|
|
|
@ -556,6 +556,7 @@ namespace cv { |
|
|
|
|
{ |
|
|
|
|
int kernel_size = getParam<int>(layer_params, "size", -1); |
|
|
|
|
int pad = getParam<int>(layer_params, "pad", 0); |
|
|
|
|
int padding = getParam<int>(layer_params, "padding", 0); |
|
|
|
|
int stride = getParam<int>(layer_params, "stride", 1); |
|
|
|
|
int filters = getParam<int>(layer_params, "filters", -1); |
|
|
|
|
bool batch_normalize = getParam<int>(layer_params, "batch_normalize", 0) == 1; |
|
|
|
@ -563,13 +564,13 @@ namespace cv { |
|
|
|
|
if (flipped == 1) |
|
|
|
|
CV_Error(cv::Error::StsNotImplemented, "Transpose the convolutional weights is not implemented"); |
|
|
|
|
|
|
|
|
|
// correct the strange value of pad=1 for kernel_size=1 in the Darknet cfg-file
|
|
|
|
|
if (kernel_size < 3) pad = 0; |
|
|
|
|
if (pad) |
|
|
|
|
padding = kernel_size / 2; |
|
|
|
|
|
|
|
|
|
CV_Assert(kernel_size > 0 && filters > 0); |
|
|
|
|
CV_Assert(current_channels > 0); |
|
|
|
|
|
|
|
|
|
setParams.setConvolution(kernel_size, pad, stride, filters, current_channels, |
|
|
|
|
setParams.setConvolution(kernel_size, padding, stride, filters, current_channels, |
|
|
|
|
batch_normalize); |
|
|
|
|
|
|
|
|
|
current_channels = filters; |
|
|
|
|