=[0, 1, 1, 1],
            dtypes=["float32", "float32", "int32", "int32"],
            use_double_buffer=True)
        with fluid.unique_name.guard():
            image, gt_box, gt_label, difficult = fluid.layers.read_file(py_reader)
            locs, confs, box, box_var = mobile_net(class_num, image, image_shape)
            if is_train:
                with fluid.unique_name.guard("train"):
                    loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box,
                        box_var)
                    loss = fluid.layers.reduce_sum(loss)
                    optimizer = optimizer_setting(train_params)
                    optimizer.minimize(loss)
                outs = [py_reader, loss]
            else:
                with fluid.unique_name.guard("inference"):
                    nmsed_out = fluid.layers.detection_output(
                        locs, confs, box, box_var, nms_threshold=0.45)
                    map_eval = fluid.evaluator.DetectionMAP(
                        nmsed_out,
                        gt_label,
                        gt_box,
                        difficult,
                        class_num,
                        overlap_threshold=0.5,
                        evaluate_difficult=False,
                        ap_version=ap_version)
                # nmsed_out and image is used to save mode for inference
                outs = [py_reader, map_eval, nmsed_out, image]
    return outs