internal static InternalArray bin_conv2d(InternalArray input, InternalArray weight, InternalArray bias, InternalArray alpha, int[] kernel_size, int[] stride, int[] padding) { var col_tensor = THWrapper.THFloatTensor_new(); var output = THWrapper.THFloatTensor_new(); var _alpha = alpha.ToTHTensor(); var _input = input.ToTHTensor(); var _weight = weight.ToTHTensor(); IntPtr _bias; if (bias == null) { _bias = THWrapper.THFloatTensor_new(); } else { _bias = bias.ToTHTensor(); } binop.THNN_Bin_SpatialConvolutionMM_updateOutput(_input, output, _weight, _bias, col_tensor, _alpha, kernel_size[0], kernel_size[1], stride[0], stride[1], padding[0], padding[1]); THWrapper.THFloatTensor_free(col_tensor); var ret = InternalArray.FromTHFloatTensor(output); THWrapper.THFloatTensor_free(output); THWrapper.THFloatTensor_free(_bias); THWrapper.THFloatTensor_free(_input); THWrapper.THFloatTensor_free(_alpha); return(ret); }
internal static InternalArray fpbin_conv2d(InternalArray input, InternalArray weight, InternalArray bias, InternalArray alpha, int[] kernel_size, int[] stride, int[] padding) { var col_tensor = THWrapper.THFloatTensor_new(); var output = THWrapper.THFloatTensor_new(); var _alpha = alpha.ToTHTensor(); var cln = new InternalArray(input.Shape); for (int i = 0; i < cln.Data.Length; i++) { cln.Data[i] = input.QIntData[i]; } var _input = cln.ToTHTensor(); var _weight = weight.ToTHTensor(); IntPtr _bias; if (bias == null) { _bias = THWrapper.THFloatTensor_new(); } else { _bias = bias.ToTHTensor(); } binop.THNN_Bin_SpatialConvolutionMM_updateOutput(_input, output, _weight, _bias, col_tensor, _alpha, kernel_size[0], kernel_size[1], stride[0], stride[1], padding[0], padding[1], true); THWrapper.THFloatTensor_free(col_tensor); var ret = InternalArray.FromTHFloatTensor(output); ret.QIntData = new short[ret.Data.Length]; for (int i = 0; i < ret.Data.Length; i++) { ret.QIntData[i] = (short)ret.Data[i]; } ret.Data = null; THWrapper.THFloatTensor_free(output); THWrapper.THFloatTensor_free(_bias); THWrapper.THFloatTensor_free(_input); THWrapper.THFloatTensor_free(_alpha); return(ret); }
public IntPtr ToTHTensor() { IntPtr ret = (IntPtr)0; if (IntData != null) { ret = THWrapper.THIntTensor_new(); CreatedTensors.Add(ret); if (Shape.Length == 2) { THWrapper.THIntTensor_resize2d(ret, Shape[0], Shape[1]); for (int i = 0; i < Shape[0]; i++) { for (int j = 0; j < Shape[1]; j++) { THWrapper.THIntTensor_set2d(ret, i, j, Get2DInt(i, j)); } } } if (Shape.Length == 3) { THWrapper.THIntTensor_resize3d(ret, Shape[0], Shape[1], Shape[2]); for (int k = 0; k < Shape[0]; k++) { for (int i = 0; i < Shape[1]; i++) { for (int j = 0; j < Shape[2]; j++) { THWrapper.THIntTensor_set3d(ret, k, i, j, Get3DInt(k, i, j)); } } } } if (Shape.Length == 4) { THWrapper.THIntTensor_resize4d(ret, Shape[0], Shape[1], Shape[2], Shape[3]); for (int k1 = 0; k1 < Shape[0]; k1++) { for (int k = 0; k < Shape[1]; k++) { for (int i = 0; i < Shape[2]; i++) { for (int j = 0; j < Shape[3]; j++) { THWrapper.THIntTensor_set4d(ret, k1, k, i, j, Get4DInt(k1, k, i, j)); } } } } } return(ret); } if (Data != null) { ret = THWrapper.THFloatTensor_new(); CreatedTensors.Add(ret); if (Shape.Length == 2) { THWrapper.THFloatTensor_resize2d(ret, Shape[0], Shape[1]); for (int i = 0; i < Shape[0]; i++) { for (int j = 0; j < Shape[1]; j++) { THWrapper.THFloatTensor_set2d(ret, i, j, Get2D(i, j)); } } } if (Shape.Length == 3) { THWrapper.THFloatTensor_resize3d(ret, Shape[0], Shape[1], Shape[2]); for (int k = 0; k < Shape[0]; k++) { for (int i = 0; i < Shape[1]; i++) { for (int j = 0; j < Shape[2]; j++) { THWrapper.THFloatTensor_set3d(ret, k, i, j, Get3D(k, i, j)); } } } } if (Shape.Length == 4) { THWrapper.THFloatTensor_resize4d(ret, Shape[0], Shape[1], Shape[2], Shape[3]); for (int k1 = 0; k1 < Shape[0]; k1++) { for (int k = 0; k < Shape[1]; k++) { for (int i = 0; i < Shape[2]; i++) { for (int j = 0; j < Shape[3]; j++) { THWrapper.THFloatTensor_set4d(ret, k1, k, i, j, Get4D(k1, k, i, j)); } } } } } } return(ret); }
internal static InternalArray fpbin_linear(InternalArray input, InternalArray weight, InternalArray bias, InternalArray alpha) { var m = input.Shape[0]; var n = input.Shape[1]; var k = weight.Shape[0]; /** * * m = input.data.shape[0] * n = input.data.shape[1] * k = weight.data.shape[0] * out_tensor = torch.FloatTensor() * bin_input = torch.IntTensor() * use_cuda = input.is_cuda * binop.encode_rows_cpu(input.data, bin_input) * binop.binary_gemm_cpu(bin_input, weight.data, output.data, m, n, k, 1, 0, 0, alpha.data) * output.data.mul_(alpha.data.t().expand(output.shape)) * if bias is not None: * output.data.add_(bias.data.expand(output.shape)) * return output * */ //InternalArray output = new InternalArray(new int[] { }); var cln = new InternalArray(input.Shape); for (int i = 0; i < cln.Data.Length; i++) { cln.Data[i] = input.QIntData[i]; } var _input = cln.ToTHTensor(); var bin_input = THWrapper.THIntTensor_new(); encode_rows_cpu(_input, bin_input); //var temp = InternalArray.FromTHIntTensor(bin_input); var _alpha = alpha.ToTHTensor(); //var _bin_input = bin_input.ToTHTensor(); var _weight = weight.ToTHTensor(); var _output = THWrapper.THFloatTensor_new(); binop.fpbinary_gemm_cpu(bin_input, _weight, _output, m, n, k, 1, 0, 0, _alpha); var temp2 = InternalArray.FromTHFloatTensor(_output); THWrapper.THFloatTensor_free(_input); THWrapper.THIntTensor_free(bin_input); //var tt = alpha.ToTHTensor(); var ttt = alpha.Transpose2D(); ttt.QIntData = new short[ttt.Data.Length]; for (int i = 0; i < ttt.Data.Length; i++) { ttt.QIntData[i] = (short)(ttt.Data[i] * 256); } ttt.Data = null; //var newt=THWrapper.THFloatTensor_newTranspose(tt, 0, 1); /*output.data.mul_(alpha.data.t().expand(output.shape)) */ if (bias != null) { throw new NotImplementedException(); /* * if bias is not None: * output.data.add_(bias.data.expand(output.shape))*/ } var output = InternalArray.FromTHFloatTensor(_output); output.QIntData = new short[output.Data.Length]; for (int i = 0; i < output.QIntData.Length; i++) { output.QIntData[i] = (short)(output.Data[i] * 256); } output.Data = null; for (int i = 0; i < ttt.QIntData.Length; i++) { var val4 = (short)((int)(output.QIntData[i] * ttt.QIntData[i]) >> 8); //output.QIntData[i] = output.Data[i] * ttt.Data[i]; output.QIntData[i] = val4; } THWrapper.THFloatTensor_free(_output); return(output); }
