為什么“conv1d”在 C 代碼、python 和 pytorch 中不同

Question

我想在 C 代碼中重現 pytorch 的“Conv1D”結果。

我嘗試使用三種方法（C 代碼、Python、Pytorch）實現“Conv1D”，但結果不同。 只有七個小數位是合理的。 假設結構中有多層conv1d，小數位數精度會逐漸降低。

根據大家的推薦，我嘗試將輸入數據的C代碼類型改為double但結果還是不正確。 我做錯了什么嗎？

例如：

Pytorch 的輸出：0.2380688339471817017

Python的輸出：0.2380688637495040894

C代碼的輸出（浮點數）：0.2380688637

C代碼的輸出（double）：0.238068885344539680

這是我目前的實現

• 輸入：

  輸入暗。 = 80，輸出變暗。 = 128，內核大小 = 5

  Pytorch：Conv1D_input.npy、Conv1D_weight.npy

  Python：Conv1D_input.npy、Conv1D_weight.npy（與Pytorch相同）

  C代碼：Conv1D_input.txt、Conv1D_weight.txt（從Pytorch轉換而來，IEEE 754單精度）

  火炬

  import torch import numpy as np from torch import nn from torch.autograd import Variable import torch.nn.functional as F import argparse import sys import io import time import os class RNN(nn.Module): def __init__(self, input_size, hidden_size): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.c1 = nn.Conv1d(input_size, hidden_size, kernel_size = 5, bias=False) self.c1.weight = torch.nn.Parameter(torch.Tensor(np.load("CONV1D_WEIGHT.npy"))) def forward(self, inputs): c = self.c1(inputs) return c input_size = 80 hidden_size = 128 kernel_size = 5 rnn = RNN(input_size, hidden_size) inputs = torch.nn.Parameter(torch.Tensor(np.load("CONV1D_IN.npy"))) print("inputs", inputs) outputs = rnn(inputs) sub_np456 = outputs[0].cpu().detach().numpy() np.savetxt("Pytorch_CONV1D_OUTPUT.txt", sub_np456) print('outputs', outputs)

  Python

   import struct import numpy as np if __name__ == "__main__": row = 80 col = 327 count = 0 res_out_dim = 128 in_dim = 80 kernel_size = 5 filter = np.zeros((80, 5), dtype = np.float32) featureMaps = np.zeros((128, 323), dtype = np.float32) spectrum = np.load("CONV1D_INPUT.npy") weight = np.load("CONV1D_WEIGHT.npy") spectrum_2d = spectrum.reshape(80, 327) for i in range(res_out_dim): for j in range(in_dim): for k in range(kernel_size): filter[j][k] = weight[i][j][k] while count < (col-kernel_size+1): for j in range(in_dim): for k in range(count, kernel_size+count): featureMaps[i][count] = featureMaps[i][count] + spectrum_2d[j][k]*filter[j][k-count] count = count + 1 count = 0 np.savetxt("Python_CONV1D_OUTPUT.txt", featureMaps)

  C 代碼（浮點數）

   #include<stdio.h> #include<stdlib.h> #include<math.h> #include<time.h> const char CONV1D_WEIGHT[] = "CONV1D_WEIGHT.txt"; const char CONV1D_INPUT[] = "CONV1D_INPUT.txt"; void parameterFree(float **matrix, int row) { int i = 0; for(i=0; i<row; i++) free(matrix[i]); free(matrix); } float** createMatrix_2D(int row, int col) { int i = 0; float **matrix = NULL; matrix = (float**)malloc(sizeof(float*) * row); if(matrix == NULL) printf("Matrix2D malloc failed\\n"); for(i=0; i<row; i++) { matrix[i] = (float*)malloc(sizeof(float) * col); if(matrix[i] == NULL) printf("Matrix2D malloc failed\\n"); } return matrix; } float** conv_1D(const char weightFile[], float **source, int *row, int *col, int in_dim, int res_out_dim, int kernel_size) { float **filter = createMatrix_2D(in_dim, kernel_size); float **featureMaps = createMatrix_2D(res_out_dim, *col-kernel_size+1); int i = 0, j = 0, k = 0, count = 0; char str[10]; float data = 0.0; FILE *fp = fopen(weightFile, "r"); if(fp == NULL) printf("Resnet file open failed\\n"); else { /*initial featureMaps*/ for(i=0; i<res_out_dim; i++) { for(j=0; j<*col-kernel_size+1; j++) { featureMaps[i][j] = 0.0; } } /*next filter*/ for(i=0; i<res_out_dim; i++) { /*read filter*/ for(j=0; j<in_dim; j++) { for(k=0; k<kernel_size; k++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); filter[j][k] = data; } } /* (part of source * filter) */ while(count < *col-kernel_size+1) { for(j=0; j<in_dim; j++) { for(k=count; k<kernel_size+count; k++) { featureMaps[i][count] += source[j][k]*filter[j][k-count]; } } count++; } count = 0; } fclose(fp); } parameterFree(source, *row); parameterFree(filter, in_dim); *row = res_out_dim; *col = *col-kernel_size+1; return featureMaps; } int main() { int row = 80; int col = 327; int in_dim = 80; int res_out_dim = 128; int kernel_size = 5; int i, j; float data; char str[10]; float **input = createMatrix_2D(row, col); FILE *fp = fopen(CONV1D_INPUT, "r"); FILE *fp2 = fopen("C code_CONV1D_OUTPUT.txt", "w"); if(fp == NULL) printf("File open failed\\n"); else { for(i=0; i<row; i++) { for(j=0; j<col; j++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); input[i][j] = data; } } } float **CONV1D_ANS = conv_1D(CONV1D_WEIGHT, input, &row, &col, in_dim, res_out_dim, kernel_size); for(i=0; i<row; i++) { for(j=0; j<col; j++) { fprintf(fp2, "[%.12f] ", CONV1D_ANS[i][j]); } fprintf(fp2, "\\n"); } return 0; }

