private static int TestReLU(float slope, bool usePackingLayout)
{
using var a = TestUtil.TestUtil.RandomMat(6, 7, 8);
using var pd = new ParamDict();
pd.Set(0, slope);//slope
using var tmp = new Mat();
var weights = new[] { tmp };
using var vector = new StdVector <Mat>(weights);
using var mb = new ModelBinFromMatArray(vector);
using var opt = new Option
{
NumThreads = 1,
UseVulkanCompute = true,
UseFP16Packed = false,
UseFP16Storage = false,
UseFP16Arithmetic = false,
UseInt8Storage = false,
UseInt8Arithmetic = false,
UsePackingLayout = usePackingLayout
};
var ret = TestUtil.TestUtil.TestLayer <ReLU>("ReLU", pd, mb, opt, a);
if (ret != 0)
{
Console.Error.WriteLine($"test_relu failed slope={slope} use_packing_layout={usePackingLayout}");
}
weights?.DisposeElement();
return(ret);
}
private static int TestDeconvolutionInt8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
using var a = TestUtil.TestUtil.RandomMat(w, h, c);
using var pd = new ParamDict();
pd.Set(0, outch); // num_output
pd.Set(1, kernel); // kernel_w
pd.Set(2, dilation); // dilation_w
pd.Set(3, stride); // stride_w
pd.Set(4, pad); // pad_w
pd.Set(5, bias); // bias_term
pd.Set(6, outch * c * kernel * kernel);
pd.Set(8, 1); // int8_scale_term
var weights = new Mat[bias > 0 ? 4 : 3];
weights[0] = TestUtil.TestUtil.RandomMat(outch * c * kernel * kernel);
if (bias > 0)
{
weights[1] = TestUtil.TestUtil.RandomMat(outch);
weights[2] = TestUtil.TestUtil.RandomMat(outch);
weights[3] = TestUtil.TestUtil.RandomMat(1);
}
else
{
weights[1] = TestUtil.TestUtil.RandomMat(outch);
weights[2] = TestUtil.TestUtil.RandomMat(1);
}
using var vector = new StdVector <Mat>(weights);
using var mb = new ModelBinFromMatArray(vector);
using var opt = new Option
{
NumThreads = 1,
UseVulkanCompute = false,
UseInt8Inference = true,
UseFP16Packed = false,
UseFP16Storage = false,
UseFP16Arithmetic = false,
UseInt8Storage = false,
UseInt8Arithmetic = false,
UsePackingLayout = false
};
var ret = TestUtil.TestUtil.TestLayer <Deconvolution>("Deconvolution", pd, mb, opt, a);
if (ret != 0)
{
Console.Error.WriteLine($"test_convolution failed w={w} h={h} c={c} outch={outch} kernel={kernel} dilation={dilation} stride={stride} pad={pad} bias={bias}");
}
return(ret);
}
private static int TestConvolutionDepthWise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group, bool usePackingLayout)
{
using var a = TestUtil.TestUtil.RandomMat(w, h, c);
using var pd = new ParamDict();
pd.Set(0, outch); // num_output
pd.Set(1, kernel); // kernel_w
pd.Set(2, dilation); // dilation_w
pd.Set(3, stride); // stride_w
pd.Set(4, pad); // pad_w
pd.Set(5, bias); // bias_term
pd.Set(6, outch / group * c / group * kernel * kernel * group);
pd.Set(7, group);
var weights = new Mat[bias > 0 ? 2 : 1];
weights[0] = TestUtil.TestUtil.RandomMat(outch / group * c / group * kernel * kernel * group);
weights[1] = TestUtil.TestUtil.RandomMat(outch);
using var vector = new StdVector <Mat>(weights);
using var mb = new ModelBinFromMatArray(vector);
using var opt = new Option
{
NumThreads = 1,
UseVulkanCompute = true,
UseInt8Inference = false,
UseFP16Packed = false,
UseFP16Storage = false,
UseFP16Arithmetic = false,
UseInt8Storage = false,
UseInt8Arithmetic = false,
UsePackingLayout = usePackingLayout
};
var ret = TestUtil.TestUtil.TestLayer <ConvolutionDepthWise>("ConvolutionDepthWise", pd, mb, opt, a);
if (ret != 0)
{
Console.Error.WriteLine($"test_convolutiondepthwise failed w={w} h={h} c={c} outch={outch} kernel={kernel} dilation={dilation} stride={stride} pad={pad} bias={bias} group={group} use_packing_layout={usePackingLayout}");
}
weights?.DisposeElement();
return(ret);
}
