Created by: ylj135cool

PaddlePaddle：1.8.4 PaddleHub：1.8.2 模型：chinese_text_detection_db_mobile=1.0.3 系统：win10 编译环境：VS2019

问题描述： 使用C++部署方式，按照文档提示，测试PaddleHub中的文字检测模型“chinese_text_detection_db_mobile”时，在预测时报错。

报错内容如下：

`--------------------------------------------
C++ Call Stacks (More useful to developers):
--------------------------------------------
Windows not support stack backtrace yet.

----------------------
Error Message Summary:
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InvalidArgumentError: Broadcast dimension mismatch. Operands could not be broadcast together with the shape of X = [1, 96, 12, 40] and the shape of Y = [1, 96, 11, 40]. Received [12] in X is not equal to [11] in Y at i:2.
  [Hint: Expected x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1 == true, but received x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1:0 != true:1.] at (D:\1.8.2\paddle\paddle/fluid/operators/elementwise/elementwise_op_function.h:157)`

实际的调用代码如下：

        void Pridict(cv::Mat& mat)
        {
            int batch_size = 1;
            int channels = mat.channels();
            int height = mat.rows;
            int width = mat.cols;
            int nums = batch_size * channels * height * width;

            float* input = new float[nums] {0};
            cv::Mat newMat;
            mat.convertTo(newMat, CV_32FC3);

            // data format NHWC to NCHW
            int idx = 0;
            for (int i = 0; i < batch_size; ++i)
            {
                int offsetBatch = i * width * height * channels;
                for (int j = 0; j < height; ++j)
                {
                    for (int k = 0; k < width; ++k)
                    {
                        auto data = newMat.at<cv::Vec3f>(j, k);
                        int offset = j * width + k;
                        input[offsetBatch + offset] = data[0];
                        input[offsetBatch + width * height + offset] = data[1];
                        input[offsetBatch + width * height * 2 + offset] = data[2];
                    }
                }
            }
            //for (int i = 0; i < channels; ++i) {
            //    cv::extractChannel(newMat, cv::Mat(height, width, CV_32FC1, input + i * height * width), i);
            //}
            auto input_names = predictor->GetInputNames();
            auto input_t = predictor->GetInputTensor(input_names[0]);
            input_t->Reshape({ batch_size, channels, height, width });
            input_t->copy_from_cpu(input);

            //此处执行后报错
            predictor->ZeroCopyRun();

            std::vector<float> out_data;
            auto output_names = predictor->GetOutputNames();
            auto output_t = predictor->GetOutputTensor(output_names[0]);
            std::vector<int> output_shape = output_t->shape();
            int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
                std::multiplies<int>());

            out_data.resize(out_num);
            output_t->copy_to_cpu(out_data.data());
            delete[] input;
        }

其中mat是直接从文件加载的一个jpg文件，又遇到类似情况的没有？还请各位大佬指导一下是什么原因，如何解决？ 谢谢大家。