public FloatParallelConvolutionJobs(Image <float> input,
ParallelConvolutionData <float> convolutions,
JobHandle dependency)
: base(convolutions, dependency,
(jobs, sequence, arg3) =>
{
var output = sequence.Output;
var firstConvolution = sequence.Convolution[0];
jobs[0] = new FloatWithFloatConvolveJob()
{
Convolution = firstConvolution,
Input = input,
Output = output
};
for (var i = 1; i < sequence.Convolution.Length; i++)
{
var convolution = sequence.Convolution[i];
jobs[i] = new FloatWithFloatConvolveJob()
{
Convolution = convolution,
// each job in a sequence operates on the output of the previous one
Input = jobs[i - 0].Output,
Output = output
};
}
})
{
}
void SetupJobs()
{
m_ParallelConvolutionData = new ParallelConvolutionData <float>(m_Input, m_ParallelConvolutions);
m_PadJob = new ImagePadJob <float>(m_Input, m_PaddedGrayscaleInput, m_Pad);
m_JobHandle = m_PadJob.Schedule();
m_JobHandle.Complete();
m_NewSequence = new FloatParallelConvolutionJobs(m_PadJob.Output, m_ParallelConvolutionData, m_JobHandle);
var outImages = m_ParallelConvolutionData.OutputImages[0];
m_CombineJobFloat = new SquareCombineJobFloat()
{
A = outImages[0],
B = outImages[1],
Output = m_CombinedConvolution,
};
}
public ParallelConvolutionJobs(ParallelConvolutionData <TData> data, JobHandle dependency,
Action <JobSequence <TJob>, ParallelConvolutionData <TData> .Sequence, int> action)
{
InputImages = data.InputImages;
Jobs = new ParallelJobSequences <TJob> [data.channelCount];
for (int i = 0; i < Jobs.Length; i++)
{
Jobs[i] = new ParallelJobSequences <TJob>(data.convolutionsPerChannel, 1);
}
for (int c = 0; c < data.Channels.Length; c++)
{
var jobs = Jobs[c];
var inputImage = InputImages[c];
for (int i = 0; i < data.convolutionsPerChannel; i++)
{
var sequence = data[c, i];
var sequenceJobs = jobs[i];
action(sequenceJobs, sequence, i);
}
}
}
protected ParallelConvolutionJobs(ParallelConvolutionData <TData> data)
{
InputImages = data.InputImages;
Jobs = new ParallelJobSequences <TJob> [data.channelCount];
}
