我正在研究鼠尾草jupyter笔记本（环境：anaconda3/envs/mxnet_p36/lib/python3.6）。
我成功运行了本教程：https://github.com/onnx/tutorials/blob/master/tutorials/MXNetONNXExport.ipynb
然后，在相同的环境下，我尝试将相同的过程应用于由贤者训练工作生成的文件。因此，我将 S3模型工件文件用作输入，更改了教程代码的某些行以满足我的需要。 我使用内置对象检测SSD VGG-16网络，其超参数image_shape：300。
sym = './model_algo_1-symbol.json'
params = './model_algo_1-0000.params'
input_shape = (1,3,300,300)
并将verbose=True作为export_model()方法中的最后一个参数：
converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file, True)
运行代码时，我得到了该错误（帖子末尾的详细输出）：
MXNetError: Error in operator multibox_target: [14:36:32] src/operator/contrib/./multibox_target-inl.h:224: Check failed: lshape.ndim() == 3 (-1 vs. 3) : Label should be [batch, num_labels, label_width] tensor
到目前为止，我找不到任何解决方案：
input_shape = (1,3,300,300)是错误的，但我无法
找出来； 有人知道解决此问题的方法或在本地计算机上使用模型的解决方法吗？
（我的意思是不必部署到AWS）
infer_shape error. Arguments:
data: (1, 3, 300, 300)
conv3_2_weight: (256, 256, 3, 3)
fc7_bias: (1024,)
multi_feat_3_conv_1x1_conv_weight: (128, 512, 1, 1)
conv4_1_bias: (512,)
conv5_3_bias: (512,)
relu4_3_cls_pred_conv_bias: (16,)
multi_feat_2_conv_3x3_relu_cls_pred_conv_weight: (24, 512, 3, 3)
relu4_3_loc_pred_conv_bias: (16,)
relu7_cls_pred_conv_weight: (24, 1024, 3, 3)
conv3_3_bias: (256,)
multi_feat_5_conv_3x3_relu_cls_pred_conv_weight: (16, 256, 3, 3)
conv4_3_weight: (512, 512, 3, 3)
conv1_2_bias: (64,)
multi_feat_2_conv_3x3_relu_cls_pred_conv_bias: (24,)
multi_feat_4_conv_3x3_conv_weight: (256, 128, 3, 3)
conv4_1_weight: (512, 256, 3, 3)
relu4_3_scale: (1, 512, 1, 1)
multi_feat_4_conv_3x3_conv_bias: (256,)
multi_feat_5_conv_3x3_relu_cls_pred_conv_bias: (16,)
conv2_2_weight: (128, 128, 3, 3)
multi_feat_3_conv_3x3_relu_loc_pred_conv_weight: (24, 256, 3, 3)
multi_feat_5_conv_3x3_conv_bias: (256,)
conv5_1_bias: (512,)
multi_feat_3_conv_3x3_conv_bias: (256,)
conv2_1_bias: (128,)
conv5_2_weight: (512, 512, 3, 3)
multi_feat_5_conv_3x3_relu_loc_pred_conv_weight: (16, 256, 3, 3)
multi_feat_4_conv_3x3_relu_loc_pred_conv_weight: (16, 256, 3, 3)
multi_feat_2_conv_3x3_conv_weight: (512, 256, 3, 3)
multi_feat_2_conv_1x1_conv_bias: (256,)
multi_feat_2_conv_1x1_conv_weight: (256, 1024, 1, 1)
conv4_3_bias: (512,)
relu7_cls_pred_conv_bias: (24,)
fc6_bias: (1024,)
conv2_1_weight: (128, 64, 3, 3)
multi_feat_2_conv_3x3_conv_bias: (512,)
multi_feat_2_conv_3x3_relu_loc_pred_conv_weight: (24, 512, 3, 3)
multi_feat_5_conv_1x1_conv_bias: (128,)
relu7_loc_pred_conv_bias: (24,)
multi_feat_3_conv_3x3_relu_loc_pred_conv_bias: (24,)
conv3_3_weight: (256, 256, 3, 3)
conv1_2_weight: (64, 64, 3, 3)
multi_feat_2_conv_3x3_relu_loc_pred_conv_bias: (24,)
conv1_1_bias: (64,)
multi_feat_4_conv_3x3_relu_cls_pred_conv_bias: (16,)
conv4_2_weight: (512, 512, 3, 3)
conv5_3_weight: (512, 512, 3, 3)
relu7_loc_pred_conv_weight: (24, 1024, 3, 3)
multi_feat_3_conv_3x3_conv_weight: (256, 128, 3, 3)
conv3_1_weight: (256, 128, 3, 3)
multi_feat_4_conv_3x3_relu_cls_pred_conv_weight: (16, 256, 3, 3)
relu4_3_loc_pred_conv_weight: (16, 512, 3, 3)
multi_feat_5_conv_3x3_conv_weight: (256, 128, 3, 3)
fc7_weight: (1024, 1024, 1, 1)
conv4_2_bias: (512,)
multi_feat_3_conv_3x3_relu_cls_pred_conv_weight: (24, 256, 3, 3)
multi_feat_3_conv_3x3_relu_cls_pred_conv_bias: (24,)
conv2_2_bias: (128,)
conv5_1_weight: (512, 512, 3, 3)
multi_feat_3_conv_1x1_conv_bias: (128,)
multi_feat_4_conv_3x3_relu_loc_pred_conv_bias: (16,)
conv1_1_weight: (64, 3, 3, 3)
multi_feat_4_conv_1x1_conv_bias: (128,)
conv3_1_bias: (256,)
multi_feat_5_conv_3x3_relu_loc_pred_conv_bias: (16,)
multi_feat_4_conv_1x1_conv_weight: (128, 256, 1, 1)
fc6_weight: (1024, 512, 3, 3)
multi_feat_5_conv_1x1_conv_weight: (128, 256, 1, 1)
conv3_2_bias: (256,)
conv5_2_bias: (512,)
relu4_3_cls_pred_conv_weight: (16, 512, 3, 3)