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https://issues.apache.org/jira/browse/SINGA-249?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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wangwei closed SINGA-249.
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Resolution: Fixed
> Convolution BP
> --------------
>
> Key: SINGA-249
> URL: https://issues.apache.org/jira/browse/SINGA-249
> Project: Singa
> Issue Type: Wish
> Environment: ubuntu 14.04，singa 1.0
> Reporter: hacker99
>
> I'm curious about how to calculate the gradient of the back propagation
> algorithm eg. Convolution layer. Can anyone explain to me the details of the
> implementation of the formula and the code? Very grateful, if there is some
> documents or just tell why dw += Mult(grad_b, col_data.T())?
> #code from src/model/layer/convolution.cc
> const std::pair<Tensor, vector<Tensor>> Convolution::Backward(
> int flag, const Tensor &grad) {
> CHECK_EQ(grad.device()->lang(), kCpp);
> CHECK_EQ(grad.nDim(), 4u);
> CHECK(!buf_.empty());
> Tensor src_data = buf_.top();
> buf_.pop();
> vector<Tensor> param_grad;
> Tensor dx;
> Tensor db, dw;
> dx.ResetLike(src_data);
> db.ResetLike(bias_);
> dw.ResetLike(weight_);
> dw.SetValue(0.0f);
> size_t batchsize = grad.shape(0);
> size_t imagesize = src_data.Size() / batchsize;
> if (bias_term_) {
> Tensor tmp1 =
> Reshape(grad, Shape{batchsize * num_filters_,
> grad.Size() / (batchsize * num_filters_)});
> Tensor tmp2(Shape{batchsize * num_filters_});
> SumColumns(tmp1, &tmp2);
> Tensor tmp3 = Reshape(tmp2, Shape{batchsize, num_filters_});
> SumRows(tmp3, &db);
> }
> auto in_data = src_data.data<float>();
> Tensor col_data(Shape{col_height_, col_width_});
> float *data_col = new float[col_height_ * col_width_];
> float *dx_b = new float[imagesize];
> for (size_t b = 0; b < batchsize; b++) {
> Im2col(in_data + b * imagesize, channels_, height_, width_, kernel_h_,
> kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data_col);
> col_data.CopyDataFromHostPtr(data_col, col_height_ * col_width_);
> Tensor grad_b(Shape{num_filters_, conv_height_ * conv_width_});
> CopyDataToFrom(&grad_b, grad, grad_b.Size(), 0, b * grad_b.Size());
> dw += Mult(grad_b, col_data.T());
> Tensor dcol_b = Mult(weight_.T(), grad_b);
> auto dcol_data = dcol_b.data<float>();
> Col2im(dcol_data, channels_, height_, width_, kernel_h_, kernel_w_,
> pad_h_,
> pad_w_, stride_h_, stride_w_, dx_b);
> dx.CopyDataFromHostPtr(dx_b, imagesize, b * imagesize);
> }
> param_grad.push_back(dw);
> param_grad.push_back(db);
> delete[] data_col;
> delete[] dx_b;
> return std::make_pair(dx, param_grad);
> }
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