Processing batches, offset in output

Robotics & Edge Computing Jetson & Embedded Systems Jetson TX2
1

Hello,

I am trying to process batches on my custom caffe model.
I downloaded this github repository https://github.com/yankailab/jetson-inference-batch and it works fine with usual model like googlenet, but not with mine, so I might have wrongly set something.

Let me explain, when I infer with a batch of 1 image, the output is fine but I noticed that when I infer with a batch of 2 or more images, there is an offset of 18 in the array

mOutputs[0].CUDA

, even though its length is correct.
I have 6 labels, so the offset is 3 times my number of labels and I noticed a factor 3 in the code of the function

gpuPreImageNetMeanBatch

which I tried to remove. It worked well, but googlenet didn’t work anymore.

So I assume that googlenet should work fine and that I am setting something wrong. I am using basically the source code of the github repo, except from the loading of the image, which I do using openCV.

My images are 80x80 and B&W.
googlenet images have different size and 3 or 4 color channels, but I process them with the same code.

My openCV loading source code :

// I get the filename(s) from the command line and do this for each one
cv::Mat image;
image = cv::imread(filename);
uchar* camData = new uchar[image.total()*4];
cv::Mat imgRGBA(image.size(), CV_8UC4, camData);
cv::cvtColor(image, imgRGBA, CV_BGR2RGBA, 4);
cv::Mat imgFloat;
imgRGBA.convertTo(imgFloat, CV_32F);
// Then I use imgFloat to set the image width, height and to set cpuPtr content like in the original loadImageRGBA function

Is this here that I am doing some wrong stuff?

I can provide more details, I just don’t know what right now.

Thanks in advance.

2

Hi,

Does your use cause only work on the custom branch?

If not, could you try if there is the same issue from our official jetson_inference sample?

Thanks.

3

Well, as far as I know, your official github does not allow batches of more than 1 images, does it?

But yes I tested on your official github with batches of 1 and it works as expected. I even tried to replace your file loadImage.cpp (and header) with mine and I got the same results, this is why I was not worried about my custom image loading file.

To clarify a little, I took the files required to build the imagenet-console target from [url]https://github.com/dusty-nv/jetson-inference[/url]. Then I replaced the loadImage files with mine. I customized the CMake to fit my only target. Everything worked as expected.

Then, I learned about [url]https://github.com/yankailab/jetson-inference-batch[/url] and wanted to integrate this feature in my custom repo, so I took the methods I needed from imageNet.h/cpp/cu and modified my imagenet-console.cpp to fit the new requirements. And I got this offset issue.

Could it be the image format? My custom model eats B&W, fixed sized images, while googlenet eats random sized, 3 or 4 channels images.

4

Also, I noticed that this github [url]https://github.com/yankailab/jetson-inference-batch[/url] does not free the CPU pointer after each image but after each batch, is it relevant?

Because when I run this, I clearly see in the debug logs that for a batch size of n, the n-1 first images of the batch are not free’d (the memory allocation of the next batch begins where the n-th image was).

Edit: I tried using my B&W images with googlenet, and the results are consistent between two different batch sizes. So there has to be something wrong with my custom model. What can I provide so you can help me?

5

Hi,

Want to clarify first:
You can get the expected results for gray images with jetson_inference but error with the custom branch, is it correct?
If yes, it’s recommended to enable jeston_inference for multiple batch support:

Modify here:
[url]https://github.com/dusty-nv/jetson-inference/blob/master/tensorNet.h#L78[/url]
[url]https://github.com/dusty-nv/jetson-inference/blob/master/imageNet.cpp#L306[/url]

Thanks

6

Hi,

Clarification: I get erroneous results when I use a batch size >=2 on the custom branch. (erroneous meaning with a weird offset)

I enabled batch support on jetson_inference.

The results for a given image are sometimes different when I use a different batch size (e.g. 1 and 2).
I always put as many images in a batch as the maximum batch size.

Is there anything else I need to modify?

7

Hi,

I figured the solution out.

My model eats grayscale images, so I had to build a custom loadImageBW function and I had to build another cuda function like cudaPreImageNetMean but without mean (image is pre-processed) and with adjustements when filling the output pointer.

See the cuda code below (code of loadImageBW is obvious but I can provide it if needed).

// gpuPreImageNetBatchBW
__global__ void gpuPreImageNetBatchBW( int numBatch, float2 scale, float* input, int iWidth, float* output, int oWidth, int oHeight )
{
	const int x = blockIdx.x * blockDim.x + threadIdx.x;
	const int y = blockIdx.y * blockDim.y + threadIdx.y;
	const int n = oWidth * oHeight;
	
	if( x >= oWidth || y >= oHeight )
		return;

	const int dx = ((float)x * scale.x);
	const int dy = ((float)y * scale.y);

	const float px  = input[ dy * iWidth + dx ];
	
	output[n * numBatch + y * oWidth + x] = px;
}


// cudaPreImageNetBatchBW
cudaError_t cudaPreImageNetBatchBW( int numBatch, float* input, size_t inputWidth, size_t inputHeight,
				             float* output, size_t outputWidth, size_t outputHeight )
{
	if( !input || !output )
		return cudaErrorInvalidDevicePointer;

	if( inputWidth == 0 || outputWidth == 0 || inputHeight == 0 || outputHeight == 0 )
		return cudaErrorInvalidValue;

	const float2 scale = make_float2( float(inputWidth) / float(outputWidth),
							    float(inputHeight) / float(outputHeight) );

	// launch kernel
	const dim3 blockDim(8, 8);
	const dim3 gridDim(iDivUp(outputWidth,blockDim.x), iDivUp(outputHeight,blockDim.y));

	gpuPreImageNetBatchBW<<<gridDim, blockDim>>>(numBatch, scale, input, inputWidth, output, outputWidth, outputHeight);

	return CUDA(cudaGetLastError());
}

Thank you for your time!

8

Thanks for your feedback : )