{
options_.str(""); options_.clear(); // clear contents and state flags
createBasicKernel(1, 1, 1);
+ CV_Assert(!kernelQueue.empty()); // basic kernel must be available
kernel_index_ = kernelQueue.size() - 1;
convolve(bottom, verifyTop, weight, bias, numImages, kernelQueue[kernel_index_]);
CV_Assert(phash.find(kernelQueue[kernel_index_]->kernelName) != phash.end());
tunerItems[i]->blockHeight,
tunerItems[i]->blockDepth))
{
+ CV_Assert(!kernelQueue.empty()); // basic kernel must be available
int kernelIdx = kernelQueue.size() - 1;
kernelConfig* config = kernelQueue[kernelIdx].get();
bool failed = false;
CV_LOG_INFO(NULL, "fallback to basic kernel");
options_.str(""); options_.clear(); // clear contents and state flags
createBasicKernel(1, 1, 1);
+ CV_Assert(!kernelQueue.empty()); // basic kernel must be available
kernel_index_ = kernelQueue.size() - 1;
}
this->bestKernelConfig = kernelQueue[kernel_index_];
#if APPLY_BIAS
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
#else
- ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], biasIndex + kern);
+ ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], kernelNum + kern);
#endif
}
}
if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
- // bug: https://github.com/opencv/opencv/issues/17953
- if (actType == "ChannelsPReLU" && bias_term == false &&
- backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
- {
- applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
- }
-
Net net;
int convId = net.addLayer(convParams.name, convParams.type, convParams);
int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);