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https://github.com/opencv/opencv.git
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Use Mat::total() in Darknet IO
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@@ -1008,7 +1008,6 @@ namespace cv {
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if (layer_type == "convolutional" || layer_type == "connected")
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if (layer_type == "convolutional" || layer_type == "connected")
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{
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{
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size_t weights_size;
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int filters;
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int filters;
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bool use_batch_normalize;
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bool use_batch_normalize;
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cv::Mat weightsBlob;
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cv::Mat weightsBlob;
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@@ -1023,7 +1022,6 @@ namespace cv {
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CV_Assert(tensor_shape[0] > 0);
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CV_Assert(tensor_shape[0] > 0);
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CV_Assert(tensor_shape[0] % groups == 0);
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CV_Assert(tensor_shape[0] % groups == 0);
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weights_size = filters * (tensor_shape[0] / groups) * kernel_size * kernel_size;
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int sizes_weights[] = { filters, tensor_shape[0] / groups, kernel_size, kernel_size };
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int sizes_weights[] = { filters, tensor_shape[0] / groups, kernel_size, kernel_size };
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weightsBlob.create(4, sizes_weights, CV_32F);
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weightsBlob.create(4, sizes_weights, CV_32F);
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}
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}
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@@ -1034,7 +1032,6 @@ namespace cv {
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CV_Assert(filters>0);
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CV_Assert(filters>0);
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weights_size = total(tensor_shape) * filters;
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int sizes_weights[] = { filters, total(tensor_shape) };
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int sizes_weights[] = { filters, total(tensor_shape) };
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weightsBlob.create(2, sizes_weights, CV_32F);
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weightsBlob.create(2, sizes_weights, CV_32F);
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}
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}
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@@ -1051,7 +1048,7 @@ namespace cv {
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ifile.read(reinterpret_cast<char *>(meanData_mat.ptr<float>()), sizeof(float)*filters);
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ifile.read(reinterpret_cast<char *>(meanData_mat.ptr<float>()), sizeof(float)*filters);
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ifile.read(reinterpret_cast<char *>(stdData_mat.ptr<float>()), sizeof(float)*filters);
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ifile.read(reinterpret_cast<char *>(stdData_mat.ptr<float>()), sizeof(float)*filters);
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}
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}
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ifile.read(reinterpret_cast<char *>(weightsBlob.ptr<float>()), sizeof(float)*weights_size);
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ifile.read(reinterpret_cast<char *>(weightsBlob.ptr<float>()), sizeof(float)*weightsBlob.total());
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// set conv/connected weights
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// set conv/connected weights
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std::vector<cv::Mat> layer_blobs;
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std::vector<cv::Mat> layer_blobs;
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