mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
Added tt::resize_bilinear() and tt::resize_bilinear_gradient().
This commit is contained in:
Davis King
parent
9cd06ce372
commit
198569460e
@ -1401,6 +1401,107 @@ namespace dlib
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}
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}
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// ----------------------------------------------------------------------------------------
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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)
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{
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DLIB_CASSERT(is_same_object(dest, src)==false);
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DLIB_CASSERT(dest.num_samples() == src.num_samples());
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DLIB_CASSERT(dest.k() == src.k());
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if (dest.size() == 0 || src.size() == 0)
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return;
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const float* s = src.host();
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float* d = dest.host();
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const float x_scale = (src.nc()-1)/(float)std::max<long>((dest.nc()-1),1);
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const float y_scale = (src.nr()-1)/(float)std::max<long>((dest.nr()-1),1);
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for (long samp = 0; samp < dest.num_samples(); ++samp)
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{
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for (long k = 0; k < dest.k(); ++k)
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{
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for (long r = 0; r < dest.nr(); ++r)
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{
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const float y = r*y_scale;
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const long top = static_cast<long>(std::floor(y));
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const long bottom = std::min(top+1, src.nr()-1);
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const float tb_frac = y - top;
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for (long c = 0; c < dest.nc(); ++c)
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{
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const float x = c*x_scale;
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const long left = static_cast<long>(std::floor(x));
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const long right = std::min(left+1, src.nc()-1);
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const float lr_frac = x - left;
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float tl = s[top*src.nc()+left];
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float tr = s[top*src.nc()+right];
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float bl = s[bottom*src.nc()+left];
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float br = s[bottom*src.nc()+right];
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float temp = (1-tb_frac)*((1-lr_frac)*tl + lr_frac*tr) +
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tb_frac*((1-lr_frac)*bl + lr_frac*br);
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d[r*dest.nc()+c] = temp;
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}
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}
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d += dest.nr()*dest.nc();
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s += src.nr()*src.nc();
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}
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}
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}
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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)
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{
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DLIB_CASSERT(is_same_object(grad, gradient_input)==false);
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DLIB_CASSERT(gradient_input.num_samples() == grad.num_samples());
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DLIB_CASSERT(gradient_input.k() == grad.k());
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if (gradient_input.size() == 0 || grad.size() == 0)
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return;
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const float* gi = gradient_input.host();
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float* g = grad.host();
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const float x_scale = (grad.nc()-1)/(float)std::max<long>((gradient_input.nc()-1),1);
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const float y_scale = (grad.nr()-1)/(float)std::max<long>((gradient_input.nr()-1),1);
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for (long samp = 0; samp < gradient_input.num_samples(); ++samp)
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{
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for (long k = 0; k < gradient_input.k(); ++k)
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{
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for (long r = 0; r < gradient_input.nr(); ++r)
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{
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const float y = r*y_scale;
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const long top = static_cast<long>(std::floor(y));
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const long bottom = std::min(top+1, grad.nr()-1);
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const float tb_frac = y - top;
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for (long c = 0; c < gradient_input.nc(); ++c)
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{
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const float x = c*x_scale;
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const long left = static_cast<long>(std::floor(x));
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const long right = std::min(left+1, grad.nc()-1);
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const float lr_frac = x - left;
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const float tmp = gi[r*gradient_input.nc()+c];
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g[top*grad.nc()+left] += tmp*(1-tb_frac)*(1-lr_frac);
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g[top*grad.nc()+right] += tmp*(1-tb_frac)*(lr_frac);
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g[bottom*grad.nc()+left] += tmp*(tb_frac)*(1-lr_frac);
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g[bottom*grad.nc()+right] += tmp*(tb_frac)*(lr_frac);
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}
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}
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g += grad.nr()*grad.nc();
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gi += gradient_input.nr()*gradient_input.nc();
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}
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}
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}
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// ------------------------------------------------------------------------------------
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// ------------------------------------------------------------------------------------
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// ------------------------------------------------------------------------------------
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@ -296,6 +296,18 @@ namespace dlib
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const tensor& gradient_input
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);
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// ----------------------------------------------------------------------------------------
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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);
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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);
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// -----------------------------------------------------------------------------------
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class pooling
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@ -1137,6 +1137,116 @@ namespace dlib
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param.device(), params_grad.device());
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}
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// ----------------------------------------------------------------------------------------
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__global__ void _cuda_resize_bilinear(size_t dsize, size_t dchan_size, size_t dnc, float* d,
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size_t schan_size, int snr, int snc, const float* s,
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const float x_scale, const float y_scale)
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{
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for(auto i : grid_stride_range(0, dsize))
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{
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const int idx = i%dchan_size;
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const int channel = i/dchan_size;
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const int sidx = channel*schan_size;
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const int r = idx/dnc;
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const int c = idx%dnc;
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const float y = r*y_scale;
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const int top = static_cast<int>(::floor(y));
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const int bottom = ::min(top+1, snr-1);
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const float tb_frac = y - top;
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const float x = c*x_scale;