  C 代碼（雙）

   #include<stdio.h> #include<stdlib.h> #include<math.h> #include<time.h> const char CONV1D_WEIGHT[] = "CONV1D_WEIGHT.txt"; const char CONV1D_INPUT[] = "CONV1D_INPUT.txt"; void parameterFree(double **matrix, int row) { int i = 0; for(i=0; i<row; i++) free(matrix[i]); free(matrix); } double** createMatrix_2D(int row, int col) { int i = 0; double **matrix = NULL; matrix = (double**)malloc(sizeof(double*) * row); if(matrix == NULL) printf("Matrix2D malloc failed\\n"); for(i=0; i<row; i++) { matrix[i] = (double*)malloc(sizeof(double) * col); if(matrix[i] == NULL) printf("Matrix2D malloc failed\\n"); } return matrix; } double** conv_1D(const char weightFile[], double **source, int *row, int *col, int in_dim, int res_out_dim, int kernel_size) { double **filter = createMatrix_2D(in_dim, kernel_size); double **featureMaps = createMatrix_2D(res_out_dim, *col-kernel_size+1); int i = 0, j = 0, k = 0, count = 0; char str[10]; float data = 0.0; FILE *fp = fopen(weightFile, "r"); if(fp == NULL) printf("Resnet file open failed\\n"); else { /*initial featureMaps*/ for(i=0; i<res_out_dim; i++) { for(j=0; j<*col-kernel_size+1; j++) { featureMaps[i][j] = 0.0; } } /*next filter*/ for(i=0; i<res_out_dim; i++) { /*read filter*/ for(j=0; j<in_dim; j++) { for(k=0; k<kernel_size; k++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); filter[j][k] = (double)data; } } /* (part of source * filter) */ while(count < *col-kernel_size+1) { for(j=0; j<in_dim; j++) { for(k=count; k<kernel_size+count; k++) { featureMaps[i][count] += source[j][k]*filter[j][k-count]; } } count++; } count = 0; } fclose(fp); } parameterFree(source, *row); parameterFree(filter, in_dim); *row = res_out_dim; *col = *col-kernel_size+1; return featureMaps; } int main() { int row = 80; int col = 327; int in_dim = 80; int res_out_dim = 128; int kernel_size = 5; int i, j; float data; char str[10]; double **input = createMatrix_2D(row, col); FILE *fp = fopen(CONV1D_INPUT, "r"); FILE *fp2 = fopen("C code_CONV1D_OUTPUT.txt", "w"); if(fp == NULL) printf("File open failed\\n"); else { for(i=0; i<row; i++) { for(j=0; j<col; j++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); input[i][j] = (double)data; } } } double **CONV1D_ANS = conv_1D(CONV1D_WEIGHT, input, &row, &col, in_dim, res_out_dim, kernel_size); for(i=0; i<row; i++) { for(j=0; j<col; j++) { fprintf(fp2, "[%.18f] ", CONV1D_ANS[i][j]); } fprintf(fp2, "\\n"); } return 0; }

Answer 1

浮點數不精確（按設計）。 根據執行操作的順序，結果可能會有所不同。 更糟糕的是，一些公式是直接數值不穩定的，而另一個具有相同解析表達式的公式可能是穩定的。

編譯器經常重新排列語句作為優化措施。 卷積是一種眾所周知的包含許多操作和循環的操作。 所以除非你直接比較執行的字節碼，否則這種推測是沒有意義的。