internal static InternalArray bin_linear(InternalArray input, InternalArray weight, InternalArray bias, InternalArray alpha) { var m = input.Shape[0]; var n = input.Shape[1]; var k = weight.Shape[0]; /** * * m = input.data.shape[0] * n = input.data.shape[1] * k = weight.data.shape[0] * out_tensor = torch.FloatTensor() * bin_input = torch.IntTensor() * use_cuda = input.is_cuda * binop.encode_rows_cpu(input.data, bin_input) * binop.binary_gemm_cpu(bin_input, weight.data, output.data, m, n, k, 1, 0, 0, alpha.data) * output.data.mul_(alpha.data.t().expand(output.shape)) * if bias is not None: * output.data.add_(bias.data.expand(output.shape)) * return output * */ //InternalArray output = new InternalArray(new int[] { }); var _input = input.ToTHTensor(); var bin_input = THWrapper.THIntTensor_new(); encode_rows_cpu(_input, bin_input); var temp = InternalArray.FromTHIntTensor(bin_input); var _alpha = alpha.ToTHTensor(); //var _bin_input = bin_input.ToTHTensor(); var _weight = weight.ToTHTensor(); var _output = THWrapper.THFloatTensor_new(); binop.binary_gemm_cpu(bin_input, _weight, _output, m, n, k, 1, 0, 0, _alpha); var temp2 = InternalArray.FromTHFloatTensor(_output); THWrapper.THFloatTensor_free(_input); THWrapper.THIntTensor_free(bin_input); //var tt = alpha.ToTHTensor(); var ttt = alpha.Transpose2D(); //var newt=THWrapper.THFloatTensor_newTranspose(tt, 0, 1); /*output.data.mul_(alpha.data.t().expand(output.shape)) */ if (bias != null) {/* * if bias is not None: * output.data.add_(bias.data.expand(output.shape))*/ } var output = InternalArray.FromTHFloatTensor(_output); for (int i = 0; i < ttt.Data.Length; i++) { output.Data[i] *= ttt.Data[i]; } THWrapper.THFloatTensor_free(_output); return(output); }
/*public static void memcpy(float[] a1, long dst, float[] a2, long dst2, long size) * { * for (int i = 0; i < size / 4; i++) * { * a1[i + dst] = a2[i + dst2]; * } * }*/ /* * THFloatTensor *input, * THFloatTensor *output, * THIntTensor *weight, * THFloatTensor *bias, * THFloatTensor *columns, * THFloatTensor *alphas, * int kH, int kW, * int dH, int dW, * int padH, int padW)*/ public static void THNN_Bin_SpatialConvolutionMM_updateOutput( IntPtr input, IntPtr output, IntPtr weight, IntPtr bias, IntPtr columns, IntPtr alphas, int kH, int kW, int dH, int dW, int padH, int padW , bool quantOutput = false) { int ndim = THWrapper.THFloatTensor_nDimension(input); int dimf = 0; int dimh = 1; int dimw = 2; if (ndim == 4) { dimf++; dimh++; dimw++; } var nInputPlane = THWrapper.THFloatTensor_size(input, dimf); var inputHeight = THWrapper.THFloatTensor_size(input, dimh); var inputWidth = THWrapper.THFloatTensor_size(input, dimw); var nOutputPlane = THWrapper.THFloatTensor_size(weight, 0); var outputHeight = (inputHeight + 2 * padH - kH) / dH + 1; var outputWidth = (inputWidth + 2 * padW - kW) / dW + 1; //InternalArray ones = new