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fix alpha compositing
Summary: Fix division by zero when alpha is 1.0 In this case, the nominator is already 0 and we need to make sure division with 0 does not occur which would produce nans Reviewed By: nikhilaravi Differential Revision: D21650478 fbshipit-source-id: bc457105b3050fef1c8bd4e58e7d6d15c0c81ffd
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@ -11,6 +11,8 @@
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#include <stdio.h>
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#include <vector>
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__constant__ const float kEpsilon = 1e-9;
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// TODO(gkioxari) support all data types once AtomicAdd supports doubles.
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// Currently, support is for floats only.
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__global__ void alphaCompositeCudaForwardKernel(
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@ -126,7 +128,7 @@ __global__ void alphaCompositeCudaBackwardKernel(
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atomicAdd(
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&grad_alphas[batch][t][j][i],
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-grad_outputs[batch][ch][j][i] * features[ch][n_idx] * cum_alpha *
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alpha / (1 - alpha_tvalue));
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alpha / (1 - alpha_tvalue + kEpsilon));
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}
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cum_alpha = cum_alpha * (1 - alphas[batch][k][j][i]);
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@ -5,6 +5,9 @@
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#include <cmath>
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#include <vector>
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// Epsilon float
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const float kEps = 1e-9;
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torch::Tensor alphaCompositeCpuForward(
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const torch::Tensor& features,
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const torch::Tensor& alphas,
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@ -101,7 +104,8 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeCpuBackward(
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}
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float alpha_tvalue = alphas_a[b][t][j][i];
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grad_alphas_a[b][t][j][i] -= grad_outputs_a[b][c][j][i] *
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features_a[c][n_idx] * cum_alpha * alpha / (1 - alpha_tvalue);
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features_a[c][n_idx] * cum_alpha * alpha /
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(1 - alpha_tvalue + kEps);
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}
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cum_alpha = cum_alpha * (1 - alpha);
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