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const int left = static_cast<int>(::floor(x));
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const int right = ::min(left+1, snc-1);
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const float lr_frac = x - left;
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float tl = s[sidx+top*snc+left];
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float tr = s[sidx+top*snc+right];
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float bl = s[sidx+bottom*snc+left];
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float br = s[sidx+bottom*snc+right];
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float temp = (1-tb_frac)*((1-lr_frac)*tl + lr_frac*tr) +
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tb_frac*((1-lr_frac)*bl + lr_frac*br);
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d[i] = temp;
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}
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}
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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)
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{
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DLIB_CASSERT(is_same_object(dest, src)==false);
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DLIB_CASSERT(dest.num_samples() == src.num_samples());
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DLIB_CASSERT(dest.k() == src.k());
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if (dest.size() == 0 || src.size() == 0)
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return;
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const float x_scale = (src.nc()-1)/(float)std::max<long>((dest.nc()-1),1);
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const float y_scale = (src.nr()-1)/(float)std::max<long>((dest.nr()-1),1);
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launch_kernel(_cuda_resize_bilinear,
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dest.size(), dest.nr()*dest.nc(), dest.nc(), dest.device(),
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src.nr()*src.nc(), src.nr(), src.nc(), src.device(),
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x_scale, y_scale);
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}
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__global__ void _cuda_resize_bilinear_gradient(size_t dsize, size_t dchan_size, size_t dnc, const float* d,
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size_t schan_size, int snr, int snc, float* s,
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const float x_scale, const float y_scale)
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{
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for(auto i : grid_stride_range(0, dsize))
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{
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const float tmp = d[i];
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const int idx = i%dchan_size;
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const int channel = i/dchan_size;
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const int sidx = channel*schan_size;
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const int r = idx/dnc;
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const int c = idx%dnc;
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const float y = r*y_scale;
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const int top = static_cast<int>(::floor(y));
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const int bottom = ::min(top+1, snr-1);
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const float tb_frac = y - top;
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const float x = c*x_scale;
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const int left = static_cast<int>(::floor(x));
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const int right = ::min(left+1, snc-1);
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const float lr_frac = x - left;
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atomicAdd(s+sidx+top*snc+left, tmp*(1-tb_frac)*(1-lr_frac));
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atomicAdd(s+sidx+top*snc+right, tmp*(1-tb_frac)*(lr_frac));
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atomicAdd(s+sidx+bottom*snc+left, tmp*(tb_frac)*(1-lr_frac));
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atomicAdd(s+sidx+bottom*snc+right, tmp*(tb_frac)*(lr_frac));
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}
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}
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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)
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{
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DLIB_CASSERT(is_same_object(grad, gradient_input)==false);
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DLIB_CASSERT(gradient_input.num_samples() == grad.num_samples());
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DLIB_CASSERT(gradient_input.k() == grad.k());
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if (grad.size() == 0 || gradient_input.size() == 0)
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return;
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const float x_scale = (grad.nc()-1)/(float)std::max<long>((gradient_input.nc()-1),1);
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const float y_scale = (grad.nr()-1)/(float)std::max<long>((gradient_input.nr()-1),1);
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launch_kernel(_cuda_resize_bilinear_gradient,
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gradient_input.size(), gradient_input.nr()*gradient_input.nc(), gradient_input.nc(), gradient_input.device(),
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grad.nr()*grad.nc(), grad.nr(), grad.nc(), grad.device(),
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x_scale, y_scale);
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}
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// ----------------------------------------------------------------------------------------
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void copy_tensor(
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@ -346,6 +346,21 @@ namespace dlib
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tensor& params_grad
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);
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// ----------------------------------------------------------------------------------------
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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);
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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);
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// ----------------------------------------------------------------------------------------
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void copy_tensor(
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tensor& dest,
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size_t dest_k_offset,
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@ -353,6 +368,7 @@ namespace dlib
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size_t src_k_offset,
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size_t count_k
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);
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// ------------------------------------------------------------------------------------
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// ------------------------------------------------------------------------------------
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// ------------------------------------------------------------------------------------
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@ -838,6 +838,32 @@ namespace dlib { namespace tt
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#endif
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}
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// ----------------------------------------------------------------------------------------
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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)
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{
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#ifdef DLIB_USE_CUDA
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cuda::resize_bilinear(dest,src);
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#else
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cpu::resize_bilinear(dest,src);
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#endif
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}
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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)
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{
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#ifdef DLIB_USE_CUDA
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cuda::resize_bilinear_gradient(grad,gradient_input);
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#else
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cpu::resize_bilinear_gradient(grad,gradient_input);
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#endif
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}
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// ------------------------------------------------------------------------------------
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void copy_tensor(
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@ -1350,6 +1350,41 @@ namespace dlib { namespace tt
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is_same_object(grad, gradient_input)==true
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!*/
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// ----------------------------------------------------------------------------------------
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void resize_bilinear (
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tensor& dest,
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const tensor& src
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);
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/*!
