手写四维卷积，python and C

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数据结构

数据:NHWC
权重:OIHW

C:

void convOp(Blob input,Blob* output,conv_s convInfo, wtParam param){int sh = convInfo.strideH, sw = convInfo.strideW,ph = convInfo.convPad.padH, pw = convInfo.convPad.padW,kh = convInfo.kernelH, kw = convInfo.kernelW;Blob padedblob;padedblob.n = input.n;padedblob.h = input.h + 2 * ph;padedblob.w = input.w + 2 * pw;padedblob.c = input.c;padedblob.dataFmt = input.dataFmt;padedblob.dataType = input.dataType;padedblob.data = malloc(sizeof(float) * padedblob.n * padedblob.h * padedblob.w * padedblob.c);memset(padedblob.data, 0, sizeof(float) * padedblob.n * padedblob.h * padedblob.w * padedblob.c);//补边for(int n = 0; n < input.n; n++){for(int c = 0; c < input.c; c++){for(int h = 0; h < input.h; h++){for(int w = 0; w < input.w; w++){blobSet(n,h + ph,w + pw,c,padedblob,blobGet(n,h,w,c,input));}}}}output->data = malloc(sizeof(float) * output->n * output->h * output->w * output->c);memset(output->data, 0, sizeof(float) * output->n * output->h * output->w * output->c);//卷积计算for(int n = 0; n < padedblob.n; n++){for(int oc = 0; oc < param.n; oc++){// wt[oc, 256, 3, 3]for(int inh = 0; inh <= padedblob.h - kh; inh += sh){for(int inw = 0; inw <= padedblob.w - kw; inw += sw){float sum = 0;for(int inc = 0; inc < padedblob.c; inc++){for(int offx = 0; offx < kh; offx++){for(int offy = 0; offy < kw; offy++){sum += blobGet(n, inh + offx, inw + offy, inc, padedblob) *wtGet(oc, offx, offy, inc, param);}}}blobSet(n, inh / sh, inw / sw, oc, *output, sum);}}}}}

Python:

def convOp(inblob, node_size, node_shape, param):"""param : OIHWreturn: outblob->nparray"""param = param[0]kh = int(node_shape[1][0])kw = int(node_shape[1][1])sh = int(node_shape[2][0])sw = int(node_shape[2][1])ph = int(node_shape[3][0])pw = int(node_shape[3][1])if ph != 0 or pw != 0:# 补边儿newn = inblob.shape[0]newh = inblob.shape[1] + 2 * phneww = inblob.shape[2] + 2 * pwnewc = inblob.shape[3]paddedblob = np.zeros((newn, newh, neww, newc), dtype=np.float32)paddedblob[:, ph:ph + inblob.shape[1], pw:pw + inblob.shape[2], :] = inblobelse:paddedblob = inbloboutblob = np.zeros(tuple(node_size), dtype=np.float32)# 权重是OIHW,而数据是NHWC，为了H与W能够在numpy中通过广播的方式计算，需要把权重转置成OHWI，numpy的转置几乎不消耗时间，因为该转置是不改变数据在内存中表示的。# 权重：OIHW 		OHWI# 数据：   	     	NHWCparam = np.transpose(param, [0, 2, 3, 1])#卷积计算for oc in range(param.shape[0]):  # O of weightfor h in range(outblob.shape[1]):  # H of outblobfor w in range(outblob.shape[2]):outblob[:, h, w, oc] = np.sum(paddedblob[:,h * sh: h * sh + param.shape[1],w * sw: w * sw + param.shape[2],:] * param[oc, :, :, :])return outblob

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