InternalArray(new int[] { 1 }); IntPtr ones = THWrapper.THFloatTensor_new(); if (bias != null && THWrapper.THFloatTensor_nDimension(bias) == 1) { THWrapper.THFloatTensor_resize2d(bias, THWrapper.THFloatTensor_size(bias, 0), 1); //THFloatTensor_resize2d(bias, bias.Shape[0], 1); } THWrapper.THFloatTensor_resize2d(ones, 1, outputHeight * outputWidth); //THFloatTensor_resize2d(ones, 1, outputHeight * outputWidth); THWrapper.THFloatTensor_fill(ones, 1); //THFloatTensor_fill(ones, 1); var T = THWrapper.THFloatTensor_size(input, 0); //InternalArray bin_col = new InternalArray(new int[] { 1 }); var bin_col = THWrapper.THIntTensor_new(); THWrapper.THFloatTensor_resize4d(output, T, (int)nOutputPlane, outputHeight, outputWidth); //THFloatTensor_resize4d(output, T, (int)nOutputPlane, outputHeight, outputWidth); THWrapper.THFloatTensor_resize3d(columns, T, kW * kH * nInputPlane, outputHeight * outputWidth); //THFloatTensor_resize3d(columns, T, kW * kH * nInputPlane, outputHeight * outputWidth); THWrapper.THIntTensor_resize3d(bin_col, T, (int)nOutputPlane, outputHeight * outputWidth); //THIntTensor_resize3d(bin_col, T, (int)nOutputPlane, outputHeight * outputWidth); for (int t = 0; t < T; t++) { /*var input_t = input.Get2DImageFrom4DArray(0, t); * var columns_t = columns.Get2DImageFrom4DArray(0, t); * var bin_col_t = bin_col.Get2DImageFrom4DArray(0, t);*/ //var _bin_col = bin_col.ToTHTensor(); var input_t = THWrapper.THFloatTensor_newSelect(input, 0, t); var columns_t = THWrapper.THFloatTensor_newSelect(columns, 0, t); var bin_col_t = THWrapper.THIntTensor_newSelect(bin_col, 0, t); /* var bbb = InternalArray.FromTHFloatTensor(columns_t); * for (int i = 0; i < bbb.Data.Length; i++) * { * var res = bbb.Data[i]; * if (res != 0) * { * * } * * }*/ THNN_unfolded_copy( columns_t, input_t, kW, kH, dW, dH, padW, padH, (int)nInputPlane, (int)inputWidth, (int)inputHeight, (int)outputWidth, (int)outputHeight ); // bbb = InternalArray.FromTHFloatTensor(columns_t); //Debug.Assert(bbb.Data[1800] == -0.053401); //Debug.Assert(bbb.Data[4200] == -0.852360); /* for (int i = 0; i < bbb.Data.Length; i++) * { * var res = bbb.Data[i]; * if (res != 0) * { * * } * }*/ encode_cols_cpu(columns_t, bin_col_t); // var bb = InternalArray.FromTHIntTensor(bin_col_t); /* for (int i = 0; i < bb.IntData.Length; i++) * { * var res = bb.IntData[i]; * if (res != 0) * { * * } * }*/ } for (int t = 0; t < T; t++) { /* THFloatTensor* output_t = THFloatTensor_newSelect(output, 0, t); * THIntTensor* bin_col_t = THIntTensor_newSelect(bin_col, 0, t); */ /*var output_t = output.Get2DImageFrom4DArray(0, t); * var bin_col_t = bin_col.Get2DImageFrom4DArray(0, t);*/ //var _output = output.ToTHTensor(); //var _bin_col = bin_col.ToTHTensor(); var output_t = THWrapper.THFloatTensor_newSelect(output, 0, t); var bin_col_t = THWrapper.THIntTensor_newSelect(bin_col, 0, t); THNN_Bin_SpatialConvolutionMM_updateOutput_frame( output_t, weight, bias, ones, bin_col_t, alphas, kW, kH, dW, dH, padW, padH, nInputPlane, inputWidth, inputHeight, nOutputPlane, outputWidth, outputHeight, quantOutput ); } }