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requires
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- is_same_object(dest, src)==false
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- dest.num_samples() == src.num_samples()
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- dest.k() == src.k()
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ensures
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- for all valid i,k: image_plane(dest,i,k) is a copy of image_plane(src,i,k)
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that has been bilinearly interpolated to fit into the shape of
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image_plane(dest,i,k).
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!*/
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void resize_bilinear_gradient (
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tensor& grad,
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const tensor& gradient_input
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);
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/*!
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requires
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- is_same_object(grad, gradient_input)==false
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- gradient_input.num_samples() == grad.num_samples()
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- gradient_input.k() == grad.k()
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ensures
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- Suppose that DEST is the output of resize_bilinear(DEST,SRC) for some SRC
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tensor, let f(SRC) == dot(gradient_input,DEST). Then this function computes
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the gradient of f() with respect to SRC and adds it to grad. It should be
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noted that we don't need to know the contents of DEST to compute this
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gradient. All that matters is that gradient_input have the same dimensions
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as DEST.
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!*/
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// ----------------------------------------------------------------------------------------
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class multi_device_tensor_averager
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@ -2303,6 +2303,69 @@ namespace
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"Number of correctly classified elements = " << num_correct << ", required = " << num_correct_required);
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}
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// ----------------------------------------------------------------------------------------
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void test_tensor_resize_bilienar(long samps, long k, long nr, long nc, long onr, long onc)
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{
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resizable_tensor img(samps,k,nr,nc);
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resizable_tensor out(samps,k,onr,onc);
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resizable_tensor out2(samps,k,onr,onc);
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dlib::rand rnd;
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for (int iter = 0; iter < 10; ++iter)
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{
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print_spinner();
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const size_t idx = rnd.get_random_64bit_number()%img.size();
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img = 1;
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img.host()[idx] = 2;
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cpu::resize_bilinear(out, img);
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#ifdef DLIB_USE_CUDA
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cuda::resize_bilinear(out2, img);
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DLIB_CASSERT(max(abs(mat(out)-mat(out2))) < 1e-5);
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#endif
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resizable_tensor gradient_input;
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gradient_input.copy_size(out);
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tt::tensor_rand rnd;
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rnd.fill_uniform(gradient_input);
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const float h = 1e-2;
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img.host()[idx] = 2;
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cpu::resize_bilinear(out, img);
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float f1 = dot(out, gradient_input);
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img.host()[idx] = 2+h;
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cpu::resize_bilinear(out, img);
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float f2 = dot(out, gradient_input);
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const float numerical_grad = (f2-f1)/h;
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dlog << LINFO << "numerical grad: " << numerical_grad;
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resizable_tensor grad, grad2;
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grad.copy_size(img);
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grad = 0.1;
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grad2.copy_size(img);
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grad2 = 0.1;
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cpu::resize_bilinear_gradient(grad2, gradient_input);
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dlog << LINFO << "analytic grad: "<< grad2.host()[idx]-0.1;
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DLIB_CASSERT(std::abs(numerical_grad - grad2.host()[idx]+0.1) < 1e-2, std::abs(numerical_grad - grad2.host()[idx]+0.1) << " numerical_grad: " << numerical_grad);
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#ifdef DLIB_USE_CUDA
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cuda::resize_bilinear_gradient(grad, gradient_input);
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dlog << LINFO << "analytic grad: "<< grad.host()[idx]-0.1;
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DLIB_CASSERT(std::abs(numerical_grad - grad.host()[idx]+0.1) < 1e-2, std::abs(numerical_grad - grad.host()[idx]+0.1) << " numerical_grad: " << numerical_grad);
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DLIB_CASSERT(max(abs(mat(grad)-mat(grad2))) < 1e-5);
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#endif
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}
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}
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// ----------------------------------------------------------------------------------------
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class dnn_tester : public tester
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@ -2337,6 +2400,9 @@ namespace
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compare_adam();
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test_copy_tensor_gpu();
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#endif
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test_tensor_resize_bilienar(2, 3, 6,6, 11, 11);
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test_tensor_resize_bilienar(2, 3, 6,6, 3, 4);
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test_tensor_resize_bilienar(2, 3, 5,6, 12, 21);
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test_max_pool(1,1,2,3,0,0);
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test_max_pool(3,3,1,1,0,0);
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test_max_pool(3,3,2,2,0,0);
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