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YOLO VS2015 c++ code, Only need opencv, do not rely on the Caffe Library
I am getting nan while training immediately after the first batch
Hi there, sorry about that to post this here, i was trying to write yolo in torch but struggled the backward function for so many days, but the gradients are always exploding slowly, could you kindly shed a light on my codes? Thank you so much.
gradInput[{ {}, {}, 1, {}, {} }] = self.mse:backward(torch.cmul(self.x_buffer, x, coord_mask), tx)
gradInput[{ {}, {}, 2, {}, {} }] = self.mse:backward(torch.cmul(self.y_buffer, y, coord_mask), ty)
gradInput[{ {}, {}, 3, {}, {} }] = self.mse:backward(torch.cmul(self.w_buffer, w, coord_mask), tw)
gradInput[{ {}, {}, 4, {}, {} }] = self.mse:backward(torch.cmul(self.h_buffer, h, coord_mask), th)
gradInput[{ {}, {}, 5, {}, {} }] = self.mse:backward(torch.cmul(self.conf_buffer, conf, coord_mask), tconf)
gradInput[{ {}, {}, { 6, 5 + nC }, {}, {} }][self.cls_mask] = self.ce:backward(torch.cmul(self.cls_buffer, cls), tcls)
是我哪里设置的不对吗
in forward_convolutional_layer sub-function, it calls "gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);", for my understanding, 'a' point to weight, 'b' point to input data, and 'c' is output. in general convolution calculation, each weight value in a conv kernel is multiplied with corresponding input data and calculate partial sum, that's to say, each weight value will multiply different value and do partial sum. but in gemm_nn sub-function, the same weight value 'A[i*lda+k]' multiply different 'B' value, and do partial sum, i am puzzled by this, could you help me ?
void gemm_nn(int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float *C, int ldc)
{
int i,j,k;
for(i = 0; i < M; ++i){
for(k = 0; k < K; ++k){
register float A_PART = ALPHA*A[i*lda+k];
for(j = 0; j < N; ++j){
C[i*ldc+j] += A_PART*B[k*ldb+j];
}
}
}
}
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